APPLICATION OF THERMAL REMOTE SENSING IN EARTHQUAKE PRECURSOR STUDIES
A THESIS
Submitted in partial fulfilment ofthe requirements for the award of the degree of DOCTOR OF PHILOSOPHY in EARTH SCIENCES
by VINEETA RAWAT
DEPARTMENT OF EARTH SCIENCES INDIAN INSTITUTE OF TECHNOLOGY ROORKEE ROORKEE-247 667 (INDIA) SEPTEMBER, 2010 ©INDIAN INSTITUTE OF TECHNOLOGY ROORKEE, ROORKEE, 2010 ALL RIGHTS RESERVED INDIAN INSTITUTE OF TECHNOLOGY ROORKEE ROORKEE
CANDIDATE'S DECLARATION
I hereby certify that the work which is being presented in the thesis entitled
APPLICATION OF THERMAL REMOTE SENSING IN EARTHQUAKE PRECURSOR STUDIES, in partial fulfilment of the requirements for the award of the degree of Doctor of Philosophy and submitted in the Department of Earth Sciences of the Indian Institute of Technology Roorkee, Roorkee is an authentic record of my own work carried out during a period from July 2006 to August 2010 under the supervision of Dr. Arun K. Saraf, Professor, Department of Earth Sciences, Dr. Josodhir Das, Scientific Officer, Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee.
The matter presented in this thesis has not been submitted by me for the award of any other degree of this or any other Institute. b& (VINEETA RAWAT)
This is to certify that the above statement made by the candidate is correct to the best of our knowledge.
(Josodhir Das ) (ArtnTK. Saraff Supervisor Supervisor Date: o-2s SeJaJ-. 2.o1 o
The PhD Viva-Voce Examination of Ms. Vineeta Rawat, Research Scholar, has been held on OZ Aterr/lC//
Signature of Supervisors Signature of External Examiner ABSTRACT
Earthquakes are the most unexpected and devastating natural phenomenon occurring on Earth. Uncertainty involved in time and place of their occurrence has intrigued scientists globally but still years of research have failed to reliably predict / forecast earthquakes in terms of location, time and magnitude. Perhaps knowledge about earthquakes is limited and requires a multidisciplinary approach to widen the window of our understanding. There is however, no doubt that preparatory processes to earthquake rupture exist and huge amounts of energy release might be preceded by some precursory phenomena that could be consistently observed and identified. The ever advancing techniques of remote sensing have the potential to contribute and assist human in evaluating natural disasters. Stresses building up during an earthquake preparation phase lead to enhanced thermal infrared emission from earth's surface prior to earthquakes. In this study efforts have been made to establish correlation between transient temporal TIR anomalies, OLR variability and earthquake events through post-earthquake analysis of NOAA-AVHRR thermal images. In this study, nine earthquakes from different parts of the world; Jabalpur earthquake (21 May 1997), Chamoli earthquake (29 Mar 1999), and Yamnotri earthquake (22 Jul 2007) from India; Dabiran earthquake (10 Jul 2003), Kerman earthquake (21 Aug 2003), Ravar earthquake (14 Oct 2004), Fin earthquake (25 Mar 2006) from Iran; Balochistan earthquake (29 Oct 2008) from Pakistan and Vrancea earthquake (27 Oct 2004) from Romania; were investigated for pre-earthquake TIR anomaly and OLR variabilitydetection. Theories explaining physical phenomena of pre-earthquake TIR anomaly generation are widely debated since the first realization ofthis phenomenon in 1988 by Gorny and his co-workers. According to one of the most accepted theory of Earth- degassing; stresses building up in earthquake preparation zone reduce pore spaces in rocks and gases are squeezed out. Localized greenhouse effect created due to the increased concentration of optically absorbing gases and alteration of hydro-geological regime of the region under stress leads to TIR anomaly. P-hole or positive hole, activation theory is empirical theory that explains not only pre-earthquake TIR anomaly but also other precursory signals. P-hole awakening under high pressure conditions and their subsequent migration, accumulation and recombination at Earth's surface leads to libration of energy. This elevates the LST of the epicenter region and
l enhanced TIR emission takes place. Seismo-ionosphere coupling theory takes into account cumulative effect of lithosphere, atmosphere and ionospheric processes. Several Remote Sensing Rock Mechanics (RSRM) experiments have also validated the observations of rise in thermal emission from stressed rock volume. Relevant parameters investigated are land surface temperature (LST) and outgoing longwave radiation (OLR). It has been observed that earthquake with magnitude higher than 5 may be preceded by detectable rise in LST and OLR. The LST was seen to increase by 2° - 11°C about 7-13 days before the main shock. Thermal anomaly attains it peak temperature and return to normal conditions once the main event is over. The transient period may range from 9-17 days. Study of earthquakes with series of aftershocks viz. Dabiran, Iran (10 Jul 2003), Kerman, Iran (21 Aug 2003), Vrancea, Romania (27 Oct 2004) reveals that the occurrence of aftershocks prevents the re-establishment of normal conditions even after the main event is over. It was also noticed that magnitude and focal depth play a vital role in intensity and spatial extent of the thermal anomaly. Higher earthquake magnitude and shallower focal depth are favorable conditions for the appearance of intense thermal anomaly with larger spatial extent and vice versa. A prominent observation regarding the earthquakes of moderate magnitude [Fin, Iran (25 Mar 2006) and Balochistan, Pakistan (29 Oct 2008)] is the appearance of a dual TIR peak instead of the single rise observed previously. This may lead us to infer that perhaps the energy accumulated in the stressed rocks may be released sporadically in the form of apparent temperature increment or any other geophysical earthquake precursor. The surface expression of TIR anomaly is also found to be governed by fault characteristics so it may or may not coincide with the epicenter. Analyses of NOAA-AVHRR derived OLR data also reveal significant pre-earthquake and co-seismic variability. OLR values rose to 20 - 55 W/m2 and 7 -14 days before the earthquake attaining maximum which may or may not be followed by a low before the earthquake. Total OLR variability transient period ranged from 11-23 days. Similarity found in development trend, overlapping transient period, disappearance with the earthquake event implies interrelation of TIR anomaly and OLR variability and their connection with the earthquake preparation process.
This study also reports Himalayan Thermal Line (HTL) and impact of increased stress conditions on HTL. HTL phenomenon was studied with respect to Chamoli earthquake, India (29 Mar 1999). Coinciding with the contact zone of frontal fold belt and moisture rich, porous, reworked soils; HTL development is affected the II -
lithological, structural and hydrological conditions of the region. It shows varying intensity indicating stress conditions of the region.
This research strengthens the concept of TIR anomaly phenomenon as a pre- earthquake process which can be monitored through satellites equipped with thermal sensors. Outgoing longwave radiation variability in response to impending earthquakes has been studied and it has been correlated with TIR anomaly for the first time. Himalayan Thermal Line has also been reported for the first time. Further HTL sensitivity analysis on regional scale may lead to establishing a reasonable correlation between thermal line, tectonic stress and fault/thrust.
in ACKNOWLEDGEMENT
First and foremost, I would like to thank the Almighty, with all my heart, for being my ultimate strength and for instilling in me the patience, optimism and competence to successfully accomplish set goals. I sincerely express my gratitude to my supervisors, Prof. Arun Kumar Saraf and Dr. Josodhir Das for considering me worthy of their able guidance and introducing me to this entirely new arena of remote sensing and GIS.
I deeply thank Saraf Sir for his continuous encouragement, care and nurturing. His vision and scientific temperament have been instrumental in shaping my own capabilities. His distinctive qualities of dedication, organization, team spirit, constant involvement, and that never-let-go attitude have been instrumental in my achievements. Learning doesn't stop here. His Quest for perfection, Will to conquer obstructions and Aiming high approach is inspirational and will always be with me.
I also express my thankfulness to Das Sir. His emotional and intellectual guidance and mentoring are deeply acknowledged. I thank him for lending me his guidance, advice, support, and experience which have indeed gone a long way in bringing this research to fruition.
I would like to thank the Heads of the Department of Earth Sciences, IIT Roorkee, during the tenure of my research, namely: Dr. V. N. Singh, Dr. R. P. Gupta and Dr. P. K. Gupta for their academic help and encouragement.
I wish to sincerely acknowledge the critical financial support by the Ministry of Human Resource Development (MHRD), Govt, of India, without which it would have become difficult to take up and complete this research.
For being the most direct source of inspiration and support, I would like to thank my former lab seniors and current lab-mates Santosh K. Panda, Dr. Ajanta Goswami, Kanika Sharma, Yazdana Shujat, Priyanka Banerjee, Ajoy and
Md. Zia.
I also thank the members of the non-teaching staff at the Department of Earth Sciences, IIT Roorkee, especially Nair ji, Rakesh ji and Saini ji for their open- hearted support and administrative help daring the course of stay at Roorkee. A special place holds for Rahil ji. His constant round the clock lab support and our tea- breaks will always be part of my fond memories. I consider myself especially lucky to be blessed with some truly special friendships. I wish to dedicate this space to Aparna, Vijay, Neelam, Meenakshi and Ashish Sir for their unsolicited, unspoken and happily lent friendly support, concern and love. My stay in Roorkee will always be lovingly remembered and cherished due to my hostel friends Nidhi Goel, Priti, Amrita, Jyoti, Rashmi, Deepmala, Gunjan etc.
And in the end, I dedicate my work to my Parents. I am deeply indebted in gratitude to my parents for their faith in me throughout, for their inspiration and for being with me through all troubles and tribulations. I also thank my sweet brother Mukul and lovely sister Tanu for supporting me. I am thankful to them for numerous naughty moments, laughs and definitely fights, which keeps me going.
Last but not the least I thank all those who have helped me directly or indirectly at various stages of this work.
VINEETA RAWAT
VI CONTENTS
Page no. ABSTRACT I
ACKNOWLEDGEMENT V
LIST OF FIGURES XIII
LIST OF TABLES XXI
CHAPTERS
1. INTRODUCTION 1-19
1.1 Preamble 1
1.2 Global and Indian Seismic scenario 2
1.2.1 Global distribution of earthquakes 2
1.2.2 Earthquake prone areas in India 3
1.3 Remote sensing in Earthquake precursor studies 4
1.3.1 Radar and GPS observations 9
1.3.2 Electromagnetic field 10
1.3.3 Thermal infrared abnormity 15
1.4 Research Objectives 16
1.5 Organization of Thesis 19
2. SATELLITE DETECTION OF EARTHQUAKE THERMAL 21-40 PRECURSOR : A REVIEW
2.1 Introduction 21
2.2 Application of Thermal Remote Sensing for Detection of 21 Thermal Anomaly Detection
2.3 Advances in Understanding of Mechanism of Thermal 25 Infrared Anomaly 2.2.1 Mechanism of thermal infrared anomaly 26
2.3.1.1 Earth-degassing theory and gas-thermal theory 26
2.3.1.2 Seismo-ionospheric coupling theory 28
2.3.1.3 p-hole activation theory 29
2.3.1.4 Remote Sensing Rock Mechanics (RSRM) 35
2.4 Outgoing Longwave Radiation Variability prior to 36 Earthquakes
2.5 Summary 39
3. DATA AND METHODOLOGY 41-53
3.1 Introduction 41
3.2 Data Used 41
3.2.1 NOAA-AVHRR data 41
3.2.1.1 Advanced Very High Resolution Radiometer 42 (AVHRR) Sensor
3.2.2 Outgoing Longwave Radiation (OLR) data 45
3.2.2.1 NOAA daily (non-interpolated) OLR data 45
3.2.2.2 NOAA interpolated OLR data 46
3.3 Data Sources 46
3.3.1 NOAA - AVHRR satellite earth station 47
3.3.2 Online sources 47
3.4 Software Used 48
3.5 Methodology 48
3.5.1 Physical nature of Thermal Anomaly 48
3.5.2 Methodology for NOAA - AVHRR Image processing and 49 Analysis » 3.5.2.1 Preparation of land surface temperature (LST) 49 maps
50 3.5.2.2 Preparation of LST time-series layout 3.5.3 Methodology for OLR data processing and analysis 50
3.5.3.1 Data processing 50
* 3.6 Remarks 53
4. EARTHQUAKE THERMAL PRECURSOR DETECTION USING 55-120 NOAA-AVHRR DATA: ANALYSIS AND OBSERVATIONS
4.1 Introduction 55
4.2 Pre-earthquake TIR anomaly detection using NOAA-AVHRR 56 data
4.2.1 Jabalpur earthquake (Mw 5.8 on 21 May 1997), India 56
4.2.1.1 Analysis 56
4.2.1.2 Observations 61
4.2.2 Dabiran Earthquake (Mw 5.8 on 10 July 2003), Iran 62
4.2.2.1 Analysis 62
4.2.2.2 Observations 67
4.2.3 Kerman Earthquake (Mw 5.9 on 21 August 2003), Iran 68
4.2.3.1 Analysis 71
4.2.3.2 Observations 71
4.2.4 Ravar Earthquake (Mw 5.1 on 14 October 2004), Iran 75
4.2.4.1 Analysis 75
4.2.4.2 Observations 75
4.2.5 Vrancea Earthquake (Mw 5.9 on 27 October 2004), 81 Romania
4.2.5.1 Analysis 81
4.2.5.2 Observations 81
4.2.6 Fin Earthquake (Mw 5.9 on 25 March 2006), Iran 87
4.2.6.1 Analysis 87
4.2.6.2 Observations 87 4.2.7 Yamnotri Earthquake (Mw 5.1 on 22 July 2007), India 88
4.2.7.1 Analysis 97
4.2.7.2 Observations 97
4.2.8 Balochistan Earthquake (Mw 5.0 on 29 October 2008), 98 Pakistan
4.2.8.1 Analysis 101
4.2.8.2 Observations 101
4.2.9 Chamoli earthquake and Thermal Line in Himalayan 105 Foothills
4.2.9.1 Analysis 106
4.2.9.2 Thermal Line Phenomena 111
4.3 Summary 117
5. PRE-EARTHQUAKE OUTGOING LONGWAVE RADIATION 121-165 VARIABILITY
5.1 Introduction 121
5.2 OLR Dependence on Temperature 121
5.3 Pre-earthquake OLR Variability Detection 122
5.3.1 Jabalpur Earthquake 122
5.3.1.1 Analysis 127
5.3.1.2 Observations 127
5.3.2 Dabiran Earthquake 128
5.3.2.1 Analysis 128
5.3.2.2 Observations 133
5.3.3 Kerman Earthquake 134
5.3.3.1 Analysis 134
5.3.3.2 Observations 139 5.3.4 Ravar Earthquake 140
5.3.4.1 Analysis 140
5.3.4.2 Observations 145
5.3.5 Vrancea Earthquake 146
5.3.5.1 Analysis 146
5.3.5.2 Observations 146
5.3.6 Fin Earthquake 151 * 5.3.6.1 Analysis 151
5.3.6.2 Observations 152
5.3.7 Yamnotri Earthquake 157
5.3.7.1 Analysis 157
5.3.7.2 Observations 158
5.4 Summary 163
6. SUMMARY AND CONCLUSIONS 167-176
6.1 Introduction 167
6.2 Summary 168
6.3 Conclusions 171
6.4 Scope for Future Research 175
BIBLIOGRAPHY
ANNEXURE - 1: LIST OF PUBLICATIONS OUT OF RESEARCH WORK
ANNEXURE - 2: REPRINTS/PRINTS OF SELECTED PUBLICATIONS LIST OF FIGURES
Page no.
Figure 1.1 Global Earthquake Distribution {source: httpJ/www. solarviews.com).
Figure 1.2 India Seismic Zone map as per IS: 1893 (Part 1) - 2002 Bureau of Indian Standards {source: http://asc-india.org).
Figure 1.3 Schematic representation of earthquake precursory signals that 11 can be detected by remote sensing sensors {source: www.isfep. com/precursors.htmf).
Figure 2.1 Schematic representation of p-hole activation, their subsequent 31 accumulation and combination at Rock-Air interface resulting in the release of energy {after Takeuchi et al., 2005).
Figure 2.2 Schematics of p-hole activation and spreading in igneous or 33 high-grade metamorphic rock, (a) The p-holes equivalent to O- are usually in a dormant state as spin-paired peroxy bonds that are also known as PHPs. (b) When stresses break a peroxy bond, two p-holes are activated, (c) An electron jumps in from a neighboring O2" and the p-hole moves out. (d) The p holes can spread across grain boundaries and through thick layers of igneous rocks.
Figure 2.3 Thermal increase with loading in Lavasan granite {Pellet et. al., 33 2007).
Figure 2.4 IR radiation earthquake model, where bi-shear (horizontal 37 stress: 50 MPa, vertical stress 0-35.4 MPa) test of frictional sliding on a gabbro sample was conducted for simulating earthquake action. It could be seen that IR radiation temperature along contact-face increased gradually as vertical load became higher, and four stress-locking points were very clear {Wu era/., 2000).
XIII Figure 3.1 Coverage Area of Indian Institute of Technology Roorkee- 51 Satellite Earth Station (IITR-SES). IITR-SES has been operating since October 2002, and acquring day and night data from NOAA and FY series of satellites for important tectonic locations in and around India, which has been used for the present study {Chaudhury, 2005).
Figure 4.1 Locations of epicenter of main event of Jabalpur earthquake, 57 historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model).
Figure 4.2 Daytime NOAA-AVHRR LST time series map of part of India 59 before and after the earthquake in Jabalpur on 21 May 1997. An intense TIR anomaly can be seen on 15 May 1997, eight days before the earthquake.
Figure 4.3 Locations of epicenter of main event of Dabiran earthquake of 63 Iran, historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model).
Figure 4.4 Daytime NOAA-AVHRR LST time series map of part of Iran 65 before and after the earthquake in Dabiran on 10 July 2003. An intense thermal anomaly can be seen on 03 July 2003, seven days before the earthquake.
Figure 4.5 Locations of epicenter of main event of Kerman earthquake of 69 Iran, historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model).
Figure 4.6 Daytime NOAA - AVHRR LST time series map of part of Iran 73 before and after the earthquake in Kerman on 21 August 2003. Thermal peak can be seen on 11 August 2003.
Figure 4.7 Locations of epicenter of main event of Ravar earthquake, 77 historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model).
XIV Figure 4.8 Nighttime NOAA-AVHRR LST time series map of part of Iran 79 before and after the earthquake in Ravar on 14 October 2004. An intense thermal anomaly can be seen on 08 October 2004, six days before the earthquake.
Figure 4.9 Locations of epicenter of main event of Vrancea earthquake 83 and historical seismicity of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model).
Figure 4.10 Daytime NOAA-AVHRR LST time series map of Romania 85 before and after the earthquake in Vrancea on 27 October 2004. An intense thermal anomaly can be seen on 25 October 2004, two days before the earthquake.
Figure 4.11 Location of epicenter of main event of Fin earthquake of Iran, 89 historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model).
Figure 4.12 Daytime NOAA-AVHRR LST time series map of Iran before and 91 after the earthquake in Fin on 25 Mar 2006.
Figure 4.13 Locations of epicenter of main event of Yamnotri earthquake of 93 India and historical seismicity of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model).
Figure 4.14 Daytime NOAA-AVHRR LST time series map of India before 95 and after the earthquake in Yamnotri on 22 July 2007. An intense thermal anomaly can be seen on 20 July 2007, two days before the earthquake.
Figure 4.15 Locations of epicenter of main event of Balochistan earthquake 99 of Pakistan and historical seismicity of the region. Epicenter and other information are shown over GTOPO30 (global digital elevation model).
Figure 4.16 Daytime NOAA-AVHRR LST time series map of part of 103 Pakistan before and after the earthquake in Balochistan on 29 October 2008.
XV Figure 4.17 Location of epicenter of main event of part of Chamoli 107 earthquake of India, historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model).
Figure 4.18 Nighttime NOAA-AVHRR LST time series map of part of India 109 before and after the earthquake in Chamoli on 29 March 1999.
Figure 4.19 A schematic depiction of the "Himalayan Thermal Line" at the 112 foot hills the Himalayas. Direction arrows show the movement of ground water. 4
Figure 4.20 Location of epicenter of main event of Chamoli earthquake of 113 India, aftershock actvity and tectonics (faults) of the region.
Figure 4.21 Nighttime NOAA-AVHRR LST time series map of part of India 115 (Chamoli region) in year 2003.
Figure 5.1a Outgoing Longwave Radiation plot for Jabalpur earthquake, 123 India. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR (from 07 May 1997 to 06 Jun 1997). OLR values between 70°E - 85°E longitudes have been averaged.
Figure 5.1b Outgoing Longwave Radiation plot for Jabalpur earthquake, 125 India. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 77.5°E longitude have been taken.
Figure 5.1c Daily mean and daily longterm mean OLR vs. time graph for 128 Jabalpur earthquake, India showing OLR variability trend during a period of 30 days (07 May 1997 - 06 Jun 1997).
*
XVI Figure 5.2a Outgoing Longwave Radiation plot for Dabiran earthquake, 129 Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR ♦ and bottom plot is Daily Longterm Mean OLR. OLR values between 50°E-60°E longitudes have been averaged.
Figure 5.2b Outgoing Longwave Radiation plot for Dabiran earthquake, 131 Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 55°E longitude have been taken.
Figure 5.2c Daily mean and daily longterm mean OLR vs. time graph for 134 Dabiran Earthquake, Iran showing OLR variability trend during a period of 30 days (25 Jun 2003 - 25 Jul 2003).
Figure 5.3a Outgoing Longwave Radiation plot for Kerman earthquake, 135 Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values between 55°E-65°E longitudes have been averaged. * Figure 5.3b Outgoing Longwave Radiation plot for Kerman earthquake, 137 Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 57.5°E longitude have taken.
Figure 5.3c Daily mean and daily longterm mean OLR vs. time graph for 139 Kerman Earthquake, Iran showing OLR variability trend during a period of 30 days (21 Jul 2003 - 21 Sep 2003).
* Figure 5.4a Outgoing Longwave Radiation plot for Ravar earthquake, Iran. 141 Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm MeanOLR. OLR values between 50°E-65°E longitudes have been averaged.
Figure 5.4b Outgoing Longwave Radiation plot for Ravar earthquake, Iran. 143 Here x-axis has 'time' dimension and y-axis represents ^ latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 55°E longitude have been taken.
XVII Figure 5.4c Daily mean and daily longterm mean OLR vs. time graph for 145 Ravar Earthquake, Iran showing OLR variability trend during a period of 30 days (29 Sep 2004 - 29 Oct 2004).
Figure 5.5a Outgoing Longwave Radiation plot for Vrancea earthquake, 147 Romania. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values between 20°E-30°E longitudes have been averaged.
Figure 5.5b Outgoing Longwave Radiation plot for Vrancea earthquake, 149 Romania. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 20°E longitude have been taken.
Figure 5.5c Daily mean and daily longterm mean OLR vs. time graph for 151 Vrancea Earthquake, Romania showing OLR variability trend during a period of 30 days (12 Oct 2004 - 11 Nov 2004).
Figure 5.6a Outgoing Longwave Radiation plot for Fin earthquake, Iran. 153 * Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values between 52.5°E-57.5°E longitudes have been averaged.
Figure 5.6b Outgoing Longwave Radiation plot for Fin earthquake, Iran. 155 Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 57.5°Elongitude have been taken.
Figure 5.6c Daily mean and daily longterm mean OLR vs. time graph for Fin 157 Earthquake, Iran showing OLR variability trend during a period of 30 days (10 Mar 2006 - 09 Apr 2006).
Figure 5.7a Outgoing Longwave Radiation plot for Yamnotri earthquake, 159 India. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values between 75°E-90°E longitudes have been averaged.
XVIII Figure 5.7b Outgoing Longwave Radiation plot for Yamnotri earthquake, 161 India. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR ♦ and bottom plot is Daily Longterm Mean OLR. OLR values at 77.5°E longitude have been taken.
Figure 5.7c Daily mean and daily longterm mean OLR vs. time graph for 163 Yamnotri Earthquake, India showing OLR variability trend during a period of 30 days (07 Jul 2007 - 06 Aug 2007).
XIX LIST OF TABLES
Page no.
Table 1.1 Satellite based monitoring of earthquake precursors {after 13 Hayakawa et al., 2000).
Table 1.2 Details of studied earthquakes {USGS reports). 17
Table 3.1 Launch dates and transmission frequencies of NOAA-series of 42 satellites.
Table 3.2 NOAA-AVHRR channels specifications and typical use. 44
Table 4.1 Details of daytime NOAA 14-AVHRR (LAC) datasets for year 61 1997 used to study the thermal scenario during Jabalpur earthquake (Mw 5.8), India.
Table 4.2 List of aftershocks following 10 Jul 2003 Dabiran earthquake, 67 Iran {source: http://www.iiees.ac.ir).
*
Table 4.3 Details of daytime NOAA 17-AVHRR (LAC) datasets for year 68 2003 used to study the thermal scenario during Dabiran earthquake (Mw 5.8), Iran.
Table 4.4 List of aftershocks that followed the Kerman Earthquake 71 {source: http://www. iiees.ac.if).
♦
XXI Table 4.5 Details of daytime NOAA 17-AVHRR (LAC) datasets for year 72 2003 used to study the thermal scenario during Kerman earthquake (Mw 5.9), Iran.
Table 4.6 Details of nighttime NOAA 16-AVHRR (LAC) datasets for year 76 2004 used to study the thermal scenario during Ravar earthquake (Mw5.1), Iran.
Table 4.7 Details of daytime NOAA-AVHRR (GAC) datasets for the year 82 2004 used to study the thermal scenario during Vrancea earthquake (Mw 5.9), Romania.
Table 4.8 Details of daytime NOAA-AVHRR (LAC) datasets for the year 88 2006 used to study the thermal scenario during Fin earthquake (Mw5.9), Iran.
Table 4.9 Details of daytime NOAA-AVHRR (LAC) datasets for the year 98 2007 used to study the thermal scenario during Yamnotri earthquake (Mw 5.1), India.
Table 4.10 Details of daytime NOAA-AVHRR (LAC) datasets for the year 102 2008 used to study the thermal scenario during Balochistan earthquake (Mw 6.4), Pakistan.
Table 4.11 Details of nighttime NOAA 14 - AVHRR data for the year 1999 106 used to study the thermal scenario during Chamoli earthquake (Mw6.6), India.
Table 4.12 Details of nighttime NOAA 17-AVHRR datasets for the year 111 2003 of Chamoli region.
Table 4.13 List of earthquakes studied through NOAA-AVHRR datasets 118 and specifications of the number of days prior to the earthquake in which the thermal anomaly was seen to occur and reach the maximum amplitude.
XXII Table 5.1 List of earthquakes studied through AVHRR derived OLR 165 datasets and observed OLR variability.
Table 6.1 A comparative analysis of TIR anomaly and OLR variability 173 observed prior to studied earthquakes. Yellow colored box indicate days with anomalous TIR emission and green colored boxes indicate days showing anomalous OLR behavior. Red colored box shows thermal-peak / OLR peak. "00" box is earthquake occurrence day. Pink colored part of the table is pre-earthquake period and blue colored part is post-earthquake period.
*
XXIII CHAPTER 1
Introduction
1.1 Preamble
Nature's fury is frequently presented before us in the form of devastating earthquakes. Earthquakes become more disastrous as present seismological techniques are still unable to make any precise earthquake forecast in terms of accurate time, location and magnitude. Researchers like Geller et al., (1997) even think that earthquakes cannot be predicted and geophysicists like Campbell (1998) believe that efforts should be directed towards hazard mitigation, understanding source mechanisms and response of infrastructures to seismic vibrations. But devastating earthquakes keep happening and foster challenges to those scientists who think that natural processes, which are currently not yet understood should be studied with greater zeal. Scientists have begun to estimate the locations and likelihoods of future damaging earthquakes. Sites of greatest hazard are being identified and possibility of longterm prediction is not a far goal. Novel inter disciplinary approach to utilize full potential of scientific methods will definitely aid in understanding earthquakes and earthquake-precursor signals. Earlier also similar criticism was noted about weather or monsoon related hazards. Contrary to such beliefs prediction efforts have gradually progressed and weather forecasting is now possible to reasonable accuracy.
£ The havoc caused by the recent major earthquakes like magnitude 8.8 Maule offshore earthquake (27 Feb 2010), Chile; magnitude 7.0 Haiti earthquake (12 Jan 2010); magnitude 7.9 Sichuan earthquake (12 May 2008), China; magnitude 7.6 POK earthquake (8 Oct 2005), Pakistan; magnitude 9.1 Sumatra earthquake (26 Dec 2004), Indonesia; magnitude 7.7 Bhuj earthquake (26 Jan 2001), India etc. is still lingering in our memories. In fact our earth is shaken frequently by earthquakes as evident through seismic recordings which indicate that several millions earthquakes occur in <♦ the world each year (USGS) and about 50 earthquakes are located each day (NEIC). Many go unnoticed because they hit remote areas devoid of seismicity recording networks or have very small magnitudes. If such earthquakes occur in a populated area, it may cause many deaths and injuries and extensive property damage. Application of Thermal Remote sensing in Earthquake Precursor studies
The evolving techniques of remote sensing have the potential to contribute and assist human research in evaluating natural processes and events occurring daily on the Earth's surface on a global basis. Sensors onboard satellites having sensing capabilities in different bands of electromagnetic spectrum allow user to interpret various earth objects and physical phenomena with more precision and reliability. Use of remote sensing in earthquake studies have found different dimensions and have proved its utility in different realms of life and property. Optical remote sensing is directly used in the disaster management and related tasks. High spatial resolution remote sensing data provides minute details of the affected region and allows planners and rescue workers to plan and approach to the affected people in more effective * manner. Microwave remote sensing [Interferometric Synthetic Aperture Radar (InSAR)] is probably most novel remote sensing application in earthquake studies. The deformation studies under InSAR domain are helpful in measuring the deformation suffered by earth during the earthquake. The use of thermal remote sensing in earthquake started way back in nineteen eighties in Russia. Gorny et al., 1988 introduced the possibility of satellite detection of earthquake precursor thermal infrared anomaly. In the subsequent chapters observations made in different earthquake cases using thermal remote sensing data have been presented. This thesis also documents the major contributions made in the development of earthquake precursor thermal anomaly studies.
1.2 Global and Indian Seismic Scenario
1.2.1 Global distribution of earthquakes
+ Until the early 20th century, seismologists had a mixed and warped picture of the distribution of earthquakes because their knowledge was restricted mostly to the earthquakes felt on the continents in which they resided. As the 20th century advanced through technology, the efforts of a global seismograph network to locate earthquakes began to become a reality. This provided more improved recording and cataloguing for earthquakes occurring world over. In 1990, there were approximately 3300 seismographic observatories participating in international data exchange. Catalogues provide the basis for studies of the tectonic deformations of the Earth and for calculation of earthquake hazard in countries around the world. Today global and
2 Chapter 1: Introduction regional catalogs are being compiled by various agencies, such as the ISC and NEIC. From antiquity it has been known that some regions are more prone to the occurrence of earthquakes than are others and improved cataloguing has provided a clear picture in support of this idea. Thus we can separate seismically active regions from those that are more stable. Once the determination of epicentres had become sufficiently accurate, it was observed that earthquakes occur in narrow zones that surround relatively stable regions. According to plate tectonics, the global distribution of epicenters is related to boundaries between lithospheric plates (figure 1.1). Earthquakes at plate boundaries are called interplate earthquakes. Less commonly, earthquakes also take place in plate interiors and these are called intraplate earthquakes. The most active region in the world corresponds to the margins of the Pacific Ocean also known as "Ring of Fire". About 90% of the world's earthquakes and 80% of the world's largest earthquakes occur along the Ring of Fire. Earthquakes with large magnitudes take place along this zone in the Americas from the Aleutian Islands to southern Chile and from the Kamchatka peninsula in Asia to New Zealand. Besides shallow earthquakes, throughout most of this long region, intermediate and deep shocks take place along the margin of Central and South America and on the other side of the Pacific along the systems of island arcs (Aleutians, the Kuriles, Japan, the Philippines, Fiji, etc.). Another large seismically active region is known as the Mediterranean-Alpine-Himalayas region and extends from west to east. This region is related to the boundary between the plates of Eurasia to the North and Africa, Arabia, and India-Australia to the South. Its seismicity involves shallow, intermediate, and deep earthquakes. It accounts for the (5 - 6% of world's total earthquakes and 17% of the world's largest earthquakes. A third seismic region is formed by earthquakes located on ocean ridges that form the boundaries of oceanic plates, such as the Mid- Atlantic Ridge, East Pacific Rise, etc. In these regions earthquakes of shallow depths are concentrated in relatively narrow bands following the trend of the oceanic ridges (USGS Earthquakes).
1.2.2 Earthquake prone areas of India
The varying geological conditions prevailing in the country imply that likelihood of earthquake damage taking place at different locations is different. Thus, a seismic Application of Thermal Remote sensing in Earthquake Precursor studies zone map is required to identify these regions. Based on the levels of intensities sustained during damaging past earthquakes, the 1970 version of the zone map subdivided India into five zones - I, II, III, IV and V. The maximum Modified Mercalli (MM) intensity of seismic shaking expected in these zones were V or less, VI, VII, VIII, and IX and higher, respectively. Parts of Himalayan boundary in the north and northeast, and the Kachchh area in the west were classified as zone V. The seismic zone maps are revised from time to time as more understanding is gained on the geology, the seismo-tectonics and the seismic activity in the country. The Indian Standards provided the first seismic zbne map in 1962, which was later revised in * 1967 and again in 1970. The map has been revised again in 2002 (figure 1.2), and it now has only four seismic zones - II, III, IV and V The areas falling in seismic zone I in the 1970 version of the map are merged with those of seismic zone II. Also, the seismic zone map in the peninsular region has been modified. This 2002 seismic zone map is not the final word on the seismic hazard of the country, and hence there can be no sense of complacency in this regard. The Seismic Zone Map of India presents a large-scale view of the seismic zones in the country (Murthy, 2004).
1.3 Remote sensing in Earthquake Precursor Studies
Recent theoretical and experimental studies explicitly demonstrated the ability of space technologies to identify and monitor the specific variations at the near-earth space, atmosphere and ground surface (named as earthquake precursors) associated with approaching severe earthquakes which appear several days (from 1 to 5) before the seismic shock over the seismically active areas (Pulinets, 2006).
Even after the years of research and study impending earthquake forecast is an unsolvable scientific question. Though there are some successful examples (Haicheng Earthquake, Magnitude 7.3, 04 Feb 1975, China) (Raleigh et al., 1977) in the past decades but the majority is failure. It is not clear yet whether the abnormal precursor relates to the earthquake determinately and quantitatively or not. This situation illuminates that there exists some limited, unilateral and even completely wrong understanding for the earthquakes (Ziqi et al., 2001, 2002). The randomness and insufficient density of observation points' distribution adds to the limitation of earthquake precursor studies. *
Figure 1.1: Global Earthquake Distribution {source: http://www.solarviews.com) 34°N 34°N
•30° N 30°N
•26°N 26°N-
22°N 22°N-
18°N 18°N
14°N 14°N-
10°N 10°N
80°E 84°E 88°E 92°E 96°E Figure 1.2: India Seismic Zone map as per IS : 1893 (Part-1) - 2002, Criteria for Earthquake Resistant Design of Structures; General Provisions & Buildings, Bureau of Indian Standards, New Delhi Chapter 1: Introduction
The remote sensing technology provides systematic and synoptic Earth observation. Low price, higher area coverage, faster, more information, unbiased direct perception and continual observations are characteristics of application of remote sensing technology, which compensate the inadequacy in earthquake stations on the ground and improve present system of earthquake monitoring and forecasting (Ziqietal.,2001).
Earthquake is a dynamic process which is due to recognition of the simple concept that an earthquake shock itself is a climax of stress-strain process (so called earthquake sequence), which begins a few days or weeks before the main shock and continues a few weeks after it (Hayakawa et al., 2000). It usually causes earth's crust deformation and physical-chemical properties change in source zone and adjoining areas; before or during the earthquake. These types of changes lead to the emanation of several type of energy affecting earth-surface and atmosphere thus making it possible to detect an earthquake precursor using remote sensing technology (figure 1.3).
It is now known that some seismo-associated (SA) phenomena (Hayakawa et al., 2000) can be supported by satellite monitoring. The possible methods are presented in table 1.1. Material energy exchanges before earthquake mainly influence the following aspects: 1) crust displacement field (by rock's elasticity, plasticity); 2) heat flow field (by rock's thermal conductance, heat capacity); 3) geo-electric field (by rock's resistance rate) and geo-magnetic field (by rock's magnetic conductance) (Ziqi etal.,2001).
1.3.1 Radar and GPS observations
Remote sensing techniques to survey the crust displacement field are as follows: Very Long Base Line Interferometry (VLBI), Satellite Laser Range Finding (SLR), Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR) etc. Presently, GPS with VLBI and SLR can be used in the measure of the relative tectonic plate movement, usually combined with the gravimeter and the levelling to improve the reliability of GPS observation network (Ziqi et al., 2001). The GPS provides the millimetre-level differential accuracy that is used by regional ground deformation networks to monitor inter-seismic ground deformation and co-seismic 9 Application of Thermal Remote sensing in Earthquake Precursor studies displacements. GPS monitoring networks are leading to better definition of fault surface deformation rates; timely detection of diagnostic changes in the fault environment; and constraints on the extent of surface fault creep and its significance to potentially significant earthquakes. GPS and modern digital seismic data can be combined with satellite remote sensing, such as InSAR, to provide spatially continuous deformation with millimetre accuracy (Tralli et al., 2005). Satellite based InSAR technology flourished in the 1990's. It is based on the base line information and phase interferometer, and has many advantages in crust's vertical displacement measurement (Ziqi et al., 2001). Radar Differential Interferometer (DifSAR) technique, due to its high sensitivity to dynamic change, high spatial resolution and wide covering area has advantage over InSAR. Using several correlated SAR images of dynamic changes differential interferogram can be generated with the precision reaching centimetres. Massonnet et al., (1993) first established the utility of SAR Differential Interferometer technique in surveying 28 June 1992 Landers (M 7.3) earthquake's co- seismic displacement field. The precision of generated interferogram was improved (Zebker et al., 1994) by eliminating topographic phase from earthquake interferogram. This post-seismic research has aroused the geophysicists' interest widely. Now a days, JERS-1, ERS-2, RADARSAT, ENVISAT satellites offer large numbers of original data for differential interferometry research.
1.3.2 Electromagnetic field
Recently role of electromagnetic methods in earthquake studies has become quite significant. The DEMETER mission (Parrot, 2002) successfully executes a science program on seismo-ionospheric research (Tronin, 2010). The low frequency (LF) or very low frequency (VLF) electrical and magnetic abnormal signals before the earthquake is also an information source that can be captured with spatial detect technique (Ziqi et al., 2001). Asymmetrical nature of underground geological material results in local breakage, tiny fractures and local friction under stress conditions. The electronic and magnetic wave radiation occurs along with these processes. There are two possibilities of electric field penetration to the ionosphere layer. The first kind relates to unstable electric field's degradation with the atmosphere height increase. The experiment has shown that due to the increase of the path of electronic particle, the value of penetration decreases exponentially with atmosphere altitude increase. 10 Figure 1.3: Schematic representation of earthquake precursory signals that can be detected by remote sensing sensors {source: www.isfep.com/precursors.html) ElectromagneticEffects
(GPS,WPM-Wave
PlasmaMonitoring,
VLFR)
Table 1.1: Satellite based monitoring of earthquake precursors (afterHayakawaetal., 2000).
Target of Observed Index/Name Equipment Main References Observation Precursor Gorny (1988), Tronin (1996 - 2010) ; Tronin et al. 2002, Remote sensing Qiang et al. (1991- 98), Saraf and Choudhury (2003 - 2005) TRS (Thermal of thermal AVHRR, MODIS, Temperature ; Saraf et al. (2007 - 2009); Choudhury et al. (2005 - 2006); Remote radiation near SSM/I changes Singh and Ouzounov (2003); Ouzounov and Freund (2004); Sensing) ground Panda et al. (2007); Tramutoli et al. (2001 - 2005); Filizolla et i^j al. (2004); Genzano et al. (2007); Remote sensing SAR (SAR Synthetic Aperture Crust Massonet et al. (1993); Zebker et al. (1994); Parson, (2005); of tectonic Seismo-associated interferometry) Radar deformation Talebian et al. (2006)
deformations perturbationsinthe
andatmosphere
Topside sounder, ionosphere Gokhberg et al., (1982 - 92); Afronin et al. (1999) Sounding of TOPEX, Pulinets et al. (1994 - 98); Parrot et al. (1996 - 98); Parrot ionosphere and VLF onboard (2002); Hayakawa and Fuzinawa (1994); Molchanov et al. atmosphere receiver, (1995) GPS transmitters Chapter 1: Introduction
The second possibility relates to the arousal of local resonance between earth and ionosphere layer system. It can cause cross-change electric current of ionosphere to splash, and may arouse additional heating and ionization because of the local decrease of heating of lower ionosphere and its density. The direct reflection of the ionosphere property before the earthquake provides the physical foundation for the application of the spatial detecting technique (Ziqi et al., 2001). Scientists have found that the VLF radiation apparently becomes enhanced in epicenter area of Ms 5 or higher earthquake (Hayakawa and Fuzinawa, 1994). Modelling computations of direct penetration of electromagnetic energy from a seismic source into the atmosphere and ionosphere showed that it is not very effective and we need to seek for other ways of energy transformation (Molchanov et al., 1995).
1.3.3 Thermal infrared abnormity
The modern operational space-borne sensors in the infra-red (IR) spectrum allows monitoring of the Earth's thermal field with a spatial resolution of 0.5 - 5km and with a temperature resolution of 0.12 - 0.5°C. Surveys are repeated every 12 hours for the polar orbit satellites, and 30 minutes for geostationary satellites. The operational system of polar orbit satellites (2-4 satellites on orbit) provides whole globe survey at least every 6 hours or more frequently. Such sensors may closely monitor seismic prone regions and provide information about the changes in surface temperature associated with an impending earthquake Tronin (2006). Gorny et al. (1988) first used the NOAA-AVHRR thermal data to indicate seismic activity of middle Asia region and suggested that abnormal IR radiation observed from meteorological satellites could be taken as an indicator of seismic activity. Later many earthquakes with thermal anomalous region were analyzed in China, Japan (Tronin 1996, 2000; Tronin et al. 2002), Italy (Tramutoli et al., 2001, 2005; Filizolla et al., 2004; Genzano et al., 2007), USA (Ouzounov and Freund, 2004) and India, Iran (Saraf and Choudhury, 2003; 2005 a-c; Saraf et al., 2008; Choudhury et al., 2006 a; Singh and Ouzounov, 2003; Panda et al., 2007) etc. Enhanced Thermal Infrared (TIR) emission from the Earth's surface retrieved by satellites prior to earthquakes is also known as "Thermal Anomaly" (Freund et al., 2005). Thermal rise in tectonically active areas may be an expression of building stresses in the Earth's crust. Temperature increases with the increase in
15 Application of Thermal Remote sensing in Earthquake Precursor studies pressure and stresses in such locations may augment the LST of the near Earth's surface (Choudhury et al., 2006). Thermal observations from satellites indicate the significant change of the Earth's surface temperature and near-surface atmosphere layers. The observed thermal anomalies are related to earth degassing (Ziqi et al., 2002); p-hole activation (Freund 2000, 2002, 2003; Freund et al., 2007) and lithosphere-atmosphere-ionosphere (LAI) coupling (Pulinets et al., 2000) under the influence of prevailing stresses in earthquake preparation zone.
1.4 Research Objectives
Prime objective of the thesis is to apply thermal remote sensing for the detection of pre-earthquake thermal infrared (TIR) anomalies in case of studied earthquakes (table 1.2). The present study is a post-earthquake attempt to identify the correlation between transient temporal thermal infrared anomalies and earthquakes. This work is not intended towards forecasting or prediction of earthquakes. The main objectives of the research can be enumerated as follows:
1. Develop a better understanding of the mechanism of pre-earthquake TIR anomaly. The observed nature of TIR anomalies remained a significant input in understanding anomalous pre-earthquake TIR emission in earthquake preparation zone.
2. Post-event analysis of thermal remote sensing data for the detection of pre- earthquake TIR anomaly and correlation between earthquakes and TIR
anomalies.
3. Quantification of TIR anomaly parameters i.e. intensity change in land surface temperature (LST) values (°C) preceding an earthquake with reference to background LST; and estimation of maximum spatial extent of anomalous
emission. 16 Table 1.2: Details of studied earthquakes {USGS reports).
Origin Location Magnitude Focal S.N. Earthquake Time Depth Date Lat. (°N) Long. (°E) (Mw) (UTC) (km)
1 Jabalpur earthquake, India 21 May 1997 21:51 23.08 80.06 5.8 35 2 Dabiran earthquake, Iran 10 Jul 2003 17:06 28.35 54.17 5.8 10 3 Kerman earthquake, Iran 21 Aug 2003 04:02 29.50 59.77 5.9 20
4 Ravar earthquake, Iran 14 Oct 2004 02:28 31.69 57.01 5.1 35 5 Vrancea earthquake, Romania 27 Oct 2004 20:34 45.78 26.62 5.9 96 6 Fin earthquake, Iran 25 Mar 2006 10:04 27.57 55.69 5.9 18
7 Yamnotri earthquake, India 22 Jul 2007 23:02 30.93 78.27 5.1 35
8 Balochistan earthquake, Pakistan 29 Oct 2008 11:32 30.56 67.48 6.4 14
9 Chamoli earthquake, India 29 Mar 1999 07:05 30.49 79.28 6.6 12 Chapter 1: Introduction
4. Explore the possibility of Outgoing Longwave Radiation (OLR) dataset in detection of TIR anomalies. OLR analysis conducted for the validation of TIR anomalies observed in case of studied earthquakes.
1.5 Organization of Thesis
The thesis has been organized in six chapters. Basic aims of each of these six chapters have been briefly discussed below -
1. Chapter 2 gives detailed review of the application of thermal remote sensing for detection of TIR anomaly as earthquake precursor. This work explores development occurred and contributions made by the techniques applied. This chapter also reviews advances in the understanding of the mechanism of TIR anomaly. Emerging scope of top of atmosphere radiation i.e. OLR recorded by satellite thermal sensors; has also been discussed in this chapter. -
2. Chapter 3 provides details of the data used in this work together with a description of the processing techniques applied to NOAA-AVHRR and OLR dataset. An overview of methodology has also been given.
3. Chapter 4 describes the analysis and observations made using NOAA-AVHRR thermal dataset for the validation of earthquake thermal precursors in studied earthquakes.
4. Chapter 5 analyses suitability of Outgoing Longwave Radiation data in detection of thermal infrared anomalies. In this study, however, OLR data also
utilized for the validation of results obtained from NOAA-AVHRR thermal data.
5. Chapter 6 summarizes the analysis, observations and the conclusions drawn from the study and also proposes few recommendations. CHAPTER 2
Satellite Detection of Earthquake Thermal Precursors -A Review
2.1 Introduction
The endeavour towards understanding the pre-earthquake energy transformation from the Earth and their detection through satellite sensors could generate considerable amount of information and a strong database. The pertinent knowledge on remote sensing based earthquake thermal precursor detection study has been reviewed and presented in this chapter. The focus is placed on the concomitant advances in the observations made and development of understanding in physical mechanism field.
2.2 Application of Thermal Remote Sensing for Detection of Thermal Infrared anomaly
The application of thermal imaging techniques in earthquake studies started in Russia in 1985 and first results were published in 1988 (Gorny et al. 1988). Gorny introduced a new trend in Thermal Aerial and Satellite Survey (TASS). The earth's surface temperature is a sensitive indicator of physical-chemical processes therefore the resulting thermal band emission often makes it possible to develop interesting trends in research, e.g. application of thermal surveys from satellites to average-range earthquake prediction, unlike earlier existing conventional techniques TASS, records the Earth's Surface Temperature (EST) being formed under the influence of heat balance (including both conductive and convective components of heat flow from the heat producing sources within the Earth) and it provides real-time data registration with a high sensitivity and spatial resolution for large areas (Gorny and Shilin, 1992). The consideration of high variability of EST when interpreting TASS data was one of the problems identified.
Earthquake thermal precursor study with thermal channels of multispectral satellite systems became possible after the extensive work done by Tronin (1996) for the Central Asian region. Thorough examination of around 10000 thermal images for the region collected by the NOAA series satellites led to conclusion that occasional Application of Thermal Remote sensing in Earthquake Precursor studies positive temperature anomaly at regional scale can be correlated with the seismicity of the region. It was discovered that there are sites in the earth's crust situated within 500 km distance spatially from epicentre of the impending earthquake, which most m intensively change their characteristics with the physico-chemical processes preceding the earthquake. Tronin studied thermal anomaly phenomena for Central Asia (Tronin, 1996); North-east China (Tronin, 2000); Japan (Tronin et al., 2002); Kamchatka Peninsula in Russia (Tronin et al., 2004). His studies showed the presence of positive anomalies of the outgoing Earth radiation flux recorded at night time and associated with large linear structures and fault systems of the crust. The analysis of continuous •4. series of night-time satellite thermal image (STI) data for long periods allowed identification of a set of infrared (IR) radiation anomalies in studied seismo active regions. It was actually discovered that there was a significant correlation between the activity of IR anomalies (mean value of area per year or month) and the seismic activation of seismo active regions. Stable IR (permanent relation with large tectonic structures) and non-stable / non-stationary IR (variable component of area and intensity) anomalies were identified. The duration (several days) of non-stable anomalies and their permanent spatial position confirm their reality selection among ^ temporary anomalies produced by meteorological factors (Tronin, 1996). Study in Middle Asia seismo-active region established existence of thermal anomaly prior to earthquakes. Tronin indicated in his results that (a) thermal anomalies appear few days (6 - 24) days before and continue for about a week after the earthquake; (b) the anomaly was sensitive to crustal earthquakes with magnitude greater than 4.7; (c) the size of anomaly was estimated about hundreds of km long and tens of km wide and; (d) the amplitude of anomaly was in the range of 2° - 10°C. Tronin also observed that earthquakes with depths more than 60 km do not respond to thermal anomalies. The y limitations of the method like clouds, improper meteorological conditions and complex relief were also identified. Subsequent studies by Tronin et al. (2004) added ground observation analysis in support of satellite detected thermal anomaly. Ground observation data (water temperature, pH and flow rate measurements done in Kamchatka peninsula, Russia fulfilled the need of supporting ground observations. The response of water in wells and surface temperature in the thermal anomaly looks similar (Tronin et al., 2004).
Contemporary to Tronin, Chinese scientists (Qiang et al., 1991, 1993, 1997, 1999) also contributed to the thermal anomaly studies. They used geostationary 22 Chapter 2: Satellite Detection of Earthquake Thermal Precursors -A Review
meteorological satellite (Meteosat) thermal data to monitor the seismicity and tried to predict the earthquakes of 18 Oct 1989 Datong, Shanxi province, China. They reported a 4°C rise in temperature in the region before the earthquake and defined pattern of thermal anomaly in stages: strengthening stage (temperature gradually picks up to reach peak), steady stage (temperature drops and recovers to still and normal statue). Earthquake was followed by a period of quiescence. Qiang and co workers claim to predict two Taiwan earthquake using "meteorological satellite quantitative image" and TIROS Operational Vertical Sounder (TOVS) data. Their predictions were based on the satellite thermal infrared temperature increase characteristics in migration route, earthquake ocation and earthquake magnitude. In his paper Qiang et al. (1999) also accounts for the long term study 1990 - 99 and reports about 40 short term and impending earthquakes, with 9 precise ones (whose three main factors of an earthquake are clearly depicted), and 12 fairly good ones. It was observed that huge isolated temperature increasing area appears far from epicenter before the earthquake. The temperature raises 2° - 6°C higher than that in peripheries. Magnitude was decided by the anomalous area. The bigger the magnitude is, the larger the brightness temperature (e.g. area is equal to or over 700000 km2 it means the magnitude is >7). The evolutionary characteristics of temperature increasing anomaly were used in detecting the future earthquake epicenter. It was usually predicted to be at the place where the fringe of temperature increasing anomaly area structure intersects the earthquake belt or in the hollow place of an isolated anomalous temperature increasing area or the movement structure or the intersection of two stress hot lines. The time of earthquake occurrence is when the brightness temperature anomaly reaches peak, usually from several to 60 days (Qiang etal., 1999).
In India, significant contribution has been made in this field by using NOAA- AVHRR thermal data. Preparation of land surface temperature (LST) maps and continuous time series based analysis technique was used by Indian workers (Saraf and Choudhury, 2003; 2005 a-c; Saraf et al., 2008, 2009; Choudhury, 2005; Choudhury et al., 2006a; Panda et al., 2007). Positive thermal anomaly for some of the major earthquakes around the world [Mw 7.9 Bhuj earthquake, India; Mw 6.8 Boumerdes earthquake, Algeria; Mw 6.6 Bam earthquake and Mw 6.4 Zarand earthquake in Iran and Mw 9.0 Sumatra earthquake, Indonesia] was reported. It was concluded that regional scale transient thermal anomalies appear a few days to a few 23 Application ofThermal Remote sensing in Earthquake Precursor studies hours preceding the earthquakes. The increase in temperature varied from 2° - 13°C than the usual temperature of the region close to epicentral area and varied from earthquake to earthquake. The temperature peak was followed by a period of ^ quiescence before the main shock.
After the established use of NOAA-AVHRR thermal data scientists also tried to find similar signatures in other types of satellite datasets. Qiang et al. (1991, 1993, 1997 and 1999) have used Meteosat data for detection of earthquake thermal anomaly and subsequently earthquake prediction. Passive microwave SSM/I datasets have also yielded positive thermal anomaly for the earthquakes in Mw7.9 Bhuj earthquake, -» India; Mw 6.2 Xinjiang and Zhangbei earthquake, China; Mw 7.6 Izmit earthquake, Turkey; Mw 7.4 and Mw6.1 earthquakes in Hindukush, Afghanistan and Mw6.1 Kalat earthquake, Pakistan (Choudhury et al., 2006 b). In recent years MODIS on board Terra/Aqua has drawn attention of scientific community due to its improved sensing capabilities. Ouzounov and Freund (2004) has reported positive thermal anomaly of about 4°C in case of Bhuj earthquake, India using MODIS data. The anomaly seems to fluctuate rapidly and dissipates within a few days after the earthquake. Similarly, anomaly in case of Kashmir (POK) (Mw 7.6) earthquake (08.10.2005) was detected ^ using MODIS-Terra (Panda et al., 2007) land surface temperature and emissivity products. A 4° - 8°C rise in LST to the south of the earthquake epicenter was observed seven days before the major event. There has been an attempt to study pre- earthquake thermal anomaly using polar orbiting and geostationary satellite simultaneously. This approach utilizes both a mapping of surface TIR transient fields from polar orbiting (Terra/Aqua-MODIS or NOAA-AVHRR) data and co-registering geosynchronous weather satellite (GOES or Meteosat) images (Ouzounov et al., 2006). This complex analysis also confirmed transient TIR anomalies prior to studied earthquakes. It concludes initiation of process along the main tectonic fault zones and variations could be seen in a radius of approximately 100km around the epicenter over the land and sea. The optimal conditions for detecting such anomalies include dry weather, cloud free, low vegetation scenes with a long observation base line. Recently, a robust satellite data analysis technique (RAT) for NOAA-AVHRR dataset is also being widely used for detection ofearthquake precursor thermal anomalies due to its capability to identify anomalous space-time TIR signal even in very variable observational (satellite view angle, land topography and coverage, etc.) and natural (e.g. meteorological) conditions (Tramutoli et al., 2001; Filizzola et al., 2004; Tramutoli 24 Chapter 2: Satellite Detection of Earthquake Thermal Precursors -A Review et al., 2005; Genzano et al., 2007). It's possible application to satellite TIR surveys in seismically active regions has been tested in the case of several earthquakes [Mw 6.9 Irpinia earthquake; Mw5.9 Athens earthquake; Mw7.6 Izmit earthquake] of magnitude higher than 5.5 by using a validation/confutation approach, devoted to verify the presence/absence of anomalous space time TIR transients in the presence/absence of seismic activity (Corrado et al., 2005). It suggests the possible presence of large scale (up to several hundred kilometers) effects. A sensitivity analysis on 9 medium-low (4 Throughout the development of earthquake thermal precursor detection research since 1988; workers have also tried to explain the mechanism behind the phenomena. The detection of thermal precursors and understanding the physical mechanism behind it are inseparable studies. The following section reviews different explanation provided to define the enhanced thermal infrared emission prior to earthquakes. 2.3 Advances in the understanding of the Mechanism of Thermal Infrared Anomaly Precursor substantiation demands existence of a well explained physical mechanism for their appearance (Pulinets, 2006). Here we discuss various propounded explanations to the precursory signals that could be detected through satellite sensors. 25 Application of Thermal Remote sensing in Earthquake Precursor studies 2.3.1 Mechanism of thermal infrared anomaly Freund et al. (2005) defines enhanced thermal IR emission from the Earth's surface retrieved by satellites prior to an earthquake as 'thermal anomaly'. Explanations to understand the mechanisms which lead to the increment in outgoing IR radiation ahead of an impending earthquake are mainly based on phenomena like release of greenhouse gases, characteristics of soil dynamics, including soil moisture and gas content, and crystal structure of rock masses under stress. Broadly, the mechanisms explaining the generation of thermal anomaly can be grouped into two categories, the first accounting atmospheric processes responsible for the appearance of thermal anomaly, and the second attributing rise in LST due to ground related processes. Here, mainly four mechanisms of TIR anomalies, which are based on observed earthquake associated phenomena and experimental results - evidences of which are detectable by thermal sensors are discussed. 2.3.1.1 Earth-degassing theory and gas-thermal Theory Two decades back the 'earth-degassing theory' was proposed (Qiang et al., 1991). This theory believed in the initiation of micro cracks and release of pore gases into the lower atmosphere with strengthened stress conditions in and around the earthquake affected region. It was proposed that with further increase of stress and spread of degassing sites, the temperature reached a maximum when the released gases enhanced the greenhouse effect in the atmosphere. A further rise in stress conditions will eventually close crack conduits and degasification will stop. It is important to note that Qiang and his co-workers believe that in such a situation, the temperature will drop and the earthquake should happen after a period of quiescence. Whether this quiescence appears before the earthquake event, coincides with the event or occurs after the event, are believed to be different conditions, which can very well happen in different earthquake process situations. It might also be feasible that a drop of temperature below usual temperature of the region happens around the earthquake period and subsequently return to normal. Tronin (1996), based on ground data from a few fault zones, proposed that the atmosphere influences the outgoing IR flux. According to this model, during earthquake preparation period, gases like H2, He, CH4, C02, 03, H2S, Rn along with 26 Chapter 2: Satellite Detection of Earthquake Thermal Precursors -A Review water vapor and associated heat (Wakita et al., 1978) reach the Earth's surface and here the litho-atmosphere coupling starts. Emission of these gases has been reported from tectonically active regions of the world (Virk et al., 2001; Salazar et al., 2002). At first, convection heat flux (hot water and gas) changes the temperature of the earth's surface. In a second phase, a change in the water level with usual temperature alters soil moisture and consequently the physical properties of the soil. The different physical properties determine the different temperature on the surface. Third is the greenhouse effect. Greenhouse gases absorb a part of the earth's IR radiation and thus lead to the accumulation of heat near surface. The amount of solar energy absorbed by a 1 - 10 cm thick layer of pure C02 (which corresponds to 1 m thick atmosphere layer with a 1 - 10% C02 concentration) is in the range of 10 - 60 W/m2 (Tronin, 1996). Thereafter, transfer of the energy to the upper atmosphere and ionosphere is believed to occur. It is reported that atmospheric gravity waves (AGW) might be the most possible carrier for energy from lithosphere to the ionosphere (Korepanov and Lizunov, 2008). However, there is great uncertainty about how AGWs are generated except by the up and down motion of the earth surface during an earthquake or an ocean surface during a large tsunami. T According to Tronin and his co-workers (2002) geological structures like faults, fractures, cracks, etc. serve as preferred conduits for convective fluids (water and gases) to the upper levels of the lithosphere. This also explains the association of the geological structures with the thermal anomaly. Geological conduits present in the crust increase considerably the transport of the heat. Tronin also agrees that the cause of the TIR anomalies lies in the lithosphere and is related to stress changes. Thermal changes in materials due to the stress fields have also been determined in laboratory studies (Brady and Rowell, 1986; Qiang et al., 1997). Later Qiang et al. (1997) proclaimed the 'Geo-thermal Theory' to be the cause of the thermal rise. According to them the temperature increasing mechanism of satellite thermo-infrared of lower air may be caused by paroxysmal releasing of crustal gas and sudden changing of lower atmosphere electrostatic field. Experimental studies suggested that mixed gases C02 and CH4, etc. (owing to their greenhouse nature) in different ratios under the action of transient electric field may cause temperature increase of about 6° - 8°C, while under the solar irradiation may lead temperature to rise around 3° - 8°C. They observed that there was no change in temperature with a constant electric field and can occur only under the action of transient electric field. They explained the 27 Application of Thermal Remote sensing in Earthquake Precursor studies creation of electric field was due to the piezoelectric effect of rocks and earthquake lightening. In this theory the cause of anomalous electric field was explained by piezoelectric effect ofthe rocks and earthquake lightening. While it is common practice ^ to quote the piezoelectric effect of the rocks, this idea is based on a complete lack of understanding of piezoelectricity of any system (like granite) that contains billions of randomly oriented piezoelectric quartz crystals. Stressing such a system may not produce sizeable electric field. The experimental setup also needs to involve role of rocks and behavior of rocks under 'regional stress field'. 2.3.1.2 Seismo-lonosphere coupling theory Lithosphere-lonosphere coupling or seismo-ionosphere coupling has been proposed to explain the physical phenomena behind the thermal infrared anomaly and surface latent heat flux variations. The concept propounds that at least two processes can essentially change the thermodynamics of the lower atmosphere layers: the action of ionization source and the strong electric fields (Pulinets, 2004). In seismically active areas the increased radon emanation from active faults and cracks before r earthquakes is thought to be the primary source of air ionization. The airborne ions can subsequently form cluster ions, small and large. During this condensation of water on existing airborne ions the "heat of condensation" is released. This leads to changes in the air humidity and air temperature. The part of model describing the generation of anomalous electrical field in the zones of earthquake preparation involves the gaseous aerosols with water molecules attachment, which is accompanied by latent heat variations. During these processes the large amount of heat (800-900 cal/g) is released (Sedunov et al., 1997). In normal conditions when the number of large ionized clusters is small, the meteorological processes prevail. But when the concentration of hydrated particles created by radon ionization increases up to anomalous values are thought to provide a detectable contribution in the surface latent heat flux (SLHF) variations up to anomalous values registered by the satellites (Pulinets et al., 2006). The observed variations in the air temperature, air humidity and atmospheric electricity have been attributed mainly to the changes of the chemical potential of the newly formed particles. This model however needs a understandable correlation of the physical mechanism to Earth crust processes, role of rocks. 28 Chapter 2: Satellite Detection of Earthquake Thermal Precursors -A Review 2.3.1.3 P-hole activation theory A mechanism of strong low frequency electromagnetic emission has been proposed by Freund and his co-workers through a solid-state physics viewpoint (Freund, 2002, 2003, 2007; Freund et al., 2005). The proposed mechanism combines the critical earthquake concept of crust acting as a charging electric battery under increasing stress. The electric charges are released by activation of dormant charge carriers which consist of defect electrons in the oxygen anion sublattice, called positive holes (p-holes). Their dormant precursors are called peroxy bonds in language of chemistry or Positive Hole Pairs (PHP). A PHP forms by two positive holes, i.e. -1 oxygen states (0~), combining and forming a peroxy link. When the PHP breaks an electron from an outside -2 oxygen state (02~) jumps into the broken bond. This electron becomes a "weakly bound electron", while the -2 oxygen (O2) which had donated the electron becomes an O" site. A PHP represents an O3X/00\YO3, with X, Y = Si4+, Al3+, i.e., O" in a matrix of O2" ofsilicates (figure 2.1). Crystallographically, rocks forming mineral structures are three-dimensional arrays of oxygen; where it is assumed that oxygen always occur in O2' state. An O" is an anion with an incomplete valence shell, seven electrons instead of the usual eight. If the O" is part of a X044+ polygon (usually a tetrahedron), X = Si4+, Al3+, etc., it might be written either as X043+ or as 03X/°. Being unstable radicals, O" and X04 3+ or 03X/° form pairs, generating a PHP, which is, chemically equivalent to a peroxy anion, O" + O" = 022" or a peroxy link as follows: 03X/° + °\ X03 = O3X/00\XO3.When are associated with certain defect sites in the host crystal structure, thermodynamically metastable peroxy links can exist (Freund, 2002, 2003). Introduction of PHPs in minerals during rock-genesis and alteration has been explained by Freund (2002). Interestingly, O- O" bond-distance (1.5 A") is almost half of O2"- O2" bond-distance (2.8 - 3.0A°). This implies that the peroxy-bond O" has a small partial molar volume, thus having a tendency to be favored by high pressure. Igneous and metamorphic rocks, which make up a major portion of the Earth's crust, contain these electronic charge carriers, which have been overlooked in the past. In the process of igneous rock formation that begins with the crystallization from H20 laden magmas, small amounts of water are structurally incorporated in the minerals, even into those that are normally considered anhydrous. The mode of incorporation leads to the formation of hydroxyl, 03X-OH with X= Si4+, Al3+, etc. During cooling, hydroxyl pairs undergo an electronic rearrangement 29 - Application of Thermal Remote sensing in Earthquake Precursor studies that results in the formation of peroxy links; 03X-00-X03 + H2 (figure 2.2a). When dissociated under stress a PHP introduces two holes (charge deficiencies) into the valence bond, causing the insulator to become a p-type semiconductor (figure 2.2b). Positive holes propagate through an oxide or silicate structure by electron hopping (figure 2.2c), whereby electrons from neighbouring O2" can hop onto the O" site. Following their concentration gradient between stressed and unstressed rocks, the p- hole spread out of the stressed rock volume. The estimated maximum speed at which a positive hole could propagate by hopping is in the order of 100 - 300 ms"1 (Freund, 2002). Since the positive holes travel via the O 2p-dominated valence bond, they can easily cross grain boundaries (figure 2.2d) without being scattered or annihilated. Laboratory experiments have proved the generation of excess IR intensity and electric potentials on the surface of dry rock when subjected to heavy stress, long before failure (Ouzounov and Freund, 2004; Freund, 2003; Freund et al., 2005). Traveling in the valence band of otherwise insulating silicate minerals, p-holes are capable of spreading from where they are generated. They can cover macroscopic distances, of the order of meter found in the laboratory test, possibly kilometers in the crust. The current carried by p-holes is not stopped by intergranular water films (Freund, 2007). It flows through centimeter-thick water layers, though the charge carriers change in the water. Important for pre-earthquake research is the fact that PHPs can be activated by stress. It costs energy to break PHPs and to activate the p-holes. When p-holes recombine, some of this energy will be regained. Theory predicts that p-holes accumulate at the rock-to-air interface (King and Freund, 1984). Stimulated mid-IR emission takes place from the rock surface within seconds of the application of stress 40 - 50 cm away from the emitting rock surface. This observation and the spectral signature of the emitted radiation provide strong evidence that the underlying effect is a kind of mid-IR luminescence arising from the recombination of p-holes at the rock surface (Freund et al., 2007). 30 Thermal Anomaly A 0 O •S 0) o o a: P -ho es accumu ate & recombine ® ® ® ® ® © ® A Vk^ fp?® 0) 0 Stress Faiy/f zone Stress Q. (0 O ®^y9 ®/ fol \>v^i® Outflow of p-ho\es Figure 2.1: Schematic representation of p-hole activation, their subsequent accumulation and combination at Rock-Air interface resulting in the release of energy {after Takeuchi et al., 2005). 31 (a) dormant state •Hi spreading-out a grain boundan. 1 a peroxy bond (PHP) (b) break and activation OO *\jk^> o Si4*. Al3*. etc breaking O normal oxygen atom. O2 (c) electric replacement o p-hole. O -* moving-out of p-hole M KJ jumping-in of electron Figure 2.2: Schematics of p-hole activation and spreading in igneousor high-grade metamorphic rock, (a) The p-holes equivalent to O" are usually ina dormant state as spin-paired peroxy bonds that are also known as PHPs. (b) When stresses break a peroxy bond, two p-holesare activated, (c)An electronjumps in from a neighboring 02"and the p-hole moves out. (d)The pholes can spread across grain boundaries and through thicklayers of igneous rocks (Takeuchietal., 2005). <6 - J* o ° » 4 • ^9y s 1 B. n - E L m *- 1 - 0 4• ♦ ^^^ar 0.0 2 0 4.0 6 0 3 0 10 0 12.0 14 0 * Force (KN) Figure 2.3: Thermal increase with loading in Lavasan granite (Pellet et. al., 2007) 33 Chapter 2: Satellite Detection of Earthquake Thermal Precursors -A Review 2.3.1.4 Remote Sensing Rock Mechanics (RSRM) Remote sensing rock mechanics (RSRM) is a new interface subject, which is based on remote sensing, rock mechanics, geophysics, physics, chemistry and informatics. This concept was put forth by Geng, Cui and Deng in 1992 (Wu et al., 2000) in the wake of improved remote sensing instrumentation and requirement of prediction of rock failure. Part of RSRM which deals with the detection of material behavior on stress application and time space forecast of rock failure can be utilized in earthquake forecasting studies. The experimental results show that the rock's IR radiation (8 - 14 um) temperature increases with loading (figure 2.3). In general, the increment of IR temperature is 0.2° - 1°C. But even higher IR temperature, 1.4° - 3°C, was detected at the fracturing position. After the rock failure, the IR temperature dropped with the decrease of the vertical stress. It was also discovered that the rock's IR radiation intensity increased gradually with increasing stress. The variation of the IR spectral curve reflects the variation of the rock sample's IR radiation energy. During the loading process, the stress transfer - because of rock deformation and cracking - could be clearly anticipated in the IR thermal images. A bi-shear test of frictional sliding was conducted for simulating earthquake action. The sample was made from gabbro. The horizontal stress was kept at 50 MPa. The vertical stress on the central block was loaded from 0 MPa to 35.4 MPa. It could be seen that the IR radiation temperature along the contact-face increased gradually as the vertical load became higher, and the four stress locking points were very clear (figure 2.4). The highest IR radiation temperature at the stress-locking location was 5°C higher than the original value (Wu et al., 2000). Wu and his co-workers also found that IR radiation temperature increases with depth inside the rock specimen surface and also increases with rock strength. IR thermal image can therefore anticipate the stress transfer process and rock fracturing location. 35 Application of Thermal Remote sensing in Earthquake Precursor studies 2.4 Outgoing Longwave Radiation (OLR) variability prior to earthquakes OLR represents total amount of radiation that is emitted from the earth- ^ atmosphere system to outer space in 3- 100 um wavelength bands. It includes all the emission from ground, atmosphere and cloud formation and can be computed using AVHRR infrared channel data. Recently, Ouzounov et al., 2007 analysed OLR data to investigate anomalous pre-earthquake OLR variations for four major earthquakes [Mw 7.9 Bhuj earthquake, India; Mw 6.8 Boumerdes earthquake, Algeria; Mw 6.6 SE Iran earthquake and Mw 9.0 Sumatra earthquake]. The possible link of ^ transient thermal fields on the ground with pre-earthquake processes establishes the rationale to explore the radiation budget. The analysis showed consistent occurrence of positive OLR anomalies prior to studied earthquakes. The process starts along the main tectonic fault zone with variations with a radius of approximately 2.5° or more around the epicenter. The time frame of strong OLR over the epicentral areas was found to be a month or more before seismic event. The magnitude of anomalous OLR values ranged between 6 - 22 W/m2. In case of Sumatra earthquake anomalous OLR values were as high as 80 W/m2. Some Chinese workers have also $_ claimed to have studied 30 earthquake examples with positive OLR anomalies (Guangmeng, 2008). 36 * Figure2.4:IRradiationearthquakemodel,wherebi-shear(horizontalstress: 50 MPa,verticalstress 0-35.4 MPa)test of frictionalsliding on a gabbro sample was conducted for simulating earthquake action. It could be seen that IR radiation temperature along contact-face increased graduallyas verticalload became higher,and fourstress-lockingpointswere very clear (Wu et al., 2000). Chapter 2: Satellite Detection of Earthquake Thermal Precursors -A Review 2.5 Summary Observed and empirical data of earth processes related to stress conditions prior to earthquakes substantiate the appearance of thermal anomalies, which can be detected by satellite sensors. The geophysical community has, however, upheld apprehensions to the occurrence of pre-earthquake thermal anomalies primarily for two obvious reasons that Freund (2007) notes: (1) not every earthquake is preceded by a reported precursor, just like foreshocks that might precede an earthquake or might not, and (2) there is no consistency in the mechanism proposed that might explain a certain precursor. An earthquake succeeds progressive stress conditions before rupture occurrs. Such stress conditions will definitely bring numerous physical changes in the rock mass, which if detected can be a clue to an impending event. Localized greenhouse effect created due to the increased concentration of optically absorbing gases released from stressed rock spaces is still debated by many researchers in this field a major causative factor leading to enhanced IR emission and is integral part of most of the propositions to explain the physical mechanism of the TIR anomaly. Stresses before an earthquake significantly alter hydrogeological regime of the region and its thermal emission. It has been proposed that all these developments taking place in the lithosphere also manifest themselves by changing lower atmosphere properties like gaseous composition, air humidity, air temperature, etc. This calls for a close understanding of lithosphere-atmosphere coupling. The enhancement of surface temperature called 'an anomaly' and subsurface processes which make the rock masses behave like a charged battery, are explained by the PHP theory. Once the positive holes are generated, current propagate through the rocks leading to electromagnetic emission, to positive surface potentials, to corona discharges, to positive ion emission, and to mid-infrared radiation. These phenomena are expressions of the same fundamental process: the 'awakening' of dormant positive hole charge carriers that turn rocks momentarily into p-type semiconductors. Experimental studies conducted on various rock samples, in rock mechanics have also started providing significant evidences in support of theories accounting to charge generation in the earth's crust. The thermal field of an object is decided by the material's thermal properties, the inner physical and chemical processes it experiences from and its thermal 39 Application of Thermal Remote sensing in Earthquake Precursor studies exchange with the outside world. The detected information relating to the thermal field can therefore be reasonably explained and utilized after the physical concepts are understood. The ability of satellite sensors to map LST conditions around the time of ^ an earthquake has brought important breakthrough for earthquake thermal precursor studies. ? 40 CHAPTER 3 Data and Methodology 3.1 Introduction With the advent of remote sensing technique and subsequent advances in remote sensing systems, data communications and processing technologies, now satellite data are available in diverse spatial, temporal and spectral resolutions. Different applications require different resolution remote sensing data. The important aspect is to lay less emphasis upon the image resolution than the application or to lay no emphasis on one specific resolution and accept medium or high spatial resolution. The Advanced Very High Resolution Radiometer (AVHRR) at 1.1 km maximum spatial resolution have widespread applications in meteorology, oceanography, climatology, agriculture, hydrology, forestry and many other disciplines. AVHRR are maintained in continuous operational mode, borne by the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites. This operational system provides imagery of all parts of the globe at least four times daily, large portions of which are archived centrally in several data centers around the world. Through the direct-broadcast system, environmental data from the AVHRR and other sensors have been made available around the world to properly equipped users of all nations. This chapter provides an account of the data used in this study and a brief overview of the methodology adopted. 3.2 Data used Remote sensing data and data products used in this research are as follows: a) NOAA-AVHRR data (from NESDIS website and IITR-SES); b) Interpolated Outgoing Long wave Radiation product (from NCEP Reanalysis data product website). 3.2.1 NOAA-AVHRR data National Oceanic and Atmospheric Administration (NOAA) operates sun- synchronous, polar-orbiting environmental satellites (POES) for long term forecasting. Application of Thermal Remote sensing in Earthquake Precursor studies POES satellite program has evolved over many years of experiments and satellite operations after the launch of their first satellite program TIROS-N in 1978. Subsequent generations were enhanced further in terms of sensor and machine capacity. The NOAA-6, launched in 1979, was the first operational satellite in TIROS-N series. The last in the series NOAA-14 came in 1994. The KLM series of NOAA satellites (NOAA-15, 16 and 17) were introduced with dedicated microwave instruments for generation of temperature, moisture and surface hydrological products in cloudy regions where visible and infrared instruments have reduced capabilities (http://www.class.ncdc.noaa.gov/saa/products/nmmr/Classic/AVHRR). Launch of NOAA-18 marks the beginning of the NOAA and European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Initial Joint Polar System (UPS) agreement. The newest, NOAA-19, was launched February 06, 2009. NOAA-18, NOAA-17, NOAA-16 and NOAA-15 all continue transmitting data as stand-by satellites. NOAA-19 is classified as the "operational" satellite (table 3.1). A new series of polar orbiters, with improved sensors, will begin with the launch of NPP in May 2011 and NPOESS-C1 in September 2014. Table 3.1: Launch dates and transmission frequencies of NOAA-series of satellites Transmission Frequencies Satellite Launch Date (MHz) NOAA-14 (J) 30 Dec 1994 HRPT-1707.0 NOAA-15 (K) 13 May 1998 HRPT-1702.5 NOAA-16 (L) 21 Sep 2000 HRPT-1702.5 NOAA-17 (M) 24 Jun 2002 HRPT-1698.0 NOAA-18 (N) 20 May 2005 HRPT-1707.0 NOAA-19 (N') 06 Feb 2009 HRPT-1698.0 3.2.1.1 Advanced Very High Resolution Radiometer (AVHRR) sensor The Advanced Very High Resolution Radiometer (AVHRR) is a cross-tracking system with five spectral bands; sensing in the visible, near-infrared, and thermal 42 Chapter 3: Data and Methodology infrared portions of the electromagnetic spectrum (table 3.2); having a resolution of 1.1 km and a frequency of Earth scans twice per day (0230 and 1430 local time). This sensor has evolved from AVHRR to AVHRR/2 and AVHRR/3 in subsequent generation of NOAA series satellites. The AVHRR sensor provides global (pole to pole) on board collection of data from all spectral channels. Each pass of the satellite provides about 2400 km wide swath. The satellite orbits the Earth 14 times each day. The average instantaneous field-of-view (IFOV) of 1.4 mill radians yields a LAC/HRPT ground resolution of approximately 1.1 km at the satellite nadir from the nominal orbit altitude of 833 km. The GAC data are derived from an on board sample averaging of the full resolution AVHRR data yielding 1.1 km by 4 km resolution at nadir. AVHRR data provide opportunities for studying and monitoring vegetation conditions in ecosystems including forests, tundra, and grasslands. Applications include agricultural assessment, land cover mapping, producing image maps of large areas such as countries or continents and tracking regional and continental snow cover. AVHRR data are also used to retrieve various geophysical parameters such as sea surface temperatures and energy budget data (Kidwell and Katherine, 1995). AVHRR data are acquired in four formats: a) Automatic Picture Transmission (APT) The Automatic Picture Transmission (APT) system provides a reduced resolution data stream from the AVHRR/3 instrument. Any two of the AVHRR channels can be chosen by ground command for processing and ultimate output to the APT transmitter. Data resolution is 4 km (NOAA KLM user's guide, Section 4.2). b) High Resolution Picture Transmission (HRPT) The High Resolution Picture Transmission (HRPT) system provides data from all spacecraft instruments in real-time. The real time transmission consists of the digitized unprocessed output of five AVHRR/3 channels (out of six channels in AVHRR/3 only five works at a time). All information necessary to calibrate the instrument outputs is included in the data stream (NOAA KLM user's guide, Section 4.1). HRPT ground resolution is of approximately 1.1 km at the satellite nadir from the nominal orbit altitude of 833 km. 43 Application of Thermal Remote sensing in Earthquake Precursor studies Table 3.2: NOAA-AVHRR channels specifications and typical use NOAA-AVHRR Channels Typical Use Channel Bandwidth (urn) 1 0.58-0.68 (Visible) Daytime cloud, ice and snow, vegetation 0.73-1.10 (NIR) Daytime cloud, vegetation Soil humidity, ice/snow distinguishing (operates only 3A 1.58-1.64 (IR) at daytime) 3B 3.55-3.93 (NIR) Heat source, nightcloud (operates only at nighttime) 10.3-11.3 (TIR) SST, LST, day/night cloud 11.5-12.5 (TIR) SST, LST, day/night cloud c) Local Area Coverage (LAC) LAC is full resolution data that are recorded on board tape for subsequent transmission during a station overpass. It is recorded onboard at original resolution (1.1 km) for part of an orbit and later transmitted to Earth. d) Global Area Coverage (GAC) The GAC data set is reduced resolution (4 km) image data that is processed onboard the satellite taking only one line out of every three and averaging every four of five adjacent samples along the scan line. A fourth data type, Full Resolution Area Coverage (FRAC), 1.1 km, is now also available daily for the entire globe with the launch of Metop-A, on October 19, 2006, Europe's first polar orbiting operational meteorological satellite system and the first of the European contribution to the Initial Joint Polar-Orbiting Operational Satellite System (UPS). 44 Chapter 3: Data and Methodology 3.2.2 Outgoing Longwave Radiation (OLR) data The total amount of the radiation that is emitted from the earth-atmosphere system to the outer space in 3-100 pm wavelength bands is called Outgoing Long wave Radiation (OLR). OLR is an important value for the earth radiation budget. Absorption of solar radiation and emission of terrestrial radiation drive the general circulation of the atmosphere and are largely responsible for the earth's weather and climate. The outgoing longwave radiation (OLR) is estimated from AVHRR observations using an empirical regression equation (Abel and Gruber, 1979) which relates the flux equivalent temperature 7) to the radiance equivalent brightness temperature Tr Tf = Tr(a+ b.Tr) (3.1) where a and b are coefficients determined from a regression analysis of radiation model calculations. Ohring et al., 1984 modified this equation by taking satellite zenith angle (a) into account Tf = Tr(a + b. Tr. cosa) (3.2) Tf and OLR are related by OLR = aTf (3.3) where a is the Stefan-Boltzmann constant. OLR has been computed in units of Watts/m2. There are at least two available versions of OLR data: a) NOAA Daily (non- interpolated) Outgoing Long wave Radiation and b) NOAA interpolated Outgoing Long wave Radiation. 3.2.2.1 NOAA daily (non-interpolated) OLR data NOAA daily non-interpolated OLR data is derived from twice daily AVHRR soundings for OLR. Daily OLR values from January 2002 to present are available at 45 Application of Thermal Remote sensing in Earthquake Precursor studies spatial resolution of 2.5° latitude x 2.5° longitude global grid (144 x 73) covering 90°N - 90.0°S, 0.0°E - 357.5°E. This data set was switched from the NOAA -16 (last day 07 Aug 2005) satellite to the NOAA -18 satellite. Due to the sun-synchronous orbit of NOAA satellite at some locations and times 'night' will follow 'day', while at others 'day' will follow 'night', and there is a longitude at which the local time switches by nearly 24 hours. However, taking a daily mean of the day and night passes, as is done for this dataset, avoids the issue of which is observed first, and serves to reduce the effective time jump that occurs at the aforementioned longitude to 12 hours. 3.2.2.2 NOAA interpolated OLR data The NOAA Interpolated Outgoing Long wave Radiation Data was used in the present study. Spatial and temporal interpolation results in gap-free data which is an advantage in time-series analysis. This gridded data set (2.5° latitude x 2.5° longitude) has global (90°N - 90.0°S, 0.0°E - 357.5°E) spatial coverage with temporal coverage of monthly, daily (for a period of June 1974 - November 2009) and long term means for monthly and daily values for 1979 -1995. OLR data from NCAR archives with gaps were then filled with spatial and temporal interpolation (Liebmann and Smith, 1996). This data is available in Earth System Research Laboratory (ESRL), Physical Sciences Division (PSD) standard NetCDF format. NOAA observed top of atmosphere, cloud free, continuous estimates of OLR values (W/m2) are based on algorithms explained by Gruber and Krueger (1984). The problem of missing grid and missing values within grid was overcome by the spatial and temporal interpolation (Liebmann and Smith, 1996). 3.3 Data Sources The remote sensing data used in this study was primarily made available from two sources: (1) NOAA-AVHRR Satellite Earth Station installed at Indian Institute of Technology Roorkee, India (IITR-SES) and (2) from online resources. 46 Chapter 3: Data and Methodology 3.3.1 NOAA-AVHRR Satellite Earth Station Indian Institute of Technology Roorkee - Satellite Earth Station (IITR-SES) is the first ever satellite earth station in any educational institute or university in India. IITR-SES sponsored by Ministry of Earth Sciences (earlier Department ofScience and Technology), New Delhi became fully operational from 24 Oct 2002. This NOAA-HRPT and FY-CHRPT satellite Earth station use to acquire AVHRR (Advanced Very High Resolution Radiometer and MVISR (Multi-spectral Visible and Infrared Radiometer) data (almost 12-16 scenes per day) from NOAA and Feng-Yun series of polar orbiting satellites but now acquires four scenes per day because of only availability of NOAA - 19 series ofsatellites. Data from satellite sensors is transmitted to the ground via a broadcast called the High Resolution Picture Transmission (HRPT).It has vast coverage of >3000 km radius (figure 3.1) which not only includes India but also many neighbouring countries like Nepal, Bangladesh, Bhutan, Myanmar, Thailand, Laos, Sri Lanka, Oman, UAE, Iran, Turkmenistan, Uzbekistan, Kazakhstan, Pakistan, Afghanistan, parts of Saudi Arabia, Mongolia, China and Russia. 3.3.2 Online Resources a) NOAA- Comprehensive Large Array data Stewardship System (CLASS) is NOAA's premier on-line facility (electronic library) for the distribution of NOAA and US Department of Defense (DoD) Polar-orbiting Operational Environmental Satellite (POES) data, NOAA's Geostationary Operational Environmental Satellite (GOES) data, and derived data. This web site provides capabilities for finding and obtaining all the above data (http://www.class.ncdc.noaa.gov). b) Earth System Research Laboratory (ESRL), Physical Sciences Division (PSD) provides gridded climate data. OLR data from National Center for Atmospheric Research (NCAR) with sophisticated spatial and temporal interpolation was used in this study was acquired from (http://www.esrl.noaa.gov/psd/data/gridded/data.interp OLR.html). c) Information on earthquakes (as regards to location of epicenter, focal depth, magnitude, time of event, casualty, faults and tectonism etc.) was obtained from reliable sources or refereed journals and articles. The United States Geological 47 Application of Thermal Remote sensing in Earthquake Precursor studies Survey (USGS) (http://www.usgs.gov/), National Earthquake Information Center (NEIC) (http://neic.usqs.gov/), Amateur Seismic Center (ASC) (http://www.asc- india.org/), International Institute of Earthquake Engineering and Seismology (IIEES) (http://www.iiees.ac.ir/) etc. have catered information for the study. 3.4 Software used For implementation of required image processing and analysis tasks on the remote sensing data various commercial softwares were used. The image processing software tools used include HRPT Reader, ERDAS Imagine (version - 8.7 and 9.2), ENVI (version - 4.6), and ESRI ArcGIS (version - 9.3). Most of the image processing operations such as geometric and radiometric correction of the remote sensing data, automated geo-referencing, etc. have been carried out using ERDAS Imagine software. ENVI has also been used for AVHRR data calibration and thermal image generation. HRPT Reader also provides land surface temperature image generation capabilities with user defined temperature range. Panoply and ESRI ArcGIS software has been used to display and process OLR datasets available in netCDF format. ESRI ArcGIS software has also been used for preparation of the thematic data layers and study area maps. In addition, the statistical data analysis has been performed using Microsoft Office Excel and ORIGIN software. 3.5 Methodology 3.5.1 Physical nature of thermal anomaly The infrared field of the earth's surface is generated due to its temperature. This is formed by the absorbed part of the solar energy and to lesser extent, due to geothermal fluxes. The main parameters defining the conditions of IR signal generation are: the emissivity of the surface (es), the albedo {A) and the thermo- physical properties of the rocks. The following equation of heat balance on the earth surface (z=0) could be written: dT/dZ = qr + qt + Rev + Rg (3-4) 48 Chapter 3: Data and Methodology where, qr is the radiation balance, qt is the heat loss due to turbulent exchange between atmosphere and ground surface, qev is the heat loss due to the evaporation, qg is the geothermal flux, z is depth (Tronin 1996; 1999; 2000). Geothermal flux {qg) is usually considered to be less than other terms in the equation (3.4) by several orders of magnitude. Variation of this parameter is unlikely to explain the thermal IR anomaly. The convective component of geothermal flux related to the fluid movement can be as high as 10-50 W/m2 (Gorny et al., 1997) and can affect surface temperature as well as solar and meteorological fluxes. Moisture content in soil and humidity in air were also set down as important factors controlling surface temperature. These processes influence other processes like evaporation and moisture condensation {qev) leading to the surface temperature alteration. 3.5.2 Methodology for NOAA-AVHRR image processing and analysis Passively measured TIR spectral radiations through AVHRR sensor (channels 4 and 5) of NOAA provide temperature of radiating surfaces. Data calibration and temperature calculation is based on the method provided in NOAA (2006). 3.5.2.1 Preparation of Land Surface Temperature (LST) maps NOAA - AVHRR data for one fortnight before and after the earthquakes (depending on the availability of error-free scenes with minimum or no cloud cover) were used to study ground thermal conditions in and around epicenter of the earthquakes. First, a visual analysis of the thermal channels of the data was carried out and later detailed analysis of images showing appearance and disappearance of thermal anomaly was done. The digital datasets used were tried best to keep a consistency in the time of acquisition of all scenes. For the preparation of time series LST maps, the data was treated uniformly. LST calculation was based on algorithm given at http://www2.ncdc.noaa.goV/docs/klm/html/c7/sec7-1.htm#sec71-2 (NOAA KLM User's Guide). A user specified range of temperature, which was consistent for all the scenes of a particular earthquake, was used. The temperature was calculated within a continuous color range for point thermal data calculation of the image and the 49 Application of Thermal Remote sensing in Earthquake Precursor studies area outside this range was masked. Cloud covers were also delineated and avoided for any temperature calculation. 3.5.2.2 Preparation of LST time series layout LST maps for studied earthquakes are then arranged in a time-series manner. AVHRR thermal images which are best suited i. e. adequate coverage of epicenter region, cloud-free are included in time-series layout. This type of representation is helpful in better understanding and interpreting gradually developing thermal regime of * the epicentral area and pattern of the TIR anomaly. 3.5.3 Methodology for OLR data Processing and Analysis OLR data is made available in netCDF (Network Common Data Form) format. The times are encoded as the number of hours (or days) using the UD UNITS library. Panoply and Arc GIS 9.3 software packages were used as display and analysis tools. 3.5.3.1 Data Processing OLR data for epicenter and adjoining areas were analyzed for detecting any kind of variability in case of each earthquake. Interpolated OLR data is 2.5° X 2.5° gridded data thus providing OLR values at an interval of 2.5°. Interpolated OLR data has four dimensions i.e. latitude, longitude, time and outgoing longwave radiation. Daily Mean OLR (interpolated in time and space from NOAA twice-daily OLR values and averaged to once daily) and Daily Longterm Mean OLR data (calculated from > interpolated NOAA OLR daily data) for a period of 30 days (15 days before and after the earthquake) was included in this study. Daily mean OLR plots, where x-axis has 'time' dimension, y-axis represents 'latitude' and OLR values are third dimension were compared with the daily longterm mean OLR plots in order to distinctly identify anomalous OLR zones. OLR range was kept consistent for both the plots. As 'longitude' dimension was kept constant, longitudinal variation in OLR was not observable. Therefore, for each earthquake at least two OLR plots were prepared: + time vs. latitude and taking averaged OLR values between selected longitudinal range; and time vs. latitude taking OLR values at a longitude near epicenter. 50 Ul Figure 3.1: Coverage Area of Indian Institute of Technology Roorkee-Satellite Earth Station (IITR-SES). IITR-SES has been operating since October 2002, and acquring day and night data from NOAA and FY series of satellite for important tectonic locations in and around India, which has been used for the present study (Chaudhury, 2005). Chapter 3: Data and Methodology 3.6 Remarks NOAA-AVHRR thermal channels data remained to be the main data source for study. NOAA 14, 17 and 18 Local Area Coverage (LAC), High Resolution Picture Transmission (HRPT), Global Area Coverage (GAC) data were used in this study. Land surface temperature images and outgoing longwave radiation image are prepared for detecting pre-earthquake thermal infrared (TIR) anomaly and OLR variability, respectively. Stress accumulation in earthquake preparation zone may manifest in form of enhanced thermal flux or longwave radiation. LST is earth's skin temperature and plays direct role in estimating longwave fluxes. Thus LST maps and their time-series layouts provide clear picture of pre-earthquake appearance of TIR anomaly, its development, spatial coverage, intensity of thermal rise and intrinsic pattern. OLR is measure of total outgoing longwave radiation and includes longwave emission from earth's surface, atmosphere and cloud tops. Any pre-earthquake variability in OLR emission may be indicative of anomalous behaviorof any of the contributing source. In this study, observations made in case of pre-earthquake OLR variability have been used to validate/ support anomalous LST observations prior to studied earthquakes. Remote sensing data utility in investigating pre-earthquake thermal rise lies in itssynoptic overview, spatial- temporal coverage and easy availability of data. 53 CHAPTER 4 Earthquake Thermal Precursor Detection using NOAA-AVHRR Data: Analysis and Observations 4.1 Introduction Stresses accumulating in rocks in earthquake preparation zone manifest itself as various observable precursory signals viz. low frequency electromagnetic emission, earthquake lights from ridges and mountain tops, magnetic field anomalies, changes in the plasma density of the ionosphere, temperature anomalies by several degrees over wide areas as detected by sensor (Freund, 2003). Pre-earthquake enhanced thermal infrared (TIR) emission from the earth's surface which is noticed as rise in land surface temperature (LST) may be identified by satellites equipped with thermal infrared sensors like AVHRR (onboard NOAA), MODIS (onboard Terra and Aqua), SSM/I (onboard DMSP) etc. LST is generally defined as the skin temperature of the Earth surface (Qin and Karnieli, 1999) and play direct role in estimating long wave fluxes. The time series satellite thermal imaging data indicates observable appearance of transient TIR anomalies of few degrees weeks to days before the earthquakes. Analyses of NOAA-AVHRR thermal datasets for nine recent earthquakes in India, Iran, Pakistan and Romania have been done using pre- and post-earthquake NOAA- AVHRR datasets. Data analysis revealed a transient TIR rise in LST ranging 2° - 11°C in or near epicentral areas. The thermal anomalies started developing about 7-13 days prior to the main event depending upon the magnitude and focal depth, and disappeared after release of stress during the main shock. In some cases of moderate earthquakes a dual thermal peak instead of the single rise has also been observed. This may lead us to understand that perhaps pre-event sporadic release of energy from stressed rocks leads to a reduction of stress condition. This study also reports appearance of Himalayan Thermal Line (HTL) along the foothills in response to seismic activity in region. Satellite thermal image of Himalayan region during Chamoli earthquake (Mw 6.4) of 29 March 1999 revealed high intensity HTL. This might be due to the enhanced tectonic stress generated in the Himalayan region due to the continuous northward Indian tectonic plate movement. Application of Thermal Remote sensing in Earthquake Precursor studies 4.2 Pre-earthquake TIR Anomaly Detection using NOAA-AVHRR data The thermal channels 4 and 5 of NOAA-AVHRR have been successfully used to study the effect of earthquake occurred in Jabalpur on 21 May 1997 (India); Yamnotri on 22 Jul 2007 (India); Dabiran on 10 Jul 2003 (Iran); Kerman on 21 Aug 2003 (Iran); Ravar on 14 Oct 2004; Fin on 25 Mar 2006 (Iran); Balochistan on 29 Oct 2008 (Pakistan); Vrancea on 27 Oct 2004 (Romania) (table 1.2). All these earthquakes show definite built up of pre-earthquake transient thermal anomalies. * 4.2.1 Jabalpur Earthquake (Mw 5.8) of 21 May 1997, India The lower crustal (focus at 35 km), magnitude 5.8 (USGS) Jabalpur earthquake (table 1.2 and figure 4.1) occurred in the mid-continental fracture zone of the Indian Peninsular Shield. Fault plane solution indicated a high angled reverse fault with small component of left-lateral strike slip. Its epicenter was centered about 8 km south-east of the city of Jabalpur at 23.08°N latitude and 80.06°E longitude. Historical earthquakes and recent earthquake swarms indicate a moderate to high seismicity in Son-Narmada-Tapti mega lineament belt. Seismicity is attributed to strain accumulation, flexuring of the crust and neo tectonic movements of the faults (Rao and Rao, 2006). The Jabalpur earthquake was spatially associated with a well- defined tectonic structure with significant background seismicity. A remarkable feature of this earthquake is its deep focus, not commonly observed in stable continent region (SCR) seismicity and its association with fewer aftershocks unlike most other SCR earthquakes (Rajendran and Rajendran, 1999). The earthquake was felt in much of Madhya Pradesh, Allahabad, Delhi, Nagpur and in parts of western Orissa. 4.2.1.1 Analysis The 21 May 1997, Jabalpur earthquake was studied for detection of pre- earthquake TIR anomaly using NOAA-14 AVHRR thermal data. Detailed analysis of the data began with a visual analysis of thermal channels of 30 days (07 May 1997 - 06 Jun 1997) and checked for cloud-cover. Acquisition time of all scenes used was tried to keep consistent to best possible. Later, images significant for the analysis of thermal anomalies before the earthquake were identified (table 4.1). 56 78° E 79° E 80° E -4 21 May 1997 Jabalpur Earthquake Mw 5.8 (USGS) j^ Jabalpur Epicenter CM O Historical Seismicity 1990-2009 Narmada River Faults 0.5 II km Figure 4.1: Locations of epicenter of main event of Jabalpur earthquake, historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model). I80°E1 80°EI 180°EI J J J 25!N "- [25^ i2yNl ^V '-& 12 May 97 113 May97 !15May97 ]80°EI |80°E| I J 25°N 25°N ^ # ' 16 May 97: 18 May 97 Je\J . I80°EI I ^'•-Mii [25*N £3 May 97i T80!ET ;80°Ej -30*C -20°C -10'C 0°C 10°C 20"C 30'C 40°C 50°C I J <%-^% Figure 4.2: Daytime NOAA-AVHRR LST time series map of part of India before and after the earthquake in Jabalpur on 21 May 1997. An intense TIR anomaly can be seen on 15 May 1997, eight days before the earthquake. 59 Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data A user specified range of temperature between -40°C - 50°C was used. LST maps were arranged in time series to study TIR anomalous behavior prior to earthquake. Cloud pixels were excluded from LST calculations. Table 4.1: Details of daytime NOAA 14-AVHRR (LAC) datasets for year 1997 used to study the thermal scenario during Jabalpur earthquake (Mw5.8), India. S. No. Date Time (UTC) Time (1ST) Scene 1 11.05.1997 09:16 14:46 Scene 2 12.05.1997 09:05 14:35 Scene 3 14.05.1997 08:43 14:13 Scene 4 15.05.1997 08:32 14:02 Scene 5 16.05.1997 08:21 13:51 Scene 6 18.05.1997 07:59 13:29 Scene 7 19.05.1997 07:47 13:17 Scene 8 21.05.1997 09:07 14:37 Scene 9 22.05.1997 08:56 14:26 Scene 10 23.05.1997 08:45 14:15 Scene 11 25.05.1997 08:23 13:53 Scene 12 26.05.1997 08:13 13:43 Scene 13 28.05.1997 09:28 14:58 Scene 14 29.05.1997 09:17 14:47 Scene 15 30.05.1997 09:11 14:41 ST=lndian Standard Tiime 4.2.1.2 Observations Jabalpur earthquake shook parts of central India on 21 May 1997 at 22:51 hrs (UTC). LST daytime time series layout (figure 4.2) shows first anomalous rise on 12 May 1997 i.e. 9 days prior to earthquake. Temperature was about 5° - 7°C higher than the background temperature in the area around that period of the year. Six days before on 15 May 1997 LST attained its peak and intensity of thermal rise was about 5° - 11°C higher than the background temperature. On this day anomaly was spread in about 262,000 km2 area north and south of Narmada river valley. This valley is a 'graben' bounded by two normal faults i. e. Narmada north fault and Narmada south 61 Application of Thermal Remote sensing in Earthquake Precursor studies fault. This type of association of anomalous area with structurally weak zones is quite significant. Such weak zones serve as channels of greenhouse gas emission and also facilitate fluid flow to surface and consequently might be responsible for LST rise. Sparse clouds were present on earthquake day hindering LST observations. Analysis shows that LST remained to higher side till 26 May 1997 and limped back to normal conditions afterwards. The TIR anomaly is short-lived in nature as in case of Jabalpur earthquake total period of disturbance was of about 17 days i.e. from 12 May 1997-26 May 1997. 4.2.2 Dabiran Earthquake (Mw 5.8) of 10 July 2003, Iran Dabiran earthquake took place in parts of Fars Province of Iran on 10 Jul 2003. This moderate earthquake magnitude 5.8 (USGS) has 10 km focal depth. Epicenter was located at 28.35°N latitude and 54.17°E longitude (table 1.2 and figure 4.3). Dabiran earthquake epicenter was about 51 km from the Zagros Fault system. Proximity to major fault systems renders this area as high seismicity region (figure 4.3). Movement along local faults like Beriz fault (distance to epicenter was about 41 km) and Bakhtegan fault (distance to epicenter was about 60 km) can be attributed as triggering factor to the major shock. This earthquake event was characterized by continued aftershock activity (table 4.2). This earthquake affected many major cities like Dabiran, Shahr-e-Pir, Juyam, Hajiabad, Khorramabad, Jahron etc. causing heavy losses to life and property. 4.2.2.1 Analysis NOAA 17-AVHRR thermal dataset were studied for a period of 30 days (25 Jun 2003 - 25 Jul 2003). Daytime thermal scenes (table 4.3) used in this study were tried best to keep consistent in time of acquisition. Scenes were first visually analyzed for their suitability and significance for the study and then processed to prepare LST map and time series layout. All cloud cover scenes were eliminated from analysis. Temperature range of -20°C - 50°C was defined and temperature outside this range was masked. TIR anomaly development behavior was studied from time series layout of LST maps. 62 ON Figure 4.3: Locations of epicenter of main event of Dabiran earthquake of Iran, historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model). -"A - c \ 30°N, — ' < 30°^ v„ —-— j' CO — i o "£ ^-- o "S^iiH^" CM TFSrf^* _>« ^ "' * c |01 July 2003 55°E [02 July 2003I 5goE O c CO 1— 30^1 CO ^ - w** Q 30°N "•" *-~i- \^^^J^^ . 4£ CO =3 , ^ 0" . .«• -C CD CO CD 03 July 2003 04 J uly 2003 1 CD 3 k 55°E *-v «l i_ CO cu 2? I # < £ 5? J- B I- Z E p cd o |s 12 July 2003 55°E | 13July 2003 k TO ^ Q E 10 July 2003 1 Dabiran Earthquake,Iran 10° 0° 10° 20° 30° 40° 50' Mw 5.8, Depth 10 km LST Scale if .E 65 Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data 4.2.2.2 Observations Pre- and post-earthquake LST maps were generated using NOAA-AVHRR thermal data. Daytime time series layout for Dabiran earthquake shows development of high LST areas near epicenter on 02 Jul 2003 (figure 4.4). The temperature of the region was about 4° - 5°C higher than the normal. Gradually, area and intensity of thermal anomaly grew in earthquake affected region with higher intensity towards north-east of epicenter. Itwas maximum on 03 Jul 2003 and had amplitude of about 7° - 10°C. Thermal anomaly weakens in following days to again pick up 6 days later on 10 Jul 2003 i.e. the earthquake day. Region remained warmer than the normal for a few more days before attaining usual LST conditions. This earthquake event was followed by several aftershocks (table 4.2), which may explain longer duration anomalous LST conditions in and around epicentral area after the earthquake. TIR anomaly started developing about 8 days (02 Jul 2003) before, reached its peak LST conditions just 7 days (03 Jul 2003) prior to the main shock. TIR anomaly is regional in nature and maximum spatial extent was around 182,500 km2. The anomalous period in case of Dabiran earthquake was for 12 days from 02 Jul 2003 - 13 Jul 2003. Table 4.2: List of aftershocks following 10 Jul 2003 Dabiran earthquake, Iran {Source: http://www.iiees. ac. ir). Time Depth S.N. Date Latitude(°N) Longitude(°E) Mag. (UTC) (km) 1 10.07.2003 17:40 14:46 28.30 10 5.6 2 10 .07. 2003 19:26 14:35 28.41 10 4.3 3 10.07.2003 19:37 14:13 28.27 10 4.1 4 11 .07.2003 10:23 14:02 28.15 10 4.5 5 11 .07.2003 23:55 13:51 28.46 10 4.8 6 13.07.2003 01:15 13:29 27.63 10 4.3 7 13.07.2003 09:33 13:17 28.07 10 4.3 8 14 .07. 2003 02:26 14:37 28.13 10 4.5 67 Application of Thermal Remote sensing in Earthquake Precursor studies Table 4.3: Details of daytime NOAA 17-AVHRR (LAC) datasets for year 2003 used to study the thermal scenario during Dabiran earthquake (Mw 5.8), Iran. S. No. Date Time (UTC) Time (IRDT) Scene 1 01 .07. 2003 07:31 12:01 Scene 2 02.07. 2003 07:09 11:39 Scene 3 03.07. 2003 06:46 11:16 Scene 4 04.07. 2003 07:15 11:45 Scene 5 06 .07. 2003 07:41 12:11 Scene 6 07 .07. 2003 06:56 11:26 Scene 7 08.07. 2003 06:33 11:03 Scene 8 10.07. 2003 06:39 11:09 Scene 9 12.07.2003 06:43 11:13 Scene 10 13.07. 2003 06:20 10:50 RDT= Iran Standard Time 4.2.3 Kerman Earthquake (Mw 5.9) of 21 August 2003, Iran The Kerman Earthquake occurred on the 21 Aug 2003 at 04:02 hrs (UTC), in the south-eastern part of Iran. It rocked the Kerman province with a magnitude of Mw 5.9 (USGS). The epicenter was located at latitude of 29.50°N and longitude of 59.77°E and the focal depth was estimated at around 20 km (table 1.2 and figure 4.5). Kerman earthquake activity occurred in vicinity of mainly two faults - Bakhtameh normal fault and Nosaratabad thrust. Local adjusting movements along these faults can be a source of seismicity in this part of Kerman. Aftershock activity was relatively sparse with Kerman earthquake but latest aftershock (Mb 4.6) occurred 20 days later on 11 Sep 2003 (table 4.4). 68 57° E 58° E 59° E 60° E tr o -SB o o ° ft °o o o V. IRAN OXDo O O g(j 8 * \ o o o • 21 August 2003 Kerman Earthquake Mw 5.9 (USGS) 7^ Kerman Epicenter o Historical Seismicity 1990-2009 -*—^Thrust fault -Of Strike slip fault Figure 4.5: Locations of epicenter of main event of Kerman earthquake of Iran, historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model). Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data 4.2.3.1 Analysis Kerman earthquake of Iran was investigated for TIR anomaly using NOAA 17 - AVHRR thermal data. Data for a period of 30 days (06 Aug 2003 - 05 Sep 2003) was studied (table 4.5). All data were visually examined for cloud cover, epicenter area coverage and data errors. User specified temperature range of -20°C - 50°C was assigned and temperature outside this range was masked. Cloud covers were delineated and avoided for any temperature calculation. LST maps were then arranged date-wise to bring out gradual development pattern. Table 4.4: List of aftershocks that followed the Kerman Earthquake {Source: http://www. iiees.ac. ir). Focal Time Latitude Longitude Magnitude S.N. Date Depth (UTC) (°N) (°E) (NEIC) (km) 1 28.08.2003 18:31 28.37 54.07 33 Mb 4.7 2 29.08.2003 06:55 28.38 51.52 33 Mb4.9 3 11.09.2003 19:31 28.39 54.02 33 Mb4.6 4.2.3.2 Observations The daytime time series LST maps prepared from the AVHRR thermal datasets (table 4.5 and figure 4.6) for this earthquake indicate a gradual rise in the surface temperature that reaches its peak on 11 Aug 2003, which was consistently high till 16 Aug 2003 (figure 4.6). TIR anomaly which first appeared around the epicenter gradually grew and spread to the south and south-east of epicenter. The anomalous area was about 175,000 km2. Generally, region was about 5° - 11 °C higher than the usual temperature of the area. Thereafter, the LST again subsided back to normal conditions. Due to persistent cloud cover over the epicenter area on the 21 Aug 2003, the LST map could not be prepared. The higher LST seen after 21 Aug 2003 may be attributed to the aftershocks (table 4.5) that continued for about 20 days after the main event. Region showed normal LST conditions on 25 Aug 2003 and had a larger transient period of 16 days. 71 Application of Thermal Remote sensing in Earthquake Precursor studies Table 4.5: Details of daytime NOAA 17-AVHRR (LAC) datasets for year 2003 used to study the thermal scenario during Kerman earthquake (Mw 5.9), Iran. S. No. Date Time (UTC) Time (IRDT) Scene 1 10.08.2003 06:07 10:37 Scene 2 11.08.2003 06:02 10:32 Scene 3 12.08.2003 06:43 11:13 Scene 5 14.08.2003 06:20 10:50 Scene 6 15.08.2003 07:15 11:45 Scene 7 16.08.2003 06:52 11:12 Scene 9 18.08.2003 06:07 10:37 Scene 10 19.08.2003 07:25 11:55 Scene 11 20.08.2003 07:02 11:32 Scene 12 21.08.2003 06:39 11:09 Scene 13 22.08.2003 06:16 10:46 Scene 14 23.08.2003 07:35 12:05 Scene 15 24.08.2003 07:12 11:42 Scene 16 25.08.2003 06:49 11:19 Scene 17 26.08.2003 06:26 10:56 Scene 18 27.08.2003 07:45 12:15 Scene 19 28.08.2003 07:22 11:52 IRDT= Iran Standard Time 72 CO 30°N D) < CM C * o c CO E CD 10 Aug 2003 CD 60°E J* CO 30°N ~Wt4 a- •e CO CD ft *< CD CD ? *i \ CO T3 c CO CD 60°E 15 Aug 2003 60°E 16 Aug 2003 CD c 30*N CO f 3im / ^ » o ml ** •c CO * Q. o a. co / E CD o te O CD CM 60°E 19 Aug 2003 60°E |20 Aug 2003 to™ CD CO .11 ^< _l T- jjj? O X c => 73 Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data 4.2.4 Ravar Earthquake (Mw 5.1) of 14 October 2004, Iran A magnitude 5.1 (IIEES) moderate earthquake shook parts of Kerman Province, Iran on 14 Oct 2004 at 2:28 hrs (UTC). The epicenter of the earthquake was at 31.69°N and 57.01 °E (table 1.2 and figure 4.7) near Ravar in the Loot and Tabas deserts, southeast-central Iran. The area is part of the Golbaf-Sirj seismogenic zone, and is surrounded by active faults that are often source of seismic activity. Nayband fault is suit of strike slip faults and movement along these faults is main cause behind the high seismicity of the region (IIEES). 4.2.4.1 Analysis NOAA 16 AVHRR data were used for the study. Data for 30 days (01 Oct 2004 - 30 Oct 2004) were visually analyzed to select scenes significant for analysis (table 4.6). Acquisition time of all scenes used was tried to keep consistent to best possible. In this case nighttime scenes were processed to prepare LST maps for the epicenter region; which were subsequently arranged in time-series manner to study TIR anomaly growth pattern. LST maps were assigned a user specified range of -40°C - 30°C for calculation and temperature outside this range was masked. Cloud pixels were excluded from any kind of calculation. Time series layout is prepared to study nature of TIR anomaly (figure 4.8). 4.2.4.2 Observations The nighttime LST time series layout for the Ravar earthquake shows presence of pre-earthquake TIR anomaly (figure 4.8). During its transient period of 12 days anomaly pattern was conformable with observations in case of other earthquakes. The anomalous field appeared first on 06 Oct 2004 to the south-east of epicenter. LST was about 3° - 5°C higher than the background temperature of the region. Anomaly intensified in subsequent days and peak was observed on 08 Oct 2004. In terms of area and intensity TIR anomaly was maximum on this day. The observed LST was about 5° - 7°C higher than the normal and during peak temperature conditions anomalous area was about 53,000 km2. This earthquake occurred on 14 Oct2004, but due to the unavailability of cloud-free scene LST image could not be prepared for the 75 Application of Thermal Remote sensing in Earthquake Precursor studiles day. On 15 Oct 2004 temperature high is indicative of stress conditions during and after the main shock. Temperature conditions attained normalcy three days later on 17 Oct 2004. The TIR anomaly is spatially associated with the Nayband fault and Bakhtameh faults. Other local faults are also seen to coincide with anomalous region. Table 4.6: Details of nighttime NOAA 16-AVHRR (LAC) datasets for year 2004 used to study the thermal scenario during Ravar earthquake (Mw 5.1), Iran. S. No. Date Time (UTC) Time (IRDT) Scene 1 01.10.2004 22:19 01:49(02.10.04) Scene 2 02.10.2004 22:08 01:38(03.10.04) Scene 3 03.10.2004 21:56 01:26(04.10.04) Scene 5 06.10.2004 23:05 02:35(07.10.04) Scene 6 07.10.2004 22:54 02:24(08.10.04) Scene 7 08.10.2004 22:40 02:10(09.10.04) Scene 9 09.10.2004 22:29 01:59(10.10.04) Scene 10 10.10.2004 22:17 01:47(11.10.04) Scene 11 11.10.2004 22:05 01:35(12.10.04) Scene 12 12.10.2004 21:54 01:24(13.10.04) Scene 13 15.10.2004 23:02 02:32(14.10.04) Scene 14 16.10.2004 22:50 02:20(15.10.04) Scene 15 17.10.2004 21:54 01:24(18.10.04) Scene 16 18.10.2004 23:02 02:32(19.10.04) IRDT= Iran Standard Time 76 54° E 55° E -J o Historical Seismicity 1990-2009 -m»- Strike slip faults -»—^Thrust faults Figure 4.7: Locations of epicenter of main event of Ravar earthquake, historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model). —k g o ^He^H o -J My CD *r" o o O o r+ ol o o M /• p! ro M O o 1 o O o c 1 o O / * 1•* co A rm '*•' J * \ jfi /^ I T ^ jJ /' , v » -*- £ o 0> J o •s o rri ao a+a\Jmm\ ni 11 rn 1 fp P I j ' (J KfFi u o o 0 0 *c z Co o O o L CO R °l o z O o o f-t- -a N> O ^1 o 1 M i O jM •U fl o O o O o O s ^ w o O Figure 4.8: Nighttime NOAA-AVHRR LST time series map of part of Iran before and after the earthquake in Ravar on 14 October 2004. An intense thermal anomaly can be seen on 08 October 2004, six days before the earthquake. Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data 4.2.5 Vrancea Earthquake (Mw 5.9) of 27 October 2004, Romania The 27 Oct 2004 moderate size Vrancea earthquake, Romania occurred at an epicenter location of 45.78°N and 26.62°E. This earthquake had magnitude 5.9 (Mw) and focal depth of 96 km (USGS) (table 1.2 and figure 4.9). The Vrancea region represents the most concentrated seismically active area in Europe, located at the south-eastern Carpathian bend in Romania. The event was felt over a large area from Bulgaria to the Republic of Moldova, but without noticeable damage and human injuries. The maximum observed intensity was VI. 4 4.2.5.1 Analysis NOAA 17-AVHRR GAC data was used to study thermal scenario before Vrancea earthquake. GAC data has 4 km spatial resolution. Data for a period of 30 days (12 Oct 2004 - 12 Nov 2004) was first visually examined and selected for preparing LST image (table 4.7). A user defined temperature range of -50°C - 30°C was specified and temperature outside this range was masked. Cloud-covers were delineated and avoided from any calculation. Since AVHRR cannot penetrate clouds, the temperature of cloudy areas will be only the temperature of cloud tops and not the actual LST of the area. LST images were arranged in a time-series manner to show the gradual development of TIR anomaly. 4.2.5.2 Observations The daytime time series layout for Vrancea earthquake, Romania shows definite appearance of transient TIR anomaly which started developing about 7 days before the main event, attained peak temperature and faded with the earthquake shock (figure 4.10). The region showed first anomalous temperature conditions on the 20 Oct 2004 i.e. 7 days before the main event. Gradually the region started getting warmer and this anomalous rise which started mainly on south-west of epicenter spread in other parts too. At this time temperature was about 3° - 5°C higher than normal. On 25 Oct 2004 (2 days before the earthquake) temperature was at its peak and about 7°-10°C higher than normal temperature. The extent of this anomalous temperature region was approximately 125,700 km2. The anomaly started fading and 81 Application of Thermal Remote sensing in Earthquake Precursor studies normal temperature was observed on 02 Nov 2008. Region attained normal temperature conditions soon after the main shock. Further, the aftershock activity was relatively sparse. Twelve aftershocks were identified during the first month after Oct 27. The largest aftershock (Mw 3.2) occurred at about 10 days after the main shock on 04 Nov 2004. All other events had magnitudes < 3 (Radulian et al. 2007). Table 4.7: Details of daytime NOAA-AVHRR (GAC) datasets for the year 2004 used to study the thermal scenario during Vrancea earthquake (Mw 5.9), Romania S. No. Date Time (UTC) Time (EET) 15.10.2004 Scene 1 08:34 11:34 Scene 2 16.10.2004 08:11 11:11 Scene 3 20.10.2004 08:20 11:20 Scene 5 21.10.2004 09:40 12:40 Scene 6 22.10.2004 09:17 11:17 Scene 7 23.10.2004 08:52 11:52 Scene 9 24.10.2004 08:29 11:29 Scene 10 25.10.2004 08:06 11:06 Scene 11 26.10.2004 09:27 12:27 Scene 12 27.10.2004 09:01 12:01 Scene 13 28.10.2004 08:38 11:38 Scene 14 29.10.2004 08:15 11:15 Scene 15 01.11.2004 08:48 11:48 EET= Eastern European Time 82 ^~^~ 26° E 27° E 28° E \ [— c- ** £""" ROMANIA pri ^k 1 A \_—^^""""-^ f3 k CD 00 27 October 2004 Vrancea Earthquake LO Mw 5.9 (USGS) • Vrancea Epicenter o Historical Seismicity 1990-2009 0 J km 5 Figure 4.9: Locations of epicenter of main event of Vrancea earthquake and historical seismicity of the region.Epicenter and other information is shown over GTOPO30 (global digital elevation model). * W^""""co! "^ o o " O .. IO life D K... 'n """—.....2 m Z "Tn " J* M1 PS;V"-t---- byroll ••—t. •• ° m — ^^^^m M ^ m ,7—~*fl «3 to . : i m-tCO o. m j .--I 131 *'' v . —>0\ CI> W .£v o z. =* Figure 4.10 Daytime NOAA-AVHRR LST time series map of part of Romania before and after the earthquake in Vrancea on 27 October 2004. An intense thermal anomaly can be seen on 25 October 2004, two days before the earthquake. Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data 4.2.6 Fin Earthquake (Mw 5.9) of 25 March 2006, Iran An earthquake occurred in the region northwest of Bandar-e-Abbas between Ruydar and Fin at 10:58 hrs local time (07:28 UTC) on 25 Mar 2006. The magnitude was estimated Mw 5.9 by Iran's National Broadband Seismic Network (INSN) and the depth estimated was 18 km (table 1.2 and figure 4.11). The earthquake epicenter was at 27.57°N and 55.69°E. As per preliminary reports there were slight damages such as cracks in the walls of houses near Ruydar and also the occurrence of landslides in the area between Fin and Ruydar. The occurrence of earthquakes in this region with activity of main seismogenic trends are accounted for by the northeast-southwest bearing and near the contact area of the Bandar Abbas - Minab Zendan structure (INSN). 4.2.6.1 Analysis NOAA 18-AVHRR thermal data were used for the detection of pre-earthquake TIR anomaly in case of 25 Mar 2006 Fin earthquake of Iran. Dataset for 30 days (10 Mar 2006 - 09 Apr 2006) was analyzed (table 4.8). Cloud-free daytime scenes were visually selected and LST maps were prepared. A user specified temperature range of -30°C - 40°C was provided and pixels outside this were masked. Cloud pixels were excluded from any further analysis. LST maps were arranged date-wise to study growth pattern of TIR anomaly prior and after the main shock (figure 4.12). 4.2.6.2 Observations The LST time series prepared from the AVHRR data for this earthquake shows a marked peak in TIR anomaly on the 15 Mar 2006 (10 days before) and then again on the 23 Mar 2006 (2 days before the main shock). Anomalous area first appeared on 12 Mar 2006 near epicenter and then spread gradually to east of epicenter (figure 4.12). The maximum temperature was about 2° - 5°C higher than the background temperature ofthe region and it occupied area of about 154,800 km2. Higher LST after the earthquake day is attributed to number of aftershocks that followed the event. In this time-series LST layout rise in temperature is manifested in two sporadic peaks instead of one prominent anomaly. High temperature was observed even after the main shock and region remained under anomalous conditions for a longer period due 87 Application of Thermal Remote sensing in Earthquake Precursor studies to aftershock activity. The transient period for TIR anomaly associated with Fin earthquake was of 15 days. Table 4.8: Details of daytime NOAA-AVHRR (LAC) datasets for the year 2006 used to study the thermal scenario during Fin earthquake (Mw 5.9), Iran S. No. Date Time (UTC) Time (IRST) Scene 1 11.03.2006 10:01 13:31 Scene 2 12.03.2006 09:51 13:21 Scene 3 13.03.2006 09:40 13:10 Scene 5 14.03.2006 09:30 13:00 Scene 6 15.03.2006 09:20 12:50 Scene 7 16.03.2006 09:09 12:39 Scene 9 18.03.2006 08:48 12:18 Scene 10 20.03.2006 10:10 13:40 Scene 11 21.03.2006 09:59 13:29 Scene 12 23.03.2006 09:39 13:09 Scene 13 24.03.2006 09:28 12:58 Scene 14 25.03.2006 09:18 12:48 IRST= Iran Standard Time 4.2.7 Yamnotri Earthquake (Mw 5.1) of 22 July 2007, India A moderate earthquake struck the Yamnotri region, India on 22 Jul 2007 at 04:32 hrs local time (23:02 UTC) causing a few injuries and minor damage to property in parts of Uttarakhand, India. The earthquake had a magnitude of 5.1 (Mw) and was felt at many places in Uttarakhand and adjoining parts of north India. The epicenter of the earthquake was located at 30.93°N, 78.27°E and focal depth was estimated to be 35 km (USGS) (table 1.2 and figure 4.13). The earthquake was strongly felt in western Uttarakhand, in bordering areas of eastern Himachal Pradesh and parts of Uttar Pradesh and Delhi. * * 00 y^ Fin Epicenter o Historical Seismicity 1990-2009 -a—^Thrust fault -jft- Strike slip fault Figure 4.11: Location of epicenter of main event of Fin earthquake of Iran, historical seismicity and tectonics (faults) of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model). CD O O 30°N 3Q°N CM i_ j 1 co • 4 CNJ c o * L CD 13 Mar 2006 0 ^ ^ CD O" •e 30°N CD CD CD J£ CD "O C 05 CD CD 20 Mar 2006 -Q •^M. C CD _ 30°N ""30-°* O EL. w (D Q. ' Q. If^' . (D E * CO K. CD "l_ • & CD k. CO 25 Mar 2006 ||23 Mar 2006 55°E 55°E CD u E -*-» h- CO _l DT or X Fin Earthquake CD i_ -20°-10° 0° 10° 20° 30° 40° Mw5.9, Depth 18km 91 76° E 77° E 78° E * °1L^ uo r- <5)0CO ° 4* o o > o 22 July 2007 Yamnotri Earthquake Mw 6.0 (USGS) O O o o y^ Yamnotri Epicenter • Historical Seismicity 1990-2009 Figure 4.13: Locations of epicenter of main event of Yamnotri earthquake of India and historical seismicity of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model) 24 July 2007 22 July 2007 -50°-40°-30°-20°-10° 0° 10° 20° 30° 40° Yamnotri Earthquake, India Mw5.1, Depth 35 km Figure 4.14: Daytime NOAA-AVHRR LST time series map of part of India before and after the earthquake in Yamnotri on 22 July 2007. An intense thermal anomaly can be seen on 20 July 2007, two days before the earthquake. 95 Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data 4.2.7.1 Analysis NOAA-AVHRR thermal dataset were studied for a period of 30 days (07 Jul 2007 - 06 Aug 2007). NOAA 17 (LAC) daytime scenes (table 4.9) used in this study were tried best to keep consistent in time of acquisition. First, scene were visually analyzed for their suitability and significance for the study and then processed to prepare LST map and subsequently time series layout. All cloud cover scenes were eliminated from further analysis. User specified temperature range of -50°C - 40°C was used and temperature outside this range was masked. Cloud pixels were delineated and avoided for any temperature calculation. 4.2.7.2 Observations Yamnotri LST layout for the region during pre- and post-earthquake period shows conspicuous rise in LST conditions prior to main shock (figure 4.14). The TIR anomaly first appeared on 15 Jul 2007 i.e. 7 days prior to main shock. The temperature was around 2° - 4°C higher than the normal on this day. The anomalous area occurred north-east of epicenter. The region attained peak temperature on 21 Jul 2007, just one day before the earthquake. On an average temperature was around 5° - 8°C higher than the normal temperature of the area. The epicenter region remained covered with clouds (partially or fully) for two days, including the day of main shock. 24 Jul 2007 LST map of area shows lowered temperature conditions however, it was still at elevated temperatures than the normal. After that area shows continuously decreasing temperature till normalcy is achieved. The maximum extent of anomaly was about 257,500 km2. The anomaly took three days to return to normal conditions and transient period for Yamnotri earthquake was of about 11 days. 97 Application of Thermal Remote sensing in Earthquake Precursor studies Table 4.9: Details of daytime NOAA-AVHRR (LAC) datasets for the year 2007 used to study the thermal scenario during Yamnotri earthquake (Mw 5.1), India. S. No. Date Time (UTC) Time (1ST) Scene 1 11.07.2007 05:26 10:56 Scene 2 14.07.2007 05:57 11:27 Scene 3 15.07.2007 05:34 11:04 Scene 4 16.07.2007 05:10 10:40 Scene 5 17.07.2007 04:47 10:17 Scene 6 19.07.2007 05:41 11:11 Scene 7 20.07.2007 05:18 10:48 Scene 8 21.07.2007 04:55 10:25 Scene 9 24.07.2007 05:26 10:56 Scene 10 25.07.2007 05:03 10:33 IST=lndian Standard Time 4.2.8 Balochistan Earthquake (Mw 6.4) of 29 October 2008, Pakistan An earthquake of 6.4 (USGS) magnitude struck major parts of Balochistan including provincial capital Quetta at 11:32 hrs (UTC) on 29 October 2008 morning. Epicenter of earthquake was at 30.56°N latitude and 67.48°E longitude (table 1.2 and figure 4.15). The tremors originated 60 km NNE of Quetta along Zhob thrust near Pishin, Gwal and Khanozai areas. It was a shallow tremor which originated from a depth of 15 km. Two active major thrust faults; Gwal Bagh and Ghazaband strike slip fault run in close proximity of affected area. The whole area lies in severe damage Zone IV and aftershocks of lesser intensity than the main event were also experienced in the area. According to the estimates of Government of Pakistan about 28 people were killed and dozens hurt in nearby cities of Quetta, Khanozia, Pishin, Ziarat, Loralai and Matung area including tremendous loss of life and property (Geological Survey of Pakistan). 98 * * 4 66° E 67° E 68° E 69° E 70° E co © to o Historical Seismicity 1990-2009 Figure 4.15: Locations of epicenter of main event of Balochistan earthquake of Pakistan and historical seismicity of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model). Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data 4.2.8.1 Analysis NOAA-AVHRR thermal dataset for a period of 30 days (14 Oct 2008 - 13 Nov 2008) were studied. AVHRR scenes used in this study were tried to keep consistent in time of acquisition and were processed identically. Daytime scenes (table 4.10) were used in preparing LST maps and time series layout for detecting pre-earthquake TIR anomaly for Balochistan earthquake. All cloud cover scenes were eliminated from analysis and cloud pixels if present in scene were delineated and avoided for any temperature calculation. User specified temperature range of -35°C - 40°C was used * and temperature outside this range was masked. 4.2.8.2 Observations Daytime LST time series layout for Balochistan earthquake shows that 7 days before main shock on 22 Oct 2008, east and southeast of epicenter temperature started to pick up with respect to the surrounding regions (figure 4.16). Next day, on 23 Oct 2008 TIR anomaly spread in a north, north-east and south-east directions and it occupied an area of around 112,500 km2. In case of Balochistan earthquake too, dual thermal peaks were observed. After 23 Oct 2008 temperature subsided temporarily to again pick up on 27 Oct 2008 i.e. just 2 days before earthquake. The temperature was about 6° - 10°C higher than the background temperature. In highly anomalous regions it was about 10° - 12°C higher. Epicenter and surrounding region gained normal land surface conditions on 30 Oct 2008; just next day after the earthquake. For Balochistan earthquake the anomalous transient period was of 8 days. 101 Application of Thermal Remote sensing in Earthquake Precursor studies Table 4.10: Details of daytime NOAA-AVHRR (LAC) datasets for the year 2008 used to study the thermal scenario during Balochistan earthquake (Mw 6.4), Pakistan. S. No. Date Time (UTC) Time (PKT) Scene 1 19.10.2008 05:41 10:41 Scene 2 20.10.2008 05:18 10:18 Scene 3 21.10.2008 04:54 09:54 Scene 4 23.10.2008 05:49 10:49 Scene 5 24.10.2008 05:25 10:25 Scene 6 25.10.2008 05:02 10:02 Scene 7 26.10.2008 04:38 09:38 Scene 8 27.10.2008 05:56 10:56 Scene 9 28.10.2008 05:32 10:32 Scene 10 29.10.2008 04:45 09:45 Scene 11 31.10.2008 05:40 10:40 Scene 12 01.11.2008 05:16 10:16 KT=Pakistan Time 102 * n o i Mr si m| i ^ | v * ~ I* o j ft) i-K D 3 O : * ••<••, "o nio IKS j ••+ Q) "i CD ^^ IO o -v 3- O O Ul.fi O O loo o 1 Si 1 * IRS O o |mp° O J0j? Figure 4.16: Daytime NOAA-AVHRRLSTtime series map of part of Pakistan before and after the earthquake in Balochistan on 29 October 2008. Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data 4.2.9 Chamoli earthquake and Thermal Line of Himalayan foothills The Chamoli earthquake occurred at midnight (00:35 hrs UTC) of 29 Mar 1999 with its epicenter (30.49°N and 79.28°E) 13 km northwest of Chamoli town located in the Garhwal Lesser Himalayas (figure 4.17 and table 1.2). The estimated magnitude for this earthquake was 6.4 (Mw) with its source at a depth of 12 km (India Meteorological Department) and was felt over large area including the adjoining plains. This event was responsible for loss of more than 150 lives, properties and triggered landslides. Predominantly thrust type focal plane solution showing northwesterly fault planes are arguably aligned with the Himalayan structural trends. The prime tectonic stress orientation to cause this earthquake should have maintained northeasterly trend. Thermal satellite data of corresponding period was examined for the detection of TIR anomaly in Chamoli region. No TIR anomaly could be detected from the region in and around Chamoli epicenter, however, a high temperature band following the Himalayan foothills (south of earthquake zone) was observed on nighttime NOAA- ^. AVHRR thermal image (figure 4.18). This high temperature band has been named and interpreted as 'Himalayan Thermal Line (HTL)' having a possible tectonic relation (figure 4.19). In fact, the thermal line can be observed all along the Himalayan foothills with varying intensity. This line follows alignment of the Himalayan foothills structural belt and the high temperature areas are concentrated mainly along the contact zone of frontal fold belt and the soil deposits. The moisture rich soil derived mainly from external and internal sources (figure 4.19). The thermal line exhibits varying temperature intensity during the period coinciding with the 29 Mar 1999 Chamoli earthquake (figure 4.18). Tronin (1996) also reported the presence of the positive anomalies of the outgoing earth radiation flux recorded at nighttime and associated with the largest linear structures and fault systems of the crust coinciding with the "thermal line" (Nikshich, 1925) of Kopetdag water basin. Anomalies of several degrees Celsius are found at the foot of Kopetdag (Turkmenistan) and Karatau ranges (south Kazakhstan), of linear shape along the boundary structure of first order. 105 Application of Thermal Remote sensing in Earthquake Precursor studies 4.2.9.1 Analysis Two data sets were used in order to study the thermal line phenomena. One for the months of March - April 1999 (i.e. the year of earthquake) and the other set for the months of March - April 2003. The NOAA -14 thermal infrared datasets for the March- April 1999 used in the present work were acquired through NIO-SES (National Institute of Oceanography Satellite Earth Station, India) (table 4.11). Whereas, NOAA-17 data used for the year 2003 were acquired through IITR-SES (Indian Institute of Technology Roorkee Satellite Earth Station, India) (table 4.12). Datasets for a period of fortnight prior to and after the earthquake (depending on the availability of the scenes with no or minimum cloud cover) were processed to study the thermal condition of the study area. A detailed analysis was performed to know the approximate time of appearance of a "thermal line" (in terms of days), its spatial extent and possible relationship with stress conditions in an earthquake preparation zone. For preparation of time series LST maps the datasets were treated identically and a user- specified temperature range of -20°C - 30°C consistent for all scenes was set so as to distinctly delineate the thermal line. Temperature outside this range was masked. Cloud-covered pixels were delineated and avoided for any temperature calculation. The digital datasets used were kept consistent in terms of the time of acquisition of all the scenes. Table 4.11: Details of nighttime NOAA 14 -AVHRR data forthe year 1999 used to study the thermal scenario during Chamoli earthquake (Mw 6.6), India. S. No. Date Time (UTC) Time (1ST) Scene 1 14.03.1999 16:36 22:06 Scene 2 19.03.1999 17:22 22:52 Scene 3 22.03.1999 16:47 22:17 Scene 4 23.03.1999 16:36 22:06 Scene 5 28.03.1999 17:23 22:51 Scene 6 30.03.1999 16:58 22:28 Scene 7 01.04.1999 16:36 22:06 Scene 8 05.04.1999 17:32 23:02 IST= Indian Standard Time 106 81° E o 7^ Chamoli Epicenter ° Historical Seismicity 1990-2009 Figure 4.17: Location of epicenter of main event of Chamoli earthquake of India and historical seismicity of the region. Epicenter and other information is shown over GTOPO30 (global digital elevation model). o O o o O o o O o o O Figure 4.18: Nighttime NOAA-AVHRR LST time series map of part of India before and after the earthquake in Chamoli on 29 March 1999 showing varying intensity of Thermal Line along Himalayan foothills. Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data Table 4.12: Details of nighttime NOAA 17-AVHRR datasets for the year 2003 of Chamoli region. S. No. Date Time (UTC) Time (1ST) Scene 1 14.03.2003 16:18 21:48 Scene 2 19.03.2003 16:06 21:36 Scene 3 22.03.2003 16:38 22:08 Scene 4 23.03.2003 16:16 21:46 Scene 5 28.03.2003 16:05 21:35 Scene 6 30.03.2003 16:58 22:28 Scene 7 01.04.2003 16:13 21:43 Scene 8 05.04.2003 16:23 21:53 IST= Indian Standard Time 4.2.9.2 Thermal Line Phenomena Himalayan Thermal Line formation along the contact zone of Siwaliks and soil deposits seem to have been affected by lithological, structural and hydrological conditions of the region. The juvenile Siwalik fold belt is made up of reworked materials from the Himalayas subjected to less compaction comparatively however suffered considerable fracturing. South of Siwalik occurs narrow zone of soil deposit formed all along the foothills of the Himalaya known as Bhabhar which is made up of porous and rocky soils of debris washed down from the highly fragile Siwalik ranges. At several places piedmont zones are seen to have developed. Also, intermountain valleys, among which the prominent one is the Dehradun, constituted of soil and pebble deposits have developed north of Siwalik hills. These deposits with high moisture content remain warmer in the night and appear as bright band on thermal satellite images. The similarity in trend of HTL and Himalayan Frontal Thrust (HFT) where it is exposed on the surface, certainly enlighten a likely association of the faults and sites of high moisture flow. It is well known that the Siwalik fold belt has been affected by faulting and especially the southern part where the HFT is developing although remaining hidden for most of its parts. Fractures and faults may have a major role in controlling fluid distribution and migration, acting as conduits both for inflow and 111 Application of Thermal Remote sensing in Earthquake Precursor studies outflow of the water. It has been suggested that the main fluid flow systems in foreland-orogen contexts are driven by tectonics (Oliver, 1986; Machel and Cavell, 1999) and topography (Garven 1995). Oliver (1986) opines that the orogenic compression drives fluid migration directly. Significant fluid flow may migrate towards the foreland coevally with thrusting (Ge and Garven, 1992). The Siwalik hills must be allowing the water to percolate in due to its present less compacted fractured rocks. Water entering from the higher topographical region north of HFT and from the south should partly remain at shallow depth as well as move deeper. Under the compressional tectonic stress condition water should be expelled out towards the surface obviously through faulted rocks acting as a conduit. The developed HFT along the southern margin of the Siwaliks can serve as a regional scale conduit for the upward flow of warm fluid from the earth. The mechanism of inflow and outflow of water in the context of Himalayan tectonic set up is illustrated in figure 4.19. Alluvium Up. Siwaliks ElIlo. Siwaliks Basement Figure 4.19: A schematic depiction of the "Himalayan Thermal Line" at the foot hills the Himalayas. Direction narrow arrows show the movement of ground water. 112 Bareilly Lucknow 0 50 100 200 0°E 30 March 1999 Figure 4.20: Location of epicenter of main event of Chamoli earthquake of India, aftershock actvity and tectonics (faults) of the region. CO o o CM ro cd >^ 25°N [19 Mar 03! c o Bfc^at^?^ -:2.T3 0 >Fi^^H|\ ••"lja • jaar^at 30°N u^^* * o E CO **^ .c O ' •H^^\J::^B CO c o . 122 Mar 031 r|23Mar03| t ro _g. <4— O Q. CO E 25°N 28 Mar 03 h- CO •fRSM dpi or or 30°N x i 25°N ^^^n E s|01 Apr03| * Chamoli CM Earthquake Epicenter 115 Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data The analysis of NOAA-AVHRR thermal images shows that the HTL was only noticeable in nighttime temperature images. This temperature band shows conspicuous higher temperature than the surroundings and coincides with the high moisture retaining soil zones of Himalayan foothill. It is also interesting to note that HTL follows the trend of Himalayan Frontal Thrust (HFT) (figure 4.20). The temperature started to rise along thermal line 15 days prior to the main shock and interestingly the maximum temperature was noticed on the 30 March 1999 i.e. one day after the earthquake. Temperature of thermal line was 2°- 5°C higher on 14 Mar 1999 than normal temperature conditions around that period of the year. After 14 Mar 1999 the temperature continued to rise steadily along the foothills of Himalayas i.e. HTL. Satellite scene acquired on 30 Mar 1999 shows an elevated temperature conditions. The soil band of HTL showed highest temperature of about 7°- 10°C on the day subsequent to the earthquake i.e. 30 Mar 1999. It is evident that the region remained under tectonic stress for long time even after the occurrence of main shock as the region experienced around 30 aftershocks with maximum magnitude 5.5 on Richter scale. Prevailing stress conditions also affect the restoration of the normal LST conditions in the epicentral region. In order to verify the characteristics of thermal line along the foothills of the Himalayas, data acquired in 2003 were also analyzed. The date and time of acquisition of scenes were kept closer to the acquisition dates for the year 1999 (figure 4.21). The layout shows weaker appearance of thermal line. Although the HTL is visible but with considerably lower intensity as indicated by indistinct and irregular development of bright tone band. 4.3 Summary Thermal sensors like NOAA-AVHRR have proved to be very useful in detecting earthquake thermal precursors before an earthquake actually strikes, though these analyses have been done in hind side. Nevertheless, the understanding of the buildup of pre-earthquake TIR anomalies and their detection provides possibilities of a reliable potential thermal precursor. The post-event investigation of pre-earthquake thermal anomalies by analyzing pre- and post-earthquake LST images reveals valuable information about the changes in the TIR regime of the affected area. 117 Table 4.13: List of earthquakes studied through NOAA-AVHRRdatasets and specifications of the number of days prior to the earthquake in which the thermal anomaly was seen to occur and reach the maximum amplitude. TIR anomaly Focal Intensity of Mag. (days before earthquake) Anomalous Transient S.N. Earthquake Depth thermal rise (Mw) area (km2) Period (km) Rise Maximum rise (°C) started observed Jabalpur Earthquake, India 1 5.8 9 days 6 days (22 May 1997) 35 5-11 262,100 17 days Dabiran Earthquake, Iran 2 5.8 8 days 7 days (10 Jul 2003) 10 7-10 182,500 12 days Kerman Earthquake, Iran 3 5.9 11 days 10 days (21 Aug 2003) 20 5-10 175,000 16 days Ravar Earthquake, Iran 4 5.1 8 days 6 days (14 Oct 2004) 18 5-7 53,000 12 days Vrancea Earthquake, Romania 5 5.9 7 days 2 days (27 Oct 2004) 96 7-10 125,700 14 days Fin Earthquake, Iran Dual peak 6 5.9 12 days 2-5 154,800 15 days (25 Mar 2006) 14 10 days / 2 days Yamnotri Earthquake, India 7 5.1 7 days 1 day 257,500 (22 Jul 2007) 35 5-8 11 days Balochistan Earthquake, Dual peak 8 6.4 7 days 6-10 112,500 9 days Pakistan (29 Oct 2008) 14 6 days / 2 days 4 Chapter 4: Earthquake Thermal Precursor Detection using NOAA- AVHRR data Earthquakes from different parts of the world; India, Iran, Pakistan, Romania have been investigated depending upon the availability of data for earthquake period. The analyses of time series LST maps for the past eight moderate-to-strong earthquakes with magnitude ranging 5.1 - 6.4 (Mw) showed 2° - 11°C pre-earthquake rise in LST. Transient TIR anomalies appeared 13-7 days before the earthquake, attained their peak and subsided instantly after the earthquake or anomaly remained till the restoration of normal stress conditions (table 4.13). The total transient period ranges from 8-14 days, however, in earthquakes with dual pre-earthquake thermal peak this period may increase. Post-earthquake achievement of normal conditions may be influenced by presence of aftershocks in epicenter region and takes about 1-8 days. Study of other earthquakes with series of aftershocks viz. Dabiran (10 Jul 2003), Kerman (21 Aug 03), Vrancea (27 Oct 04) reveals that the occurrence of aftershocks prevents the re-establishment of normal conditions even after the main event is over. It was also noticed that magnitude and focal depth play a vital role in intensity and spatial extent of the thermal anomaly. Higher earthquake magnitude and shallower focal depth are favorable conditions for the appearance of intense thermal anomaly with larger spatial extent and vice versa. A prominent observation regarding the earthquakes of moderate magnitude is the appearance of a dual TIR peak in surface temperatures instead of the single rise observed previously. The first peak appears about 10-6 days before the earthquake and the second temperature peak relatively closer to the main shock, i.e. 6-2 days. This may lead us to infer that perhaps the energy accumulated in the stressed rocks has been released sporadically in the form of the electromagnetic emission, apparent temperature increment or any other geophysical earthquake precursor, which in turn might reduce the magnitude of the main shock. In cases of Fin (25 Mar 06) and Balochistan (29 Oct 2008) earthquakes this phenomenon was observed. This study also reports Himalayan Thermal Line (HTL) and impact of increased stress conditions on HTL. NOAA-AVHRR thermal data for Chamoli earthquake (29 Mar 1999) was analyzed. As discussed earlier, thermal infrared anomaly could not be observed in the Chamoli region. This may have been shrouded due to presence of high ruggedness in terrain, vegetation cover, and unsteady meteorological conditions. Nevertheless, increased tectonic stress condition during the Chamoli earthquake in the Himalayan region could be inferred through the study of thermal line. It also may be noted that the characteristics of thermal line as presented in this study is different from the other typical examples of pre-earthquake 119 Application of Thermal Remote sensing in Earthquake Precursor studies thermal infrared anomaly associated with the earthquakes. Although the appearance of HTL in nighttime TIR images is mainly dependent on the moisture and heat content of the soil, the increase in these has certain relation to the enhanced tectonic stress conditions. Further, for the purpose of moisture flow presence of conduits is essential which is served by the HFT in this case. Therefore, the thermal line should align itself along with the weak zone which may be marked by exposed or hidden fault/thrust. Further; exploring the temperature sensitivity of the thermal line prior or after an earthquake event may lead to establishing a reasonable correlation between thermal line, tectonic stress and fault/thrust. 120 CHAPTER 5 Pre-earthquake Outgoing Longwave Radiation Variability 5.1 Introduction This chapter presents observations made using outgoing longwave radiation data for earthquakes which have been studied for pre-earthquake thermal anomalies (table 1.2). Pre and co-seismic OLR variability has been correlated with enhanced thermal infrared emission prior to main earthquake event and OLR dependence on land surface temperature (LST). Earth's radiation budget depends on the total incoming solar flux and the outgoing top of the atmosphere (TOA) radiative fluxes. The outgoing radiative fluxes consist of the reflected part of the incoming solar flux, as well as the thermal flux emitted by the earth-atmosphere system. This thermal flux is often referred to as Outgoing Longwave Radiation (OLR) and encompasses all of the emission from the ground, atmosphere and clouds formation. This energy is often derived from window channel measurements from satellites in polar orbits. Its dependence on surface temperature and possible link of transient thermal fields on the ground with pre- earthquake processes establishes the rationale to explore the radiation budget prior to earthquakes under study. The relevant parameters investigated here are Daily Mean (DM) OLR and Daily Longterm Mean (DLM) OLR (Section 3.2.2). This interpolated OLR data has four dimensions: time, latitude, longitude and outgoing longwave radiation. Daily mean OLR data for the selected study period gives OLR scenario of the epicenter region while give a general status of OLR emission in the region and serve as a standard normal dataset facilitating the deduction of anomalous OLR emissions during the earthquake events. 5.2 OLR dependence on Temperature i It is useful to examine radiative transfer equation to express clear sky OLR conditions: Application of Thermal Remote sensing in Earthquake Precursor studies olr =/;;;[{^(o). ttv(Ps, o)} + rp° bm ^pdp] dv (5.1) where, Bv(p) denotes the blackbody emission at pressure, p, and 7Yv(p,0) denotes the transmittance between the surface pressure (ps) and the top of atmosphere. The subscript v denotes wave number, and both B and Tr include implicit integration over the zenith angle to convert radiance to irradiance. Equation 5.1 may be simplified into: OLR = aTs\ Tr + 0LRa. (T(p), q(p)) (5.2) where, oTs4 is black-body surface emission, Tr is effective atmospheric transmittance and OLRa is atmospheric component of OLR and a is Stefan-Boltzman constant (5.67 x 10"8 W/m2T4) (Allan et al., 1999). From equation 5.2; dependence of OLR on temperature component either surface temperature or air temperature is clear. Increases in T in equation 5.2 act to increase the longwave emission to space. Because atmospheric temperature is coupled to certain degree with surface temperature; so a change in may have considerable impact on outgoing longwave radiation. 5.3 Pre-earthquake OLR variability detection Here, earthquakes showing positive TIR anomaly have been analyzed for comparable pre-earthquake OLR variability. It is believed that anomalous LST will have its impression on OLR budget of the earthquake affected region. Using NOAA - AVHRR derived interpolated OLR data (daily mean and daily longterm mean) an attempt has been made to validate positive thermal infrared anomaly observations for studied earthquake (table 1.2) 5.3.1 Jabalpur Earthquake, India Jabalpur earthquake (Mw 5.8) occurred on 21 May 1997 at 22:51 (UTC) affecting parts of central India. The epicenter of earthquake was located at 23.08°N latitude and 80.06°E longitude (table 1.2 and figure 4.1). 122 Daily Mean OLR i^i—i—i—i—i—i—i—i—i—i—i—i—i—r -|—I 1—I—i—i—i—I 1 1 1—i 1—r- 30 w a •a 25 20 J I I I I I I I I I I I I I I I I I I I • < '' 1997-05-06 1997-05-22 1997-06-06 Daily Longterm Mean OLR -i—i—i—i—i—i—i—i—i—i—i—i—i—i 1—i—i—i—i—i—i—i—i—i—i—i—i—i—r- 30 h 20- -i i i i i i i i • i i——j i i__i i i i_i i i i i i i i i i_ 0001-05-06 0001-05-22 0001-06-06 OLR scale (W/m2) i 236.0 256.5 277.0 297.5 Figure 5.1a: Outgoing Longwave Radiation plot for Jabalpur earthquake, India. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR (from 07 May 1997 to 06 Jun 1997). OLR values between 70°E - 85°E longitudes have been averaged. 123 Daily Mean OLR "I—i—i—i—i—i—i—r 30 "2 25 20 _i i i i I i i i i I I i I I l I i i I i i I i I I i i i i_ 1997-05-06 1997-05-22 1997-06-06 Daily Longterm Mean OLR *>i—i—i—i—i—i—i—i—i—i—i—i—i—i—i r~~i—r—i—i—i—i—i—i—i—i—i—i—i—r 30 "2 25 1' _i I I I I i i_ _i i i l I i i_j i i i I i i i i i i_ 0001-05-06 0001-05-22 0001-06-06 OLR scale (W/m2) I 1— 196.6 222.4 248.3 274.1 299.9 325.8 Figure 5.1b: Outgoing Longwave Radiation plot for Jabalpur earthquake, India. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 77.5°E longitude have been taken. 125 Chapter 5: Pre-earthquake Outgoing Longwave Radiation variability 5.3.1.1 Analysis OLR data for epicenter and adjoining region between 70°E - 85°E and 20°N - 30°N was selected for study. This 2.5° X 2.5° gridded data provides OLR values at 70°E, 72.5°E, 75°E, 77.5°E, 80°E, 82.5°E, 85°E longitudes and 20°N, 22.5°N, 25°N, 27.5°N, 30°N latitudes. Daily Mean OLR and Daily Longterm Mean OLR Data for one fortnight (07 May 1997 - 06 Jun 1997) before and after the earthquake was analyzed for examining pre-earthquake OLR variability. Interpolated OLR data has four dimensions i.e. latitude, longitude, time and outgoing longwave radiation; so its representation requires taking at least one dimension to be invariable. In figure 5.1a and 5.1b OLR is shown in time vs. latitude plot. Time is plotted on x-axis and y-axis represents latitude. Two types of plots are prepared - a) OLR values averaged between longitudes 70°E - 85°E in time vs. latitude plot (figure 5.1a); b) OLR values at 77.5°E longitude in time vs. latitude plot (figure 5.1b). This latitude has been chosen on the basis of proximity to earthquake epicenter and LST anomaly location. 5.3.1.2 Observations Daily mean OLR and daily longterm mean OLR values were plotted as above and OLR scale range was kept common. OLR values in time vs. latitude show development of high OLR zone prior and during the earthquake. This OLR anomaly appeared first on 08 May 1997 gradually grew in extent and intensity. Peak OLR values were noticed between 12 and 18 May 1997. OLR started decreasing from 19 May - 20 May 1997. On 21 May 1997 OLR again picks up and fluctuates between highs and lows till 30 May 1997. There occurred a magnitude 3.8 earthquake on 28 May 1997 (USGS). The post earthquake OLR high can be attributed to this type of reminiscent seismicity (figure 5.1a). Daily mean OLR and daily longterm mean OLR plot for longitude 77.5°E and 20°N - 30°N latitudinal range was also plotted and this too shows similar nature of OLR variability (figure 5.1b). The anomalous transient period was of 23 days. OLR values were about 30 - 40 W/m2 higher than the normal on maximum OLR anomaly day. Graph (figure 5.1c) distinctly shows single long-duration OLR high prior to 127 Application of Thermal Remote sensing in Earthquake Precursor studies earthquake. Daily mean and daily longterm mean OLR values have been plotted graphically. DM-AV and DLM-AV lines show averaged OLR between 70°E - 85°E longitudes and at 25°N, whereas DM-77.5°E and DLM-77.5°E show OLR values at 77.5°E and 25°N. 340 320 IT 300 1 5 280 O 260 •*— DM-AV -•—DM-77.5°E/25°N 240 DLM-AV DIM-77.5°E/25''N 220 ~i 1 1 1_ Ullt||Sflf|iif!ffI|f||iii5555J?i5J5i5J22i35J3J53*5Jl 33 33 3 3 3 —I rM m it in \o r* DATE DM-AV =daily mean - average, DLM-AV=daily longterm mean - average Figure 5.1c: Daily mean and daily longterm mean OLR vs. time graph for Jabalpur earthquake, India showing OLR variability trend during a period of 30 days (07 May 1997-06 Jun 1997). 5.3.2 Dabiran Earthquake, Iran Dabiran earthquake (Mw5.8) occurred on 10 Jul 2003 at 17:06 hrs (UTC) in Fars province of Iran. The epicenter of earthquake was located at 28.35°N latitude and 54.17°E longitude (table 1.2 and figure 4.3). 5.3.2.1 Analysis The study area for Dabiran earthquake lies between 50°E - 60°E longitudes and 25°N - 30°N latitudes and study period of 30 days is from 25 Jun 2003 - 25 Jul 2003. The OLR data is 2.5° X 2.5° gridded data and thus provides values at 50°E, 52.5°E, 55°E, 57.5°E, 60°E longitudes and 25°N, 27.5°N, 30°N latitudes. Two types of plots were prepared - a) OLR values averaged between longitudes 50°E - 60°E in 128 . Daily Mean OLR ^1—i—I—i—i—I—i—I—i—i—i—i—I—r—i—I—|—i—i—i—i—i—i—i—i—i—i—i—i—i—r 30 24- J I I I I I I I I I I I I—I I I I I I I I I I I I I I I I L. 2003-06-24 2003-07-10muifjii 2003-07-25 Daily Longterm Mean OLR "i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i i 30 "28 « 26 .1 24 i i l—i I I I I I I i_—I I I I I i i- -i 1 1 i I I i_ 0001-06-24 0001-07-25 0001-07-10 OLR scale (W/m2) —I— 227.6 250.0 272.4 294.8 Figure 5.2a: Outgoing Longwave Radiation plot for Dabiran earthquake, Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values between 50°E-60°E longitudes have been averaged. 129 Daily Mean OLR -I—i—i—I—I—I—I—i—i—i—i—i—I—I—I—i—i—i—i—i—i—i—i—i—i—i—i—i—r 30 28 •a 3 «26 24 -J i—i I i -i i_i i i i i i i i_ 2003-06-24 2003-07-10 2003-07-25 Daily Longterm Mean OLR 321—i—i—i—i—i—i—i—i—i—i i i—r—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r 30 1 * «j 26 24 i i i i I i i i i i i I I I i__i I I i i i i i I i i i i i i_ 0001-06-24 0001-07-10nnn^ri7.4n 0001-07-25 OLR scale (W/m2) ♦ * 198.0 223.9 249.8 275.7 301.6 Figure 5.2b: Outgoing Longwave Radiation plot for Dabiran earthquake, Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 55°E longitude have been taken. 131 Chapter 5: Pre-earthquake Outgoing Longwave Radiation variability time vs. latitude plot (figure 5.2a); b) OLR values near the epicenter at 55°E longitude in time vs. latitude plot (figure 5.2b). This latitude has been chosen on the basis of proximity to earthquake epicenter and LST anomaly location. 5.3.2.2 Observations Daily mean OLR, in time vs. latitude plot clearly shows pre-earthquake anomaly. On comparison with daily longterm mean (plotted using identical parameters) this disturbance in normal conditions can be properly studied. The OLR high values first appeared on 26 Jun 2003 i.e. 14 days before the main shock. On 30 Jun 2003 region showed highest values during the study-period and was followed by a decreasing trend between 02 Jul 2003 and 08 Jul 2003. Just one day before the earthquake region started getting warmer, indicated by high OLR values on 09 Jul 2003. This transient anomalous period was of about 22 days and high OLR was observed till 17 Jul 2003 i.e. after 7 days of the main shock (figure 5.2a). OLR values during this entire period of disturbance were about 20 - 30 W/m2 higher than the normal. Figure 5.2b too shows almost similar OLR variable conditions. This plot shows averaged OLR values between 50°E - 60°E longitudes in time vs. latitude plot. Graph shows two OLR highs: a pre-earthquake high and other high appeared just one day after the earthquake and remained till 12 Jul 2003 (figure 5.2c). DM-AV and DLM-AV lines show averaged OLR between 50°E - 60°E longitudes and at 27.5°N, whereas DM-55°E and DLM-55°E show OLR values at 55°E and 27.5°N. [33 Application of Thermal Remote sensing in Earthquake Precursor studies • DM-AV 240 • -DM-55°N/27.5°E • DLM-AV 220 DLM-55°N/27.5°E 200 _i i i * • _i i i i i i_ 333333333333333333333 rMm*srmior*cOCT*o «Nm*futu)f*co(rto H H ri rI rH .H »H —I N DATE DM-AV = daily mean - average, DLM-AV = daily longterm mean - average Figure 5.2c: Daily mean and daily longterm mean OLR vs. time graph for Dabiran Earthquake, Iran showing OLR variability trend during a period of 30 days (25 Jun 2003 - 25 Jul 2003). 5.3.3 Kerman Earthquake, Iran Kerman earthquake shook parts of south-east Iran on 21 Aug 2003 at 04:02 hrs (UTC). Its epicenter was located at 29.05°N latitude and 59.77°E longitude and magnitude was measured 5.9 (Mw) (table 1.2 and figure 4.5). 5.3.3.1 Analysis Data to detect OLR variability prior to Kerman earthquake was studied for a period of 30 days (21 Jul 2003 - 21 Sep 2003). 2.5° X 2.5° gridded data for study region lying between 55°E - 65°E longitudes and 22.5°N - 32.5°N latitudes was procured. OLR values are available at 55°E, 57.5°E, 60°E, 62.5°E, 65°E longitudes and 22.5°N, 25°N, 27.5°N, 30°N, 32.5°N latitudes. Two types of plots are prepared - a) OLR values averaged between longitudes 55°E - 65°E in time vs. latitude plot (figure 5.3a); b) OLR values near the epicenter at 57.5°E longitude in time vs. latitude 134 Daily Mean OLR }bi—,—,—,—_,—f—,—,—,—,—,—,—,—,—,—,—,—,—,—,—,—,—,—r 30 25 i j I I i_ -I I I I I I I I I I I I I I I I I I l__l I L 2003-07-20 2003-08-21 2003-09-21 Daily Longterm Mean OLR 0001-07-20 0001-08-21 0001-09-21 OLR scale (W/m2) 200.2 232.2 264.2 296.3 328.4 Figure 5.3a: Outgoing Longwave Radiation plot for Kerman earthquake, Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values between 55°E-65°E longitudes have been averaged. 135 Daily Mean OLR 3!>i—i—i—i—i 1—i—i—i—i—i—r—i—i—i 1—i—i—i—r t—i—r -I I I I L. -I I I I I I I L. -J I I I I I I I 1- 2003-07-20 2003-08-21 2003-09-21 Daily Longterm Mean OLR 30- 1 I 0001-07-20 0001-08-21 0001-09-21 OLR scale (W/m2) ;• 168.1 200.2 232.2 264.2 296.3 328.4 Figure 5.3b: Outgoing Longwave Radiation plot for Kerman earthquake, Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 57.5°E longitude have taken. 137 Chapter 5: Pre-earthquake Outgoing Longwave Radiation variability plot (figure 5.3b). This latitude has been chosen on the basis of proximity to earthquake epicenter and LST anomaly location. 340 r 320 j**#t^v jgr _**^„ _*-tr*^ 300 ^ 280 5 260 • / w • DC O 24° —*—DM-AV 220 —•— DM-S7.5°F/30"N DLM-AV 200 i i i i i i i i i i i i i i i oe33SSo^:1'^'"!•,!:T?4•1•1'"!•?1:4•?4',?,f'?,?,f"?,^',?"?,?,? DATE DM-AV = daily mean - average, DLM-AV = daily longterm mean - average Figure 5.3c: Daily mean and daily longterm mean OLR vs. time graph for Kerman Earthquake, Iran showing OLR variability trend during a period of 30 days (21 Jul 2003 -21 Sep 2003). 5.3.3.2 Observations Interpolated OLR data (figure 5.3a) in time vs. latitude plot shows a well developed high OLR zone prior to 21 Aug 2003 Kerman earthquake. This anomaly first appeared on 12 Aug 2003 i.e. 9 days before the main event. OLR intensified and grew in extent and intensity; reaching maximum on 16 Aug 2003. This anomaly subsequently faded; but continued till the main shock jolted the region. After the earthquake OLR values returned to normal conditions. Anomalous OLR was about 20 - 35 W/m2 higher than the normal. Daily longterm mean OLR plot, with like factors serves as normal OLR conditions for the region. Figure 5.3b displaying OLR plot near longitude 57.5°E also shows existence of pre-earthquake OLR anomaly which went away after the earthquake. The time of appearance, trend of development is almost similar to as seen in figure 5.1a. Graph (figure 5.3c) showing OLR vs. time plot at 57.5°E longitude and 30°N latitude also shows distinct pre-earthquake rise in OLR values. High OLR values can be seen from 04 Aug 2003 onwards and it anomaly went away only after the earthquake on 21 Aug 2003. Post-earthquake study period shows normal conditions for the region. 139 Application of Thermal Remote sensing in Earthquake Precursor studies 5.3.4 Ravar Earthquake, Iran A magnitude (Mw5.1) (IIEES) moderate earthquake shook parts of Kerman Province, Iran on 14 Oct 2004 at 2:28 hrs (UTC). The epicenter of the earthquake was at 31.73°N and 57.11°E (table 1.2 and figure 4.7) near Ravar. 5.3.4.1 Analysis OLR data for Ravar earthquake was studied for a period of 30 days (29 Sep 2004 - 29 Oct 2004). The study area lies between 50°E - 65°E longitudes and 25°N - 35°N latitudes. This 2.5° X 2.5° gridded data provides values at 50°E, 52.5°E, 55°E, 57.5°E, 60°E, 62.5°E, 65°E longitudes and 25°N, 27.5°N, 30°N, 32.5°N, 35°N latitudes. Interpolated OLR data has four dimensions: time, latitude, longitude and OLR. This requires at least one dimension to be invariable for representation. Longitude has been taken constant and two types of plots are prepared - a) OLR values averaged between longitudes 50°E - 65°E in time vs. latitude plot (figure 5.4a); b) OLR values near the epicenter at 55°E longitude in time vs. latitude plot (figure 5.4b). This latitude has been chosen on the basis of proximity to earthquake epicenter and LST anomaly location. 140 Daily Mean OLR 40| 1 1 1 1 1 1 1 1 1 1 1 1 1 1 , 1 1 [ 1 ] 1 1 1 1 1 1 ) 1 r 35 • •a S 30- 25- J I I I l__l I l__l I L. -I I I I I I L__J •• 2004-09-29 2004-10-14 2004-10-29 Daily Longterm Mean OLR «oi—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r 35 25" * -I I I I I I I I I I I 1_J I I I l_l I 1—_l u 0001-09-29 0001-10-14 0001-10-29 OLR scale (W/m2) I I- 253.3 268.2 283.1 298.0 313.0 Figure 5.4a: Outgoing Longwave Radiation plot for Ravar earthquake, Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values between 50°E-65°E longitudes have been averaged. 141 Daily Mean OLR »|—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r i i i -I i i i i I I I I i i i i u 2004-09-29 2004-10-14 2004-10-29 Daily Longterm Mean OLR -|—i—i—i—i 1—i 1 1—i 1—I 1 1 1 1 1—i 1—i r 35- j i i i i I i i i i l i i I i I i i I i I i i I i i i i i_ 0001-09-29 0001-10-14 0001-10-29 OLR scale (W/m2) * t 223.4 243.0 262.5 282.0 301.5 321.0 Figure 5.4b: Outgoing Longwave Radiation plot for Ravar earthquake, Iran.Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 55°E longitude have been taken. 143 Chapter 5: Pre-earthquake Outgoing Longwave Radiation variability 310 r 300 290 -§. 280 tc 270 —i O * DM-AV 260 DLM-AV • DM-55°E/32,S°N 250 240 ''•• I I I L_ _1 L_ _L_ -J I I I I I I ' t i $#999999999999999999999999999999fk %f M M u u u u u u u u o u DATE DM-AV = daily mean - average, DLM-AV = daily longterm mean - average Figure 5.4c: Daily mean and daily longterm mean OLR vs. time graph for Ravar Earthquake, Iran showing OLR variability trend during a period of 30 days (29 Sep 2004 - 29 Oct 2004). 5.3.4.2 Observations Daily mean OLR vs. time and latitude plot for Ravar earthquake shows pre- earthquake OLR variability. This OLR high is about 40 - 45 W/m2 higher than the normal. Its comparison with daily longterm mean OLR plot with identical parameters facilitate in identifying temporal and spatial variability in OLR values. OLR anomaly first appeared on 01 Oct 2004 i.e. 13 days before the earthquake. Gradually it grew in intensity and extent; and reached its maximum on 05 Oct 2004. This peak was followed by a decrease in OLR, to again pick up just before the earthquake on 10 Oct 2004. Region showed warmer conditions during earthquake period and normal was seen only after 17 Oct 2004 i.e. 3 days after the earthquake (figure 5.4a). The transient anomalous period in case of Ravar earthquake was of 17 days. OLR vs. time and latitude plot at longitude 55°E shows fairly similar trend. OLR anomaly plot shows consistently high before the earthquake and decreases only after the main event (figure 5.4b). Graph shows two OLR highs: a pre-earthquake high and other high appeared four days before the earthquake continued through 14 Oct 2004 till 17 Oct 2004 (figure 5.4c). DM-AV and DLM-AV lines show averaged OLR between 50°E - 65°E longitudes and at 32.5°N, whereas DM-55°E and DLM-55°E show OLR values at 55°E and 32.5°N. 145 Application of Thermal Remote sensing in Earthquake Precursor studies 5.3.5 Vrancea Earthquake, Romania Magnitude 5.9 (Mw) Vrancea earthquake of Romania occurred on 27 Oct 2004 at 20:34 hrs (UTC). Its epicenter location was at 45.78°N and 26.62°E with a focal depth of 96 km (table 1.2 and figure 4.9). 5.3.5.1 Analysis OLR data for region lying between 20°E - 30°E longitudes and 40°N - 50°N latitudes has been studied for identifying pre-earthquake OLR variability. This 2.5° X 2.5° gridded data provides OLR values at 20°E, 22.5°E, 25°E, 27.5°E, 30°E longitudes and 40°N, 42.5°N, 45°N,47.5°N, 50°N latitudes. 30 days data from 12 Oct 2004 - 11 Nov 2004 was analyzed. OLR values averaged between longitudes 20°E - 30°E in time vs. latitude plot (figure 5.5a); and b) OLR values near the epicenter at 20°E longitude in time vs. latitude (figure 5.5b) were plotted to examine temporal and spatial variability before the main event. This latitude has been chosen on the basis of proximity to earthquake epicenter and LST anomaly location. 5.3.5.2 Observations Time vs. latitude and daily mean OLR plot for Vrancea earthquake of Romania distinctly shows anomalously high OLR values before and during the earthquake. During entire anomalous period from 20 Oct 2004 - 06 Nov 2004 i.e. 17 days, consistently high OLR values were observed in the region. Anomalous OLR was about 30 - 40 W/m2 higher than the normal. On comparing with daily longterm mean OLR, it was noticed that anomaly first appeared on 20 Oct 2004. It gradually grew in intensity and extent; reached its peak on 24 Oct 2004 and faded away after 06 Nov 2004 (figure 5.5a). Daily mean OLR plot at longitude 20°E also suggests high OLR values in the study region and follows almost similar pattern (figure 5.5b). Graph also shows constantly higher values than normal. Anomaly started developing before main event and continued till 06 Nov 2004. DM-AV and DLM-AV lines show averaged OLR between 20°E - 30°E longitudes and at 45°N, whereas DM-20°E and DLM-20°E show OLR values at 20°E and 45°N (figure 5.5c). 146 Daily Mean OLR i^i—i—i—i—i—i—i—i—i—i—i—i—i i -I—i—r—i—i—i—i—i—i—i—i—i—i—r- 50 f 40 A „,L_—I I I 1—1 L. l I 1 I I 1 1 1 1—-J I I I I I |_ 2004-10-11 2004-10-27 2004-11-11 Daily Longterm Mean OLR "I—i—I—i—I—I—I—I—i—i—i—i—i—i—r "1—i—I—I—I—i—I—I—I—I—I—I—i—r M 50 Z L •o45 40 -l l I l i i i i i -i 1—I—I i i i I I I •' • • 0001-10-11 0001-10-27 0001-11-11 OLR scale (W/m2) < 221.1 238.6 Figure 5.5a: Outgoing Longwave Radiation plot for Vrancea earthquake, Romania. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values between 20°E-30°E longitudes have been averaged. 147 Daily Mean OLR !>|—i—i—I—I—I—I—I—i—I—i—i—I—i—I 1—i—i—i—i—i—i—i—i—i—i—i—i—i—r 50 <- •5 45 40r _i I l l I I i_ -i—i—; 1—i—i—i—i—i—i_j i i i i i i i_ 2004-10-11 2004-10-27 2004-11-11 Daily Longterm Mean OLR "|—r—i—i—i—i—i 1—i—i—i—i—i—i—i 1—i—i—i—i—i—i—i—i—i—i—i—i—i—r 50 •§45 3 40 I I I I I I I I I I I I I I I I I I I ••• 0001-10-11 0001-10-27 0001-11-11 OLR scale (W/m2) —( 137.5 165.3 193.1 220.9 248.7 Figure 5.5b: Outgoing Longwave Radiation plot for Vrancea earthquake, Romania. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 20°E longitude have been taken. 149 Chapter 5: Pre-earthquake Outgoing Longwave Radiation variability 280 260 240 220 — 200 1 <180 2-160 K - DM-AV O140 • • DLM-AV - DM-20°E/45*N 120 DLM-20°E/45°N 100 _i 1_ _j i i i_ 00090000090000009090 T 9 9 *HH«-4 DATE DM-AV = daily mean - average, DLM-AV = daily longterm mean - average Figure 5.5c: Daily mean and daily longterm mean OLR vs. time graph for Vrancea Earthquake, Romania showing OLR variability trend during a period of 30 days (12 Oct 2004 - 11 Nov 2004). 5.3.6 Fin Earthquake, Iran Fin earthquake occurred on 25 March 2006 at 10:58 hrs (UTC) and its epicenter was located at 27.57°N latitude and 55.69°E longitude. This magnitude 5.9 (Mw) earthquake had a focal depth of about 14 km (table 1.2 and figure 4.11). 5.3.6.1 Analysis Interpolated OLR data for region lying between 52.5°E - 57.5°E longitudes and 25°N - 30°N latitudes and a period of 30 days from 10 Mar 2006 - 09 Apr 2006 was analyzed. OLR data available at a resolution of 2.5°; provide OLR values at 52.5°E, 55°E, 57.5°E longitudes and 25°N, 27.5°N, 30°N latitudes. Averaged OLR values between 52.5°E - 57.5°E longitudes and OLR values at 57.5°E were shown in time vs. latitude graph. Interpolated OLR data has four dimensions: time, latitude, longitude, OLR; therefore one dimension is taken as invariable (longitude) to study pre- earthquake OLR variability (figure 5.6a and 5.6b). This latitude has been chosen on the basis of proximity to earthquake epicenter and LST anomaly location. 151 Application of Thermal Remote sensing in Earthquake Precursor studies 5.3.6.2 Observations Daily mean OLR and daily longterm mean OLR values are shown on a time vs. latitude plot. Comparing these plot clearly indicate development of high OLR zones prior and during the earthquake. This first appeared on 11 Mar 2006 and gradually grew in extent and intensity. Peak OLR values were noticed intermittently between 12 Mar 2006 - 15 Mar 2006 and 23 Mar 2006 - 27 Mar 2006. Region does show reduction in OLR values in between. The epicenter region exhibits disturbed OLR conditions for a period of 21 days from 11 Mar 2006 - 01 Apr 2006 (figure 5.6a). OLR values at 57.5°E were shown in time vs. latitude plot and also show similar trend of OLR variability (figure 5.6b). OLR values were about 30 - 50 W/m2 higher than the normal on maximum OLR anomaly day. Graph distinctly displays intermittent OLR high periods during the entire transient period. Daily mean and daily longterm mean OLR values have been plotted graphically. DM-AV and DLM-AV lines show averaged OLR between 52.5°E - 57.5°E longitudes and at 27.5°N, whereas DM-57.5°E and DLM-57.5°E show OLR values at 57.5°E and 27.5°N (figure 5.6c) * > 152 Daily Mean OLR ^i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r- 301- 25 -I—I 1 1 1 1 1 1 1 1 1 1 I I I I I I I I I I I I I I I I •' 2006-03-09 2006-03-25 2006-04-09 Daily Longterm Mean OLR i i i i i • 30 Z (•. 0) •D „ 3 B (0 _l A g 25 % I • i 1 1 1 1 0001-03-09 0001-03-25 0001-04-09 OLR scale (W/m2) —i— —I 218.6 244.5 270.3 Figure 5.6a: Outgoing Longwave Radiation plot for Fin earthquake, Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values between 52.5°E-57.5°E longitudes have been averaged. 153 Daily Mean OLR 3b|—i—r—i—i—i—i—i—i—i—i—i—i—i—i—i—j—i—i—i—i—i—i—r—i—i—i—i—i—i—r 3a. 25 201 1 1—' 1 U—J 1 1 1 1 1 1 1 1 1 1 I I I l__l I • 2006-03-09 2006-03-25 2006-04-09 Daily Longterm Mean OLR Jb|—'—i—i—i—i—i—i—i—i—r—i—i—r—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r nay 30 i M 25 -i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i i i i i i i_i 0001-03-09 0001-03-25 0001-04-09 OLR scale (W/m2) * —I 164.8 192.8 220.8 248.8 276.8 Figure 5.6b: Outgoing Longwave Radiation plot for Fin earthquake, Iran. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 57.5°Elongitude have been taken. 155 Chapter 5: Pre-earthquake Outgoing Longwave Radiation variability en O r-l fs| •tiAtorsoomor-iiNm^tintiirs.Mcno-H—• (Nro*»inior*.cocno irHrHTHrHr-tN(NrS(Mr>jrM(N|r«JfNfSmr^t —I Date DM-AV = daily mean - average, DLM-AV = daily longterm mean- average Figure 5.6c: Daily mean and daily longterm mean OLR vs. time graph for Fin Earthquake, Iran showing OLR variability trend during a period of 30 days (10 Mar 2006 - 09 Apr 2006). 5.3.7 Yamnotri Earthquake, India Yamnotri earthquake (Mw5.1) occurred on 22 Jul 2007 at 23:02 (UTC) in parts of northern India. The epicenter of earthquake was located at 30.93°N latitude and 78.27°E longitude (table 1.2 and figure 4.13). 5.3.7.1 Analysis Interpolated OLR data for a period of 30 days i.e. 07 July 2007 - 06 Aug 2007 (15 days before and after the earthquake) was studied. Daily mean OLR and daily longterm mean OLR data is 2.5° X2.5° gridded data comprise four dimensions namely latitude, longitude, time and outgoing longwave radiation. Region under study falls between longitudes 75°E - 90°E and latitudes 25°N - 35°N and thus OLR values are available at 75°E, 77.5°E, 80°E, 82.5°E, 85°E, 87.5°E, 90°E longitudes and 25°N, 27.5°N, 30°N, 32.5°N, 35°N latitudes. Daily mean OLR and daily longterm mean OLR plots were compared to visualize spatial and temporal variability of OLR. Two plots were prepared: a) averaged OLR values between 75°E - 90°E and time vs. latitude (figure 5.7a); b) OLR plot at longitude 77.5°E and time vs. latitude (figure 5.7b). This 157 Application of Thermal Remote sensing in Earthquake Precursor studies latitude has been chosen on the basis of proximity to earthquake epicenter and LST anomaly location. 5.3.7.2 Observations Daily mean OLR plot shows development of high OLR zones north of 30°N near earthquake epicenter. Initially this anomalous OLR was observed 14 days prior to earthquake south of epicenter which gradually shifted towards north. High OLR zones are seen in two phases with short duration low OLR phase. From 14 Jul 2007 (i.e. 8 days before earthquake) onwards OLR increased in intensity and extent till 22 Jul 2007. An evident fall in OLR values was seen just after the earthquake. Post- earthquake this region shows relatively lower longwave emission and resumes normal conditions with intermittent highs (figure 5.7a). Similarly, daily mean OLR plot and daily longterm mean OLR plot was prepared for a location near earthquake epicenter. At 77.5°E longitude, the plot shows comparable pre-earthquake OLR variability (figure 5.7b). Graph plotted between time and OLR shows matching OLR trends. On comparing daily mean and daily longterm mean OLR; two OLR highs can be clearly seen (figure 5.7c). First peak is observed on 10 Jul 2007 and OLR values were about 35 - 55 W/m2 higher than the normal. OLR suddenly fall on 12 Jul 2007 to again pick up from 14 Jul 2007 - 19 Jul 2007. Earthquake occurred on 22 Jul 2007 and was followed by relatively low OLR. This can be attributed to earlier disturbances in OLR. 158 Daily Mean OLR -io i—i—i—I—I—I—I—I—I—i—i—i—I—I—i 1—i—I—I—i—i—i i i—i—i—i—i—i—r- 35 3 30 f 25- -1 I I t I I I I I I I I I I I I I I I I I |_ 2007-07-06 2007-07-22 2007-08-06 Daily Longterm Mean OLR «0|—i—i—I—i—I—I—I—I—I—I—I—I—I—I—I—I—I—I—i—i—i—i—i—i—i—i—i—i—i—r 351- a 130- 25 -i i i i I u -I I I I I I I I 1—1 I I I I I I I i i 0001-07-06 0001-07-22 0001-08-06 OLR scale (W/m2) -—4— I ' 160.8 179.6 198.4 217.2 236.0 Figure 5.7a: Outgoing Longwave Radiation plot for Yamnotri earthquake, India. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values between 75°E-90°E longitudes have been averaged. 159 Daily Mean OLR «Oi—i—i—i—i—i—i—i—i—i—i—i—i—i—i 1—i—i—i—i—i—i—i—i—i—r—i—i—i—r 35- 3 30 25- j i i I I I I i I i i I i I I I u -I I I I I I I I i_ 2007-07-06 2007-07-22 2007-08-06 Daily Longterm Mean OLR «oi—i—i—r—i—r—i—i—i—r -i—i—i—i—i—i—i—i—i—i—i—i—i—p 35- 25 J 1 1 1 . _l : 1 L I I -I I I I I l—l I I I U 0001-07-06 0001-07-22 0001-08-06 OLR scale (W/m2) 136.2 165.2 194.2 223.1 Figure 5.7b: Outgoing Longwave Radiation plot for Yamnotri earthquake, India. Here x-axis has 'time' dimension and y-axis represents latitudinal range of OLR variability. Top plot is Daily Mean OLR and bottom plot is Daily Longterm Mean OLR. OLR values at 77.5°E longitude have been taken. 161 Chapter 5: Pre-earthquake Outgoing Longwave Radiation variability iUU r 280 260 240 ^220 ir£>»;^*^~v5* ..^T*^..i^£^ter*-x 3 200 . / § 180 • DM-AV 160 • DLM-AV 140 • DM-77.5°E/30°N 120 DLM-77.5°E/30°N • i i i i i_ 100 —i 1 1 1 1 1 i * i —t 1 i ! i 1 1 -i 1 i i_ 3 3 3 •4 .? .? 5 .? 3 3 33333333 *arvOOCh©rH DATE DM-AV =daily mean - average, DLM-AV =daily longterm mean - average Figure 5.7c: Daily mean and daily longterm mean OLR vs. time graph for Yamnotri Earthquake, India showing OLR variability trend during a period of 30 days (07 Jul 2007 - 06 Aug 2007). 5.4 Summary Pre-earthquake OLR variability analysis has been performed to investigate anomalous nature of outgoing longwave radiation. Interpolated data for a period of 30 days (15 days before and 15 days after the earthquake) in all the test cases have been examined for detection of seismicity associated OLR variability. Outgoing longwave radiation is a measure of Earth's radiation budget and has significant contribution from earth's surface. Enhanced longwave emission may be reflected in form of high OLR flux which can be detected through satellites equipped with thermal sensors. The OLR is estimated from AVHRR observations using an empirical regression equation developed by Abel and Gruber (1979) which relates the flux equivalent temperature to the radiance equivalent brightness temperature (chapter 3 section 3.2.2). Here, OLR data was explored to substantiate TIR anomaly observations. Available OLR data has four dimensions namely time, latitude, longitude and OLR. Longitude has been taken as invariable dimension in time vs. latitude plots. OLR's latitudinal spread can be easily studied in these plots. Daily mean OLR plot has been compared with daily longterm mean OLR plots with identical parameters. Daily longterm mean data serves as base information for anomaly detection studies. Daily 163 Application of Thermal Remote sensing in Earthquake Precursor studies mean OLR in time vs. latitude plot clearly shows development of high OLR zones prior to earthquakes. Anomalous OLR appears 14-7 days before the earthquake, attains maximum which may or may not be followed by a low before the earthquake. The OLR was about 20 - 50 W/m2 higher than the normal. It was noticed that during the period of main shock too OLR was towards higher side. Jabalpur (Mw 5.8), Kerman (Mw 5.9) and Vrancea (Mw 5.9) earthquakes OLR shows constantly high prior to earthquakes. Whereas in Dabiran (Mw 5.8), Ravar (Mw 5.1), Fin (Mw 5.9) and Yamnotri (Mw 5.1) earthquakes; pre-earthquake OLR maximum subsided again to pick up 1 - 2 days before the main event. These pre-earthquake short-lived variations in outgoing longwave radiation may depend on the stress conditions of the epicenter region and its resultant enhanced longwave emission. The transient period may range from 14 - 21 days as indicated by study (table 5.1). Comparison of observations for pre-earthquake TIR anomaly and OLR variability reveal similarity in spatial and temporal development pattern. Their time of appearance, time of reaching maximum (in terms of days) and subsequent decrease during or after the main shock indicate a close relation. As have been stated earlier, land surface longwave emission forms a significant contribution to OLR calculation so; any increase in TIR emission may be reflected in OLR anomalous values. These positive pre-earthquake OLR variations thus help to validate TIR anomaly observations. > 164 Table 5.1: List of earthquakes studied through AVHRR derived OLR datasets and observed OLR variability. TIR anomaly Focal Intensity of Mag. (days before earthquake) Transient S.N. Earthquake Depth OLR rise (Mw) Maximum rise Period (km) Rise started (W/m2) observed Jabalpur Earthquake, India 1 5.8 13 days 7 days 30-40 23 days (22 May 1997) 35 Dabiran Earthquake, Iran 2 5.8 14 days 10 days 20-30 22 days (10 Jul 2003) 10 Kerman Earthquake, Iran 3 5.9 9 days 7 days 20-35 11 days (21 Aug 2003) 20 Ravar Earthquake, Iran 4 5.1 13 days 10 days 40-45 17 days (14 Oct 2004) 18 Vrancea Earthquake, 5 5.9 7 days 10 days 30-40 17 days Romania (27 Oct 2004) 96 Fin Earthquake, Iran 6 5.9 14 days 1 day 30-50 22 days (25 Mar 2006) 14 Yamnotri Earthquake, India 7 5.1 14 days 7 days 35-55 16 days (22 Jul 2007) 35 CHAPTER 6 Summary and Conclusions 6.1 Introduction Within last several years major earthquakes in different parts of the world caused devastation and significant loss of life and property. USGS records around 15000 earthquakes of higher than 4 magnitudes annually. Despite great efforts, the short-term earthquake prediction has not been achieved and earthquakes continue to surprise mankind with its fury. For successful prediction of earthquake with a view of issuing forewarning to people it is necessary to study short time precursors. Earthquakes occur when stresses in the earth's crust exceed rock's strength to resist, thus causing the sudden rupture of rocks and displacement along a fracture plane called a fault. Energy from the fault rupture is transmitted as seismic waves that cause all damaging effects. It will not be unreasonable to presume that stress energy, accumulated through the relative motion of two plates, would not be released all at once only during the moment of the earthquake (Freund, 2003). Instead, some of the stress energy should have activated dormant electric charges in rock forming minerals, leakage channels like crystal lattice dislocations, microcracks etc. before the final release of stress energy. Remote sensing technology provides systematic and synoptic earth observation and compensates the inadequacy in earthquake monitoring / recording stations on the ground and improves present system of earthquake monitoring and possible forecasting. GPS and radar observations, electromagnetic field, ground thermal infrared abnormity are the challenging areas of remote sensing application in earthquake science. Application of thermal remote sensing in earthquake precursor studies is rather an unconventional approach in field of seismological research and undoubtedly provides a new perspective in use of remote sensing technology. Gorny et al. (1988) introduced the idea of thermal imaging of earthquake precursory signals. Since then advances in remote sensing technologies and improved sensor capabilities have allowed workers to discover earthquake thermal precursors and establish its utility for Application of Thermal Remote sensing in Earthquake Precursor studies possible earthquake predictions. Stresses building up in earthquake preparation zone may augment land surface temperature (LST) in and around epicenter area along with other precursory symptoms. Earth-degassing through squeezed pore spaces and p- hole activation in stressed rock volume is possible mechanism causing this LST rise. Lithosphere-Atmosphere-lonosphere (LAI) coupling is also deemed to cause thermal abnormity prior to earthquakes. Satellites equipped with thermal sensors and regular monitoring ability is capable of detecting this anomalous rise. Meteorological satellite NOAA - AVHRR having spatial resolution of 1.1 km, two thermal bands (4 and 5) and twice daily temporal resolution (0230 and 1430 local time) provides suitable data for this kind of study. 6.2 Summary NOAA-AVHRR thermal data has been used to examine nine moderate to strong earthquakes from India, Iran, Pakistan and Romania for detection of pre- earthquake TIR anomaly. This thesis work is dedicated to post-earthquake study for investigating pre-earthquake thermal precursors. Rise in land surface temperature (LST) in response to enhanced thermal infrared emission due to accumulating stresses in earthquake preparation zone; forms the basis of the study. LST is skin temperature of earth's surface and satellite sensing provides unbiased, regular, cost- effective and large coverage for the region of interest. Thermal imaging of ground surface can provide useful information on any anomalous development in earthquake preparation zone prior to imminent earthquake. Another approach to further widen the scope of AVHRR thermal data was to study outgoing longwave radiation variability. OLR is an important measure of earth's radiation budget and dependent on surface temperature and possible link of transient thermal fields on the ground with pre- earthquake processes establishes the rationale to explore the radiation budget of epicenter region prior to earthquakes under study. Transient TIR anomaly may stay 8-18 days from its first appearance till normalcy is reached in and around earthquake epicenter. It has been observed in studied earthquakes that TIR anomaly appears about 7-11 days before the main event. OLR gradually grows in size and intensity and attains a peak temperature usually 7 - 1 day prior to earthquake. The pre-earthquake LST rise was about 2° - 168 Chapter 6: Summary and Conclusions 13°C higher than the background temperature of the region. The anomaly disappears after the earthquake once accumulated stresses are released. In some earthquake cases (Fin, Mw5.9; Balochistan, Mw6.4) dual thermal peak was observed before the main event. Post-earthquake anomaly fades due to release of stresses and region shows usual conditions. TIR anomaly in case of 21 May 1997, Jabalpur earthquake (Mw 5.8), India was seen to develop 9 days before the main event. The total transient period for Jabalpur earthquake was 17 days during which anomaly appeared, reached its maximum, subsided before main event and then went away after the earthquake. The LST rose to about 5° - 11°C than the usual temperature in affected area. OLR observations for Jabalpur earthquake also show similar trend of appearance, attaining maximum and then subsiding during or after the earthquake. However, transient period for OLR anomaly is slightly higher than the TIR anomaly transient period (table 6.1). It is interesting to note that anomalous area is spread along the SONATA (Son-Narmada- Tapti) lineament. OLR high was seen 4 days before the first appearance of TIR anomaly on 13 May 1997 and it also remained for a longer period than the TIR anomaly. OLR values were about 30- 40 W/m2 higher than the normal. Magnitude 5.8 Dabiran earthquake, Iran has a transient period of 12 days. Anomaly started developing from 01 Jul 2003, reached peak temperature two days later on 03 Jul 2003. Earthquake occurred on 10 Jul 2003 and region returned back to normal conditions on 13 Jul 2003. This anomalous rise in LST conditions was 7° - 10°C higher than the background temperature. OLR variability is seen to develop 14 days before the main shock, and region showed disturbed OLR conditions till 17 Jul 2003. OLR transient period was of 22 days and during this period OLR values rose as much as 20 - 30 W/m2 higher than the normal. Dabiran earthquake also shows a longer transient period of OLR anomaly. Magnitude 5.9 Fin, Iran and magnitude 6.4 Balochistan, Pakistan earthquakes show an interesting observation of dual thermal peak. In case of Fin earthquake the temperature maximum were observed 10 and 2 days earlier than main shock. Balochistan earthquake shows a relatively shorter transient period of 9 days with thermal peak appearing just 6 and 2 days before the earthquake. The intensity of thermal rise was about 2° - 13°C. The difference between the two peak temperatures was about 4° - 5°C. Pre-earthquake OLR abnormity does not show dual peak in OLR 169 Application of Thermal Remote sensing in Earthquake Precursor studies anomaly and the single peak is closer to second TIR anomaly maximum. OLR values were about 20 - 50 W/m2 higher than the normal. In case of Balochistan earthquake OLR anomaly was not observed. Analysis of AVHRR thermal data for magnitude 5.1 Ravar earthquake, Iran show distinct development of thermal anomaly 8 days prior to earthquake. In a transient period of 12 days thermal peak of about 5° - 7°C developed 6 days before on 8 Oct 2004. Region returned to normal conditions 3 days later on 17 Oct 2004. OLR anomaly appeared 13 days before on 01 Oct 2004. This is 7 days earlier to first appearance of TIR anomaly. However normal OLR and LST conditions were observed almost on same day i.e. 3 days after main event. Vrancea earthquake, Romania of magnitude 5.9 occurred on 27 Oct 2004. This is a case of deep focus earthquake with widespread TIR anomaly that almost covered most of Romania. Anomaly appeared 7 days before on 22 Oct 2004, attained maximum 2 days before and limped back to normal condition in about 7 days time. The last aftershock of magnitude Mw 3.2 was observed 10 days later. The extended time taken to return to normal may be attributed to aftershock activity in affected region. Analysis of thermal LST images for magnitude 5.8 Dabiran earthquake and magnitude 5.9 Kerman earthquake also show a delay in reaching normal conditions in affected region. Dabiran earthquake, Iran was succeeded by aftershock activity for 3 days (table 4.2) and thus LST normal conditions were not observed before that. In Kerman earthquake, Iran case latest LST high was seen 7 days after the earthquake owing to associated aftershock activity. The remarkable correlation between OLR anomaly and TIR anomaly in case of Vrancea earthquake is interesting. OLR anomaly also first appeared on 22 Oct 2004 i.e. 7 days before and it went away after the main shock almost concurrently. OLR valueswere about 30 - 40 W/m2 higher than the normal. Magnitude 6.4 Balochistan earthquake, Pakistan (29 Oct 2008) has a relatively short transient anomalous period of 9 days. In daytime LST layout for Balochistan earthquake, TIR anomaly was first seen on 22 Oct 2008 i.e. 7 days before the main shock. Dual thermal peak was observed on 23 Oct 2008 and 27 Oct 2008. The land surface temperature of the region was about 6° - 10°C higher than the normal. No significant pre-earthquake variability in OLR development was seen. 170 Chapter 6: Summary and Conclusions Chamoli earthquake, India of magnitude 6.4 (Mw) occurred on 29 Mar 1999. Instead of pre-earthquake TIR anomaly a high temperature band following the Himalayan foothills (south of earthquake zone) was observed on nighttime NOAA- AVHRR thermal image. This high temperature band has been interpreted as 'Himalayan Thermal Line (HTL)' having a possible tectonic relation and effect of increased stress conditions in earthquake preparation zone is reported. HTL coincides with the high moisture retaining soil zones of Himalayan foothill which are made up of reworked porous and rocky soils. It is also interesting to note that HTL follows the trend of Himalayan Frontal Thrust (HFT) at some places. On maximum temperature day i.e. 30 Mar 1999 temperature of thermal line was 5° - 7°C higher than usual temperature. TIR anomaly could not be observed in the Chamoli region which may have been masked due to presence of high ruggedness in terrain, vegetation cover, and unsteady meteorological conditions. 6.3 Conclusions The present study using NOAA - AVHRR thermal dataset for investigating pre- earthquake anomalous TIR emission reveals that earthquake with magnitude higher than 5 may be preceded by detectable rise in land surface temperature and outgoing longwave radiation. The LST was found to increase by 2° - 13°C about 7-13 days before the main shock and OLR values rose to 20 - 55 W/m2 about 7-14 days before the main shock. Similarity in anomaly development trend, overlapping transient period, disappearance with the earthquake event (table 6.1) imply interrelation of TIR anomaly and OLR variability and their connection with the earthquake preparation process. Cloud-cover over epicenter hinders studying LST condition of affected region. OLR, which is top of atmosphere radiation, can serve as an important indicator of enhanced TIR emission and solve the problem presented by cloud-cover to certain extent. The anomalous region may or may not coincide with the epicenter location. The surface expression of TIR anomaly is governed by structural settings of the region. This offset from epicenter and alignment of TIR anomaly along weak zones is probably because the it facilitate easy and rapid flow of warm greenhouse gases and fluids to earth's surface thus inducing rise in LST. 171 Application of Thermal Remote sensing in Earthquake Precursor studies The development and detection of TIR anomaly depends on terrain, meteorological conditions, vegetation cover and satellite sensor capabilities. Earthquake magnitude, which is a measure of energy release, has effect on TIR anomaly appearance. The earthquakes with higher magnitude have higher intensity and spatial extent of anomalous signals, although focal depth also affects intensity and extent of thermal rise. Deep focus earthquake tend to have larger spatial extent than the earthquakes whose focal depth is relatively shallow. Aftershock activities play important role in attainment of normal temperature conditions of the region. The occurrence of aftershocks prolongs anomaly period and prevents or delays re-establishment of normal conditions after main earthquake event. Due to prevailing residual stresses, the epicenter and adjoining areas experience aftershocks and thus the disappearance of the TIR anomaly takes a longer time. An interesting observation regarding the earthquakes of moderate magnitude in the present study is the appearance of a dual peak in LST instead of the single rise observed previously. The first peak appears about 10 days before the earthquake and the second temperature peak occurs relatively closer to the main shock, i.e. 2-6 days. This may lead us to infer that perhaps the energy accumulated in the stressed rocks has been released sporadically in the form of the electromagnetic emission, apparent temperature increment or any other geophysical earthquake precursor. Analysis of AVHRR derived Outgoing Longwave Radiation (OLR) data also reveal significant pre-earthquake and co-seismic variability. This has been correlated with enhanced thermal infrared emission prior to main earthquake event and OLR dependence on land surface temperature. OLR anomaly studied in context of seismic precursors clearly shows development of high OLR zones prior to earthquakes. Anomalous OLR appeared 14-7 days before the earthquake, attains maximum which may or may not be followed by a low before the earthquake. OLR conditions return to normal after the earthquake event is over. 172 Table 6.1: A comparative analysis of TIR anomaly and OLR variability observed prior to studied earthquakes. Yellow colored box indicate days with anomalous TIR emission and green colored boxes indicate days showing anomalous OLR behavior. Red colored box shows thermal-peak / OLR peak. "00" box is earthquake occurrence day. Pink colored part of the table is pre-earthquake period and blue colored part is post-earthquake period. 15 14 13 12 11 10 09 08 07 06 05 04 03 02 01 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 TP 17 J 1 1 1 1 1 1 1 II III 1 II 23 1 1 12 D • 22 1 1 1 1 16 K 11 12 R 17 1 1 1 1 1 14 V 17 1 1 1 1 1 1 1 1 1 1 II 15 F ^^^^^^^^^^^H 22 11 Y 16 09 B • J = Jabalpur eq., D= Dabiran eq., K= Kerman eq., R= Ravar eq., V= Vrancea eq., F= Fin eq., Y= Yamnotri eq., B= Balochistan eq. and TP= Transient Period (in days). Chapter 6: Summary and Conclusions Comparison of observations for pre-earthquake TIR anomaly and OLR variability reveal similarity in spatial and temporal development pattern (table 6.1). Their time of appearance, time of achieving maximum (in terms of days) and subsequent drop during or after the main shock indicate a close relation. OLR values were 20 - 55 W/m2 higher than the normal OLR emission. As have been stated earlier, land surface longwave emission forms a significant contribution to OLR calculation so; any increase in TIR emission may be reflected in OLR anomalous values. These positive pre-earthquake OLR variations thus help to validate TIR anomaly observations. In case of Chamoli earthquake (29 Mar 1999) unstable meteorological conditions and rugged topography of high Himalayan region has hindered detection of pre-earthquake TIR anomaly. Instead enhanced stress conditions were reflected in the nighttime LST images through the development of high temperature linear zone named as Himalayan Thermal Line (HTL). HTL coincides with the Himalayan foothill zone made up of reworked, porous, water saturated soils. Increased tectonic stress condition during the Chamoli earthquake in the Himalayan region could be inferred through the study of thermal line. It also may be noted that the characteristics of thermal line as presented in this study is different from the other typical examples of pre-earthquake thermal infrared anomaly associated with the earthquakes. 6.4 Scope for Future Research Present work is aimed towards application of thermal remote sensing in identifying correlation between transient thermal anomalies and earthquake events. This research strengthens the concept ofTIR anomaly phenomenon as an earthquake precursory process. It is a post-earthquake analysis and is not intended for earthquake prediction or forecast. However in near future it can serve as one ofthe parameters for near real-time monitoring of impending earthquakes. For exploring further the potential of thermal remote sensing other type of available thermal datasets (e.g. MODIS - Terra/Aqua) can be used. These observations can be refined by taking longterm datasets (e.g. LST normals) into account. Longterm data serves as ideal base information for all future anomalies calculations. 175 Application of Thermal Remote sensing in Earthquake Precursor studies A correlation of InSAR generated deformation maps with thermal anomalies (Saraf et al., 2008) can be utilized to support thermal anomaly as a ground-related phenomenon. Himalayan Thermal Line (HTL) sensitivity to accumulating stresses need regular monitoring. A detailed research on response of HTL to accumulating stresses can throw light on seismicity of the region. 176 BIBLIOGRAPHY • 1. 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URL Links: 1. http://dmsp.nqdc.noaa.gov/dmsp.html 2. http://drarunsaraf.tripod.com/iitr-ses.htm 3. http://earthobservatorv.nasa.qov/Newsroom/Newlmaqes/images.php37imq id: 10933 4. http://earthquake.usqs.gov/faq/hist.html 5. http://eoweb.dlr.de:8080/short quide/index.html 6. http://freethouqhts.org/archives/000389.php 7. http://meteo.infospace.ru 8. http://monitoring.llnl.gov/regionalization/tect map.html 9. http://nsidc.org/data/modis/order data.html xxi 10. http://orfeus.knmi.nl/newsletter/vol4no2/afqhan.html 11. http://periqee.ncdc.noaa.goV/docs/klm/html/c7/sec7-1.htm#sec71-2 12. http://perigee.ncdc.noaa.gov/docs/podug/html/c1/sec1-410.htm 13. http://pubs.usqs.gov/qip/earthq1/where.html 14. http://rapidfire.sci.qsfc.nasa.gov/ 15. http://science.nasa.gOv/headlines/y2003/11 aug earthquakes.htm 16. http://seamless.usgs.gov/index.php 17. http://solidearth.ipl.nasa.gov/qess2.html 18. http://srtm.csi.cgiar.org/SELECTION/inputcoord.asp 19. http://theearthguakemuseum.blogspot.com/ 20. http://vulcan.wr.usgs.govA/olcanoes/JuanDeFucaRidge/description iuan de fu ca.html 21. http://www.cas.sc.edu/geog/research/gisciences/RS/index.html 22. http://www.cdc.noaa.gov/cdc/data.ncep.reanalysis.html 23. http://www.cgd.ucar.edu/cas/catalog/ 24. http://www.class.ncdc.noaa.gov/saa/products/nmmr/Classic/AVHRR 25. http://www.cnn.eom/WORLD/9801/10/china.guake.update/mackinnon.30.aiff 26. http://www.cosis.net/abstracts/EAE03/08814/EAE03-J-08814.pdf 27. http://www.crcnetbase.com/doi/pdfplus/10.1201/9780203964439.pt3 28. http://www.dlr.de/dlr 29. http://www.emsc-csem.org 30. http://www.esrl.noaa.gov/psd/data/gridded/data.interp OLR.html 31. http://www.ess.washington.edu/SEIS/PNSN/INFO GENERAL/eg prediction.ht ml 32. http://www.geotimes.org/mar02/WebExtra0326.html 33. http://www.qisdevelopment.net/aars/acrs/1999/ps6/ps61179a.shtml xxii 34. http://www.ictp.trieste.it/~attia/alqeriaseis.htm 35. http://www.imd.ernet.in/section/seismo/static/siqnif.htm 36. http://www.isfep.com/indexamembers.html 37. http://www.itc.nl/research/products/sensordb/qetsat.aspx?name=NOAA-18 38. http://www.meteoquake.org/indexa1.html 39. http://www.n2yo.com/satellites/?c=4 40. http://www.osd.noaa.gov/class/ 41. http://www.reliefweb.int/rw/rwb.nsf/db900SID/RMOI-6AQ9UK7QpenDocument 42. http://www.solarisviews.com/ 43. http://www.srces.spb.org/tronin/seis/seis index.html 44. http://www.stvincent.ac.uk/Resources/Weather/NOAA/svsteminfo.html 45. http://www.terraresearch.net/articles/earthquakeclouds articlel .htm 46. http://www.weatherwise.org/. 47. http://www.wunderground.com/global/Region/A2/Temperature.html 48. http://www2.ncdc.noaa.gov/docs/klm/html/c3/sec3-1.htm 49. http://www.eic.eri.u-tokvo.ac.ip/index-e.html 50. https://wist.echo.nasa.gov/v0ims/DATAPRODSERV/sample data.html XXlll ANNEXURE-I LIST OF PUBLICATIONS OUT OF THE RESEARCH WORK International Journals 1. Saraf, A. K., S. Choudhury, S. K. Panda, S. Dasgupta and V. Rawat, (2007), Does a major earthquake precede a thermal anomaly? International Journal ofGeoinformatics, Vol. 3, No.3, pp. 1-8. 2. Saraf, A. K., V. Rawat, P. Banerjee, S. Choudhury, S. K. Panda, S. Dasgupta and J.D. Das (2008), Satellite detection of earthquake thermal precursors in Iran, Natural Hazards, Vol. 47, pp. 119-135 (DOI: 10.1007/s11069-007-9201-7). 3. Saraf, A. K., V. Rawat, S. Choudhury, S. Dasgupta and J. D. Das (2009), Advances in understanding of the mechanism for generation of earthquake thermal precursors detected by satellites, International Journal of Applied Earth Observation and Geoinformation, Vol. 11, pp. 373-379 (DOI: 10.1016/j.jag.2009.07.003). 4. Rawat, V., A. K. Saraf, J. D. Das, K. Sharma and Y. Shujat (2010), Remote Sensing Observation of Earthquake Thermal Precursors in India and Iran, International Journal ofApplied Earth Observation and Geoinformation (submitted). 5. Rawat, V., A. K. Saraf, J. D. Das and K. Sharma (2010), Anomalous land surface temperature and outgoing long wave radiation observations prior to earthquakes in India and Romania, International Journal ofRemote Sensing (submitted). National Journals 6. Saraf, A. K, V. Rawat, J. D. Das, A. Tronin, S. Choudhury and K. Sharma, (2010), NOAA-AVHRR data displaying Thermal Line in the Himalayan foothills and its association with Frontal Thrust and Chamoli earthquake, Indian Journal of Earth Sciences (submitted). International Conference 7. Saraf, A. K., J. Das and V. Rawat, (2009), Can satellite detect early signatures of earthquake-induced landslides of Himalayas?, Proceedings of 2nd International Conference on Geoinformation Technology for Natural Disaster Management and Rehabilitation, held between 30-31 January 2009 at, AIT, Bangkok, Thailand, p. 256. 8. Saraf, A. K., V. Rawat, S. Choudhury and J. Das, (2009), Earthquake thermal precursors: Their detection and analysis, Proceedings of 2nd International Conference on Geoinformation Technology for Natural Disaster Management and Rehabilitation, held between 30-31 January 2009 at, AIT, Bangkok, Thailand, p. 256. 9. Saraf, A. K., V. Rawat, S. Choudhury, P. Banerjee, S. Dasgupta, and J.D. Das (2009), Remote Sensing Observations of Earthquake Thermal Precursors of India and Iran, Proceedings of 1st International Workshop on Validation of Earthquake precursors by Satellite and Terrestrial Observations (VESTO), Chiba University, Chiba Japan, March 26-28, 2009, p.17. National Conferences 10. Saraf, A. K., S. Choudhury, V. Rawat, P. Banerjee, S. Dasgupta and J.D. Das, (2008), Detecting Earthquake Precursor: A Thermal Remote Sensing Approach, Map India- 2008, held between 6-8 Feb. 2008, organised by GIS Development, NOIDA, India. 11. Saraf, A. K., V. Rawat, J. D. Das and Kanika Sharma (2010) Remote Sensing Observations of Earthquake Thermal Precursors, Proceedings of National Seminar on Geoscience for Society and Environment, Department of Geology, Utkal University, Bhubaneshwar, 22nd February 2010, p. 5. ANNEXURE-II REPRINTS OF FEW SELECTED PUBLICATIONS .. .•••'••• U.K. Saraf et al.I InternationalJi•aurnal ofApplied Earth Observation and Geoinformation 71 (2009) 373-379 t \ points were veryclear (Wu et al.,2000). A.K. Saraf et al./International Journal of Applied Earth Observation and Geoinformation 11 (2009) 373-379 377 insulator to become a p-type semiconductor (Fig. 3b). Positive holes propagate through an oxide orsilicate structure by electron image can therefore anticipate thestress transfer process and rock hopping (Fig. 3c), whereby electrons from neighbouring O2" can fracturing location. hop onto the 0 site. Following their concentration gradient between stressed and unstressed rocks, the p-hole spread out of 4. Discussions the stressed rock volume. The estimated maximum speed atwhich Observed andempirical dataofearthprocesses related tostress a positive hole could propagate byhopping is in the orderof100- conditions prior to earthquakes substantiate the appearance of 300ms (Freund, 2002). Since the positive holes travel viathe O thermal anomalies, which canbedetected bysatellite sensors. The 2p-dominated valence bond, they can easily cross grain boundaries geophysical community has. however, upheld apprehensions to (Fig. 3d) without being scattered or annihilated. the occurrence ofpre-earthquake thermal anomalies primarily for Laboratory experiments have proved thegeneration ofexcess IR two obvious reasons that Freund (2007) notes: (1) not every intensity and electric potentials onthesurface ofdry rock when earthquake is preceded by a reported precursor, just like subjected to heavy stress. long before failure (Ouzounov and foreshocks that might precede an earthquake ormight not. and Freund, 2004; Freund, 2003; Freund et al., 2005). Traveling inthe (2) there is no consistency in the mechanism proposed that might valence band ofotherwise insulating silicate minerals, p-holes are explain a certain precursor. An earthquake succeeds progressive capable ofspreading from where they are generated. They can stress conditions before thevisible rupture one can actually sense. cover macroscopic distances, of the order of meter in the Such stress conditions will definitely bring numerous physical laboratory, possibly kilometers in the crust. The current carried changes in the rock mass, which ifdetected can be a clue to an by p-holes is not stopped by intergranular water films (Freund, impending event. 2007). Itflows through centimeter-thick water layers, though the Localized greenhouse effect created due to the increased charge carriers change in the water. Important for pre-earthquake concentration of optically absorbing gases released from research is the fact that PHPs can be activated bystress. It costs stressed rock spaces is still thought by many researchers in energy to break PHPs and toactivate the p-holes. When p-holes this field a major causative factor leading to enhanced IR recombine, some ofthis energy will be regained. Theory predicts emission and is integral part of most of the propositions to that p-holes accumulate at the rock-to-air interface (King and" explain the physical mechanism of the thermal infrared (TIR) Freund, 1984). Stimulated mid-IR emission takes place from the anomaly. Stresses before an earthquake significantly alter rock surface within seconds ofthe application ofstress40-50cm away from the emitting rock surface. This observation and the hydrogeological regime of the region. Consequent changes in spectral signature ofthe emitted radiation provide strong evidence ground water table affect physiochemical properties of the soil that the underlying effect is akind ofmid-IR luminescence arising and its thermal emission. It has been proposed that all these from the recombination of p-holes at the rock surface (Freund developments taking place in the lithosphere also manifest et al., 2007). themselves by changing lower atmosphere properties like gaseous composition, air humidity, air temperature, etc. This calls for a close understanding of lithosphere-atmosphere 3.4. Remote sensing rock mechanics (RSRM) coupling. TIR anomaly explained on the basis ofair temperature rise isbased on the physical and chemical processes taking place Remote sensing rock mechanics (RSRM) is a new interface in the atmosphere as a result ofaction ofionizing radiation by subject, which is based on remote sensing, rock mechanics, radon gas (and/or its progeny products). The ions thus formed geophysics, physics, chemistry and informatics. This concept was act as the nuclei for water vapor condensation. During put forth by Geng, Cui and Deng in 1992 (Wu et al.. 2000) in the condensation large amount of latent heat is released, which wake of improved RS instrumentation and requirement of leads to the changes in the air temperature. Further, the prediction of rock failure. Part of RSRM which deals with the enhancement of surface temperature we call 'an anomaly' and detection of material behavior on stress application and time- subsurface processes which make the rock masses behave like a space forecast of rock failure can be utilized in earthquake charged battery, are explained by the PHP theory. Once the forecasting studies. positive holes are generated, currents propagate through the The experimental results show thatthe rock's IR radiation (8- rocks leading to electromagnetic emission, to positive surface 14u,m) temperature increases with loading. In general, the potentials, to coronadischarges, to positive ionemission, and to increment of IR temperature is 0.2-1 °C. But even higher IR mid-infrared radiation. These phenomena areexpressions ofthe temperature, 1.4-3°C, was detected at the fracturing position. same fundamental process: the'awakening' ofdormant positive After the rock failure, the IR temperature dropped with the hole charge carriers that turn rocks momentarily into p-type decrease ofthevertical stress.Itwasalsodiscovered that the rock's semiconductors. Experimental studies conducted on various infrared (IR) radiation intensity increased gradually with increas rock samples, in rock mechanics have also started providing ing stress. The variation of the IR spectral curve reflects the significant evidences insupport oftheories accounting tocharge variation of the rock sample's IR radiation energy. During the generation in the earth's crust. A correlation or InSAR deforma loading process, the stress transfer - because of rock deformation tion maps and LST map (Fig. 2) of the epicentral area has shown and cracking - could be clearly anticipated in the IR thermal plausible relationship between the deformation and thermal images. A bi-shear test of frictional sliding was conducted for anomalous region location. This also suggests that the appear simulating earthquake action. The sample wasmade from gabbro. ance of TIR anomalies is a ground related phenomena (Saraf The horizontal stress was kept at 50 MPa.The vertical stress on the et al., 2008). The thermal field of an object is decided by the central block was loaded from 0 MPa to 35.4 MPa. It could be seen material's thermal properties, the inner physical and chemical that the IR radiation temperature along the contact-face increased processes it experiences fromand its thermalexchange with the gradually as the vertical load became higher, and the four stress- outside world.The detected information relatingto the thermal locking points were very clear (Fig. 4). The highest IR radiation field can therefore be reasonably explainedand utilized after the temperature at the stress-lockinglocationwas 5 °Chigher than the physicalconcepts are understood. Theabilityof satellite sensors original value (Wu et al., 2000). Wu and his co-workers also found to map LST conditions around the time of an earthquake has that IR radiation temperature increases with depth inside the rock brought important breakthrough for earthquake thermal pre specimensurfaceand alsoincreaseswith rockstrength. IR thermal cursor studies. : •• • • A.K. Saraf et al./International Journal ofApplied Earth Observation andGeoinformation 11 (2009)373-379 the thermodynamicsof the lower atmosphere layers: the action of holes). Their dormant precursors are called peroxy bonds in ionization sourceand the strong electricfields (Pulinets, 2004). In language ofchemistryor positivehole pairs(PHP). APHP forms by seismically active areas the increased radon emanation from active two positive holes, i.e. -1 oxygen states (O-). combining and faults and cracks before earthquakes is thought to be the primary forming a peroxy link. When the PHP breaks an electron from an sourceof air ionization. Theairborne ions can subsequently form outside -2,oxygen state (O2")jumps into the broken bond. This cluster ions, small and large. During this condensation of water on electron becomes a "weakly bound electron",while the -2 oxygen existing airborne ions the "heat of condensation" is released. This (O2-) which had donated the electron becomes an O" site. A PHP leads to changesin the air humidity and air temperature. The part represents an 03X/°°\Y03, with X.Y- Si.**, Al3*, i.e., O" in a matrix of model describing the generation of anomalous electrical field in of O2" of silicates. the zones of earthquake preparation involves the gaseous aerosols Crystallographically, rocks forming mineral structures are with water moleculesattachment, which isaccompanied by latent three-dimensional arrays of oxygen; where it is assumed that heat variations. During these processes the large amount of heat oxygen always occur in O2- state. An 0" is an anion with an (-800-900 cal/g) is released (Sedunov et al„ 1997). In normal incomplete valence shell, seven electrons instead of the usual conditions when the number of large ionized clusters is small, the eight. Ifthe 0" is part of a X044* polygon (usuallya tetrahedron), meteorological processes prevail. But when the concentration of X- Si4*, Al3*, etc.. it mightbe writteneitheras XO„3* or as 03X/°. hydrated particles created by radon ionization increases up to Being unstable radicals, 0" and X043* or 03X/° form pairs, anomalous values are thought to provide a detectable contribution generating a PHP, which is, chemically equivalent to a peroxy in the surface latent heat flux (SLHF) variations up to anomalous anion, 0" +0--022" or a peroxy link as follows: 03X/° +o/ values registered by the satellites (Pulinets et al., 2006). The X03 - 03X/°°\X03. When are associated with certain defect sites in observed variations in the air temperature, air humidity and the host crystal structure, thermodynamically metastable peroxy atmosphericelectricityhavebeen attributed mainly to the changes links can exist (Freund, 2002, 2003). Introduction of PHPs in of the chemical potential of the newly formed particles. The mineralsduring rock-genesis and alteration has beenexplained by inability of this model to correlate the physical mechanism to earth Freund (2002). Interestingly, 0~-0" bond-distance (1.5 A) is crust processes, role of rocks and the fact that all the chemical almost half of 02~-02- bond-distance (2.8-3.0 A). This implies processes described to explain the mechanismare not unique to that the peroxy-bond 0~ has a small partial molar volume, thus earthquakes and take place continuously; make this model having a tendency to be favored by high pressure. unsustainable. Igneous and metamorphic rocks, which make up a major portion of the Earth's crust, contain these electronic charge 3.3. p-hole activation theoiy carriers, which have been overlooked in the past. In the process of igneous rock formation that begins with the crystallization from A mechanism of strong low frequency electromagnetic emis H20 laden magmas, small amounts of water are structurally sion has been proposed by Freund and his co-workers through a incorporated in the minerals, even into those that are normally solid-state physics viewpoint (Freund, 2002. 2003, 2007; Freund considered anhydrous. The mode of incorporation leads to the et al., 2005). The proposed mechanism combines the critical formation of hydroxyl, 03X-OH with X- Si4*, Al3*. etc. During earthquake conceptof crust acting as a chargingelectric battery cooling,hydroxyl pairs undergo an electronic rearrangement that under increasing stress. The electric charges are released by results in the formation of peroxy links, 03X-0O-XO3 + H2 activation of dormant charge carriers which consist of defect (Fig. 3a). When dissociated under stress a PHP introduces two electrons in the oxygen anion sublattice, called positiveholes(p- holes (charge deficiencies) into the valence bond, causing the (ii1dormantslate (d) spreading-out O o O Q n grain boundaiy O OO £p|L n peroxy bond (PHP) (h) break and activation Q \J' Q Q CO o sftytfV* O normal oxygen atom, 02~ p-holc. O" (c) electric replacement 0 o o o o - moving-outof p-hole (M _jfMb KJ jumping-in ofelectron Fig. 3.Schematic diagram showing p-hole activation andspreading in igneous or high-grade metamorphic rock, (a) p-holes equivalent to 0" isusually ina dormant state peroxy bonds thatarealso known asPHPs. (b)When stresses break aperoxy-bond. twop-holes areactivated, (c)An electron jumpsinfrom neighboring 0*"andp-hole moves out. (d) p-holes can spreadacrossgrain boundariesand through thick layersof igneous rocks(Takeuchi et al.. 2005). \i : AX Saraf et al. /International Journal of Applied Earth Observation and Geoinformation U(2009) 373-379 and the thermophysical properties of the rocks. The following equation of heat balance on the earth surface (z=0) could be IR flux. According to this model, during earthquake preparation written: period, gases like H2, He, CH«, C02. 03. H2S. Rn, along with water vapor andassociated heat(Wakita et al.. 1978) reach theearth's cIT - or + qt + qEV + q. surface and here the litho-atmosphere coupling starts. Emission or T\z (1 these gases has been reported from tectonically active regions or where, q, is the radiation balance, qt is the heat loss due to the world (Biagi etal., 2000; Virk etal.. 2001; Salazar etal 2002) turbulent exchange between atmosphere and ground surface q.v is At first, convection heat flux (hot water and gas) changes the the heat loss due to the evaporation, qg is the geothermal flux zis temperature orthe earth's surface. In asecond phase, achange in depth (Tronin, 1996, 2000). the water level with usual temperature alters soil moisture and Geothermal flux (cjg) isusually considered tobeless than other consequently the physical properties or the soil. The different terms in Eq. (1) by several orders ofmagnitude. Variation ofthis physical properties determine the different temperature on the parameter is unlikely to explain the thermal IR anomaly The surface. Third is the greenhouse effect. Greenhouse gases absorb a convective component of geothermal flux related to the fluid part ortheearth's IR radiation and thus lead tothe accumulation or movement can be as high as10-50 Wirr2 (Gorny and Shilin. 1992) heat near surface. The amount orsolar energy absorbed by a 1- and can affect surface temperature as well as solar and 10 cm thick layer orpure C02 (which corresponds to 1m thick meteorological fluxes. Moisture content in soil and humidity in atmosphere layer with a 1-10% C02 concentration) is in the range air were also set down as important factors controlling surface or 10-60 Wm-2 (Tronin. 1996). Thereafter, transfer or the energy tothe upper atmosphere and ionosphere isbelieved tooccur Itis temperature. These processes influence other processes like reported that atmospheric gravity waves (AGW) might be the most evaporation and moisture condensation (qc„) leading to the possible carrier ror energy from lithosphere to the ionosphere surface temperature variations. (ICorepanov and Lizunov, 2008). However, there is great uncer 3. Mechanism of thermal anomaly tainty knows how AGWs are generated except by the up and down motion or the earth surface during an earthquake or an ocean Explanations to understand the reasons which lead to the surface during a large tsunami. increment in outgoing IR radiation ahead of an impending According to Tronin and his co-workers (2002) geological earthquake are mainly based on phenomena like the release of structures like faults, fractures, cracks, etc. serve as preferred greenhouse gases, characteristics ofsoil dynamics, including soil conduits for convective fluids (water and gases) to the upper levels moisture and gas content, and crystal structure of rock masses of the lithosphere. This also explains the association of the under stress. Freund et al. (2005) defines enhanced thermal IR geological structures with the thermal anomaly. Geological emission from the Earth's surface retrieved by satellites prior to an conduits present in the crust increase the transport orthe heat earthquakeas 'thermal anomaly'. by one order orthe magnitude above the diffusive flow. Tronin also Broadly, themechanisms explaining thegeneration ofthermal agrees that the cause of the thermal IR anomalies lies in the anomaly can be grouped into two categories, the first accounting lithosphere and is related to stress changes. Thermal changes in atmospheric processes responsible for theappearance ofthermal materials due to the stress fields have also been determined in anomaly, and the second attributing rise in LST due to ground laboratory studies (Brady and Rowell. 1986; Qiang etal. 1997) related processes. In this paper we discuss mainly four mechan Later Qiang etal. (1997) proclaimed the 'Geo-thermal Theory' to isms of thermal anomalies, based on observed earthquake be the cause of the thermal rise. According to them the associated phenomena and experimental results and detectable temperature increasing mechanism of satellite thermo-infrared by satellite sensors. of lower air may be caused by paroxysmal releasing or crustal gas and sudden changing or lower atmosphere electrostatic field. 3.1. Earth degassing theory and gas-thermal theory Experimental studies suggested that mixed gases C02 and CH„, etc. (owing to their greenhouse nature) in different ratios under the action ortransient electric field may cause temperature increase o( The history ofthermal remote sensing inearthquake research about 6"C, while under the solar irradiation may lead temperature dates back to the late 80s (Gorny etal.. 1988,1997). Gorny. Xu and to rise around 3°C. They observed that there was no change in Dian first suggested that TIR anomalies detected by meteorological temperature with aconstant electric field andcan occur only under satellites provide indications to seismic activities (Lixin et al.. theaction ortransient electric field. They explained thecreation or 2000). Two decades back the 'earth degassing theory' (Qiang etal." electric field was due to the piezoelectric effect of rocks and 1991) was proposed. This theory believed in the initiation or earthquake lightening. / microcracks and release ofpore gases into the lower atmosphere In this theory the cause of anomalous electric field was with strengthened stress conditions around the earthquake explained by piezoelectric effect of the rocks and earthquake location. It was proposed that with further increase of stress lightening. While it iscommon practice toquote the piezoelectric and spread or degassing sites, the temperature reached a effect or the rocks, this idea is based on a complete lack or maximum when the gases enhanced the greenhouse effect in understanding ofpiezoelectricity ofany system (like granite) that the atmosphere. Afurther rise instress conditions will eventually contains billions of randomly oriented piezoelectric quartz close crack conduits and degasification will stop. Itisimportant to crystals. Stressing such a system cannot produce any sizeable note thatQiang and his co-workers believe thatinsuch asituation, electric field. Besides, theirexperimental setup neither involve role the temperature will drop and the earthquake should happen after ofrocks nor comment on the behavior of rocks under 'regional a period ofquiescence. Whether thisquiescence appears before the stress field'. earthquake event, coincides with the event or occurs after the event, we believe, are different conditions, which can very well 3.2. Seismo-ionosphere coupling theory happen indifferent earthquake process situations. Itmight also be feasible thatadrop oftemperature below usual temperature ofthe lonosphere-lithosphere coupling or seismo-ionosphere cou region happens around the earthquake period and subsequently pling has been proposed to explain the physical phenomena return to normal. Tronin(1996). based on ground data horn a few behind the thermal infrared anomaly and surface latent heat fiux Tault zones, proposed that the atmosphere influences the outgoing variations. Itsays thatatleast two processes can essentially change V ' . 374 AXSaraf et at./International Journal ofApplied Earth Observation and Geoinformation 11 (2009) 373-379 !. Introduction DETECTION BYSATELLITE THEFjIVIAr. SENSORS Earthquakes are probably the mostunexpectedand devastating natural phenomenon. Present seismological techniques are still unable to make any precise earthquake forecast in terms of accurate time, location and magnitude. Geller et al. (1997) have Thermometer even declared, "Earthquakes cannot be predicted". There are also sheltered geophysicists like Campbell (EOS, 1998) who believe that a better in meterological station focus would be for hazard mitigation and understanding source mechanisms and response of infrastructures to seismic vibrations rather than applying efforts for what he calls 'pseudoscientific' attempts for understanding precursor earthquake signals. But devastating earthquakes keep happening and foster challenges to those scientists who think that natural processes, which are currently not yet understood should be studied with greater efforts not with less. Our understanding ot the earth and or seismology keeps evolving. Ourpresent knowledge or complexsubsurfacephenom ena and their influence on the atmosphere encompass societal need fora reliable, testable interpretation or the subtle signatures that the earth throws at us. A scientific approach to monitor precursors based on laboratory experiments for the observations or any precursors continue to pour in from different workers. Electromagnetic satellite sensor techniques have shown an excellent forecasting potential (Fig. 1). Recognition or the simple concept that an earthquake shock itself is a climax of some processes (so called earthquake sequence), which begins some time before the main shock and continues for some time after it Fig. 1.Schematic diagram showing twowidely accepted theories ofgeneration of pre-earthquake thermal IR anomaly that can be detected by satellite thermal (Hayakawa et al.. 2000), has opened avenues for satellite monitoring or seismic hazards. Identification o( thermal infrared (TIR) precursors as pre-seismic signal has gained wide support spatial extent vary fromearthquake to earthquake and dependson world over, especially in Russia, China, India, United States, Italy magnitude, focal depth and terrain conditions. (Corny et al., 1988; Qiang et al., 1991; Tronin, 1996, 2000; However, precursor substantiation (Pulinets, 2006) demands Tramutoli et al., 2001; Saraf and Choudhury, 2003, 2005a,b,c; existence of a well-explained physical mechanism for their Choudhury et al.. 2006a.b; Sarar et al., 2006, 2007, 2008: Panda appearance. We discuss in this paper various propounded et al.,2007; Ouzounov and Freund. 2004). We observed such short- explanations to the precursory signals that we could detect term anomalies around the epicentral region for earthquakes in through satellite sensors. India. Algeria, Iran (Fig. 2),China, Pakistan and Indonesia through NOAA-AVHRR, Terra/Aqua-MODIS and passive microwave DMSP- 2. Physical nature of thermal anomaly SSM/I satellite data and call them 'transient TIR anomalies'. The anomalies are seen to appear a few days before the earthquakes. The infrared (IR) fieldof the earth's surface is generated due to The rise in radiation temperature was seen to vary within 5-12 "C. its temperature. This is formed by the re-emission or the absorbed There is a time on the order or maximum expression or the part or the solar energy and to lesser extent, bygeothermal fluxes. anomaly and it generally goes away soon after the earthquake The main parameters defining the conditions or IR signal event.Thecharacteristic or the TIR anomalies, their intensity and generation are: the emissivity or the surface (es), the albedo (A) ' -*0 -30 -20 -10 0 10 20 30 Temperature (BC) Parsons?. CciKffiTv Univ. Oxford (L astfc- ' -i. -JI... -Jf Fig. 2. Acomparison of thermal anomaly field of 22 February, 2005 earthquake derived from daytime NOAA-AVHRR image (a); with InSAR deformation map (b)generated for earthquake (Source: Parsons. 2005) shows agood correlation of location or thermal anomaly with co-seismic deformation. Red star indicates location of Zarand earthquake epicenter. (For interpretation ofthe references tocolor inthis figure legend, the reader isreferred totheweb version ofthearticle.) IfSrt'* iH^-ON^J CC;p-,v' International Journal of Applied Earth Observation and Geoinformation H (2009)373-379 Contents lists available at ScienceDirect International Journal of Applied Earth Observation and Geoinformation ^lER journal Homepage: www.elsevier.com/locate/jag —• '•• "V •• ! •. ...i •!••• ,.., m _,„,, _____ ._.[ Review Advances in understanding of the mechanism for generation of earthquake thermal precursors detected by satellites Arun K. Sarafa,*f Vineeta Rawat3, Swapnamita ChoudhuryM, Sudipta Dasguptac,\ Josodhir Dasd -1 Department of Earth Sciences. Indian Institute of Technology Roorkee. Roorkee 247667. India bWndia Institute afHimalayan Geology. Dehradun. India c Reliance Industries Limited. Navi Mumbai. India ''Department ofEarthquake Engineering. Indian Institute of Technology Roorkee. Roorkee 247667, India ARTICLE INFO ABSTRACT Article history: Stressesbuildingupduringanearthquakepreparationphasealsomanifestthemselves inthe form ofa so Received 19 September 7.008 calIed increased land surface temperature (LST) leading toa thermal precursor prior tothe earthquake cccp e juy event. This phenomenon has now been validated byourobservations ofshort-term thermal anomalies — detected by infrared satellite sensors for several recent past earthquakes around the world. The rise in Keywords: infrared radiance temperature was seen to vary between 5 and 12°C for different earthquakes. We , . _. discuss in this paper different explanations for the generation of such anomalies that have been offered. ThermalLand surfaceinfraredtemperatureanomaly Emissionr- •• ofr gases duej to.•_•the opening and••*••closure ofmicropores upon ...induced stresses and also the Positive hole theory ' participation of ground water have been propounded as a possible cause for generation ofthermal anomalies. Seismo-ionosphere coupling, by which gases like radon move to the earth-atmosphere interface and cause air ionization thus bringing about a change in air temperature, relative humidity, etc.. has been put forth by some workers. A mechanism of low frequency electromagnetic emission was tested and experimented by scientists with rock masses in stressed conditions as those that exist at tectonic locations. The workers proposed the positive hole pair theory, which received support from several scientific groups. Positive holes (sites of electron deficiency) are activated in stressed rocks from pre-existingyet dormant positive hole pairs (PHPs) and their recombination at rock-air interface leads to a LST rise. A combination of remote sensing detection of rock mechanics behavior with a perception of chemistry and geophysics has been applied to propose the remote sensing rock mechanics theory. Remote sensing detections of such anomalies confirm so far proposed lab theories for such a hotly debated field as earthquake precursor study by providing unbiased observations with consistency in time and space distribution. © 2009 Elsevier B.V. All rights reserved. 1. Introduction 374 2. Physical nature of thermal anomaly 374 3. Mechanism of thermal anomaly 375 / 3.1. Earth degassing theory and gas-thermal theory 375 3.2. Seismo-ionosphere coupling theory 375 3.3. p-hole activation theory 376 3.4. Remote sensing rock mechanics (RSRM) 377 4. Discussions 377 References 379 * Corresponding author. Tel: +91 1332 285549; Tax: +91 1332 285638. E-mail addresses: [email protected], arun.k.saraWgmail.com (A.K. Saraf)- 1 Present address. 0303-2434/$ - see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2009.07.003 Table7 Characteristicsoftheshort-termpre-earthquakethermalanomaliesassociatedwiththeIranearthquakesstudiedthroughNOAA-AVHRRthermaldatasets BO S. no. Earthquake i Magnitude(A/w) Focal Pre-earthquakethermalanomaly(beforetheearthquake) Intensityof Spatialextent dePth ~ — thermal of thermal (km) Risestarted(days) Maximumriseobserved(days) rise ~q anomaly (km2) 1. Changureh-A vaj* 6.5 10 2 5-10 1,63,243 22 Jun 02 2 Jahron 5.8 10 7 5-7 13,82,543 10 Jul 03 3 Kerman* 5.9 20 11 10** and 6 5-10 949,780 21 Aug 03 4 Bam* 6.6 10 7 (.nighttime data) 5 (nighttime data) 7-13 (nighttime) 3,08,000 (nighttime) 25 Dec 03 4 (daytime data) 2 (daytime data) 7-10 (daytime) 3,28,200 (daytime) s Firozabad-Kajoor 6.3 28 5 1 4-6 71,22,452 28 May 04 6 Zarand* 6.4 14 S 1 10-12 75,600 22 Feb 05 7 Qeshm 6.0 10 7 3-4 2-3 3,82,074 27 Nov 05 8 Faryab 6.0 18 4-8 7,29,344 28 Feb 06 9 Fin 5.9 18 13 10** and 2 5-7 9,63,072 25 Mar 06 10 Persian Gulf 5.8 10 11 2-4 7,70,457 28 Jun 06 \& on Earthquakes discussed here in details 1. Changureh-Avaj* 6.5 10 7 2 5-10 1,63,243 22 Jun 02 2 Jahron 5.8 10 9 7 5-7 13,82,543 10 Jul 03 3 Kerman* 5.9 20 11 10** and 6 5-10 949,780 21 Aug 03 4 Bam* 6.6 10 7 (nighttime data) 5 (nighttime data) 7-13 (nighttime) 3,08,000 (nighttime) 25 Dec 03 4 (daytime data) 2 (daytime data) 7-10 (daytime) 3,28,200 (daytime) 5 Firozabad-Kajoor 6.3 28 5 1 4-6 71,22,452 28 May 04 6 Zarand* 6.4 14 5 1 10-12 75,600 22 Feb 05 7 Qeshm 6.0 10 7 3-4 2-3 3,82,074 27 Nov 05 8 Faryab 6.0 18 5 2 4-8 7,29,344 28 Feb 06 9 Fin 5.9 18 13 10** and 2 5-7 9,63,072 25 Mar 06 10 Persian Gulf 5.8 10 11 8 2^ 7,70,457 28 Jun 06 lfi> on Earthquakes discussed here in details 13 -t 5' Earthquakes with dual thermal peak era 134 Nat Hazards (2008) 47:119-135 A prominent observation regarding the earthquakes of moderate magnitude, i.e. less than magnitude 6 in the present study, is the appearance of a dual TIR peak in surface temperatures instead of the single rise observed previously. The first peak appears about 10 days before the earthquake and the second temperature peak relatively closer to the main shock, i.e. 2-6 days. This may lead us to infer that perhaps the energy accumulatedin the stressed rocks has been released sporadically in the form of the electromagnetic emission, apparent temperature increment or any other geophysical earthquake precursor, which in turn might reduce the magnitude of the main shock. Study of other earthquakes ((Jahron (10 Jul 03), Kerman (21 Aug 03), Fin (25 Mar 06), Persian Gulf (28 Jun 06)) showing a series of aftershocks reveals that the occurrence of aftershocks prevents the re-establishment of normal conditions even after the main event is over. Due to prevailing residual stresses, the epicentre and adjoining areas experience aftershocks and thus the disappearance of the IR anomaly takes a longer time. The observation of a common field for TIR anomaly and surface displacement of Bam and Zarand earthquakes is a significant contribution. The concurrences of both the fields provide enough reason to believe plau sible relationship between surface deformations and appearance of the TIR anomaly. The observation substantiates the explanations for pre-earthquake TIR anomalies given above. This is an important contribution to the knowledge of pre-earthquake TIR anomaly phenomena and for developing it as reliable and potential earthquake precursors. Acknowledgement We are greatly indebted to the Department of Science and Technology (Seismology Division), New Delhi, for financial assistance. References Banerjee P (2007) Analysis of thermal remote sensing data in earthquake studies. M.Tech Dissertation, Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee (unpublished) BarryLP, PhilipsT (2003) Anticipating earthquakes. Available onlineat: http://science.nasa.gov./headlines/ y2003/llaug_earthquakes.htm?list50946 (accessed on 19 Sept 2006) Becker F, Li ZL (1990) Towards a local split window method over land surfaces. Int J Remote Sens 11: 369-393 Choudhury S (2005) Development of remote sensing based geothermic techniques in earthquake studies. Ph.D. Thesis, Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee (unpublished) Choudhury S, Dasgupta S, Saraf AK, Panda S (2006) Remote sensing observations of pre-earthquake thermal anomalies in Iran. Int J Remote Sens 27(20):4381-4396 Freethoughts(2006)Earthquakesin Iran:a geologicalperspective.Availableonline at: http://freethoughts.org/ archives/000389.php (accessed on 10 Aug 2007) Freund F (2000) Time resolved study of charge generation and propagation in igneous rocks. J Geophys Res 105:11001-11019 Freund F (2002) Charge generation and propagation in igneous rocks. J Geodyn 33:543-570 Freund F (2003) Rocks that crackle and sparkle and glow: strange pre-earthquake phenomena. J Sci Explor 17(1):37-71 Freund F, Keefner J, Mellon JJ, Post R, Takeuchi A, Lau BWS, La A, Ouzounov D (2005) Enhanced mid-infrared emission from igneous rocks under stress. Geophys Res Abstr 7:09568 Freund F, Takeuchi A, Lau BWS, Al-Manaseer A, Fu CC, Byrant NA, Ouzounov D (2007) Stimulated infrared emission from rocks: assessing a stress indicator. Earth 2:1-10 Gorny VI, Salman AG, Tronin AA, Shilin BB (1988) The earth's outgoing IR radiation as an indicator of seismic activity. Proc Acad Sci USSR 301:67-69 Ghafory-Ashtiany M (1999) Rescue operation and reconstruction of recent earthquakes in Iran. Disaster Prev Manage 8(l):5-20 Nakamura T, Suzuki S, Matsushima T, Ito Y, Hosseini SK, Gandomi AJ, Sadeghi H, Maleki M, Aghda SMF (2004) Source fault structure of the 2003 Bam earthquake, Southeast Iran, inferred from aftershock _: Springer Nat Hazards (2008) 47:119-135 distribution and its relation to the heavily damaged area: existence of the Arg-e-Bam fault proposed oVTSoV'S^ NOAA (2006) NOAA KLM User's guide. Available online at: http://www2.ncdc.noaa.gov/docs/klm/html /c7/sec7-l.htm (accessed on 28 April 2006) Ouzounov D Freund FT (2004) Mid-infrared emission prior to strong earthquakes analyzed by remote sensing data. Adv Space Res 33:268-273 ' 'c",ole Parsons B(2005) ERS and Envisat missions: data and services. Available online at: http://earth.esa int/ tnnge2005/proceedings/presentations/800_Iaur.pdf (accessed on 30 Oct 2006) Pellet F Keshavarz M Jafari K, Hosseini KA (2007) Rock deformation and rock failure precursors application for earthquake prediction. Presentation made at SEE 5Conference, held between 13-16 May 2007 at Tehran, Iran Qiang ZJ, Kong LC, Zheng LZ, Guo MH, Wang GP, Zhao Y(1997) An experimental study on the temperature increasing mechanism ofsatellite thermo-infrared. Acta Seismol Sin 10247-252 Saraf AK, Choudhury S(2003) Earthquakes and thermal anomalies. Geospatial Today 2(2)-18-20 Saraf AK, Choudhury S(2005a) NOAA-AVHRR detects thermal anomaly associated with 26 January 2001 Bhuj Earthquake, Gujarat, India. Int J Remote Sens 26(6)'1065-1073 Saraf AK Choudhury S(2005b) Satellite detects surface thermal anomalies associated with the Algerian earthquakes of May 2003. IntJ Remote Sens 26(13)2705-2713 Saraf AK, Choudhury S(2005c) Thermal remote sensing technique in the study of pre-earthquake thermal anomalies. J Indian Geophys Union 9(3): 197-207 Saraf AK, Choudhury S (2005d) SSM/I applications in studies of thermal anomalies associated with earthquakes. Int J Geoinf 2(3):197-207 Saraf AK, Choudhury S, Panda S, Dasgupta S, Rawat V(2007) Does amajor earthquake precede athermal anomaly. Int J Geoinf (in press) Shanjun L, Lixin W(2005) Study on mechanism of satellite IR anomaly before tectonic earthquake Available online at: http://ieeexplore.ieee.org/iel5/10226/32601/OI5268l6.pdf (accessed on 15 Dec 2007) Stramondo S, Moro M, Doumaz F, Cinti FR (2005a) The 26 December 2003, Bam Iran earthquake: surface displacement detection from Envisat ASAR Interferometry. Int J Remote Sens 26(5)1027-1034 Stramondo S, Moro M, Tolomei C, Cinti FR, Doumaz F(2005b) InSAR surface displacement field and fault modeling for the 2003 Bam earthquake (southeastern Iran). J Geodyn 40347-353 Talebian M, Biggs J, Bolourchi M, Copley A, Ghassemi A, Ghorashi M, Hollingsworth J, Jackson J Nissen E, Oveisi B, Parsons B, Pristley K, Saiidi A(2006) The Dahuiyeh (Zarand) earthquake of 2005 February 22 in Central Iran: reactivation of an intramountain reverse fault Gephys J Int 164137-148 Tramutoli V, DiBello G, Pergola N, Piscitelli S(2001) Robust satellite techniques for remote sensing of seismically active areas. Ann Geofis 44:295-312 Tronin AA (1996) Satellite thermal survey—a new tool for the study of seismoactive regions Int JRemote Sens 17:1439-1455 Tronin A(2000) Thermal IR satellite sensor data application for earthquake research in China Int JRemote Sens 21(16):3169-3177 Tronin AA, Hayakawa M, Molchanov OA (2002) Thermal IR satellite data application for earthquake research in Japan and China. J Geodyn 33:519-534 USGS (2002) International Institute of Earthquake Engineering and Seismology. Available online af http://www.iiees.ac.ir/english/bank/Avaj/avaj_report.html (accessed 27 Aug 2007) Wu L, Cui C, Geng N, Wang J(2000) Remote sensing rock mechanics (RSRM) and associated experimental studies. Int J Rock Mech Min Sci 37:879-888 ZiQi G, ShuQuing Q, Chao W, Zhi L, Xiang G, Weiguo Z, Yong Y, Hong Z, Jishuang Q(2004) The mechanism of earthquake's thermal infrared radiation precursory on remote sensing images Available online at: http://ieeexplore.ieee.org/iel5/7969/22039/01026436.pdf (accessed on 15 Dec 2007) __ Springer 132 Nat Hazards (2008) 47:119-135 30"N 30°N 60"E SO'E • / i 30'N <•, / >wfl / 60*0 &m 16 Aug 2003 '- • IK 30'N 30*N 30*N 30*N .. fir _ji. , ___•_» / * *•*! m 60*E f ^18 Aug 2003 60"E 60*E 5C 6(fE^ ; 23 Aug 2003 30*N 30«N * tp #v. l\ / .-V •*%»**:;:: .** * BAoiMral 60'J; * 2?Aui);iroj 60*£; 28aub 60*E 29 Aug 2003 •kEpicenter 21 Aug 2002 -10X Kerman earthquake O'C 1 Mag: 5.9, Depth: 20km 2G"C 1 40"C 50*C m LST Scale| Fig. 8 Daytime NOAA-AVHRR LSTtime series map of Iranbefore andaftertheearthquake in Kerman in Iran on 21 Aug 2003. Red colour-filled star symbol indicates the occurrence of the earthquake, whereas without colour-filled star signifies location of the impending earthquake epicentre of 21 Aug 2003 Table 6 List of aftershocks that followed the Kerman earthquake S. Date Time Latitude Longitude Depth Magnitude no. (UTC) (N)(°) (E)(°) (km) (NEIC) (Source: http://www.iiees.ac.ir) 1 21Aug03a 04:02 29.09 59.81 20 Afw5.9 2 28 Aug 03 18:31 28.37 54.07 33 Mb4.7 3 29 Aug 03 06:55 28.38 51.52 33 A/b4.9 4 11 Sep 03 19:31 28.39 54.02 33 Mb4.6 a Main earthquake event _ Springer Nat Hazards (2008) 47:119-135 131 Table 5 Details of nighttime S. no. Date Time of acquisition (UTC) and daytime NOAA-AVHRR data of the year 2003, used to Nighttime Daytime prepare LST time series maps as shown in Fig. 8 to study pre- 1 11 Aug 03 18:18 - earthquake thermal anomaly for Kerman earthquake 2 12 Aug 03 17:55 6:43 3 13 Aug 03 17:32 - 4 14 Aug 03 17:09 6:20 5 15 Aug 03 18:28 7:15 6 16 Aug 03 - 6:52 7 17 Aug 03 17:42 - 8 18 Aug 03 17:19 6:07 9 19 Aug 03 - 7:25 10 20 Aug 03 - 7:02 11 21 Aug 03 - 6:39 12 22 Aug 03 17:29 6:16 13 23 Aug 03 17:06 7:35 14 24 Aug 03 - 7:12 15 25 Aug 03 18:02 6:49 16 26 Aug 03 17:39 6:26 17 27 Aug 03 18:57 7:45 18 28 Aug 03 18:34 7:22 4 Discussion and conclusions Thermal sensors like NOAA-AVHRR have proved to be very useful in detecting TIR precursors before an earthquake actually strikes, though this analysis has been a post-event study. Nevertheless, the understanding of the buildup of pre-earthquake TIR anomalies and their detection provides possibilities of a reliable potential precursor. The post-event investigation of pre-earthquake thermal anomalies by analysing pre- and post-earthquake LST images reveals valuable information about the changes in the TIR regime of the affected area. The analyses of time series LST maps for the past ten moderate-to-strong earthquakes show a 2-13°C rise in LST 1-10 days before the earthquake struck (Table 7). It was also noticed that magnitude and focal depth play a vital role in intensity and spatial extent of the thermal anomaly. Higher earthquake magnitude and shallower focal depth are favourable conditions for the appearance of intense thermal anomaly with larger spatial extent and vice versa. In this study, moderate-to-strong magnitude earthquakes were selected. It was seen that there was a thermal anomaly of higher-than-usual temperature of the region. Daytime and nighttime data of the Bam earthquake and other studied earthquakes show a difference in the appearance of the TIR anomalies and peak anomalous temperatures. This is probably due to the typical meteorological phenomena or the appearance of clouds during the day or night. Clouds during the day may lead to a comparatively cooler day, but at night may lead to a hotter night since clouds prevent the Earth's heat from escaping. In case of cloud-free weather conditions this difference may be attributed to differential heating contrary to differential cooling. As such, the diurnal thermal regime may not be conformable at daytime or nighttime. But it was a regular observation that the anomalies appear first in nighttime thermal images. _. Springer 130 Nat Hazards (2008) 47:119-135 \ 16Jun02|^ 1 7Jun 02 •¥ + \ 35*N 35aN -V "55'E ss*e • 50'E\ ••.. 30*N 30"N 18Jun02j_jj! J20Jun02 !2l7un02 ' t V"! k ... t • ♦ + 35'N 35*N .- 35*N 55"E 55'E 55'E 30'N 50"£', s.S S«E_ .... 30!N ' 50*E*2:.„ - 30'N .A 22 Jun 02 23 Jun 02 25 Jun 02 sT*«t - j "4** "I .V, 35*N '^K*". 35*N "--"I. ^ . n t Clouds H8» Mtf*•'•«& __. 55'E 50'Et. •:•... 55*E:. 30*N 5Q°E \ • 50*E ^ -' 30*N -^- Epicentre 22 June 2002 20eC 10*C OX -10°C -20*C Changureh-Avaj Earthquake Magnitude 6.5, Depth 10km i Fig. 7 Nighttime NOAA-AVHRR LST time series map of Iran before and after the earthquake in Changureh-Avaj in Iran on 22 Jun 2002. Red colour-filled star symbol indicates the day of the earthquake, whereas without colour-filledstar signifies locationof the impending earthquake epicentre of 22 Jun 2002 temperatures isabout6°C.Thereafter,thetemperatureof thelandsurfaceagainsubsidesback to normal conditions. Due to persistent cloud cover over the epicentre area on the 21 Aug 2003,theLSTmapcouldnotbeprepared. Thehotterlandsurfaceseenafter21Aug2003may be attributed to the aftershocks (Table 6) that continued for about 20 days after the main event.The anomalous temperature after the mainearthquake event as seen on LSTmapsof 28-29 Sep 2003 coincides with the aftershocksthat the region suffered (Table 6). Thus, in this earthquake a dual peak in the surface temperatureconditions instead of the single rise, that has been observed previously, is seen. This may lead us to consider a possibility of sporadic release of accumulated energy in the stressed rocks, which might lead to the reduction of the magnitude of the main shock. _: Springer • Nat Hazards (2008) 47:119-135 129 -40 JO JO -10 0 10 20 30 Temperature (°CJ Fig. 6 A comparison of thermal anomaly field of 22 Feb 2005 derived from daytime NOAA-AVHRR image (a) with surface displacement field derived from InSAR study (b) (Parsons 2005). Red star indicates the location Zarandearthquake epicentre Table 4 Details of nighttime and daytime NOAA-AVHRR S. no. Date Time of acquisition (UTC) data of the year 2002, used to prepare LST time series maps as Nighttime Daytime shown in Fig. 7 to study 1 15 Jun 02 pre-earthquake thermal anomaly 23:39 10:49 for Changureh-Avaj earthquake 2 16 Jun 02 23:27 _ 3 17 Jun 02 23:16 _ 4 18 Jun 02 23:05 10:39 5 19 Jun 02 - 10:28 6 20 Jun 02 22:44 10:16 7 21 Jun 02 22:32 10:05 8 22 Jun 02 22:21 09:53 9 23 Jun 02 22:10 09:42 10 24 Jun 02 - 09:31 11 25 Jun 02 23:29 - anomalous region was not available due to persistent cloud cover but adjoining areas certainly show lowering of the temperature after the earthquake event. Daytime LST maps show a relatively late appearance of the thermal anomaly. The anomaly started developing on 17 Jun 2002, reached its maximum on 20 Jun 2002 and returned to normal conditions after the main shock on 22 Jun 2002. In case of the Changureh-Avaj earthquake time ofappearance ofthe thermal peak is same in daytime as well as in nighttime LST maps. 3.4 Kerman earthquake (21 Aug 2003 at 8:32 LT) Thedaytime andnighttime time series LST maps prepared from theNOAA-AVHRR datasets (Table 5; Fig. 8) for this earthquake indicate a gradual rise in the surface temperature that reaches its peak on 11 Aug 2003, which isfollowed bya temporary fall ofthe temperature to again pick up a rise on 15 Aug 2003 (Fig. 8). The difference between the two peak _Springer 128 Nat Hazards (2008) 47:119-135 Fig. 5 Daytime •••, NOAA-AVHRR LST time series 1 [LLELL map of Iran before and after the /: \ earthquake in Zarand, Iran, on 22 Feb 2005 (Choudhury 2005). Red colour-filled star symbol • _,, indicates the day of the \ > . a earthquake, whereas without \ colour-filled star signifies AK N location of the impending V i . _.i- earthquake epicentre |19£Kb 05j"*^ '-~^ j 320Feb 05s t of 22 Feb 2005 -\.y *1*0 v ' K\ < -. X # , £_ # 1 ^^X *•'* •* ^k^~ j*__'<_ Kit :!a • 3*ffl !^B^..$ - * 22 Feb 2005. Central Iran Earthquake Epicentre, Magnitude: 6.4 field observations, i.e. uplift on the north side of a roughly E-W fault, and subsidence to the south of it (Fig. 6). The InSAR surface deformation field falls within the anomalous temperature field developed before the Zarand earthquake. This observation suggests a relationship between pre-earthquake thermal anomaly phenomena and surface deforma tion. A detailed investigation of such observations in several other earthquakes may provide a better understanding of earthquake rupture process. 3.3 Changureh-Avaj earthquake (22 Jun 2002 at 7:28 LT) The analysis of nighttime and daytime LST maps of the region shows the presence of an anomalous area around the earthquake epicentre. In nighttime LSTs it started developing on 15 Jun 2002 and reached its maximum on 20 Jun 2002, i.e. 2 days prior to the main event (Table 4; Fig. 7). The maximum spatial extent of this anomalous region was about 1,64,000 km2 and intensity of thermal rise was as high as 5-10°C. LST map of the _Spriringer Nat Hazards (2008) 47:119-135 127 Bam fault v . Bavarat 2KN 29°N 60*1 ~i Bam fault N M~ 4 km « a « •» -jo ii o to _> afcrr? 'f» ;— Temperature (°C) [Source: Stramondo et at, 2005 (a) & (b)] Fig. 4 A comparison of thermal anomaly field of 26 Dec 2003 derived from nighttime NOAA-AVHRR image (a) and surface displacement field derived by unwrapping interferometric phase onto the SAR amplitude image (b). The black line in (b) indicates the SAR inferred earthquake fault location. Red star indicates the location of Bam earthquake epicentre Table 3 Time of acquisition of S. no. Date Time of GAC daytime NOAA-AVHRR (GAC) acquisition (UTC) data for the year 2005 used to study the thermal scenario over 15 Feb 05 06:48-08:34 Iran before the Zarand earth quake on 22 Feb 2005 16 Feb 05 06:28-08:12 19 Feb 05 06:57-08:43 20 Feb 05 06:42-08:20 21 Feb 05 06:12-07:57 22 Feb 05 05:49-07:34 23 Feb 05 07:06-08:52 anomaly gradually increased till 21 Feb 2005. On 21 Feb 2005, the temperature was the highest (about 10-12°C higher than the usual temperature normally observed in the region), with a large patch of intense anomalous region covering an area of about 75,600 km2. On the day of the earthquake (i.e. 22 Feb 2005) the epicentral area was completely covered with clouds. On 23 Feb 2005, one day after the main earthquake event, the temperature was normal (Fig. 5) again. A series of tremors continued around the epicentre of the main shock in Iran as aftershocks following the main shock of 22 Feb 2005. The 2005 Zarand earthquake was caused by movement along an intra-mountain reverse fault, striking E-W and dipping North at ~60° to a depth of about 10 km (Talebian et al. 2006). The InSAR studies (Parsons 2005) using Envisat ASAR data reveal a maximum ~20cm line-of-sight displacement towards the satellite and ~20 cm away from the satellite for the ascending interferogram, and ~40 cm towards and ~45 cm away from the satellite for the descending interferogram. The interferograms are consistent with the _. Springer 126 Nat Hazards (2008) 47:119-135 Fig. 3 Daytime NOAA-AVHRR LST time series map of Iran before and after the earthquake in Bam, Iran, on 26 Dec 2003 (Choudhury 2005). Red colour-filled star symbol indicates the day of the earthquake, whereas without colour-filled star signifies location of the impending earthquake epicentre of 26 Dec 2003 the main shock revealed the source of earthquake as the "Arg-e-Bam" fault (Nakamura et al. 2004). The unwrapped interferometric phase onto the SAR amplitude image displays the surface displacement pattern, extends to around 30 km in N-S and to around 20 km in the E-W direction (Fig. 4b). It was observed that the co-seismic InSAR surface deformation field isin concurrence with thepre-seismic thermal anomalous areadeveloped to theeastof theepicentre on 26 Dec2003. This analysis indicates a plausible relationship between the two and suggests that LST pre-earthquake phenomena are land related. 3.2 Zarand earthquake (22 Feb 2005 at 5:55 LT) In case of the 22 Feb 2005 Zarand earthquake, daytime NOAA-AVHRR LST time series maps (Table 3; Fig. 5) show a build up of positive thermal anomaly near the epicentral region. The anomaly spread over an area of about 75 km diameter on 16 Feb 2005. This _. Springer Nat Hazards (2008) 47:119-135 125 pre-earthquake thermal anomaly study. Earlier, workers (Saraf and Choudhury 2003, 2005a-d; Choudhury et al. 2006) had only studied earthquakes of magnitude >6; however,' in the present attempt earthquakes ofmagnitude <6 have also been studied to look into the possibilities of detecting pre-earthquake thermal anomaly and its behaviour. 3.1 Bam earthquake (26 Dec 2003 at 5:26 LT) NOAA-AVHRR time series LST maps for the Bam earthquake show that there was a definite sharp rise in LST, which appeared before the devastating earthquake of 26 Dec 2003 (Table 2; Fig. 3). The average temperature rise ofepicentre region was about 5-7°C, which was at places as high as 6-10°C above normal. In nighttime LST maps (not shown in this paper), the thermal regime of the region was normal on 18 Dec 2003. An anomaly first appeared on 21 Dec 2003 with around 7-13°C increase in surface temperature relative to the temperature on 18 Dec 2003 (Fig. 3). It is however, 7-8°C higher than the normal temperature in the region around that period ofthe year. On 21 Dec 2003 the temperature was 7-13°C higher than the normal, whereas on 22 Dec 2003 the temperature was less than this boost and was normal on 23 Dec 2003. The earthquake occurred on 26 Dec 2003, 5 days after the thermal peak. This release of accumulated stresses results in the normal temperature conditions. Analysis and similar processing of nighttime NOAA-AVHRR data of the year 2004 acquired around the same time and onthe same day asthe 2003 data showed no such abnormal behaviour ofthe LST. Daytime LST time series maps show that the rise intemperature started on 22Dec 2003 and attained the peak on24Dec 2003. The normal temperature was around 22-25°C on 21 Dec 2003, whereas on 24 Dec 2003 temperature was around 29-32°C (i.e. 7-10°C higher than usual temperature ofthe region around that period ofthe year). The InSAR co-seismic surface deformation study (Stramondo et al. 2005a, b) of the same Bam earthquake revealed the existence ofan unmapped fault 4 km west ofBam fault, the only active tectonic feature in the epicentral area (Fig. 4). Analysis ofthe hypocentre distribution of aftershocks recorded bya temporal seismic network installed 6 weeks after Table 2 Details of nighttime and daytime NOAA-AVHRR data (acquired by IITR-SES) ofthe year 2003 used to prepare LST time series maps as shown in Fig. 3to study pre-earthquake thermal anomaly of26 Dec 2003 Bam earthquake S. no. Date Time of acquisition (UTC) Nighttime Daytime 21:30-23:00 (h) 16:30- 18:15 (h) 09:00-10:00 (h) 1 18 Dec 03 21:40 18:00 2 19 Dec 03 - 17:36 _ 3 20 Dec 03 - 17:03 _ 4 _ 21 Dec 03 22:50 _ 5 22 Dec 03 22:38 - 10:01 6 23 Dec 03 10:25 - 09:46 7 24 Dec 03 22:14 - 09:34 8 25 Dec 03 22:02 - 09:23 9 26 Dec 03 21:50 - 09:11 10 27 Dec 03 21:39 - - _. Springer 124 Nat Hazards (2008) 47:119-135 apparent surface temperature. Several Remote Sensing Rock Mechanics (RSRM) (Wu et al. 2000) experiments and other studies (Pellet et al. 2007) conducted to predict material behaviour on stress application and time space forecast of rock failure have validated the infrared emission from the rocks under stress. However, it should be noticed that this heat is starkly different from the frictional heat that develops at the fault surfaces during rupture. This frictional heat, that takes a lot more time to come up to the surface, actually develops at the time of the earthquake itself, and hence has no contribution towards the pre-earthquake thermal anomaly (Banerjee 2007). 2 Data and methodology The NOAA-AVHRR TIR datasets used in the present work were acquired through IITR- SES and some datasets were procured from the National Environmental Satellite Data and Information Services (NESDIS) website. The NOAA-AVHRR series of satellites allow effective monitoring of the earth's thermal field due to its high spatial (1.1 km), temporal (four scenes daily per satellite) and temperature (0.5°C) resolution. Day and night time High-Resolution Picture Transmission (HRPT), Local Area Coverage (LAC) and GAC (Global Area Coverage) data acquired through NOAA 14, 15, 16, 17 and 18 were used in present study. Passively measured TIR spectral radiations through AVHRR sensor (channels 4 and 5) of NOAA provide temperature of radiating surfaces. Data calibration and temperature calculation is based on the method provided in NOAA (2006). Daytime and nighttime datasets for around a fortnight prior to earthquake and one fort night after the earthquake (depending on the availability of the scenes with no or minimum cloud cover) were processed to study the thermal condition around the epicentre. A visual analysis ofthermal imagesfollowed bya detailed analysis wasdonetoknowtheapproximate time of appearanceof a thermal anomaly (in terms of days), intensity of thermal rise and its spatial extent.SinceAVHRR cannotpenetrate clouds, cloudyareaswillgivethetemperature of the cloud top and not the actual LST of the area. So images with dense cloud cover were excluded from the study. This paper presents time series LST layouts for the studied earth quakesthusfacilitatinga comparativeanalysisofgradualdevelopmentof thermalanomalies, thermal peak and return of thermal conditions of the affected region to normal after the earthquake. Image co-registrationand correctionfor different satellite view angles were not done since any image overlay analysiswas not intended.For preparationof time series LST mapsthe datasetsweretreatedidentically anda user-specified temperature rangeconsistent for all scenesof a particular earthquake wasdefined so as to distinctly delineatethe thermal anomalous area. Temperature outside this range was masked. Cloud-covered pixels were delineated and avoided for any temperature calculation. The digital datasets used were kept consistent in terms of the time of acquisition of all the scenes. The generation of all the LST maps is based on the Becker and Li (1990) split window algorithm, which uses the differential absorption effect in channels 4 and 5 of NOAA-AVHRR forcorrecting theatmospheric attenuation mainly caused by water vapour absorption. This is followed bythetime series layouts of these LSTs thatarefinally studied to analyse the temperature variation of the ground surface. 3 Observations Ten earthquakes (Table 1) occurring in Iran during the periodbetween Jun 2002 and Jun 2006 and ranging between magnitudes 5.8 and 6.6 (USGS) were selected for the _. Springer Nat Hazards (2008)47:119-135 123 studied earthquakes: Earth degassing theory (ZiQi et al. 2004) and p-hole activation theory (Freund 2000, 2002, 2003; Freund et al. 2007). During the preparatory phase of an earthquake, under high tectonic stress, pore spaces in the rocks are reduced due to increasing subsurface pressure releasing trapped gases. These gases on reaching earth's surface are already at an elevated temperature with respect to the air temperature due to subsurface geothermal heat and secondly, it also creates a local greenhouse effect on the land surface thus serving as the sourceof outgoing anomalous radiation. Positive thermal anomalies at a regional scale were observed with the examination of around 9,000 thermal images for the Middle Asian earthquake. These anomalies were attributed to the green house effect thatwas caused before the earthquake byanincrease ingases such asC02and CHt (Tronin 1996). A new theory of charge generation in rocks prior to earthquakes is given byFreund (2000, 2002, 2003). This theory keeps parity with laboratory experiments (Qiang et al. 1997; Ouzounov andFreund 2004) andalsoprovides anexplanation forother observed geophysical precursors. Electronic chargecarrierscan be free electronsor sitesof electron deficiency in the crystal structure, the latter known as p-holes (Freund 2000). Crystallographically, rocks forming mineral structures are practically three-dimensional arrays of oxygen, where it is tacitly assumed thatoxygen always occurin O2"state. AnO" is an anion with an incomplete valence shell, seven electrons instead of the usual eight. If the 0~ is part ofan X044+ polygon (usually a tetrahedron) (X = Si4+, Al3+, etc.), it might be written either as X043+or as 03X/°. Being unstable radicals, O" and X043+ or O3X/0 form pairs, generating a positive hole pair (PHP), chemically equivalent to a peroxy anion, 0~ + O- = O2 ~~ or a peroxy link as follows: O3X/0 + „/X03 = 03X/°°\XOi Such peroxy links may be associated with certain defect sites in the host crystal structure, but thermodynamically they are metastable (Freund 2002, 2003). Introduction of PHPs in minerals during rock genesis and alteration has been explained explicitly by Freund (2002). It should be noted that the 0"-0~ bond distance (1.5 A) is almost half of 02_-02_ bond distance (2.8-3.0 A). This implies that the peroxy-bound O- has a small partial molar volume, thus having a tendency to be favoured by high pressure. When dissociated under deviatoric stress a PHP introduces two holes (charge deficiencies) into the valence bond, causing the insulator to become a p-type semiconductor. Positive holes propagate though an oxide or silicate structure by electron hopping, whereby electrons from neighbouring O2- can hop onto the O" site. Laboratory experiments have proved the generation of excess IR intensityand electric potentials on the surface of dry rock that is subjected to heavy stress (Ouzounov and Freund 2004; Freund et al. 2007). Excess IR radiation is especially generated along surfaces with maximum curvature, i.e. at the edges and corners where positive holes accumulate. An infrared camera can monitor the thermal emissions from the surface of the rock under stress. Mobile charges of the samesignin anymedium will spread out in response to their mutual electrostatic repulsion. Because they try to get away from each other as far as possible, they accumulate at the surface. There the 0~ can recombine to form again a peroxy bond. The recombination leads to vibrationally highly excited 0~-0~ bonds. These 0~-0~ bonds can de-excite either by emitting specific mid-IR photons in the 11-12 urn region or by channelling the excess energy into neighbouring Si-O and Al-O bonds, which in turn will emit in the 8-10 urn region (Freund et al. 2005, 2007). The observation and the spectral signature of the emitted radiation provides strong evidence that the underlying effect is a kind of mid-IR luminescence arising from the recombination of p-holes at the rock surface. The energy therefore emitted by this electron acquisition increases the _. Springer 122 Nat Hazards (2008) 47:119-135 1.1 Tectonics of the region The tectonic belt of Iran forms a linear NW-SE trending intra-continental fold and thrust belt between the Arabian shield and Central Iran. The seismic activity of the belt is remarkably highas the region hasbeenexperiencing frequent earthquakes up to magnitude 7. The Zagros Thrust Zone (ZTZ) constitutes the boundary between the two colliding plates (Fig. 2). In fact, the highest frequency of earthquakes in Iran occurs in the Zagros region. However, due to the diffuse nature of the deformation, the intensities of these tremors are generally low. Five out of the ten studied earthquakes fall in the area known as Central-East Iran. Here, deformation takes place largely in the form of strike-slip move ments focused along a complex array of intersecting faults. In sharp contrast to that in Zagros, the seismic activity associated with Central Iranian faults is sporadic but much more localized and occurs with significantly higher magnitudes (Freethoughts 2006). 1.2 Concept of pre-earthquake thermal anomaly and its satellite-based detection Enhanced Thermal Infrared (TIR) emission from the Earth'ssurface retrieved by satellites prior to earthquakes is also known as "Thermal Anomaly" (Freundet al. 2005). Thermal rise in a tectonically active area may be an expression of building stresses in the Earth's crust. Temperature increases with the increase in pressure and stresses in such locations may augment the LST of the near Earth's surface (Choudhury et al. 2006). It has been established that some major earthquakes are preceded by a remotely detectable thermal anomaly (Saraf and Choudhury 2003, 2005a-d; Choudhuryet al. 2006). There are various physical explanations for thermal anomalies appearing before an impending earthquake. Two leading theories predict thermal anomaly patterns that match the observed pattern of Fig.2 Main tectonics of Iran and active tectonic faults (Choudhury 2005) _. Springer Nat Hazards (2008) 47:119-135 121 Table 1 List ofstudied earthquakes that occurred during the period ofJun 2002-Jun 2006 in Iran S. no. Earthquake Origin Location Magnitude Focal - (USGS)MW depth Date Time Latitude Longitude (km) (UTC) (N) (°) (E) O 1 Changureh-Avaj" 22 Jun 02 02:58 35.63 49.05 6.5 10 2 Jahron 10 Jul 03 17:40 28.35 54.17 5.8 10 3 Kerman" 21 Aug 03 04:02 29.05 59.77 5.9 20 4 Bam" 25 Dec 03 01:56 29.00 58.34 6.6 10 5 Firozabad-Kajoor 28 May 04 12:38 36.29 51.59 6.3 28 6 Zarand" 22 Feb 05 02.25 30.75 56.82 6.4 14 7 Qeshm 27 Nov 05 10:22 26.77 55.86 6.0 10 8 Faryab 28 Feb 06 07:31 28.12 56.87 6.0 18 9 Fin 25 Mar 06 07:28 27.57 55.69 5.9 18 10 Persian Gulf 28 Jun 06 21:02 26.82 55.90 5.8 10 Earthquakes discussed here in detail Fig. 1 Epicentres of the studied earthquakes (details of earthquakes are in Table 1). Different coloured boxes show the extent of LST images (used in Figs. 3,5, 7 and 8)of different earthquakes studied Besides these, six more earthquakes namely Jahron, Firozabad-Kajoor, Qeshm, Faryab, Fin and Persian Gulf (Table 1) were also studied but only the four above-mentioned earth quakes are discussed here in detail. _. Springer 120 Nat Hazards (2008) 47:119-135 stressed rock volume and their further recombination at the rock-air interface. A precise correlation of LST maps of Bam and Zarand with InSAR-generated deformation maps also provides evidence that the thermal anomaly is a ground-related phenomenon, not an atmospheric one. Keywords Earthquake • Thermal infrared anomaly 1 Introduction Theenergythatanearthquake releases buildsupformonths andyearsbeforehand in theform ofstresses within theEarth's crust. Atthe moment, forecasters have nodirect way ofseeing these stresses or detecting when they reach critically high levels(Barry and Philips 2003). There areseveral satellite-based methods thatshow potential asprecursors to theearthquake activity. Oneof thesemethods is tolookforsurges in anomalous outgoing infrared radiation (IR). Gorny et al. (1988) first used theNOAA-AVHRR data to indicate seismic activity of middle Asiaregion(Shanjun andLixin2005) andfirst suggested that abnormal IR radiation observed from meteorological satellites could be taken as an indicator of seismic activity. Latersimilar researches werecarriedoutin China, Japan (Tronin 2000; Tronin et al. 2002), India, Iran(Saraf andChoudhury 2003, 2005a-d; Sarafet al. 2007; Choudhury et al. 2006), Italy (Tramutoliet al. 2001), USA (Ouzounovand Freund 2004) and other countries. Iran being part of theAlpine-Himalayan Belt falls in one of the most earthquake-prone regions of the world. It hasexperienced more than 130strong earthquakes witha maximum magnitude of 7.5 or more in the past century. In twentieth century alone, 20 large earth quakes have claimed more than 1,00,000 lives, destroyed many towns and thousands of villages and caused extensive economic damage (Ghafory-Ashtiany 1999). Besides itshigh seismicity, its relatively cloud-free and stable weather conditions during most parts of the year and its sparse vegetation cover make Iran a suitable study area. The Indian Institute of Technology Roorkee-Satellite Earth Station (IITR-SES) has been operational since Oct 2002 and also acquires NOAA-AVHRR data covering most parts of Iran and several neighbouring countries of India. Table 1 lists the studied earth quakes that occurred between Jun 2002 and Jun 2006 in Iran (Fig. 1). The devastating earthquake of Bam, Iran, struck on 26 Dec 2003 at 01:55 (UTC) with a magnitude of 6.6 (USGS). The earthquake focus was at a depth of 10 km and the epicentre was located at 29.00° N latitude and58.33° E longitude near theancient 2000-year-old cityof Bam. This earthquake was caused by right-lateral strike-slip motion on the N-S trending Bam fault, which passes from the vicinity of the city of Bam and Baravat (Saraf and Choudhury 2005c). The epicentre of the 22 Feb 2005 Dahoeieh-Zarand earthquake (about 250 km from the destructive Bam earthquake of 26 Dec 2003) was situated near Zarand in the province of Kerman. Changureh-Avaj region of northwestIran shook on 22 Jun 2002 due to shallow earthquake (focal depth 10 km) of magnitude 6.5 (USGS) at about 02:58 h (UTC). Epicentre of this earthquake was about 225 km west of Tehran at a latitude 35.67° N and longitude 48.93° E (NEIC). Places of severe damage were the villages of Abdarreh and Changureh. The fault planesolution (USGS 2002) indicates that the seismic event occurred on a reverse fault having a trend about 115° N and located 8 km north of the Avaj Fault. The Kerman earthquake of magnitude 5.9 (USGS) occurred on 21 Aug 2003 at 04:02 h (UTC) in the south eastern part of Iran. The epicentre was located at latitude 29.05° N and longitude 59.77° E and its focal depth was reported to be 20 km. _Springer Nat Hazards (2008) 47:119-135 DOI 10.1007/sl 1069-007-9201-7 ORIGINAL PAPER Satellite detection of earthquake thermal infrared precursors in Iran Arun K. Saraf • Vineeta Rawat • Priyanka Banerjee • Swapnamita Choudhury • Santosh K. Panda • Sudipta Dasgupta • J. D. Das Received: 26September 2007 / Accepted: 4 December 2007 / Published online: 19 January 2008 © Springer Science+Business Media B.V. 2008 Abstract Stress accumulated in rocks in tectonically active areas may manifest itself as electromagnetic radiation emission and temperature variation through a process of energy transformation. Land surface temperature (LST) changes before an impending earthquake can be detected with thermal infrared (TIR) sensors such as NOAA-AVHRR, Terra/Aqua-MODIS, etc. TIR anomalies produced by 10 recent earthquakes inIran during the period ofJun 2002-Jun 2006 in the tectonically active belt have been studied using pre- and post-earthquake NOAA-AVHRR datasets. Dataanalysis revealeda transientTIRriseinLST ranging 2-13°C in and around epicentral areas. The thermal anomalies started developing about 1-10 days prior tothe main event depending upon the magnitude and focal depth, and disappeared after themain shock. Inthe case ofmoderate earthquakes (<6magnitude) adual thermal peak instead ofthesingle rise has been observed. This may leadustounderstand that perhaps pre-event sporadic release of energy from stressed rocks leads to a reduction in magnitude of the main shock. This TIR temperature increment prior to an impending earthquake canbeattributed todegassing from rocks under stress ortop-holeactivation inthe A. K. Saraf (El) • V. Rawat • P. Banerjee •S. Choudhury • S. K. Panda •S. Dasgupta •J. D. Das Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee 247667, India e-mail: [email protected] J. D. Das Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India Present Address: S. Choudhury Wadia Institute of Himalayan Geology, Dehradun, India Present Address: S. K. Panda Geophysical Institute, University of Alaska Fairbanks, Fairbanks, USA Present Address: S. Dasgupta Reliance Industries Ltd., Navi Mumbai, India _Spn nger SI AX Saraf et al./International Journal of Applied Earth Observation and Geoinformation 11(2009) 373-379 379 References Qiang, Z„ Kong. LC. Zheng. L.Z.. Guo. M.H.. Wang. G.P.. Zhao. 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