Evidence for Fluid Migration During the 2016 Meinong,Taiwan, Aftershock SequenceY.‐F. Hsu1,2, H.‐H. Huang2 , M.‐H. Huang3 , V. C. Tsai2,4 , R. Y. Chuang1 , K.‐F. Feng2,5,and S.‐H. Lin6

1Department of Geography, National Taiwan University, Taipei, Taiwan, 2Institute of Earth Sciences, Academia Sinica,Taipei, Taiwan, 3Department of Geology, University of Maryland, College Park, MD, USA, 4Department of Earth,Environmental and Planetary Sciences, Brown University, Providence, RI, USA, 5Department of Geosciences, NationalTaiwan University, Taipei, Taiwan, 6Central Geological Survey, New Taipei City, Taiwan

Abstract An important question of earthquake science is to what extent fluids such as water play animportant role in modulating seismicity. While many models of earthquake rupture assume that fluidsare critical for dynamic weakening during slip and evidence for fluids is well documented for shallowinjection‐induced or hydrothermally induced earthquakes, there has been limited conclusive evidence forthe role of fluids for tectonic earthquakes in the middle‐to‐lower crust. Here, we provide evidence for fluidmigration during the 2016 Meinong, Taiwan, aftershock sequence, occurring at 10–20 km depth within aclassic fold‐and‐thrust belt. We find high Vp/Vs ratios, characteristic of highly fluid saturated regions, in theMeinong aftershock region and that the Vp/Vs ratios in the central aftershock region change with timeduring the aftershock sequence. The central aftershock sequence distinguishes itself from other aftershocksby having a swarm‐like magnitude distribution and aftershock decay rate, as well as a slower migrationrate that may be related to fluid diffusion. The estimated permeability (~3.8 × 10−15 m2) and temporalchanges in Vp/Vs suggest that moderate earthquakes may be able to strongly affect permeabilities in themidcrust. The results also suggest that fluid processes play a critical role in regulating seismicity in a classiccontinental collision tectonic setting and may also have a role in modifying earthquake hazards moregenerally.

Plain Language Summary A long‐standing question is how fluids like water influenceearthquakes. While the effect of fluids on earthquake rupture is widely observed for shallowinjection‐induced or hydrothermally induced earthquakes, we know relatively less about their effect on deepearthquakes. Here, we provide evidence for fluid movement during the 2016 Meinong aftershock sequencein southwestern Taiwan. The analysis shows that seismic waves traveling through the aftershock regionchange their speed during the aftershock sequence, which is strongly suggestive of fluid movement.An estimate of the speed of fluidmigration also suggests that themidcrust of southwestern Taiwan can easilytransport fluids. These results suggest that fluids may generally play an important role for deep crustalearthquakes and affect earthquake hazards.

1. Introduction

Fault‐fluid interactions have been widely documented to regulate fault failure and crustal stress transfer,particularly in the shallow crust and in volcanic or hydrothermal areas (Bosl & Nur, 2002; Hainzl &Fischer, 2002; Nur & Booker, 1972; Shelly et al., 2015, 2016). With fluids present, increasing pore pres-sures reduce the effective normal stress and facilitate faulting (Sibson, 1981) and pore pressure maymigrate diffusively (Brown & Ge, 2018), as expected from theoretical considerations (Shapiro et al., 1997).Fluids are also critical in dynamical models of earthquake rupture to produce the frictional weakeningobserved in the laboratory and in situ experiments (Cappa et al., 2019; French & Zhu, 2017; Xing et al., 2019).However, despite these indications, observations that fluids are involved in deep crustal tectonic earth-quakes has been limited. Fluid‐induced seismicity has been suggested for a subset of aftershocks, such asthe 1992 Landers aftershocks based on a poroelastic Coulomb stress calculation (Bosl & Nur, 2002), the1997 Umbria‐Marche aftershocks based on pore pressure modeling (Miller et al., 2004), and the 2010 ElMayor‐Cucapah aftershocks (Ross et al., 2017) and the 2014 Ubaye Valley aftershocks (De Barros et al., 2019)based on spatiotemporal distributions of seismicity. Vp/Vs ratios have also been previously used to probe

©2020. American Geophysical Union.All Rights Reserved.

RESEARCH ARTICLE10.1029/2020JB019994

Key Points:• Temporal Vp/Vs changes and

aftershock migration suggest thatfluid migration occurred during the2016 Meinong, Taiwan, earthquakesequence

• Spatial correlation analysis suggeststhat seismic hazards may be higheraround the fringes rather than at thecenter of fluid‐rich regions

• Midcrustal permeability may varytemporally due to deep crustalearthquakes

Supporting Information:• Supporting Information S1• Data Set S1

Correspondence to:H.‐H. Huang,hhhuang@earth.sinica.edu.tw

Citation:Hsu, Y.‐F., Huang,H.‐H.,Huang,M.‐H.,Tsai, V. C., Chuang, R. Y., Feng, K.‐F., &Lin, S.‐H. (2020). Evidence for fluidmigration during the 2016 Meinong,Taiwan, aftershock sequence. Journal ofGeophysical Research: Solid Earth, 125,e2020JB019994. https://doi.org/10.1029/2020JB019994

Received 15 APR 2020Accepted 25 AUG 2020Accepted article online 27 AUG 2020

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https://orcid.org/0000-0002-1115-2427https://orcid.org/0000-0003-2331-3766https://orcid.org/0000-0003-1809-6672https://orcid.org/0000-0002-1071-9161https://doi.org/10.1029/2020JB019994https://doi.org/10.1029/2020JB019994http://dx.doi.org/10.1029/2020JB019994http://dx.doi.org/10.1029/2020JB019994http://dx.doi.org/10.1029/2020JB019994http://dx.doi.org/10.1029/2020JB019994http://dx.doi.org/10.1029/2020JB019994mailto:hhhuang@earth.sinica.edu.twhttps://doi.org/10.1029/2020JB019994https://doi.org/10.1029/2020JB019994http://publications.agu.org/journals/http://crossmark.crossref.org/dialog/?doi=10.1029%2F2020JB019994&domain=pdf&date_stamp=2020-09-05

fault‐fluid processes (Bachura & Fischer, 2016; Gritto & Jarpeb, 2014). Yet the evidence in these cases is notconclusive, andmost other crustal earthquakes lack the data needed to definitively determine whether fluidsplayed a significant role.

The 5 February 2016 Meinong earthquake (Mw 6.4) and its aftershock sequence, which occurred in a fold‐and‐thrust belt of southern Taiwan, was well documented due to the high density of seismic stations inTaiwan (Figure 1; Lee et al., 2016; Kanamori et al., 2017). Despite being relatively deep (~15 km) and small,the Meinong earthquake caused strong ground motions, extensive damage and 117 fatalities in the city ofTainan (Jan et al., 2018; Kanamori et al., 2017). Its rupture was also complex and triggered seismic and aseis-mic faulting at multiple depths (Huang et al., 2016; Lin et al., 2018). Interestingly, the Meinong sequencealso occurred in a region imaged tomographically to have relatively high Vp/Vs ratios (Huang et al., 2014),with the aftershocks clustering into three groups, primarily near the edges of the region with highest inferredVp/Vs ratios (Figure 2). Triggering of the shallow (5–10 km) aseismic faulting has also been suggested to befacilitated by tectonic conditions such as high fluid pressure and high background stress levels (Huanget al., 2016).

The observations suggestive of fluids in theMeinong regionmotivates this work in which we perform amoredetailed analysis of whether fluids were likely to play a role during the Meinong aftershock sequence. Weestimate time‐dependent near‐source Vp/Vs ratios during the aftershock sequence, relocate the seismicityusing double‐difference relocation to better constrain its spatiotemporal time history and expansion, anduse the results to estimate crustal hydraulic properties. We further discuss the likely implications for earth-quake hazards in southwestern Taiwan and earthquake physics more generally.

2. Data and Methods2.1. Earthquake Sequence Data

In this work, we analyze earthquake data from the Meinong aftershock sequence between February andJune 2016 collected by the Central Weather Bureau of Taiwan. We choose events to associate with the

Figure 1. Map of stations and the relocated Meinong earthquake sequence. Green and pink dots are the 2016 Meinongand 2011 Jiasian earthquake sequences, respectively. Yellow stars denote the hypocenters of the 2016 Meinong(green focal mechanisms) and 2011 Jiasian earthquakes (pink focal mechanism). Black triangles denote seismic stations.Blue boxes divide the Meinong earthquake sequence into three clusters. The red lines are active faults. The blacklines denote the cross sections shown in Figure 2. The inset map shows the distribution of all stations (white triangles)and the stations used in this study (black triangles). The red box denotes the study area.

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aftershock sequence using the double‐link declustering method (Davis & Frohlich, 1991; Wu & Chiao, 2006)with time/space linking distances of 2 days and 4 km. Each subsequent event that occurs within 2 days and4 km of a previous event is associated. This process continues sequentially until no further events can beassociated. The associated events are defined as an aftershock sequence and the event with the largest mag-nitude within the sequence is defined as the mainshock. The 525 extracted aftershocks in a local magnituderange of 0.5–6.6 cluster into three distinct groups (Figures 1 and 2) and last from February 5 through the endof February 2016. Changing the linking distances makes almost no change to the spatiotemporal pattern ofthe central and western clusters and instead mainly results in a different number of linked aftershocks in thenorthern cluster and farther north (Figure S1 in the supporting information).

2.2. In Situ Vp/Vs Ratio Estimation Method

To estimate Vp/Vs ratios of event clusters, we use the method of Lin and Shearer (2007). For a sufficientlycompact cluster of events, this method assumes the Vp/Vs ratio to be constant within the cluster so thatthe demeaned P and S wave differential times from any pair of events to any station should theoreticallylie on a line with slope given by the estimated Vp/Vs ratio. The Vp/Vs ratio is determined by linear regressionof all of the differential data for each cluster. Themethod has better precision than is possible to obtain tomo-graphically (Lin et al., 2015) or with other methods that separately estimate Vp and Vs (Gritto & Jarpe, 2014)and allows us to examine subtle temporal changes that occur during the aftershock sequence.

Figure 2. Comparison of earthquake locations between (a and b) the raw CWB catalog, (c and d) the 3D‐DD‐relocated catalog, and (e and f) thehypoDD‐relocated catalog. Green dots represent the Meinong earthquake sequence. Earthquakes within 5 km are projected on the cross sections. Backgroundcolors show Vp/Vs ratio variations from the velocity model of Huang et al. (2014). The topography with a vertical exaggeration of 10 is shown in the top panel.Focal mechanisms for events with the largest magnitude in each aftershock cluster are plotted as representative of each cluster (Wu et al., 2008).

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2.3. A 3‐D Double‐Difference Relocation Algorithm (3D‐DD)

To more accurately locate the aftershocks, we relocate all aftershocks using a modified double‐differencerelocation program named 3D‐DD (Lin, 2014), which incorporates a 3‐D velocity model into a standarddouble‐difference location estimation scheme (Waldhauser & Ellsworth, 2000). Details about how themethod differs from the hypoDD routine of Waldhauser and Ellsworth (2000) and validation tests of the3D‐DDmethod are described in detail in Text S1 and Figures S2–S5. We use a high‐resolution velocity modelof Taiwan (Huang et al., 2014) in the 3D‐DD procedure, which reduces path effects and near‐source struc-tural heterogeneity effects, resulting in a more compact distribution of aftershocks (Figure 2). The root‐mean‐square (RMS) differential travel time residual is reduced from 0.58 to 0.27 s after seven iterations(Figure S6). To ensure robustness of the 3D‐DDmethodology, we also relocated events with the unmodifiedhypoDD routine. Relocations using hypoDD are only mildly different from the 3D‐DD results and are shownin Figures 2e and 2f for comparison. The average location errors are about 158, 109, and 180 m in E‐W, N‐S,and vertical directions, respectively (Waldhauser & Ellsworth, 2000).

3. Results and Analysis3.1. In Situ Vp/Vs Estimation

We separately estimate the Vp/Vs ratios for the three somewhat distinct clusters of aftershocks: awell‐separated western cluster, a more diffuse northern cluster, and a central compact cluster of events thatgroup together near the mainshock hypocenter (see Figure 1). Using the Lin and Shearer (2007) methodresults in estimated in situ Vp/Vs ratios of 1.72, 1.88, and 1.95 for the western, northern, and central clusters,respectively (Figures 3 and S7). The values for the northern and central clusters are modestly higher (by ~0.2)than the Vp/Vs ratios estimated tomographically (see Figure 2) and is not surprising given the smoothing andexpected underestimation of the tomographic method. The spatial pattern is consistent with the tomo-graphic results, with higher Vp/Vs ratios for the central cluster, suggesting that these events may be moreaffected by fluids.

We further examine the temporal evolution of Vp/Vs for the central cluster by dividing the central after-shocks into five periods that each have a similar number of paired P and S wave differential times (1,600–1,800 pairs each). We choose this number of periods so that the number of pairs is high enough to robustlydetermine each set's Vp/Vs ratios independently. Using fewer pairs would increase the estimation uncertain-ties and prevent us from obtaining robust Vp/Vs temporal variations. Estimated Vp/Vs ratios and

Figure 3. Near source Vp/Vs ratios for each aftershock clusters. Panels (a) and (b) show the aftershocks color coded by estimated Vp/Vs ratios in the two crosssections shown in Figure 1. Panel (c) shows the Vp/Vs ratios estimated with the method of Lin and Shearer (2007) compared with Vp/Vs ratios estimated fromthe tomographic model (Huang et al., 2014). Vp/Vs ratio regression results for the three clusters are shown in Figure S7.

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corresponding scatter plots for the five time periods are shown in Figure 4. In situ Vp/Vs ratios varysignificantly with time, with the most significant temporal departure being an ~0.2 increase for the Day0.6–3 time window. Considering that the uncertainties are around 0.04–0.07 as derived from jackknifetests (Efron, 1982), the peak Vp/Vs ratio is robustly different from those during the other four timeperiods, suggesting a transient change in Vp/Vs ratio that is likely due to fluid migration (Bachura &Fischer, 2016). Further tests using different time cutoffs also corroborate the robustness of the observedtemporal changes (see Figure S8).

3.2. Spatiotemporal Distribution of Relocated Aftershocks

After applying the 3D‐DD relocation, each of the three clusters of aftershocks has a more compact spatialdistribution compared to the original distribution (Figure 2). The central cluster, in particular, becomesmore distinct and mostly confined to between the 1.8–1.9 contours of Vp/Vs of the tomographic results(Figure 2). The northern cluster also appears to delineate two clear subhorizontal planes (depths of 7 and11 km). The spatiotemporal evolution of the Meinong relocated aftershock sequence is further explored inFigure 5. Comparing the three clusters separately (Figures 5b–5d), it becomes clear that the central cluster'sevents are more prolonged in time (>20 days) and compact in space (mostly

time that regular mainshock‐aftershock sequences usually have (Figure 6b), instead again appearing moreswarm‐like with relatively random fluctuations in magnitude and event rate as is typical of hydrothermalevents (Vidale & Shearer, 2006). In contrast, the northern and western clusters have relatively typicalmainshock‐aftershock type spatiotemporal distributions (Figures 6c and 6d). More specifically, if wefurther divide the central cluster into a swarm aftershock group and a stress‐triggering group (with theswarm group defined as events below the D ¼ 1.5 m2/s curve, Figure 5b), the swarm group has aGutenberg‐Richter b value of 0.55, whereas the other group has a more typical b value of 0.91 (Figures 7aand 7b). The b values are calculated by maximum likelihood estimation (Aki, 1965; Utsu, 1965) withcompleteness magnitude given by the magnitude bin with the peak number of events within 0.2 (Wiemeret al., 1998). While it is still debated whether the presence of pore fluids should increase or decrease bvalues (Hainzl & Fischer, 2002; Shelly et al., 2016; Wiemer et al., 1998), it is clear that the b values aresubstantially different from the typical value of 1, again indicating that the mechanism driving theseswarm‐like aftershocks may be different from traditional aftershock stress triggering and hinting at therole of fluids.

4. Discussion

Both the time‐variable high Vp/Vs values and the swarm‐like activity of the central cluster of aftershocks ofthe Meinong earthquake suggest that they may have been driven by fluid migration (Bachura&Fischer, 2016). Swarms may also be driven by other processes like magmatic intrusions (Whiteet al., 2011) or afterslip (Koper et al., 2018), but magmatic intrusion in southern Taiwan is unlikely andtime‐variable Vp/Vs would not be expected from afterslip, leaving fluid migration as the most likely

Figure 5. Spatiotemporal earthquake evolution of the Meinong earthquake sequence (a) and individual three aftershockclusters (b–d). Panels (b)–(d) are for the central, western, and northern clusters, respectively. The black linesdenote the timing of the first event in each plot, and the color of the dots represents focal depth. The blue dashed curvesin (b) show how fast fluid would diffuse with diffusivity (D) equal to 1 and 1.5 m2/s, for reference.

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candidate. Given the low 10° dip angle of the Meinong mainshock (Figure 1), faulting is also not favorableunless fluid pressures are high (Sibson, 1985), as is also suggested by the high Vp/Vs ratios (Figure 2). Thus,both the mainshock rupture and the subsequent central cluster aftershocks may have been facilitated byfluids that flowed through the fault zone or locally formed cracks and fractures (Figure 8).

The gradual return of Vp/Vs ratios following their initial increasesuggests that the temporal variations in Vp/Vs ratio may reflectchanges in pore pressure rather than in fluid content itself. If anincrease in fluid content around the mainshock area were whatcaused the high Vp/Vs ratios, it would be difficult to imagine allof the fluid draining away at the deep depth of 15 km in such ashort time; in that scenario, then, the Vp/Vs ratios would beexpected to remain at high values afterward. Thus, we argue thatit is more likely that the time‐varying Vp/Vs ratios we observedare in response to pore pressure changes. In this scenario, theearthquake rupturing redistributes the existing pore fluid in a faultzone with locally small‐scale fluid flow (Figure 8). The inducedpore pressure then triggers the subset of aftershocks in the centralcluster (Figures 5 and 6). Owing to poroelastic relaxation or fluiddissipation in fractures/cracks, the pore pressure then graduallyreduces to the background level as the Vp/Vs ratio returns to its ori-ginal state.

If pore pressure diffusion occurs as envisioned above, the diffusivitycurve in Figure 5b may be used to estimate the crustal permeabilityin southwestern Taiwan. Following Shapiro et al. (1997), the perme-

ability k ¼ DηN

, where η is the pore‐fluid dynamic viscosity and N is

the poroelastic modulus. Using η ¼ 1.0 × 10−4 Pa s for water at adepth of ~14 km (Wagner & Kretzschmar, 2008) and N ¼ 3.9 × 1010

Figure 6. Magnitude‐time distribution of the Meinong earthquake sequence (a) and for the individual three aftershock clusters (b–d). Panels (b)–(d) are for thecentral, western, and northern clusters, respectively.

Figure 7. b value calculation for the swarm‐like aftershock group (black dots)and stress‐triggered aftershock group of the central cluster (blue dots). The reddots indicate the magnitude of completeness. The lines show the regressionresults of 0.55 and 0.91 for the swarm‐like and stress‐triggered (“M‐A”)aftershocks, respectively.

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Pa (see Appendix A) yields a value of k ≈ 3.8 × 10−15 m2. As shown in Figure 9, this value is 3 orders ofmagnitude higher than the mean permeability (k) versus depth (z) relation derived from geothermal andmetamorphic data (black dashed curve, Manning & Ingebritsen, 1999) but consistent with the high k‐zrelation derived from primarily seismic observations in the upper crust (blue dashed curve, Ingebritsen &Manning, 2010). Our estimates support the high k‐z relation at midcrustal depths and suggest that theoccurrence of large and transient permeability variations could indeed be possible and caused by deepmoderate earthquakes. The high value of permeability also suggests that advective heat transport mayoccur transiently (k > 10−16 m2), which could have important controls on deep geological processes suchas orogenic prograde metamorphism (Manning & Ingebritsen, 1999).

We speculate that the high Vp/Vs anomalies in the midcrust are from an accumulation of fluids that mayresult from the high flux of sediments deposited by the rapidly growing Taiwan mountain belt (Hsuet al., 2016), with the fine‐grained mudstone of the Gutingkeng formation serving as an impermeable caplayer storing the fluids to form an overpressurized fluid reservoir. This interpretation is supported by theobservation of NE‐SW trending mud volcanoes distributed throughout the region (Hui et al., 2018), whichhas geochemical signatures of a fluid source from actively dewatering sedimentary pore waters (Youet al., 2004). We further surmise that the lack of both aftershocks and long‐term seismicity at the locationof highest Vp/Vs (Figure 10) may be due to aseismic creep caused by high pore pressure stabilizingrate‐state frictional sliding (French & Zhu, 2017; Xing et al., 2019). This may also explain how southwesternTaiwan deforms so rapidly without asmuch seismic energy released as would be expected (Ching et al., 2016;Hu et al., 2007; Huang & Evans, 2019; Wu et al., 2008). If true, seismic hazards may actually be higher

Figure 8. Seismogenic structures (a) and schematics of a fault‐fluid interaction process for the Meinong earthquakesequence (b and c). (a) The green and light blue dots are the Meinong earthquake sequence and fluid‐drivenaftershocks. The yellow star denotes the Meinong mainshock. The black dashed lines are proposed fault structures. Theblue arrows schematically illustrate the fluid flow directions. Background colors show Vp/V s ratio variations from thevelocity model of Huang et al. (2014). (b) The enlarged area is the red box in (a), schematically showing the faultinitiation responsible for the Meinong earthquake due to high fluid pressure. (c) Schematic for the near source fault/fracture network thought to be triggered by fluid flow and migration.

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Figure 10. Locations of the Meinong earthquake sequence (a and b), fluid‐driven central aftershocks (c and d), and1990–2018 background seismicity (e and f) in the Cross Sections AA′ and BB′. The location of the cross sections isindicated in Figure 1. Earthquakes within 5 km are projected on the cross sections. Background colors show Vp/V s ratiovariations from the velocity model of Huang et al. (2014). The pink dots in (e) and (f) denote the 2010 Jaisian earthquakesequence (Huang et al., 2011). Topography with a vertical exaggeration of 10 is shown in the top panel.

Figure 9. Global compilation of permeability (k) depth (z) data and regression curves for tectonically active continentalcrust. Data referenced are listed in the legend. Uncertainties and measurement extents are indicated by error bars.The black and blue dashed curves denote the mean and high permeability regression k‐z curves from different data sets(see text for more details).

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around the fringes rather than the center of the high Vp/Vs anomaly, including where the northern and cen-tral aftershock clusters occurred. When fluids are active during faulting, as for the central aftershock cluster,aftershock activity may be prolonged and with relatively larger magnitudes that could cause more significantsecondary hazards (Figure 6b).

5. Conclusions

By analyzing a subset of the Meinong, Taiwan, aftershock sequence, we have found clear evidence for tem-poral changes in already high Vp/Vs ratios as well as slow migration of seismicity that does not followOmori's law and has a low Gutenberg‐Richter b value of 0.55. A reasonable interpretation of these observa-tions is that pore pressure changes resulting from localized fluidmigration with an enhanced permeability of3.8 × 10−15 m2 caused the changes in Vp/Vs ratios and the swarm‐like seismicity. This suggests that deepmoderate earthquakes may drive transient fluid flow in the midcrust of southwestern Taiwan, which mayin turn also modulate heat flow. The detailed seismic observations further allow us to discriminate betweendifferent aftershock driving mechanisms, which is important for furthering our understanding of the regio-nal seismotectonics and earthquake hazards more generally.

Appendix A: Permeability Calculation

To estimate crustal permeability, we use the formulak ¼ DηN

of Shapiro et al. (1997). The poroelastic modulus

N requires further calculations as

N ¼ φKf

þ αKg

� �−1; (A1)

where φ, Kf, and Kg are porosity, bulk moduli of the fluid, and solid grain material, respectively. The coef-

ficient α is a ratio between bulk moduli of drained matrix (Kd) and grain material, given by α ¼ 1 − KdKg

� �.

Bulk and shear modulus can be converted from seismic velocity and density (ρ) as

VP ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiK þ 4

3μ

ρ

vuut; VS ¼

ffiffiffiμρ

r: (A2)

We take an average solid P wave velocity (VP) of 5.7 km/s and S wave velocity (VS) of 3.3 km/s from thesurrounding areas of high Vp/Vs (Huang et al., 2014), and density is derived to be 2.65 g/cm

3 based on anempirical relationship with P wave velocity (Equation 1 in Brocher, 2005). We then obtain Kg ¼ 47.6 GPa.Likewise, given VP ¼ 1.37 km/s and ρ ¼ 0.82 g/m3 for water at 300°C (Lin, 2000) and 10 Mpa correspond-ing to the depth of ~15 km (Wagner & Kretzschmar, 2008), the bulk modulus for fluid (Kf) is estimated tobe 1.5 GPa. Other fluids such as supercritical CO2 are also possible. However, without additional informa-tion, we simply assume water here for our calculation.

Next, we use the Gassmann formula (Gassmann, 1951) to assess the porosity φ as

KeffKg − Kd

¼ KdKg − Kd

þ Kfφ Kg − Kf� �; μeff ¼ μd; (A3)

where additional Keff, μeff represent effective bulk and shear moduli of a fluid‐filled porous rock mediumwe seek and μd is shear modulus of drained matrix. By introducing a critical porosity (φc) that defines thetransition from a medium which is supported by the frame to a medium where solid materials are sus-pended in the fluid (Nur et al., 1995), their relations can be further expressed as

Kd ¼ Kg 1 − φφc

� �; μd ¼ μg 1 −

φφc

� �; (A4)

where the critical porosity of 0.05 for mudstone/shale (Revil et al., 2002) is chosen for themudstone‐dominated Gutingkeng formation widely present in the region. Thus, changing porosity φ

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will change Kd, μd, and in turn the Keff and μeff via Equation A3. To best fit the high in situ Vp/Vs ratio of~1.95 for central aftershock group (Figure 3), we obtain φ ~ 2.3% (Figure S9) and N ~ 3.9 × 1010 Pa when

substituting all parameters back into Equation A1. Substituting N back into k ¼ DηN, we arrive at a perme-

ability k ~ 3.8 × 10−15 m2. We acknowledge that large uncertainties are inherent in the chosen physicalparameters for grain material and pore fluids at depths of 15 km so that our permeability estimate maynot be accurate to within an order of magnitude.

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AcknowledgmentsWe thank the Central Weather Bureaufor providing the earthquake data foranalysis (https://www.cwb.gov.tw/V8/E/) and provide the relocated catalog ofthe Meinong aftershock sequence asData Set S1 in the supportinginformation. We thank Matteo Lupi forproviding critical comments on anearlier version of this work. We alsothank Editor Rachel Abercrombie andtwo anonymous reviewers for theirconstructive comments, whichsubstantially improved the work. Thiswork was partially supported byCentral Weather Bureau Grant MOTC‐CWB‐107‐E‐09 and Ministry of Scienceand Technology Grant 108‐2116‐M‐001‐009.

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