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Bulletin of the Seismological Society of America
Aftershock sequence and statistics of the 2017 MW 5.5 Pohang earthquake, SouthKorea: implication of fault heterogeneity and post-seismic relaxation
--Manuscript Draft--
Manuscript Number:
Article Type: Article
Section/Category: Regular Issue
Full Title: Aftershock sequence and statistics of the 2017 MW 5.5 Pohang earthquake, SouthKorea: implication of fault heterogeneity and post-seismic relaxation
Corresponding Author: Junkee Rhie, Ph.D.Seoul National UniversitySeoul, KOREA, REPUBLIC OF
Corresponding Author's Institution: Seoul National University
Corresponding Author E-Mail: [email protected]
Order of Authors: Jeong-Ung Woo
Minook Kim
Junkee Rhie, Ph.D.
Tae-Seob Kang, Ph.D.
Abstract: The sequence of foreshocks, mainshock, and aftershocks associated with a faultrupture are the result of interactions of complex fault systems, the tectonic stress field,and fluid movement. Analysis of shock sequences can aid our understanding of thespatial distribution and magnitude of these factors, as well as providing a seismichazard assessment. The 2017, M W 5.5 Pohang earthquake sequence occurredfollowing fluid-induced seismic activity at a nearby enhanced geothermal system siteand is an example of reactivation of a critically stressed fault system in the PohangBasin, South Korea. We created an earthquake catalog based on unsupervised data-mining and measuring the energy ratio between short- and long-window seismogramsrecorded by a temporary seismic network. The spatial distribution of approximately4,000 relocated aftershocks revealed four fault segments striking southwestward. Wealso determined that the three largest earthquakes ( M > 4) were located at theboundary of two fault segments. We infer that locally concentrated stress at thejunctions of the faults caused such large earthquakes and that their ruptures onmultiple segments can explain the high proportion of non-double couple components.The area affected by aftershocks expands to the southwest and northeast by 0.5 and 1km decade -1 , respectively, which may result from post-seismic deformation orsequentially transferred static Coulomb stress. The b -values of the Gutenberg-Richter relationship temporarily increased during the aftershock period, suggesting thatthe stress field was perturbed. The b -values were generally low (< 1) and locallyvariable throughout the aftershock area, which may be due to the complex faultstructures and material properties. Furthermore, the mapped p -values of the Omorilaw vary along strike, which may indicate anisotropic expansion speeds in theaftershock region.
Author Comments: The first author, Jeong-Ung Woo will receive a Ph.D degree in February 2020. In theauthor affiliation, I fill the blank for the current state.
Suggested Reviewers: Kwang-Hee KimPusan National [email protected] is the first author of the paper "Assessing whether the 2017 Mw 5.4 Pohangearthquake in South Korea was an induced event" published at Science.
Francesco [email protected] is the first author of the paper "The November 2017 Mw 5.5 Pohang earthquake: A
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possible case of induced seismicity in South Korea" published at Science.
Grzegorz [email protected] is the first author of the paper "Controlling fluid-induced seismicity during a 6.1-km-deep geothermal stimulation in Finland" published at Science Advances.
Chang-Soo ChoKorea Institute for Geosciences and Mineral [email protected] is one of the authors of "Surface Deformations and Rupture Processes Associatedwith the 2017 Mw 5.4 Pohang, Korea, Earthquake" published at BSSA and he did lotsworks on relocated seismicity.
Opposed Reviewers:
Additional Information:
Question Response
<b>Key Point #1: </b><br><i>Key Pointsare now mandatory for BSSA, and willappear at the front of articles starting in2020. Please submit three COMPLETEsentences addressing the following: 1)what problem did you address?; 2) whatconclusions did you come to?; and 3)what are the implications of your findings?Each point must be 110 characters or less(including spaces).
Three largest M > 4 earthquakes of the 2017 MW 5.5 Pohang sequence was located atjunctions of fault segments.
Key Point #2: Along-strike expansion of aftershock area was observed, implying afterslips orCoulomb stress transfer.
Key Point #3: Generally low b-values (<1) and variations in p-values along northeast direction weremapped.
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04 February 2020
Betty Schiefelbein
Manuscript Coordinator
Bulletin of the Seismlogical Society of America
Dear Editor:
I wish to submit an article for publication in the Bulletin of the Seismological Society of America titled
“Aftershock sequences and statistics of the 2017 MW 5.5 Pohang earthquake, South Korea: implication of
fault heterogeneity and post-seismic relaxation.” The paper was coauthored by Jeong-Ung Woo, Minook
Kim, Junkee Rhie, and Tae-Seob Kang.
This study analyzed over 4,000 earthquakes that were part of the foreshock-mainshock-aftershock
sequence associated with the main 2017 MW 5.5 Pohang earthquake. It was of interest to us that a high
proportion of non-double-couple events in the large magnitude earthquakes were recorded as part of the
Pohang event. Analysis of these data using the double-difference method confirmed the presence of a
complex network of four faults in the immediate vicinity of the Pohang enhanced geothermal site. Further
analysis of the Gutenberg-Richter b-values and the Omori p-values for the mainshock and aftershock
sequences indicated that they varied both temporally and/or spatially in the 2017 Mw 5.5 Pohang sequence
indicating a certain degree of subsurface heterogeneity than previously understood. We believe that our
study makes a significant contribution to the literature because, although our paper essentially presents a
case study located on the Korean Peninsula, the methods of analysis of b- and p-values that we describe
are of wider relevance to seismic hazard assessment and prediction in general.
Further, we believe that this paper will be of interest to the readership of your journal because it falls
within the remit of the BSSA to report on seismology and seismic hazard analyses. Specifically, we
believe that it fits well within the category of characterization of fault systems and seismotectonics
relevant to understanding seismicity and carrying out seismic hazard assessments, as described by Pratts.
Please consider, as potential referees, Kwang-Hee Kim (Pusan National University;
[email protected]), Francesco Grigoli (ETH; [email protected]), Grzegorz Kwiatek
(GFZ; [email protected]), and Chang-Soo Cho (Korea Institute for Geosciences and Mineral
Resources; [email protected]).
This manuscript has not been published or presented elsewhere in part or in entirety and is not under
consideration by another journal. We have read and understood your journal’s policies, and we believe
that neither the manuscript nor the study violates any of these. There are no conflicts of interest to declare.
Thank you for your kind consideration of this manuscript. I look forward to hearing from you.
Sincerely,
Junkee Rhie
School of Earth and Environmental Sciences, Seoul National University
08826
+82-2-880-6731
+82-2-871-3269
Letter to editor Click here to access/download;Letter to Editor;letter.doc
1
Aftershock sequence and statistics of the 2017 MW 5.5 Pohang earthquake, South Korea: 1
implication of fault heterogeneity and post-seismic relaxation 2
3
Jeong-Ung Woo 1, Minook Kim2a, Junkee Rhie 1*, Tae-Seob Kang 3 4
Corresponding author: Junkee Rhie ([email protected]) 5
School of Earth and Environmental Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-6
gu, Seoul 08826, Republic of Korea 7
Phone: +82-2-880-6731 8
Fax: +82-2-871-3269 9
1 School of Earth and Environmental Sciences, Seoul National University, Seoul 08826, Republic 10
of Korea 11
2 Department of Structural Systems and Site Evaluation, Korea Institute of Nuclear Safety, 12
Daejeon 34412, South Korea 13
3 Division of Earth Environmental System Science, Pukyong National University, Busan 48513, 14
Republic of Korea 15
16
Key points 17
18
1. Three largest M > 4 earthquakes of the 2017 MW 5.5 Pohang sequence was located at 19
junctions of fault segments. 20
a also at Division of Earth Environmental System Science, Pukyong National University, Busan
48513.
Manuscript Click here to access/download;Manuscript;manu.docx
2
2. Along-strike expansion of aftershock area was observed, implying afterslips or Coulomb 21
stress transfer. 22
3. Generally low b-values (< 1) and variations in p-values along northeast direction were 23
mapped. 24
25
Abstract 26
27
The sequence of foreshocks, mainshock, and aftershocks associated with a fault rupture are the 28
result of interactions of complex fault systems, the tectonic stress field, and fluid movement. 29
Analysis of shock sequences can aid our understanding of the spatial distribution and magnitude 30
of these factors, as well as providing a seismic hazard assessment. The 2017, MW 5.5 Pohang 31
earthquake sequence occurred following fluid-induced seismic activity at a nearby enhanced 32
geothermal system site and is an example of reactivation of a critically stressed fault system in 33
the Pohang Basin, South Korea. We created an earthquake catalog based on unsupervised data-34
mining and measuring the energy ratio between short- and long-window seismograms recorded 35
by a temporary seismic network. The spatial distribution of approximately 4,000 relocated 36
aftershocks revealed four fault segments striking southwestward. We also determined that the 37
three largest earthquakes (M > 4) were located at the boundary of two fault segments. We infer 38
that locally concentrated stress at the junctions of the faults caused such large earthquakes and 39
that their ruptures on multiple segments can explain the high proportion of non-double couple 40
components. The area affected by aftershocks expands to the southwest and northeast by 0.5 and 41
1 km decade-1, respectively, which may result from post-seismic deformation or sequentially 42
transferred static Coulomb stress. The b-values of the Gutenberg-Richter relationship 43
3
temporarily increased during the aftershock period, suggesting that the stress field was perturbed. 44
The b-values were generally low (< 1) and locally variable throughout the aftershock area, which 45
may be due to the complex fault structures and material properties. Furthermore, the mapped p-46
values of the Omori law vary along strike, which may indicate anisotropic expansion speeds in 47
the aftershock region. 48
49
4
Text 50
51
INTRODUCTION 52
On 15 November 2017, a moderate-sized earthquake of moment magnitude (MW) 5.5 or local 53
magnitude (ML) 5.4 struck the city of Pohang, located in the southeastern part of the Korean 54
Peninsula, which damaged infrastructure, injured 90 people, and made 1500 homeless (Kim et al., 55
2018a). The earthquake (hereafter referred to as the mainshock) was the second-largest 56
earthquake event among earthquakes recorded instrumentally in South Korea since 1978, 57
according to the catalog of the Korea Meteorological Administration (KMA). A close 58
examination of the seismic source characteristics of such a rarely observed moderate-sized 59
earthquake and its foreshock-mainshock-aftershock sequence is necessary not only to evaluate 60
the current stress field (Zoback, 1992; Soh et al., 2018) and fault properties, but also to 61
understand aftershock triggering mechanisms (King et al., 1994; Kilb et al., 2000). Estimation of 62
statistical parameters (i.e., the Gutenberg-Richter b-value and the Omori law p-value) from a 63
large number of microearthquakes in conjunction with the seismic source properties of 64
aftershocks can give information on fault heterogeneities, such as crack density, slip distribution, 65
applied shear stress, viscoelastic properties, and heat flow (Wiemer and Katsumata, 1999; Murru 66
et al., 2007). 67
One important point to note is that the mainshock occurred near an enhanced geothermal 68
system (EGS) site (Grigoli et al., 2018; Kim et al., 2018b; Lee et al., 2019). A body of evidence 69
supports the claim that the mainshock was triggered by five fluid-injection experiments as well 70
as an associated loss of heavy drilling muds and released tectonic energy on a critically stressed 71
fault (Ellsworth et al., 2019; Woo et al., 2019a). The periods of stimulation experiments 72
5
conducted on two hydraulic wells (PX-1 and PX-2) were closely correlated with microseismicity 73
observed near the wells. Induced seismicity mapped in the vicinity of the EGS indicated the 74
presence of a previously unmapped fault. Microseismicity triggered on this fault migrated to the 75
location of the mainshock. A breakout was observed in the PX-2 well at intervals corresponding 76
to the assumed fault. The groundwater levels of PX-1 and PX-2 decreased abruptly by 121 m and 77
793 m, respectively, immediately after the mainshock but gradually recovered by 0.078 m/day 78
and 0.198 m/day, respectively (Lee, 2019). 79
Previous studies of aftershock distributions in the Pohang Basin determined the presence of 80
complex fault geometries (Hong et al., 2018; Kim et al., 2019). Separately, Grigoli et al. (2018) 81
reported that obtaining a significant non-double-couple (non-DC) component when inverting the 82
moment tensor for a mainshock can be attributed to the complexity of the rupture process in a 83
multi-fault system. The spatial pattern of early aftershocks associated with two 2016 Gyeongju 84
earthquakes (ML 5.1 and ML 5.8), which occurred on two sub-parallel faults approximately 40 km 85
away from the Pohang mainshock, is differentiated from the presence of two or three fault 86
segments with varying strikes and dips for the early aftershocks associated with the 2017 Pohang 87
earthquakes (Uchide and Song, 2017; Son et al., 2017; Woo et al., 2019b). 88
In this study, we created an earthquake catalog for the foreshock-mainshock-aftershock 89
sequence from data recorded by local permanent seismic networks, temporary seismometers 90
deployed as part of the aftershock monitoring system, and the temporary Pohang EGS 91
monitoring system. Earthquakes were detected using a machine-learning data mining technique 92
for data obtained during the first ten days and a conventional automatic detection algorithm was 93
employed for the aftershock monitoring system as a whole. Each detected earthquake was 94
located by manual picking and visual inspection and then precisely relocated by the double-95
6
difference method (Waldhauser and Ellsworth, 2000). Using the spatial distribution of over 4000 96
earthquakes, we modeled fault systems as a series of multiple fault segments by mapping the 97
spatio-temporal distribution of the statistical parameters b and p. 98
Mapping the distribution of earthquake magnitudes provides an independent analysis of the 99
characteristics of aftershock activities and can be used to analyze spatial heterogeneities of 100
material properties, such as stress state, level of asperities, and heat flow rate (Scholz, 1968; 101
Wiemer and Katsumata, 1999; Wiemer and Wyss 2000; Ávila-Barrientos et al., 2008); assess 102
seismic hazards via epidemic-type aftershock sequence modeling (ETAS; Ogata, 1998); and 103
conduct probabilistic seismic hazard analysis (PSHA; Cornell, 1968). In this study, we evaluated 104
the relative magnitude of each earthquake by using amplitude ratios relative to earthquakes of 105
known ML. 106
107
DATA AND METHOD 108
Seismic Networks 109
Continuous seismic waveform data used to detect and analyze seismic source parameters were 110
collected from four different networks (Figure 1). The first data set was obtained from a 111
combined permanent seismic network operated by KMA, the Korea Institute of Geoscience and 112
Mineral Resources (KIGAM) and the Korea Hydro and Nuclear Power (KHNP). The permanent 113
seismic networks of KMA, KIGAM, and KHNP are named KS, KG, and KN, respectively. The 114
second set of continuous waveform data were recorded by nine borehole seismometers installed 115
at depths of between 100 and 150 m, which operated to monitor microseismic events for the 116
Pohang EGS project. Three of the temporary borehole seismometers recorded the mainshock, 117
while the operation of the other borehole seismometers started within the next 2 days; all of them 118
7
operated until the end of November 2017. The third continuous waveform data set was collected 119
by 37 temporary broad-band seismometers installed after the mainshock by the university 120
consortium (Pukyong National University and Seoul National University) and KIGAM 121
independently. The first seismometer installed temporarily for monitoring aftershocks started its 122
operation approximately 1 h after the onset of the mainshock. Lastly, we used waveforms of 214 123
early aftershocks, occurred within four hours from the mainshock, recorded at eight short-period 124
temporary seismometers deployed by Pusan National University (Kim et al., 2018b). The 125
seismograph stations of these temporary networks were densely spaced and located within the 126
radius of 20 km from the EGS site, respectively (Figure 1). 127
128
Detection and Hypocenter Determination 129
Since stabilizing temporary seismometers for aftershock monitoring can take many hours, 130
conventional algorithms for earthquake detection, such as STA/LTA (Withers et al., 1998; 131
Trnkoczy 2002), are of limited use for locating early aftershocks because of the incompleteness 132
of the local seismometer network. In this study, we utilized the Fingerprint and Similarity 133
Threshold (FAST) data-mining algorithm that uses waveform similarity to detect such early 134
aftershock sequences (Yoon et al., 2015; Yoon et al., 2017; Bergen et al., 2018) with a 135
conventional energy-based algorithm for the period for aftershock monitoring system. The FAST 136
algorithm finds pairs of waveforms having similar spectrograms without any prior information, 137
allowing us to obtain pairs of earthquake candidates with correlative signals. The performance of 138
the FAST algorithm to discriminate true events from earthquake candidates can be improved by 139
measuring similarity at multiple stations (Bergen et al., 2018). 140
8
We applied the FAST method to ten days of continuous seismograms recorded between 14 Nov 141
2017 and 23 Nov 2017 to cover the period of operation of the aftershock monitoring system. We 142
used three-component seismograms obtained from two short-period (PHA2 and DKJ) and one 143
broadband (CHS) seismometers, which are located within 30 km from the mainshock. The three 144
borehole seismometers of the EGS monitoring system that were operational at the onset of the 145
mainshock were not used in detection due to the high level of ambient background noise and 146
regularly observed pulse-like signals. The sampling rate of the seismograms was fixed at 100 Hz 147
and the frequency range of the bandpass filter was set to 2 – 20 Hz. All parameters employed in 148
the FAST algorithm routines were either determined manually from performance trials and or 149
were previously applied values (Yoon et al., 2017; Yoon et al., 2019a) and are summarized in 150
Table A1 and A2. 151
We detected 1580 candidate events via the FAST search, leading to a subset of 1357 locatable 152
earthquakes from visual inspection. This is comparable to the number of events in the earthquake 153
catalog published by Kim et al. (2018b), which utilized eight local seismographs deployed within 154
3 km of the EGS site for earthquake detection: the FAST algorithm successfully detected 155
169/174 or 97% of earthquakes for the same period. 156
While the aftershock monitoring network was operational (i.e., from 15 November 2017 to 28 157
February 2018), we applied an automatic algorithm to detect and locate microseismic 158
earthquakes (Sabbione and Velis, 2013). Continuous waveforms were transformed into 159
characteristic functions for measuring the ratio between the short-term average (STA) and the 160
long-term average (LTA). We declared candidate earthquakes when the STA/LTA ratio 161
exceeded 5 for a given time window of 4 s at more than three stations. For each triggered time 162
window, the normalized squared envelope functions of Baer and Kradolfer (1987) were 163
9
calculated to determine the time at which to maximize the function value (hereafter referred to as 164
the BK function). Since the BK function can be maximized for the arrivals of either the P-wave 165
or the S-wave, the maximum value of the BK function was tested to discriminate whether the 166
measured local maximum corresponded to the first arrival. If we observed a local high BK 167
function value before the maximum of the BK function in a given time window, we set two 168
consecutive time samples as the arrivals of the P- and S-waves. Otherwise, we searched for other 169
local maximum after the triggered time window and set the maxima as the P- and S-wave phase 170
arrivals when a secondary maximum was available. The phase arrivals determined in this way 171
were visually confirmed by using a Wadati plot (Wadati, 1933). 172
We determined the initial hypocenters of the detected earthquakes via Hypoellipse (Lahr, 1999), 173
with phase arrival times being determined by manual inspection and a 1-D layered seismic 174
velocity model for the Pohang EGS site (Woo et al., 2019a; Table 1). In this procedure, we 175
combined the earthquakes detected from either the FAST algorithm or the STA/LTA method 176
with events with ML > 2.0 listed in the KMA and Kim et al. (2018b) event catalogs. Earthquakes 177
with an onset difference of less than 2 s were regarded as duplicate events. Station corrections 178
were calculated based on a comparison of the theoretical arrival times for five immediate 179
foreshocks reported by Woo et al. (2019a) and their picked arrival times. 180
Initial hypocenters were relocated with hypoDD (Waldhauser and Ellsworth, 2000) by using 181
travel time differences obtained from waveform cross-correlation measurements as well as 182
picked phase times as inputs to the double-difference algorithm. The 1-D velocity model of Woo 183
et al. (2019a) was applied for the relocation procedure, again (Table 1). All relocated events were 184
shifted by 39 m, 28 m, and 96 m in eastwards, northwards, and downwards, respectively, to 185
match the centroid of the five immediate foreshocks with the results of Woo et al. (2019a), of 186
10
which recordings at 17 PX-2 borehole chains were applied to obtain accurate hypocenters. We 187
resampled waveforms to 1,000 Hz with a cubic spline after first having applied a 2–10 Hz 188
bandpass filter. Each seismogram was reduced to a 1 s time window centered at each phase 189
arrival time. We allow a time shift up to 0.1 s for the cross-correlation measurements. Time 190
shifts that maximized the cross-correlation coefficient (CC) between two pairs of waveforms 191
were used only if the maximum CCs were greater than 0.85. The squared maximum CCs were 192
used to weight the measurements. The relative locations were calculated by least-squares fitting 193
of the data and the location uncertainties were evaluated by using bootstrapping analysis 194
(Waldhauser and Ellsworth, 2000). Synthetic travel time differences between paired events were 195
reconstructed by random selection of a set of residuals and relative locations for these synthetic 196
travel times were calculated 200 times. 197
198
Magnitude Estimation and Statistical Analysis 199
Waveform similarity can be assessed to estimate the relative magnitudes of earthquakes (Shelly 200
et al., 2016; Yoon et al., 2019b). We adopted a simple magnitude-amplitude relationship 201
modified from the equation of Shelly et al. (2016) that considers the differences in hypocentral 202
distance between two earthquakes: 203
dm = clog10(a/r), (1) 204
where dm, a, and r represent the ratios of magnitude, amplitude, and hypocenteral distance, and c 205
is a coefficient for the magnitude-amplitude relationship (Shelly et al. 2016). The coefficient c in 206
Equation 1 varies with the earthquake magnitude scale that is used: for example, Shelly et al. 207
(2016) reported that c = 1 for ML and c = 2/3 for MW. In this study, we used a set of MLs of 208
aftershocks and Equation (1) to estimate the coefficient c, following the method of Woo et al. 209
11
(2019b). If the CC of a waveform pair was greater than 0.85, we calculated the amplitude ratio as 210
the slope of the eigenvector for the largest eigenvalue of the covariance matrix of the two 211
waveforms (Shelly et al., 2016). Thus, for earthquakes with known values of ML, we were able to 212
estimate the parameter c. 213
We can also determine relative magnitudes of earthquakes by using our estimated value of c in 214
Equation (1). Estimated relative magnitudes (MRel) were arithmetically averaged to produce a 215
representative value and uncertainties were obtained from their standard deviations. 216
The Gutenberg-Richter law (G-R law) describes the relationship between earthquake frequency 217
and magnitude. Its statistical properties are widely accepted and applied to the investigation of 218
seismo-tectonic properties in a specific region over a certain time period. Examples of 219
application of the G-R law include work on aftershock sequences by Wiemer and Katsumata 220
(1999) and Woo et al. (2019b), on earthquake swarms by Farrell et al. (2009), on induced 221
seismicity by Shapiro (2007), and in laboratory experiments by Scholz (1968). The earthquake 222
frequency distribution with magnitude can be written as: 223
log10 N(≥ M) = a - bM, (2) 224
where N is the number of earthquakes equal to or greater than a magnitude M, and a and b are 225
scaling constants. a is proportional to the overall seismicity in a given spatio-temporal interval, 226
whereas b represents the ratio of the number of large earthquakes to small earthquakes. The 227
behavior of b-values has been attributed to crack density (Mogi, 1962), stress drop (Wyss, 1973), 228
and tectonic stresses (Schorlemmer et al., 2005, Scholz, 1968), and slip distribution (Wiemer and 229
Katsumata, 1999). 230
We determined the magnitude of completeness (MC) for 3,521 magnitudes based on a modified 231
goodness-of-fit method of Wiemer and Wyss (2000), following Woo et al. (2019b). Then, we 232
12
evaluated the b-value for a set of magnitudes using the maximum likelihood method of Aki 233
(1965) with a magnitude bin of 0.1. The uncertainty of b-values was estimated with the method 234
of Shi and Bolt (1982). 235
Omori’s law describes the decay rate of aftershocks. Its parameters are also broadly applied to 236
interpret regional seismic and tectonic properties (Omori, 1894; Utsu, 1961). The extended form 237
of Omori’s law can be written as: 238
R(t) = K(t+c)-p, (3) 239
where K, c, and p are the scaling coefficients that describe the aftershock decay rates in a given 240
region. p, which represents the power of the aftershock decay rates, has a range of 0.6 to 1.8 and 241
is considered to be a function of stress and temperature in the crust (Utsu and Ogata, 1995; 242
Wiemer and Katsumura 1999). We mapped the spatial variation of p-values by binning 250 243
magnitudes and by selecting magnitudes greater than MC. The three parameters and their 244
associated uncertainties were determined following the maximum likelihood method presented 245
by Ogata (1983). 246
247
RESULTS 248
Of the 4,446 earthquakes with initial locations, we relocated seven foreshocks, the mainshock, 249
and 3,938 aftershocks using hypoDD (Waldhauser and Ellsworth, 2000), having excluded 250
earthquakes with fewer than seven traveltime difference measurements. Uncertainties of relative 251
locations to within two standard deviations were estimated as 25 m in the eastwest direction, 18 252
m in the northsouth direction, and 37m vertically. Figure 2 presents the spatial distribution of 253
aftershocks, both in plan view and cross-sections, four in the dip direction (A1-A2, B1-B2, C1-C2, 254
and D1-D2) and one in the strike direction (E1-E2). From the map, we determined the apparent 255
13
strike of aftershocks (crossline of E1-E2) to be 210°, which corresponds to the azimuth of the first 256
principal vector obtained from two-dimensional principal component analysis (PCA) (Jollifle, 257
2002). From cross-sections in the dip direction (A1-A2 to D1-D2), we observed that the spatial 258
distribution of aftershocks delineates at least four different fault segments (Figure 2). In the most 259
northeastern part of the study area, a sub-vertical fault was identified from the aftershock 260
distribution. An ML 3.5 earthquake on 21:05:15, 19 November 2017; UTC with a focal 261
mechanism (strike: 234°, dip: 85°, rake: -174°) is consistent with the inferred fault. Among the 262
relocated earthquakes, the first observed event on the fault plane occurred within 72 s of the 263
onset of the mainshock (Figure A1), which indicates that reactivation of the fault segment was 264
initiated by the mainshock rupture or soon afterward. Two slightly different fault geometries, 265
both dipping northwestward, are distinguished in the middle of sections B1-B2 and C1-C2 from 266
the spatial distribution of the aftershocks. The aftershock distribution along B1-B2 has a wider 267
range of focal depths, a shallower dip, and a strike closer to north-south than that of C1-C2. Both 268
the mainshock and the ML 4.3 aftershock are located adjacent to a virtual boundary of B1-B2 and 269
C1-C2 and their focal mechanisms are consistent with the observed fault geometry. Earthquakes 270
in the southwestern part of D1-D2 occurred after the largest aftershock (ML 4.6) (Figure A1) and 271
their focal depths deepened to the south-east, dipping in the opposite direction to the three other 272
fault segments observed on A1-A2, B1-B2 and C1-C2. Such a conjugate fault geometry is matched 273
with one nodal plane of the focal mechanism (strike: 34°, dip: 52°, rake: 136°) of the largest ML 274
4.6 aftershock. 275
From the complex fault geometry delineated by the four cross-sections, we constructed a 276
simplified fault model to describe the observed aftershock distribution. For the three segments 277
that reactivated with the occurrence of the mainshock, we described their geometry using the 278
14
aftershocks that occurred within one day of the mainshock. Because the mainshock was situated 279
on a virtual boundary between two faults (F2 and F3) with slightly different strikes and dips, we 280
divided the aftershock area based on the hypocenter of the mainshock and an apparent strike of 281
210°, which we estimated from PCA of data in map view. The aftershocks on the most 282
northeasterly fault segment (F1) were de-clustered from the adjoining fault (F2) using the simple 283
assumption that the Heunghae Fault (i.e., Song et al., 2015; Yun et al., 1991) vertically intersects 284
them both. Earthquakes that occurred up to 1 day after the largest ML 4.6 aftershock were used to 285
investigate the most southwesterly fault segment (F4). Faults F1F4 were used to divide the 286
study area into four regions and earthquakes were assigned to a region on the basis of the 287
location of their hypocenter. We applied PCA analysis with bootstrapping to earthquakes that 288
were resampled 200 times to estimate strike, dip, fault length, and fault width. The fault length 289
and width were determined as the difference between the 2.5th and 97.5th percentile of the strike 290
and dip components. The resulting fault geometry is summarized in Table 2. 291
We determined c using the 266 relocated earthquakes with known ML. We evaluated c as 0.85 292
by PCA (Figure 3), which is larger than the case for the MW magnitude scale (c = 2/3) scale but 293
smaller than the case for the ML magnitude scale (c = 1). The difference in c implies that the ML 294
magnitude does not naturally match MW for earthquakes within the range of magnitudes included 295
in this study, filtered to a frequency range of 2 – 10 Hz. The observed value of c is relatively 296
high compared with 0.7 that was estimation using the MLs of the Gyeongju aftershock sequences 297
(Woo et al., 2019), which may be the result of systematic differences between ML and KMA 298
magnitude. 299
We estimated the magnitudes of 3,521 earthquakes with measurements ≥ 5. Figures 3b and 3c 300
illustrate the comparison of MRel with ML and the variations of MRel with time. Since MRel is 301
15
exactly proportional to ML without any scaling parameters, we propose that MRel can replace ML 302
as the magnitude scale for subsequent analysis. 303
We examined temporal variations of seismic b-values by binning 600 earthquake magnitudes 304
(MRel) into a set (Figure 4a). There was an overlap of four hundred earthquakes between two 305
consecutive bins. The MC decreased from 0.8 to 0.2 during the first 3 days of the early aftershock 306
sequence, which is indicative of a decrease in the background noise level for that period. The b-307
value for the first bin was evaluated as 0.66, which is consistent with b-values for earthquakes 308
detected during fluid injection into the Pohang EGS site before the occurrence of the mainshock 309
(Woo et al., 2019). The b-value increased with time for the first three days up to a maximum of 310
0.98 and fluctuated during a month. After a month, it decreased to 0.77 until the largest 311
aftershock of ML 4.6 occurred. We tested the temporal changes of b-values with a fixed MC of 312
0.8, corresponding to the maximum values over the whole period, to investigate whether the 313
observed temporal variations of b-values were biased by the choice of MC (gray dots of Figure 4a) 314
and confirmed that the main features were not significantly changed. Figure 4b illustrates the 315
magnitude-frequency distributions of three data sets highlighted in Figure 4a. 316
The spatial variation of b-values was investigated for the vertical cross-section along the 317
apparent strike of 210°. Earthquakes within 1.5 km of each 0.5 0.5 km grid cell on the cross-318
section were binned into that cell. We analyzed the b-value only if each bin contained at least 319
250 earthquakes. Figure 4c illustrates the spatial distribution of b-values on the vertical cross-320
section. The estimated b-values are between 0.63 and 0.86, all of which are lower than the 321
typically assumed b-value of 1 (Wyss, 1973). Since ML is approximated by MRel, such low b-322
values can be interpreted as an increase in applied shear stress and effective stress (Scholz, 1968; 323
Wyss 1973), low material heterogeneity (Mogi, 1962), or a high stress drop (Wyss, 1973). 324
16
Considering that the slip tendency of the mainshock is indicative of a critically stressed fault 325
(Chang et al., 2020) and the stress drop of 5.6 MPa for the mainshock is not higher than that of 326
other earthquakes in South Korea (Rhee and Sheen 2016; Woo et al., 2019a), our preferred 327
interpretation is that the generally low b-values in the aftershock area may result from high 328
applied stress in this region. We estimated a b-value of 0.69 near the hypocenter of the 329
mainshock, which is comparable to the values observed for the earthquakes during the fluid 330
injection (= 0.66). 331
The significance of temporal and spatial differences in b-values can be verified by Utsu’s test 332
(Utsu, 1992), in which the probability that the b-values between two sets of earthquakes are the 333
same is defined via Akaike Information Criterion (Akaike, 1974). We first tested the statistical 334
significance of the temporal differences of b-values among early (< 1 day), intermediate (~ 3 335
days), and late aftershocks (~ 80 days), which are highlighted in green, red, and blue, 336
respectively, in Figures 4a and 4b. The probability that the b-value for the intermediate period is 337
not significantly higher than those of the early and late aftershocks was estimated as 2.6×10-7 and 338
1.0×10-3, respectively, indicating that the temporal increase and decrease of b-values are 339
statistically reasonable with a significance level of 5%. Similar variations of b-values with time 340
can be found for the 2016 Gyeongju earthquake (Woo et al., 2019b) and other cases (Smith, 341
1981; Chan et al., 2012; Gulia et al., 2018), which can be interpreted as local stress changes due 342
to the mainshock rupture or a mixed effect of a changing spatial distribution of b-value and a 343
heterogeneous population of aftershocks with time (Figure 4c). 344
We also applied Utsu’s test for all pairs of spatially varying b-values for which the difference is 345
statistically significant with a significance level of 5% if Δb > 0.1 for half the cases and Δb > 346
0.135 for all cases (Figure 4d). Therefore, we roughly divided the aftershock area into three sub-347
17
regions: R1 with relatively low b-values; R2 with high b-values and Δb > 0.1; and R3 with high 348
b-values and Δb > 0.135 (Figure 4c). The ML 4.3 and ML 4.6 earthquakes are located near R2 and 349
R3 and have high b-values relative to the values of the hypocenter area (R1), which can be 350
interpreted as indicating material heterogeneity with respect to the conjugate fault system 351
(Figures 2c and 2e). Alternatively, spatial variations of pore pressure or applied stress may 352
contribute to b-value heterogeneity. 353
The p-values that describe the power law decay rate of aftershocks were estimated for two data 354
sets: (1) period A, between the onset of the mainshock and the ML 4.6 aftershock; and (2) period 355
B, after the onset of ML 4.6 aftershock. This grouping was chosen because the occurrence of the 356
largest aftershock resulted in increased seismicity, which resets the decay rate for the mainshock 357
(Figure 3c). For each data set, we estimated the p-value that represents the whole data set and the 358
spatial variation of p-values at the cross-sections along the apparent strike of 210°, with the same 359
bins used for estimating the spatial variations of b-values. The p-value of period A was estimated 360
as 1.10, which is larger than the value for period B (= 0.78). Such a difference may result from 361
differing initial stress levels for periods A and B with respect to the stress perturbation of the 362
mainshock sequence, spatial heterogeneity of the internal structure for the conjugate fault system 363
(Figure 2e; Wiemer and Katsumata, 1999), or just an insufficient number of earthquakes in the 364
calculation of p-values for period B. With the exception of p-values for period B, the p-values of 365
the period A were higher in the southwestern region than those in the northeastern region. This 366
could be indicative of a spatial variation of heat flow (Kisslinger and Jones, 1991), heterogeneity 367
of fault strength (Mikumo and Miyatake, 1979) or an insufficient number of aftershocks to allow 368
accurate fitting of the aftershock power decay law for the southwestern aftershock region prior to 369
the occurrence of the ML 4.6 aftershock. 370
18
371
DISCUSSION 372
Expansion of aftershock areas with time 373
Expansion of early aftershock sequences is widely observed (Tajima and Kanamori 1985; 374
Fukumaya and Ellsworth, 2000; Peng and Zhao, 2009; Kato et al., 2014). Some temporal 375
evolution of aftershock areas have been interpreted to be the result of afterslip or post-seismic 376
deformation (Helmstetter and Shaw 2009; Peng and Zhao 2009; Perfettini et al., 2017; Ross et al., 377
2017). Speeds of along-strike expansion of the aftershock zone were measured on a logarithmic 378
time scale and showed that propagating aftershock can cause the expansion of aftershocks (Peng 379
and Zhao 2009; Frank et al., 2017; Perfettini et al., 2017; Ross et al., 2017). In the present study, 380
we examined the spatio-temporal distribution of aftershocks on a logarithmic time scale to 381
consider possible post-seismic deformation following the mainshock (Figure 6). In a map view, 382
we observed that the aftershock zone has roughly expanded along the apparent strike direction, 383
especially during the first day (Figures 6a and c), whereas no clear trends were observed in a 384
vertical sense (Figure 6b). 385
The speed of virtual aftershock migration fronts for the bilateral expansion along the strike 386
direction were ~1 km decade-1 northeastward and ~0.5 km decade-1 southwestward (Figure 6c), 387
which may indicate post-seismic deformations related to aseismic afterslip (Peng and Zhao 2009; 388
Perfettini et al., 2018). The difference in the migration speeds can be attributed to different rate-389
and-state parameters described by Dieterich (1994) following the equations published by 390
Perfettini et al. (2018). However, in our case, we also observe a significant p-value variation in 391
the northeastern and southwestern parts of the study area (Figure 5a). Such variations of p-value 392
require a different model rather than the rate-dependent friction law (Helmstetter and Shaw 2003; 393
19
Mignan 2015). Assuming that the power-law rheology governs post-seismic velocity which is 394
proportional to (1+t/t*)-p (Montési, 2004), where t* is a characteristic time of the aftershock, the 395
slip velocity or the aftershock occurrence rate decays with time as a power of p. For regions with 396
low p-value, the slip velocity decreases relatively slowly and the accumulated post-seismic 397
displacement required to rupture asperities can takes short time compared to that of the regions 398
with high p-value. Therefore, the p-value variation observed for the aftershock area during the 399
period A may be related to the different seismic migration speeds (Figures 5a and 6c). We did 400
not further compare p values and the migration speed in this study, since it may require more 401
complex analysis than a simplified form of the Omori’s law (Narteau et al., 2002). Furthermore, 402
there is an absence of data for very early (« 1 day) or late (> 100 days) aftershock rates. 403
The expansion of the aftershock zone can also be explained by a cascade of sequentially 404
triggered aftershocks in terms of changes to the static Coulomb stress (Ellsworth and Bulut, 405
2018). Since no clear evidence of post-seismic deformation was observed from differential 406
InSAR analysis (Song and Lee, 2019), the observed expansion of aftershocks during a single day 407
could possibly be attributed to changes to the static stress field caused by the aftershock 408
sequences rather than a result of aseismic deformation. However, the descending image of 409
differential InSAR reveals surface deformation during the first day after the mainshock, whereas 410
the ascending image reveals deformation during the next 19 days. This implies that the co-411
seismic deformation associated with afterslips related to the expansion of aftershock zone that 412
occurred within 20 days of the mainshock might be captured by the InSAR image. Therefore, the 413
possibility of afterslip-driven aftershocks cannot be discounted, even without the observation of 414
post seismic deformation. 415
20
High percentage of non-DC components observed for the mainshock and two largest 416
aftershocks 417
The moment tensor solutions of the mainshock and two largest earthquakes have high 418
percentages (> 30%) of non-DC components (Grigoli et al., 2018; Hong et al., 2018), in contrast 419
to the normally observed moment tensor solutions in South Korea. Such high non-DC 420
components of the moment tensor solutions of the three largest earthquakes can result from 421
complex shear faulting of multiple DCs, tensile opening/closing, and shear faulting in anisotropic 422
and heterogeneous media (Miller et al., 1998). It has already been established that the spatial 423
distribution of the Pohang earthquake sequence indicates that multiple fault segments were 424
reactivated in a complex fault system and the faulting types of the focal mechanism vary 425
throughout the aftershock area (i.e., Kim et al., 2019; Chang et al., 2020). Hence, a combination 426
of multiple DC moment tensor solutions with varying senses of slip motion could be one of the 427
causes of the three largest earthquakes having high non-DC components. 428
We propose the following sequence of events to explain the mainshock and major aftershock 429
sequence associated with the MW 5.5 Pohang earthquake. We infer that the nucleation of the 430
mainshock rupture was initiated at the junction between F2 and F3 and that the rupture 431
propagated along F2 and F3 with possible intervention of F1. Later, the ML 4.3 earthquake was 432
initiated between two adjacent conjugate faults dipping southwestward and northeastward in the 433
deeper aftershock region below the mainshock (Figure 2c). Finally, the ML 4.6 earthquake 434
nucleated at the southwestern tip of the aftershock area and subsequent aftershocks occurred on a 435
previously unrecorded southeastward dipping fault, suggesting that the rupture of the ML 4.6 436
earthquake sequences was initiated at the intersection of conjugate faults F3 and F4. 437
21
Although the three earthquakes were located at the intersection of multiple fault planes, it is hard 438
to envisage that all the earthquakes located in the surrounding area ruptured on multiple fault 439
planes. Some MRel 3 3.6 earthquakes without non-DC components were located in the vicinity 440
of the interconnecting faults (Choi et al., 2018), which may suggest that a certain amount of 441
seismic energy is required for the simultaneous movement of multiple fault segments. The fault 442
dimensions for the three largest earthquakes are inferred to be greater than 1 km, based on the 443
assumption of a constant stress drop of 5.6 MPa on a circular crack (i.e., Figure 2f), leading us to 444
propose that a kilometer rupture scale is the threshold to rupture multiple fault planes. Low b 445
values observed throughout the aftershock area can be considered as stress concentrations within 446
areas of high asperities (Wimmer and Wyss, 2000). High asperities in the regions adjoining two 447
or more fault segments may concentrate tectonic energy either as an earthquake nucleation point 448
or as barriers to rupture propagation. This may explain why only ML > 4 non-DC component 449
earthquakes were observed. The sonic log data of the PX-2 borehole recorded the existence of 450
anisotropic structures in the Pohang Basin (Ellsworth et al., 2019). Such anisotropic materials 451
can also cause earthquakes with high non-DC components. However, it is our preferred 452
interpretation that non-DC components in the three largest earthquakes result from the fault 453
complexity because low, non-DC earthquakes for ML 3 – 3.6 earthquakes were also observed. 454
455
Comparison between aftershock activities and induced seismicity at the EGS site during 456
stimulation. 457
The seismicity recorded during the five hydraulic stimulation experiments at the Pohang EGS 458
site and the inferred focal mechanisms revealed a fault plane located near the PX-2 well (Woo et 459
al., 2019a). PX-2 seismicity was clustered on a plane with a strike of 214° and a dip of 43° and 460
22
migrated southwestward, heading toward the location of the mainshock (Woo et al., 2019a). 461
However, the fault geometry for the induced earthquakes related to the PX-2 well has a 20° 462
shallower dip angle than the moment tensor solution of the mainshock and aftershocks. It 463
suggests that complex fault segments exist locally throughout the aftershock region and that a 464
simple fault plane does not explain the detailed fault structures. The ML 4.3 earthquakes have 465
deeper focal depths and their focal mechanism has steeper dips than that of the mainshock, which 466
can also be regarded as a result of complex fault geometry. Observation of various types of focal 467
mechanisms in aftershock sequences (Kim et al., 2019; Chang et al., 2020) are also a 468
manifestation of the complex geometry, which is in contrast to the nearly identical focal 469
mechanisms for the PX-2 seismicity (Woo et al., 2019a). 470
The b-values observed during the Pohang EGS project have insignificant variations, with an 471
average value of 0.66 (Woo et al., 2019a, Langenbruch et al., 2020); whereas, the b-values 472
estimated for the early aftershock sequences are statistically different from the b-values for a bin 473
of approximately 3 days after the mainshock (Figure 4). If we assume that b-values act as a 474
stress-meter (Scholz, 2015; Rigo et al., 2018; Woo et al., 2019b) and temporal variation of b-475
values during the aftershock period represents the level of stress state, the invariant b-values 476
observed during the stimulation period suggest that stress perturbations caused by fluid injection 477
may be far lower than the accumulated tectonic stress, indicating the existence of a critically 478
stressed fault system before the mainshock. 479
480
Reactivation of a multi-segment fault system and spatial variations of b-values and p-values 481
The complexity of the Pohang aftershock distributions was modeled as four fault segments, 482
following the approach of Hong et al. (2018) and Kim et al. (2018b, 2019) (Figure 7). The 483
23
seismicity along a subvertical fault, F1, in the northeastern of the study area clearly represents 484
migration of the aftershock front northeastward during the first day of the aftershock sequences 485
(Figure 6). Although this fault plane is located about 3 km away from the mainshock hypocenter, 486
it may have been reactivated as a part of the mainshock rupture process. Alternatively, it may 487
have been dynamically triggered by the mainshock considering circumstantial evidence that 488
aftershock activity on the fault segments was initiated within just 2 min (Figure A1) and the slip 489
distribution of the mainshock calculated from the static deformations with InSAR data is largest 490
in the northeastern part of the fault model (Song and Lee, 2019). Aftershocks on F1 are bounded 491
by the Heunghae Fault, which has surface expression (Figure 1), detaching F1 from F2 and F3. 492
Therefore, in either case, the reactivation of F1 may require a certain stress threshold to be 493
ruptured preferentially to F2 and F3. 494
Two slightly different geometries of F2 and F3 are suggested by Hong et al. (2018), reflecting a 495
complex fault system near the Pohang EGS site. While the b values vary slightly on F2, the 496
observed p values were higher for F3, at least until the occurrence of the ML 4.6 event. The 497
different behaviors of the two statistical parameters imply that the two fault segments exist under 498
different physical conditions, such as: differential stress states (Scholz, 1968), local 499
heterogeneity of the rock matrix that may interact with viscous materials (Wyss, 1973; Bayrak et 500
al., 2013), or variable spatial distribution of heat flow (Kisslinger and Jones, 1991). 501
The b values decreased to ~0.7 when fault segment F4 was reactivated by the ML 4.6 aftershock. 502
The lower b-values may indicate F4 was already highly stressed when the ML 4.6 earthquake was 503
triggered. The observed p-values for period B were generally much lower than those for period A, 504
which may be the result of using short time periods for analysis during period B or just uneven 505
seismicity observed for periods A and B. 506
24
507
CONCLUSIONS 508
In this study, we detected over 4,000 earthquakes related to the MW 5.5 (ML 5.4) Pohang 509
earthquake by using both unsupervised data-mining and a conventional automatic earthquake 510
detection method. From the spatio-temporal distribution of relocated seismicity, we observed 511
that four fault segments were responsible for the aftershocks. All the faults strike 512
northeastsouthwest, but have different dip angles and dip directions. The three largest 513
earthquakes are located at the boundaries of two adjoining fault segments, which may have 514
focused the stress released by multiple faults, resulting in high, non-DC earthquake mechanisms. 515
By measuring amplitude ratios between two similar earthquakes, we estimated relative 516
magnitudes of earthquakes to infer the statistical parameters related to earthquake frequency and 517
magnitude. The observed spatio-temporal distribution of b-value indicates that they were 518
spatially variable, but generally as low as ~0.7, and temporarily increased with time. The 519
observed p values were different for the northeastern and southwestern parts of the study area, 520
implying that heterogeneities in material properties such as frictional heat can lead to two 521
different speeds of aftershock expansion rate with logarithmic time. The complexity of faulting 522
in the aftershock zone will influence the duration and magnitude of seismic activity that is 523
caused by the locally perturbed stress field that is a result of the mainshock. We hope that our 524
findings can be applied to an interpretation of aftershock mechanisms under the general complex 525
fault systems and can be utilized to perform a seismic hazard assessment lowering the epistemic 526
uncertainty about the characteristics of the fault sources and their contemporary seismic activity. 527
528
Data and Resources 529
25
530
The unpublished manuscript by Chang et al. (2020), “Stress state and fault slip susceptibility 531
prior to the 2017 MW 5.5 Pohang earthquake triggered by enhanced geothermal system 532
stimulation” has been submitted to Geochemistry, Geophysics, Geosystems. The earthquake 533
catalog used in this study will be released at zenodo website (with doi number) when it is 534
published in journal. 535
536
Acknowledgments 537
538
We thank the Korea Institute of Geoscience and Mineral resources (KIGAM), the Korea 539
Meteorological Administration (KMA), the Korea Hydro & Nuclear Power (KHNP), and the 540
K.‐ H. Kim for providing seismic data used in this study. We appreciate C. E. Yoon and G. C. 541
Beroza for comments on FAST usage, W. L. Ellsworth for advice on visualizing the seismicity, J. 542
Song for discussion on non-DC earthquakes. This work was conducted during the Korean 543
Government Commission (KGC) on the relations between the 2017 Pohang earthquake and EGS 544
project, funded by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) 545
grant from the South Korean government (MOTIE) (no. 2018‐ 3010111860). This work was 546
supported by the Nuclear Safety Research Program through the Korea Foundation of Nuclear 547
Safety (KoFONS) using the financial resource granted by the Nuclear Safety and Security 548
Commision (NSSC) of the Republic of Korea (No. 1705010). 549
550
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749
Full mailing address for each author 750
1 School of Earth and Environmental Sciences, Seoul National University, Seoul 08826, 751
Republic of Korea 752
35
J.-U.W., [email protected] 753
J.R., [email protected] 754
2 Department of Structural Systems and Site Evaluation, Korea Institute of Nuclear Safety, 755
Daejeon 34412, South Korea 756
M.K., [email protected] 757
3 Division of Earth Environmental System Science, Pukyong National University, Busan 48513, 758
Republic of Korea 759
T.-S.K., [email protected] 760
761
Tables 762
763
Table 1. The 1-D layered seismic velocity structure for the Pohang EGS site. 764
Depth to the top of the layer (km) P-wave velocity (kms-1) S-wave velocity (kms-1)
0.0 1.67 0.48
0.203 4.01 2.21
0.67 5.08 3.03
2.4 5.45 3.07
3.4 5.85 3.31
7.7 5.91 3.51
12 6.44 3.70
34 8.05 4.60
765
Table 2. Parameters of the faults involved in the aftershock sequences. 766
36
Properties Fault 1 (F1) Fault 2 (F2) Fault 3 (F3) Fault 4 (F4)
Strike (°)
Median 222.7 207.4 223.1 25.8
Median
absolute
deviation
1.1 1.2 0.4 3.9
Dip (°)
Median 77.4 59.8 61.2 68.2
Median
absolute
deviation
2.0 1.3 0.6 5.0
Fault length (km) 2.8 2.4 3.4 2.1
Fault width (km) 1.9 3.5 2.9 1.4
Fault thickness (km) 0.9 0.8 0.7 0.8
767
37
List of figure captions 768
769
Figure 1. Map of (a) temporary and (b) permanent seismic stations used for analysis of source 770
parameters, geologic lineaments, faults, and relocated hypocenters. Three surface ruptures near 771
the study area are illustrated in (a) (Song et al., 2015; Yun et al., 1991). The focal mechanism of 772
the mainshock that was determined from the polarity of first arrivals is illustrated in (b). (c) 773
shows the location of the Gyeongsang Basin (GB) and the Yeonil Basin (YB) where many 774
NENNE sinistral strike-slip surface ruptures and NW transfer faults have developed. The red 775
boxes in (b) and (c) represent the domain of (a) and (b), respectively. 776
777
Figure 2. (a) Distribution of the 3946 epicenters relocated via hypoDD (Waldhauser and 778
Ellsworth, 2000) by using traveltime differences. The earthquakes projected onto each of the 779
cross-sections A1-A2 to E1-E2 shown in (b) to (f) fall within the rectangles denoted by dashed 780
black lines in (a). The trajectory of two stimulation wells PX-1 and PX-2 are illustrated as gray 781
lines in (c) with open sections colored in blue and red. The mainshock and two largest 782
aftershocks (ML 4.3 and ML 4.6) are denoted as red, blue, and green stars, respectively. (b–f) 783
Depth distribution of the relocated hypocenters along the cross-sections of A1-A2 to E1-E2. 784
Possible interpretations for delineated faults from the aftershock distribution are marked as gray 785
lines in (b), (c), (d), and (e). The circles in (f) represent the rupture radii of earthquakes with MRel 786
> 1.5, assuming a stress drop of 5.6 MPa, which corresponds to an approximated value for the 787
mainshock estimated by the spectral ratio method (Woo et al., 2019a). The red, blue, and green 788
circles in (f) indicate the rupture size of the three largest earthquakes with ML 5.4, 4.3, and 4.6, 789
respectively. 790
38
791
Figure 3. (a) Determination of the scaling parameter c in Equation (1) from known ML 792
magnitudes. The amplitude ratios measured from two similar waveforms observed at a station 793
are measured and counted to estimate the scaling parameter for given MLs. The red line indicates 794
the scaling parameter c of 0.74 calculated from the slope of the first principal components 795
between magnitude differences and the ratio of amplitude divided by hypocenteral distances. (b) 796
Comparison between ML and MRel. The red line indicates identity relation. (c) The distribution of 797
earthquake magnitudes with their origin time. The three largest earthquakes (ML 5.4, 4.3, and 4.6) 798
are denoted as red, blue, and green stars, respectively, with their MLs. The microearthquakes of 799
which magnitudes cannot be measured from Equation (1) are denoted as square symbols at the 800
bottom of the graph. 801
802
Figure 4. (a) Temporal variations of seismic b-values and MC for each bin of each time period. 803
We combined MRels obtained from Equation (1) with MLs of the three largest earthquakes. A set 804
of 600 earthquakes constitute a bin for measuring b-values and there is an overlap of 400 805
earthquakes between two consecutive bins. The standard deviations of each of the magnitude 806
bins are represented as vertical and horizontal error bars. The black, red, and blue dots indicate 807
three typical bins for the evaluation of b-values in (b). The gray dots and error bars represent b-808
values and their standard errors calculated based on a maximum MC of 1 for all bins. (b) Four 809
examples of the curves used to estimate the two scaling parameters of the G-R law. For each case, 810
the color used for plotting the data and the equation of G-R law corresponds to a specific bin of 811
(a). The filled circles indicate the cutoff magnitudes of MC that honor Equation (1) for larger 812
magnitudes. (c) Two dimensional spatial variations of b-values at a vertical profile along both 813
39
the apparent strike of 210°. Hypocenters of three largest earthquakes are denoted as red, blue, 814
and green stars, respectively. Dashed lines and solid lines indicate the significant difference level 815
of 5% with median and conservative thresholds. (d) Two-sigma interval distribution of 816
probabilities that differences of paired b-values (Δb) in (c) is insignificant. The Δbs are binned by 817
0.05. The difference is statistically significant with a significance level of 5% if Δb > 0.1 for half 818
the cases and Δb > 0.135 for all cases. 819
820
Figure 5. (a) Two dimensional spatial variations of p-values for a cross-section along the 821
apparent strike 210° for earthquake sequences before the occurrence of the largest aftershock 822
(ML 4.6). (b) Two dimensional spatial variations of p-values for the same depth profile shown in 823
(a), but for seismic sequences after the onset of the ML4.6 earthquake. (c and d) aftershock decay 824
rates and their corresponding Omori’s law plots with the estimated parameters obtained from the 825
whole data set used for mapping p-values in (a) and (b), respectively. 826
827
Figure 6. Temporal distribution of seismicity presented in (a) plan view, (b) a depth profile along 828
the apparent strike of 210°, and (c) a unidirectional projection along the apparent strike. We 829
illustrate the radius of earthquakes with MRel ≥ 1.5 by assuming a circular crack rupture and a 830
stress drop of 5.6 MPa from Woo et al. (2019a). In (a) and (b), the location of the mainshock and 831
the two largest consecutive aftershocks of ML 4.6 and ML 5.4 are represented as red, blue and 832
green stars, respectively. The rupture radii of the three largest earthquakes are displayed in (c) 833
with colors to match the star symbols in (a) and (b). The trajectory of two stimulation wells PX-1 834
and PX-2 are illustrated as gray lines in (a) and (b). The thick gray lines in (c) represent the 835
40
linear scaling relationship between the along-strike expansion of earthquake sequences with 836
logarithmic time scale. 837
838
Figure 7. Schematic diagram that illustrates four fault segments inferred from the hydraulic 839
stimulation wells of PX-1 and PX-2 and the distribution of aftershocks. The three cubes colored 840
in red, blue, and green show the hypocenters of the mainshock and the two largest aftershocks of 841
ML 4.3 and 4.6, respectively. The occurrence of the mainshock triggered seismicity on fault 842
segments F2 and F3, and possibly affected the re-activation of F1. Fault segment F4, located to 843
the southwest of F3, was not delineated until the largest aftershock (ML 4.6) occurred. 844
845
846
41
Figures 847
848
849
Figure 1. Map of (a) temporary and (b) permanent seismic stations used for analysis of source 850
parameters, geologic lineaments, faults, and relocated hypocenters. Three surface ruptures near 851
the study area are illustrated in (a) (Song et al., 2015; Yun et al., 1991). The focal mechanism of 852
the mainshock that was determined from the polarity of first arrivals is illustrated in (b). (c) 853
shows the location of the Gyeongsang Basin (GB) and the Yeonil Basin (YB) where many 854
NENNE sinistral strike-slip surface ruptures and NW transfer faults have developed. The red 855
boxes in (b) and (c) represent the domain of (a) and (b), respectively. 856
42
857
858
Figure 2. (a) Distribution of the 3946 epicenters relocated via hypoDD (Waldhauser and 859
Ellsworth, 2000) by using traveltime differences. The earthquakes projected onto each of the 860
cross-sections A1-A2 to E1-E2 shown in (b) to (f) fall within the rectangles denoted by dashed 861
black lines in (a). The trajectory of two stimulation wells PX-1 and PX-2 are illustrated as gray 862
lines in (c) with open sections colored in blue and red. The mainshock and two largest 863
aftershocks (ML 4.3 and ML 4.6) are denoted as red, blue, and green stars, respectively. (b–f) 864
Depth distribution of the relocated hypocenters along the cross-sections of A1-A2 to E1-E2. 865
Possible interpretations for delineated faults from the aftershock distribution are marked as gray 866
43
lines in (b), (c), (d), and (e). The circles in (f) represent the rupture radii of earthquakes with MRel 867
> 1.5, assuming a stress drop of 5.6 MPa, which corresponds to an approximated value for the 868
mainshock estimated by the spectral ratio method (Woo et al., 2019a). The red, blue, and green 869
circles in (f) indicate the rupture size of the three largest earthquakes with ML 5.4, 4.3, and 4.6, 870
respectively. 871
872
44
873
874
Figure 3. (a) Determination of the scaling parameter c in Equation (1) from known ML 875
magnitudes. The amplitude ratios measured from two similar waveforms observed at a station 876
are measured and counted to estimate the scaling parameter for given MLs. The red line indicates 877
the scaling parameter c of 0.74 calculated from the slope of the first principal components 878
between magnitude differences and the ratio of amplitude divided by hypocenteral distances. (b) 879
Comparison between ML and MRel. The red line indicates identity relation. (c) The distribution of 880
earthquake magnitudes with their origin time. The three largest earthquakes (ML 5.4, 4.3, and 4.6) 881
are denoted as red, blue, and green stars, respectively, with their MLs. The microearthquakes of 882
which magnitudes cannot be measured from Equation (1) are denoted as square symbols at the 883
bottom of the graph. 884
885
45
886
887
Figure 4. (a) Temporal variations of seismic b-values and MC for each bin of each time period. 888
We combined MRels obtained from Equation (1) with MLs of the three largest earthquakes. A set 889
of 600 earthquakes constitute a bin for measuring b-values and there is an overlap of 400 890
earthquakes between two consecutive bins. The standard deviations of each of the magnitude 891
bins are represented as vertical and horizontal error bars. The black, red, and blue dots indicate 892
three typical bins for the evaluation of b-values in (b). The gray dots and error bars represent b-893
values and their standard errors calculated based on a maximum MC of 1 for all bins. (b) Four 894
examples of the curves used to estimate the two scaling parameters of the G-R law. For each case, 895
the color used for plotting the data and the equation of G-R law corresponds to a specific bin of 896
(a). The filled circles indicate the cutoff magnitudes of MC that honor Equation (1) for larger 897
magnitudes. (c) Two dimensional spatial variations of b-values at a vertical profile along both 898
the apparent strike of 210°. Hypocenters of three largest earthquakes are denoted as red, blue, 899
46
and green stars, respectively. Dashed lines and solid lines indicate the significant difference level 900
of 5% with median and conservative thresholds. (d) Two-sigma interval distribution of 901
probabilities that differences of paired b-values (Δb) in (c) is insignificant. The Δbs are binned by 902
0.05. The difference is statistically significant with a significance level of 5% if Δb > 0.1 for half 903
the cases and Δb > 0.135 for all cases. 904
47
905
906
Figure 5. (a) Two dimensional spatial variations of p-values for a cross-section along the 907
apparent strike 210° for earthquake sequences before the occurrence of the largest aftershock 908
(ML 4.6). (b) Two dimensional spatial variations of p-values for the same depth profile shown in 909
(a), but for seismic sequences after the onset of the ML 4.6 earthquake. (c and d) aftershock decay 910
rates and their corresponding Omori’s law plots with the estimated parameters obtained from the 911
whole data set used for mapping p-values in (a) and (b), respectively. 912
913
48
914
Figure 6. Temporal distribution of seismicity presented in (a) plan view, (b) a depth profile along 915
the apparent strike of 210°, and (c) a unidirectional projection along the apparent strike. We 916
illustrate the radius of earthquakes with MRel ≥ 1.5 by assuming a circular crack rupture and a 917
stress drop of 5.6 MPa from Woo et al. (2019a). In (a) and (b), the location of the mainshock and 918
the two largest consecutive aftershocks of ML 4.6 and ML 5.4 are represented as red, blue and 919
green stars, respectively. The rupture radii of the three largest earthquakes are displayed in (c) 920
with colors to match the star symbols in (a) and (b). The trajectory of two stimulation wells PX-1 921
and PX-2 are illustrated as gray lines in (a) and (b). The thick gray lines in (c) represent the 922
linear scaling relationship between the along-strike expansion of earthquake sequences with 923
logarithmic time scale. 924
49
925
926
Figure 7. Schematic diagram that illustrates four fault segments inferred from the hydraulic 927
stimulation wells of PX-1 and PX-2 and the distribution of aftershocks. The three cubes colored 928
in red, blue, and green show the hypocenters of the mainshock and the two largest aftershocks of 929
ML 4.3 and 4.6, respectively. The occurrence of the mainshock triggered seismicity on fault 930
segments F2 and F3, and possibly affected the re-activation of F1. Fault segment F4, located to 931
the southwest of F3, was not delineated until the largest aftershock (ML 4.6) occurred. 932
933
934
50
Appendices 935
936
Table A1. Fingerprint extraction parameters for the FAST algorithm to detect earthquakes with 937
waveform similarity. 938
Fingerprint extraction parameter Value
Time-series window length of generated spectrogram 6.0 s
Time-series window lag of generated spectrogram 0.1 s
Spectral image window length 64
Spectral image window lag 10
Fingerprint sparsity 400
Final spectral image width 32
Number of hash functions per hash table 4
Number of hash tables 100
Number of votes 2
Near-repeat exclusion parameters 5
939
Table A2. Input parameters for the network detection in the FAST algorithm (Bergen et al., 2016; 940
Bergen and Beroza, 2018; Rong et al., 2018) 941
Event-pair extraction, pruning, and network detection parameters values
Time gap along diagonal 3 s
Time gap adjacent diagonal 3 s
Adjacent diagonal merge iteration 2
51
Number of votes 10
Minimum fingerprint pairs 3
Maximum bounding-box width 5 s
Minimum number of stations for detection 1
Arrival time constraint: maximum time gap 5
942
943
944
945
52
Figure A1. Snapshots of the distribution maps of aftershocks at (a) 10 min, (b) 1 h, (c) 5 h, (d) 1 946
day, (e) 30 day, and (f) 4mo from the onset of the mainshock. For each subfigure, earthquakes 947
until Red and gray dots 948
949