Geotechnische Kartierung und Suszeptibilitätsanalyse für Hangrutschungen in der Gemeinde Badong (Drei-Schluchten-Region / China) ------Geotechnical mapping and landslide susceptibility analysis in Badong county (Three Gorges Region / China)
der Naturwissenschaftlichen Fakultät
der Friedrich-Alexander-Universität Erlangen-Nürnberg
zur
Erlangung des Doktorgrades Dr. rer. nat.
vorgelegt von
Renneng Bi
Als Dissertation genehmigt von der Naturwissenschaftlichen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg
Tag der mündlichen Prüfung: 01.04.2015
Vorsitzender des Promotionsorgans: Prof. Dr. Jörn Wilms
Gutachter: Prof. Dr. Joachim Rohn Prof. Dr. Wei Xiang Selbständigkeitserklärung zur Dissertation
Ich erkläre ausdrücklich, dass es sich bei der von mir eingereichten Dissertation mit dem Titel:
Geotechnical mapping and landslide susceptibility analysis in Badong county (Three Gorges Region / China) um eine von mir selbstständig und ohne fremde Hilfe verfasste Arbeit handelt. Ich erkläre, dass ich sämtliche in der oben genannten Arbeit verwendeten fremden Quellen, auch aus dem Internet (einschließlich Tabellen, Grafiken u. Ä.) als solche kenntlich gemacht habe. Insbesondere bestätige ich, dass ich ausnahmslos sowohl bei wörtlich übernommenen Aussagen bzw. unverändert übernommenen Tabellen, Grafiken u. Ä. (Zitaten) als auch bei in eigenen Worten wiedergegebenen Aussagen bzw. von mir abgewandelten Tabellen, Grafiken u. Ä. anderer Autorinnen und Autoren (Paraphrasen) die Quelle angegeben habe. Ich erkläre hiermit weiterhin, dass die vorgelegte Arbeit zuvor weder von mir noch – soweit mir bekannt ist – von einer anderen Person an dieser oder einer anderen Hochschule eingereicht wurde. Mir ist bewusst, dass Verstöße gegen die Grundsätze der Selbstständigkeit als Täuschung betrachtet und dass die Unrichtigkeit dieser Erklärung eine Benotung der Arbeit mit der Note nicht ausreichend zur Folge hat und dass Verletzungen des Urheberrechts strafrechtlich verfolgt werden können.
______Erlangen, ______Unterschrift Datum Abstract
The Three Gorges Dam Project (TGDP) on the Yangtze River is the dam project with the largest installed capacity in the world. In the Three Gorges Reservoir Area (ca. 56,700 km2), 4,469 landslides have been registered. The study area lies in Badong county, 70 km upstream of the Three Gorges Dam in the foreland of the reservoir and has an extremely high density of landslides. In the study area (ca. 64 km2), 103 landslides with a total area of 4.8 km2 are mapped. Tectonically the study area is located in regional folding belt in the middle part of the Yangtze plate. In the study area mainly the bedrocks of the Badong formation (Middle Triassic) crop out. They are mainly composed of clayey limestones and clayey siltstones. Due to the extremely strong tectonic pressure from north-south direction, different types of tectonic structures can be observed in the bedding layers and the bedrocks are intensely fractured. A geotechnical mapping was performed in order to find all landslides in the investigation area. The geological and geotechnical mapping in the area showed that tectonic structures and the river incision are the essential factors of the landslide development in this region. Especially the occurrences of rock slides and large scale debris slides are tightly correlated with the longitudinal extension of the Guandukou syncline and its secondary folds. In order to investigate the susceptibility for landslides in a local and in a regional scale an artificial neural network (ANN) method was applied. The multilayer feed-forward (MLF) neural network in the artificial neural network method is a computational tool for classification. It is firstly applied for lithology recognition for the study area. The results show that areas with two definitely different lithologies can be well discriminated in most parts of the study area. Secondly the MLF neural network is used for classification of landslide susceptibility in a local scale in the study. The results show that the riversides in the west part of North Badong and in South Badong are the areas at most sensitive for new landslides. As a comparison, a study with the MLF neural network in a large catchment in the Three Gorges region is also attached. The study shows that the MLF neural network is also an efficient tool for recognition of existing landslides in a regional scale. Kurzfassung
Das Three Gorges Dam Project (TGDP) am Yangtze Fluss ist das Dammprojekt mit der größten installierten Leistung der Welt. Im Einzugsgebiet des Drei-Schluchten-Stausees (ca. 56.700km²), wurden 4.469 Hangrutschungen registriert. Im Forschungsgebiet liegt der Kreis Badong ca. 70 km stromaufwärts des Drei-Schluchten-Staudamms im Vorland des Stausees und zeigt eine extrem hohe Verteilungsdichte von Hangrutschungen. Im Untersuchungsgebiet (ca. 64 km²) wurden 103 Rutschungen mit einer Fläche von 4,8 km² kartiert. Tektonisch liegt das Untersuchungsgebiet im regionalen Faltengürtel im mittleren Teil der Yangtze-Platte. Im Untersuchungsgebiet ist hauptsächlich das Felsgestein der Badong-Formation (Mittlere Trias) aufgeschlossen. Es setzt sich zum größten Teil aus tonhaltigen Kalkstein und tonhaltigen Siltstein. Aufgrund des extrem starken tektonischen Drucks aus nordsüdlicher Richtung, sind verschiedene Typen tektonischer Strukturen in den Bankung sichtbar und das anstehende Gestein ist stark zerklüftet. Es wurde eine geotechnische Kartierung in diesem Gebiet durchgeführt, um alle Hangrutuschungen im Untersuchungsgebiet aufzufinden. Die geologische und geotechnische Kartierung im Gebiet zeigten, dass tektonische Strukturen und die Einschneidungen der Flüsse die Hauptfaktoren der Entwicklung von Hangrutschungen in dieser Region sind. Besonders das Auftreten von Felsrutschungen sowie großräumigen Schuttrutschungen steht besonders eng in Wechselwirkung mit der Längenausdehnung der Guandukou-Synkline und ihren sekundären Falten. Zur Untersuchung der Suszeptibilität für Hangrutschungen im lokalen und regionalen Maßstab wurde eine künstliche neuronale Netz-Methode verwendet. Das mehrschichtige Feedforward (MLF) neuronale Netz der künstlichen neuronalen Netz-Methode ist ein computergesteuertes Klassifizierungswerkzeug. Dieses Werkzeug wurde zunächst zur Erkennung der Lithologie des Untersuchungsgebiets angewendet. Das Ergebnis zeigt, dass Gebiete mit zwei definitiv differierenden Lithologien gut in den meisten Teilen des Untersuchungsgebietes unterschieden werden können. Darüber hinaus wurde das MLF-neuronale Netz für die Klassifizierung der Suszeptibilität für Hangrutschungen im lokalen Maßstab der Studie angewendet. Die Ergebnisse zeigen, dass sich die Uferregionen im Westen von Nord- und Süd-Badong als besonders suszeptibel für neue Hangrutschungen erweisen. Zum Vergleich wurde eine Studie mit dem MLF-neuronalem Netz in einem großräumigen Reservoir der Drei-Schluchten-Region angefügt. Die Studie ergab, dass das MLF-neuronale Netz auch ein effizientes Werkzeug zur Erkennung existierender Hangrutschungen in regionalem Maßstab ist. Acknowledgements
Firstly I owe my deepest gratitude to Prof. Dr. Joachim Rohn for your outstanding supervision over my dissertation with extraordinary patience and constructive critics. I feel lucky I have such an approachable, supportive and generous supervisor. I thank you for all your help in the last years.
I also would like to thank Prof. Dr. Xiang Wei from the China University of Geosciences (Wuhan). I am very grateful for your support and encouragement in any situation for so many years. Under the supervision from you and Mr. Rohn, the experiences in Xiangxi and Badong have greatly enriched my field knowledge. Thank you for all the chances that you gave me, especially for connecting my life with Germany.
Many thanks go to my good friends and colleagues Markus Schleier, Johannes Wiedenmann, Christian Dumperth, Luo Jin and Dominik Ehret, who helped me in intensive discussions in discovering flaws and solving problems. You also helped me to get used to living in Germany and made my studies here interesting and colorful.
I also want to thank many students of Xiang Wei, especially Gong Siyi, for your accompaniment and support in the field.
I thank all my Chinese friends in Erlangen. I will always remember the time we spent together. Without you I would have felt much lonelier.
And last but not least I thank my parents and my wife who always believe in me. Your lovely and sincere encouragement has strengthened me to finish my doctoral study.
Contents
Contents 1 Introduction...... 1 1.1 Landslides in the Three Gorges Dam area...... 1 1.2 Targets of the study ...... 2 1.3 Approaches of the study...... 2 2 Overview ...... 4 2.1 Location ...... 4 2.1.1 The Yangtze River...... 4 2.1.2 The Three Gorges...... 5 2.1.3 The Gezhouba Dam Project (Dec. 1970 - Dec. 1988) ...... 6 2.1.4 The Three Gorges Dam Project (Dec. 1994 - Dec. 2009) ...... 6 2.1.4.1 Three Gorges Reservoir (TGR) ...... 7 2.1.4.2 Three Gorges Reservoir Area (TGRA) ...... 7 2.1.5 The study area ...... 8 2.2 Climate...... 9 2.2.1 The climate in Three Gorges Reservoir Area...... 9 2.2.2 The climate in the Badong study area ...... 10 2.3 Hydrology...... 11 2.3.1 South Badong...... 12 2.3.2 North Badong...... 13 2.3.3 West Badong...... 13 2.3.4 East Badong...... 13 3 Geological background...... 15 3.1 Regional Geology...... 15 3.1.1 The present tectonic configuration ...... 15 3.1.1.1 The coverage of Yangtze plate ...... 15 3.1.1.2 The main tectonic structures in the middle part of the Yangtze plate ...... 15 3.1.1.3 The regional tectonic situation of Badong study area ...... 18 3.1.2 The tectonic history in the Middle Yangtze plate...... 19 3.1.2.1 Before the Indosinian orogeny (before 260 Ma) ...... 19
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Contents 3.1.2.2 Indosinian orogeny (260 Ma - 227 Ma)...... 19 3.1.2.3 Yanshanian orogeny (227 Ma - 96 Ma)...... 19 3.1.2.3.1 The conformation of Dabashan folding zone...... 20 3.1.2.3.2 The conformation of Chuan East - E’xiang West folding zone folding zone ...... 20 3.1.2.4 Himalayan orogeny (96 Ma - now) ...... 20 3.2 Geology in the Badong study area...... 20 3.2.1 The geological formations in the Badong study area...... 20 3.2.1.1 Early Triassic - Upper Jialingjiang formation (T1j3)...... 22 3.2.1.2 Middle Triassic - Badong formation (T2b) ...... 23 3.2.1.2.1 The first sub-formation of Badong formation (T2b1)...... 23 3.2.1.2.1.1 T2b11 ...... 23 3.2.1.2.1.2 T2b12 ...... 24 3.2.1.2.2 The second sub-formation of Badong formation (T2b2)...... 25 3.2.1.2.3 The third sub-formation of Badong formation (T2b3) ...... 26 3.2.1.2.4 The forth sub-formation of Badong formation (T2b4) ...... 28 3.2.1.2.5 The fifth sub-formation of Badong formation (T2b5) ...... 29 3.2.1.3 Late Triassic - Shazhenxi formation (T3s)...... 30 3.2.2 The tectonic structures in the Badong study area...... 31 3.2.2.1 Folds ...... 32 3.2.2.1.1 Guandukou syncline...... 32 3.2.2.1.2 Xinwu anticline...... 34 3.2.2.2 Faults ...... 36 3.2.2.3 Joints...... 39 3.2.2.3.1 Conjugate joints...... 39 3.2.2.3.2 Dip joints...... 41 3.2.2.4 Cleavage ...... 42 3.2.2.5 Crush zone in T2b3 bedding layers ...... 45 3.3 Landform evolution in the Badong study area ...... 46 3.3.1 The incision rate of Yangtze River in the river section of Three Gorges.... 46
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Contents 3.3.2 The landform change in the Badong study area...... 47 3.3.3 The characteristics of the drainage system in the study area ...... 48 4 Investigation of landslides in the Badong study area ...... 50 4.1 Classification of landslides...... 50 4.2 Stability of landslides...... 50 4.3 Landslides in the Badong study area ...... 51 4.3.1 Overview ...... 51 4.3.1.1 Slope angle of slide bodies ...... 57 4.3.1.2 Elevation of slide bodies...... 57 4.3.1.3 Slope structure...... 58 4.3.1.4 Lithology ...... 59 4.3.1.5 Tectonic structures...... 59 4.3.1.6 Land use...... 60 4.3.2 Slides...... 60 4.3.2.1 Rock slides...... 61 4.3.2.1.1 Rock slide RS8...... 61 4.3.2.1.2 Rock slide RS9...... 64 4.3.2.1.3 Rock slide RS10...... 65 4.3.2.1.4 Rock slide RS18 (Zhaoshuling landslide)...... 67 4.3.2.1.5 Rock slide RS22...... 69 4.3.2.2 Debris slides...... 72 4.3.2.2.1 Debris slide DS23...... 73 4.3.2.2.2 Debris slide DS30...... 74 4.3.2.2.3 Debris slide DS44...... 77 4.3.2.2.4 Debris slide DS45...... 79 4.3.2.2.5 Debris slide DS70...... 82 4.3.3 Fall ...... 83 4.3.4 Old slide accumulations in North Badong ...... 84 4.3.5 Case study: the Huangtupo landslide...... 86 4.3.5.1 Overview of the Huangtupo landslide ...... 86
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Contents 4.3.5.2 Stability situation of the debris mass DS73...... 89 4.3.5.3 The tunnel project of the China University of Geosciences (Wuhan)...... 90 4.3.5.3.1 Sliding movement along the crush zone between the bedding layers ...... 91 4.3.5.3.2 Sliding movement along the border between the bedding layers and the debris mass ...... 94 4.4 Conclusion ...... 96 5 Theory of the artificial neural network (ANN) method...... 97 5.1 Introduction ...... 97 5.1.1 Classification with machine learning...... 97 5.1.2 Artificial neural network...... 97 5.2 A simple neuron...... 99 5.2.1 Scalar input...... 99 5.2.2 Vector input...... 99 5.2.3 Common transfer functions for neural networks...... 100 5.3 Layered neural networks ...... 100 5.3.1 One layer neural network...... 101 5.3.2 Multilayer feed-forward (MLF) neural network ...... 101 5.4 Learning with error back-propagation in the multilayer feed-forward network ...... 102 5.5 Generalization performance of neural networks...... 103 5.5.1 Regularization in neural networks ...... 103 5.5.2 Early stopping...... 104 6 Lithology recognition with remote sensing image data...... 105 6.1 Introduction ...... 105 6.2 The general principle of remote sensing for land cover classification...... 105 6.2.1 Digital images...... 105 6.2.2 Characteristic spectra...... 106 6.2.3 Concept of a classification with multispectral image data ...... 107 6.3 Data preparation with Landsat TM image for land cover classification ...... 108 6.4 Land cover classification based on the artificial neural network (ANN) method ...... 109
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Contents 6.4.1 Training data preparation...... 109 6.4.2 Classification result...... 110 6.5 Discussion ...... 111 6.6 Conclusion ...... 112 7 Local scale landslide susceptibility mapping in the Badong study area ...... 114 7.1 Introduction ...... 114 7.2 Slope stability evaluation with the multilayer feed-forward (MLF) neural network...... 115 7.2.1 Neural network data processing ...... 115 7.2.1.1 Input data processing...... 116 7.2.1.1.1 Elevation ...... 116 7.2.1.1.1.1 Values for neural network training...... 116 7.2.1.1.1.2 Values for neural network application...... 116 7.2.1.1.2 Slope angle...... 117 7.2.1.1.2.1 Values for neural network training...... 117 7.2.1.1.2.2 Values for neural network application...... 117 7.2.1.1.3 Slope structure...... 118 7.2.1.1.3.1 Values for neural network training...... 118 7.2.1.1.3.2 Values for neural network application...... 119 7.2.1.1.4 Lithology...... 120 7.2.1.1.4.1 Values for neural network training...... 120 7.2.1.1.4.2 Values for neural network application...... 121 7.2.1.1.5 Tectonics...... 122 7.2.1.1.5.1 Values for neural network training...... 122 7.2.1.1.5.2 Values for neural network application...... 123 7.2.1.1.6 Land use...... 124 7.2.1.1.6.1 Values for neural network training...... 124 7.2.1.1.6.2 Values for neural network application...... 125 7.2.2 Neural network execution...... 127 7.2.3 Neural network outputs (slope stability classification for all unit areas).. 128
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Contents 7.3 Discussion ...... 130 7.4 Conclusion ...... 131 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment...... 133 8.1 Introduction ...... 133 8.2 Study area...... 133 8.2.1 Location of Xiangxi catchment ...... 133 8.2.2 Climate in Xiangxi catchment...... 134 8.2.3 Landslides in Xiangxi catchment...... 134 8.3 Geological background...... 136 8.4 Data preparation and processing ...... 139 8.4.1 Geology...... 140 8.4.2 Slope angle and slope curvature ...... 140 8.4.3 River network...... 140 8.4.4 Landslides ...... 141 8.5 Relationships between landslides and their causative factors...... 141 8.5.1 Lithology ...... 142 8.5.2 Slope angle...... 143 8.5.3 Slope curvature...... 143 8.5.4 River network...... 144 8.6 ANN application ...... 144 8.7 Result ...... 148 8.8 Discussion ...... 148 8.9 Conclusion ...... 150 Summary ...... 151 References...... 152
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Figures Figures Fig. 1. 1: Landslide distribution in the Three Gorges Reservoir Area (revised from Guo et al., 2008)...... 1
Fig. 2. 1: The Yangtze River basin in China (revised, http://zh.wikipedia.org/wiki/%E9%95%BF%E6%B1%9F) ...... 4 Fig. 2. 2: The water downgrade in the mainstream of the Yangtze River (revised, http://www.teacher.com.cn/) ...... 5 Fig. 2. 3: Overview of Three Gorges in Google map...... 6 Fig. 2. 4: Longitudinal elevation profile of the river bed in the Three Gorges Reservoir (Schönbrodt-Stitt, 2013)...... 7 Fig. 2. 5: Overview of the Three Gorges Reservoir Area (the area with white border) in Google map ...... 8 Fig. 2. 6: Overview of the Badong study area from Google map (taken on Jan. 3. 2005)...... 9 Fig. 2. 7: The average yearly precipitation in the Three Gorges Reservoir Area (Guo, 2008) 10 Fig. 2. 8: The maximum daily precipitation in the Three Gorges Reservoir Area (Guo, 2008) ...... 10 Fig. 2. 9: The drainage system in the Badong study area...... 12
Fig. 3. 1: Tectonic map of China ...... 15 Fig. 3. 2: Geological map of the Middle Yangtze plate (Modified from Hu, 2011)...... 16 Fig. 3. 3: The folds in the geological map for the northern part of the Middle Yangtze plate (Modified from Hu, 2011)...... 17 Fig. 3. 4: Regional geological map around the Badong study area (Simplified from BGEDHP, 1987)...... 18 Fig. 3. 5: Geological map of the Badong study area...... 21 Fig. 3. 6: The sketch showing the profile at the location in figure 3.5...... 21 Fig. 3. 7: Typical outcrop of T1j3 sub-formation...... 23 Fig. 3. 8: The typical outcrop of T2b11...... 24 Fig. 3. 9: The typical outcrop of upper layers of T2b12 ...... 25 Fig. 3. 10: Typical outcrop of T2b2 sub-formation on the riverside in North Badong ...... 26 Fig. 3. 11: Two sites of T2b31 formation outcrop in South Badong...... 27 Fig. 3. 12: The T2b32 outcrop in Badong study area...... 28 Fig. 3. 13: Typical outcrop of T2b4 in East Badong ...... 29 Fig. 3. 14: Typical outcrop from T2b5 sub-formation in East Badong ...... 30 Fig. 3. 15: Outcrop of T2b5 bedding layers (Photos from Claudia Muschick)...... 31 Fig. 3. 16: Tectonic map of the Badong study area...... 32 Fig. 3. 17: The sketch showing the extension of Guandukou syncline at the profile A - A’.... 32 Fig. 3. 18: The axis of Guandukou syncline intersects the outlet of S3 ravine in South Badong ...... 33 Fig. 3. 19: The folding zone in the south limb of Guandukou syncline in the Liangshuixi ravine...... 34 Fig. 3. 20: The south border of T2b2/T2b3 in the Xinwu anticline on the eastern riverside of Dongrang River...... 35
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Figures Fig. 3. 21: The invert-lying layers in the southern limb of Xinwu anticline on the western riverside of Dongrang River (Photo taken by Joachim Rohn) ...... 36 Fig. 3. 22: A depict of the orientation of the layers along the street (Godzik, 2013)...... 36 Fig. 3. 23: The outcrop of Guandukou fault zone in the upstream area of S5 ravine (660 m asl.) ...... 37 Fig. 3. 24: Fault in the T2b3 layers on the west side of S2 ravine in South Badong (400 m asl.) ...... 38 Fig. 3. 25: Outcrops of the fault in both sides of S2 ravine ...... 38 Fig. 3. 26: Fault in T2b2 clayey siltstone bedrock upstream of S4 ravine in South Badong (550 m asl.)...... 39 Fig. 3. 27: Conjugated shear joints in the landslide body of Zhaoshuling landslide (joints and the bedding plane are displayed with the software “stereonet” and the red lines stand for the positions of bedding layers) ...... 40 Fig. 3. 28: Conjugate shear joints in the sandstone layers of T2b2 formation (joints and the bedding plane are displayed with the software “stereonet” and the red lines stand for the positions of bedding layers)...... 41 Fig. 3. 29: Dip joint in the T2b2 formation in South Badong (joints and bedding planes are displayed with the software “stereonet” and the red lines stand for the positions of bedding layers) ...... 42 Fig. 3. 30: An outcrop of spaced cleavage in the T2b3 formation bedrock in South Badong (cleavage and bedding planes are displayed with the programme “stereonet” and the red lines stand for the positions of bedding layers)...... 43 Fig. 3. 31: An outcrop of spaced cleavage in T2b3 sub-formation in East Badong...... 44 Fig. 3. 32: Continuous cleavage in T2b3 sub-formation...... 44 Fig. 3. 33: Spaced cleavage in clayey siltstone s of T2b4 sub- formation in East Badong...... 45 Fig. 3. 34: An outcrop of crush zone in bedding layers ...... 46 Fig. 3. 35: The speculated geological border between T2b2 layers and T2b3 layers on an elevation of 250 m asl. (Profile B - B’ stands for the location of the figure 3.38).. 47
Fig. 4. 1: Mapped landslides in the Badong study area...... 52 Fig. 4. 2: General slope angles of the slide bodies...... 57 Fig. 4. 3: Elevation values of the top of slide bodies ...... 58 Fig. 4. 4: Slope classification referring to the relationship between the relative aspects of slope and bedding layers (Chen, 2009)...... 59 Fig. 4. 5: Location of the introduced slides in the study area ...... 61 Fig. 4. 6: Rock slides RS8, RS9 and RS10 on the riverside in North Badong...... 62 Fig. 4. 7: Overview of the rock slide RS8 and the underlying bedding layers from its west side ...... 63 Fig. 4. 8: An outcrop of the border between the rock slide body and the underlying bedrocks ...... 64 Fig. 4. 9: Profile of the rock slide RS8 in the location marked in the figure 4.6 ...... 64 Fig. 4. 10: An outcrop of the rock slide body RS 9 beside the street...... 65 Fig. 4. 11: An outcrop of the rock slide body RS10 from beside the street ...... 66 Fig. 4. 12: Rock slide RS18 rock slide on the riverside of the Yangtze River in South Badong
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Figures
...... 67 Fig. 4. 13: Outcrops of rock slide body on an elevation of 250 m asl...... 68 Fig. 4. 14: Different material compositions of the rock slide body RS18...... 68 Fig. 4. 15: Profile of the Zhaoshuling rock slide (Revised from Chai, 2008)...... 69 Fig. 4. 16: Overview of rock slide RS22...... 70 Fig. 4. 17: Rock slide materials of the rock slide RS22...... 71 Fig. 4. 18: Bedding layers in and near the main scarp ...... 71 Fig. 4. 19: Geotechnical profile for the rock slide RS22 at the location marked in the figure 4.16...... 72 Fig. 4. 20: Overview of the debris slide DS23 on the east side of the N2 ravine close to the outlet in North Badong...... 73 Fig. 4. 21: Overview of the debris slide RS30 ...... 74 Fig. 4. 22: The middle part of the debris slide body ...... 75 Fig. 4. 23: An outcrop of debris slide body and bedding layers of debris slide DS30...... 76 Fig. 4. 24: Geotechnical profile of the debris slide RS30 ...... 77 Fig. 4. 25: Overview of the debris slide DS44 ...... 78 Fig. 4. 26: Accumulation of slope deposits immediately close to the main scarp of debris slide DS44...... 79 Fig. 4. 27: Overview of rock slide DS45...... 80 Fig. 4. 28: Observation of debris slide body on the street...... 81 Fig. 4. 29: Geotechnical profile for the debris slide DS45...... 81 Fig. 4. 30: The debris slide event on June 10, 1995 (Photo from PPT by Hongbiao Jia) ...... 82 Fig. 4. 31: Overview of the rock wall with potential of rock fall at the east side of the N2 ravine...... 83 Fig. 4. 32: The rock fall site on the east side of the N2 ravine on an elevation of ca. 200 m asl...... 84 Fig. 4. 33: Areas with residual material from the T2b3 sub-formation in the West of North Badong ...... 85 Fig. 4. 34: The residual slide mass from T2b3 covering on the T2b2 bedrock...... 85 Fig. 4. 35: Speculated process of slope evolution in the profile A - A’ in North Badong in figure 4.33 ...... 86 Fig. 4. 36: An overview of the Huangtupo landslide (Google map) ...... 87 Fig. 4. 37: Distribution of the four debris masses of the Huangtupo landslide according to HEGTHP (2001)...... 88 Fig. 4. 38: Cumulative displacements from 4 GPS measuring points on the debris mass DS73 following the water level change in the Yangtze River (Wang et al., 2014)...... 90 Fig. 4. 39: Geotechnical profile of the debris mass DS73 (Wang et al., 2014)...... 91 Fig. 4. 40: Shear zone between the bedding layers at the cross of the main tunnel and the branch tunnel 3...... 92 Fig. 4. 41: Two dimensional X-Ray diffraction test for the scratch surface of shear zone ...... 93 Fig. 4. 42: The corresponding dimensional X-Ray test for the scratch surface of shear zone. 93 Fig. 4. 43: Western and eastern secondary tunnels of the branch tunnel 3 at a horizontal depth of 140 m depth...... 95
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Figures
Fig. 6. 1: Technical characteristics of the digital image data (Richards and Jia, 2005) ...... 106 Fig. 6. 2: Spectral reflectance characteristics of vegetation and three kinds of sedimentary rocks in a wavelength between 0.4 - 2.5 μm (Sabins, 1999)...... 107 Fig. 6. 3: Pixels can be classified according to the reflectance of every band (Richards and Jia, 2006)...... 108 Fig. 6. 4: A diagram for classification with artificial neural network ...... 109 Fig. 6. 5: Sampling of training data based on the Landsat 5 (date: August 15, 1995) image 110 Fig. 6. 6: Result of land cover classification compared with the mapped geological borders for the study area...... 111
Fig. 7. 1: General concept of stability evaluation with artificial neural network...... 115 Fig. 7. 2: Digital elevation model (DEM) for the study area ...... 117 Fig. 7. 3: Slope angle of the study area ...... 118 Fig. 7. 4: Classification of slide bodies referring to slope structures...... 119 Fig. 7. 5: Slope structure classification in the study area...... 120 Fig. 7. 6: Classification of slide bodies referring to lithology...... 121 Fig. 7. 7: Lithological influence classification in the study area...... 122 Fig. 7. 8: Classification of slide bodies according to tectonics...... 123 Fig. 7. 9: Tectonic influence classification in the study area ...... 124 Fig. 7. 10: Classification of slide bodies according to land use...... 125 Fig. 7. 11: Influence classification of land use in the study area ...... 126 Fig. 7. 12: Classification of slide bodies referring to their activity states...... 127 Fig. 7. 13: Diagram of ANN for the slope stability evaluation ...... 128 Fig. 7. 14: Slope stability classification in the Badong study area...... 129 Fig. 7. 15: Accumulative distribution of stability values ...... 130
Fig. 8. 1: Location of the Xiangxi catchment (red color area) (after YPIS 2002, unpublished Yangtze-Project Information, University Giessen) ...... 134 Fig. 8. 2: a, Digital Elevation Model (DEM) (150 m × 150 m resolution) of Xiangxi catchment based on ASTER Global DEM; b, Slope angle based on DEM for Xiangxi catchment; c, Slope curvature based on DEM for Xiangxi catchment; d, River network and its order classification based on DEM for Xiangxi catchment135 Fig. 8. 3: Landslide examples on Xiangxi River...... 136 Fig. 8. 4: a, The general geological background relevant to Table 8.1 (simplified from 1: 200 000 geological map, Bureau of Geological Exploration & Development of Hubei Province, 1984) and landslide inventory in Xiangxi catchment; b, A typical profile (W-E) for the river section at Xiakou county...... 138 Fig. 8. 5: The landslide area (× 104 m2) in three buffer areas along the Xiangxi River...... 143 Fig. 8. 6: Training area and application area division in the Xiangxi catchment...... 145 Fig. 8. 7: General concept of landslide susceptibility analysis for the Xiangxi catchment ... 145 Fig. 8. 8: Flow chart of the artificial neural network model ...... 146 Fig. 8. 9: a Landslide susceptibility classification for Xiangxi catchment; b, an example of the susceptibility classification for single landslides based with the terrain unit size of 150 m × 150 m ...... 148
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Tables
Tables
Table 2. 1: The monthly precipitation in the Badong study area (HEGTHP, 2001) ...... 11 Table 2. 2: Geometric description of the large ravines (watershed area > 1 km2) in South Badong ...... 13 Table 2. 3: Geometric description of large ravines (watershed area > 1 km2) in North Badong ...... 13 Table 2. 4: The geometric description of the large ravine (watershed area > 1 km2) in West Badong ...... 13 Table 2. 5: The geometric description of the large ravines (watershed area > 1 km2) in East Badong ...... 14
Table 3. 1: Short description of the geological formations in the Badong study area (BGEDHP, 1987)...... 22 Table 3. 2: The elevation and the age of the terraces in the study area...... 47
Table 4. 1: Abbreviated classification of slope movements (Varnes, 1978)...... 50 Table 4. 2: The states of landslide activity (modified from WP/WLI, 1993)...... 51 Table 4. 3: Information about mapped slides in the study area...... 53
Table 6. 1: Seven spectral bands of the Thematic Mapper (TM) sensor (USGS, 2012)...... 109
Table 7. 1: Data source for neural network application ...... 116 Table 7. 2: Area percentage of unit areas in different stability states...... 130 Table 7. 3: Classification of active slides compared with the result from neural network output ...... 130
Table 8. 1: Geology in Xiangxi catchment and the mapped landslide area in every formation area ...... 139 Table 8. 2: Data preparation ...... 140 Table 8. 3: Indices for geological formations as applied for the ANN analysis...... 140 Table 8. 4: Indices for different stream orders as applied for the ANN analysis ...... 141 Table 8. 5: Slope angle for landslide bodies in different geological formations (SD: standard deviation)...... 143 Table 8. 6: Curvature of landslide body (SD: standard deviation)...... 144 Table 8. 7: Arrangement of the input values and the target output values for training phase 146 Table 8. 8: Weight and bias in the ANN model from input layer to hidden layer...... 147 Table 8. 9: Weight and bias in the model from hidden layer to output layer ...... 147
xi
Chapter 1 Introduction
1 Introduction
1.1 Landslides in the Three Gorges Dam area The Three Gorges Dam has the largest reservoir area (1 084 km2) in the world. Since the impoundment of the reservoir, the water level has been raised ca. 100 m at most in the reservoir area and ca. 632 km2 of riverside have been submerged. Accompanied by the water level rising, the hydrogeological conditions of the submerged area and its surrounding area have been enormously changed. At the same time, the situation of the newly submerged riverside has also been essentially changed through which the stability of the riverside has also been changed. Landslides on the riversides are the common consequence during the rebalancing process. A landslide is the “downwards movement of the soil, debris or rock under gravity” (Varnes, 1978). If their occurrence endangers people and their property, this phenomenon is a natural hazard. To mitigate the possible danger, the causative factors of this phenomenon have to be firstly ascertained. Landslides often occur in mountain areas. Rainfall, snowmelt, earthquakes, river erosion, human activities etc. are possible impacting factors for landslides. According to Liu et al. (2007), 4 469 landslides have been registered in the Three Gorges Reservoir Area, from which 3 830 sites are slides, 549 sites are falls and 90 sites are flows, 684 landslides have volumes larger than 105 m3, from which 215 landslides are located in the main stream and 469 landslides are located in the tributaries. Especially 4 landslides of them have volumes larger than 108 m3 (Xue et al., 2009). Figure 1.1 shows the landslide distribution in the Three Gorges Reservoir Area.
Badong
N Fengjie
Three Gorges Dam Wanzhou
Landslide Magmatic or metamorphic rock
Carbonate rock
Chongqing Clasolite rock
Alternation of carbonate and clasolite 100 km bedding layers
Fig. 1. 1: Landslide distribution in the Three Gorges Reservoir Area (revised from Guo et al., 2008) 1
Chapter 1 Introduction Badong county town is a town center with ca. 60 800 inhabitants (until Oct. 2010, www.esz.gov.cn) and lies ca. 70 km upstream of the Three Gorges dam. It suffers directly from the reservoir impoundment. Due to the special geological background, landslides develop extremely densely in this area. 109 landslides have been mapped in an area of ca. 64 km2 and 20 of them are observed to be instable. The Huangtupo landslide in the Badong study area is one of the largest landslides in the Three Gorges region. It has a volume of ca. 6.9 × 107 m3. It is actually a landslide group composed of four landslide bodies. The monitoring data from 2003 to 2008 show a cumulative movement of 80 - 148 mm at the toe of the landslide body. The maximal velocity reaches 12 mm/month (Wang et al., 2014). Due to the large deformation of the landslide body and the potential possibility of large scale movement in the future, the inhabitants (ca. 18 000 people) on the landslide have been organized to move out of this area since 2007.
1.2 Targets of the study Due to the expansion of the town center of Badong county, more and more farmlands have been utilized for new residential zones or business quarters. The potential damages from landslides will increase with the land use change in the area. To minimize the risk from landslides in future, an evaluation of landslide susceptibility is necessary for this area. In the aspect of landslide research, the Badong study area is a representative fore-field of the reservoir, which is sensitive for landslides and directly influenced by the reservoir impoundment. How the hill slopes react under the yearly water level change in the reservoir is the kernel question for the riverside slopes. “The best prophet of the future is the past” (Lord Byron). The regularities of the occurred landslides can be used to estimate where landslides may occur in the future. Based on this methodology, a landslide susceptibility map for the study area is aimed to be completed. To conduct this susceptibility analysis for landslides, the relevant impacting factors will be taken into account. In this study, slope angle, distance to the Yangtze River, slope structure, lithology, bedrock integrity and land cover will be applied as impacting factors in evaluating the landslide susceptibility in the study area.
1.3 Approaches of the study To collect the original information of the area, the geological mapping and landslide mapping are the basic preparation. In this procedure, information about the lithology, tectonic, drainage system, etc. should be collected. Furthermore, the landslides in the study area will also be mapped, so that the correlations between landslide occurrence and the geological background can be investigated. In the second, third and fourth chapters, the geographical and geological background, the landslide development in the area will be introduced in succession. The available data about the geology and the landslides in the area can be used to predict the possible areas of landslides in the future. To realize this process, a pattern recognition method, which is called artificial neural network (ANN), will be adopted. Artificial neural network (ANN) is the main methodology used for data analysis. Its fundamental function is pattern recognition based on building correlations between the input and output data. The theory of
2
Chapter 1 Introduction the ANN-method will be introduced in the fifth chapter to get an essential knowledge about the basic idea of this method, which kinds of data are needed for the application of this method, and how to evaluate the results. Firstly the ANN will be used to classify the land cover of the area. Due to the obvious difference between the two main types of lithology in the study area, areas with different lithology can be distinguished as two different types of land cover (the areas of clayey limestone and the areas of clayey siltstone). A scene of satellite data from Landsat 5 will be selected for land cover classification. The result of the land cover classification should be helpful for the revise of the geological map. The application of the ANN-method on lithology recognition will be introduced in chapter 6. Secondly the ANN-method will be applied for landslide susceptibility in the study area. The mapped landslides will be adopted as training samples for the network to build internal correlations between the causative factors and the susceptibility for landslides. The application of the ANN-method for landslide susceptibility analysis will be introduced in chapter 7. In the end the essential results from the field work and the indoor-analysis will be summed up. The advantages and disadvantages about the application of the ANN-method for landslide susceptibility analysis will be shortly discussed.
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Chapter 2 Overview
2 Overview
2.1 Location
2.1.1 The Yangtze River The Yangtze River is about 6 300 km long and the third longest river in the world. It originates from an elevation of ca. 5 400 m asl. in the Tibetan Plateau, and flows through the southern part of the Sichuan basin, the Three Gorges and flows into the East China Sea in Shanghai city. The Yangtze River catchment lies completely in Chinese territory and covers an area of 180 million km2, which is 18.8% of the land area of China (figure 2.1) (http://zh.wikipedia.org/wiki/%E9%95%BF%E6%B1%9F).
500 km
Yichang Hukou
Fig. 2. 1: The Yangtze River basin in China (revised, http://zh.wikipedia.org/wiki/%E9%95%BF%E6%B1%9F)
The upstream area of the Yangtze River stretches 4 504 km long from the river source until Yichang city, which is ca. 72% of the total length. The middle stream is the river section from Yichang city to Hukou county on the Yangtze River, which is 858 km long and 14% of the total length. The downstream begins in the Jiujiang city and injects in the East China Sea in Shanghai city, which is in total 938 km long and ca. 15% of the total length (http://www.cjw.com.cn/zwzc/cjcd/lyzs/). The water level in Yichang city is only ca. 45 m asl., which means that 72% of the total length has ca. 99% of the total vertical height (figure 2.2).
4
Chapter 2 Overview
Fig. 2. 2: The water downgrade in the mainstream of the Yangtze River (revised, http://www.teacher.com.cn/)
The average discharge of the Yangtze River into the East China Sea is 30 166 m3/s, which is ca. 10 times as much as the discharge of the Rhine River into the North Sea in Europe. The maximum discharge is 110 000 m3/s and the minimum discharge is 2 000 m3/s (http://zh.wikipedia.org/wiki/%E9%95%BF%E6%B1%9F).
2.1.2 The Three Gorges The Three Gorges is the 192 km long river section from Yichang city upstream to Fengjie town in the upper reaches of the Yangtze River, which is named after three successive gorges in the river (figure 2.3). These three gorges, which are 119 km long in total, are characterized by a deep-cut landform with vertical limestone cliffs. From upstream to downstream, they are separately called Qutang Gorge (8 km long), Wu Gorge (45 km long) and Xiling Gorge (66 km long). The remaining 73 km river segments have a relatively broad river channel and a gentle riverside. Before 2002, the river channels in Three Gorges were mostly 100 m to 300 m wide but it had also been as narrow as only 75 m wide at most. The hilltops in this area are mostly as high as 1 000 m asl. to 1 500 m asl. (Chen, 1992) In the Yangtze River, the average yearly flow discharge through the Yichang Hydrologic Station, where is the end of the upper reaches, is 451 billion m3 and its average discharge is 14 300 m3/s. The maximum discharge is 71 100 m3/s and the minimum discharge is 2 770 m3/s. In July and August the flow has 70 - 80 % of the yearly discharge (Guo, 2008).
5
Chapter 2 Overview
N
Fengjie Wushan
Badong
Gezhouba Dam
Three Gorges Dam Yichang Three Gorges
50 km
Fig. 2. 3: Overview of Three Gorges in Google map
2.1.3 The Gezhouba Dam Project (Dec. 1970 - Dec. 1988) The Gezhouba Dam Project is the first hydraulic project on the Yangtze River. The construction of the Gezhouba Dam lasted from 30. Dec. 1970 to 10. Dec. 1988. The dam is located 2.3 km downstream of Nanjingguan town in Yichang city, which has been denoted in figure 2.3. The dam is maximum 53.8 m high and 2606.5 m long. The dam top is at 70 m asl. and the water level at the upstream side of the dam is controlled to be constantly at 66±1 m asl. during the whole year (http://www.hbhp.net/sdz/gzbsdz.html). The reservoir impoundment influenced a 200 km long river segment of the Yangtze River, which extended from the dam site upstream to Fengjie county town. Its reservoir has a storage capacity of 1.58 billion m3. Because of its small storage capacity, the reservoir has almost no function of flood prevention.
2.1.4 The Three Gorges Dam Project (Dec. 1994 - Dec. 2009) The Three Gorges Dam Project (TGP) includes two phases. The first phase was the construction of the Three Gorges Dam (TGD) and its relevant facilities. It was begun on 14. Dec. 1994 and was finished in Dec. 2009. The dam is located 38 km upstream of the Gezhouba Dam in the Xiling Gorge, which has been denoted in figure 2.3. The second phase was the operation of water level regulation and electricity production since the year 2002. The two phases have a few years of overlapping. The discussions about the advantages and the disadvantages of the Three Gorges project have been intensively performed since the beginning of its planning.
6
Chapter 2 Overview 2.1.4.1 Three Gorges Reservoir (TGR) The Three Gorges Reservoir (TGR) is the inundation area in the Yangtze River catchment, which is built by reservoir impoundment upstream of the Three Gorges Dam. It stretches in total 663 km long in the Yangtze River catchment, from which 574 km expand in the mainstream and 89 km expand in the tributaries. The reservoir segment in the mainstream is much longer than the Three Gorges (192 km) and the end of the reservoir reaches upstream to Jiangjing county town, which is ca. 40 km upstream of Chongqing city (figure 2.4). The reservoir covers a maximal water area of 1 084 km2 and has a total storage capacity of 39.3 billion m3. Its storage capacity is almost 25 times as large as that of the Gezhouba Reservoir (http://www.hbhp.net/sdz/sxsdz.html). Because of its huge storage capacity and the large rise of water level in the reservoir, the project has a composite function of electricity generation, flood prevention and shipping improvement. Since the impoundment in the Three Gorges Reservoir in 2002, the water level was intermittently raised from 66 m asl. up to higher than 145 m asl.. Since 2008, the water level has been controlled between 145 m asl. and 175 m asl..
(m) (km) Fig. 2. 4: Longitudinal elevation profile of the river bed in the Three Gorges Reservoir (Schönbrodt-Stitt, 2013)
2.1.4.2 Three Gorges Reservoir Area (TGRA) The Three Gorges Reservoir Area (TGRA) is a definition for the total administrative coverage of 20 counties, which get involved in the impoundment of the Three Gorges Reservoir (figure 2.5). It covers a total area of ca. 56 700 km2 in the Yangtze River catchment. This term should be clearly differentiated from the definition of Three Gorges Reservoir (TGR).
7
Chapter 2 Overview
N
Three Gorges Dam
Badong
Chongqing
200 km
Fig. 2. 5: Overview of the Three Gorges Reservoir Area (the area with white border) in Google map
2.1.5 The study area The Badong study area covers an area surface of 64 km2 on both sides of the Yangtze River. It is situated ca. 70 km upstream of the Three Gorges Dam. The hilltops around the Badong study area reach mostly ca. 700 m asl. to ca. 1 200 m asl. The Yangtze River flows from the southwest direction into the study area and changes in the southeast direction out of the study area. The Badong county town with ca. 67 000 inhabitants is situated in the study area. The town centre has been two times resettled in the Badong study area. Firstly it has been resettled from the old Badong county town ((1) in figure 2.6), which has been now submerged by the Yangtze River, to Huangtupo town ((2) in figure 2.6) in the 1980s due to the impoundment of the Gezhouba Dam project. Huangtupo town is located ca. 2 km west of the old town. In the beginning of the 1990s, Huangtupo town has been confirmed locating on a huge old landslide, which could be instable due to the impoundment of the Three Gorges Reservoir. Since the end of the 2000s, the government organizations have been gradually moved to Xinling town ((3) in figure 2.6), which is ca. 5 km west of Huangtupo town on the south riverside of the Yangtze River and is planned mainly to serve as an administrative and business center of the county. Since 2011, the inhabitants in Huangtupo town began to be relocated to Tonggubao town ((4) in figure 2.6), which lies opposite to Xinling town on the north riverside of the Yangtze River.
8
Chapter 2 Overview
N
(4) Tonggubao town
(3) Xinling town (2) Huangtupo
(1) Yangtze River old Badong county town
5 km
Fig. 2. 6: Overview of the Badong study area from Google map (taken on Jan. 3. 2005)
Due to the two times of resettlement and rebuilding, buildings have now fully covered the area on the south riverside of the Yangtze River on an elevation lower than 400 m asl. and in Tonggubao county on the north riverside. Except of the building areas, the rest of the land has been as much as possible reclaimed as farm land.
2.2 Climate
2.2.1 The climate in Three Gorges Reservoir Area The climate of the Three Gorges Reservoir Area is subtropical. The average yearly precipitation changes between 987 mm/a and 1 326 mm/a (Fig. 2.7). Generally the area around Wanzhou county has the highest precipitation. In the areas east and southwest of Wanzhou county the average yearly precipitation is lower in comparison. The rain season lasts from May to September and has often rainstorm days. The maximum daily precipitation changes between 127 mm/d to 229 mm/d, which is around 10% of the yearly precipitation (Fig. 2.8). Chongqing city, Kaixian county , Wanzhou county and Yichang city have relative high values of the maximum daily precipitation. The average yearly temperature is 16.7 °C to 18.7 °C. July is the hottest month of a year and the average temperature is 28 °C to 30 °C. The temperature in one year reaches at most 41 °C. (Guo, 2008)
9
Chapter 2 Overview
100 km Kaixian Badong Wanzhou
Yichang
Chongqing Precipitation (mm)
Fig. 2. 7: The average yearly precipitation in the Three Gorges Reservoir Area (Guo, 2008)
Kaixian 100 km Badong Wanzhou
Yichang
Chongqing Precipitation (mm)
Fig. 2. 8: The maximum daily precipitation in the Three Gorges Reservoir Area (Guo, 2008)
2.2.2 The climate in the Badong study area The average yearly precipitation in Badong is 1 100.7 mm (1954 - 2000) (HEGTHP, 2001). The maximum yearly precipitation is 1 522.4 mm (1954) and the minimum yearly precipitation is 694.8 mm (1960). The maximum weekly precipitation is 237.5 mm (from 07. to 14. August 1991); the maximum daily precipitation is 193.3 mm (15. July 1962) and the maximum one-hour precipitation is 75.2 mm (6. August 1991). From May to September is the rainy season generating 60% - 70% of the yearly precipitation (Xu, 1994).
10
Chapter 2 Overview
Table 2. 1: The monthly precipitation in the Badong study area (HEGTHP, 2001) Month Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Yearly Precipitation (mm) 14.6 24.98 53.0 90.5 144.5 168.6 178.5 145.1 128.8 89.7 44.1 18.1 1100.7 Percentage of the yearly precipitation 1.3 2.3 4.8 8.2 13.2 15.3 16.2 13.2 11.7 8.1 4 1.7 100 (%)
In Badong county town, the average yearly temperature is 17.5 °C. July and August are the hottest months of the year. The average of the maximum daily temperature in July and August is 35.3 °C. The maximum temperature was 41.4 °C on 6. August 1981. January and February are the coldest months in the year. The average of the minimum daily temperature in January and February is 3.8 °C. The minimum temperature was -9.4 °C on 03. January 1977 (HEGTHP, 2001).
2.3 Hydrology Before the impoundment of the Gezhouba Reservoir, the water level in the Badong study area changed notably with the seasons in the river segment of Badong county town. July to September was the flooding period of a year. The water level was at most 112.85 m asl. in the year 1970 and at lowest 54.77 m asl. in the year 1979 (HEGTHP, 2001). Due to the impoundment of the Gezhouba Reservoir, the water level is controlled to stay at around 66 m asl. during the whole year since 1988. Because of the impoundment in the Three Gorges Reservoir, the water level in Badong county town is kept between a range from 145 m asl. to 175 m asl. since 2009. The river segment is 700 - 1 100 m broad. Two northern tributaries of the Yangtze River, the Shennong River in the northwest part of the area and the Dongrangkou River in the northeast of the area, flow from north to south direction into the Yangtze River in the study area (figure 2.9). The Shennong River stretches ca. 60 km long in northern direction. Its watershed covers an area of 1 031.5 km2. It derives from the Shennongjia Nature Reserve Area, whose highest point is 3 059 m asl. (http://wenku.baidu.com/view/098fbe3483c4bb4cf7ecd12c.html). The Dongrang River stretches totally ca. 18 km long also in northern direction and it has a 126.9 km2 large watershed. The height difference is 685 m from the source area to the output. (http://www.cjbd.com.cn/html/dongrangkou/) The Yangtze River and the two northern tributaries of it divide the study area into four sub-areas: South Badong, North Badong, West Badong and East Badong (figure 2.9). South Badong is the drainage area south of the Yangtze River in figure 2.9. North Badong is the drainage area north of the Yangtze River but between the Shennong River and the Dongrang River. West Badong is the drainage area north of the Yangtze River and west of the Shennong River. East Badong is the drainage area north of the Yangtze River and east of the Dongrang River. Based on the DEM for the study area with a minimum elevation of 100 m asl., the drainage system has been calculated with ArcGIS software (figure 2.9). Totally five stream orders have been divided.
11
Chapter 2 Overview
North Badong
East West Badong Badong
South Badong
Fig. 2. 9: The drainage system in the Badong study area
Generally the large ravines in the study area stretch in the north-south direction or in the east-west direction, which is correlated with the regional tectonic situation. Comparing the ravines in east-west direction with the ravines in north-south direction, the general gradients of the ravines in east-west direction are gently lower than those in north-south direction. The gradient of the main stream of ravines has a clear relationship with the lithology of the area. The water flow in the ravines can be only temporarily observed after rainfall. The ravines, whose watershed is larger than 1.0 km2, have been separately introduced in four sub-areas.
2.3.1 South Badong South Badong exhibits a fan-shaped form facing north and is cut by 5 by ravines, which are separately called S1 ravine, S2 ravine, S3 ravine, S4 ravine and S5 ravine (see figure 2.9). S1, S2, S3 and S5 have the source from limestone bearing mountains of Lower Triassic formations (T1), on an elevation between 1 100 m asl. and 1 500 m asl. south of the Badong study area. This formation has built high mountains and deep gorges in the Three Gorges region. Only S4 has the complete watershed in the study area and the source from clayey siltstone area. The watershed of S5 extends out of the drainage area as shown in figure 2.9. The S1, S2, S3 and S4 ravines have a similar general gradient along the main stream of the watershed (table 2.2). All of the five ravines flow almost in the north direction into the Yangtze River.
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Chapter 2 Overview
Table 2. 2: Geometric description of the large ravines (watershed area > 1 km2) in South Badong Main stream Highest point in the Main stream Elevation of the General gradient of Watershed abbreviation watershed (m asl.) length (m) outlet (m asl.) the main stream (°) area (km2) S1 1 487 4 213 100 14 6.3 S2 1 311 4 597 100 13 3.4 S3 1 251 4 168 100 14 3.8 S4 860 1 756 100 15 1.1 S5* > 1 400 > 4 008 100 - > 10.3 * (The watershed of S5 ravine has a large area extending out of the study area and the data is not completely available)
2.3.2 North Badong In North Badong three ravines develop mainly: N1 ravine, N2 ravine and N3 ravine. N1 and N2 flow parallel in the south direction into the Yangtze River. N3 lies in the northern part of North Badong and flows from West to East into the Dongrang River. North Badong covers the watersheds of all three ravines. The N1 ravine has the lowest general gradient in the ravines stretching in north-south direction (table 2.3).
Table 2. 3: Geometric description of large ravines (watershed area > 1 km2) in North Badong Highest point in Main Elevation of General gradient of Main stream Watershed the stream the the main stream abbreviation area (km2) watershed (m asl.) length (m) outlet (m asl.) (°) N1 931 3 411 100 12 4.7 N2 925 3 203 100 13 2.4 N3 931 2 316 160 14 3.4
2.3.3 West Badong The W1 ravine is the only large ravine in West Badong. It flows in eastern direction into the Shennong River. The whole watershed of the W1 ravine lies in the West Badong area. The W1 ravine has the lowest general gradients of the ravines stretching in east-west direction (table 2.4).
Table 2. 4: The geometric description of the large ravine (watershed area > 1 km2) in West Badong Highest point in Main Elevation of General gradient of Main stream Watershed the stream the the main stream abbreviation area (km2) watershed (m asl.) length (m) outlet (m asl.) (°) W1 759 1 435 100 11 1.9
2.3.4 East Badong In East Badong two ravines develop mainly: The E1 ravine (6.7 km2 watershed area) and the E2 ravine (3.9 km2, watershed area). But the study area covers only the western part of them. 13
Chapter 2 Overview E1 and E2 are tributaries of the Dongrang River and flow from western direction into the Dongrang River. The E1 ravine has a similar watershed size than the S1 ravine, which stretches in north-south direction. But the general gradient of E1 is clearly lower than that of S1 (table 2.2 and 2.5). There is also a similar regularity between E2 and S3.
Table 2. 5: The geometric description of the large ravines (watershed area > 1 km2) in East Badong Highest point in Main Elevation of General gradient of Main stream Watershed the stream the the main stream Abbreviation area (km2) watershed (m asl.) length (m) outlet (m asl.) (°) E1 1 116 3 024 120 11 6.7 E2 1 116 3 314 160 13 3.9
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Chapter 3 Geological background
3 Geological background
3.1 Regional Geology
3.1.1 The present tectonic configuration
3.1.1.1 The coverage of Yangtze plate The Yangtze plate is one of the most important tectonic plates in the Chinese territory. It was formed in the Neoproterozoic period (ca. 1 000 Ma. - 542 Ma.) (Huang et al., 1977). It adjoins Qinling plate with the northern border, Songpan plate with the west border, North China plate with the northeast border and Huaxia plate with the south border. Generally it is composed of sediments from Cambrian period to Cretaceous period. The study area lies in the middle part of Yangtze plate on the Yangtze River (figure 3.1).
1000 km
N
Qinling plate North China plate
Songpan plate
Badong Yangtze plate
Huaxia plate
Fig. 3. 1: Tectonic map of China (Source: http://www.sjdkj.net/index.php?m=content&c=index&a=show&ca tid=24&id=1 079)
3.1.1.2 The main tectonic structures in the middle part of the Yangtze plate As shown in figure 3.2, the middle part of the Yangtze plate consists of mainly the Sichuan basin, the South Dabashan folding zone and the Shennongjia anticline, the Huangling 15
Chapter 3 Geological background anticline and the Jianghan basin, the Chuan East folding zone and the E’xiang West folding zone. The Chuan East folding zone and the E’xiang West folding zone are mostly called Chuan East - E’xiang West folding zone. This area has collected Cambrian to Cretaceous sediments (figure 3.2). The metamorphic stone in the kernel part of Huangling anticline, which has been tested to be ca. 3 290 Ma old, is considered to be one of the oldest rocks in the Yangtze plate (Xiong et al., 2004).
(2)
(1) (4)
N (5) (8) (12) (9) (3) (13) Badong (10) (6) (7)
(14)
(11)
Fig. 3. 2: Geological map of the Middle Yangtze plate (Modified from Hu, 2011) (The area in the black dotted line is the coverage of figure 3.3) (1) Songpan plate; (2) Qinling orogenic belt; (3) Sichuan basin; (4) North Dabashan folding zone; (5) South Dabashan folding zone; (6) Chuan East folding zone; (7) E’xiang West folding zone; (8) Shennongjia anticline; (9) Huangling anticline; (10) Jianghan basin; (11) Xuefengshan orogenic belt; (12) Huayingshan fault; (13) Qiyueshan fault; (14) Zhangjiajie - Huayuan fault
16
Chapter 3 Geological background Both the South Dabashan folding zone and the Chuan East - E’xiang West folding zone exhibit regular arc forms, which indicate strong tectonic movement in the tectonic history (figure 3.3). The fold axes in the South Dabashan folding zone bulge in the southwest direction, while the folding axes in the Chuan East - E’xiang West folding zone bulge in the northwest direction. In the South Dabashan folding zone, sediments from Pre-Cambrian to Middle Triassic crop out. Its extension in the northwest direction has been obstructed by the Hannan - Micangshan upheaval and its extension in the east direction has been resisted by the Shennongjia anticline. The Chuan East - E’xiang West folding zone indicates the folding area between the Huayingshan fault belt and the Zhangjiajie - Huayuan fault belt. The Qiyueshan fault divides the folding zone into the northwest part which is called Chuan East folding zone and the southeast part which is called E’xiang West folding zone. The Chuan East folding zone belongs to the east part of the old Sichuan basin.
North Dabashan folding zone
South Dabashan folding zone
Chuan East Badong folding zone Huayingshan fault
E’xiang West folding zone N
Qiyueshan fault 100 km
Fig. 3. 3: The folds in the geological map for the northern part of the Middle Yangtze plate (Modified from Hu, 2011) (The quadrangle area in the black line is the coverage of figure 3.4)
17
Chapter 3 Geological background In the Chuan East folding zone, the anticlines are strait and the synclines are broad, which are usually called comb-like folding. In the E’xiang West folding zone, the anticlines are broad and the synclines are strait, which are usually called trough-like folding (figure 3.3).
3.1.1.3 The regional tectonic situation of Badong study area The Badong study area lies in the axis of a synclinorium between Shennongjia anticline in the north and Xianglongshan anticline in the south. The main axis and the secondary axes lie in the east-west direction. The synclinorium has built the west flank of the spoon-shaped Zigui syncline and the axes line inclines generally slightly in eastern direction. The Zigui syncline is an important tectonic unit in the Three Gorges region. It is surrounded by the dome-shaped Huangling anticline in the east, the dome-shaped Shennongjia anticline in the north and the Xianglongshan anticline in the south (figure 3.4).
Shennongjia anticline
Zigui syncline Huangling
Badong anticline
Xianglongshan anticline
Fig. 3. 4: Regional geological map around the Badong study area (Simplified from BGEDHP, 1987)
In this synclinorium between Shennongjia anticline and the Xianglongshan anticline, two series of tectonic structures can be observed: the tectonic structures (folds and faults) in the ENE (east-northeast) or E-W (east-west) direction and the tectonic structures in the NNE (north-northeast). The structures in the ENE or E-W direction have been cut across by the structures in the NNE direction, which means that the structures in ENE direction were formed earlier than the faults in NNE direction. The faults in NNE direction extend also in the Jurassic formations, which indicate that they are formed at least later than Jurassic.
18
Chapter 3 Geological background 3.1.2 The tectonic history in the Middle Yangtze plate
3.1.2.1 Before the Indosinian orogeny (before 260 Ma) In the Paleozoic era the Yangtze plate was relative stable and has suffered a few times of “uplift and subsidence” processes, which has brought about sediment interruption in the periods of Devonian and Carboniferous, gentle upheaval and settlement in the plate. This situation maintained until end of Permian (Chen et al, 1992).
3.1.2.2 Indosinian orogeny (260 Ma - 227 Ma) The Indosinian orogeny lasted from the beginning of the Later Permian to the end of Middle Triassic (Liu, 2012). In this period a series of orogenic belts in the territory of China have been formed. Qinling orogenic belt and the Ganzi - Songpan orogenic belt north of the Yangtze plate were mainly formed in this period (Ren, 1980). In the middle part of the Yangtze plate, there have been a few times of “uplift and subsidence” processes before the Middle Triassic period. The deposition interruption between the Later Permian and Early Triassic, and the deposition interruption between Middle Triassic and Late Triassic have indicated a process of firstly the transgression of palaeo-ocean and then the regression of palaeo-ocean in the region. During the Later Indosinian orogeny, the regional undulation became much stronger. The Huangling anticline and the Shennongjia anticline were raised above the sea level at that time. A large palaeo-basin was formed in the area, which covers the area of the present Zigui syncline, the area of the present Sichuan basin and the area between them (Liu and Zhang, 2008).
3.1.2.3 Yanshanian orogeny (227 Ma - 96 Ma) The Yanshanian orogeny is the tectonic movement period after the Indosinian orogeny, which lasted from the beginning of the Late Triassic to the end of Early Cretaceous (227 Ma - 96 Ma) (Liu et al., 2012). This orogeny has caused strong deformations between the continent plates and in the continent plates (Dong et al., 2007). It was also the most important period to form mineral resources. Based on the slight tectonic deformation in the Indosinian orogeny, the Yanshanian orogeny has thoroughly changed the tectonic configuration of the middle part of Yangtze plate. This orogeny has built the general configuration of the present tectonic situation of the Middle Yangtze plate. With the regression of the palaeo-ocean from the southeast part to the northwest part in the Sichuan basin, the middle part of Yangtze plate has almost finished the process of receiving deposition. The Huangling anticline rose up continuously, while the Zigui basin on its west side and the Jianghan basin on its east side sank relatively (Chen et al., 1992). The South Dabashan folding zone and the Chuan East - E’xiang West folding zone were built mainly during this orogeny.
19
Chapter 3 Geological background 3.1.2.3.1 The conformation of Dabashan folding zone Due to the crash between the Yangtze plate and the Qinling plate, the north margin of Yangtze plate submerges under the Qinling plate. In this process, the South Dabashan folding zone, which lies on the north edge of Yangtze plate, has been formed.
3.1.2.3.2 The conformation of Chuan East - E’xiang West folding zone folding zone The conformation of the arc shaped Chuan East - E’xiang West folding zone is generally considered to be a gradual process (Feng, 2003; Liu, 2008; Hu, 2009; Mei, 2010). According to the formation period, Hu (2009) divided the Chuan East - E’xiang West folding zone into the northwest part (Chuan East folding zone) and the southeast part (E’xiang West folding zone) by the Qiyueshan fault, which lies in NNE direction (figure 3.3). He thinks that the northwest part was formed until the Late Jurassic period and the southeast part was formed until the end of the Early Cretaceous. The folding zone had been formed gradually from the southeast part to the northwest part. Tectonically the Three Gorges region lies in the north margin of Chuan East folding zone.
3.1.2.4 Himalayan orogeny (96 Ma - now) Himalayan orogeny is the period of the tectonic movement since the beginning of the Late Cretaceous (96 Ma), which has totally changed the landform of West China. In the middle part of Yangtze plate, the western part of Sichuan basin has been raised to be higher than its eastern part. The Three Gorges region has generally experienced an intermittent upheaval process. Since Tertiary, the west side of Three Gorges region has been raised higher than the east side. Generally five levels of planation surface can be found in the Three Gorges region (Chen, 1992). Huangling anticline continues upheaving during this orogeny. From 65 Ma to 7 Ma, the kernel part has risen by 1 426 - 429 m. Since 7 Ma, the kernel part has risen by ca. 1 428 m (Xiang et al, 2009). The rise of the kernel part of the Huangling anticline is considered to be the essential factor of the earthquakes in the Three Gorges region. The largest earthquake, which has been recorded since 1948 around Huangling anticline, is 5.2 Richter grade in 1961 (Tang and Huang, 1983).
3.2 Geology in the Badong study area
3.2.1 The geological formations in the Badong study area The Triassic sediments in Three Gorges region are geologically divided into 4 formations: Early Triassic formations: Daye formation T1d and Jialingjiang formation T1j, Middle Triassic formation: Badong formation T2b, Late Triassic formation: Shazhenxi formation T3s. (All the coordinates for the locations are based on the WGS_1984_UTM_Zone_49N coordinate system.) The T1j formation can be subdivided into 3 sub-formations: T1j1, T1j2 and T1j3 (from old to new). T2b can be subdivided into 5 sub-formations:T2b1, T2b2, T2b3, T2b4 and T2b5. In the
20
Chapter 3 Geological background Badong study area, T1j3 sub-formation, T2b and T3s formations are relevant. Figure 3.5 is a geological map for the Badong study area. Table 3.1 gives a short introduction about the formations. Deng (2000) concluded the sedimentary process as two deposition cycles: limestone (T1j3) →clayey limestone (T2b1) → clayey siltstone (T2b2) limestone (T2b31) →clayey limestone (T2b32) →clayey siltstone (T2b2).
A’
Xinwu anticline
(2) (9) (5) (11) (13) (8) Guandukou syncline (12) (6) (10) (14)
(7) (4) (1)
(3) A
Fig. 3. 5: Geological map of the Badong study area (The locations of the numbers in figure 3.5 stand for the locations of corresponding figures, e.g. the location of (1) in the figure stands for the location of figure 3.6) (1): fig. 3.7, (2): fig.3.8L and fig. 3.9R, (3): fig. 3.8R, (4): fig. 3.9L, (5): fig. 3.10, (6): fig. 3.11L, (7): fig. 3.11R, (8): fig. 3.12L, (9): fig. 3.12R, (10): fig. 3.13L, (11): fig. 3.13R, (12): fig. 3.14L, (13): fig. 3.14 R, (14): fig. 3.15
A 13° A’
1 km Yangtze River
145 m asl.
0
Fig. 3. 6: The sketch showing the profile at the location in figure 3.5
21
Chapter 3 Geological background
Table 3. 1: Short description of the geological formations in the Badong study area (BGEDHP, 1987) Formation Formation Thickness Age name Lithology subdivision (m) (Abbreviation) Rhaetian Thick yellow sandstone intercalated with thin Shazhanxi - 120 coal seams. Norian (T3s) The outcrop has not been closely observed. Carnian - - - - Medium thick to thick grey clayey limestone, T2b5 20 thick black limestone. Medium thick to thick fuchsia clayey siltstone T2b4 400 with the intercalation of grey green clayey Ladinian siltstone. Thin to medium thick grey or grey yellow T2b3 200 - 300 limestone or clayey limestone. Clay content tends is increasing from the base to top. Badong Thin to medium thick fuchsia clayey siltstone (T2b) T2b2 290 - 430 with intercalations of grey green clayey siltstone.
Grey or black limestone breccias. Brown Anisian clayey material fills between the breccias. T2b12 50 - 100 The cementation degree changes very much in different layers. Thin layered grey green or yellow clayey T2b11 20 - 50 limestone. Thick layers of homogenous grey to black Jialingjiang Olenekian T1j3 130 limestone. Only the youngest segment T1j3 (T1j) crops out in the study area.
3.2.1.1 Early Triassic - Upper Jialingjiang formation (T1j3) Age: Olenekian, Early Triassic Thickness: ca. 130 m (BGEDHP, 1987) Description: It consists of homogenous medium thick to thick layers of grey or black limestone. The layers are mostly ca. 20 cm to ca. 50 cm thick. Some layers can be as thick as 1 m. The outcrops are mostly not weathered and can be hardly broken off by hammer. The outcrop in the figure 3.6 was mapped in South Badong on an elevation of ca. 400 m asl.. Normally a set of conjugate joints can be observed from the broken surface of the large outcrops in the old stone pits. The density of the joints is ca. 0.5 /m. No infilling material can be observed in the joints. The joints are hardly to be observed in the intact bedding layers. The content of dolomite mineral in the bedrock changes largely in this formation. It can be higher than 70% but it can be also as little as 9% (Deng, 2000). This formation built mostly high mountains in the study area and in the whole Three Gorges region. 22
Chapter 3 Geological background Stretch in the study area: The study area does not cover the complete sediment of T1j3 sub-formation. This sub-formation crops out in the southern part of South Badong, north of W1 ravine in West Badong and in a section on the riverside on Shennong River in North Badong (figure 3.5).
Fig. 3. 7: Typical outcrop of T1j3 sub-formation (X: 435435, Y: 3434727)
3.2.1.2 Middle Triassic - Badong formation (T2b) The Badong formation is the most important formation in the study area. According to the similarity of lithology, the 5 sub-formations can be divided into two groups. T2b1, T2b3 and T2b5 are mainly yellow or grey limestone or clayey limestone layers; T2b2 and T2b4 are mainly fuchsia clayey siltstone layers.
3.2.1.2.1 The first sub-formation of Badong formation (T2b1) The T2b1 sub-formation is ca. 50 m to ca. 150 m thick in the study area. This sub-formation can be lithologically further divided into two segments: T1b11 (old) and T2b12 (new). Due to the obvious lithological difference between T1j formation and T2b formation, T2b1 is mostly the transition belt between the steep landform and the gentle landform, such as the landforms differ between north and south of W1 ravine in West Badong, or the steep landform in the southern part of South Badong and the relatively gentle landform north of it.
3.2.1.2.1.1 T2b11 Age: Anisian, Middle Triassic Thickness: ca. 20 - 50 m Description: It consists of thin to medium thick layers of grey black limestone breccias. The
23
Chapter 3 Geological background layers are most a few centimeters to ca. 20 cm thick. The diameters of breccias are mostly ca. 0.5 cm to ca. 3 cm. The outcrop in figure 3.8 (left side) was found in the south side of W1 ravine in West Badong (see the location in figure 3.5). Clayey material can be also observed filled between the breccias in the fresh outcrop. Both argillaceous and calcareous cementation of breccias can be observed in the bedding layers. The argillaceous cemented layers have obviously lower mechanical strength than that of the calcareous cemented layers. The weathered outcrop in the figure 3.8 (right side) was found in South Badong (see the location in figure 3.5). Karst phenomena in the bedrock can be often found in the outcrops of this sub-formation. Karst holes with a size of ca. 2 cm - ca. 10 cm can be observed in the weathered outcrop and they are mostly filled with brown clayey material which should be the residual material from the weathered bedrock (figure 3.8 (right side)). Stretch in the study area: It crops out in the southern part of South Badong, in West Badong at the south side of E1 ravine and on a section of the riverside of Shennong River in North Badong. (The stretch of this sub-formation in North Badong has been speculated.)
Left side: The fresh outcrop of T2b11 Right side: Strongly weathered outcrop of T2b11 (X: 434466, Y:3437890) (X: 439474, Y: 3433965) Fig. 3. 8: The typical outcrop of T2b11
3.2.1.2.1.2 T2b12 Age: Anisian, Middle Triassic Thickness: ca. 50 m - 100 m. Description: It consists of thin layers of grey yellow clayey limestone. The sediment in the fresh outcrop is well stratified and the layers are mostly 1 - 3 cm thick. The fresh outcrop in figure 3.9 (left side) was found in the south side of W1 ravine in West Badong (see the location in figure 3.5). The border between the T2b12 layers and the underlying T2b11 can be clearly observed in the field (figure 3.9(right side)). Both fresh and weathered outcrops can be found in the field (figure 3.8). They both have very brittle structure. Either rocks from fresh outcrop or rocks from weathered outcrop can be broken off by hand. On the broken surface the slight silty clay can be felt by finger. The strongly weathered outcrop in South Badong (figure 3.9(right side)) was mapped in South
24
Chapter 3 Geological background Badong on the riverside of Yangtze River (see the location in figure 3.5). The surface looks dark green. A set of joints stretching in north - south direction can be obviously observed. The joint fractures are 1 - 5 cm wide open and the fracture surfaces are flat and straight. The joint fractures penetrate almost the whole bedding layers of the outcrop (ca. 2 m high) with ca. 80º dip angle. The intervals between the joints are ca. 30 cm to 50 cm. Loose yellow clayey infilling can be observed in the joint fractures. This set of joints has not been found in the outcrop in West Badong (figure 3.9(right side)). Stretch in the study area: T2b11 crops out along the foot of steep mountains, which are built by T1j formation in South Badong. It crops out also on the south side of W1 ravine in West Badong and on a section on the riverside of Shennong River in North Badong (figure 3.5). (The stretch in North Badong has been speculated)
T2b12 T2b11
Left side: Weathered T2b12 outcrop in South Badong Right side: Fresh outcrop of T2b11 and T2b12 at the (X: 435178, Y: 3435081) border. (X:434523, Y: 3436389) Fig. 3. 9: The typical outcrop of upper layers of T2b12
3.2.1.2.2 The second sub-formation of Badong formation (T2b2) Age: Anisian, Middle Triassic Thickness: ca. 290 m to ca. 430m. Description: It consists of thin to medium thick fuchsia clayey siltstone with intercalations of thin to medium thick grey green clayey siltstone. The layers are mostly a few centimeters to a few decimeters thick. Very seldom it can be as thick as 1 m. The outcrop in figure 3.10 lies on the riverside of Yangtze River west of N1 ravine in North Badong on an elevation of ca. 170 m asl.. All the outcrops appear weathered in some degree. The density of fractures changes very much in the area. Joints and cleavages are two main kinds of fractures in bedding layers. In South Badong, West Badong and in the western part of North Badong, due to the high density of the cleavages and the strongly weathering in the outcrop, the joints are difficult to be recognized. Mostly a cleavage density of 10 - 15 /m can be observed in the bedding layers. Partly the cleavage fractures can be observed every 1 - 2 cm in the bedrock. The rocks can be broken off with hammer. The outcrop of T2b2 sub-formation on the west side of Dongrang River in North Badong has
25
Chapter 3 Geological background a complete different development of the joints. Two sets of joints have been measured (Godzik, 2013). One set stretches in northeast direction and the other set stretches in east-west direction. The density of the joint fractures is 5 - 7 /m. The outcrop is slightly weathered. The joint fractures are flat and straight. Additionally a layer of ca. 3 m thick yellow clayey limestone crops out in North Badong. A set of grey green fine sandstone layers with a total thickness of about 5 m has also been mapped in this sub-formation in South Badong and North Badong. Stretch in the study area: This sub-formation crops out in the southern part of South Badong, in the west of North Badong, in West Badong south of the W1 ravine and in the northern parts of East Badong and North Badong along the Dongrang River (figure 3.5).
Fig. 3. 10: Typical outcrop of T2b2 sub-formation on the riverside in North Badong (X: 435940, Y: 3435813)
3.2.1.2.3 The third sub-formation of Badong formation (T2b3) Age: Ladinian, Middle Triassic Thickness: ca. 200 m - 300 m Description: It consists of thin to medium thick grey or yellow limestones or clayey limestones (figure 3.11). Due to the different clay content, this sub-formation can be divided into the lower layers (T2b31) and the upper layers (T2b32). The T2b32 layers have a definitely higher clay content than the T2b31 layers. But the border between them could not be exactly determined in the field. It is a gradual change in the bedrock layers. Roughly the T2b32 layers are totally ca. 50 - 100 m thick.
26
Chapter 3 Geological background Joints and cleavages are the main two kinds of fractures in the bedding layers. The cleavages stretch mostly in the east-west direction perpendicular to the bedding plane. The density changes between ca. 10 - 50 /m. A set of conjugate joints and a set of dip joints develop in T2b3 layers. The tectonic structures in this sub-formation will be described in detail in the next sub-chapter 3.2.2. The outcrop in the figure 3.11(left side) was found in the S3 ravine on an elevation of ca. 325 m asl.. The outcrop in the figure 3.11(right side) was mapped in the S3 ravine on the elevation of ca. 625 m asl.. Both grey limestone layers and grey yellow clayey limestone layers crop out in the T2b31 layers. The layers at the bottom of the lower layers consist of black limestone, which shows higher mechanical strength than that of the grey limestone layers and grey yellow clayey limestone layers. The outcrops of T2b31 layers in the field are mostly fresh or slightly weathered. The layers are mostly ca. 10 - 40 cm thick. Mostly a clayey infilling can be observed in the fractures of the clayey limestone layers. The infilling thickness changes with the fracture spaces between a few millimeters to ca. 2 centimeters.
Left side: Grey limestone layers in T2b31 Right side: Grey yellow clayey limestone layers in (X: 439641, Y: 3435989) T2b31 (X: 439255 ,Y: 3435216) Fig. 3. 11: Two sites of T2b31 formation outcrop in South Badong
The outcrop in figure 3.12 (left side) was found in the area between S2 ravine and S3 ravine on an elevation of 350 m asl. in South Badong. The outcrop in the figure 3.12 (right side) was found east of N2 ravine close to the geological border between the T2b32 layers and T2b4 layers on an elevation of ca. 350 m asl.. The layers of T2b32 are mostly ca. 10 cm - ca. 20 cm thick (figure 3.12). The weathered outcrops appear mostly slightly pink or yellow. The outcrops are mostly characterized by densely developed joints and thick infilling materials. The outcrop surfaces are mostly covered with a few millimeters thick clayey lamina, which is the residual material from the weathered bedrock. In the joints of the layers develop stretching perpendicular to the bedding planes. The density of joints is 7 - 10 /m. Maximal 2 centimeters thick of grey or grey yellow clayey infillings can be observed both in the joint fractures and between the bedding layers. According to the relative location in tectonic structures, high density cleavages exist in the area in both sides of Guandukou syncline. The weathering degree and the relative spatial position according to sedimentary sequence of bedding layers are the most important
27
Chapter 3 Geological background characters to discriminate T2b32 layers from T2b31 layers in the field.
Left side: T2b32 outcrop in South Badong Right side: T2b32 outcrop in North Badong (X: 439330, Y: 3436664) (X: 440919, Y: 3437811) Fig. 3. 12: The T2b32 outcrop in Badong study area
Stretch in Badong study area: This formation crops out in the northern part of South Badong, in the southern part of West Badong, in the eastern part of North Badong and in the northern part of East Badong (figure 3.5).
3.2.1.2.4 The forth sub-formation of Badong formation (T2b4) Age: Ladinian, Middle Triassic Thickness: ca. 400 m. Description: It mainly consists of medium thick to thick layers of fuchsia clayey siltstone with intercalations of grey green or grey yellow clayey siltstone (figure 3.13). The outcrop in figure 3.12 was found on the riverside of Yangtze River in East Badong on an elevation of ca. 260 m asl.. All the outcrops from this sub-formation appear weathered in some degree. Small pieces of shards, which are the dropped weathered material, can be mostly found at the foot of the outcrop. Both joints and cleavages can be observed in the outcrops of the bedding layers. The density of joints is ca. 4 /m and they extend almost in the north-south direction. Besides the joints, cleavages develop mostly also densely in the layers. In many outcrops, the joints are difficult to be recognized due to the high density of the cleavages and the strong weathering of the bedrock. Mostly a density of ca. 10 - 30 /m for cleavages can be seen in the bedding layers. The outcrops of T2b4 layers are mostly weathered and can be broken off with hammer. Generally the sub-formation T2b4 has a similar lithology as the sub-formation T2b2. The outcrop of fuchsia clayey siltstone in T2b2 and T2b4 sub-formations are mostly so seriously weathered that the joint planes and the bedding planes cannot be measured. Generally T2b4 sub-formation has a similar lithology as T2b2 sub-formation. The difference between them is the calcite content in bedding layers. The fuchsia color of T2b4 bedding layers appears mostly slightly pale. Calcite druses with a size of 1 - 3 cm can be often found
28
Chapter 3 Geological background in the bedrocks of T2b4 sub-formation in North Badong and East Badong (figure 3.13 (right side)). From samples taken from the T2b4 layers in North Badong, the carbonate content is ca. 33%, which is much higher than that of the T2b2 layers (ca. 8%) (Muschick, 2013). Stretch in the Badong study area: It crops out on the riverside of Yangtze River close to the mouth of S3 ravine in South Badong, on the western riverside of the Dongrang River in the eastern part of North Badong and on the east side of the Dongrang River in the south of East Badong (figure 3.5).
Left side: Typical outcrop of T2b4 in East Badong Right side: Calcite druses in a T2b4 rock block in (X: 442734, Y: 3436514) North Badong. (X: 441834, Y:3437844 ) Fig. 3. 13: Typical outcrop of T2b4 in East Badong
3.2.1.2.5 The fifth sub-formation of Badong formation (T2b5) Age: Ladinian, Middle Triassic Thickness: ca. 20 m Lithological description: It consists of medium thick to thick grey black limestone. The layers are mostly ca. 30 cm to ca. 60 cm thick. It can be maximal 2 m thick. The fractures, which have a density of ca. 3 - 5 /m, can be observed in the bedding layers in the direction perpendicular to the bedding plane. A few millimeter clayey infilling can be observed in the fractures of the bedrock. Two outcrops in the figure 3.14 are mapped on the west side of the outlet of Shennong River on an elevation of ca. 400 m asl. (see the location in the figure 3.5). The bedrock in the outcrop can be broken with hammer. The outcrop is mostly slightly weathered. Additionally a layer of 3 - 5 m thick dark grey limestone can be found in this section. The T2b5 sub-formation can be easily differentiated with the underlying T2b4 sub-formation in the field (figure 3.14 (right side)). Stretch in the area: It crops out only in the area south of E1 ravine in East Badong above T2b4 sub-formation. This sub-formation is relative thin but it can be continuously observed in the field.
29
Chapter 3 Geological background
T2b5
T2b4
Left side: T2b5 outcrop on the hill slope close to the Right side: Outcrop of the border between T2b4 and outlet of the Dongrang River in East Badong T2b5 on the east hill slope close to the mouth of (X: 442833, Y:3437063) Dongrang River. (X: 443009, Y: 3436720) Fig. 3. 14: Typical outcrop from T2b5 sub-formation in East Badong
3.2.1.3 Late Triassic - Shazhenxi formation (T3s) Age: Norian to Rhaetian, Late Triassic Thickness: ca 120 m. Lithological description: It is grey green or grey yellow medium thick to thick quartzitic sandstone. The layers of this formation crop out on steep cliffs with bedding layers dipping in the reverse direction as slope direction (figure 3.15 (right side)). The outcrop in figure 3.15 (left side) was found on a slope of the outlet of Shennong River on a elevation of ca. 480 m asl.. In the outcrop T2bs layers are composed of grey green or grey yellow fine sandstone with intercalations of clayey siltstone. The layers are ca. 30 - 50 cm thick. The sandstone layers are slightly weathered and the interlayers are strongly weathered. A set of conjugate joints can be observed in the bedding layers and the density changes between 5 - 8 /m in different layers. The interlayers are mostly a few centimeters thick and weathered. The underlying T2b5 layers are strongly weathered, which shows a definite difference with T3s layers. A small obsolete coal factory has been found on a slope dipping in southern direction in East Badong. According to the geological report for this region, T3s layers are lying unconformable on the underlying T2b5 sediment with a small included angle and a few layers of coal can be exploited in this sub-formation (BGEDHP, 1987). Stretch in the area: This formation crops only out south of E1 ravine in the southern part of East Badong. It is located on an elevation between 500 m asl and 650 asl. (figure 3.5).
30
Chapter 3 Geological background
T3s
T2b5
Left side: Border between T2b5/T3s Right side: Cliffs built by T3s bedding layers (X: 442841, Y: 3435664) Fig. 3. 15: Outcrop of T2b5 bedding layers (Photos from Claudia Muschick)
3.2.2 The tectonic structures in the Badong study area Since the Yanshanian orogeny this area has experienced a strong tectonic compression in north-south direction. The folds, faults, joints and cleavages in this Badong study area are the consequence of this compression process. Generally due to the difference on the mechanical strength between the bedding layers, the layers of limestones and clayey limestones should have undertaken higher compression stress during the tectonic movement and exhibit more obvious deformation characters than clayey siltstone layers. Badong study area belongs to the synclinorium between Shennongjia anticline and the Xianglongshan anticline (see figure 3.4). Two fold structures, the Guandukou syncline and the Xinwu anticline, control the overall tectonics in the Badong study area. Each of them has also a few secondary folds in the limbs. The axes of both folds are almost parallel and orient in east-west direction (figure 3.16). The secondary folds, faults, joints and cleavages have also indicated an extremely strong compression process in the north-south direction in the Badong study area.
31
Chapter 3 Geological background
Xinwu (3) (4) B
(12)
(1) Guandukou (8) (2) (11) (6)
(7) (10) (9) (5) B’
Guandukou
Fig. 3. 16: Tectonic map of the Badong study area (The locations of the numbers in figure 3.16 stand for the locations of corresponding figures, e.g. the location of (1) in the figure stands for the location of figure 3.18) (1): fig. 3.18, (2): fig. 3.19, (3): fig. 3.20, (4): fig. 3.21, (5): fig. 3.23, (6): fig. 3,24 and fig. 3.25, (7): fig. 3.26, (8): fig. 3.27, (9): fig. 3.28, (10): fig. 3.29, (11): fig. 3.33, (12): fig. 3.34
3.2.2.1 Folds Figure 3.17 shows the speculated schema of the geological profile of Guandukou syncline at the location B - B’ in figure 3.16.
B B’ 1 km
0
Fig. 3. 17: The sketch showing the extension of Guandukou syncline at the profile B - B’ (The black straight line in figure 3.16)
3.2.2.1.1 Guandukou syncline The axis of Guandukou syncline extends along the riverbank of South Badong in the west-northwest (WNW) direction (figure 3.5). In western direction the axis stretches into West Badong and in eastern direction it stretches into East Badong. South Badong lies in the south limb of Guandukou syncline. The north river bank of Yangtze River in North Badong is the north limb of Guandukou syncline and is also the south limb of Xinwu anticline at the
32
Chapter 3 Geological background same time. The south limb and north limb of Guandukou syncline are not symmetrical. The south limb dips in northern direction, while the dip direction of the north limb changes from southwest direction close to the Shennong River to southeast direction close to Dongrang River in North Badong.
N
Fig. 3. 18: The axis of Guandukou syncline intersects the outlet of S3 ravine in South Badong
The south limb of the syncline includes one large secondary anticline and one secondary syncline in South Badong. The secondary anticline is totally ca. 4 400 m long and stretches from the area between S1 ravine and S2 ravine to the area between S4 ravine and S5 ravine. The secondary anticline has been divided into 3 sections by S2 and S3 ravines. They can be measured on different elevations, which indicated a probably curved original fold axis. The section between S2 ravine and S3 ravine is ca. 670 m long; the section between S2 ravine and S3 is ca. 500 m long; the section between S3 ravine and S5 ravine is ca. 2 300 m long. According to the dip directions of the bedding layers in the study area, the axial line of the Guandukou syncline inclines gently in eastern direction. The inter-limb angle of Guandukou syncline changes in the Badong study area. It is around 90° - 100° in West Badong, around 110° - 120° in South and East Badong, around 140° - 150° in East Badong. In the area between S3 and S4 ravines, a large profile which extends in the north - south direction exhibits a series of tight secondary folds in the south limb of Guandukou syncline. The profile is the west side of a ravine which is called Liangshuixi ravine. It has represented a cross section of the south limb of Guandukou syncline. Tectonically the profile is situated tightly close to the axis of Guandukou syncline, where the deformation and the tectonic stress should have been extremely strong. From the sediment sequence, the folded layers belong to the upper layers of T2b3 sub-formation, whose clay content is relatively higher and the mechanical strength is relatively lower than that of the lower layers. In the east-west direction, the folding belt extends parallel to the axis of Guangukou syncline along the Yangtze River. It stretches in western direction until the area between S2 ravine and
33
Chapter 3 Geological background S3 ravine. In the eastern direction, it can be last seen at the west side of the outlet of S4 ravine. The east side of the outlet has been covered by the colluvial material and cannot be observed. But it can be reasonably speculated that it has also extended into the area east of S4 ravine. The strongly brittle bedding layers could be an important factor for the occurrence of Huangtupo landslide and other landslides located along the folding belt on the southern riverside of Yangtze River, because a slope with brittle bedding layers is more possible to be instable than a slope with relative integrated bedding layers.
N
Fig. 3. 19: The folding zone in the south limb of Guandukou syncline in the Liangshuixi ravine
3.2.2.1.2 Xinwu anticline The Xinwu anticline stretches in east-west direction in West Badong, North Badong and East Badong. The Dongrang River flows from north to south across the anticline and has uncovered a complete arc form of the Xinwu anticline (figure 3.20 and 3.21). Comparing the elevation of the outcrop of the anticline axis along the border T2b2/T2b3 separately in the middle of North Badong and in the east of North Badong, the axis of Xinwu anticline is gently inclined in eastern direction. In East Badong and in the area east of N1 ravine in North Badong, the axis of the anticline is unified, but in the area west of N1 ravine in North Badong, the anticline presents the structure of an anticlinorium, which is composed of two secondary anticlines and one secondary syncline. The transition part between the west section of the anticline and the east section has been eroded by the N1 ravine (figure 3.5).
34
Chapter 3 Geological background
N
T2b3 T2b2
Fig. 3. 20: The south border of T2b2/T2b3 in the Xinwu anticline on the eastern riverside of Dongrang River (The yellow line stands for the border between T2b2 and T2b3 sub-formations)
On the western riverside of Dongrang River, it can be noticed that locally the layers of T2b2 sub-formation lie on the layers of T2b3 sub-formation in the south limb. The invert-lying layers in T2b2 sub-formation had been measured along the western riverside of Dongrang River (Godzik, 2013), which shows a gradual change of the dip direction of the layers. The layers on the most southern side dip in south southeast (SSE) direction with 40° slope angle, while the layers on the most northern side dip in north northwest (NNW) direction with 10° slope angle (figure 3.22).
35
Chapter 3 Geological background
N
T2b3 T2b2
Fig. 3. 21: The invert-lying layers in the southern limb of Xinwu anticline on the western riverside of Dongrang River (Photo taken by Joachim Rohn) (The yellow line stands for the border between T2b2 and T2b3 sub-formations. The black line stands for the location of profile in figure 3.22.)
NE
Fig. 3. 22: A depict of the orientation of the layers along the street (Godzik, 2013) (The values in the brackets stand for the dip direction and dip angle of the layers.)
3.2.2.2 Faults Faults have not been found notably in the study area. Mostly only short faults have been mapped from the outcrops (figure 3.16). The Guandukou fault is the only large scale fault mapped in the study area. It extends along the border between T1j3 sub-formation and T1j sub-formation in South Badong and has a total length of ca. 9 km in South Badong. In western direction it extends also across the Yangtze River (BGEDHP, 1987). Figure 3.23 shows an outcrop of Guandukou fault. It was found upstream in S5 ravine on an elevation of ca. 660 m asl.. The footwall block is composed T1j3 bedding layers dipping in a direction of 16° with a dip angle of 75°. The fault zone is in total ca 1 m thick and consists of fault breccias with different grain sizes. The breccias are badly cemented and can be broken off by hammer. Most of the gravels are smaller than ca. 5 cm. Yellow clayey infilling can be observed between the gravels. The gravels are angular and appear grey or grey yellow and
36
Chapter 3 Geological background originate probably from T2b1 layers. The gravels in the outcrop are partly oriented and show a downwards movement of the hanging wall block. About 3 m north to this outcrop the T2b1 bedding layers can be seen. But on the rock surface of footwall block, which is the top layer of T1j3 sub-formation, scratches can be felt by finger on the surface of the layer, which indicates an upwards movement the hanging wall block. But the orientation of gravels in the fault zone shows now a relative downwards movement of the hanging wall. It can be speculated that the fault was once a reverse fault and now is a normal fault. With the general strong compression characters on tectonic in this region, it should be reasonable to speculate that this fault was a reverse fault.
N
Fig. 3. 23: The outcrop of Guandukou fault zone in the upstream area of S5 ravine (660 m asl.) (X: 440836, Y: 3432774)
Additionally four short faults have been also found in the field, both in the T2b2 and T2b3 layers. Two of them have been mapped in North Badong and the other two in South Badong (figure 3.16). Figure 3.24 shows the reverse fault found in the T2b3 layers in the S2 ravine on an elevation of ca. 400 m asl.. In the footwall block, a ca. 10 - 15 cm thick cleaved fault zone can be observed. The contact surface in the footwall block is highly oriented under shear stress. The cleavage spacing which can be observed is only a few millimeter thick. Additionally the layers in the direction vertical to the fault zone have a bend of almost 90°. In the hanging wall block of the fault, some shear zones can be observed along the contact surface between the bedding layers. The shear zones are ca. 2 - 10 cm thick and extend along the bedding plane. The fault can be found on both sides of S2 ravine. In the west and east direction they extend into the hill slopes and cannot be traced.
37
Chapter 3 Geological background
N
Shear zone in the bedding layers
Fig. 3. 24: Fault in the T2b3 layers on the west side of S2 ravine in South Badong (400 m asl.) (X: 438083, Y: 3436064)
N N
Left side: Overview of the fault in the west side of S2 Right side: Overview of the fault in the east side of ravine S2 ravine Fig. 3. 25: Outcrops of the fault in both sides of S2 ravine
Figure 3.26 shows a normal fault on the west side of S4 ravine in South Badong. T2b2 layers are dipping steeply in the north direction. At the fault zone, it can be seen that a short section of bedding layers has been drug in northern direction by the footwall block, which indicates a character of a normal fault. In the north of the profile Due to the low mechanical strength of the layers, the fault zone does not exhibit so strong deformation in the neighboring zone as it
38
Chapter 3 Geological background was observed near comparable faults in T2b3 layers. The fault zone is about 20 cm thick and extends straight in the north and south direction in the profile. The layers in both the hanging wall block and the footwall block have kept their original straight bedding planes. The lateral separation can not be determined in this profile but it should be longer than 10 m according to the correspondence of the layers in the hanging wall block and the footwall block.
N
Fig. 3. 26: Fault in T2b2 clayey siltstone bedrock upstream of S4 ravine in South Badong (550 m asl.) (X:440676, Y: 3435013)
3.2.2.3 Joints Joints are preferentially formed accompanied with folds under strong tectonic stress. Conjugate joints develop in pairs and are both oblique to the fold axis. The strike joints are roughly parallel to the fold axial plane. The dip joints are generally parallel to the dip direction of the fold limb. A set of conjugate joints and a set of north-south directed dip joints are developed in both limbs of Guandukou syncline. In the kernel part of Xinwu anticline, a set of strike joints and a set of dip joints have been measured.
3.2.2.3.1 Conjugate joints In the Badong study area, conjugated joints can be observed in the T1j and in T2b3 layers. Conjugates joints can be mainly found in the limestone layers of T2b3 sub-formation. They exist in all parts of the study area composed of limestone layers and clayey limestone layers..
39
Chapter 3 Geological background The density of the conjugated joints varies between ca 1 - 6 /m. Figure 3.27 shows a conjugate shear zone in Zhaoshuling, which is the area between S1 and S2 ravine. The joint surfaces are generally flat and a few millimeter of brown clayey infilling can observed. The density is ca. 5 /m. According to the sediment sequence, it belongs to the bottom layers of T2b3 sub-formation.
Fig. 3. 27: Conjugated shear joints in the landslide body of Zhaoshuling landslide (joints and the bedding plane are displayed with the software “stereonet” and the red lines stand for the positions of bedding layers) (X: 437328, Y: 3438985)
Conjugate shear joints can be observed in fine sandstone layers of the T2b2 formation (Figure 3.28) in South Badong and North Badong. But generally the sandstone layer crops very seldom out in the study area but it has recorded the direction of the historical stress. The density of joint is ca. 1 - 5 /m. The joint surface is very flat and the joint fractures are mostly tight close. Infilling materials cannot be observed in the fractures. Comparing the deformation characters of the clayey siltstone layers and the fine sandstone layers, it can be concluded that the brittle deformation exist mostly in the layers with high mechanical strength and plastic deformation exist mostly in the layers with low mechanical strength.
40
Chapter 3 Geological background
Fig. 3. 28: Conjugate shear joints in the sandstone layers of T2b2 formation (joints and the bedding plane are displayed with the software “stereonet” and the red lines stand for the positions of bedding layers) (X: 438732, Y: 3434477)
3.2.2.3.2 Dip joints A set of dip joints stretching in the north-south direction can be measured in the whole study area. The joint density is mostly ca. 1 /m or lower than 1 /m. They can be observed both in the clayey limestone layers and in clayey siltstone layers. The joint surfaces are mostly straight and flat. Figure 3.29 shows an outcrop of dip joints in the T2b2 sub-formation in the S5 ravine on an elevation of ca. 200 m asl. in South Badong. Their extension is ca. 20 m. Infilling can not be found in the joints.
41
Chapter 3 Geological background
Fig. 3. 29: Dip joint in the T2b2 formation in South Badong (joints and bedding planes are displayed with the software “stereonet” and the red lines stand for the positions of bedding layers) (X: 442481, Y: 3433541)
3.2.2.4 Cleavage There are different opinions and various usages of the term “cleavage”. Van der Pluijm and Marshak (2004) have defined cleavage as “a secondary fabric element, formed under low-temperature conditions, which impacts on the rock a tendency to split along planes”. According to the domain spacing of cleavages, they divided the cleavages into spaced cleavages and continuous cleavages. The spaced cleavages have a domain spacing larger than 1 mm and the continuous cleavages have a domain spacing lower than 1 mm. According to the morphologic characters, they can be classified after Van der Pluijm & Marshak (2004) into four categories: disjunctive cleavage, pencil cleavage, slaty cleavage and crenulation cleavage. Cleavages can be observed mainly in both limbs of Guandukou syncline, both in the clayey siltstone layers and the clayey limestone layers. Generally the closer to the axis of Guandukou anticline, the more often cleavages can be observed in the field. In the kernel part of Xinwu anticline (outcrop of T2b2 sub-formation) and in the north limb of Xinwu anticline, cleavages are not developed. Figure 3.30 shows a typical outcrop with spaced cleavage in the T2b3 layers. The layers dip in northern direction. A set of cleavages stretching in the east-west direction and a set of dip joints stretching in the north-south direction can be observed in the layers. The cleavage fractures are mostly tightly closed. In the outcrop the broken surface tends to break off preferentially along the cleavages and joints. On the weathered faces of cleavage fractures, a few millimeters thick of yellow clayey material can be observed.
42
Chapter 3 Geological background
Fig. 3. 30: An outcrop of spaced cleavage in the T2b3 formation bedrock in South Badong (cleavage and bedding planes are displayed with the programme “stereonet” and the red lines stand for the positions of bedding layers) (X: 439905, Y: 3436148)
Figure 3.31 shows a large outcrop of T2b32 sub-formation with cleavage on the eastern riverside of Dongrang River north of E1 ravine in East Badong. Tectonically it is the transition zone between the north limb of Guandukou syncline and the south limb of Xinwu anticline. The layers are ca. 10 cm to ca. 50 cm thick and dip steeply in southern direction. The cleavages stretching in east-west direction are oriented vertical to the bedding plane. The cleavage density changes in different layers between ca. 5 /m and ca. 20 /m. Figure 3.32 shows two examples of cleavaged outcrops, whose cleavage domains are only a few millimeters to ca. 2 centimeters apart. In figure 3.28L, cleavages develop densely in a layer but the underlying layer does not present a continuous character of cleavage. In figure 3.32(right side) brown yellow clayey laminates can be observed in the cleaved domains. The broken surface extends preferentially along the cleavage domains. But generally the rock layers have kept their integrity.
43
Chapter 3 Geological background
N
Fig. 3. 31: An outcrop of spaced cleavage in T2b3 sub-formation in East Badong (X: 443173, Y: 3439215)
Left side: Densely developing spaced cleavages in a Right side: Spaced cleavages in intact bedrock of high T2b3 bedding layer (X: 439905, Y: 3436148) integrity Fig. 3. 32: Continuous cleavage in T2b3 sub-formation
Disjunctive cleavages can be found in clayey siltstone layers. The outcrop in figure 3.33 was mapped in a hill slope on the Yangtze River in North Badong. The strongly cleavaged bedding layers are ca. 10 cm to ca. 30 cm thick and dip in northeast direction. The cleavage planes are parallel and the spacing of the cleavage domains is a few millimeters to a few centimeters. In the cleavage fractures thin calcite lamina can be observed. It is assumed that this is the product of weathering but not the process of cleavage conformation.
44
Chapter 3 Geological background
Compression stress
bedding plane
Fig. 3. 33: Spaced cleavage in clayey siltstone s of T2b4 sub- formation in East Badong (X: 442832, Y: 3436412)
3.2.2.5 Crush zone in T2b3 bedding layers In South Badong and North Badong, crushed bedding layers can be found in the outcrops of T2b3 bedding layers in both limbs of Guandukou syncline. The crush zones extend mostly roughly along bedding layers or slightly cross bedding layers. Their thickness changes mostly between a few centimeters and ca. 10 cm. But they can be maximal ca. 1 m thick. The strong compression stress during the tectonic movement and the mechanical strength difference between different bedding layers should have brought about these disassembled bedding layers. Figure 3.34 shows a typical outcrop of disassembled bedding layers in T2b3 bedding layers. In the crush zone angular or cooky-shaped gravels in different sizes compose the disassembled bedding layers. The diameters of gravels are mostly not bigger than 10 cm. Grey or grey yellow clayey infillings can be observed between gravels. But the percentage of clayey infillings appears not higher than 10%.
45
Chapter 3 Geological background
Fig. 3. 34: An outcrop of crush zone in bedding layers (X: 436956, Y: 3435984)
3.3 Landform evolution in the Badong study area Since the breakthrough of Yangtze River, the relative incision rate in the Three Gorges region has largely increased due to the higher discharge and the regional uplift of the Yangtze plate (Chen et al., 2013). River incision is the essential external factor for the landform evolution in the Three Gorges region.
3.3.1 The incision rate of Yangtze River in the river section of Three Gorges The river incision rate is mostly extrapolated by the deposit on the riverside by comparing the age of the deposit and its relative position. The incision rate here is actually the relative incision rate referring to the riverside. It is generally considered that the earth crust in Three Gorges region has been intermittently cut down by 1 700 m to 2 000 m in the last 25 Ma (Xie, 2006). Since 3.0 - 3.4 Ma the Yangtze River has incised by ca. 1 300 - 1 500 m (Xie, 2006) m and since Quaternary (ca. 2.5 Ma) the Yangtze River has incised by ca. 1 000 - 1 200 m (Chen et al., 2013), which means the average incision rate of ca. 0.5 mm/a. (Xie, 1990) holds that Badong is the uplift center in the Three Gorges region. In this way we can roughly calculate the incision depth with the average rate of river incision of the Yangtze River in Three Gorges region. The Yangtze River should have been cut down ca. 200 m in the last 0.5 Ma.
46
Chapter 3 Geological background 3.3.2 The landform change in the Badong study area The present elevation of the river bed in Badong is ca. 40 - 50 m asl. It can be speculated that the old river bed was located on an elevation between ca. 250 m asl. before the perforation of Yangtze River. Under the elevation of 300 m asl., five grades of river terraces can be observed in Badong (table 3.2) (Deng, 2000; Liu, 2010).
Table 3. 2: The elevation and the age of the terraces in the study area Terrace grade Age (104 a) Elevation (m asl.) 6 73 325 - 350 5 49 250 - 260 4 11.2 180 - 200 3 9.1 160 - 170 2 2.4 130 - 140 1 0.8 100 - 110
According to the dip direction of the bedding layers and the sediment sequence, the geological border between T2b2 sub-formation and T2b3 sub-formation on the 250 m asl. elevation has been reasonably estimated for the study area (figure 3.35). The border shows the unsymmetry of the Guandukou syncline. In South Badong, the T2b2 bedrock thickness is thin and is only 40 - 50 m thick (Li et al, 1996). Deng (2000) has noticed that the area between S1 ravine and the S2 ravine has an especially retrograde landform compared with the area west of S1 ravine and the area east of S3 ravine. It indicates that this area has experienced intensive landslides.
892 m
588 m
100 m
100 m
675 m 771 m
Fig. 3. 35: The speculated geological border between T2b2 layers and T2b3 layers on an elevation of 250 m asl. (Profile B - B’ stands for the location of the figure 3.38)
47
Chapter 3 Geological background The most notable change of the landform lies in the area west of N1 ravine in North Badong. According to the sediment sequence of the bedding layers, it could be reasonably speculated that the T2b3 bedrock layers have once covered the present area west of N1 ravine in North Badong (figure 3.317). Just now the old T2b3 bedrock layers have been eroded away due to the river incision. Some reasons can be found for the erosion process, why the T2b3 bedrock has not been maintained as high mountains. Firstly the strong regional structural movement in the geological period has brought about densely developed cleavages and joints in the bedrock of this area. Secondly the bedrock dips in the same direction as the slope dip in this area, which is a slope structure that promotes the occurrence of landslides. Thirdly, due to the flow direction change of Yangtze River, the toe of the slope in this area has suffered persistent strong hydraulic scouring, which has kept a sustained steep slope at the frontier of the slope. The densely distributed landslides, which lie on the riverside of this area, have also proved the high frequency of landslides in this area. The accumulation of the old landslide bodies originating from T2b3 bedrock have been found located in the areas of T2b2 bedding layers, which is speculated to be the consequence of this erosion process.
3.3.3 The characteristics of the drainage system in the study area Accompanying with the river incision, some large ravines have been formed in the study area. The characteristics of the drainage system show correlations with lithology and regional joint orientation in the bedding layers. The lithology in the Badong study area can be roughly divided into massive limestone layers, clayey limestone layers and clayey siltstone layers. The areas of massive limestone layers have mostly steep landform and the slope surfaces in these areas are mostly smooth. The gullies have usually a low discharge but high gradient. Generally in the area of clayey siltstone layers, the superficial drainage system often exhibits a branch form with more shallow gullies and a wide-open landform. But in the areas of clayey limestone layers, the gullies gather together into one deep gully and built deep-cut ravines. The probable reason is that the low permeability of clayey siltstone disperses and mitigates the water erosion, while the fractured and karstic clayey limestone layers offer preferential routes along the north-south stretching joint fractures for the incision of drainage. For example, the W1 ravine in West Badong flows generally along the outcrop of T2b1 sub-formation. Massive limestone layers crop out north of W1 ravine and clayey siltstone layers crop out south of W1 ravine. In the area north of the W1 ravine, steep cliffs and deep gorges have been built. In the area south of the W2 formation, the landform is relative gentler and the area has be explored as farm land. Secondly the drainage system shows correlations with the regional joint extension. The study area is characterized by the regional joints, which stretch in the north-south direction. The extension of the ravines shows a tendency of follow this orientation. As it has been introduced in chapter 2, the large ravines in the study area stretch either in the east-west direction or in the north-south direction. The large ravines (S1, S2, S3 and S4), which have the outlets in T2b3 layers and extend in the north-south direction, have their outlets the regular vertical-standing cliffs, such as in the
48
Chapter 3 Geological background figure 3.15. The large ravines (N3, E1 and E2), which have also the outlet in T2b3 layers but stretch in the east-west direction, have also a steep landscape but no regular vertical-standing rock cliffs.
49
Chapter 4 Investigation of landslides in the Badong study area
4 Investigation of landslides in the Badong study area
4.1 Classification of landslides The term landslide is a worldwide used term to describe the phenomena of “the movement of a mass of rock, debris or earth down a slope” (Varnes, 1978). The landslide type classification by Varnes (1978) will be adopted in this study (Table 4.1). In this classification, both the material pattern and also the movement type have been taken into account to nominate the mechanism of a landslide.
Table 4. 1: Abbreviated classification of slope movements (Varnes, 1978) Type of material Type of Movement Engineering soils Bedrock Predominantly coarse Predominantly fine Falls Rock fall Debris fall Earth fall Topples Rock topple Debris topple Earth topple Rotational Slides Rock slide Debris slide Earth slide Translational Lateral spreads Rock spread Debris spread Earth spread Rock flow Debris flow Earth flow Flows (deep creep) (soil creep) Complex Combination of two or more principal types of movement
4.2 Stability of landslides Landslides are not always dangerous. They can be in different stability status: active, reactivated, suspended, etc.. The potential threat of landslides is one of the main reasons, why so much attention has been paid to landslides. In this thesis, the classification of landslide status from WP/WLI (1993) will be adopted. Landslides may occur anywhere if the boundary conditions are fulfilled. But here we have to differentiate the natural landslides from the simple slope failures triggered by human activities. Landslides occur with a complex background composed of many impacting factors, such as geology, climate, hydrology etc.. Artificial slope failures are mainly caused by unsuitable measures during construction. In this study we focus mainly on the so called “natural landslides”.
50
Chapter 4 Investigation of landslides in the Badong study area
Table 4. 2: The states of landslide activity (modified from WP/WLI, 1993) Status Description definition active The landslide is currently moving reactivated The landslide is active again and moves on pre-existing shears whose strength parameters approach residual values. suspended The landslide, which has moved within the last annual cycle of seasons, is not moving at present. inactive The landslide last moved more than one annual cycle of seasons ago. dormant The causes of movement remain apparently. abandoned The causes of movement do not exist anymore. stabilized Artificial remedial measures have stopped the movement of the landslide. relict The landslide has clearly developed under different geomorphological or climatic conditions.
4.3 Landslides in the Badong study area
4.3.1 Overview The Badong study area is a landslide-prone area covering ca. 64 km2. According to the classification of landslides in table 4.1, 102 slides and one rock fall are mapped (figure 4.1). With the 102 slides in the study area, some general regularities of landslide distribution can be concluded. The rock fall is extra described in the end. Firstly the slides lie mostly near the riverside of the Yangtze River, especially on an elevation lower than 300 m asl. and on an elevation between 450 - 600 m asl.. The river incision is the essential factor of slides development in the area. Secondly the types and the scales of the landsides are correlated to the lithology. If we divided the study area according to the lithology of the bedding layers, it can be classified into limestone areas, sandstone areas, clayey limestone areas and clayey siltstone areas. Slides have been mostly found in the clayey limestone areas and the clayey siltstone areas. The scales of the slides in the clayey limestone areas are larger than in clayey limestone areas. Thirdly the slides, especially rock slides, occur most frequently in areas, where the bedding layers dip out of the slopes. A statistic analysis is done from many aspects as follows about slide distribution in the study area.
51
N3 E2
Rock fall E1
W1 N1 S2 N2 S1 S3 S4
Yangtze
S5
Fig. 4.1: Mapped landslides in the Badong study area Table 4.3: Information about mapped slides in the study area (Red color for active slides; black color for inactive slides) Toe Top Average Average Slope Type Height Length Area Bedrock type Material source elevation elevation breadth thickness angle Activity +No. (m) (m) (m2) /geological formation /geological formation (m asl.) (m asl.) (m) (m) (°) DS1 195 260 65 170 85 14 300 15 21 inactive CL/T1b1 CL/T1b1 DS2 170 200 30 55 30 1 700 3 30 inactive CL/T2b1 CL/T2b1 DS3 95 255 160 400 190 78 000 20 22 active CS/T2b2 CS/T2b2 DS4 175 275 100 195 95 18 500 5 27 inactive CL/T2b3 CL/T2b3 DS5 115 175 60 170 90 15400 10 20 inactive CL/T2b3 CL/T2b3 DS6 155 195 40 75 45 3 600 5 28 inactive CS/T2b2 CS/T2b2 DS7 185 235 50 105 40 4 300 8 25 active CS/T2b2 CS/T2b2 DS8 80 235 155 390 125 48 300 7 22 inactive CS/T2b2 CS/T2b2 DS9 165 230 65 160 75 11 900 5 22 inactive CS/T2b2 CS/T2b2 DS10 80 235 155 500 125 63 100 13 17 inactive CS/T2b2 CS/T2b2 DS11 80 170 90 335 175 58 500 17 15 inactive CS/T2b2 CS/T2b2 DS12 80 175 95 170 290 78 400 17 20 inactive CS/T2b2 CS/T2b2 DS13 455 540 85 180 65 11 800 4 25 inactive CS/T2b2 CS/T2b2 DS14 475 530 55 125 90 11 300 8 24 inactive CS/T2b2 CS/T2b2 DS15 460 490 30 90 65 5 200 7 19 inactive CS/T2b2 CS/T2b2 DS16 450 485 35 170 85 14 500 12 12 inactive CS/T2b2 CS/T2b2 DS17 360 370 10 50 30 1 400 5 14 inactive CS/T2b2 CS/T2b2 DS18 490 565 75 350 90 32 000 10 12 inactive CS/T2b2 CS/T2b2 DS19 570 595 25 65 40 2 500 5 21 inactive CS/T2b2 CS/T2b2 DS20 530 575 45 200 70 13 700 12 13 inactive CS/T2b2 CS/T2b2 DS21 115 180 65 195 130 24 800 15 19 inactive CS/T2b2 CS/T2b2 DS22 145 175 30 75 40 3 000 7 21 active CS/T2b2 CS/T2b2 DS23 175 240 65 110 30 3 000 2 31 active CS/T2b2 CS/T2b2 DS24 335 435 100 275 140 39 200 15 20 active CS/T2b2 CL/T2b3 DS25 295 325 30 55 40 2 100 3 28 inactive CS/T2b2 CS/T2b2 DS26 705 725 20 40 15 700 2 27 inactive CS/T2b2 CS/T2b2 DS27 740 760 20 45 35 1 500 3 24 inactive CS/T2b2 CL/T2b3 DS28 745 780 35 70 40 2 800 3 26 inactive CS/T2b2 CS/T2b2 DS29 80 210 130 365 135 50 100 7 20 inactive CL/T2b3 CL/T2b3 DS30 170 305 135 460 125 58 200 5 16 inactive CL/T2b3 CS/T2b4 DS31 100 160 60 210 190 39 800 20 16 inactive CS/T2b4 CS/T2b4 DS32 180 225 45 150 65 9 600 5 17 active CS/T2b4 CS/T2b4 DS33 540 610 70 185 20 14 100 6 20 inactive CS/T2b4 CS/T2b4 DS34 445 475 30 100 25 2 600 3 17 inactive CS/T2b4 CS/T2b4 DS35 390 445 55 180 30 5 500 5 17 active CS/T2b4 CS/T2b4 DS36 210 310 100 200 105 21 400 10 27 inactive CL/T2b3 CL/T2b3 DS37 140 175 35 45 20 900 2 31 active CL/T2b3 CL/T2b3 DS38 215 285 70 160 75 12 300 13 23 inactive CL/T2b3 CL/T2b3 DS39 275 355 80 160 30 5 200 5 26 inactive CS/T2b2 CS/T2b2 DS40 200 235 35 70 45 2 900 5 27 inactive CS/T2b2 CS/T2b2 DS41 160 230 70 190 40 7 700 5 20 inactive CS/T2b2 CS/T2b2 DS42 175 220 45 80 55 4 600 6 29 inactive CL/T2b3 CS/T2b2 DS43 150 240 90 215 65 14 100 7 23 inactive CS/T2b4 CS/T2b4 DS44 80 220 140 370 195 72 300 10 21 inactive CS/T2b4 CS/T2b4 DS45 100 385 285 600 95 57 800 6 25 active CS/T2b4 CS/T2b4 DS46 260 315 55 105 55 6 000 5 27 inactive CS/T2b4 CS/T2b4 DS47 370 460 90 230 90 21 100 8 21 inactive CS/T2b4 S/T3s, CS/T2b4 DS48 265 525 260 585 105 61 000 7 24 inactive CS/T2b4 S/T3s, CS/T2b4 DS49 80 225 145 340 145 48 700 5 23 inactive CS/T2b2 CS/T2b2 DS50 395 425 30 70 120 8 200 3 25 inactive CS/T2b2 CS/T2b2 DS51 400 520 120 375 210 79 200 8 18 inactive CS/T2b2 CL/T2b3 DS52 340 410 70 130 75 9 500 5 28 inactive CL/T2b3 CL/T2b3 DS53 520 555 35 85 45 3 600 3 23 inactive CL/T2b3 CL/T2b3 DS54 585 620 35 50 20 1 100 3 36 inactive CS/T2b2 CL/T2b3 DS55 485 510 25 115 75 8 400 5 12 inactive CL/T2b3 CL/T2b3 DS56 515 550 35 130 50 6 700 5 15 inactive CL/T2b3 CL/T2b3 DS57 670 710 40 55 35 1 800 3 37 inactive CL/T2b3 CL/T2b3 DS58 510 555 45 50 35 1 700 5 40 inactive CL/T2b3 CL/T2b3 DS59 535 590 55 155 75 11 700 8 20 inactive CS/T2b2 CS/T2b2 DS60 140 225 85 180 100 18 500 14 25 active CL/T2b3 CS/T2b4 DS61 180 205 25 60 45 2 900 3 23 inactive CL/T2b3 CL/T2b3 DS62 185 240 55 180 65 12 000 10 17 inactive CS/T2b4 CS/T2b4 DS63 145 195 50 145 90 1 300 6 19 inactive CL/T2b3 CL/T2b3 DS64 670 730 60 120 75 8 800 4 27 inactive L/T1j L/T1j DS65 615 670 55 170 50 8 500 5 18 inactive L/T1j L/T1j DS66 640 680 40 110 40 4 300 10 20 inactive CS/T2b2 CS/T2b2 DS67 560 640 80 175 55 10 100 10 24 inactive CS/T2b2 CS/T2b2 DS68 465 530 65 135 70 9 500 71 26 inactive CS/T2b2 CS/T2b2 DS69 160 240 80 130 120 15 700 5 32 active CS/T2b2 CS/T2b2 DS70 160 215 55 100 65 6 500 10 29 active CL/T2b3 CL/T2b3 DS71 555 645 90 245 110 26 400 8 20 inactive CS/T2b2 CS/T2b2 DS72 530 600 70 245 145 35 300 5 16 inactive CS/T2b2 CS/T2b2 DS73 70 290 220 775 470 325 000 70 19 active CL/T2b3 CL/T2b3 DS74 50 250 200 510 500 320 000 61 26 active CL/T2b3 CL/T2b3 DS75 160 600 440 1 200 440 381 000 35 17 inactive CL/T2b3 CS/T2b2 DS76 220 520 300 1 100 500 326 000 42 21 inactive CL/T2b3 CS/T2b2 DS76.1 350 405 55 155 135 21 400 10 19 inactive CL/T2b3 CS/T2b2 RS1 80 180 100 315 140 44 600 15 30 inactive CL/T2b3 CL/T2b3 RS2 80 225 145 410 165 67 800 15 20 inactive CS/T2b2 CS/T2b2 RS3 175 250 75 195 130 25 400 6 21 active CS/T2b2 CS/T2b2 RS4 90 295 205 635 190 119 400 15 18 inactive CS/T2b2 CL/T2b3, CS/T2b2 RS5 375 495 120 415 130 54 200 10 16 inactive CS/T2b2 CL/T2b3 RS6 390 500 110 425 325 138 000 5 14 inactive CS/T2b2 CL/T2b3 RS7 785 845 60 110 60 6 800 4 28 inactive CL/T2b3 CL/T2b3 RS8 125 230 105 295 100 29 100 5 20 inactive CS/T2b2 CS/T2b2 RS9 80 265 185 455 140 62 600 7 22 inactive CS/T2b2 CS/T2b2 RS10 80 325 245 600 110 66 200 7 22 inactive CS/T2b2 CS/T2b2, CL/T2b3 RS11 120 225 105 211 100 20 900 10 26 inactive CL/T2b3 CL/T2b3 RS12 125 355 230 520 305 159 400 5 24 inactive CL/T2b3 CL/T2b3 RS13 675 715 40 85 40 3 600 4 24 inactive CL/T2b3 CL/T2b3 RS14 255 290 35 120 40 4 700 3 16 inactive CL/T2b3 CS/T2b4 RS15 150 225 75 300 105 31 900 15 14 inactive CS/T2b4 CS/T2b4 RS16 80 210 130 295 205 60 800 15 24 active CS/T2b4 CS/T2b4 RS17 80 255 175 530 190 99 700 4 18 active CS/T2b2 CS/T2b2 RS18 100 400 300 1 015 545 553 200 35 16 inactive CL/T2b3 CL/T2b3 RS19 100 310 210 345 310 107 200 10 31 active CL/T2b3 CL/T2b3 RS20 190 265 75 210 177 37 000 10 20 active CL/T2b3 CL/T2b3 RS21 405 520 115 255 155 39 600 7 24 inactive CL/T2b3 CL/T2b3 RS22 500 550 50 160 145 23 300 10 17 inactive CS/T2b2 CL/T2b3 RS23 445 480 35 70 60 4 300 3 26 inactive CS/T2b2 CS/T2b2 RS24 245 300 55 130 51 6 600 5 23 inactive CS/T2b2 CS/T2b2 RS25 160 200 40 120 105 12 700 5 18 active CS/T2b2 CS/T2b2
DS: debris slide; RS: rock slide; CS: clayey siltstone; CL: clayey limestone
Chapter 4 Investigation of landslides in the Badong study area
4.3.1.1 Slope angle of slide bodies The general slope angles of all slide bodies mapped in the study area have a mean value of 22.2° and a standard deviation of 5.5°. The lowest general slope angle is 11.5° and the highest slope angle is 39.7° (figure 4.2). But the general slope angles of most of the slide bodies lie between 11° and 30°. The general slope angles of instable slide bodies have a mean value of 23.5° and a standard deviation of 4.8°. The slope angles of the instable slide bodies lie between ca. 17° - 32°.
Slope angle of landslide body (°) 38-40
34-36 All slides Instable slides
30-32
26-28
22-24
18-20
14-16
10-12 Slides number 0 5 10 15 20
Fig. 4. 2: General slope angles of the slide bodies
4.3.1.2 Elevation of slide bodies The river incision is the essential background of the slide development in the study area. The elevation values of the slide bodies are used to represent the influence of the Yangtze River. Slides occur especially frequently near the riverside of the Yangtze River. The river bed of Yangtze River in Badong study area has a elevation of ca. 40 - 50 m asl., while the water level in the Three Gorges Reservoir changes between 145 m asl. and 175 m asl. since 2008. This means that the river banks in the study area have a depth of at least ca. 100 m submerged in the reservoir. Most of the slides lie on the riverside or near the riverside in the study area. 40 slides have their slide bodies partly submerged by the reservoir. Due to the impoundment of the reservoir, the toe of many slides on the riverside cannot be seen any more. Because of the elevations of the toes of the slide bodies are usually speculated from the topographical map if they are submerged in the river, the elevations of tops of slide bodies are statistically analyzed (figure 4.3). The tops of slide bodies concentrate specially on an
57
Chapter 4 Investigation of landslides in the Badong study area elevation lower than 300 m asl. (47 slides), and an elevation range between 450 m asl. and 600 m asl. (25 slides). The tops of the instable slide bodies lie on an elevation between 175 m asl. and 443 m asl., especially on an elevation lower than 300 m asl.. From the 20 instable slides, 13 slides have their toes lower than 175 m asl..
Elevation (m) All slides Instable slides 750-800
650-700
550-600
450-500
350-400
250-300
150-200 Slides number
0 5 10 15 20 25 30
Fig. 4. 3: Elevation values of the top of slide bodies
4.3.1.3 Slope structure The term slope structure in this study indicates a geometrical classification of slopes according to the included angle between the dip direction of the slope and the dip direction of bedding layers in the slope (figure 4.4) (Chen, 2009). If the included angle between the dip direction of the slope and the dip direction of the bedding layers in the slope is lower than 20°, the slope is a dip slope. If the included angle is between 20° and 160°, it is a cross-dip slope. If the included angle is between 160° and 180°, it is an anti-dip slope. In South Badong and North Badong, the slopes are mostly dip slopes, while in West Badong the slopes are mainly cross-dip slopes and in East Badong the slopes are mostly anti-dip slopes. The distribution of the slides shows a clear correlation with the slope structures. In the study area, the slides lie most frequently in dip slopes (59%), medium frequently in across-dip slopes (28%) and relatively seldom in anti-dip slopes in the study area (13%). In the 20 instable slides, 11 (55%) of them lie in dip slopes, 7 (35%) of them lie in cross-dip slopes and 2 (10%) of them lie in anti-dip slopes. The total area of 11 instable slide bodies in dip slopes has 55% of the total area of all instable slides. The total area of 7 instable slide bodies in cross-dip slopes has 35% of the total area of all instable slides. The total area of 2 instable slide bodies in anti-dip slopes has 10% of the total area of all instable slides. The tendency of the distribution of instable slides is roughly coincident to the distribution of all 58
Chapter 4 Investigation of landslides in the Badong study area slides according to slope structure.
Fig. 4. 4: Slope classification referring to the relationship between the relative aspects of slope and bedding layers (Chen, 2009)
4.3.1.4 Lithology In the study area slides prevail mainly in areas consisting of outcropping clayey siltstones, which have an area of 25.5 km2 in total, and in areas consisting of outcropping clayey limestones, which have an area of 22.8 km2 in total. Additionally the areas consisting of massive limestones are 8.6 km2 and the areas consisting of thick sandstones are about 1.0 km2 big. Most of the slides lie in areas consisting of clayey siltstones and clayey limestones. In the Badong study area, 62 slide bodies have their materials originating mainly from clayey siltstone bedrocks. They have an average area of ca. 39 900 m2. 36 slide bodies have their materials originating mainly from clayey limestone bedrocks. They have an average area of ca. 61 200 m2. Additionally there are two small scale slide bodies with areas of ca. 8 800 m2 (debris slide DS64) and ca. 8 500 m2 (debris slide DS65) originating from massive limestones (T1j formation), and two debris slides with areas of ca. 21 100 m2 (debris slide DS47) and ca. 61 000 m2 (debris slide DS48) originating from sandstones (T3s formation) and clayey sandstones (T2b4 sub-formation). It can be noticed that the frequency of slide occurrence in areas consisting of clayey siltstones is definitely higher than in areas consisting of clayey limestones. But the scales of the slides in areas consisting of clayey siltstones are much lower than in that of clayey siltstones. In the 20 instable slides, 13 slide bodies have their materials originating mainly from clayey siltstone bedrock and they have an average area of ca. 30 000 m2. 7 slides bodies have their materials originating mainly from clayey limestone bedrock and they have an average area of ca. 114 900 m2.
4.3.1.5 Tectonic structures Generally tectonic structures are strongly developed in the whole study area. The distribution of slides in the study area shows a high frequency correlation with the tectonic structures. Slides are especially developed in the T2b2 areas in the western part of North Badong, and in
59
Chapter 4 Investigation of landslides in the Badong study area both T2b2 and T2b3 areas in South Badong. The T2b2 areas in the western part of North Badong and the T2b2 and T2b3 areas in South Badong have a total area of 24.5 km2, which is ca. 42% of the mapped area. But there are 68 slides which are 67% from the number of slides. These 68 slides have a total area of 3.9 km2, which is 83% of the total area of all mapped slides. As introduced in chapter 3, the Xinwu anticline is composed of two secondary anticlines and one secondary syncline in the area west of N1 ravine in North Badong, which is the general tectonic background in this area. In South Badong, the Guandukou syncline and two secondary folds in the south limb build the general tectonic background. The instable slides have also been found mainly located in both limbs of the Guandukou syncline. From 20 instable slide bodies, 14 of them lie in the T2b2 areas in the western part of North Badong and in the T2b2 and T2b3 areas in South Badong. The 14 instable slide bodies have a total area of 1.0 km, which is 82 % of the total area of all instable slides.
4.3.1.6 Land use Many studies have shown that forest slopes are mostly more stable than comparable slopes in open land (Prandini et al., 1977; Rickli and Graf, 2009). They believe that the root systems of trees can stabilize a certain depth of the surface soil. But there is not enough research indicating that the forest has also positive impact on slides with deep shear zones. The land use in the study area has been rapidly changing since the construction of the Gezhouba Dam Project. The counties and the cities in the Three Gorges region became larger and larger. More and more areas have been used as farmland, which could increase the risk of shallow slides. It is difficult to measure how much influence the land use has on slides. But they do have some correlation in the Badong study area. It can be noticed that the slide bodies have been preferred to be used as farmland. A few reasons can be speculated for that. Large areas of terraces on the old riversides, which were suitable for agriculture, have been submerged by the reservoir. The consequence is that the areas which are suitable for economic crops became less, so that the hill slopes in the study area have been over-exploited for farmland. It can be also noticed in the study area that the land has been used as farmland as much as possible. In this process, the areas of slide bodies are in favor of exploited as farmland, because of the low slope angle and of the loose material composition on the slide bodies. To this manner, the distribution of farmland has correlations with the distribution of slide bodies. Most areas of slide bodies have been used as farmland. On the contrary, slides have been very seldom found in areas of forest and shrub in the study area. Besides in areas of massive limestones, forest and areas have only been found on relative steep slopes. Especially shrub grows mostly in areas with very thin soil coverage and often in dry areas, which are the least possible to be slide bodies. So it can be concluded that slides have high relativity with farmland areas and low relativity with shrub areas.
4.3.2 Slides Cruden and Varnes (1993) have defined a slide as “a slide is a downslope movement of a soil
60
Chapter 4 Investigation of landslides in the Badong study area or rock mass occurring dominantly on surfaces of rupture or on relatively thin zones of intense shear strain”. According to the materials of movement, slides can be divided into rock slides, debris slides and earth slides (table 4.1). Due to the relatively gentle landforms in this area, slides are the main type of slides. Totally 25 rock slides and 77 debris slides have been mapped. 5 representative rock slides and 5 typical debris slides are selected as an introduction of slides in the study area (figure 4.5).
RS8
DS23 DS30
N1 RS9 N2 RS10 S2 S3 Yangtze S4 DS44
DS45
RS18 DS70 S5
RS22
Fig. 4. 5: Location of the introduced slides in the study area
4.3.2.1 Rock slides Firstly rock slide occurrences have been found correlated with the slope structure. Most of the rock slides have been mapped in dip slopes. A few of them have been also found in cross-dip slopes. It is very seldom that rock slides have been mapped in anti-dip slope slopes in the study area. Secondly rock slides are mostly located on riversides of the Yangtze River, which is in accordance to the general regulars of slide distribution. Thirdly large scale rock slides are especially correlated with tectonic structures, such as Guandukou syncline and its secondary folds in both limbs. 5 rock slides in the study area have been selected to present the correlations between rock slides and slope structures, riversides and tectonics.
4.3.2.1.1 Rock slide RS8 Fig. 4.6 shows three rock slides located on the riverside of the Yangtze River in North Badong, which exhibit representative landforms of rock slides in the study area. They lie in dip slopes
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Chapter 4 Investigation of landslides in the Badong study area in areas of the T2b2 sub-formation. The rock slide RS8 lies immediately close to the N1 ravine on the east side in North Badong. It lies in the T2b2 sub-formation and has a total area of ca. 29 100 m2. The slope surface of the rock slide body dips in southern direction with a general slope angle of ca. 20°. The rock slide body is ca. 295 m long in total and ca. 100 m broad on average. The top of the rock slide body is on an elevation of ca. 230 m asl.. The toe is submerged in the reservoir and is speculated to be lying on an elevation of ca. 125 m asl. according to the topographical map. No active characters from the rock slide body can be observed. The rock slide body is mainly used as farmland. Orange trees are the main economic crop in this region. A few resident houses are built at the top of the rock slide body. A street has been built across the slide bodies on an elevation of ca. 200 m asl. and a few resident houses have been also built along the street.
A
RS10 RS9 RS8 Fig. 4.10 A’ Fig. 4.11
Fig. 4. 6: Rock slides RS8, RS9 and RS10 on the riverside in North Badong (View direction: northeast)
The figure 4.7 shows an overview of the rock slide RS8 from its west side. It can be noticed from the photo that the bedding layers under the rock slide body build an anticline, whose axis stretches in east-west direction. The bedding layers beside the street in the northern limb of the anticline are measured to dip in 23° direction with a dip angle of 31°.
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Chapter 4 Investigation of landslides in the Badong study area
N
(23° / 31°)
Bedding layers
Fig. 4. 7: Overview of the rock slide RS8 and the underlying bedding layers from its west side (View direction: east) (The black square in figure 4.7 is the area of figure 4.8)
The thickness of the rock slide is speculated to be ca. 5 m on average. Figure 4.8 shows an outcrop of the border between the rock slide body and the underlying bedrocks. The photo was taken at the west flank of the rock slide body, which has been excavated due to the street construction. The street in figure 4.8 stretches in north-south direction. The materials of the rock slide body consist of a mixture of rock blocks and soils which originate from the T2b2 bedding layers. The maximal size of the blocks is ca. 0.5 m and the blocks are angular. The bedding layers are generally 20 - 40 cm thick and the density of fractures is ca. 5 - 10 /m.
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Chapter 4 Investigation of landslides in the Badong study area
N
Rock slide body
Bedding layers
Fig. 4. 8: An outcrop of the border between the rock slide body and the underlying bedrocks (The location of the figure is marked in figure 4.7) (X: 438904, Y: 3436390)
The main scarp of the slide can be recognized as a relatively steeper landform than the rock slide body. It appears as a curved armchair-shaped form and is covered mainly by shrub. It reaches an elevation of ca. 275 m asl. at most, which is ca. 45 m higher than the top of the rock slide body. The bedding layers in the main scarp are also speculated to be dipping in north-northeast direction. Referring to this photo and the distribution of the rock slide body, a geotechnical profile has been prepared based on a DEM with a resolution of 10 m (figure 4.9).
Main scarp Profile A-A’ of the rock slide RS8
27°
T2b2
Street
175 m asl.
145 m asl.
Fig. 4. 9: Profile of the rock slide RS8 in the location marked in the figure 4.6
4.3.2.1.2 Rock slide RS9 The rock slide RS9 lies ca. 50 m east of the rock slide RS8 on the riverside of the Yangtze River in North Badong (figure 4.6). It lies in the T2b2 sub-formation and covers an area of ca.
64
Chapter 4 Investigation of landslides in the Badong study area 62 600 m2. The slope surface of the rock slide body dips in southern direction with a general slope angle of ca. 22°. The rock slide body is ca. 455 m long in total and ca. 140 m broad on average. The top of the rock slide body reaches as high as ca. 260 m asl.. The toe is submerged in the reservoir and is speculated to be lying on an elevation of ca. 80 m asl.. The rock slide body is observed to be stable.
Fig. 4. 10: An outcrop of the rock slide body RS 9 beside the street (X: 349237, Y: 3436246)
The rock slide body is speculated to be ca. 7 m thick on the average. The rock slide body is mainly used as farmland. Orange trees are planted on it. A street stretches cross the rock slide body on an elevation of ca. 200 m asl. in east-west direction. A few resident houses have been built at the top of the rock slide body and along the street at the middle part of the rock slide body. Due to the street construction, a thickness of ca. 3 - 4 m of the rock slide body has been exposed. From the outcrop in figure 4.10, it can be observed that the materials of the rock slide body are mainly composed of fuchsia clayey silt mixed with fuchsia rock blocks, gravels and soils originating from the T2b2 sub-formation. The blocks are mostly not larger than 50 cm. The main scarp of the slide can be clearly recognized from the landform, which is partly covered by forest and partly by farmland. It reaches as high as ca. 275 m asl., which is ca. 12 m higher than the top of the rock slide body.
4.3.2.1.3 Rock slide RS10 The rock slide body RS10 lies tightly close to the rock slide body RS9 on the east side (figure 4.6). The rock slide body lies close to the geological border between the T2b2 and T2b3 sub-formations. The rock slide body RS10 covers an area of ca. 66 200 m2. The slope surface of the rock slide body dips in southern direction with a general slope angle of ca 22°. The rock
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Chapter 4 Investigation of landslides in the Badong study area slide body is ca. 600 m long and ca. 110 m broad on average. The top of the rock slide body is as high as ca. 325 m asl.. The toe is submerged in the reservoir and is speculated to be lying on an elevation of ca. 80 m asl.. The rock slide body is considered to be stable.
Fig. 4. 11: An outcrop of the rock slide body RS10 from beside the street (X439372, Y: 3436217)
The rock slide body is speculated to have an average thickness of ca. 7 m. It is mainly used as farmland. Orange trees are planted on the rock slide body. A street stretches cross the rock slide body on an elevation of ca. 200 m asl. in east-west direction. Resident houses have been built on both sides of the street. Due to the street construction, a thickness of ca. 4 - 5 m of the rock slide body can be seen at the north side of the street. Figure 4.11 was taken from the excavation besides the street. It can be seen that the materials are composed of rock blocks, gravels and soils from both the T2b2 and T2b3 sun-formations. The rock blocks are as large as ca. 0.5 m at most. The main scarp of the slide exhibits a concave form and is mainly covered by forest. It reaches an elevation of ca. 375 m asl., which is ca. 50 m higher than the top of the rock slide body. The main scarp lies in the T2b3 sub-formation area. The bedding layers in the main scarp dip in 108° direction with a dip angle of 20°. The bedding layers are mostly 30 - 50 cm thick and the fractures have a density of ca. 2 - 5 /m. The density of fractures in the bedrocks is definitely lower than in most areas in South Badong.
The rock slides RS8, RS9 and RS10 are lying on the riverside in North Badong and represent the typical landforms of rock slides in the study area. The dip slopes, the locations on the river side and the local structure (the anticline introduced in figure 4.8) have built the general background of the three rock slides. The speculated thicknesses of the rock slide bodies are definitely lower than the corresponding heights of the main scarps, which indicates that a large part of the rock slide bodies have been eroded.
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Chapter 4 Investigation of landslides in the Badong study area 4.3.2.1.4 Rock slide RS18 (Zhaoshuling landslide) The rock slide RS 18 (Zhaoshuling rock slide) is the largest rock slide in the study area. The rock slide body lies in the area between the S1 and S2 ravines on the riverside of the Yangtze River in South Badong (Fig. 4.5). It lies in a dip slope in the T2b2 sub-formation. The rock slide body has an area of ca. 553 200 m2. The slope surface of the rock slide body dips in northern direction with a general slope angle of ca. 16°. The rock slide body is ca. 1 015 m long in total and ca. 545 m broad on average. The top of the rock slide body reaches an elevation of ca. 400 m asl. The toe of rock slide body is submerged in the reservoir and is reported as low as ca. 100 m asl., which is the elevation of the first grade terrace before the reservoir impoundment (Ma et al., 2006). No signs have been found indicating the instability of the rock slide body. The average thickness of the rock slide body is ca. 35 m (Li et al., 1996). The top of the rock slide body is covered by buildings. The biggest part of the rock slide body is covered by forest and shrub. The middle and the lower part of the rock slide body are mainly covered by forest and shrub (figure 4.12). A lot of resident houses distribute on the rock slide body. A street, which stretches in east-west direction, has been built through the rock slide body on an elevation of ca. 200 m asl..
N
A
A’
Fig. 4. 12: Rock slide RS18 rock slide on the riverside of the Yangtze River in South Badong (View direction: south southwest)
Figure 4.13 presents two photos of outcrops of the rock slide body on an elevation of ca. 250 m asl.. The photo of figure 4.13 (left side) is located close to the eastern flank of the rock slide body. The material is mainly composed of limestone blocks, gravels and grey clayey soil. The blocks are at most ca. 40 cm thick. The photo of figure 4.13 (right side) is located in the middle of the rock slide body. The stratified character of the original bedding layers dipping in the slope is somehow preserved in the rock slide body. But brown residual soils have filled the space between the moved bedding layers and the fractures. 67
Chapter 4 Investigation of landslides in the Badong study area
Left side: An outcrop of the rock slide body RS18 Right side: An outcrop of rock slide body RS18 on 250 on 250 m asl. (X: 437726, Y: 3435121) m asl. (X: 437522, Y: 3435069) Fig. 4. 13: Outcrops of rock slide body on an elevation of 250 m asl.
Figure 4.14 shows two outcrops of the rock slide body on an elevation of ca. 165 m asl.. As shown in figure 4.14 (left side), the material is mainly composed of rock blocks and gravels. The sizes of the blocks are maximal ca. 1 m thick. Most of them have diameters between 10 - 30 cm. Brown or grey brown weathered fine material fills the spaces between blocks and gravels. It can be seen from figure 4.14 (right side) that the stratified character of the old bedding layers has been partly preserved. According to the outcrops in figure 4.13 and in figure 4.14, the middle part of the rock slide body has kept very well stratified character of the old bedding layers. Along the flanks of the rock slide body, the composition of rock blocks and gravels filled with weathered material can be mainly observed. According to the geotechnical profile of the slide (figure 4.15), the shear zone is along the border between T2b2 bedding layers and T2b3 bedding layers.
Left side: rock block accumulation in the rock slide Right side: layered bedrock in the rock slide body body (X: 437648, Y: 3435429) (X: 437432, Y: 345441) Fig. 4. 14: Different material compositions of the rock slide body RS18
The main scarp of the rock slide reaches an elevation of ca. 475 m asl. at most, which is ca. 75 m higher than the top of the rock slide body. It has a definitely steeper landform than the rock
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Chapter 4 Investigation of landslides in the Badong study area slide body and the outcrops of clayey limestone bedding layers can be observed in the main scarp. In addition, a small area outcropping T2b2 bedrock has been mapped in the main scarp, which is speculated to be exposed by the sliding of the overlying T2b3 bedding layers. But the T2b3 bedding layers crop out in the areas east and west of the rock slide body. According to the thermoluminescence (TL) dating test with a sample from the shear zone of the slide, the Zhaoshuling rock slide occurred ca. 11.7 ± 0.9 × 104 a BP (Chen et al., 2013).
A’ A Profile A’-A of the rock slide RS22
Fig. 4. 15: Profile of the Zhaoshuling rock slide (Revised from Chai, 2008)
Some common characters of the rock slides RS8, RS9, RS10 and RS18 can be concluded. Firstly the rock slides occur close to the secondary anticlines of the Guandukou syncline near riversides whose axes lie in east-west direction. The rock slides RS8, RS9 and RS10 occur in dip slopes built by the southern limb of the secondary anticline. The rock slide RS18 occurs in a dip slope built by the north limb of the secondary anticline. Secondly the zones of depletion retreat slope upwards shortly over the axes of the secondary anticlines. The crowns of the main scarps lie in anti-dip slopes built by the other limbs of the secondary limbs. Generally the anticlines should have promoted the development of rock slides.
4.3.2.1.5 Rock slide RS22 Figure 4.16 shows a rock slide lying on the geological border between the T2b2 and T2b3 sub-formations in the upstream area of the S4 ravine in South Badong. It lies in a cross-dip slope. The rock slide body covers an area of ca. 23 300 m2. The slope surface of the rock slide body dips generally in east-northeast direction with a slope angle of ca. 17°. The rock slide body is ca. 160 m long and 145 m broad on average. The top of the rock slide body lies on an elevation of ca. 550 m asl. and the toe lies on an elevation of ca. 500 m asl.. No instable character can be observed from the rock slide body.
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Chapter 4 Investigation of landslides in the Badong study area
N
A T2b3 T2b2 (4) (3) (1)
(2)
A’
Fig. 4. 16: Overview of rock slide RS22 (View direction: west) (The locations of black dots in the figure stand for the corresponding locations of the following figures) (1): fig. 4.17 (left side), (2): fig. 4.17 (right side), (3): fig. 4.18 (left side), (4): fig. 4.18 (right side)
The rock slide body is speculated to be ca. 10 m thick on average. The rock slide body has a convex form and has been mainly used as farmland. A few resident houses have been built at the lower part of the rock slide body. Different kinds of vegetables have been planted on it. An excavation has been done on an elevation of ca. 545 m asl. across the part of the rock slide body. Figure 4.17 shows two outcrops of the rock slide body. The photo in the figure 4.17 (left side) was taken from the excavation on an elevation of ca. 545 m asl.. As shown in figure 4.17(left side), the outcrop is ca. 2 m thick in total and the material is mainly composed of limestone gravels and rock blocks originating from the T2b3 sub-formation. Many gravels are characterized by sheeted forms. The rock blocks are at most ca. 0.5 m thick. The photo in figure 4.17 (right side) was taken on an elevation of ca. 520 m asl. close to the resident houses. The materials are mainly composed of brown clayey soils, limestone gravels and limestone blocks originating from the T2b3 sub-formation. The rock blocks and gravels have different size. The rock blocks are not larger than ca. 40 cm.
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Chapter 4 Investigation of landslides in the Badong study area
Left side: Material of rock slide body from the Right side: Material of rock slide body close to the excavation (X: 440639, Y: 3433744) resident houses (X: 440735, Y: 3433796 ) Fig. 4. 17: Rock slide materials of the rock slide RS22 (The locations of the photos are marked in figure 4.16)
The main scarp reaches an elevation of ca. 600 m asl., which is ca. 50 m higher than the top of the rock slide body. The main scarp exhibits a concave land form and is mainly covered by forest. Due to the excavation across the main scarp, bedding layers can be observed. Figure 4.18 (left side) shows an outcrop of bedrocks which is exposed by the excavation. From the excavation, slope deposits composed of gravels and soils originating from the T2b3 sub-formation can be observed with a total thickness of ca. 2 m. In the bedding layers under the slope deposits, the border between the T2b2 and the T2b3 sub-formations in the main scarp of the slide can be seen.
T2b3
T2b2
Left side: Geological border between T2b2 / T2b3 in Right side: Outcrop of strongly fractured T2b3 the main scarp (X: 440669, Y: 3433659) bedrocks ca. 100 m north of the rock slide (X: 440604, Y: 3433915) Fig. 4. 18: Bedding layers in and near the main scarp (See the locations in figure 4.16)
The outcrop shown by figure 4.18 (right side) has been found north of the main scarp (see the location in figure 4.7). Strongly developed fractures can be observed in the T2b3 bedding
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Chapter 4 Investigation of landslides in the Badong study area layers from the excavation. The bedding layers dip in the slope with dip angles between 55° and 65° (in southern direction). The bedding layers of are mostly only a few centimeters thick. The joints are stretching in north-south direction and can be found in the bedrock every 10 - 20 cm. Brown weathered materials fill the space between the densely fractures. The outcrops in figure 4.18 (left side) and in figure 4.18 (right side) indicate that there could be a syncline or a tectonic structure that is more complicated than a simple syncline. The extremely fractured bedrocks due to the strong tectonic deformation could be the most important factor for the occurrence of this rock slide. According to the information introduced above, a geotechnical profile is made for the rock slide RS22 (fig. 4.19).
64° Profile A-A’ of the rock slide RS22
Main scarp
Fig. 4. 19: Geotechnical profile for the rock slide RS22 at the location marked in the figure 4.16
On an elevation between 440 m asl. and 650 m asl. in the upstream area of the S4 ravine, 5 small-scale slides and the largest slide group are located here, which shows a high frequency of slides in this area. The character of extremely developed tectonics in this region should be a most significant factor for the high frequency of slide occurrence in this area.
4.3.2.2 Debris slides Debris slides are landslides, whose landslide bodies are mainly composed of debris. 77 debris slides in total have been mapped in the study area. The materials of the debris slide bodies in the study area originate mostly either from clayey limestone bedrocks and clayey siltstone bedrocks. Debris slides have been mapped in slopes composed of different kinds of loose materials. Firstly a small scale debris slide (DS23) is introduced. Although it is small, it is one of the common types of instable debris slides in the study area. It shows a creeping movement and has a typical landform for the observed debris slides. Secondly, one debris slide in a dip slope (DS30) and two debris slides in anti-dip slopes (DS44 and DS55) on the riverside of the
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Chapter 4 Investigation of landslides in the Badong study area Yangtze River are introduced. They all have relatively high positions on the riverside with their lower parts of the debris slide bodies submerged in the reservoir. But comparatively complete landforms of debris slides can be still observed from them. At last, a debris slide event (DS70) is described.
4.3.2.2.1 Debris slide DS23 The debris slide DS23 is located on the east side of the N1 ravine ca. 400 m to the outlet of the ravine in North Badong. It lies in an anti-dip slope composed of T2b2 sub-formation. The debris slide body covers an area of ca. 3 000 m2. The slope surface of the debris slide body dips in western direction with a general slope angle of ca. 31°. The debris slide body is ca. 110 m long in total and ca. 30 m broad on average. The top of the debris slide body lies on an elevation of ca. 240 m asl. and the toe on an elevation of ca. 175 m asl.. The debris slide body is observed to be instable with a creeping movement. The debris slide body is speculated to be ca. 2 m thick. The surface of the debris slide body has been artificially managed and is used as farmland (figure 4.20). Orange trees and vegetables have been planted on it. A street has been built across the debris slide body on an elevation of ca. 200 m asl.. It consists of mainly fuchsia clayey silt soils with fuchsia silt gravels. The sizes of the gravels are maximal only a few centimeters.
N
Fig. 4. 20: Overview of the debris slide DS23 on the east side of the N2 ravine close to the outlet in North Badong (View direction: east)
The main scarp can be observed ca. 2 m higher slope upwards of the debris slide body. The slope deposit has offered the material for the debris slide. The area of main scarp has also
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Chapter 4 Investigation of landslides in the Badong study area been partly used as farmland and bedrocks can been observed on both sides of the main scarp. The debris is located in an anti-dip slope. The bedding layers ca. 50 m north of the debris slide body beside the street are measured dipping in 91° direction with a dip angle of 22°. This debris slide DS23 is a typical debris slide in the study area. They are usually found in T2b2 and T2b4 sub-formation with thin loose materials and relatively steep landforms built by bedrocks. The debris slide bodies are mostly used as farmland, due to the loose materials.
4.3.2.2.2 Debris slide DS30 The debris slide DS30 is located immediately close to the geological border between T2b3 and T2b4 sub-formations on the riverside of the Yangtze River in North Badong. It lies in a dip-slope composed of T2b3 sub-formation. The materials of the debris slide body originate from T2b4 sub-formation and the bedrocks under the debris slide body are T2b3 bedding layers. The debris slide body presents a convex form and covers an area of ca. 58 200 m2. The slope surface of the debris slide body dips in south-southeast direction with a general slope angle of ca. 16°. The debris slide body is ca. 460 m long and ca. 125 m broad on average. The top of the debris slide body reaches an elevation of ca. 305 m asl. and the toe lies on an elevation of ca. 170 m asl.. No instable signals have been found from the debris slide body. The debris slide body is speculated to have a thickness of ca. 5 m on average and is mainly used as farmland. Orange trees and vegetables have been planted on it. A street cuts through the debris slide body on an elevation of ca. 200 m asl.. So the materials of the debris slide body can be directly observed from the excavation on the street side (figure 4.22). A few resident houses can be also found at the top of the debris slide body and at the lower part of the debris slide body below the street.
N A
A’
Fig. 4. 21: Overview of the debris slide RS30 (View direction: northwest)
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Chapter 4 Investigation of landslides in the Badong study area
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Debris slide body Bedrock
Fig. 4. 22: The middle part of the debris slide body (View direction: west) (The black square in the figure 4.22 is the location of figure 4.23)
As can be seen from the excavation beside of the street (figure 4.23), the debris material originating mainly from the T2b4 sub-formation is accumulated on the bedding layers of T2b3-subformation. The bedding layers in figure 4.23 dip in 177° direction with 20° dip angle. The loose materials in the outcrop are totally ca. 4.5 m thick and can be divided into three layers: The lower layer is ca. 1 m thick and consists of old weathered material from the underlying bedrocks. It is mainly composed of angular grey yellow gravels and yellow clayey silt soils, which originate from T2b3 sub-formation. The gravels are mostly only a few centimeters large. The middle layer is ca. 1.5 m thick. It is mainly composed of fuchsia clayey silt soils and siltstone gravels embedded by small rock blocks, which originate from T2b4 sub-formation. The rock blocks are maximal ca. 40 cm large. The upper layer is ca. 2 m thick. It is composed of fuchsia clayey silt soils, siltstone and limestone gravels. Small rock blocks with a maximal size of ca. 40 cm can be seen in this layer.
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Chapter 4 Investigation of landslides in the Badong study area
(3)
(2)
(1) Debris slide body
T2b3 bedding layers
Fig. 4. 23: An outcrop of debris slide body and bedding layers of debris slide DS30 (The location of the slide has been marked in figure 4.22)
On an elevation between 170 - 175 m asl. on the riverside, the grey yellow gravels originating from the underlying clayey limestones can be seen again. So it is considered that the debris slide body stretches roughly until this elevation on the riverside. According to the observations made from the outcrop of the debris slide body in figure 4.23, it is speculated that there were at least two phases of sliding. In the first phase, the debris slide materials originate mainly from T2b4 sub-formation. In the second phase, the materials originate from both T2b3 and T2b4 sub-formations. The main scarp has reached an elevation of ca. 360 m asl., which is ca. 55 m higher than the top of the debris slide body. It exhibits a flat landform and is covered partly by farmland and partly by forest. A street has been built on an elevation of ca. 340 m asl. across the main scarp. A few resident houses can be found in the area of the main scarp. Bedding layers of clayey limestones were measured with a dip direction of 164° and a dip angle of 24°. They belong to the top of the T2b3 sub-formation. T2b4 bedrocks can be observed ca. 40 m east of the clayey limestone outcrop. According to all the information above, a geotechnical profile is made for the rock slide RS30 (figure 4.24).
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Chapter 4 Investigation of landslides in the Badong study area
354° Profile A-A’ of the rock slide RS30
Main scarp
T2b3 Street
175 m asl.
145 m asl. 177°∠22°
Fig. 4. 24: Geotechnical profile of the debris slide RS30 (The location of the profile is marked in the figure 4.21)
It is often difficult to determine whether a slide is a rock slide or a debris slide, when the materials of the slide bodies are strongly weathered. In this situation, not many rock blocks can be seen in the rock slide bodies. The bedrocks from T2b2 and T2b4 sub-formations in the study area are of this kind of easy-weathering rocks, so that it is difficult to judge the landslide type in the T2b2 and T2b4 areas. In this study, only the slides, in which rock blocks can be still definitely observed as majority in the material composition, are determined as rock slides. Otherwise they are determined as debris slides.
4.3.2.2.3 Debris slide DS44 The debris slide DS44 lies at the outlet of the Dongrang River on the east riverside in East Badong (figure 4.25). It lies in a cross-dip slope composed of the T2b4 sub-formation. The debris slide body has a slight convex form and covers an area of ca. 72 300 m2. It is totally ca. 370 m long and ca. 195 m broad on average. The slope surface of the debris slide body dips in western direction with a general slope angle of ca. 21°. The top of the debris slide body lies on an elevation of ca. 220 m asl.. The toe of the debris slide body is submerged in the reservoir and is speculated lying on an elevation of ca. 80 m asl. according to the topographical map. No signals of instability have been found from the debris slide body.
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Chapter 4 Investigation of landslides in the Badong study area
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Location of the fig. 4.26
Fig. 4. 25: Overview of the debris slide DS44 (View direction: northeast)
The debris slide body is speculated to have an average thickness of ca. 10 m. It has been mainly used as farmland. Orange trees have been planted on it. The material of the debris slide body is mainly composed of fuchsia clayey silty soil. No big natural rock blocks can be seen on the surface of the debris slide body. A few resident houses have been built on the debris slide body on an elevation of ca. 200 m asl.. The topographical map for this area shows that the old village center was located at the lower part of the debris slide body, which has been submerged now by the reservoir. The main scarp exhibits a concave landform with a relatively steep slope. In the main scarp, the T2b4 bedding layers crop out in 30° direction with a dip angle of 25°. The main scarp reaches as high as ca. 300 m asl., which is ca. 80 m higher than the top of the debris slide body. It is mainly covered by shrub. A street has been built across the main scarp on an elevation of ca. 230 m asl.. On the ridge of the slope immediately close to the main scarp, an accumulation of slope deposits composed of fuchsia silty soil with embedded rock blocks can be noticed (figure 4.26). The fuchsia rock blocks are mostly smaller than ca. 20 cm. But a few of them can be as large as ca. 40 cm.
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Chapter 4 Investigation of landslides in the Badong study area
Fig. 4. 26: Accumulation of slope deposits immediately close to the main scarp of debris slide DS44 (See the location in figure 4.25) (X: 442518, Y: 3435151)
4.3.2.2.4 Debris slide DS45 The debris slide DS 45 lies on the riverside of the Yangtze River in East Badong. The debris slide body lies in an anti-dip slope composed of T2b4 sub-formation. The debris slide body has an area of ca. 57 800 m2 and is located ca. 300 m east of the debris body of DS44. The slope surface of the debris slide body dips in south-southwest direction with a general slope angle of ca. 25°. The debris slide body is ca. 600 m long in total and ca. 95 m broad on average. The top of the debris slide body lies on an elevation of ca. 385 m asl.. The toe of the debris slide body has been submerged by the reservoir and is speculated lying on an elevation of ca. 100 m asl.. The debris slide body can be observed to be active.
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Chapter 4 Investigation of landslides in the Badong study area
N
A
Fig. 4.28
A’ Fig. 4. 27: Overview of rock slide DS45 (View direction: northeast)
The thickness of the debris slide body is speculated to be ca. 5 m on average. A street has been built across the debris slide body on an elevation of ca. 270 m asl.. The part of the debris slide body higher than the street is mainly covered by shrub and the part below the street is used as farmland. A few resident houses can be found at the top of the debris slide body and on the debris slide body lower than the street. As can be seen in figure 4.28 (left side), the materials of the debris slide body DS45 consist of mainly fuchsia clayey silty soil with silt gravels. Small limestone rock blocks originating from the T2b5 sub-formation can be also observed on the surface of the debris slide body. The rock blocks are mostly smaller than ca. 30 cm. From the surface of the debris slide body beside the street, fresh shallow sliding movements can be observed (figure 4.28 (left side)). The debris slide body can be observed as instable. A section of the retaining wall was broken by the movement of the debris slide body and is now newly rebuilt. The old retaining wall beside the street has been pushed into the direction out of the slope (figure 4.28 (right side)).
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Chapter 4 Investigation of landslides in the Badong study area
Left side: Materials of the debris slide body Right side: Inclined retaining wall Fig. 4. 28: Observation of debris slide body on the street (X: 442720, Y: 3435074)
The main scarp is reaching the geological border between T2b4 and T2b5 sub-formation. The crown of the main scarp reaches maximal an elevation of ca. 450 m asl., which is ca. 65 m higher than the top of the debris slide body. The main scarp appears slightly concave with a steep landform and is covered by shrub. In the main scarp, the bedding layers of T2b4 and T2b5 sub-formations dip in 35° direction with a dip angle of 20°. The accumulation of colluvial material is the main material source for the debris slide. According to the information introduced above, a geotechnical profile of debris slide DS45 is made (figure 4.29).
35°∠20° 44° Main scarp Profile of the debris slide DS45
Street
175 m asl. 145 m asl.
Fig. 4. 29: Geotechnical profile for the debris slide DS45 (The location of the profile is marked in figure 4.27)
Similar to debris slide DS23, the accumulation of loose materials on anti-dip slopes is often
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Chapter 4 Investigation of landslides in the Badong study area instable due to the high slope angle. Additionally, in the situation of debris slide DS45, the debris slide is also accompanied with rock falls, because the overlying limestone layers from T2b5 sub-formation are over steepened. In the geotechnical situation which is called “hard on soft”, rock falls occur often in anti-dip slopes in the relatively harder overlying hard rocks due to the loss of mechanical support of the underlying soft bedrocks.
4.3.2.2.5 Debris slide DS70 A debris slide occurred on June 10, 1995 ca. 350 m west of the S5 ravine near the riverside of the Yangtze River in South Badong (figure 4.30). It occurred in old slide deposits. The debris slide body covers an area of ca. 6 500 m2. It was about 65 m broad and 100 m long. The sliding body was 5 - 13 m thick. It has a volume of ca. 6.7 × 104 m3 (Deng et al., 2000). The slope dips in north-northwest direction. According to the topographical map for this area, the slope angle before the slide event was ca. 29°.
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Fig. 4. 30: The debris slide event on June 10, 1995 (Photo from PPT by Hongbiao Jia)
The crown of the main scarp lies on the height of the street, which lies on an elevation of 215 m asl.. The toe of the surface of rupture lies on an elevation of ca. 160 m asl.. The debris slide event caused 5 deaths and 9 injuries. The long-term uncontrolled discharge of waste water is considered to have triggered the debris slide (Deng and Wang, 2000). After the occurrence of the slide, the slope has been treated with mitigation measures.
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Chapter 4 Investigation of landslides in the Badong study area 4.3.3 Fall “A fall starts with the detachment of soil or rock from a steep slope along a surface on which little or no shear displacement takes place” (Cruden and Varnes, 1993). Due to relative gentle landforms, only one site of rock fall has been mapped in the study area. The vertical rock wall is situated on the east side of the N2 ravine in North Badong (figure 4.31). It is ca. 100 m long in total and ca. 20 m high.
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Fig. 4. 31: Overview of the rock wall with potential of rock fall at the east side of the N2 ravine (View direction: northeast) (The black square in the figure is the location of the figure 4.32)
Limestones of the T2b3 sub-formation have built the rock wall. The bedding layers in the rock wall dip in south-southeast direction with a dip angle of 15° - 21°. The bedding layers are mostly 10 - 50 cm thick. Cleavages and a set of conjugate joints develop in the bedding layers. The integrity of bedrock changes very much in different bedding layers. The lowest density of fractures is ca. 10 /m. Brown clayey infillings with a thickness of up to 2 cm can be observed between the bedding layers. Parts of the bedrocks are strongly crushed into highly broken gravels with grey clayey infillings (figure 4.32 (left side)). They stretch in belts or in zones. The crush belt stretches roughly along the bedding layers with a thickness of ca. 1 m. Crush zones can be found with a diameter of a few meters in the rock wall. These crush belts and crush zones have been formed during strong tectonic movements. The brittle bedding layers and the crushed belt in rock slopes increases the potential danger of rock fall. During the earthquake on 16, Dec. 2013, a small scale rock fall happened (figure 4.32 (right side)). The Richter magnitude scale was 5.1 and the epicenter was about 6 km away from the 83
Chapter 4 Investigation of landslides in the Badong study area rock fall site (http://www.bdntv.cn/html/2013/bdsp_1217/25633.html). The size of the rock blocks is maximal ca. 40 cm. The rock wall above the street has a high potential for rock falls during earthquakes in future. Since the reservoir impoundment, low-grade earthquakes happen more often than before.
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Crush zone
Crush belt
Left side: Strongly crushed bedrocks in the rack wall Right side: Rock fall triggered by the earthquake of (View direction: southeast) Dec. 16, 2013. (http://news.sina.com.cn) (View direction: south) Fig. 4. 32: The rock fall site on the east side of the N2 ravine on an elevation of ca. 200 m asl. (X: 440137, Y: 3436276)
4.3.4 Old slide accumulations in North Badong Totally 7 sites of residual material from T2b3 sub-formation have been mapped in the area west of N1 ravine in North Badong (Fig. 4.33). Three sites of them are debris accumulations of large scale clayey limestone blocks and gravels in the clayey soil. Slide should be the formation modus of these accumulations. The common material source should be the T2b3 outcrop upwards of the accumulation. Generally the grain size of the accumulation changes with the elevation in every accumulation site. The fine weathered material is situated in the higher part of the accumulation. Along the slope downwards, the amount of fine material tends to be lower and more rock blocks can be seen in the lower part of each accumulation. Rock slide is the probable modus of sliding. According to the brittle structure of the clayey limestone layers in the study area and the steep south dipping layers, the old clayey limestone layers could not be able to build steep cliffs on the riversides. It can be speculated that rock slides have been the probable mode of landsliding under the rapid incision of the Yangtze River, which could be similar as the rock slide RS18 (Zhaoshuling landslide) located in the area between S1 ravine and S2 ravine (figure 4.35 (left side)).
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Chapter 4 Investigation of landslides in the Badong study area
A
(2)
(1)
A’ Fig. 4. 33: Areas with residual material from the T2b3 sub-formation in the West of North Badong ((1) in the figure stands for the location of figure 4.34 (left side), (2) in the figure stands for the location of figure 4.34 (right side))
Left side: T2b3 material above T2b4 bedding Right side: T2b3 material above T2b4 bedding layers layers (X: 438245, Y: 3439168) (X: 348215, Y: 3437350) Fig. 4. 34: The residual slide mass from T2b3 covering on the T2b2 bedrock
Figure 4.35 (right side) is a sketch describing the present situation of the area west of N1 ravine in North Badong. The landslide process from the T2b3 bedding layers has stopped until the bedrock layers retreated to the axis of the anticline. The reverse dip direction of the bedding layers could be the advantageous factor for the stability.
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Chapter 4 Investigation of landslides in the Badong study area
Left side: The speculated situation before 0.5 Ma of the Right side: The present situation of the slope slope Fig. 4. 35: Speculated process of slope evolution in the profile A - A’ in North Badong in figure 4.33
4.3.5 Case study: the Huangtupo landslide
4.3.5.1 Overview of the Huangtupo landslide The Huangtupo landslide is one of the largest landslides in the Three Gorges Reservoir Area. It lies on the riverside of the Yangtze River in South Badong between the S4 and the S5 ravine (see Fig. 4.36). Its landslide body can be subdivided into four different landslide bodies. The whole mass movement has experienced different types of movement during different sliding phases. The landslide group covers an area of ca. 1.35 × 106 m2 with a volume of ca. 6 900 × 104 m3 (HEGTHP, 2001). The slope is composed of material from the T2b2 and the T2b3 sub-formations. The slope surface of the landslide body dips generally in north-northeast direction. The landslide body is distributed on an elevation between 50 m asl. and 640 m asl.. The toe of the sliding body is submerged in the Yangtze River. During an intense geotechnical investigation of the landslide by HEGTHP in 2001, it was found that the landslide body of Huangtupo landslide can be divided into four debris masses (DS73, DS74, DS75 and DS76 in figure 4.37). The debris masses DS73 and DS74 lie on the riverside of the Yangtze River and the debris masses DS75 and DS76 are located slope upwards on higher positions. The debris masses DS75 and DS76 lie over the debris masses DS73 and DS74 (HEGTHP, 2001). The detail information about the four debris masses is introduced as follows.
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Chapter 4 Investigation of landslides in the Badong study area
S4
S5
Fig. 4. 36: An overview of the Huangtupo landslide (Google map)
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Chapter 4 Investigation of landslides in the Badong study area
A’ S4
Entrance G2 Yangtze G7 Fig.: 4.43 (left) Fig.: 4.43 (right)
G9
G11 Exit Fig. 4.40 A S5
GPS point
Fig. 4. 37: Distribution of the four debris masses of the Huangtupo landslide according to HEGTHP (2001) (The locations of the GPS points are referred to Wang et al. (2014), The locations of branch tunnels are referred to Jian and Yang (2013))
The debris masses DS73 and DS74 were considered to be formed earlier than the debris masses DS75 and DS76. Samples from different shear zones have been taken to examine the time of sliding with the thermoluminescence (TL) dating test (Chen et al., 2013). The results indicate that the debris masses DS73 and DS74 were formed earlier than the debris masses DS75 and DS76. Between debris masses DS73, DS74, DS75 and DS76, the debris mass DS76 is formed at latest. In each landslide body there are a few layers of shear zones in different depths, which indicates that every debris mass has experienced more than one time of sliding movement. The debris mass DS73 is maximal 770 m long and ca. 450 - 500 m broad. The top reaches an elevation between 250 - 290 m asl. and the toe lies on an elevation between 70-90 m. It covers an area of ca. 32.5 × 104 m2 and has a total volume of ca. 2 300 × 104 m3. According to the borehole record, the average thickness of the debris mass is 95.27 m and the minimal
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Chapter 4 Investigation of landslides in the Badong study area thickness is 31.66 m. The debris mass has an average thickness of ca. 69.90 m (HEGTHP, 2001). The debris mass DS 74 is ca. 380 - 510 m long and ca. 400 - 600 m broad. The top reaches an elevation of ca. 210 - 250 m asl. and the toe lies on an elevation of 50 - 80 m asl.. It covers an area of ca. 32.0 × 104 m2 and has a total volume of ca. 2 000 × 104 m3. According to the borehole record, the maximal thickness is about 92 m and the minimal thickness is about 35 m. It has an average thickness of about 60 m (HEGTHP, 2001). The debris mass DS75 is ca. 1 200 m long in total and 440 m broad on average. The top reaches an elevation of ca. 600 m and the toe lies on an elevation between ca. 160 - 210 m asl.. It covers an area of ca. 38.1 × 104 m2 and has a volume of about 1 300 × 104 m3. The thickness of the debris mass lies most between 20 - 35 m and has a maximal 58 m. It has an average thickness of 35 m. The low part of the debris mass DS75 lies over the up parts of the debris masses DS73 and DS74 (HEGTHP, 2001). The debris mass DS76 is ca. 1 100 m long in total and ca. 500 m broad on average. The top reaches an elevation of ca. 520 m asl. and the toe lies on an elevation between 220 - 240 m asl.. It covers an area of ca. 32.6 × 104 m2 and has a total volume of about 1 400 × 104 m3. It has an average thickness of ca. 42m. The lower part of the debris mass of DS76 covers the top of the debris mass of DS73 and the lower part of the debris mass of DS74 (HEGTHP, 2001).
4.3.5.2 Stability situation of the debris mass DS73 The debris mass DS73 is proved to be active since ca. 20 years. On Oct. 29, 1995 a debris slide event occurred at the front part of it on the riverside. The debris slide body was ca. 3 - 20 m thick and was ca. 200 long. Its volume was ca. 200 000 m3. The main scarp lies on an elevation of ca. 150 m asl. and the toe of the debris slide body lies on an elevation of ca. 70 m asl. (Yu and Wu, 1996). Since the impoundment of the reservoir in 2003, the debris mass DS73 became instable and many superficial fissures have been found on it, which endangered dozens of resident buildings. Around 150 fissures, which indicated an obvious signal of reactivity of the Huangtupo landslide, have been found until 2008 with the water level increase (Wang et al., 2014). According to the four GPS measuring points on the debris mass DS73, it has moved 80 mm to 140 mm in the period between April 2003 and October 2007 (figure 4.38) (Wang et al., 2014). The average movement rate was ca. 2.5 mm/month.
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Fig. 4. 38: Cumulative displacements from 4 GPS measuring points on the debris mass DS73 following the water level change in the Yangtze River (Wang et al., 2014)
4.3.5.3 The tunnel project of the China University of Geosciences (Wuhan) Since 2010, a tunnel system has been built under the debris mass DS73 by the China University of Geosciences (Wuhan) in order to install a field laboratory for landslide research (see the location in figure 4.37). The tunnel system is composed of a main tunnel and five branch tunnels. The main tunnel is 908 m long in total with a diameter of 5.0 m × 3.5 m. The branch tunnels have a diameter of 3.5 m × 3 m. The branch tunnels are stretching in northeast direction and are separately 5 m, 10 m, 145 m, 5 m and 37 m long. They are successively called branch tunnel 1, 2, 3, 4 and 5 (Jian and Yang, 2013). The location of the branch tunnels are marked in figure 4.37. The construction of the whole field laboratory is not yet finished. The main tunnel has the entrance at the western side of the debris mass DS73 and the exit at the eastern side. In the main tunnel, from 4 m to the entrance to 654 m to the entrance, the tunnel section lies in bedrock under the debris mass. From 654 m to the entrance to 709 m to the entrance, the tunnel stretches along the shear zone between the bedrocks and the overlying loose material from landslide mass. From 709 m to the entrance to the exit, the tunnel section lies in loose material of landslide mass (Jian and Yang, 2013). Figure 4.39 shows a geotechnical profile of the debris mass DS73. The orientation of the profile has been marked in figure 4.37.
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A Shear zone Limestone A’
Loose debris Clayey limestone material Dense debris material
Main tunnel
Fig. 4. 39: Geotechnical profile of the debris mass DS73 (Wang et al., 2014) (The location of the profile is marked in figure 4.37) (The location of the tunnel is referred to Jian and Yang (2013))
4.3.5.3.1 Sliding movement along the crush zone between the bedding layers The branch tunnel 3 begins from the location 465 m to the entrance in bedrock. It stretches totally 145 m long horizontally in 31° (NE) direction. Movements have been observed both in the bedding layers of the branch tunnel 3 at the intersection with the main tunnel and at the border between the bedrock and the overlying loose material in the branch tunnel 3. At the beginning of the excavation of the branch tunnel 3, a sliding movement along the crush zone between the bedding layers is observed. Figure 4.40 shows a crush zone between the bedding layers, which is located at the cross between the main tunnel and the branch tunnel 3. It lies between two layers of medium thick clayey limestones, which dip in northern direction with dip angle of 25°. Crush zone between the bedding layers are formed during the strong tectonic movement. The slide movement can be observed along the crush zone and the crush zone is also the shear zone of the sliding movement. The crush zone is ca. 10 cm to 40 cm thick. It consists of strongly compacted grey green limestone gravels with clays filling the interspace. The grain size is mostly only a few millimeters to ca. 3 cm. The gravels are highly oriented, which indicates a high shear stress between the overlying and underlying bedding layers.
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Fig. 4. 40: Shear zone between the bedding layers at the cross of the main tunnel and the branch tunnel 3 (The location is marked in the figure 4.37) (X: 441195, Y: 343485)
On the contact surface between shear zone and the overlying layer, brown clayey scratches can be observed obviously, which shows the relative sliding direction of the layer. A sample of the shear zone with scratch surface has been taken for two-dimensional X-Ray test aiming to exam the mineral composition and the orientation of the clay minerals with the General Area Detector Diffraction System (GADDS). Compared with the one-dimensional (conventional) diffraction profile, the two-dimensional X-Ray diffraction collects the diffraction not in a line but in a plane. The theory of GADDS is explained in the User Manual of General Area Detector Diffraction System (GADDS). The test is performed by Prof. Dr. Jürgen Neubauer from Lehrstuhl Mineralogie (head: Prof. Dr. Matthias Göbbels) of GeoZentrum Nordbayern in the University of Erlangen - Nürnberg. Figure 4.41 shows the intensity of the diffraction intensity in the measure scope from 4° to 28° on the detector plane. The area detector image has a resolution of 1 024 × 1 024 pixel. The count is the unit used to reflect the diffraction intensity. The higher the number of the counts is, the higher is the diffraction intensity. The diffraction intensity in figure 4.41 lies between 0 and 7. The curved diffraction belts with the same diffraction angle (2-theta) indicate that the minerals are highly orientated with a mean difference of the dip angle of only about ±2°.
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28° 4° 2θ scale
Fig. 4. 41: Two dimensional X-Ray diffraction test for the scratch surface of shear zone (X-Ray analysis performed by Prof. Dr. Jürgen Neubauer, department of Applied Mineralogy, University of Erlangen - Nürnberg)
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4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 2-Theta - Scale slip_soilC_#2_4-28_800? - File: slip_soilC_#2_01.raw - Type: 2Th alone - Start: 3.900 ?- End: 28.300 ?- Step: 0.040 ?- Step time: 300. s - Temp.: 25 ? (Room) - Time Started: 0 s - 2- Theta: 3.900 ? - T h Operati ons: Back gr ound 1.778,1.000 | Impor t 00-002-0462 (D) - Illite, 1M - KAl2(Si3AlO10)(OH)2 - Y: 18.52 % - d x by: 1. - WL: 1.54056 - Monoclinic - 00-046-1045 (*) - Quartz, syn - SiO2 - Y: 15.36 % - d x by: 1. - WL: 1.54056 - Hexagonal - I/Ic PDF 3.4 - 00-007-0051 (D) - Montmorillonite - (Na,Ca)0.3(Al,Mg)2Si2O10(OH)2? H2O - Y: 29.08 % - d x by: 1. - WL: 1.54056 - 00-005-0586 (*) - Calcite, syn - CaCO3 - Y: 62.72 % - d x by: 1. - WL: 1.54056 - Rhombo.R.axes - I/Ic PDF 2. - 01-079-1570 (C) - Kaolinite - Al2(Si2O5)(OH)4 - Y: 18.83 % - d x by: 1. - WL: 1.54056 - Triclinic - I/Ic PDF 1. - 01-071-0821 (C) - Chlorite - Al4.5(Al .8Si3.2)O10(OH )8 - Y: 46.14 % - d x by: 1. - WL: 1.54056 - Monoclini c - I/Ic PDF 1. - Fig. 4. 42: The corresponding dimensional X-Ray test for the scratch surface of shear zone (X-Ray analysis performed by Prof. Dr. Jürgen Neubauer, department of Applied Mineralogy, University of Erlangen - Nürnberg)
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Chapter 4 Investigation of landslides in the Badong study area Figure 4.42 shows the diffraction intensity in every measure interval of the 2-theta-scale from 4° to 28°. The Lin value on the Y-axis stands for the total number of counts in each measure interval. The diagram shows that the chlorite is the main mineral in it. It shows intensive and stable diffraction peaks in the diagram. Additionally illite, montmorillonite and kaolinite can be also found as lower mineral proportion. Quartz and calcite can not be obviously observed from the analysis.
4.3.5.3.2 Sliding movement along the border between the bedding layers and the debris mass The border between the bedrocks and the loose material was found in a depth of 140 m in the branch tunnel 3 (Jian and Yang, 2013). To better observe the border, two secondary tunnels were further built at a depth of 140 m in branch tunnel 3. They are both ca. 5 m deep separately in western direction and in eastern direction. They are ca. 2.5 m broad and ca. 3.0 m high. In the western secondary tunnel of the branch tunnel 3, an outcrop with three layers of materials can be seen (figure 4.43 (left side)). The outcrop surface stretches in north-south direction. The lower layer (layer (1) in figure 4.43 (left side)) is composed of grey yellow limestone blocks and gravels filled by yellow clay. The rock blocks are maximal ca. 30 cm big. They originate mainly from the T2b3 sub-formation. The middle layer (layer (2) in figure 4.43 (left side)) is composed of slightly fuchsia and grey yellow clay mixed with limestone gravels. Most of the gravels have a size of less than ca. 3 cm. A few of them are as large as ca. 5 cm. This layer is bearing material from the T2b2 and the T2b3 sub-formation. Above the middle layer there are a few layers of grey blue limestones originating from the T2b3 sub-formation. In the eastern secondary tunnel of the branch tunnel 3, the bedding layers and two layers of debris materials can be observed (figure 4.43 (right side)). The bedding layers dip in 355° direction with a 45° dip angle. Above the bedrocks a layer of clay mixed with gravels can be observed. The upper part of the clayey layer looks grey and the lower part of the clayey layer looks brown. As can be seen from the excavation, this layer is ca. 30 - 40 cm thick in total. The gravels are mostly only a few millimeters big. A few of them are as large as ca. 3 cm. On the contact surface of the bedrock, scratches can be observed, which indicates a sliding movement (figure 4.44). Above the clay layer, a debris layer can be seen. From the material composition, the debris layer in the east secondary tunnel (layer (1) in figure 4.43 (right side)) is the same as the lower layer (layer (1)) in the figure 4.43 (left side) (west secondary tunnel of the branch tunnel 3). The upper layer in figure 4.43 (right side) and the lower layer in figure 4.43 (left side) belong to the same layer of debris material.
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Left side: west secondary tunnel of the third branch Right side: east secondary tunnel of the third branch tunnel tunnel (The black square shows the location of figure Layer (1): grey yellow limestone blocks and gravels 4.44) filled by yellow clay Layer (0): grey and fuchsia clayey mixed with Layer (2): slightly fuchsia and grey yellow clay limestone gravels mixed with limestone gravels Layer (1): grey yellow limestone blocks and gravels Layer (3): blue grey clayey limestone layers filled by yellow clay Fig. 4. 43: Western and eastern secondary tunnels of the branch tunnel 3 at a horizontal depth of 140 m depth (See the location of the photos in figure 4.37) (X: 441270, Y: 3434608)
Clay mixed with gravels
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Fig. 4.44: Scratches on the contact surface between the clayey layer and the bedding layer
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Chapter 4 Investigation of landslides in the Badong study area 4.4 Conclusion The study area lies mainly in the Badong formation on the Yangtze River, which is especially sensitive for landslides. With the mapped 25 rock slides and 67 debris slides, following regularities of landslide distribution can be concluded. Firstly, the lithologies of bedrocks are essential factors of landslides in the study area. Clayey limestones and clayey siltstones in the study area are bedrocks which are especially sensitive for landslides. Slides have been mostly found in these two kinds of bedrocks. The frequency of slides in clayey siltstone areas is higher than that in clayey limestone areas, while the scales of the slides in the clayey limestone areas are larger than the slides in clayey siltstone areas. Secondly, extremely developed tectonic structures are responsible for large scale slides in the study area. The stretches of secondary tectonic structures of the Guandukou syncline impact the scales of large slides, such as the introduced rock slides RS8, RS9, RS10 and RS18. The zones of depletion of rock slides retreat slope upwards shortly over the axes of the secondary anticlines. The crowns of the main scarps lie in anti-dip slopes built by the other limbs of the secondary limbs. The developments of debris slides are usually tightly correlated with rock slides, such as the Huangtupo landslide. The occurrence of large scale debris slides is usually a further evolution of old rock slides. Thirdly, river incision is a significant factor of landslide occurrences. The mapped slide bodies concentrate especially on an elevation lower than 300 m asl. and on an elevation between 450 - 600 m asl.. The incision of the Yangtze River is a significant factor of landslide occurrences on the riversides. The landslides located on an elevation between 450 - 600 m asl. lie mostly in the upstream areas of large ravines. The incision of ravines and the strongly fractured bedrocks are main factors for the occurrences of slides located on an elevation between 450 - 600 m asl..
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Chapter 5 Theory of the artificial neural network (ANN) method
5 Theory of the artificial neural network (ANN) method
5.1 Introduction
5.1.1 Classification with machine learning Classification is to catalog many objects into different groups referring to their attributes or feathers. At most generally, classification in machine learning can be divided into two types referring to the learning process: supervised learning or unsupervised learning. A classification with supervised learning process refers to assort the data by firstly learning from pre-defined examples. For different concrete purposes, the classification with supervised learning can be also called pattern recognition or pattern classification, discrimination, identification etc. (Michie et al., 2009). A classification with unsupervised learning is a self-organized classification without pre-defined rules. The applied model will decide how to categorize the data in the learning process. For supervised learning, a lot of mathematical models can be applied, such as artificial neural network (ANN), decision tree, support vector machine (SVM), k-nearest-neighbor, logistic regression, naive Bayes. For unsupervised learning, k-means clustering, hierarchical clustering, hidden Markov models etc. are the typical mathematical models. In this study, a learning algorithm for supervised learning is needed and artificial neural network is selected for applications.
5.1.2 Artificial neural network Artificial neural network (ANN), is a model following the concept of biological neural networks. It is an interconnected model composed of simple processing units (neurons). It is a computational method inspired by the biological nervous system, especially by the human brain. Since it was introduced in 1943 by the neurophysiologist W. McCulloch and the mathematician W. Pitts for the first time, Artificial neural network has experienced its golden time of theoretical promotion and application expansion since the early 1980’s. Nowadays its application has been extended in many fields, such as pattern recognition, data processing, and non-linear control, which can be regarded as complementary to conventional approaches. Artificial neural network has represented an alternative computational paradigm in which the solution of a problem is learned from a set of examples (Bishop, 1995). The substantial distinction of artificial neural network to the conventional statistical methods is its learning capability, which can be performed by model training. Here it should be firstly declared that the terms “learning” and “training” indicate the same process with network computation. In the learning process an artificial neural network may learn like the human brain by constructing and adjusting the connections between neurons of the network to optimize the performance of the whole system. The learning efficiency of an artificial neural network determines the performance of the network model. In this aspect many different training algorithms and architectures have been developed to improve the performance of neural 97
Chapter 5 Theory of the artificial neural network (ANN) method models. Another difference between the artificial neural network method and the conventional statistical methods is that the artificial neural network method is not an approach to perform accurate calculation but a tool which has the best capability to approach the real target despite errors in the input information. Artificial neural network will optimize its structure and tend to approach the target output as much as possible by adjusting the weight w and the bias b for every neuron in the learning process. Sometimes artificial neural network is also considered to be a black box tool, because the exact computation process cannot be explained for the situation, in which the result is still right although there is defect in the input information. Neural networks work like the human brain which may still make the right decisions rationally despite uncertain information in the surrounding environment. With the learning capability of neural networks, different functions can be performed, such as pattern recognition, function fitting, data clustering and time series analysis. The multilayer neural network belongs to the most popular architectures, which have been comprehensively adopted to solve the pattern recognition problems. The artificial neural network method is one of the most commonly used tools for classification. Compared with the decision tree method, the artificial neural network method shows especially better ability of learning in pattern recognition analysis, although the usage of decision tree could be easier (Brown, 1993; Goel et al., 2003). Kim (2008) has compared the artificial neural network method with the decision tree method and the linear regression method based on the number and types of independent variables and sample size. The result shows that the performance of neural networks improved faster than those of the other methods, when the number of classes of categorical variable increases. Compared with the newly developed support vector machine method, the artificial neural network method has both advantages and disadvantages in concrete cases of pattern classification (Westreich et al., 2010; Shao and Lunetta, 2012; Zanaty, 2012, Liu et al., 2013). The fundamental principle of neural network will be firstly introduced in this chapter. Then the architecture and theory of multilayer neural network and error back-propagation, which has been mainly adopted in this study, will be introduced at some length. Generally the diagram of a neural network can be divided into 6 steps: 1 prepare the input data 2 configure the network 3 initialize the weights and biases 4 train the network 5 validate the network 6 use the network
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Inputs
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Error too big Defined outputs Error
Error small enough Convergence Fig. 5. 1: The basic concept of the neural network with its learning process
The general process has been explained by the flow diagram (figure 5.1). With the inputs, the network will be trained and will try to approach the targets by adjusting the weights and biases of every network unit. In this process the model will try to build a mathematical correlation between the network outputs and the corresponding targets. The error, which means the general difference between the outputs and the corresponding targets, will be fed back to the network model to adjust the interconnection between the neurons. This process will continue until the general error is small enough.
5.2 A simple neuron All complicated neural networks are composed of many simples with different architectures. A neural network is a composition of input values, weights of input values, bias values and transfer functions. As follows the compositions of simple networks are firstly introduced.
5.2.1 Scalar input All the complicated neural networks are composed of a certain number of organized simple neurons. The basic unit for the neural network can be described with a = f(wp+b) in figure 5.2.
p w f a
b Fig. 5. 2: An example of a single-input neuron (p is the scalar input value; w is the scalar weight; b is the bias; a is the scalar output)
5.2.2 Vector input If the input data is a single vector with R elements, the network can be described as below:
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Chapter 5 Theory of the artificial neural network (ANN) method
Fig. 5. 3: A single neuron with R element inputs (Beale et al., 2010)
In this case we can express the input n in the function:
22,111,1 ... ,1 RR bpwpwpwn (5.1) The summation of the weighted inputs and the bias forms the input to the transfer function f. Neurons can use the differentiable transfer function f to generate their output.
5.2.3 Common transfer functions for neural networks Transfer functions are also called activation functions. The nonlinear transfer functions (tangent and logistic) are the most commonly used transfer functions for multilayer networks (figure 5.4). The “tangent” function scales outputs in the range -1 and 1. The “logistic” function scales outputs in the range between 0 and 1.
Fig. 5. 4: The “tangent” function curve and the “logistic” function curve
5.3 Layered neural networks Neural networks can be in various forms with different architectures. The multilayer feed-forward neural network is the network architecture, which will be mainly adopted in this study. Due to its high efficiency and simple application, it has become the most popular usage of neural network. The basic and the most important features of the multilayered network including the layered network are introduced.
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Chapter 5 Theory of the artificial neural network (ANN) method 5.3.1 One layer neural network The layered character is the intuitive property of a multilayer network. A multilayer network is composed of a few successive single-layer networks. An example of single-layer networks with S logsig neurons having R inputs is shown below in full detail on the left and with a layer diagram on the right (Fig. 5.5).
Fig. 5. 5: An example of a single layer neural network (left side in detail, right side as simple diagram) (Beale et al, 2010)
5.3.2 Multilayer feed-forward (MLF) neural network A multilayer feed-forward neural network is one pattern of multilayer neural networks. A multilayer feed-forward network has two significant characters. Firstly the network contains at least two layers (one hidden layer and one output layer). Secondly the network diagram should be feed-forward, which means the outputs of one layer will be used as inputs of the next layer. This ensures that the network contains no feed-back loops and the network outputs can be calculated as explicit functions of the inputs and the weights (Bishop, 1995). Figure 5.6 shows the diagram of a network with a hidden layer and an output layer. The outputs of the hidden layer are used as inputs of the output layer.
Fig. 5. 6: A simple diagram of a two layer feed-forward tansig/purelin network (Beale et al, 2010)
It should be noted that the number of the layers depends on the layers of the weights and bias,
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Chapter 5 Theory of the artificial neural network (ANN) method since it is the layers of the adaptive weights which are crucial in determining the properties of the network function. The inputs in a network diagram have not processed the units but only represent the values of the input variables. So the input data should not be separately considered as a layer (Bishop, 1995). In this study the term “input layer” will not be used but only “inputs” or “input values”. The mathematic theory of the multilayer feed-forward neural network is described in detailed by Bishop (1995). For the multilayer network, the output values of one layer will be delivered as input values of the next layer, which is called “forward propagation process” since it can be regarded as a forward flow of information through the network. Multilayer feed-forward networks have some advantages (Svozil 1997). Firstly it has strong learning ability and needs mostly only one hidden layer is able to settle most of the nonlinear problems. Secondly MLF neural networks are very robust. Their performance degrades gently with increasing numbers of noise. But a big problem of all neural networks is that the processes taking place during the training process are not well interpretable. Furthermore the repeatability and the stability of the learning process let also the neural network results doubtful.
5.4 Learning with error back-propagation in the multilayer feed-forward network It can be noticed that the term “back-propagation” has been used in neural computing literatures to mean different things. For example, the multilayer network architecture is sometimes called a back-propagation network. The term back-propagation is also applied to mean the training of a multi-layer network using gradient descent applied to a sum-of-squares error function. The term error back-propagation is an independent definition, which should not be chained with multilayered networks (Bishop, 1995). To clarify the usage of the term “error back-propagation”, the nature of the training process should be carefully considered (Bishop, 1995). Most training algorithms involving in interactive procedure to minimize the error function contain usually two steps. Firstly, the derivatives of the error function with respect to the weights must be evaluated. The important contribution of the back-propagation technique provides a computationally efficient method to evaluate such derivatives. Because it is at this stage that errors are propagated backwards through the network, the term “back-propagation” should be specifically used for this step to describe the evaluation of derivatives. Secondly the derivatives are used to compute the adjustments to be made to the weights. The common used gradient descent method, is one of the most popular algorithms to adjust the weights. The first step, which exhibits the essential usage of error propagation, can be applied not only by multilayer networks but also many other kinds of networks. The mathematical theory of error back-propagation is introduced in detailed by Bishop (1995). With the error back-propagation method, Kriesel (2007) has noted that the selection of the learning rate has much influence on the learning process. The learning rate does vary over the time and it changes also in different layers. It is suggested to select a larger learning rate for the weight layers close to the input layer than for the weight layers close to the output layer. For multi-layered networks the evaluation of the error function derivatives plays a central role in the majority of training algorithms. Error back-propagation is an efficient technique to find
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Chapter 5 Theory of the artificial neural network (ANN) method the derivatives of an error function with respect to the weights and bias in the network. There are actually many other algorithms in this step to adjust the weights. The back-propagation algorithm ANN model is often criticized about its learning speed and failure to guarantee its convergence. The troublesome long training process results from non-optimum learning rate (Neaupane and Achet, 2004). Additionally there is no well-defined algorithm for determining the optimal number of hidden nodes, although some guidelines are proposed (Baum and Haussler 1989). As a result, many trials are required to find a suitable number of hidden nodes and hidden layers.
5.5 Generalization performance of neural networks The dimensionality of training data including inputs and outputs for neural network training is determined by the number of inputs and the number of outputs. But the number of hidden neurons needs to be artificially changed according to the performance of the neural network. The number of hidden neurons changes the weights and biases in relevant two layers. Thus the number of hidden neuron is a significant factor for the performance of the neural network. So an optimum number of hidden neurons are expected to find the optimum balance between under-fitting and over-fitting. Bishop (2006) has given an example of fitting results with a two-layer network for the sinusoidal regression problem. The network is trained with ten training samples. The numbers of the hidden neurons (the value M in figure 5.7) are separately 1, 3 and 10. The results show that the result with 1 hidden neuron is under-fitting and the result with 10 hidden neurons is over-fitting. To avoid the over-fitting problem, the regularization in neural networks is the common method.
Fig. 5. 7: The results of a two-layered network with different numbers of hidden neurons (Bishop, 2006)
5.5.1 Regularization in neural networks To avoid the over-fitting problem, the following quadratic function is commonly used as regularizer (Kriesel, 2005):
Enew(w) = E(w)+ q· o(w) (q ≥ 0 ) (5.2) in which, w, E and q stand separately for weights, the general error of the network and a regularization coefficient. During the training process, the weights will be so adjusted that the both E(w) and o(w) will be reduced to be minimum. To avoid over-fitting during neural network training, there are a few methods, such as “weight decay”, “consistent Gaussian priors” (Bishop, 2006).
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Chapter 5 Theory of the artificial neural network (ANN) method 5.5.2 Early stopping Early stopping is an alternative to regularization to control the effective complexity of a network. During the training process, the general error E often shows firstly a decrease followed by an increase over the training steps. Training will be stopped if the validation error begins to increase. With early stopping, a network with good generalization performance can be obtained. The principle of early stopping is to divide data into two parts (Svozil, 1997). One part is used for training and the other part is used for validation. The validation data is used to compute the validation error periodically during training. Early stopping has also disadvantages. For example, it is difficult to determine how to efficiently divide data into training data and validation data; when should the training process be stopped, if the validation error does not increase at all.
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Chapter 6 Lithology recognition with remote sensing image data
6 Lithology recognition with remote sensing image data
6.1 Introduction Remote sensing data is already widely used for geological mapping. Many common, remote sensing data, such as the Landsat Thematic Mapper (TM) satellite images, hyperspectral images, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and the Advanced Land Imager (ALI) have been applied for mineral exploration (Kaufmann, 1988; Sabins, 1999; Hubbard and Crowley, 2005). Due to the improving resolution of remote sensing data, satellite images, such as SPOT images and Landsat TM images, have been used to recognize different tectonic characters (Saintot et al., 1999; Kwatli et al., 2012). Remote sensing data, such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, which have 14 bands, have been often used for lithological classification in arid areas (Rowan, 2003; Yu et al., 2012) . Only a few studies have been found using Landsat TM images for lithology classification in areas where bedrocks crop out on a large scale (Chung et al., 1993; An et al., 1995). This chapter represents a trial study with a scene of an image from the Landsat 5 satellite for lithology recognition in the Badong study area, which is a sub-tropical mountainous area. The image was taken in 1995 and is selected as the main data base for this analysis. Based on the characteristic spectra of different types of land cover in the image data, a land cover classification has been performed for the study area. The neural network tool box, which is incorporated in the ENVI (Environment for Visualizing Imaging) software (http://www.exelisvis.com/docs/using_envi_Home.html), has been adopted to execute this classification. As a result, land cover in the Badong study area has been divided into 5 types: vegetation, water, buildings, clayey limestone areas and clayey sandstone areas. The lithology recognition based on land cover classification can be used to complement the field mapping results in the study area.
6.2 The general principle of remote sensing for land cover classification
6.2.1 Digital images Digital images are a format to save the image data. The most common digital images nowadays are the photos taken by the sensors installed in the photo cameras. With this format, digital images can be easily stored, duplicated or revised like other digital data. A digital image is a digital picture or representation of an object. A digital image is saved in raster form with a few layers and every layer is composed of squared units (Fig. 6.1). Every pixel is composed of a set of units from different layers, which show the radiance in different spectral ranges from the same area of an object. The value in each unit indicates the brightness of a certain spectral band, which means the intensity of the electromagnetic spectrum of a certain spectral band. The brightness values are saved with 8 bits and are divided into 256 levels in every pixel from 0 to 255. A higher brightness value indicates a higher average radiance in a certain spectral range of the object area. 105
Chapter 6 Lithology recognition with remote sensing image data
Fig. 6. 1: Technical characteristics of the digital image data (Richards and Jia, 2005)
The digital image data from remote sensing systems are essentially similar to the photos taken by digital cameras. The difference between them is that the sensors for remote sensing are mostly carried by aircraft or spacecraft and are much more complicated and efficient than the sensors in common handheld cameras. The earth surface receives the solar energy and the solar energy can be transmitted, absorbed, reflected, scattered and emitted on the earth surface in the way of electromagnetic waves with various wavelengths. The digital images from remote sensing systems receive mainly the solar energy that was reflected by the earth surface, because the energy emitted by the objects on the earth surface is relatively low. Theoretically all spectra with different wavelengths can be taken by the sensors in the remote sensing system. But some certain wavelengths are excluded due to the selective opacity of the earth’s atmosphere, scattering from atmospheric particulates and the significance of the data. The most useful wavelengths for the land cover classification are the visible and infrared range (between 0.4 - 12 μm) and the microwave range (30 - 300 mm) (Richards and Jia, 2006).
6.2.2 Characteristic spectra The reflectance of any material varies related to the wavelength. These variations of reflectance in different spectral bands compose the spectral signature of a material and this characteristic can be used to establish the characteristic spectra for this material. Fig. 6.2 describes the reflectance spectra for vegetation and three common sedimentary rocks (Sabins, 1999). It shows the change of reflectance of different materials within a wavelength between 0.4 - 2.5 μm. It can be seen that these materials have very different reflectance in different spectral bands. Between sandstones, limestones and shales, generally sandstones have the highest reflectance and shales have the lowest reflectance in all the wave lengths between 0.4 - 2.5 μm. Additionally all the curves are characterized by the water absorption bands in the wavelengths ca. 1.4 μm and ca. 1.9 μm. As figure 4.2, in the visual light (wavelength between 0.45 - 0.69 μm) and in infrared (wavelengths between 1.9 - 2.5 μm), vegetation has definitely much lower reflectance than sandstones, limestones and shales. In infrared (wavelengths between 0.69 - 1.3 μm), vegetation has much higher reflectance than sandstones, limestones and shales. In infrared (wavelengths between 1.6 - 1.8 μm), vegetation has a reflectance that is higher than that of
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Chapter 6 Lithology recognition with remote sensing image data shales but lower than that of limestones. These reflectance differences between different materials on the earth surface let them to be able to be differentiated with each other.
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Fig. 6. 2: Spectral reflectance characteristics of vegetation and three kinds of sedimentary rocks in a wavelength between 0.4 - 2.5 μm (Sabins, 1999)
The spectral characteristics of vegetation depend on plant pigments in leaves and on the health status of the plants. The reflectance characteristics of soils are determined by its moisture content, organic content, texture, structure and iron oxide content (Sivakumar et al., 2004).
6.2.3 Concept of a classification with multispectral image data A digital image for a remote sensing system represents an area in imagery file format. A pixel in the image data represents the reflectance of a corresponding square area on the earth surface. Different objects on the earth surface have different reflectance, through which the objects can be distinguished from one another. According to the characteristic values, the pixels can be identified and differentiated from each other. Figure 6.3 depicts the concept of land cover classification based on a multispectral satellite image. Every type of land cover, such as wheat, water, woodland, buildings etc., has a set of values for characteristic reflectance, with which the different types of land cover can be discriminated from each other. The pixels with the same reflectance values will be classified into the same group, which are commonly used for land cover classification. The premise of this classification is the assumption that similar materials have similar characteristic spectra and on the contrary that similar characteristic spectra indicate also similar materials (NASA, 2011).
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Fig. 6. 3: Pixels can be classified according to the reflectance of every band (Richards and Jia, 2006)
6.3 Data preparation with Landsat TM image for land cover classification A scene of satellite image, which was taken by the TM sensor in Landsat 5 satellite on August 15, 1996, has relative low cloud coverage and has been selected for this study. On the one hand, this scene has relatively low cloud coverage. On the other hand, the building areas in the study area were comparatively small in 1990s, which is an advantage for lithology recognition in this study. Landsat 5 satellite is the fifth satellite of the Landsat program, which is a joint effort of the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration (NASA) since 1972. The goal of this program is a long-term record of the natural and human induced changes on the global landscape. The Thematic Mapper (TM) sensors, which have been installed in Landsat satellites 1, 2, 3, 4 and 5, and the strengthened similar Enhanced Thematic Mapper (ETM+) sensors, which have been installed in Landsat satellite 6, 7 and 8, have been broadly used in fields of agriculture management, forest management, disaster investigation, climate monitoring and forecasting, earth resource exploration etc.. One of the most important and efficient applications of image data from the Landsat TM sensor is land cover classification. It includes 6 bands with 30 m resolution and 1 thermal band with 120 m resolution (table 6.1). Three of the six bands with 30 m resolution are the visual wave lengths (ca. 0.39 - 0.7μm) and the other three in the near-infrared or shortwave infrared wave lengths (0.7 - 3 μm) (table 6.1). The ENVI (Environment for Visualizing Imaging) software is a widely used software for image data processing. It incorporates different kinds of tool boxes for image processing and mathematical analysis. With this software, bands 1, 2, 3, 4, 5 and 7 in a square area (20 × 14 km2) around the Badong study area has been selected from the original scene. Every pixel is composed of six values from the six bands. The values are integral numbers between 0 and 255.
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Table 6. 1: Seven spectral bands of the Thematic Mapper (TM) sensor (USGS, 2012) Wavelength Resolution Usage Band Value (micrometers) (m) Visible Bathymetric mapping; distinguishes soil from vegetation; 1 30 (0.45-0.52) deciduous from coniferous vegetation. Visible Emphasizes peak vegetation, which is useful for assessing 2 30 (0.52-0.60) plant vigor. Visible Emphasizes vegetation slopes. 3 30 (0.63-0.69) Near-infrared Emphasizes biomass content and shorelines. 4 30 (0.76-0.90) 0-255 Shortwave Discriminates moisture content of soil and vegetation; 5 infrared 30 penetrates thin clouds. (1.55-1.75) Thermal Useful for thermal mapping and estimated soil moisture. 6 120 (10.40-12.50) Shortwave Useful for mapping hydrothermally altered rocks 7 infrared 30 associated with mineral deposits. (2.08-2.35)
6.4 Land cover classification based on the artificial neural network (ANN) method The land cover classification is composed of two steps. In the first step, a suitable number of pixels is selected for each type of land cover and the neural network model will be trained with this data, through which correlations will be built the input data and the target output data. In the second step, these correlations will be applied to classify the input data. An output value can be got for each pixel, which is the predicted result of classification for the pixel (figure 6.4).
Input data Target output (Reflectance values in band 1, 2, 3, 4, 5 and 7) (Land use types) Step 1: Model training (correlation building)
Correlation
Step 2: Model application (correlation application) Input data Application output (Reflectance values in band 1, 2, 3, 4, 5 and 7) (Land use classification) Fig. 6. 4: A diagram for classification with artificial neural network
6.4.1 Training data preparation The neural network tool box in the ENVI software has been edited to be a user-friendly interface for classification, which lets the user perform some easy image processing 109
Chapter 6 Lithology recognition with remote sensing image data procedures. As input data, the training samples can be directly chosen by selecting pixels. The land cover in the Badong study area can be generally divided into 5 classes: vegetation, water, buildings, clayey limestone areas and clayey siltstone areas. As input for the neural network, 500 pixels have been selected from each type of land cover to acquire an overall classification from the study area. With the interface for neural network application, the sample selection can be easily completed by clicking on pixels. Figure 6.5 presents the pixels, which have been sampled as input data for the network training.
Fig. 6. 5: Sampling of training data based on the Landsat 5 (date: August 15, 1995) image (Landsat 5 image from August 15, 1996, 30 m × 30 m resolution)
The ultimate purpose of land cover classification in this study is lithology recognition. As we have introduced in former chapters, the lithology in the study area can be roughly divided into areas mainly composed of limestones (T1j formation), areas mainly composed of clayey limestones (T2b1, T2b3, T2b5 sub-formations), areas mainly composed of clayey siltstones (T2b2 and T2b4 sub-formations) and areas mainly composed of sandstones (T3s formation). But the areas of limestones and the areas of sandstones are mostly covered by forest, so that these areas can be only recognized as vegetation area. The areas of clayey limestones and the areas of clayey siltstones have been mostly used as farmland, in which the information of the ground surface can be reflected in the image data.
6.4.2 Classification result The accuracy of classification depends on the resolution of the image data, which has a 30 m × 30 m resolution. The result can be presented as image data, in which all the pixels have been divided into five types (figure 6.6). The land cover of the scene has been classified into
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Chapter 6 Lithology recognition with remote sensing image data five types: vegetation, water, buildings, clayey siltstones and clayey limestones. In the result, the water areas can be identified best. The areas of clayey siltstones and clayey limestones have been well differentiated in South Badong, West Badong and the southern part of North Badong. But the T2b2 area, which crops out on both riversides of Dongrang River, has not been well recognized. The areas of massive limestones in South Badong and West Badong and the areas of thick sandstones in East Badong have been mostly covered by vegetation and cannot be directly recognized.
Fig. 6. 6: Result of land cover classification compared with the mapped geological borders for the study area
6.5 Discussion The neural network tool in the ENVI software has efficiently classified the land cover in the study area. The border between the clayey limestone areas and the clayey sandstone areas can be used to modify the results of field mapping. The field work has provided a general overview of the land cover for the area, while the neural network classification based on the image data has complemented the mapping result and has largely improved the accuracy of the lithological borders. Some remarks should be noticed for the application of the method with multispectral image data: (1) Compared with computational classification with remote sensing images, field mapping is still the fundamental foundation and the preparation of the work. The computational classification is a complement of the field mapping. In the procedure of input data selection, the types of land cover should be appropriately divided during the sample selection for the training data. A suitable division of land cover types relies on a comprehensive investigation in the field. (2) The lithology recognition based on land cover classification in the area is actually a recognition and classification of the superficial weathered materials. It is very seldom that
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Chapter 6 Lithology recognition with remote sensing image data bedrocks crop out without coverage, especially the easy-weathered T2b and T2b4 bedrocks in the study area. They are commonly covered with vegetation and slope deposits. So the lithology recognition is an indirect discrimination of the weathered materials above the bedrocks. In the situation when a landslide occurs or the material has been artificially resettled to another place, which means the superficial coverage does not originate from the underlying bedrocks, the result of lithology recognition will be inaccurate. For example, the T2b2 formation areas on both sides of Dongrang River in North Badong and East Badong, where fuchsia clayey siltstones crop out, are partly recognized as areas of clayey limestones. Actually this recognition is not an error, because parts of T2b2 areas are covered by weathered materials originating from the T2b3 sub-formation, which crop out slope upwards of the T2b2 sub-formation in this region. At the same time, it proves that image data comprise the information only from a certain depth of the earth surface and the information under a certain depth cannot be reflected in image data any more, at least not in this image data from Landsat 5 satellite. (3) The types of land cover in the study area should be appropriately pre-defined for lithology recognition. It is very seldom in the study area that an area is completely bare without coverage of vegetation or buildings. The average reflectance of an area with vegetation coverage is influenced by both the density of vegetation and the type of soils on the ground. If the vegetation is too thick, such as in forest areas, the reflectance depends mainly on the vegetation. If the vegetation is not so thick, like it is often in farmland areas, the reflectance comprises the information from both, the vegetation and the soils. Most of the areas have been used as farmland when the scene of the satellite image was taken in the study area. The lithological information on the earth surface can be mainly recorded in farmland areas. The areas covered by forest are recognized as vegetation areas. (4) Due to the number of image bands, the types of land cover have to be properly divided. As we can note in table 6.1, the land cover can be generally discriminated with the bands such as between water, soil and vegetation. For example, vegetation areas cannot be divided into shrub areas and forest areas, because they cannot be well discriminated from each other. (5) The selected samples for each type of land cover should be comprehensive and include as much as all subdivided land cover types. The value in a unit of a band indicates the overall reflectance of a certain wavelength in each squared area on the earth surface. The areas with the same lithology but covered by different vegetation have different reflectance. Areas with the same lithology but with different sorts of vegetation have to be classified into the same type of land cover, so that the common characteristic of the pixels can be summarized and applied to identify the lithology in such areas. Correspondingly, the result of classification is only applicable for the study area. The results for areas outside of the study area could be inaccurate, although they have also been sorted into some classes.
6.6 Conclusion This study is a trial of lithology recognition with a scene of a satellite image from Landsat 5. The results show a good quality of lithology recognition in areas without thick vegetation. Comparing with the results from field mapping, the method can be used to improve the accuracy of the geological map. Due to the number of bands in Landsat TM image data, a few
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Chapter 6 Lithology recognition with remote sensing image data lithological types which have significant differences in their characteristic spectra, can be discriminated in farmland areas.
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Chapter 7 Local scale landslide susceptibility mapping in the Badong study area
7 Local scale landslide susceptibility mapping in the Badong study area
7.1 Introduction Although a large number of studies have been presented, the exact definition of landslide susceptibility is still confusing. Very often the concrete research objects in the studies are not clearly explained. For example, which types of landsliding are concerned, the aim is to find out existing landslides or to find out the locations of potential landslides. Aleotti and Chowdhury (1999) have noted that at least the following three questions have to be answered for an evaluation of landslide susceptibility: “(1) where will the landslides occur; (2) what type of failures will occur; (3) how will the landslides occur”. Fell et al. (2008) has defined landslide susceptibility as “a quantitative or qualitative assessment of the classification, volume (or area), and spatial distribution of landslides which exist or potentially may occur in an area.” They have furthermore noted that “susceptibility may also include a description of the velocity and intensity of the existing or potential landsliding” and “landslide susceptibility includes landslides which have their source outside the area but may travel onto or regress into the area”. Both definitions are indeed extensive and have tried to include all possible cases. The definition from Fell et al. (2008) has especially emphasized that the landslide susceptibility of an area includes also the situation that the area may passively influenced by the landslide from outside of the area. The analysis scale determines which goals are able to be achieved (Wang, 2005). At a regional scale landslide distribution analysis, landslide density analysis, geomorphologic analysis and qualitative map are suitable. The concrete states of activity are mostly not concerned. At a local scale, safety factors are usually the targets. At this scale, landslide distribution, landslide activity and geomorphologic features are usually applied. In a local scale like the Badong study area, most of the existing landslides have been mapped. It will be more meaningful to detect the potential landsliding locations than to find out more existing landslides. Thus the landslide susceptibility in this study area is firstly constrained with potential landslides in the study area. The essential concept of analysis on landslide susceptibility is the famous principle that “the past and present are keys to the future” (Varnes and IAEG, 1984). It indicates the assumptions that landslides will occur probably in the same areas with the same geological, geomorphological, hydrogeological and climatic background as in the past. To evaluate the landslide susceptibility for potential new landslides in the study area, two kinds of data can be used: the data from the original slopes before the occurrence of the mapped landslides, or the data from present active landslides. In the Badong study area, the active landslides offer a possibility to evaluate the susceptibility for potential new landslides. Slides are the main types of movement in the study area. Thus the susceptibility for potential slides is mainly concerned in this chapter. The Badong study area belongs to mountainous regions in the upstream of the Yangtze River. In a total area of ca. 64 km2, 102 slides are mapped, in which 20 slides are instable. All slides cover a total area of ca. 4.8 km2 and instable slides cover a total area of ca. 1.2 km2. The study is carried out through the artificial neural network (ANN) method with the help of 114
Chapter 7 Local scale landslide susceptibility mapping in the Badong study area the ArcGIS software and the MATLAB software. The correlated parameters of the mapped slides are firstly digitized and edited to be training samples for the neural network, and the trained neural work model is then applied to evaluate the slope stability of the whole study area. The result is presented in raster data with a resolution of 30 m, in which the study area will be divided into independent square areas with a size of 30 m × 30 m. The landslide susceptibility of the unit areas is divided into 5 levels.
7.2 Slope stability evaluation with the multilayer feed-forward (MLF) neural network The theory of the ANN method is already introduced in chapter 4. The MLF neural networks can be used for the evaluation of the stability of landslides. The performance of stability evaluation in this study can be generally divided into two steps (Figure 7.1). In the first step, correlations between correlated factors of landslides and their activity states will be built through the training process of a neural network. In the second step, these correlations will be applied to evaluate the stability states of every unit area in the whole application area.
Input data (parameters of 102 slides) Target output (elevation, slope angle, slope structure, lithology, tectonic, land use) (Activity of 102 slides) Step 1: Model training (correlation building) Correlation Step 2: Model application (correlation application) Input data (parameters of all unit areas) Application output (elevation, slope angle, slope structure, lithology, tectonic, land use) (Result: stability of unit areas) Fig. 7. 1: General concept of stability evaluation with artificial neural network
7.2.1 Neural network data processing The data for the neural network training are composed of quantified input data and target output data. The input data are composed of 6 sets of parameters including 2 sets of geomorphological parameters (elevation values of landslide bodies, general slope angles of landslide bodies), 3 sets of geological parameters (slope structure, lithology, tectonics) and 1 set of geographical parameters (land use) as input data. The target output data are the values indicating activity states of slide bodies. Due to the instability of the model, the results of model training could be every time different. With the same data format as the training data, the input data for the neural network application are also composed of 6 parameters including 2 sets of geomorphological parameters (general elevation values of unit areas, general slope angles of unit areas,), 3 sets of geological parameters (slope structure, lithology, tectonics) and 1 set of geographical parameters (land use). The network outputs are the values, which indicate the stability states of each unit area. The whole study area is divided into unit areas with a size of 30 m × 30 m.
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7.2.1.1 Input data processing As follows, both the input data for neural network training and input data for neural network application are introduced. The input data for neural network training are based on the information of the 102 slides, which are mapped in the study area. So there are 102 training samples for the neural network. The data source for neural network application is introduced in table 7.1. The topographic maps with a scale of 1: 10 000 and an accuracy of 5 m interval between counter lines have been digitized to build a digital elevation model (DEM) for the study area. The DEM is used as foundation of geometric calculation for the analysis. The whole area has been divided into squared unit areas with a size of 30 m × 30 m. The neural network model is applied in the whole study area.
Table 7. 1: Data source for neural network application Data source Precision Attribute Elevation Topography (DEM) 30 m ×30 m Slope angle Slope structure Geological Scale 1 : 10 000 Lithology mapping Tectonics Google map ca. 5 m resolution Land use
7.2.1.1.1 Elevation
7.2.1.1.1.1 Values for neural network training For each training sample, a value is needed to represent the elevation of the landslide body. As observed in the study area many large scale landslide bodies have a higher thickness in the lower part and a lower thickness in the upper part. To emphasize the influence of the Yangtze River on landslides, the elevation on a 1/3 height of the slide body is appointed to represent the location of a slide body. The elevation of a slide body is represented by the function (7.1): toe theofelevation - top theofelevation theofelevation top - theofelevation toe toe theofelevation body landslide theofelevation theofelevation landslide body theofelevation toe (7.1) 3 Every sample for the neural network training is assigned with a value, which stands for the elevation of a landslide body. The elevation values of slide bodies lie between 110 - 805 m.
7.2.1.1.1.2 Values for neural network application The elevation values of the unit areas were obtained from the digital elevation model. The middle point of a squared unit area stands for the elevation of the unit area. The elevation values in the study area are located between 95 - 1114 m asl. (figure 7.2). Every unit area is correspondingly assigned with a value in the range of 95 - 1114, which stands for the elevation of the unit area.
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Fig. 7. 2: Digital elevation model (DEM) for the study area
7.2.1.1.2 Slope angle
7.2.1.1.2.1 Values for neural network training The general slope angle of a slide body can be determined by the height and the horizontal length of the slide body, which can be measured on the topographical map if the area of the slide has been ascertained. So the general slope of a slide body can be got by the function (7.2) theofelevation top - theofelevation toe (7.2) arctan body landslide theof angle slope general slope angle theof landslide body arctan horizontal theoflength landslide body Every sample for the neural network training is assigned with a value, which stands for the slope angle of a landslide body. The values lie between a range of 12 - 40. The values of all slide bodies can be found in table 4.3
7.2.1.1.2.2 Values for neural network application The slope angles of unit areas are calculated based on the DEM for the study area with the ArcGIS software. As shown in figure 7.3, the slope angles of unit areas in the study area lying between 0 - 68°. The average slope angle of all unit areas in the study area is 24° and the standard deviation is 9°. Every unit area is correspondingly assigned with a value in the range of 0 - 68, which stands for the slope angle of the unit area.
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Fig. 7. 3: Slope angle of the study area
7.2.1.1.3 Slope structure
7.2.1.1.3.1 Values for neural network training As introduced in chapter 4, the slopes in the study area can be divided into 3 classes: class 1 (anti-dip slopes), class 2 (cross-dip slopes) and class 3 (dip slopes). The slides are at most frequently in dip slopes. The landslide bodies are divided into three classes according to slope structure of the slopes, where they are located.
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Fig. 7. 4: Classification of slide bodies referring to slope structures
The training samples in anti-dip slides are signed with the value 1, because they are at least prone to be active. The training sample in cross-dip slopes are signed with the value 2, because they are medium prone to be active. The training samples in dip slopes are signed with the value 3, because they are at most prone to be active. Every sample for neural network training is correspondingly assigned with a value of 1, 2 or 3, which stands for the influence of slope structure on the slide body activity (figure 7.5).
7.2.1.1.3.2 Values for neural network application The study area is divided into small part areas belonging to 3 classes according to different slope structures in each part area (figure 7.6). The part areas belonging to class 1 are anti-dip slopes, the part areas belonging to class 2 are cross-dip slopes and the part areas belonging to class 3 are dip slopes. All the unit areas in the same part area are assigned with the same value. Each unit area is correspondingly assigned with a value of 1, 2 or 3, which stands for the slope structure in this unit area.
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Fig. 7. 5: Slope structure classification in the study area
7.2.1.1.4 Lithology
7.2.1.1.4.1 Values for neural network training According to the material source, the slide bodies are divided into 3 classes. The slide bodies in class 1 have their material sources mainly from T1j bedrocks (massive limestone layers) and T2b3 bedrocks (thick sandstone layers). The slide bodies in class 2 have their material sources mainly from T2b1, T2b3 and T2b5 bedrocks (clayey limestone layers). The slide bodies in class 3 have their material sources mainly from T2b2 and T2b4 bedrocks (clayey siltstone layers).
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Fig. 7. 6: Classification of slide bodies referring to lithology
Active slides are found frequently both in slide bodies originating from clayey limestones and also in slide bodies originating from clayey siltstones. As introduced in chapter 4, the scales of the active slides originating from clayey limestones are larger than those from clayey siltstones. But the number of instable slide bodies originating from clayey siltstones (13 slides) is much higher than that from clayey limestones (7 slides). Thus the training samples with materials originating from clayey siltstones are signed with the value 1, because they are considered to be at most prone to be active. The training samples in class 2 are signed with the value 2, because they are considered to be medium prone to be active. The training samples in class 3 are signed with the value 3, because they considered to at least prone to be active. Every sample for the neural network training is correspondingly assigned with a value of 1, 2 or 3, which stands for the influence of lithology on slide body activity.
7.2.1.1.4.2 Values for neural network application The study area is divided into small part areas belonging to 3 classes according to the lithology in each part area (figure 7.8). The part areas outcropping in the T1j and T3s formations belong to class 1. The part areas outcropping in the T2b1, T2b3 and T2b5 sub-formations belong to class 2. The part areas outcropping in the T2b2 and T2b4 sub-formations belong to class 3. In accordance with the values for neural network training, the value 1 indicates that the part areas in class 1 are at most prone to be stable. The value 3 indicates that the part areas in class 3 are at most prone to be instable. All the unit areas in the same part area are assigned with the same value. Each unit area is correspondingly assigned with a value of 1, 2 or 3, which stands
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Chapter 7 Local scale landslide susceptibility mapping in the Badong study area for the lithology in this unit area.
Fig. 7. 7: Lithological influence classification in the study area
7.2.1.1.5 Tectonics
7.2.1.1.5.1 Values for neural network training The slides are divided into 4 classes according to the local tectonic influence in areas, where slide bodies are located. The tectonic influence here indicates the fracture development in bedrocks. The slide bodies in class 0 are located in areas, where fractures are at least developed. On the contrary, the landslide bodies in class 3 are located in areas, where fractures are at most developed. The training samples in class 0 are signed with the value 0, because they are at least prone to be active with the tectonic influence. On the contrary, the training samples in class 3 are signed with the value 3, because they are at most prone to be active with the tectonic influence. Every sample for neural network training is assigned with a value of 0, 1, 2 or 3, which stands for the influence of tectonics on the slide body activity.
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Chapter 7 Local scale landslide susceptibility mapping in the Badong study area
Fig. 7. 8: Classification of slide bodies according to tectonics
7.2.1.1.5.2 Values for neural network application The study area is divided into small part areas belonging to 4 classes according to the local tectonic development in each part area (figure 7.10). Compared with the geological map in chapter 3 (figure 3.5), the geological structure is based on the extension and the distance to the axis of the Guandukou syncline. Furthermore the fracture intensities in part areas of clayey siltstones are generally higher than in part areas of clayey limestones. The part areas outcropping in the T1j formation (massive limestones) and in the T3s formation (thick sandstones) are classified into class 0 in the study area. The part areas outcropping in the T2b3 sub-formation (clayey limestones) in North Badong, in East Badong and partly in South Badong are classified into class 1. Additionally the part areas outcropping in the T2b2 sub-formation (clayey siltstones) on both sides of the Dongrang River are also classified into class 1. The part areas outcropping in the T2b2 sub-formation (clayey limestones) in West Badong, the part areas outcropping in the T2b2 sub-formation (clayey siltstones) east of S5 ravine in South Badong , the part areas outcropping in the T2b4 sub-formation (clayey siltstones) on the western riverside of Dongrang River are classified into class 2. The part areas outcropping in the T2b2 sub-formation west of S5 ravine in South Badong, in West Badong, and in the western part of North Badong have been classified into class 3. Finally, all unit areas in the same part areas are assigned with the same value. Each unit area is correspondingly assigned with a value of 0, 1, 2 or 3, which stands for the tectonic development in the unit area. It has to be noted that although the tectonic development is a significant factor of landslide occurrences in the study area, the accuracy of this division referring to the tectonic
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Chapter 7 Local scale landslide susceptibility mapping in the Badong study area development is still very much constrained by the detail degree of field mapping. Furthermore the influences of tectonic structures change gradual in different directions and distances. It is difficult to strictly determine the intensity of tectonic disintegration. To this extend, the classification of tectonic development in different parts of the study area is only a rough division.
Dongrang River
Shennong River
Fig. 7. 9: Tectonic influence classification in the study area
7.2.1.1.6 Land use
7.2.1.1.6.1 Values for neural network training Landslide bodies are divided into 3 classes according to the types of land use on landslide bodies. The Google map from January 3, 2005 has a very high resolution for this area and no clouds covered the area. For this reason this satellite image was mainly referred to determine the main land use type on each slide body in the study area. Training samples in class 1 are mainly covered by shrub. Training samples in class 2 are mainly covered by wood. Training samples in class 3 are mainly covered by farmland or by large scale building areas. Thus the training samples in class 1 are signed with the value 1, which indicates they are at least prone to be active. The training samples in class 2 are signed with the value 2, which indicates they are medium prone to be active. The training samples in class 3 are signed with the value 3, which indicates they are at most prone to be active. Every sample for the neural network training is assigned with a value of 1, 2 or 3, which stands for the correlation of land use on slide body activity. Usually there are different types of land use on a slide body, but only the main type of land use on the slide body is taken into consideration.
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Fig. 7. 10: Classification of slide bodies according to land use
7.2.1.1.6.2 Values for neural network application The land use classification in the study area is based on the satellite image from January 3, 2005 taken from Google map. The study area is divided into small part areas belonging to 3 classes. The part areas belonging to class 1 are mainly covered by shrub. The part areas belonging to class 2 are mainly covered by forest. The part areas belonging to class 3 are mainly covered by farmland or large scale buildings. Finally, all the unit areas in the same part area are assigned with the same value. Each unit area is correspondingly assigned with a value of 1, 2 or 3, which stands for the land use in this unit area.
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Chapter 7 Local scale landslide susceptibility mapping in the Badong study area
Fig. 7. 11: Influence classification of land use in the study area
7.2.1.2 Target output data for neural network training (slide activity) The activity states of the slide bodies are processed to be the corresponding target output of the training data. The slides are divided into two classes: class 0 (inactive) and class 1 (active). The activity states of the slide bodies are determined mainly in the field and a few of them are reported by the correlated literature. The slide bodies, from which instable characters are found, are all classified as active slides. Different patterns of instable characters can be observed from slides in the study area. Firstly fissures on the slope surface can be sometimes observed at the upper part of slide bodies, which are direct indications of active slides. Secondly linear fissures in the concrete streets on slide bodes or cracks in the buildings on slide bodes can be usually observed due to the deformation of active slide bodies. Thirdly fresh scarps induced by sliding movement at the surface of slide bodies are also signals for active slides. Additionally artificial mitigation measures could be also seen broken by movement of slide bodies. In the end, every training sample is assigned with a value of 0 or 1, which stands for the activity of the slide body.
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Fig. 7. 12: Classification of slide bodies referring to their activity states
7.2.2 Neural network execution In the training step, correlations are needed to be built between input data target output data with training samples. The geomorphological parameters of the mapped slides are processed to be training samples for the neural network training. If the number of inputs is n, a recommended number of neurons in the hidden layer is (2×n+1) for a multilayer feed-forward neural network (Hecht-Nielsen, 1987). Thus a model with the structure of 6-13-1 has been adopted to train the neural network. Figure 7.14 shows the structure of the neural network model, which is used in this study. It has shown the basic structure of the multilayer feed-forward neural network. In the application step, the correlations built by the neural network are applied to classify the application inputs. The neural network outputs are obtained as the result of the classification.
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1 Elevation
2 Slope angle
3 Slope structure Slope stability 4 (0, 1) Lithology
Tectonic
Land use
13
Inputs Weight {1, 1} Transfer Hidden Weight {2, 1} Transfer Outputs function neurons function Bias {1} Bias {2} Fig. 7. 13: Diagram of ANN for the slope stability evaluation
7.2.3 Neural network outputs (slope stability classification for all unit areas) The classification of slope stability is the result of the neural network application (figure 7.15). Firstly the network outputs show that the potential instable areas are mainly located on an elevation lower than ca. 300 m asl. , especially under the elevation of 175 m asl.. Secondly in North and East Badong, the potential instable areas are mainly the areas composed of clayey siltstones. In South Badong the potential instable areas lie mainly in areas composed of clayey limestones. West Badong has instable areas on an elevation between ca. 200 m asl. and 300 m asl. in both clayey limestone areas and clayey siltstone areas.
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Fig. 7. 14: Slope stability classification in the Badong study area
The output values of the neural network application are limited in the range (0, 1). The value 0 represents the highest stability of an area and the value 1 means the highest instability of an area. Figure 7.16 shows the accumulative distribution of stability values of all pixels. According to the actual landslide distribution and their stability situations, the stability values are manually divided into 5 classes: very stable (0, 0.03), (63% of the total area), stable (0.03, 0.9), (17% of the total area), medium (0.9, 0.95), (5% of the total area), instable (0.95, 0.97), (6% of the total area) and very instable (0.97, 1), (9% of the total area), (table 7.2). According to this division 85% of the study area is considered to be stable and 15% of the area is relatively instable. In figure 7.16 it can be noticed that the “very stable” unit areas concentrate in a range of 0 - 0.03 and the “very instable” unit areas concentrate in a range of 0.97 - 1. The division of values between 0.03 - 0.97 is mainly referred to the distribution of the existing slides.
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Fig. 7. 15: Accumulative distribution of stability values
Table 7. 2: Area percentage of unit areas in different stability states Stability status Value range Area percentage Very stable (0, 0.03) 63% Stable (0.03, 0.9) 17% Medium stable (0.9, 0.95) 5% Instable (0.95, 0.97) 6% Very instable (0.97, 1) 9%
In the classification result, the unit areas of 13 active slides from all 20 active slides are mainly classified as very stable and the unit areas in 1 active slide are mainly classified as instable. Thus 14 active slides from all 20 slides, which are totally 93% on the area, have been classified as in stable or very instable (table 7.3). All large scales slides are rightly divided. Additionally, the unit areas in 1 active slide are mainly classified as medium stable. The unit areas in 3 small active slides and 2 small active slides, which have a total area percentage of 4% of all slides, are separately classified as stable and very stable.
Table 7. 3: Classification of active slides compared with the result from neural network output Stability Slide number Area percentage Very stable 2 1% Stable 3 3% Medium stable 1 2% Instable 1 3% Very instable 13 90%
7.3 Discussion The application based on the artificial neural network method has offered an approach to evaluate susceptibility for potential new slides by analyzing the mapped slides in the study 130
Chapter 7 Local scale landslide susceptibility mapping in the Badong study area area. In this study, 6 common impacting factors have been taken into account, which indicates that this classification is just a possibility evaluation based on these 6 impacting factors. In practice, the stability of a slope depends on much more concrete and local factors. Thus the evaluation based on these 6 factors is a potentiality evaluation. In figure 7.14, it can be specially noticed that large areas of riversides are also classified as very instable. But due to reservoir impoundment, the slides with the tops of slide bodies lower than 175 m asl. are not completely mapped. Small scale debris slides occur indeed often on an elevation between 145 - 175 m asl. in the reservoir. Due to the complexity of every single landslide, the stability of a landslide is usually affected also by many other concrete and local impacting factors. For instance, rainfall is indeed a significant factor of landslide occurrence, but it is difficult to be used as impacting factors. On the one hand, there are not available rainfall data in such accuracy. The different influence from rainfall on each slide body cannot be determined. On the other hand, it is almost impossible to investigation how rainfall influences very single slides or unit areas. The influence of rainfall is chained to hydrogeological background of sliding areas, which let it impossible to evaluate the impact of climate in different parts of the study area. During the processing of input data for landslide susceptibility classification, it is common that the sliding areas are divided into independent unit areas for statistic analysis. The tendency of the result is useful if they are only statistically applied to represent the slides in the form of geomorphological areas. But they do not suit to be used as input data for neural network training, because unit areas do not present the whole landslide bodies as integrated individuals. Otherwise the overall characters of landslides, such as general slope angles and curvature degree of slide bodies, cannot be represented in the training process. In this study, each slide is treated as a complete individual. At the same time, because every unit area has been regarded as an object which is independent from the neighboring areas, it does not make much sense to artificially divide the area with a higher resolution. In this study, the results suit best in practice to slopes, which have a size similar to 30 m × 30 m, because a slope, which is notably larger than this size, cannot be represented in the input data of neural networks. A landslide susceptibility classification for larger landslides needs theoretically also an output classification with larger unit areas. In this study the samples for training process of neural network originate also from the application area of neural network application. Both the training samples and the application area have the same background, which is an advantage to improve the results of the analysis. The similarities between the input data and the application data are the essential concept of classification. So the choice of training data for application data and the comparability between the training data and the application data could be important factors impacting the result.
7.4 Conclusion The analysis has proposed a case study, which abstracts information from the occurred landslides in one area to evaluate the slope stability of the whole area. It is an attempt to expand the regulars from single examples to a large area. The result has proved an efficient usage of ANN as an approach for pattern recognition. Incorporated with the geotechnical
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Chapter 7 Local scale landslide susceptibility mapping in the Badong study area mapping in the study area, the result of slope stability evaluation can be applied as an instruction for city planning for example during the expansion of the county center of Badong.
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Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment
8 Regional scale landslide susceptibility mapping in the Xiangxi catchment
8.1 Introduction The Badong area is only a ca. 64 km2 big investigation area. In order to test the susceptibility analysis for landslides in a regional scale, the Xiangxi catchment (3 209 km2) which is a large tributary of the Yangtze River upstream of the Three Gorges dam, is selected as another exemplary area for landslide susceptibility analysis with the multilayer feed-forward (MLF) neural network method. The landslide susceptibility analysis in the Xiangxi catchment concerns mainly on recognition of occurred landslides. In this scale, the study area is divided into unit areas with a size of 150 m × 150 m. With the ANN method, the study area is divided into a training area and an application area. The unit areas in the training area are classified as unit areas of landslides and unit areas of no-landslides, and they are used as training samples. Due to the resolution of the data, four impacting factors (lithology, slope angle, slope curvature, river network influence) are taken into account for the analysis. As a result, the susceptibility analysis for landslides is performed for the whole Xiangxi catchment. A landslide susceptibility map, in which 70% of all landslides are rightly classified in the training area (back-water area), is created for the Xiangxi catchment.
(The content of this chapter is mainly based on the reviewed paper: Bi R, Schleier M, Rohn J, Ehret D, Xiang W (2014) Landslide susceptibility analysis based on ArcGIS and Artificial Neural Network for a large catchment in Three Gorges region, China. Environmental Earth Sciences. Vol. 72(6): 1925 - 1938)
8.2 Study area
8.2.1 Location of Xiangxi catchment Xiangxi catchment is a mountainous landscape in Three Gorges region in China, which covers an area of 3 209 km2 (Fig. 8.1). Its main river, Xiangxi River, is a 94 km long northern tributary of Yangtze River. Its outlet is located about 30 km upstream of the Three Gorges dam. The terrain is characterized by a high relief. The elevation rises from 62 m asl. at the outlet into Yangtze River up to 3 059 m asl. at Shennongding Peak in the Shennongjia Forest Nature Reservoir region (Fig. 8.2a). More than 80% of the Xiangxi catchment area is higher than 1 200 m asl.. In 2007, 87% of the Xiangxi catchment was covered by woodland, 9.5% by arable land and 2.2% by garden land (Seeber et al., 2010).
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Fig. 8. 1: Location of the Xiangxi catchment (red color area) (after YPIS 2002, unpublished Yangtze-Project Information, University Giessen)
8.2.2 Climate in Xiangxi catchment According to the record (1961 - 1990, Xingshan Climate Station in the central Xiangxi catchment), the mean annual temperature (1961 - 1990) in this area was 15.3° C; the highest temperature was 43.1° C and the lowest was -9.3° C. The mean annual precipitation is 900 – 1200 mm/a, of which about 69 % occur between May and September.
8.2.3 Landslides in Xiangxi catchment Due to the impoundment in TGR, the water level in the river segment between Gaoyang and Xiangxi River outlet, which is about 40 km long, is correspondingly following the water level in Yangtze River. In the framework of the Sino-German BMBF “Yangtze-Project”, the back-water area of Xiangxi catchment and a part of the area north of the back-water area in Xiangxi catchment were mapped. In total, 351 landslides, which cover an area of 10.6 km2, were mapped in Xiangxi catchment. 277 of them are located in the back-water area. According to the landslide classification from Varnes (1978), the landslides in Xiangxi catchment are mainly rotational or translational slides. These types of landslides are especially prevalent in Xiangxi catchment and are considered to be research objects of this study (Fig. 8.3).
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Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment a b
c d
Fig. 8. 2: a, Digital Elevation Model (DEM) (150 m × 150 m resolution) of Xiangxi catchment based on ASTER Global DEM; b, Slope angle based on DEM for Xiangxi catchment; c, Slope curvature based on DEM for Xiangxi catchment; d, River network and its order classification based on DEM for Xiangxi catchment
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Fig. 8. 3: Landslide examples on Xiangxi River
For the south part of Xiangxi catchment where the landslides most frequently occur, a landslide susceptibility analysis was executed based on the combination of frequency ratio, heuristic GIS-method and ground truth evaluation, showing that up to 89% of all known landslides were classified correctly into high susceptibility area (Schleier et al., 2013). The landslides occur especially frequently along the 40 km long river segment close to the outlet. Some dormant landslides along this river segment were reported to be reactivated, such as the Bazimen landslide (volume 4 × 106 m3) (Bi et al., 2012), the Baijiabao landslide (14.6 × 106 m3) (Huang and Chen, 2007) and the Qiaotou landslide (1.6 × 106 m3) (Jiang et al., 2011; Ehret et al., 2010). However, because of the lower erosion energy in the tributary, the volumes of the landslides are generally much smaller than the landslides directly on Yangtze River. The total area of landslide bodies in every geological formation has been listed in Table 8.1.
8.3 Geological background Xiangxi River lies at the east flank of the Zigui syncline, which is also the west flank of the Huangling anticline. The strata are inclined and the layers are generally dipping in the western direction. The geological formations of Precambrian, Cambrian, Ordovician, Silurian, Devonian, Permian, Triassic, and Jurassic are exhibited from east to west in the southern Xiangxi catchment (Fig. 8.4a). Fig. 8.4b is a typical geological profile of the southern Xiangxi catchment. The lithology of the geological formations is briefly summarized in Table 8.1.
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The northern and eastern parts of Xiangxi catchment are mainly composed of Precambrian, Cambrian and Ordovician formations. They form high mountains with thick natural forest and a regional plateau with a relatively high elevation NE of Gaoyang. The Precambrian formations consist of granite and gneiss. The Cambrian and Ordovician formations are mainly composed of thick massive limestone and dolomite layers. Silurian formations are composed of two formations (S1, S2) in Xiangxi catchment. S1 is mainly siltstone, shale and argillite intercalated with fine grained sandstone or limestone or shell limestone. S2 is mainly fine sandstone and limestone. The total thickness of S2 is generally only 91 - 181 m thick in TGR but S1 is totally 880 - 1644 m thick. The Devonian and Permian formations have very thin sediment in the area. The Carboniferous formations are missing in the succession. Triassic formations can be divided into three formations (T1, T2 and T3). T1 is thick layered limestone and dolomite, and T2 is thick layered limestone and dolomite inter-layered with fine sandstone and siltstone. T3 is thin siltstone intercalated with sandstone, carbonaceous shale, argillite and coal seams. Jurassic formations expose four formations (J1, J1 - 2, J2 and J3) in the study area. J1 is mainly a thin layered intercalation of medium grained sandstone with clayey siltstone, carbonaceous shale and coal seams. J1 - 2 is an interim formation between J1 and J2 and is siltstone intercalated with siltstone and fine sandstone. J2 is mainly siltstone but also partly fine sandstone, and J3 is mainly fine sandstone partly intercalated with siltstone and clay stone.
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Fig. 8. 4: a, The general geological background relevant to Table 8.1 (simplified from 1: 200 000 geological map, Bureau of Geological Exploration & Development of Hubei Province, 1984) and landslide inventory in Xiangxi catchment; b, A typical profile (W-E) for the river section at Xiakou county
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Table 8. 1: Geology in Xiangxi catchment and the mapped landslide area in every formation area
Mapped landslide area Geological formation Lithology (km2)
J3 sandstone, siltstone, clay stone 0.625
J2 siltstone, sandstone 1.336 Jurassic J1 - 2 siltstone, clay stone, sandstone, marl 1.166
J1 sandstone, clayey siltstone, shale, coal, conglomerate 1.658
T3 siltstone, sandstone, shale, coal 0.389
Triassic T2 limestone, dolomite, sandstone, siltstone 0.382
T1 limestone, dolomite, shale 0.046
Permian P limestone (microcrystalline), flint (nodules), siliceous stone 0.069
sandstone (quartzose), shale, argillaceous limestone, Devonian D 0 ironstone
S2 fine sandstone (quartzose), limestone 0.179 Silurian S1 shale (carbonaceous), siltstone, fine sandstone, limestone 2.93
Ordovician O limestone (microcrystalline, siliceous, argillaceous), shale 0.548
Cambrian CB limestone and dolomite (argillaceous, layered), shale 0.570
granite, gneiss, slate, dolomite and limestone, sandstone, Precambrian PCB 0.733 conglomerate
8.4 Data preparation and processing The geological map (1: 200 000 scale), the DEM with the resolution of 30 m × 30 m and field mapping information were prepared and applied as analysis data (Table 8.2). Four factors were extracted from the maps and DEM through the ArcGIS software. The data were preliminary prepared as input data for ANN analysis. 139
Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment
Table 8. 2: Data preparation
Data source Precision Attribute
Geological map Scale 1: 200 000 Lithology
Slope angle 150 m × 150 m DEM Slope curvature
30 m × 30m River network
Landslide mapping Scale 1: 10 000 Presence of landslides
8.4.1 Geology The geological information was obtained from the geological map for this area (Fig. 8.4a). The borders of the geological formations were digitized and saved as ArcGIS data. The formations were divided into 11 classes and every formation was assigned with a value in the interval [-5, 5] (Table 8.3). The value 5 means the highest probability for landslides to occur, and the value -5 the lowest probability. The lithology of formation and its correction with landslide incidence were taken into account in the assignation of the parameter for every geological formation. The assignation was tested and varied several times until the training results fit best to the mapped result in the training area.
Table 8. 3: Indices for geological formations as applied for the ANN analysis
Geological Precambrian Cambrian Ordovician Silurian Devonian Permian Triassic Jurassic
formation PCB CB O S1 S2 D P T1 T2T3 J1 J1-2 J2J3
Index -3 -3 -4 4 -2 -5 -5 -5 -10 5 3 11
8.4.2 Slope angle and slope curvature The DEM for the area was derived from ASTER Global DEM. In consideration of the size of the study area and the analysis precision, Xiangxi catchment was divided into terrain units with a size of 150 m × 150 m based on the DEM of 30 m × 30 m resolution. Every terrain unit was an independent area with elevation and spatial position information. Through ArcGIS software the slope angle and slope curvature were also calculated for every terrain unit (Fig. 8.2b and Fig. 8.2c). They are directly used as input data for ANN analysis.
8.4.3 River network The river network was calculated based on the DEM of 30 m × 30 m resolution. The stream discharge of the river network was also acquired according to the upstream drainage area. The calculation was theoretically computed based on the relative elevation and slope aspect of the 140
Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment area. Thus a river network with different stream orders was obtained through ArcGIS (Fig. 8.2d). The terrain units in the catchment were divided into different groups according to their distance to a river and the stream order of this river. The buffer areas of the streams were further divided into 3 classes with different buffer distance for every stream order. For example, on both sides of stream order 1 the buffer area was divided into 3 classes: class 1 (0 - 600 m distance to the stream), class 2 (600 - 1200 m distance to the stream) and class 3 (1200 - 1800 m distance to the stream). Every terrain unit was quantified with a value between -5 and 5. The value 5 indicates the strongest influence from the river and the value -5 indicates the lowest influence. The values are artificially adjusted until the training result fit best in the training area. In the end, a set of assignations as shown in Table 8.4 was determined.
Table 8. 4: Indices for different stream orders as applied for the ANN analysis
Buffer distance Class 1 Class 2 Class 3 The rest area
Order 1 (600m) 5 2 0
Order 2 (500m) 4 1 -1 -5 Order 3 (400m) 3 0 -2
Order 4 (300m) 2 -1 -3
8.4.4 Landslides The landslide information was digitized as a shape file in ArcGIS, which was correlated with their areas and spatial positions. The whole catchment was divided into landslide (value 1) or no-landslide (value 0) areas. Through ArcGIS, Xiangxi catchment was divided into 150 m × 150 m terrain units with attributes of elevation, geodetic coordinates, slope angle, slope curvature and landslide presence or absence. In the training area, every terrain unit in the area was an independent area with 4 input attributes (lithology, slope angle, slope curvature and river network influence (distance to river and stream order) and 1 target output attribute (landslide presence or absence). In the application area, every terrain unit was an independent area with only 4 input attributes. ANN was implemented to determine the quantitative correlation between landslide distribution and causative factors in the training area and then applied in the whole catchment to produce a landslide susceptibility map.
8.5 Relationships between landslides and their causative factors Based on the available data, the correlation between landslide distribution and lithology, slope angle, slope curvature, and river network influence are separately qualitatively or
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Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment half-quantitatively analyzed as follows:
8.5.1 Lithology In the whole TGR region, 90 % of all landslides developed in a stratum with an intercalation of weak sediments in the Triassic (T1, T2 and T3) and Jurassic (J1, J1 - 2, J2 and J3) formations (He et al., 2008). However, in Xiangxi catchment, the landslides were prevalent in the Silurian formations in Quyuan subcatchment and in the Jurassic formations along the Xiangxi River, because the Triassic formations have only very thin sediment thickness in Xiangxi catchment. As can be seen from Table 8.1, most of the landslides were found in Silurian and Jurassic formations. The Silurian formations are sensitive for landslide occurrence, especially in Quyuan subcatchment (Fig. 8.4a), which is a part of the back-water area. Landslides in Silurian formations are abundant due to the extremely easily weathering fragile sediments, although they are not far away from the main stream. Along the Xiangxi River T1, T2, T3, J1, J1 - 2 and J2 formations are mainly exposed. T1 and T2 formations are composed of massive limestones which are not susceptible for landslides. Their outcrops are located mainly on the bank of Xiangxi River. T3 is a thin formation and only few landslides were found in this formation. In the J1 formation, many landslides with different sizes took place because landslide-sensitive layers with siltstones and soft coal layers are prevalent in this formation. The J1 formation lies along the Xiangxi River. Thus, the landslides occur especially frequently. Fig. 8.5 is a quantitative statistical analysis of the correlation between the total area of landslide bodies and the relevant geological formations along the Xiangxi River in back-water area. It indicates that the J1, J1 - 2 and J2 are definitely more sensitive for landslides to occur. As indicated in the geological map (Fig. 8.4a), some geological faults lie in Precambrian, Cambrian and Ordovician formations in the northern and eastern catchment. There are also some interlaced faults east of Gaoyang. However, a clear relation between landslides and the distance to faults was not found. Thus, the tectonic background has not been taken into account for the further landslide susceptibility analysis.
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140 T1 )
2 120 T2 m 4 100 T3
80 J1 J1-2 60 J2 40
20 area bodies of landslide (10 0 0-600 m 600-1200 m 1200-1800 m buffer area
Fig. 8. 5: The landslide area (× 104 m2) in three buffer areas along the Xiangxi River
8.5.2 Slope angle Slope angle plays an important role in slope failure. In Table 8.5, the statistical analysis of the slope angles of all landslide units in the different formations in the back-water area is presented. According to the DEM, the slope angle in the catchment ranges from 0° to 60° (Fig. 8.2b). The slope angles of all landslides in the different formations are compiled in Table 8.5. The mean values converge between 13° and 26°, The average slope angle of all landslides is 18.5 °.
Table 8. 5: Slope angle for landslide bodies in different geological formations (SD: standard deviation)
Geological Precambrian Cambrian Ordovician Silurian Devonian Permian Triassic Jurassic All formation PCB CB O S1 S2 D P T1 T2 T3 J1 J1-2 J2J3
Min. (°) 0.5 1.2 7.3 3.4 3.4 3.9 - 16.1 7.3 10.3 0.9 0.5 5.1 10.02.2
Max. (°) 43.7 35.3 43.7 29.9 32.8 40.2 - 26.5 21 27.9 29.7 40.1 30.0 30.6 30.8
Mean (°) 18.5 17.5 25.6 16.6 17.6 18.4 - 20.3 13.9 17.7 15.9 17.6 19.0 18.6 16.1
SD (°) 7.2 8.6 9.9 6.3 6.5 10.4 - 4.5 5.3 5.5 7.2 9.1 6.1 4.6 7.9
8.5.3 Slope curvature Slope curvature, by definition, describes the degree of flatness of a slope. It can be further defined with profile curvature and plan curvature. Profile curvature is the curvature of the surface in the direction of slope. Plan curvature is the curvature of the surface perpendicular to the slope direction. Curvature is a mathematical combination of profile curvature and plan curvature. A picture of slope curvature in Xiangxi catchment has been simplified as concave slopes, plan slopes and convex slopes (Fig. 8.2c). Based on DEM, the slope curvature of all landslide bodies has been statistically calculated in ArcGIS (Table 8.6). A negative value indicates a concave shape and a positive value convex shape. The result shows a normal distribution and indicates that the general slope curvature of the slopes is slightly negative. A statistical investigation for regional landslide susceptibility 143
Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment from Oh et al. (2009) indicated similar results about the relationship between landslides and slope curvature. In practice, the landslide presents different topography in different parts of the landslide mass. For example, the back part close to the main scarp is often concave, but the front of the landslide mass is commonly convex. However, the analysis shows not very significant correlation.
Table 8. 6: Curvature of landslide body (SD: standard deviation)
Geolo- Precambrian Cambrian Ordovician Silurian Devonian Permian Triassic Jurassic
gical All PCB CB O S1 S2 D P T1 T2 T3 J1 J1-2 J2 J3 formation
Min. -1.10 -0.63 -1.10 -0.68 -0.65 -0.61 - 0.00 -0.32 -0.33 -0.32 -0.85 -0.56 -0.80 -0.58
Max 0.70 0.70 0.35 0.37 0.52 0.12 - 0.43 -0.11 0.17 0.50 0.47 0.32 0.43 0.33
Mean -0.11 -0.14 -0.21 -0.07 -0.07 -0.15 - 0.16 -0.23 -0.09 0.00 -0.12 -0.08 -0.04 -0.15
SD (°) 0.22 0.26 0.35 0.21 0.19 0.18 - 0.20 0.10 0.16 0.24 0.24 0.19 0.22 0.23
8.5.4 River network The intensity of the river’s influence on landslide occurrence depends on the scale of river and the distance to the river. Generally, the area closer to the river and the area closer to a river with high discharge have a higher landslide ratio. Thus two factors, stream order and the distance to the stream, are considered to be landslide causative factors. Based on the DEM, the river network in Xiangxi catchment was calculated and classified into 4 stream orders (Fig. 8.2d). The stream order 1 is the 40 km long river segment which is affected by the impoundment of the 175 m water level. The stream order 2, including 3 streams, is 149.5 km long in total. The stream order 3 is 324 km long in total and the stream order 4 is 716 km long. The stream orders were classified according to the discharge of the stream, which was calculated based on DEM. There is stream order 1 only in the training area but not in the application area. The whole river network of the application area lies upstream of the stream order 1. Both in training area and application area there are stream orders 2, 3 and 4. So that in both areas there is a comparable river network background. Fig. 8.5 presents the landslide ratio separately in the area 0 - 600 m, 600 - 1200 m and 1200 - 1800 m from the river in different formations on both sides of Xiangxi River. The landslide frequencies in areas, which are closer to the stream, are obviously higher. The landslide frequencies in all the formation areas reduce with the increase of distance to river. Especially in the Xiangxi formation area, the landslide frequency in the buffer area 0 - 600 m is quite high. But it decreases dramatically in the buffer area 600 - 1200 m and 1200 - 1800 m to the river.
8.6 ANN application Based on the DEM of Xiangxi catchment, ArcGIS was used to prepare the input data, output data and to present the results in pictures. ANN was implemented through the software MATLAB for landslide susceptibility analysis. Within the ANN method, Xiangxi catchment was divided into two parts: the training area and the application area (Fig. 8.6). 144
Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment
Application area
Fig. 8. 6: Training area and application area division in the Xiangxi catchment
As shown in figure 8.7, the process includes two steps: the training step in the training area and the application step in the application area. In the training step, the ANN method is chosen to “train” the model with the data in the training area (back-water area). Four causative factors, including lithology, slope angle, slope curvature, and river network influence, were quantified as input data. The target output value of a terrain unit depends on the presence / absence of a landslide.
Target output Input data (parameters of unit areas in training area) (landslide or no-landslide (lithology, slope angle, slope curvature, river network influence) unit areas) Step 1: Model training (correlation building) Correlation Step 2: Model application (correlation application) Input data (parameters of all unit areas) Application output (lithology, slope angle, slope curvature, river network influence) (Result: landslide and no-landslide unit areas)
Fig. 8. 7: General concept of landslide susceptibility analysis for the Xiangxi catchment
Through the quantitative assignation, the input data and the network output data for the training area were arranged as shown in Table 8.7. Through the back-propagation learning algorithm, an ANN model with a 4-6-1 structure (Fig. 8.8) was established and well trained. The weight and bias from the input layer to the hidden layer and from the hidden layer and the
145
Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment output layer are shown in Table 8.8 and Table 8.9. Then, this model was used for the application area. Every unit area in the application area gets a target output value, which indicates the landslide susceptibility in this unit area.
Table 8. 7: Arrangement of the input values and the target output values for training phase
Input Output
Data source 1: 200 000 geological map DEM Mapping
River network Attribute Lithology Slope angle Curvature Landslide influence
Range [-5, 5] [0, 60°] [-2.35, 2.35] [-5, 5] 0 / 1
1
Lithology [-5, 5] 2
Slope angle (0, 60°) 3 Susceptibility (0, 1) Slope curvature (-2.35, 2.35) 4
River network influence [-5, 5] 5
6
Input Weight{1, 1} Hidden Weight{2, 1} Output layer layer layer
Bias{1} Bias{2}
Fig. 8. 8: Flow chart of the artificial neural network model
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Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment
Table 8. 8: Weight and bias in the ANN model from input layer to hidden layer
Nodes in hidden layer Node 1 Node 2 Node 3 Node 4 Node 5 Node 6
Lithology -40.4818 41.6396 -291.8484 0.16346 -0.27002 0.058932
Slope angle -517.2592 546.3468 -70.9329 -9.4143 -0.42704 -1.624 Weight Slope curvature -20.6304 20.7796 3.7002 17.2243 3.2238 3.7277
River network influence 21.3556 -76.3141 245.6819 4.0294 -0.52366 0.59149
Bias of node -260.6311 328.9628 180.403 0.29263 -0.31867 1.8413
Table 8. 9: Weight and bias in the model from hidden layer to output layer
Nodes in hidden layer Node 1 Node 2 Node 3 Node 4 Node 5 Node 6
Weight of node -312.7008 -312.445 -1.0009 -2.6082 -4.225 83.1432
Bias of node -84.278
Then, the model was validated for competence in the training area. In comparison with the observed landslide distribution, the network output values in the training area were divided so that areas with “high” and “very high” susceptibility appropriately cover most of the mapped landslides (Fig. 8.9b). According to the landslide distribution in the training area, the network output values were classified into 5 classes: very low (0, 0,005), low (0.005, 0.03), medium (0.03, 0.05), high (0.05, 0.1) and very high (0.1, 1). Under this division the ANN output values fit best to the target output (mapping result) in the training area. Table 8.10 shows the landslide classification for all landslides bigger than 104 m2. According to the size of the landslide area, 19.5% of all mapped landslides are within areas, which are classified as “low” and “very low”, and 69.3% of landslides are within the classes “high” and “very high”. Then the model was used in the whole catchment to produce a landslide susceptibility map.
147
Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment
Fig. 8. 9: a Landslide susceptibility classification for Xiangxi catchment; b, an example of the susceptibility classification for single landslides based with the terrain unit size of 150 m × 150 m
8.7 Result The landslide susceptibility map indicates a landslide distribution pattern in the study area. A statistic calculation of landslide classification for the training area and the application area is presented in Table 8.11. The areal statistic was calculated by coverage of the number of terrain units. 86.5% of the training area is classified as “low” and “very low”, and 7.7% is classified as “high” and “very high”. In the application area 95.7% is classified as “low” and “very low” and 1.8% is classified as “high” and “very high”. As a result a map with the network output data for every unit area was established (Fig. 8.9a). The correlations between landslide distribution and lithology, slope angle, slope curvature and river network influence offer an availability of landslide susceptibility in the whole Xiangxi catchment: (1) Landslides occur notably more frequently in areas with low mechanical strength and easily weathering layers. In Xiangxi catchment, landslides are especially frequent in the Silurian and Jurassic formations; (2) Landslides present similar terrain characters on the slope angle and slope form (convex, plan or concave). The slope angle of landslide bodies is mainly between 13° and 26° and the form of the landslide bodies are generally slightly concave; (3) Streams with higher discharge have a stronger influence on landslide occurrence and the distance to a stream also has a clear correlation with landslide occurrence. The landslides in Xiangxi catchment are mainly located along the Xiangxi River and its first class tributary.
8.8 Discussion The application of ANN for landslide susceptibility analysis in this study is actually a pattern
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Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment recognition method. It firstly summarized the correlations between landslide distribution and their causative factors and then used these correlations to evaluate a new area. Only 4 causative factors were chosen as impact factors in the paper: geology, slope angle and slope curvature, river network. Some other related factors, which were popularly adopted as input data of landslide susceptibility analysis such as elevation, slope aspect, land-use and dynamic water pressure due to the reservoir water level change, were not taken into account. The necessary and accuracy of more or too much causative factors for landslide susceptibility is discussed. The elevation itself is not a causative factor of landslide occurrence. For example, in this study, in an area with such high relief, the landslides cannot be restricted by the elevation of their location. However, it can usually be explained in relation to other landslide causative factors, such as the distance to drainage. The slope aspect itself is also not a causality of landslide occurrence. It could be an indirect impact factor in the situation if the climate situation has a local influence on the slopes in a certain direction or the dipping of sedimentary layers has a predominant direction. However for an area as large as Xiangxi catchment, it is difficult to determine, which aspects of slopes have the higher probability of landslide occurrence. The water level change in the reservoir and the related ground water table change in the slope are key factors of inducing new landslides and reactivating dormant landslides. Under the situation of homogenous slope, the rapid drawdown of reservoir water level will cause the slope instability. But how a slope with a certain kind of geological structure reacts under the water level change cannot be uniformly determined with the method in this paper. A comprehensive and detailed measures for Xiangxi catchment, such as to monitor the reaction of slopes under the situation of water level rising and lowering, has not been taken. Thus, the uncertainty of the data source itself could be more of a disadvantage. Because of the accuracy of the input data and the real relationship between landslide occurrence and the input causative factors and, the use of too much factors for the analysis of landslide susceptibility could be still doubtful, especially for a regional analysis. In comparison with the result in this study, the similar method, which has taken account of more causative factors, is not fundamentally better. On the contrary, the abuse of input may induce the accuracy of result. Based on quantitative information of landslide causative factors, the ANN model with back-propagation algorithm can be effectively adopted as a tool for landslide susceptibility in a large area. Even if the method is not so accurate in a site-specific scale (Fig. 8.9b) it offers a practical overview of landslide distribution for big areas. A landslide susceptibility map is presented with a resolution of 150 m × 150 m, which can be used as basic data to assist in landslide management and land use planning. The analysis has its limitations because of the accuracy of the data source and the complexity of such a large region. As it is known that the ANN method, which can be compared with some other statistical classification methods such as decision tree, rough set and support vector machine, is not an accurate mathematical method but a black-box approach. Its results allow some degree of reasonable aberration. Besides the common background, every landslide has its own evolutionary process and unexpected artificial intervention. Thus the local positions of single landslides were not always exactly struck in the classification. However, the regional landslide situation was well recognized and classified in a large-scale overview.
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Chapter 8 Regional scale landslide susceptibility mapping in the Xiangxi catchment
8.9 Conclusion As a classification tool, the ANN method can be used for recognition of existing landslides in a regional scale. Despite the large area and the poor availability of data, the key factors of landslides enable the access to the basic regularity of landslide distribution. Comparing with the mapped landslide distribution, the predictive landslide susceptibility provides a reliable outline of landslide distribution for the study area. With the application of the ANN method both in the Badong study area and the Xiangxi catchment, landslide susceptibility analysis are performed separately in a local scale and in a regional scale. In the local scale analysis in the Badong study area, the attention is mainly paid to the susceptibility for potential landslides. In the regional scale analysis in the Xiangxi catchment the recognition of existing landslides is mainly focused. There are advantages and disadvantages in both applications. In a local scale analysis, parameters of every single landslide are edited and characters of every single landslide can be reflected as a complete individual. With a relatively high data resolution, characters of every single landslide will be lost when landslide bodies are divided into small unit areas. Additionally, activity states of every landslide are also able to be considered in this scale. A disadvantage is that the number of training samples could be too small for neural network training. In a regional scale analysis, due to the relatively low resolution of data, the concrete activity states of landslides cannot be taken into account and it is usually enough to determine the general locations of the occurred landslides. The advantage is that people get an overview of the landslide susceptibility for a large area and save some effort of field mapping and land use planning. In this scale, landslides for neural network training are usually divided into unit areas, which is convenient for data processing but the characters of the landslides cannot be well preserved.
150
Summary
Summary
Based on geological and geotechnical mapping in the study area, the type and the distribution of all landslides are summarized and a classification of landslide susceptibility is performed. The intensely developed tectonics and the river incision are essential factors of landslide development in the Badong study area. Through geological mapping, it is found that tectonic structures including joints, faults, folds and cleavages are developed especially dense in both limbs of the Guandukou syncline. The large ravines in the study area stretch mainly parallel or rectangular to the fold axis of Guandukou syncline in north-south or east-west direction. Through geotechnical mapping, 103 landslides are mapped, from with 77 are debris slides, 25 are rock slides and one is a rock fall. Some significant regularities are found within the 102 landslides. Firstly the general slope angle of the landslide bodies lies between 11° and 30° and they have a mean value of 23.5°. Secondly landslides concentrate mainly on an elevation lower than 300 m asl. and on an elevation between ca. 450 m asl. and ca. 600 m asl.. Thirdly the landslides occurred most frequently in dip slopes (59%), medium frequently in across-dip slopes (28%) and relatively seldom in anti-dip slopes (13%). Fourthly most of the landslides develop in areas with clayey limestone bedrocks and clayey siltstone bedrocks. The number of landslides originating from clayey siltstones (62) is much higher than that from clayey limestones (36). But the average scale of slides originating from clayey limestones (ca. 61 200 m2) is much higher than that originating from clayey siltstones (ca. 39 900m2). Fifthly landslides concentrate especially in the west part of North Badong and in South Badong due to the intensely developed tectonics in these areas. Multilayer feed-forward neural networks in the artificial neural network (ANN) method are selected to solve different kinds of classification problems. The study about lithology recognition shows that it is possible to discriminate some types of lithologies, which have definitely different reflectance in satellite images from Landsat 5 satellite. The results show the best ability of lithology recognition in farmland areas, while the coverage of thick vegetation and large scale building areas are the main obstacles of lithology recognition. With geological, geomorphological and geographical characters of mapped landslides in study areas, multilayer layer feed-forward (MLF) neural networks show high capabilities both in analyzing the susceptibility for potential landslides in a local scale and in investigating the susceptibility for existing landslides in a regional scale. They are both GIS-based susceptibility evaluations through the ANN method and show generally satisfying results in both case studies. There are advantages and disadvantages both in local scale application and in regional scale application. No matter in which scale an analysis is performed, the objects of susceptibility analysis (slides, falls, flows etc.), the data feasibility (for potential landslides or occurred landslides) and the scales of analysis (regional scale, local scale, detailed scale, etc.) should be firstly clarified before further analysis.
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