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Non-planarity, scale-dependent roughness and kinematic properties of the Pidima active normal fault scarp (Messinia, Greece) using high-resolution terrestrial LiDAR data
Ioannis Karamitros, Athanassios Ganas, Alexandros Chatzipetros, Sotirios Valkaniotis
PII: S0191-8141(19)30043-4 DOI: https://doi.org/10.1016/j.jsg.2020.104065 Reference: SG 104065
To appear in: Journal of Structural Geology
Received Date: 29 January 2019 Revised Date: 7 April 2020 Accepted Date: 8 April 2020
Please cite this article as: Karamitros, I., Ganas, A., Chatzipetros, A., Valkaniotis, S., Non-planarity, scale-dependent roughness and kinematic properties of the Pidima active normal fault scarp (Messinia, Greece) using high-resolution terrestrial LiDAR data, Journal of Structural Geology (2020), doi: https:// doi.org/10.1016/j.jsg.2020.104065.
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1 TITLE:
2 Non-planarity, scale-dependent roughness and kinematic properties of the Pidima
3 active normal fault scarp (Messinia, Greece) using high-resolution Terrestrial LiDAR
4 data
5 AUTHORS:
6 Ioannis Karamitros*1,2 , Athanassios Ganas 2, Alexandros Chatzipetros 1 and Sotirios
7 Valkaniotis 3
8 1Aristotle University of Thessaloniki, Department of Geology [email protected]
10 2National Observatory of Athens, Institute of Geodynamics, 11810 Athens, Greece
12 3Koronidos Str., Trikala, Greece [email protected]
13 KEYWORDS: LiDAR, earthquakes, roughness, Peloponnese, Messinia, fault
14 ABSTRACT:
15 Terrestrial LiDAR (TLS), photogrammetric and field data were collected during the years
16 2014, 2015 and 2017, at a 20-m long limestone scarp locality along the N-S striking, 70°
17 west-dipping, active Pidima fault (Messinia, SW Peloponnese, Greece). This locality
18 presents an artificially exhumed portion of an active fault plane, thus providing a unique
19 opportunity to study kinematics and limestone scarp morphology (including its curvature
20 and roughness). The survey of 2015 offered a high-density point cloud of the fault scarp
21 with 6-mm working resolution, creating a very close to real life representation 3D model.
22 We found that scarp geometry is non-planar with increasing convexity up-dip and 1
23 increasing northwesterly dip-directions from north towards south, along strike. Non-
24 planarity is also recorded from the morphological data along strike with the appearance of
25 several, slip-parallel troughs and ridges with a mean distance (half-wavelength) of 0.75 m.
26 The fault-plane roughness (absolute values) is scale dependent. The roughness
27 configuration attains a slip-parallel pattern for observation scales above 1-cm. At
28 observations scales less than 1-cm a slip-normal pattern is weakly visible. The obtained
29 TLS results agree with field data (manual compass measurements) and macroscopic
30 observations. We infer an earthquake magnitude of 6.2±0.2 for the last event along the
31 Pidima fault based on the measured thickness of the smooth stripe that was imaged by t-
32 LiDAR along the non-exhumed surface of the scarp.
33 1. Introduction
34 The Messinia basin, SW Peloponnese, is bounded by highly active fault zones, and has
35 hosted several strong, destructive earthquakes (e.g. Galanopoulos, 1949; Ambraseys and
36 Jackson, 1990; Papoulia and Makris, 2004; Slejko et al. 2010; Kouskouna and Kaviris,
37 2014). In addition, it hosts a major city of Greece, Kalamata (pop. 70000 in the 2011
38 census; Fig. 1), and therefore it attains a significant societal value. Research on neotectonic
39 faults near Kalamata started after the September 1986 M w=6.0 strong earthquake that
40 devastated most of the old city (Anagnostopoulos et al. 1987; Lyon-Caen et al. 1988;
41 Papazachos et al. 1988; Papanikolaou et al. 1988; Kazantzidou-Fertinidou et al. 2016;
42 Kassaras et al. 2018). The structural analysis of neotectonic fault segments is essential for
43 the investigation of stress field orientations, present-day geodynamics, fault kinematics,
44 relation to historic seismic events, potential reactivations of fault segments, and for the
45 evaluation of the fault activity along strike of a fault zone. Natural fault scarps and
46 exhumed fault plane segments are indicators of shallow earthquake activity with
47 earthquakes that cut the entire seismogenic layer, involving magnitudes greater than M=6 2
48 (Stewart and Hancock, 1990; Ganas et al. 1998; Roberts and Ganas, 2000; Wiatr et al.
49 2013).
50 Several recent studies in Greece and Italy have attempted the reconstruction of past
51 earthquakes on naturally exposed limestone bedrock fault scarps by using terrestrial
52 LiDAR (t-LiDAR; e.g. Kokkalas et al., 2007; Jones et al., 2009; Wilkinson et al., 2010;
53 Bubeck et al., 2015; Wiatr et al., 2013, 2015; Mason et al. 2016). Sampled fault scarps
54 include localities along normal faults that hosted recent earthquake ruptures such as L’
55 Aquila (2009, M=6.3), Pissia (1981, M=6.7) as well as well-known active faults with Late
56 Quaternary activity in central Greece (Arkitsa fault; Jackson and McKenzie, 1999) and
57 Crete (Spili fault; Caputo et al., 2006; Mouslopoulou et al., 2013; Wiatr et al. 2013). The
58 primary aim of those works was to enable the differentiation of earthquake ruptures along
59 fault planes by exploring systematic roughness variations on fault scarps without much
60 success (e.g. Stewart, 1996; Caputo et al., 2006; Jones et al., 2009). However, the
61 advantages in terms of sampling accuracy and spatial resolution are allowing a growing
62 number of studies to focus more on quantitative research topics, in a way that was
63 impossible using traditional field data alone (e.g. Enge et al., 2007; Sagy et al., 2007).
64 LiDAR images of fault scarps can provide numerous along-strike data on fault geometry
65 (dip-direction, dip-angle), slip-rates (Mason et al. 2016) and fault kinematics (slip vector
66 plunge and azimuth; Wiatr et al. 2013; Kirkpatrick and Brodsky, 2014) that are very useful
67 in studying the evolution of fault growth and kinematic behaviour with time. T-LiDAR
68 studies on bedrock fault scarps and roughness analyses are also of decisive importance for
69 supporting cosmogenic isotope studies and dating paleoevents (e.g. in central Apennines,
70 Cowie et al. 2017; on Pisia fault, Corinth, Mechernich, et al. 2018).
71 In this article, we provide a detailed description of an artificially exhumed carbonate fault
72 scarp, along the Pidima active normal fault (southwest Peloponnese, Greece; Fig. 1), using 3
73 terrestrial light detection and ranging (t-LiDAR or TLS). The Pidima Fault is the younger
74 fault in this N-S trending fault zone (segment number 9 on Fig. 1), as its location right at
75 the border between Taygetos Mt basement and sedimentary basin suggests. In terms of
76 seismic hazard assessment, this is very important for the city of Kalamata as ground
77 shaking is expected more frequently than more “internal”, less-active faults. So, based on
78 a) the existence of a young active fault near Kalamata b) the well-preserved fault scarp, &
79 c) a possible rupture directly inducing strong ground shaking into the sedimentary basin
80 thus posing significant threat for inhabitants from a hazard assessment perspective, make
81 this locality a crucial target for a detailed study.
82 The t-LiDAR survey was conducted during May 2015 with the aim to generate a high-
83 resolution model of the scarp surface to look for variations in scarp morphology that could
84 be associated with the history of earthquake ruptures. In addition, detailed field mapping of
85 the fault surface was carried out during December 2017 and data from a field work of 2014
86 was added. We first provide a review of the tectonic setting (section 2) and local geology
87 (section 3), then we describe the TLS data and proceed with data analysis and
88 interpretation (sections 4 and 5).
89 2. Tectonic Setting
90 The region of Messinia is one of the most tectonically and seismically active in the
91 Hellenic Arc, mostly because it is located near to the Hellenic megathrust, where the
92 African plate subducts below the Eurasian one (Hatzfeld et al., 1989, 1990; Papoulia and
93 Makris, 2004; Ladas et al., 2004; Ganas and Parsons, 2009; Mariolakos and Spyridonos,
94 2010; Fountoulis et al., 2012). The carbonate (alpine) bedrock belongs to two major
95 geotectonic units of the Hellenides, the tectonic zones of Gabrovo - Tripolis and Olonos –
96 Pindos (Mountrakis, 2010). The central part of the basin is covered by sun-rift sediments of
4
97 Pliocene to Quaternary age, unconformably overlying the alpine basement (Fig. 1). The
98 thickness of these sediments in the central part exceeds 280 m. On the western boundary of
99 the basin the sediments are deposited unconformably on the Olonos – Pindos unit rocks,
100 mainly on radiolarites, Upper Cretaceous limestones and flysch. On the northern and the
101 eastern part, the sediments are tectonically in contact with the alpine formations, as a result
102 of the activity of the boundary fault zone of the roughly N-S developed graben (Fig. 1). To
103 the south and southeast, the sun-rift sediments cover the Tripolis unit flysch and limestones
104 respectively.
105 Crustal deformation of the upper plate (i.e. south Peloponnesus) is accommodated by
106 extensional faulting along NNW-SSE striking normal faults (e.g. Papanikolaou et al. 1988;
107 Armijo et al., 1992; Roberts and Ganas, 2000; Ganas et al. 2012; Papanikolaou et al.
108 2013). Most of the deformation onshore Messinia is accommodated by the Eastern
109 Messinia Fault Zone (EMFZ), a series of west-dipping normal faults bordering the western
110 part of Taygetos Mt range and the west coast of Mani peninsula (Fig. 1; Fountoulis et al.,
111 2012; Ganas et al., 2012; Kyriakopoulos et al., 2013; Valkaniotis et al., 2015;
112 Kazantzidou-Firtinidou et al., 2016). Papoulia et al. (2001) assessed the seismic hazard of
113 the offshore, basin-margin normal fault zone (whose finite throw exceeds 5.5 km) in the
114 same area and provided a minimum long-term slip rate of 1.1mm/yr.
115 Figure 1 Here
116 The Eastern Messinia Fault Zone (EMFZ) is a complex system of normal faults dipping
117 westwards with a strike of NNW-SSE to N-S, attaining a total length of more than 100 km
118 from the northern Messinia plain in the north to the southern part of Mani peninsula in the
119 south (Valkaniotis et al., 2015). EMFZ has its continuity disrupted by overlapping faults,
120 which in turn form many relay ramp structures across its length (Valkaniotis et al., 2015).
5
121 Stress analysis of fault striation measurements on fault planes along EMFZ shows a
122 present regime of WSW-ENE extension, in accordance with focal mechanisms from
123 modern seismicity (Lyon-Caen et al., 1988; Hatzfeld et al., 1990; Kassaras et al., 2014) and
124 GPS extensional strain patterns (Hollenstein et al., 2008; Chousianitis et al., 2015). A
125 seismic swarm occurred in the central part of EMFZ (south of Ichalia; Fig. 1) during 2011-
126 2012 involving three 4.6 ≤M≤4.8 shallow earthquakes that caused ground deformation and
127 secondary effects on the surface (mainly ground cracks; Ganas et al., 2012; Kyriakopoulos
128 et al., 2013; Kassaras et al., 2014). The focal mechanisms of the 2011 events (Kassaras et
129 al. 2014) point to the reactivation of west-dipping normal fault striking NNW-SSE (Fig. 1)
130 that involved slip migration from North towards South (supported by geodetic data;
131 Kyriakopoulos et al. 2013). The last event of the swarm occurred on 10 October 2011
132 (M (NOA) =4.7) about 5 km towards north of Arfara village (Fig. 1) close to the termination
133 of the Pidima normal fault. The Pidima fault belongs to EMFZ (Fault number 9 in Fig. 1);
134 its trace can be found in the western side of the Taygetus mountain range, east of the
135 Arfara and Pidima villages (Fig. 1).
136 3. Geology - The Pidima active fault
137 During 2014-2015 under the framework of project ASPIDA (Valkaniotis et al., 2015;
138 Kazantzidou et al. 2016) the central part of the EMFZ, from the town of Arfara to the city
139 of Kalamata (Fig. 1), was investigated by detailed field mapping of fault structures and
140 syn-rift sediments. The bedrock geology comprises carbonate rocks, mainly limestones
141 (the “pre-rift”). Upper Pleistocene – Holocene sediments and scree (the “syn-rift”; Fig. 2)
142 occur inside the basin and along mountain slopes, respectively. Our field mapping
143 concluded that the Pidima fault segment has a length of 8 km and it has created a footwall
144 relief of about 300 m during (Mid?-Late) Quaternary as a result of cumulative, seismic
145 slip. The fault is the youngest of two series of sub-parallel N-S striking normal faults 6
146 accommodating extension (see Fig. 1; segments 9 and 5). Its young age is documented by
147 the occurrence of marine deposits of Plio-Pleistocene age that are found today at an
148 elevation of 360 m (Marcopoulou-Diakantoni et al. 1989) in the footwall area of this fault
149 (see syn-rift area of map between segments 5 and 9 in Fig. 1). Assuming an Early
150 Quaternary age (2 Ma) of fault initiation and 600 m of throw (given that basin depth is
151 about 300 m) we obtain an average throw rate of 0.3 mm/yr.
152 In the study area (Fig. 2) the footwall block comprises Mesozoic carbonates of the Tripolis
153 geotectonic zone (Ls). The hangingwall comprises Holocene-age swampy and alluvial
154 deposits ( Hl in Fig. 2) together with Quaternary fan & terrestrial deposits ( Qsc, cs ) that are
155 found stratigraphically below. We also mapped a narrow zone of Upper Pleistocene scarp
156 debris & breccia ( Pt.c ) resting next to the fault trace. Its width ranges between 10 and 30-
157 m. We found no field evidence for bedrock fault planes in the hangingwall, so the Pidima
158 fault is the basin-bounding fault. In the limestone scarp locality, the local footwall relief is
159 70-m (see cross-section in Fig. 2b). The surface of the fault scarp is composed by breccia
160 sheets composed of limestone angular fragments on clay breccia (or compact breccia
161 sheets ; Stewart and Hancock, 1990) and it is cut by a network of extension fractures
162 (mainly slip-parallel fractures at high angles to the slip direction) without regular spacing.
163 It is worth mentioning that the fault scarp’s surface contains no comb-fractures. The site of
164 our investigation is appropriate to extract accurate information of both scarp geometry and
165 morphology as it is not affected by landslides, nor it comprises an area of high erosion or
166 deposition. We checked the scarp surface of any human-induced damage bit and/or
167 weathering marks but we found no such evidence. However, we confirmed human activity
168 to a range of a few tens of metres to the west and some agricultural activity (oil-trees)
169 nearby, but the immediate to the scarp ground surface after its exhumation was found
170 undisturbed.
7
171 In addition, during the summer of 2015 a paleoseismological trench was excavated near the
172 village Pidima a few hundred metres to the north of the fault scarp (yellow box in Fig. 2a).
173 The trench was excavated along an E-W orientation and colluvium samples were dated
174 from post-faulting colluvial deposits and fissure fills,in order to examine the
175 paleoearthquake record of the Pidima fault (Zygouri et al., 2015). The t-LiDAR field
176 campaign provided a supplementary dataset to the findings of paleoseismology, mainly on
177 fault scarp morphology, size of last earthquake and fault kinematics which is the focus of
178 this paper.
179 Figure 2 Here
180 4. Methods
181 4.1 Characteristics of Terrestrial LiDAR (TLS) Data
182
183 LiDAR is a remote sensing technology (acronym of “ Light Detection and Ranging ”) and
184 its ground-based counterpart is called TLS (Terrestrial Laser Scanning) or T-LiDAR. The
185 technique comprises a non-intrusive visual recording system, the high spatial and temporal
186 resolution of which makes it an effective method for morphological reconstructions of
187 geological settings, monitoring, and numerical modelling of geological phenomena (e.g.
188 Jones et al. 2009; Wiatr et al., 2013). This technology can generate digital elevation models
189 or virtual outcrop models, either of which retain a wealth of quantitative information (e.g.,
190 x, y, and z coordinate values). This results in a virtual model that is suitable for
191 interpretation, digitization and quantitative geological analysis (Buckley et al., 2008). The
192 device operates by emitting a laser pulse at known azimuth and angle of inclination relative
193 to the scanner towards a surface (Fig. 3b). The time of flight of the emitted pulse and its
8
194 reflected, returning counterpart is used to calculate the distance between the laser scanner
195 and a surface. The scanner emits thousands of pulses per second and incrementally is
196 adjusting the direction, horizontally and vertically to sample reflections within its 360°
197 horizontal and 90° vertical line of sight.
198 When the pulses are reflected by a surface during a scanning session the receiver detects
199 portions of the backscattered signal (Jörg et al., 2006, Wiatr et al. 2015). The infrared
200 (1500 nm) laser scanner detects monochromatic information of the backscattered signal in
201 256 intensity grey values (Fig. 3c) that can provide information about a range of the
202 scanned surface properties (Wiatr et al., 2015; Schneiderwind et al., 2016; Mechernich et
203 al., 2018).The backscatter values mainly depend on: a) the reflectivity of the target material
204 (color as well as the surface condition, roughness, etc.), b) the range to the target (due to
205 beam divergence the pulse becomes wider and less intense with distance), c) inclination
206 angle of the laser beam, d) atmospheric conditions (presence of dust, rain, humidity levels,
207 etc.).
208 Higher return intensities are associated with relatively smooth, highly reflective surfaces,
209 while the lower values with darker and rougher (they scatter some of the energy away from
210 the sensor). A smooth reflective surface may also produce lower intensity returns if it is
211 tilted away from the direction of the incoming light pulse and thus reflects most of the
212 energy away from the sensor (Fowler et al., 2011, Penansa et al., 2014).
213 The resulting raw data is in the form of a point cloud, a densely spaced network of
214 elevation points based on the Cartesian coordinates (x, y, z) of the reflected points. These
215 three-dimensional coordinates of the target objects are computed from the time difference
216 between the laser pulse being emitted and returned in association with the starting angle of
217 the pulse and the absolute location of the sensor. Multiple scans can be used to collect data
9
218 and the resultant point clouds are subsequently aligned and merged based on the
219 identification of overlapping zones and are typically assisted by algorithms implemented
220 within software platforms.
221 In this work we present an analysis of t-LiDAR data using a point-cloud resolution (or
222 scanning step) of 0.5-1 cm and we derive a number of kinematic and morphological
223 products for the Pidima fault scarp. The list of products is shown in Table 1.
224
225 Table 1. Information on the relation between processing product and analysis resolution.
226 Software used: CloudCompare
Processing Product Product resolution Figure in this study
Fault plane kinematics (dip- 1 cm 4, 5, 6, 7
angle, dip-direction, slip
vector azimuth and plunge)
Fault plane curvature 1 m 11
Fault plane roughness 6 mm, 1 cm, 2 cm, 10 cm, 1 m 8, 9
227
228 4.2 Instrument setup and data acquisition
229 In our study, we used the laser ranging system ILRIS 3D from Optech Inc., Ontario,
230 Canada (7 mm at 100 m distance accuracy according to the manufacturer’s specifications).
231 The laser scanner was accompanied with additional equipment, such as a self-rotating and
232 tilting base for the scanner, a mounting tripod and a power generator for producing power
10
233 in the field (Fig. 3b). The backscatter intensity value of each point appears on an 8-bit scale
234 (0 – 255; Fig. 3c).
235 The field work took place on May 26, 2015 and the TLS instrument was placed 10 m away
236 from the fault plane (Fig. 3a). Determined afterwards from the 3D model (see Fig. 6), the
237 length of the exhumed Pidima fault scarp is 28.237 m and its (height 7.971 m. The scanner
238 originally measured the last return with scanning step 0.005 m (5 mm) on the surface. We
239 obtained about 10.5 million initial cloud points, subsequently the scans were “cleaned”
240 (vegetation and surface noise points were removed) and aligned using Polyworks TM (for
241 data handling with Polyworks TM the reader is referred to Wiatr et al., 2013).
242 For computational purposes, the point cloud was reduced to 3.6 million cloud points (about
243 66% less data; see Table 2 for data acquisition summary) or 17k points per m 2, without
244 loss of quality, such as the resulting 3D model is of millimetre (6-mm) accuracy. After data
245 reduction the file was imported to CloudCompare (an open source software) for data
246 analysis.
247
248 Table 2. TLS survey attributes and the resultant processing output from the point cloud.
Fault No of scan Scan length along strike Point cloud Point Cloud
segments (m) (total) (working set)
Pidima 4 28.237 10582758 3621287
249
250 Figure 3 Here
251 4.3 TLS Data processing 11
252 We used the CloudCompare open software to process the TLS data (Dewez et al. 2016;
253 Valkaniotis et al. 2018; Tung et al. 2018). In order to acquire information about the
254 geometric features of the fault surface, we first computed the surface normals. From the
255 three-dimensional perspective, a normal to a surface at a specific point is a vector that is
256 perpendicular to the tangent plane to that surface at that point. The normal vector is often
257 used in computer graphics to determine a surface's orientation toward a light source for flat
258 shading, or the orientation of each of the corners (vertices). The solution of estimating the
259 surface normal, as defined by the CloudCompare and PCL (Point Cloud Library) software
260 platforms, is an analysis of the eigenvectors and eigenvalues of a covariance matrix created
261 from the nearest neighbors of the query point.
262 For each point p i, the covariance matrix C assembles as follows:
263 = ∑ ∙ ( − ̅) ∙ ( − ̅) , ∙ = ∙ , ∈ {0,1,2} [1]
264 Where k is the number of point neighbors, ̅ represents the 3D centroid of the nearest
265 neighbors, λj is the j-th eigenvalue use of the covariance matrix, and the j-th eigenvector.
266 To orient all normals consistently towards the viewpoint , they need to satisfy the
267 equation:
268 ∙ ( − ) > 0 [2]
269 We obtained the following products on scarp morphology and kinematics: computation of
270 scarp point normals (vectors going outwards from the scanned surface), scarp dip angle
271 (Fig. 4c), scarp dip direction (Fig. 4a) facets orientation and dip angle (Fig. 7,), scarp
272 surface mean curvature (Fig. 8), roughness (Fig. 9) Below we present our results in more
273 detail.
274 5. Results 12
275 5.1 On the Geometry and kinematic properties of the Pidima scarp
276 The basic kinematic properties of a fault plane are: dip angle of the fault plane, dip-
277 direction of the fault plane and slip-vector orientations along the fault plane. First, we
278 present the results on the dip-direction (Fig. 4a,b), then on dip-angle (Fig. 4c,d) and we
279 also extracted slip-vector orientations in selected locations on the scarp (Fig. 5). The mean
280 value of the dip-angle is 70.5° (Fig. 4d) with a standard deviation of 4.3°. The bell-shaped
281 histogram of the values fits to a Gaussian (normal) distribution. We also note that towards
282 the higher parts of the scarp dip-angle values become lower (Fig. 4c), i.e. the scarp attains
283 a convex geometry up-dip. The area of high values on the central part of the plane
284 corresponds to a zone of the brecciated colluvium (Fig. 6). The mean value of the dip-
285 direction is N250°E (Fig. 4b). with a standard deviation of 18°. The histogram of the dip-
286 direction values also fits to a Gaussian distribution (Fig. 4b). We observe that the dip-
287 direction of the scarp changes systematically from North to South along strike (Fig. 4a)
288 with the northern part to dip more towards the SW while the southern part to dip more
289 towards the W. This is another piece of evidence for the non-planarity of the scarp.
290 In addition, we used a method to calculate the 3D slip vector on the fault surface, which is
291 to manually create a line between two points on the TLS data (e.g. Gold et al., 2012; Wiatr
292 et al., 2013). We selected the two end-points using the 3-D model with intensity values
293 (shown in Fig. 3c) where we can follow the linear features of the scarp surface (identified
294 in the field as slickenside lineations; see inset in Fig. 6 below). This way we obtained 58
295 lines that represent the slip vector orientation (Fig. 5) and measured their attributes
296 together with the section of the fault plane that hosted the line. For each measurement, the
297 azimuth and the angle of the line were extracted. A comparison with field measurements of
298 the rake of the slip vector (see Fig. 5) showed that the difference of the means is 4.4° while
299 both datasets exhibit similar standard deviations around their means (Table 3). The rake 13
300 data indicate a small dextral-slip component (between 5 and 10°) of the slip vector at this
301 locality. We associate this small (but systematic) dextral component to the position of our
302 sampling locality along-strike the Pidima fault, i.e. the scarp is located towards the south
303 end of the northern segment of the fault (Fig. 1b) where it is expected a strike-slip
304 component of the slip vector in normal faults (Ma and Kusznir, 1995; Roberts and Ganas,
305 2000; Hampel et al. 2013). As the Pidima segment strikes N-S (on average) and dips
306 towards west, it is expected that a dextral component of slip would develop near its
307 southern end.
308 Table 3. Results for slip-vector analysis and comparison with field data. The sampled
309 surfaces are shown in Fig. 5. The field measurements were collected with a CLAR
310 compass.
Measurement T-LiDAR data (N=59) Field data (N=36)
Mean of Slip vector rake -95.25° -99.70°
St. Deviation of rake 6.91° 6.26°
311
312 The zone of high dip-angle values in the centre-north part of the scarp (Fig. 4c) was
313 identified in the field as brecciated colluvium between two fault surfaces (Fig. 6; the
314 brecciated colluvium is indicated by number 2). Field mapping showed that the north part
315 of the scarp consists of a partly polished fault plane (1) created on a colluvium breccia, the
316 clay-matrix breccia zone (2), while the south part of the scarp consists of a polished fault
317 plane on limestone breccia (3). We may consider both planes to be localised fault-bound
318 lenses of the Pidima fault although we have no more data along strike. We also identify the
319 line of intersection (branch line) between the two sections of the multi-strand fault scarp
14
320 (black line annotated in Fig. 6; Bonson et al. 2007). We then focus our analysis more on
321 the scarp sections 1 and 3 (Fig. 6). Consequently, the rough breccia zone was removed
322 from the 3D model. Both segments appear very similar in their dip angle distribution
323 resembling the distribution in Fig. 4, however, in their dip direction they show a 25°
324 degrees divergence (Fig. 7), that is the north segment shows a mean value of dip-direction
325 of N233°E, while the southern segment N258°E, respectively. This result proves again that
326 the Pidima scarp is non-planar along strike.
327 Figure 4 Here
328 Figure 5 Here
329 Figure 6 Here
330 Figure 7 Here
331 To numerically confirm the non-planarity evidence along strike we applied a second
332 method, that of facet analysis on the TLS data from the south segment (scarp surface
333 labeled 3 in Fig. 6). We used the FACETS plug-in in CloudCompare which is capable of
334 performing geological planar facets analysis by automatically extracting planar facets from
335 point clouds (Fig. 7). The analysis is based on the normal vectors of the surface it runs on,
336 and creates domains of planar facets. The program first recursively divides the point cloud
337 into sub-cells and then computes elementary planar patches and then regroups them
338 progressively according to a planarity threshold into larger polygons (Dewez et al., 2016).
339 This can be done by choosing the resulting facets minimum size (16 cloud points) and
340 angular step (5°). The fault plane, in our case, was divided in groups of small facets in
341 order to more accurately illustrate the way dip-direction changes along its surface. The dip-
342 direction data, can be exported for further analysis. The planar facets of the south segment
15
343 have a dip-direction between N244° – 295°E, giving a mean dip-direction at N259°E (see
344 stereonet in Fig 7). The average fault scarp strike is calculated at N170°E.
345 5.3 Roughness of fault plane
346 Fault scarp roughness (Fig. 8) is a fundamental geometric property of a surface and it in
347 this study is defined as the value of distance between a point and the best fitting plane
348 computed on its nearest neighbors. The radius (kernel) that CloudCompare uses to
349 compute the roughness of the surface, is defined as the radius of a sphere centered on each
350 point and can be determined by the user, in our case we set it to 1 m, , 0.1 m and 0.02 m,
351 respectively (Fig. 9a, b, c,; Table 1). Roughness provides useful information on possible
352 changes of the properties or irregularities of a fault scarp including its degradation with
353 time (e.g. Stewart, 1996). In our analysis we focus on the Euclidean description of
354 roughness (Power and Tullis, 1991), that is on the geometrical analysis of our point-cloud
355 data, providing statistical descriptions of root mean square roughness at various scales of
356 observation (Fig. 9).
357 From the roughness analysis we were able to extract information about the fault plane’s
358 surface linear features and small-scale corrugations. Larger kernel size means that more
359 neighboring points are taken into the equation and larger structures predominate the
360 smaller ones. The resulting image helps us define the corrugation’s wavelength and
361 orientation on the fault’s surface. Our results for 1-m, and 0.1-m kernel sizes indicate the
362 existence of rough, slip-parallel stripes over the fault surface and not slip-normal as it
363 would be expected intuitively if roughness was solely determined by erosion of successive
364 earthquake ruptures along the scarp base (that is scarp would be rougher as ruptures getting
365 older, i.e. rougher up-dip). An interesting result came up in the case of kernel size 0.02-m
366 where a smooth slip-normal stripe appears towards the top of the scarp (Fig. 9a) which
16
367 does not overlap with the gray stripe at the level of vegetation towards the top of the scarp
368 (see Fig. 3a, Fig. 6a, for field views). In general, the results indicate that the smaller the
369 kernel size, and thus the scale of the analysis, the smoother the surface (see Table 4 for
370 statistics). In our TLS data the scarp mean roughness decreases with reducing kernel size
371 in a non-linear relation (Fig. 10). The data are fitted better with an exponential function
372 (Fig. 10; R 2=0.99 vs R 2=0.88 for a linear fit).
373 We also calculated the terrain ruggedness index (TRI; Fig. 9) as area roughness parameters
374 provide more significant values than the profile and can help determine the spatial
375 distribution of ruggedness on a surface (e.g. Riley et al. 1999; Wiatr et al. 2015;
376 Mechernich et al. 2018). The roughness pattern resembles the one computed by
377 CloudCompare, i.e. at kernel size of 1-cm and larger the roughness features are more slip-
378 parallel (Fig. 9b, 9c) whereas at small kernel size (6-mm; Fig. 9a) a slip-normal pattern is
379 weakly visible. We note that the high slip-parallel values are associated to rapid changes in
380 dip-directions and dip-angles.
381 Figure 8 Here
382 Figure 9 Here
383 Figure 10 Here
384
17
385 Table 4 . Roughness statistics. Units in m. Notice that mean roughness (R) increases with
386 kernel size.
Kernel size R min R max R mean R st. dev.
0.02 m 0 0.006 0.002 0.001
0.1 m 0 0.012 0.003 0.002
1.0 m 0 0.114 0.023 0.018
387
388
389 6. Discussion
390 In this paper we examined the possibilities of integrating t-LiDAR and point cloud creation
391 technologies for mapping the kinematic properties of an active fault scarp as well as its
392 morphology in detail. As the t-LiDAR technique offers a lot of new information for
393 structural geology, we were able to show (see section 6.4 below) that the t-LiDAR
394 measurements were reliable as we validated our results with field measurements.
395 The Pidima area is of great seismotectonic interest due to the area’s high earthquake
396 potential and any additional data would be of significant importance. The fault plane
397 features gave many useful and interesting results based on the point cloud analyses.
398 Moreover, the structural analysis of the fault plane allowed for a detailed logging of its
399 geometrical characteristics, such as the plane’s dip and dip direction angles, lineament and
400 corrugation size and orientation. Below we discuss a few important observations including
401 the size of the last earthquake occurred on this fault.
402 6.1 Scarp morphology: Curvature of fault plane 18
403 One of the interesting areas of research on fault scarps is the relation between the
404 formation of large corrugations on the fault plane with the mechanics of the earthquake
405 ruptures. Corrugations are believed to form from overlapping faults by two main
406 mechanisms, the lateral propagation of curved fault tips and linkage by connecting faults
407 (Ferrill et al., 1999). Corrugations may have their origins in the progressive breakthrough
408 of originally segmented fault systems (Jones et al., 2009). In our study we identify two
409 scales of corrugations: a) the corrugation of the fault or a local fault-bend (Fig. 6c) which
410 is defined by the deviation of the fault trace from a straight line and b) the corrugations of
411 the fault plane itself which are developed in slip-parallel mode and maintain an almost
412 equal-half-distance of 0.75 m (in our 1-m kernel size dataset; Fig. 11). We associate the
413 former (i.e. the fault bend) with fault growth processes although more field data are needed
414 to establish the fault linkage history of EMFZ (Fig. 1). The large-scale corrugations on the
415 fault plane (Fig. 11) may be related to the mechanics of faulting; a component of strike-
416 parallel horizontal strain may exist during earthquake nucleation and rupture propagation
417 on the fault plane (i.e. Roberts, 1996; Roberts and Ganas, 2000; Hampel et al. 2013).
418 The curvature of a fault surface (see Fig. 6a for a 3-D model of the Pidima scarp showing
419 its curved morphology) is another measure of the divergence it has from planarity.
420 Curvature analysis of geological surfaces can provide useful plots for quantitative
421 measurements of folds and fault geometry, such as corrugations and has also been
422 employed to describe the geometry of strata, to quantify the degree of deformation or strain
423 in deformed strata, and to predict fracture orientation (Bergbauer and Pollard, 2003; Jones
424 et al., 2009). The computation of curvature is based on the surface normals, which were
425 automatically generated by the CloudCompare. In mathematical terms, curvature is a
426 second-order derivative of the surface and for that reason curvature values are independent
19
427 of the surface’s orientation. For a surface defined in 3-D space, the mean curvature is
428 related to a unit normal of the surface:
429 2 = −∇ ∙ [3]
-1 430 Where H is the mean curvature (in units m ) and is the normal unit.
431 The mean scarp curvature is shown in Fig. 11, on the whole Pidima scarp surface in order
432 to identify the changes of curvature along strike with kernel-size of 1-metre. The data
433 analysis shows an alternation of low-high curvature values (0-0.29) along scarp’s strike.
434 The arrangement of the curvature pattern is slip-parallel, i.e. the high and lows are sub-
435 parallel to the slip-vector. We identified in the field that most of the high values correspond
436 to large corrugations of the scarp, displaying a mean half-wavelength of 0.75 m on both
437 slip surfaces (Fig. 11b; i.e. the distance between a corrugation “ridge” and a “trough”).
438 Curvature is aligned in slip-parallel mode over the fault surface except for the area with
439 colluvium (Fig. 11e) where a deflection was introduced probably because of excavation
440 works. In comparison to other t-LiDAR studies in Greece such as at Arkitsa (Jones et al.
441 2009) our corrugation half-wavelength is smaller (0.75 m vs 4.04 m peak-to-peak in
442 Arkitsa) however, the point cloud scale of analysis is not available at the Arkitsa study.
443 Although corrugations are ubiquitous on fault scarps in Greece (e.g. Roberts, 1996;
444 Roberts and Ganas, 2000; Ganas et al. 2004; Jones et al. 2009) it is the first time that a
445 regular “half-wavelength” of corrugations is established. As we lack more data along strike
446 of the Pidima fault we are not certain that this “half-wavelength” is constant or it is a value
447 with local significance.
448 In addition, the scarp geometry-related products (dip-direction and dip-angle) show that the
449 scarp is non-planar. The range of dip-directions is N206°E – N280°E (Fig. 4a, b; 3%
450 trimmed either side of the distribution) along strike while the dip-angle range is 57°-79°
20
451 (Fig. 4c, d; 3% trimmed). We also observe that a) the distribution of the variability in the
452 dip-direction along strike is not uniform (Fig. 4a), i.e. the northern part of the scarp is
453 oriented more to the SW than its central and southern part, probably reflecting a local
454 concavity (bend) of the fault plane b) the distribution of the dip-angles strongly resembles
455 a gaussian-type with a mean value of 70° and standard deviation of 4°. We also note that
456 towards the higher parts of the scarp dip-angle values become lower (Fig. 4c), i.e. the scarp
457 attains a convex geometry up-dip. We measured lower dip-angles on both higher parts of
458 the scarp, natural and exhumed.
459 Therefore, it is demonstrated that the Pidima fault scarp is non-planar, both along-dip (slip-
460 parallel) and along-strike (slip-normal) directions.
461
462 Figure 10 Here
463
464 6.2 Roughness analysis and its kernel-size dependence
465 We investigated the roughness scale-dependence first by drawing a set of 13 orthogonal
466 line-profiles, six (6) along-strike or slip-normal (see Fig. S1; profiles 34 through 39) and
467 seven (7) along-dip or slip-parallel (Fig. S1; profiles 22-28). In all profiles we start to see
468 the roughness variations at kernel size 0.1 m and above, because at 0.02 m the fault scarp
469 appears smooth with subtle variations in roughness (see Fig. 8a).
470 In along-strike (or slip-normal) profiles the shape of the profile is similar (number of peaks
471 – number of troughs) for all kernel sizes. The roughness values scale with kernel size, i.e.
472 the larger the kernel the larger the roughness (as in the overall data; Table 4 and Fig. 10).
473 For kernel size 1 m we observe an increase in roughness from scarp bottom to scarp top 21
474 (see Fig. 8 for a “map” view). For all kernel sizes the increase is systematic if we consider
475 just the three bottom profiles (i.e. 34, 35 and 36; Fig. S2).
476 The along-dip (slip-parallel; Fig. S3) profiles display a mixed picture: some profiles
477 (e.g.26 and 27 in Fig. S3) present a similarity of shapes irrespective of kernel size,
478 however, profiles 22 to 25 present different shapes. We attribute this difference to the
479 location of these profiles at the scarp surface, i.e. they are aligned along “ridges” and
480 “troughs” of the scarp.
481 Overall, we cannot discriminate between slip-normal and slip-parallel roughness neither in
482 shape forms or in absolute values. This is due to a) the randomness in the selection of the
483 profiles, i.e. we chose regular spacing of about 1m and we did not pick the profile location
484 on purpose and b) to the local variations in scarp morphology which are not discernible at
485 the scale (kernel-size) of our investigation (lower limit 6 mm). The TRI analysis
486 demonstrated that the slip-parallel features are less dominant at 6-mm kernel (Fig. 9a) as
487 opposed to the 1-cm kernel. It is therefore probable that lower (smaller) kernel sizes are
488 needed to explore roughness variations along the Pidima fault scarp. This could be
489 achieved by reducing the scanning distance to a few metres. We note that our TLS data
490 resolution is not high enough to provide 1-mm kernel size of the scarp surface or even less,
491 so effectively our scaling-coverage is only two orders of magnitude, that is 1-cm and 1-m,
492 respectively.
493
494 6.3 Evidence for seismic slip along the non-exhumed part of the scarp
495 So far, we did not describe the non-exhumed surface of the scarp, that is the narrow stripe
496 that extends along the top of the scarp, parallel to the line of vegetation (see Fig. 3 for a
22
497 view in the field). Using the t-LiDAR data (the backscatter signal intensity image) we were
498 able to measure the two dimensions of this feature (Fig. 3c) along strike and along the dip
499 direction, i.e. the stripe’s length is equal to 12.6 m and the average width equals 0.31 m.
500 We suggest that this well-preserved feature represents the last seismic slip along this scarp
501 as it comprises a typical footwall feature of earthquake surface ruptures along limestone
502 scarps (e.g. Stewart, 1996; Ganas et al. 2004; Civico et al. 2018). Assuming this value
503 (0.31 m) is the maximum surface displacement of the last earthquake we obtain a surface
504 magnitude (Ms) of 6.2 using the Wells and Coppersmith empirical relationship (1994; W
505 & C) for the last event. We note that this estimate is in broad agreement with the length –
506 magnitude estimate by W & C (1994) because an 8 km long normal fault is expected to
507 generate an earthquake with 5.7 ≤M≤6.4 (using a standard deviation of 0.34 magnitude
508 units). Our approach is useful in terms of estimating the magnitude of the last earthquake,
509 however, more data are needed (e.g. from paleoseismology) in order to provide a more
510 detailed and complete seismic hazard assessment for the Pidima fault,
511
512 6.4 Validation of t-LiDAR results with field data
513 We compare our image processing results to a set of field data collected by a) S.
514 Valkaniotis and K. Betzelou during the summer of 2014 and b) A. Ganas and I. Karamitros
515 on 7 December 2017 along the Pidima fault scarp. We want to investigate the consistency
516 of our image processing analysis and to validate our results with field measurements.
517 Although the population of the scanned fault surface reaches a few million points of data
518 we can draw coherent conclusions when compared with field data because the exposure is
519 limited (roughly 20 m long by 7 m high) and the overall kinematics of the fault is known.
520 Our field data indicate a mainly dip-slip fault dipping at high-angle towards the west (see
23
521 Fig. 5). Our measurements are accurate to about 5° in compass azimuth / inclination. Our
522 field data have been analysed and plotted by the use of WinTensor software.
523 We validated the following products (Fig. 12): a) fault dip-angle diagram b) fault lineation
524 plunge diagram c) fault strike rose diagram d) fault lineation azimuth plot e) stereonet,
525 lower hemisphere projection of fault planes f) stereonet plot of lineations. In the field we
526 measured the pitch angle of striations using a CLAR-type compass. This set of
527 measurements was converted to line-azimuth and line-plunge by WinTensor software, so
528 that it can be directly compared with the analysis by CloudCompare (TLS measurements).
529 Our TLS results are depicted in Figure 12 (left panel) and are in close agreement with the
530 field data (Fig. 12 right panel), The small differences are due to the fact that field data were
531 collected from particular patches of the fault scarp while our TLS results originate from the
532 analysis of nearly-identical areas of scarp (see red boxes in Fig. 5) but not exactly the same
533 lines or striations. In addition, small differences may be due to the computation of the
534 plane orientation by the image processing software. Another reason is the (locally)
535 imprecise data recording of t-LiDAR as its view direction may vary locally depending on
536 fault surface details (grooves, small corrugations etc).
537 Figure 11 Here
538
539 7. Conclusions
540 1. The geometry-related products of the obtained point-cloud (dip-direction and dip-
541 angle; Fig. 4) show that the scarp is non-planar (range N206°E – N280°E) along
542 strike while the dip range is 57°-79°.
24
543 2. The TLS data indicate that towards the higher parts of the scarp dip-angle values
544 become lower (by an amount of up to 15°), i.e. the scarp attains a convex geometry.
545 3. Our TLS results are in close agreement with the field data (Fig. 7 and Fig. 21-23),
546 as our mean field measurements for dip direction is N250°E and for dip-angle is
547 70.5°.
548 4. A comparison with field measurements of the rake of the slip vector (see Fig. 5)
549 showed that the difference of the means is 4.4° while both datasets exhibit similar
550 standard deviations around their means (Table 3). The rake data indicate a small
551 dextral-slip component (between 5 and 10°) of the slip vector at this locality.
552 5. We mapped roughness patterns using the CloudCompare and the TRI methods (Fig.
553 8 & 9). The fault-plane roughness (absolute values) is scale dependent. The
554 roughness configuration attains a slip-parallel pattern for observation scales above
555 1-cm. At observations scales less than 1-cm a slip-normal pattern is weakly visible.
556 The results indicate that the smaller the processing kernel size, the smoother the
557 surface (Fig. 10).
558 6. In terms of scarp morphology, we observe that fault surface curvature develops
559 mainly in slip-parallel mode.
560 7. We mapped a 0.31 m wide, smooth stripe along the naturally exposed (non-
561 exhumed) part of the scarp that may represent the last seismic displacement along
562 this scarp. Assuming this value is the maximum surface displacement of the last
563 earthquake we infer that its magnitude was M=6.2±0.2.
564
565 8. Acknowledgments
566 We thank one anonymous reviewer and Sascha Schneiderwind for constructive reviews.
567 We thank Iannis Koukouvelas, Vassiliki Zygouri, Efstratios Delogkos, Elena Partheniou, 25
568 Konstantina Betzelou, Babis Fassoulas, Spiros Pavlides and Antonis Mouratidis for
569 comments and discussion in the field. The open-source software WinTensor &
570 GeoCalculator can be accessed from
571 http://www.damiendelvaux.be/Tensor/WinTensor/win-tensor.html and
572 http://www.holcombe.net.au/software/geocalculator.html respectively. This research was
573 funded by the project KRIPIS-ASPIDA “ Infrastructure Upgrade for Seismic Protection of
574 the Country and Strengthen Service Excellence through Action ”. This work is part of MSc
575 thesis of I.K at the Aristotle University of Thessaloniki.
576
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801 Geological Society of Greece Annual Scientific Meeting, Abstracts Volume, p. 51.
36
802 TABLES:
803 .
804
805
806
37
807 ILLUSTRATIONS:
808 809 Figure 1. a) Tectonic map of SW Peloponnese showing the N-S Eastern Messinia Fault
810 Zone. 1) Main EMFZ fault traces 2) Verga fault surface rupture due to the 1986 earthquake
811 (M w=5.9) mapped by Lyon-Caen et al. (1988) 3) Secondary active faults/Older fault
812 segments 4) Main fault segments numbers 5) location of Pidima fault scarp. Barbs towards
813 the downthrowing side of faults. Simplified syn-rift geological formations modified from
814 IGME maps (Lalechos et al. 1973; Papadopoulos et al. 1997; Perrier et al. 1989; Psonis et
815 al. 1982, 1986). Beachballs represent NOA focal plane solutions (Ganas et al., 2012;
816 Kassaras et al., 2014). Dashed box shows area of Fig. 1b and Fig. 2. b) Geological map
38
817 (simplified) of the greater Pidima fault area. Red dot 1 indicates location of TLS site, red
818 dot 2 indicates location of the paleoseismological section, respectively. Stereonet indicates
819 a lower-hemisphere projection of fault planes and striations along the Pidima fault
820 segment. Legend as follows: Q is Upper Pleistocene – Holocene, Pt is Pleistocene, Pl is
821 Pliocene and M is Mesozoic basement. Numbers indicate spot elevations across the faults.
822
39
823
824
825 Figure 2. a) Geological map of the Pidima scarp area (made by the authors). The scarp
826 locality is shown with a yellow box. Red line shows the active fault trace and older fault
827 scarps. Curved black lines are 4-m interval elevation contours (digitised from Greek Army
828 topographic maps). Black, dashed line A-A' shows the trace of the cross-section in b). c)
829 field photo (taken on 18 August 2014) of the Pidima fault plane including the exhumed
830 portion. Legend as follows: HI is Holocene deposits (and swamps), Qsc,cs is Quaternary
831 scree deposits, Pt,c is Pleistocene scree deposits, Pl is Pliocene deposits and Ls is
832 limestone (Tripolis zone).
833
40
834 835 Figure 3 . a) Pidima fault scarp panoramic photograph (taken by SV on August 2014; view
836 to the east; notice human to the right, for scale). The red rectangle indicates the area that
837 was scanned during the field trip of May 2015 b) instrument set-up against the scarp c)
838 Backscatter intensity image of the scarp (on the right gray scale 0-255). In this
839 configuration axis-X (red) is normal to scarp, axis-Y (green) is along scarp, and axis-Z
840 (blue) is vertical (along scarp height).
841
41
842
843 Figure 4. a) Cleaned scarp image showing distribution of dip directions along strike b)
844 histogram of dip-direction values (degrees clockwise from North). c) Cleaned LiDAR
845 image showing distribution of dip-angles along scarp strike (see scale on the right;) and d)
846 histogram of dip-angle values. The mean value of dip is 70.5° with a standard deviation of
847 4.3° . The dominant WSW dip-direction is at a high-angle to the Pidima fault strike (Fig. 2)
848 Data analysis with CloudCompare, N=3.6 million points.
849
850 .
851
852
42
853
854
855 Figure 5. Field photograph of the Pidima scarp showing sections sampled for slip-vector
856 data (red boxes with annotations; A1 to A8): top) lower hemisphere stereographic
857 projection of fault planes and lines from field data and b) lower hemisphere stereographic
858 projection of fault planes and lines from corresponding TLS data. Inset box shows a close-
859 up of the fault plane (pencil for scale) near section A8. The field data were collected during
860 December 2017.
43
861
862 Figure 6. Pidima scarp photographs: (left) strike normal (right) along strike (view north).
863 The branch line (black) between the two overlapping fault lenses with the colluvium slip
864 surface (1) on top of the polished limestone slip surface (3), between them the scarp area
865 (2), comprising rough colluvium breccia. Red box indicates area shown in 8b. The non-
866 exhumed part of the scarp is visible as the gray area towards the top, aligned with
867 vegetation (marked with dashed yellow line). Photograph taken on 7 December 2017. C)
868 The 3-D model of the Pidima scarp from TLS data with color integration from the
869 photographs taken on the field trip of December 2017. The model was made by the
44
870 combined use of Agisoft Photoscan and CloudCompare d) the trace of the fault plane 3 m
871 above ground (in map view). Blue is the limestone part of the scanned surface. Red is the
872 cemented colluvium part of the scanned surface. The non-planar geometry of the scarp is
873 more evident on its central part.
874
875
45
876
877
878 Figure 7. Scarp TLS Data analysis: The result of the planar facets analysis plug-in of
879 CloudCompare with an angular step at 5° (degrees). Each facet has individual dip-direction
880 and angle values. Left) a lower hemisphere stereographic projection of the results of the
881 facet analysis is shown with inner circles depicting range of dip-angles (0-90°). Outer
882 circle denotes range of dip-directions. Colours inside the stereonet denote facet density
883 (blue small – red large).
884
46
885
886 Figure 8. Surface roughness image of Pidima Scarp at various scales (kernel-size) of
887 observation. The image is a point-cloud product of CloudCompare. a) algorithm kernel
888 Size = 0.02 m b) 0.1 m c) 1.0 m. Vertical scale bars indicate absolute roughness values, i.e.
47
889 distance in m from the mean plane of the sampled population of neighbouring TLS points.
890 Notice the rough, slip-parallel stripes that are preserved at images b) to c) but not in a)
891 where a slip-normal stripe with low roughness appears at the top of the scarp. Bar scale on
892 the bottom right is in m.
893
48
894
895 896 Figure 9. Surface roughness obtained by the TRI method (Riley et al. 1999). a) kernel size 897 6 mm b) kernel size 1 cm and c) kernel size 2 cm. Slip-parallel features are visible at 898 kernel sizes above 1-cm. At kernel-size 6 mm a slip-normal pattern is weakly visible.
49
899
900 Figure 10. Plot of observation (kernel-size; X axis) vs. mean roughness values (Y)
901 showing a non-linear relation of scarp roughness with increasing kernel-size (the fitted-
902 lines to data are shown for comparison). Roughness data are those from Fig. 8.
50
903
904 Figure 11. Curvature analysis of TLS data: a) Cleaned scarp image showing distribution of
905 mean surface curvature (absolute values) along strike by use of CloudCompare (see scale
906 on the right; n=3.6 million points) b) manual tracing of max curvature values over the
907 image of a). c) histogram of curvature values and d) sketch of curvature definitions along a
908 scarp surface (not to scale; after Roberts, 2001) e) field photograph of the Pidima scarp that
909 is included inside the red box of a). Dashed red line is the same as in b). Algorithm kernel
910 size = 1 m. Zero curvatures are shown in blue, high curvatures (0.29) with red,
911 respectively.
51
912 52
913 Fig. 12 Validation of TLS data with field measurements: a) rose diagram showing dip- 914 direction b) quarter-rose diagram showing dip-angle c) rose diagram showing striation 915 azimuth d) quarter-rose diagram showing plunge-angle of striations e) stereonet projection 916 of fault planes where striations were measured f) stereonet projection of striations. 917 Sampling locations are presented in Fig. 5. Plots were produced from WinTensor.
53
Highlights
• High-density point cloud (17K points per squ. metre) of the Pidima active normal fault scarp with 6-mm resolution • detail mapping of the variation of fault plane geometry along strike and dip and along the slip vector orientation • Validation of TLS data products with field measurements • New quantitative data on corrugations and roughness properties of the fault plane author statement
Ioannis Karamitros: Conceptualization, Methodology, Software, Visualization, Writing- Original draft preparation, Writing- Reviewing and Editing,
Athanassios Ganas: Supervision, Writing- Original draft preparation, Writing- Reviewing and Editing, Funding acquisition
Alexandros Chatzipetros: Supervision, Investigation.
Sotirios Valkaniotis: Visualization, Software, Data collection Declaration of interests
☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: