1
2
Geophysical Research Letters
3
Supporting Information for
4
Reactivated faulting near Cushing Oklahoma: increased potential for a triggered
5
earthquake in an area of United States strategic infrastructure
6
7
D.E. McNamara1, G.P. Hayes1, H.M. Benz1, R.A. Williams1, N.D. McMahon3, R.C.
8
Aster3, A. Holland2, T. Sickbert6, R. Herrmann4, R. Briggs1, G. Smoczyk1, E. Bergman5,
9
P. Earle1
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11
1
US Geological Survey, MS966, Box 25046, Denver, CO 80225
12
2
Oklahoma Geological Survey, 100 East Boyd Street, Suite N131, Norman, OK 73019
13
3
Department of Geosciences, Colorado State University, Fort Collins, CO 80523
14
4
Department of Earth and Atmospheric Sciences, 3642 Lindell Boulevard, Saint Louis
15
University, St. Louis, MO 63108
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5
Global Seismological Services, 1900 19th Street, Golden, CO 80401
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6
Noble Research Center, Oklahoma State University, Stillwater, Oklahoma
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19
Introduction
20
This supplement contains supporting text, figures and tables cited in the main
21
document.
22
23
Data and Methods
24
25
Seismic Station Deployments
26
Beginning in early 2010, the USGS in cooperation with the Oklahoma Geological Survey
27
(OGS) began deploying temporary portable seismic stations in central Oklahoma in order
1
28
to improve monitoring of the increasing regional seismicity [McNamara et al., 2015]. In
29
November 2011 numerous additional stations were deployed to record aftershocks
30
associated with the M5.6 Prague earthquake sequence [McNamara et al., 2015]. In the
31
days following the 10 October 2014 Mw 4.3 Cushing earthquake, four additional portable
32
seismic stations were deployed in the vicinity of the epicenter (Figure 1).
33
Complementing the portable deployments were temporary regionally distributed stations
34
in the Earthscope Transportable (net code TA), and permanent stations operated by the
35
OGS and USGS (net codes OK and US, respectively). The seismic station deployments
36
provided high quality waveforms for subspace detection, earthquake relocation, and
37
regional moment tensor (RMT) studies.
38
39
Earthquake detection, location and source parameters
40
41
Using station OK031 (Figure 1) and template waveforms from the M 4.3 earthquake, we
42
ran a subspace detection algorithm to identify subsequent aftershocks [Benz et al., 2015],
43
resulting in the detection of 473 earthquakes over the time period from 2014-10-13 to
44
2014-12-01, with a magnitude of completeness of 0.3 and an average b-value of 1.05
45
(Figure 2). To locate these events, we determined P- and S-wave arrival times for all
46
earthquakes recorded by at least four seismic stations. Initial locations of the aftershocks
47
associated with the 2014 October Cushing M4 earthquakes were determined with a
48
standard “single-event” approach using a stand-alone version of the main processing and
49
analysis system (a.k.a Hydra) used by the USGS National Earthquake Information Center
50
(NEIC) [Buland et al., 2009]. In the initial standard “single-event” approach, the
51
aftershocks were located using the AK135 one-dimensional global velocity model
52
[Kennett et al., 1995] that is routinely used by NEIC. This system allowed us to identify
53
and locate individual earthquakes, compute network-averaged regional magnitudes (e.g.
54
ML, mblg, Md), and Mw from waveform modeling of earthquakes larger than about
55
M3.0. This resulted in a catalog of 75 single-event hypocenter locations complete to a
56
magnitude of 1.5.
57
2
58
After initial “single-event” aftershock locations and magnitudes were determined,
59
using the procedures described above, hypocenter locations and phase data were re-
60
analyzed to further refine source locations using a “multiple-event” approach based
61
on the Hypocentroidal Decomposition algorithm [HD; Jordan and Sverdrup,
62
1981]. Details on methods and application can be found in a number of recent, regional
63
seismotectonics studies [McNamara et al., 2015; McNamara et al., 2014; Hayes et al.,
64
2013; Hayes et al., 2014; Rubinstein et al., 2014]. The HD relocation method provides
65
improved hypocenter locations with minimized location bias and realistic estimates of
66
location uncertainty for each earthquake (<1 km) (Figure 3) (Table S1). Relative to the
67
single-event locations, HD relocated seismicity reduced the epicentral scatter by a factor
68
of two. In addition, an advantage of the HD method is the ability to simultaneously
69
relocate a poorly recorded mainshock (initially located 3 km southeast of the aftershock
70
cluster) with aftershocks detected after the deployment of local stations (Figure 3)
71
(Table S1). For the HD relocation study, we used a central Oklahoma velocity model
72
determined by the best fit to the travel time data [McNamara et al., 2015] (Table S2).
73
We tested the sensitivity of focal depth to the assumed velocity model by re-
74
computing hypocenter locations assuming the ak135 velocity model used to locate
75
earthquakes by the USGS NEIC. When using the AK135 velocity model, we found
76
that the hypocentroid (average) depth increased by 0.12 km (from 5.34 km to 5.45)
77
and depth uncertainty doubled from 0.434 to 0.9 km. Epicenter locations changed
78
well below the initial uncertainty due to the good azimuthal station coverage for
79
each earthquake. Based on these results we use the central Oklahoma velocity
80
model for the final hypocenter locations (Table S1).
81
82
Moment Tensor Focal Mechanisms
83
RMT solutions [Herrmann et al., 2011] were computed for the two largest earthquakes
84
in the sequence (10-10-2014, M 4.3; 10-07-2014, M 4.0) using broadband waveform data
85
observed at regional network and potable seismic stations. Both RMTs indicate near-
86
vertical, left-lateral strike-slip faulting on a N80°W striking nodal plane that aligns with
3
87
the relocated seismicity (Figure 1). Focal mechanism fault plane orientation is well
88
constrained due to the good azimuthal distribution of recording stations with
89
uncertainty of +/-20º in strike and +/-5º in dip (Figure S1). RMT uncertainty is
90
consistent with a regional sensitivity study of RMTs in Oklahoma (Johnson et al.,
91
2015).
92
93
Coulomb Stress
94
We used Coulomb failure stress (ΔCFS) analysis [Stein et al., 1997] to assess how the
95
October 2014 Cushing earthquake sequence affected loading on structures within the
96
Wilzetta-Whitetail fault zone. We calculated the Coulomb stress change imparted by the
97
earthquakes onto conjugate structures oriented 200°/90°/-180°, that is, vertical right-
98
lateral faults striking slightly west of due south (Wilzetta-Whitetail fault zone), and onto
99
vertical left-lateral faults aligning with the structure activated by the Cushing sequence
100
(280°/90°/0°) (Figure S2).
101
In Figure S3 we examine the sensitivity of the ΔCFS model to assumption of Poisson's
102
Ratio, coefficient of friction, and source strike. The largest effect on the model can be
103
seen in the variation of source strike (Figure S3a). If source strike is larger (290°-300°)
104
than our RMT suggests (280°) then modeled stress change would significantly decrease
105
in the southern lobe of the model along the Wilzetta-Whitetail fault zone. The combined
106
analysis of the spatial distribution of high-precision earthquake hypocenter relocations
107
and fault planes determined from RMT analysis of the two largest Cushing earthquakes,
108
are sufficient to provide high confidence in our assumption of source strike (Figures 3
109
and S1). The assumption of the coefficient of friction also has a small effect on the
110
modeled stress change (Figure S3b). Orientation of the east and west lobes of stress
111
increase rotate several degrees however the north and south lobes along the Wilzetta-
112
Whitetail fault zone are relatively unchanged through the range of coefficient of friction
113
assumptions shown in Figure S3b. Figure S3c and d show that Poisson’s ratio has
114
little to no effect on modeled stress changes.
4
115
116
Based on the parameter sensitivity test shown in Figure S3, we use standard values for
117
the coefficient of friction and Poisson’s Ratio, of 0.4 and 0.25, respectively. Fault
118
dimensions are assumed using the standard empirical relationships with magnitude for
119
subsurface faults (Wells and Coppersmith, 1994) and source strike is taken from the
120
strike of seismicity and RMT fault planes (280°). Results indicate that the Wilzetta-
121
Whitetail fault zone has experienced significant stress changes (> 0.1 bar) over a region
122
at least 8 km in length (Figure 1), and comparable depth extent. Similarly, the unnamed
123
left-lateral fault on which the Cushing sequence occurred has increased stress beyond the
124
ends of the recent earthquakes (west of -96°47’, east of -96°45’), and in the shallow crust
125
above the ~ 5 km depth of recent events, over a length of about 10 km (Figure S1). The
126
ΔCFS pattern is inherently three-dimensional, and areas of negative stress change are
127
interspersed with positive changes. However, net ΔCFS over the fault areas discussed
128
here are positive, and any subsequent event nucleating in such regions of positive ΔCFS
129
would generate its own dynamic stress field that further promotes rupture propagation,
130
potentially even into regions of negative ΔCFS from previous events.
131
132
Wastewater injection-seismicity correlation
133
In Figure S5, we show a high correlation between daily wastewater injection volume and
134
daily seismicity for the Cushing earthquake sequence from 2014-10-13 to 2014-12-01.
135
Two intervals of injection shutdown (10/7 and 10/22) followed by resumption of
136
operations (10/20 and 10/27) are clearly observed as strong variations in the daily
137
microseismicity rate seventeen days later. The two time series have a maximum cross-
138
correlation coefficient (0.79) at a 17-day lag. Correlations of seismicity with individual
139
wells are lower with the highest at the highest volume well (Calyx cc=0.68 at 17-day
140
lag). Correlation between injection volume and seismicity separated by many kilometers
141
with lag times on the order of days to weeks has been commonly observed in Oklahoma
142
and other regions [Talwani et al., 2007; Holland, 2013b; Keranen et al., 2013;
143
Keranen et al., 2014; Kim, 2013; Horton, 2012; Block et al., 2014]
5
144
The hydrologic property controlling pore pressure diffusion is hydraulic diffusivity, c.
145
[Talwani et al., 2007] demonstrates that if we know the time lag, Δt, between the start of
146
fluid injection in a well and the onset of seismicity at a distance, r, the hydraulic
147
diffusivity of the connecting fractures is c = r2/4Δt. By analyzing more than ninety cases
148
of fractures associated with induced seismicity, they observed a range of c from 0.1 to 10
149
m2/s. Assuming a range of injection well to earthquake distances, r, observed in the
150
Cushing sequence (5-10 km) and the maximum correlation lag time, Δt, of 17-days, we
151
determine a range of possible hydraulic diffusivity, c, from 4.25 to 17 m2/s. This is
152
generally consistent with the range observed in the central US [Talwani et al., 2007;
153
Holland, 2013b] where c = 13.8 ± 5.4 m2/s for a study that directly linked hydraulic
154
fracturing operations with seismicity in south-central Oklahoma. Only 30 days of daily
155
injection volume measurements are available at all three wells in the vicinity of the
156
Cushing sequence so clearly more data are required to establish a long-term correlation
157
with daily microseismicity. This will be possible as additional injection volume data
158
become available from the OCC.
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References
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Buland, R.P., M. Guy, D. Kragness, J. Patton, B. Erickson, M. Morrison, C. Bryan, D.
Ketchum and H. Benz (2009). American Geophysical Union, Fall Meeting 2009,
S11B-1696.
Hayes, G.P., Bergman, E., Johnson, K., Benz, H., Brown, L., and Melzer, A., (2013).
Seismotectonic framework of the February 27, 2010 Mw 8.8 Maule, Chile earthquake
sequence, Geophys. J. Int. (2013) 195, 1034–1051, doi: 10.1093/gji/ggt238.
Hayes, G. P., M.W. Herman, W.D. Barnhart, K.P. Furlong, S. Riquelme, H. Benz, E.
Bergman, S. Barrientos, P. S. Earle, and S. Samsonov, (2014). Continuing megathrust
earthquake potential in Chile after the 2014 Iquique earthquake. Nature, 512(7514),
295-298.
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184
185
186
187
188
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190
191
Herrmann, R.B., H.M. Benz, and C.J. Ammon, (2011). Monitoring the earthquake source
process in North America, Bull. Seism. Soc., Am. 101, 2609-2625.
192
193
Kennett, B.L.N., E.R. Engdahl, and R. Buland, (1995). Constraints on seismic velocities
in the earth from travel times, Geophys. J. Int. 122, 108-124.
194
195
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197
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McNamara, D.E, Benz, H.M., Herrmann, R.B., Bergman, E.A., and Chapman, M.,
(2014), The Mw 5.8 Central Virginia seismic zone earthquake sequence of August 23,
2011: Constraints on earthquake source parameters and fault geometry, Bull. Seism.
Soc. Am., 104, 1.
Johnson, K.L., G.P. Hayes, R.B. Herrmann, H.M. Benz, D.E. McNamara and E.
Bergman, (2015). Strike and dip sensitivity to seismic network geometry and faulting
style for regional moment tensors computed from waveform time-series, Seism. Res.
Lett, in prep.
199
200
201
202
Rubinstein, J.L., W.L. Ellsworth, A. McGarr, and J. Benz, (2014), The 2001-Present
induced earthquake sequence in the Raton Basin of Northern New Mexico and
Southern Colorado, Bull. Seism. Soc. Am., 104 (5), 1-20.
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204
205
206
Stein, R. S., Barka, A. A. & Dieterich, J. H., (1997). Progressive failure on the North
Anatolian fault since 1939 by earthquake stress triggering. Geophys. J. Int. 128, 594–
604.
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Supplement Figures
209
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212
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215
216
Figure S1: Focal mechanism sensitivity at the preferred depth. Green colors indicate
the best fits to the waveforms. Each solution is plotted as a vector at a given value of
strike and dip with the angle of the vector representing the rake angle, measured, with
respect to the upward vertical (N) in the figure. (a) The best fitting fault plane
parameters (strike, dip, rake) for the 07 October 2014 Mw 4.0 earthquake. (b) The best
fitting fault plane parameters (strike dip rake) for the 10 October 2014 Mw 4.3
earthquake.
8
217
218
219
220
221
222
223
Figure S2: Map of the Cushing Oklahoma region with earthquakes (white circles)
seismic stations (red triangles) and Coulomb failure stress (ΔCFS) model at several
depths. Strands of Wilzetta-Whitehorse fault zone are shown as black lines. Black
diamonds show disposal wells.
9
224
225
226
227
228
229
230
231
232
233
234
Figure S3: Coulomb failure stress (ΔCFS) model sensitivity to parameter selection.
Parameters analyzed include fault strike, coefficient of friction, and Poisson’s ratio. a)
ΔCFS at depth of 5 km for a range of fault strikes. Colors represent the preferred model
shown in Figure 1. b) ΔCFS at depth of 5 km for a range of coefficients of friction.
Colors represent the preferred model shown in Figure 1. c) ΔCFS at depth of 5 km
assuming preferred strike and coefficient of friction and Poisson’s Ratio of 0.21. d) ΔCFS
at depth of 5 km assuming preferred strike and coefficient of friction and Poisson’s Ratio
of 0.29.
10
235
236
237
238
239
240
241
242
243
244
245
Figure S4: Central Oklahoma regional map from Cushing to Prague. Circles show the
HD relocated hypocenters scaled by magnitude and colored by depth from McNamara et
al., (2015) and this study. Thick black lines are subsurface faults from Northcutt and
Campbell (1995). Dashed black lines show the inferred trace of the conjugate left-lateral
faults near Cushing and Prague. Wells associated with the oil and gas industry are
shown as grey diamonds.
11
246
247
248
249
250
251
252
253
254
Figure S5: Comparison of injected wastewater volume with microseismicity. (a) Summed
daily injection volume (red line) at three nearby wells (Wildhorse, Wilson, Calyx) with
the summed daily seismicity (black line) at two temporary seismic stations (OK030,
OK031). (b) cross-correlation coefficient versus lag. The maximum coefficient of 0.79
occurs at a seventeen-day lag.
255
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256
Table S1: HD multiple-event locations
Origin time
YYYY-M-D_H:M:S
Latitude
()
Longitude
()
Depth
(km)
Mag.
2014-9-10_21:2:46.24
2014-9-22_10:14:4.73
2014-9-28_5:35:47.12
2014-10-1_9:54:22.75
2014-10-7_16:51:13.04
2014-10-7_17:2:45.42
2014-10-7_17:19:8.21
2014-10-7_17:57:31.40
2014-10-7_23:57:39.43
2014-10-8_10:7:5.91
2014-10-11_14:14:12.64
2014-10-12_18:12:34.07
2014-10-12_22:9:28.34
2014-10-12_23:21:30.38
2014-10-10_13:51:20.94
2014-10-20_20:2:25.09
2014-10-20_15:20:54.02
2014-10-20_16:41:46.17
2014-10-21_9:41:56.52
2014-10-20_19:50:57.61
2014-10-21_11:17:9.67
2014-10-22_20:45:44.08
2014-10-17_2:31:35.79
2014-10-17_15:3:50.94
2014-10-21_13:10:46.61
2014-10-21_13:18:56.52
2014-10-23_9:53:38.24
2014-10-22_12:43:39.28
2014-10-21_17:3:34.68
2014-10-19_14:57:4.07
2014-10-18_6:36:44.43
2014-10-20_21:7:48.32
2014-10-20_21:35:1.37
2014-10-19_15:5:2.77
2014-10-23_2:30:11.86
2014-10-20_5:13:50.14
2014-10-25_16:43:48.50
2014-10-26_11:48:2.53
2014-10-25_16:36:15.52
2014-10-25_2:21:4.67
2014-10-25_16:42:15.60
2014-10-26_10:15:18.54
2014-10-25_16:38:12.00
2014-10-25_16:42:34.15
2014-10-25_16:55:40.06
2014-10-12_22:11:20.50
2014-10-12_22:10:42.10
35.93747
35.89815
35.92886
35.92957
35.94597
35.94521
35.94363
35.94433
35.94454
35.94405
35.94207
35.94353
35.94300
35.94555
35.94240
35.94637
35.94691
35.94619
35.94650
35.94592
35.94606
35.94515
35.94143
35.94472
35.94540
35.94584
35.94551
35.94550
35.94748
35.93990
35.94705
35.94636
35.94834
35.94313
35.94559
35.94714
35.94660
35.93009
35.94613
35.94619
35.94559
35.94544
35.94660
35.94572
35.94693
35.94405
35.94363
-96.78906
-96.79288
-96.80475
-96.80496
-96.77060
-96.77975
-96.76807
-96.76157
-96.78110
-96.77118
-96.75867
-96.77271
-96.76056
-96.77985
-96.75641
-96.78281
-96.78378
-96.78244
-96.78217
-96.78506
-96.77985
-96.78592
-96.77261
-96.77924
-96.78482
-96.78320
-96.77963
-96.78418
-96.78571
-96.79462
-96.77982
-96.78177
-96.77982
-96.80246
-96.77948
-96.78033
-96.78619
-96.80804
-96.78323
-96.78241
-96.78207
-96.77872
-96.78476
-96.78128
-96.78415
-96.76306
-96.76208
4.29
9.00
4.92
4.74
6.43
5.03
5.16
5.25
5.11
5.68
5.00
5.52
6.05
5.29
6.49
5.63
5.27
5.63
5.35
5.64
5.03
5.52
5.00
5.22
5.73
5.92
5.25
5.32
5.56
4.53
5.14
5.28
4.88
0.85
5.32
5.01
5.37
5.18
5.54
5.73
5.32
5.34
5.25
5.24
5.33
6.01
5.57
2.2ML
1.7ML
2.4ML
1.7ML
4.1ML
2.3ML
2.1ML
2.3ML
2.9ML
2.0ML
2.1ML
2.6ML
2.6ML
2.1ML
4.6ML
2.0ML
2.5ML
2.7ML
2.4ML
1.8ML
2.1ML
1.6ML
1.3ML
1.7ML
1.5ML
2.1ML
1.6ML
1.9ML
1.6ML
0.9Md
1.6ML
1.4ML
1.5ML
1.1ML
1.3ML
1.2ML
2.3ML
2.1ML
2.7ML
1.9ML
1.6ML
1.3ML
1.4ML
1.5ML
1.3ML
2.8ML
1.4ML
13
Semiminor
axis
azimuth
()
41
34
41
38
36
36
30
37
35
36
38
35
38
35
35
26
35
37
36
36
26
27
67
30
30
41
36
45
31
85
41
44
323
325
31
32
36
37
36
37
37
38
32
36
37
39
28
Semiminor
axis
length
(km)
0.41
0.46
0.34
0.43
0.39
0.37
0.46
0.40
0.41
0.39
0.44
0.38
0.36
0.38
0.40
0.40
0.39
0.32
0.35
0.38
0.49
0.47
0.48
0.39
0.38
0.54
0.37
0.50
0.53
0.59
0.51
0.50
0.70
0.55
0.48
0.55
0.34
0.38
0.35
0.34
0.35
0.38
0.44
0.48
0.48
0.36
0.51
Semimajor
axis
azimuth
()
131
124
131
128
126
126
120
127
125
126
128
125
128
125
125
116
125
127
126
126
116
117
157
120
120
131
126
135
121
175
131
134
53
55
121
122
126
127
126
127
127
128
122
126
127
129
118
Semimajor
axis
length
(km)
0.62
0.82
0.50
0.60
0.53
0.53
0.61
0.58
0.58
0.54
0.57
0.58
0.55
0.59
0.56
0.68
0.57
0.47
0.53
0.54
0.77
0.61
0.74
0.62
0.60
0.68
0.53
0.63
0.64
0.67
0.66
0.63
0.81
0.79
0.60
0.68
0.49
0.54
0.50
0.53
0.55
0.58
0.66
0.75
0.73
0.52
1.11
Depth
error
(km)
0.9
2.0
0.6
1.0
0.7
0.7
1.0
0.8
0.8
0.7
0.9
0.3
0.3
0.3
1.0
0.3
0.3
0.2
0.2
0.2
0.4
0.3
0.4
0.3
0.3
0.5
0.2
0.5
0.3
0.5
0.5
0.5
0.7
0.8
0.3
0.3
0.2
0.3
0.2
0.2
0.2
0.2
0.3
0.4
0.4
0.3
0.4
2014-10-27_16:44:9.67
2014-10-9_12:49:18.82
2014-10-28_11:23:7.11
2014-10-12_18:24:18.35
2014-10-29_3:2:34.55
2014-10-30_3:14:20.21
2014-10-29_16:37:18.34
2014-10-25_16:54:51.54
2014-11-1_23:18:43.69
2014-11-1_17:10:19.30
2014-10-21_19:46:8.53
2014-10-16_16:8:25.48
2014-10-20_0:42:39.31
2014-10-24_19:57:25.53
2014-10-24_6:12:12.98
2014-10-16_6:31:41.73
2014-10-23_11:1:23.89
2014-10-21_11:9:48.50
2014-10-16_7:40:5.21
2014-10-20_0:53:41.56
2014-11-3_6:40:29.88
2014-11-4_1:10:3.15
2014-11-4_8:40:20.79
2014-11-15_0:34:49.69
2014-11-25_20:15:58.27
35.94229
35.94628
35.94582
35.94084
35.94689
35.94430
35.94327
35.94559
35.94855
35.92194
35.93990
35.94217
35.94125
35.93427
35.94640
35.94637
35.94090
35.94277
35.94729
35.93930
35.94460
35.94564
35.94302
35.93353
35.94716
-96.77069
-96.78336
-96.77963
-96.76031
-96.78598
-96.77426
-96.77252
-96.78235
-96.78085
-96.77145
-96.75558
-96.75778
-96.75732
-96.77606
-96.78629
-96.78348
-96.75616
-96.75998
-96.78342
-96.75717
-96.77924
-96.78317
-96.77072
-96.78403
-96.76645
5.18
5.13
5.37
4.95
5.19
5.22
4.94
5.35
5.97
4.43
4.98
5.49
5.44
4.75
5.56
5.21
4.93
4.01
5.00
5.36
5.48
5.63
4.28
5.08
4.44
1.4ML
2.2ML
1.3ML
1.3ML
1.4ML
2.5ML
1.4ML
1.4ML
1.9ML
2.1ML
1.6ML
1.3ML
1.4ML
1.5ML
1.3ML
1.2ML
1.4ML
1.4ML
1.1ML
1.4ML
1.7ML
1.8ML
1.5ML
2.6ML
2.8ML
31
44
30
36
31
38
30
31
69
34
37
37
354
40
30
36
28
30
65
36
337
67
41
33
38
0.40
0.42
0.40
0.48
0.35
0.33
0.37
0.40
0.54
0.40
0.47
0.44
0.58
0.37
0.43
0.43
0.39
0.58
0.57
0.55
0.62
0.59
0.58
0.41
0.39
257
258
259
260
Table S2: Central Oklahoma Velocity Model (McNamara et al., 2015].
Depth
(km)
0.000
1.900
1.900
8.000
8.000
21.000
21.000
42.000
42.000
120.000
Vp
(km/s)
3.400
3.400
5.550
5.550
6.250
6.250
6.400
6.400
8.150
8.150
Vs 261
(km/s)
2.000
2.000
3.300
3.300
3.600
3.600
3.700
3.700
4.600
4.600
262
263
264
265
266
267
268
14
121
134
120
126
121
128
120
121
159
124
127
127
84
130
120
126
118
120
155
126
67
157
131
123
128
0.72
0.60
0.68
0.95
0.53
0.48
0.59
0.68
0.71
0.58
0.76
0.83
0.76
0.55
0.74
0.68
0.70
0.86
0.88
0.76
0.73
0.74
0.81
0.59
0.64
0.3
0.9
0.3
0.4
0.2
0.2
0.2
0.3
0.3
0.3
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.3
0.4
0.4
0.4
0.3
0.5
269
270
Table S3: RMT fault plane solutions
Origin time
271
2014-10-07
16:51:13
2014-10-10
13:51:21
Latitude
(°)
35.9474
Longitude
(°)
-96.7642
Depth
(km)
5.3
Mag.
4.0
strike1
(°)
97
dip1
(°)
85
rake1
(°)
4
strike2
(°)
7
dip2
(°)
86
rake2
(°)
175
35.9466
-96.7594
5.0
4.2
280
90
-4
10
86
-180
15