characteristics in hypocenters of microseismic events due to...

ArticleVol. 21, No. 5, p. 683694, October 2017http://dx.doi.org/10.1007/s12303-017-0021-9pISSN 1226-4806 eISSN 1598-7477 Geosciences JournalGJCharacteristics in hypocenters of microseismic events due to hydraulic fracturing and natural faults: a case study in the Horn River Basin, Canada

Jeong-Ung Woo1, Juhwan Kim1, Junkee Rhie1*, and Tae-Seob Kang2

1School of Earth and Environmental Sciences, Seoul National University, Seoul 08826, Republic of Korea2Department of Earth and Environmental Sciences, Pukyong National University, Busan 48513, Republic of Korea

ABSTRACT: For two to three decades, microseismic monitoring has been popular in the development of unconventional resources,because the fracture network generated by hydraulic fracturing mainly controls the productivity, and microseismic monitoringenables direct measurements for imaging the fracture network. Nevertheless, some refinements are required to make this methodmore practical. One challenge is to quantify the effects of pre-existing natural fractures for generating microseismic events. We determinethe hypocenters of microseismic events occurring in a shale gas play in the Horn River Basin, Canada, and report several interestingspatial and temporal features of the hypocenter distributions. Automatic phase-picking is applied to waveform data recorded at 98shallow buried three-component geophones, and phases thought to be from the same event are associated. The initial hypocentersof events are determined by iterative linear inversion algorithm then relocated using a double-difference algorithm, where relativetravel time measurements are obtained with the waveform cross-correlation. We group events into many clusters based on frackingstages and their hypocenters, and then define the best-fitting plane of hypocenters for each cluster. Most strikes of the best-fittingplanes are consistent with the direction of local horizontal stress maximum, indicating that hydraulic fracturing induces mostmicroseismic events. However, the best-fitting planes of several clusters have strikes similar to those of pre-existing faults or fractures,indicating that pre-existing natural faults or fractures can affect the generation of microseismic events. In addition, some observationssuggest that natural fractures can affect the temporal evolution of the spatial occurrence pattern of microseismic events. We observed spe-cific migration patterns of microseismic events around known faults in the study area. Although further work is required for com-plete understanding of this phenomenon, our observations help elucidate the nature of microseismic generation.

Key words: induced seismology, principal component analysis, Horn River Basin, fracture propagation, microseismic event location

Manuscript received September 8, 2016; Manuscript accepted May 3, 2017

1. INTRODUCTION

Microseismic monitoring is a widely used seismic tool forimaging fracture networks induced by hydraulic fracturing(Wolhart et al., 2006). Hydraulic fracturing induces cracks byinjecting fluids under high pressure into an impermeablemedia, thereby enhancing the permeability of the media. Enhancedpermeability can be long lasting because, along with fluids,proppants are injected to inhibit crack closure and enableeconomic recovery of natural gas. Thus, monitoring microseismic

events generated by the crack-opening process can give usefulinformation on the development of fracture networks (Sasaki,1998; Warpinski et al., 1998; Anikiev et al., 2014). However, itis well known that not only those fractures generated by hydraulicfracturing but also pre-existing natural fractures can affect thefinal productivity of natural gas. Therefore, an importantcurrent question is how to distinguish between events directlyrelated to hydraulic fracturing and those somehow associatedwith pre-existing natural fractures. The first step to resolvethis issue is to compile well-analyzed results from differentsituations.

In this study, we determined the hypocenters of microseismicevents occurring in a shale gas play in the Horn River Basin,Canada. Since the number of microseismic events to be locatedis usually very large, we automatized the entire procedure. Aphase-picking algorithm based on STA/LTA was used to measure

*Corresponding author:Junkee RhieSchool of Earth and Environmental Sciences, Seoul National Univer-sity, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of KoreaTel: +82-2-880-6731, Fax: +82-2-871-3269, E-mail: [email protected]

The Association of Korean Geoscience Societies and Springer 2017

684 Jeong-Ung Woo, Juhwan Kim, Junkee Rhie, and Tae-Seob Kang

http://dx.doi.org/10.1007/s12303-017-0021-9 http://www.springer.com/journal/12303

P- and S-wave arrival times, and an association algorithmusing a Wadati diagram was utilized to identify events. Theassociation process applied conservative thresholds for identifyingevents in order to prevent potential bias caused by false eventdetections. The absolute hypocenters were determined by awidely used iterative linear inversion scheme and then relocatedby double-difference algorithm to improve the precision oftheir relative locations. For relocation, the waveform cross-correlation method was applied to improve relative travel-timemeasurements. We grouped events into many clusters, mainlybased on their hypocenters, and found the best-fitting planeof hypocenters for each cluster by means of principal componentanalysis (PCA). The spatial distributions of strike and size ofthe planes were analyzed to investigate effects due to hydraulicfracturing and pre-existing natural faults or fractures. Ourobservations show that the spatial and temporal distributionsof microseismic events can be affected by natural fractures.Although it is difficult to quantify their effects based solely onthese observations, our findings can contribute to improvingthe knowledge of microseismic generation.

2. DATA AND METHODS

2.1. Data Acquisition

The Horn River Formation located in the Horn river Basinin northern British Columbia is Middle to Late Devonian inage (Ross and Bustin, 2008; Kam et al., 2014). The formationconsists of siliceous shale, calcareous shale, and argillaceouslimestone. The Muskwa and Evie formations are the sub-units of the Horn River Formation in which hydro-fracturingwas implemented. The Evie Formation is shallower than theMuskwa Formation. Four and three horizontal wells weredrilled at the Muskwa and Evie formations, respectively (Fig.1). For observing microseismic activities at the two formations, ashallow buried array was operated for a total of 70 days. Ninety-eight three-component geophones (center frequency 10 Hz,sampling rate 500 Hz) were deployed. The average interstationdistance between two adjacent geophones was only several hundredmeters. The aperture of the whole array was approximately 8km and was sufficiently large to cover the lateral extent of allhorizontal wells. Surface monitoring of microseismic eventsis cheaper than monitoring at depth, and can efficiently detectevents occurring across a broad area. However, a significantscattering of waves near the surface can reduce signal-to-noiseratio (SNR). A surface buried array used in our case is designedto mitigate this issue by reducing the noise level and enhancingSNR.

2.2. Data Processing

For P- and S-phase detection, we applied the short-termaverage to long-term average (STA/LTA) method to 5–50 Hz

Fig. 1. Layout of shallowly buried array (a–c) and hydro-fracturingwells (a–d). The coordinate origin is at the center of hydro-frackingwells. Location of subsurface array is marked as triangles and thelines of horizontal hydro-fracturing wells are distinguished by dif-ferent colors. Well log data for making the one-dimensional (1D)velocity model were obtained from the three regions, marked by pur-ple, yellow, and green stars in panel (a). Locations and names ofhydro-fracking wells in the Muskwa and Evie formations are shownin (b) and (c). Note that the locations of M1 and the M4 are the sameas E1 and E3 on the map view, but the Muskwa Formation is at shal-lower depth than the Evie Formation. Panel (d) shows the depth pro-file of the seven hydro-fracking wells. It is projected along the E2line, located at the center of the seven wells.

Characteristics of microseismic events due to hydraulic fracturing 685

http://www.springer.com/journal/12303 http://dx.doi.org/10.1007/s12303-017-0021-9

bandpass filtered data. The STA/LTA method, which is widelyused in weak-motion seismology, takes advantage of thecharacteristics that a short time window can capture transientchange due to seismic signal whereas a long time windowmostly changes with background noise (Trnkoczy, 2012). Inthe STA/LTA method, raw data are usually transformed intocharacteristic functions (CFs) containing the energy information.The STA and LTA values of the ith windows are described as:

, (1a)

. (1b)

Equations (1a) and (2a) average-out a series of traces of acharacteristic function. Then, the ratio of STA and LTA,

, (2)

becomes high when a signal arrives. However, the value ofSTA/LTA in Equation (2) can vary from case to case, requiringsome experience in selecting appropriate ns, nl, and thresholdof event detection (Trnkoczy, 2012). We use the characteristicfunction of Grigoli et al. (2013), in which the P characteristicfunction (CFp) is defined as a squared vertical component ofseismic traces, as represented in Allen (1978). The method usedto define the S characteristic function (CFS) is more complex.Initially, analytic traces are introduced by two horizontalcomponents and Hilbert transform, H:

, (3a)

. (3b)

The following work configures the covariant matrix Q fromthe analytic traces such as:

STA i 1ns---- CFj

ij i ns–==

LTA i 1nl---- CFj

ij i nl–==

STA/LTA i STA i LTA i -----------------=

X j x j iH x j +=

Y j y j iH y j +=

Fig. 2. An example of phase-picking correction for improving measurement accuracy. Each column is separated by stations. The first row isthe waveform of the vertical component. The second row is the STA/LTA of P characteristic function in the case of ns = 20 and n1 = 60. Thethird and fourth rows are the waveforms of two horizontal components. The fifth row is the STA/LTA of S characteristic function in the caseof ns = 20 and n1 = 60. The blue ‘+’ marks indicate automatically determined P-arrival times (first and second rows) and S-arrival times (third,fourth, and fifth rows) before correction; the red ‘+’ marks indicate automatically determined P-arrival times (first and second rows) and S-arrival times (third, fourth, and fifth rows) after correction.



, (4)

where represents the conjugate transform. Then, the Scharacteristic function (CFS) is defined as in Equation (5):

. (5)

Here, is a small positive number preventing STA/LTAfrom having an infinite value and l is the largest eigenvalueof Q(j) (Note that Q(j) is a Hermitian matrix, which has realpositive eigenvalues). The basic algorithm of Equations (1a)and (1b) is modified in Grigoli et al. (2013) into the recursiveSTA/LTA algorithm of Withers et al. (1998, 1999), describedas follows:

STA(j) = Ks[CF(j)] + (1Ks)STA( j1), (6a)LTA(j) = Kl[CF(jns1)] + (1Kl)LTA( j1). (6b)

Here, Ks and K1 are set to 1/ns and 1/nl, respectively. Followingmultiple trials, we fixed ns and nl to 20 and 60, and P and Sdetection thresholds to 5 and 10, respectively.

As the STA/LTA method detects the maximum value of eachpick, the measured phase arrival time can be delayed with respectto the actual phase arrival time. To improve the picking accuracy,a correction procedure was applied as follows. We set a 0.4-second time window before the measured phase arrival timeand aligned data points by order of amplitude. We then selecteddata points belonging to a low 20% rank and identified intervalswithin which more than five consecutive samples (0.01 sec inthis case) were selected. The corrected phase arrival time wasdefined as the end of the last interval within the 0.4-secondtime window. Figure 2 shows an example of the phase-pickingcorrection discussed above. The approach was utilized to overcomethe inherent drawback of STA/LTA algorithms, which is thedelay phenomenon of the measured arrival time.

2.3. Microearthquake Association

Before locating the events, it is necessary to associate thephase arrival times from the same event. First, the P- and S-arrivals at each station were paired if their time difference was0.4–1.5 s, thereby eliminating many false detections. Then, thenumber of P-arrivals paired with corresponding S-arrivalswas counted for all stations within a moving window of 0.5 sinterval and 0.1 s increment (Fig. 3a). If the number of countswas > 7, it was classified as an event detection. Due to thepossibility of false detection and inaccurate picking, somephase arrival data for each event can lower the quality of thelocation result. To resolve this, we used a Wadati plot, whichshows the relationship between P arrivals and P–S times. For

the velocity model with homogeneous Vp/Vs ratio, the timedifference of P- and S-arrivals is proportional to P-travel time.Thus, points plotted on the Wadati diagram should have a lineartrend if there is no false detection and all points are correctlymeasured. To discard erroneous phase arrival time data, wedrew a Wadati plot for each event and reselected the pairs lyingbetween two parallel trend lines. Here, two trend lines havethe same slope of 1, which means that Vp/Vs is 2, but shiftedby 0.1 s along the P–S time axis. An average Vp/Vs value canbe estimated from the well logging data (Fig. 4) and the valueis similar to the previous study (Zeng et al., 2014). In Zeng etal. (2014), the Vp/Vs ratio was set to 2.2 for shale and 1.8 forlimestone, based on the results of Castagna et al. (1985). Weidentify trend line positions that maximize the number ofpairs located between the two lines. An example Wadati diagramis shown in Figure 3b. By the series of association procedures,we detect the phase arrival time sets of more than 8000 microseismicevents.

Q j X j X j X j Y j Y j X j Y j Y j

=

CFS 1 j 2 +=

Fig. 3. Example of automatic association process. Panel (a) shows aprimitive automatic association process. Ordinates indicate the counts ofP arrivals during cumulative 0.5-seconds time window, and abscissaeindicate the end of the 0.5-seconds time window. Counts > 7 are clas-sified as an event detection. Panel (b) shows the use of Wadati dia-grams for pair reselection. The gradient of the underlying red line is1 and the overlying line is shifted by 0.1 seconds along the P–S timeaxis. Pairs are chosen for the location inversion when the red linesare positioned to maximize the pairs captured between them.



2.4. Microearthquake Location

For each phase arrival time set, we determined its hypocenterusing HYPOELLIPSE (Lahr, 1989), linear, iterative inversionsoftware that was developed to determine the locations and origintimes of shallow earthquakes by minimizing the differencesbetween theoretical and observed phase arrival times. Thealgorithm requires the calculation of theoretical travel timesfor a one-dimensional (1D) velocity model shown in Figure 4.The inversion results of the hypocenters are plotted in Figure 5.

To investigate the detailed spatial distribution of hypocenters,they were relocated using HypoDD software adopting a double-difference algorithm (Waldhauser and Ellsworth, 2000; Waldhauser,2001). This method corrects relative hypocenters by using traveltime differences between two adjacent events or stations. The timedifferences can be measured by the waveform cross-correlationrather than direct phase picking, which can significantly reducemeasurement errors (Friberg et al., 2014). Since the double-

difference algorithm is applicable to events having similarhypocenters, all the microseismic events were grouped accordingto the fracking stage and the events were relocated group bygroup. As a result, 174 groups were relocated, as shown inFigure 6. The velocity model for the relocation procedures isshown in Figure 4.

2.5. Pattern Analysis for Microearthquakes

Since the spatial and temporal distribution of all hypocentersis too complex to interpret as a whole, we divided mosthypocenters into 194 clusters. Here, we attempted to gatherhypocenters associated with the same local fracture networkas one cluster. It would be expected that the hypocenters ofevents generated by hydraulic fracturing would align on aplane, as reported by many previous studies (Yost et al., 1988;Fisher et al., 2002; Gale et al., 2007; King, 2010). Assumingthat the events will be on a specific plane, we determine thebest-fitting plane from the hypocenters for each cluster bymeans of principal component analysis (PCA). The PCA algorithmfinds independent unit basis vectors that maximize the datavariance (Jackson, 1991). The detailed processes are representedin the following steps. For the hypocenters in a three-dimensionalspace, the first unit bases vector w1, which maximizes the varianceof the hypocenters, is expressed as:

w1 = , (7a)

where xmean indicates the arithmetic mean of the hypocenters,i.e., xmean= . Here, the inner productand the number of the hypocenters are represented by <, > andN, repectively. The second unit bases vector w2 can be obtainedlikewise after removing the w1 component from the hypocentersas:

w2 = . (7b)

Using the above equations, we defined the fitting planeas that containing the average location of hypocenters andperpendicular to both unit vectors, w1 and w2.

For the best-fitting plane of each event cluster, we calculatedstrike and projected area based on the method proposed byWoo et al. (2016).

3. RESULTS

3.1. The Muskwa Formation

The Muskwa Formation contains four horizontal wells termed

argmaxw 1=

xi xmean w– 2

i 1=

N

1N---- xN

i 1= i yNi 1= i zN

i 1= i

argmaxw 1=

xi xmean xi xmean– w1 w1 w–– 2

i 1=

N

Fig. 4. Data for the three well logs and the 1D velocity model for thelocation inversion. The sites for well logging data are shown in Figure1a. At each depth interval, the velocity model utilizes median valuesof Vp and Vs. The blue and red lines indicate P-wave and S-wavevelocity models, respectively. The depths of the Muskwa and Evieformations are denoted by two arrows.



Fig. 5. Map view (a, b) and depth profiles (c, d) of microseismic event locations via HYPOELLIPSE software. The locations of the eventsoccurring during the fracking stages of the Muskwa and Evie wells are shown as gray circles in (a, c) and (b, d), respectively. The thick greenlines in the map view indicate known regional faults. The description of the seven hydro-fracking wells is the same as in Figure 1.

Fig. 6. Map view (a, b) and depth profiles (c, d) of microseismic event locations via HypoDD software. The locations of the events occurringduring the fracking stages of the Muskwa and Evie wells are shown as gray circles in (a, c) and (b, d), respectively. All symbols match thosedescribed in Figure 5.



(from northeast to southwest) M1, 2, 3, and 4 (Fig. 1). The totalnumber of events occurring in the Muskwa Formation is 3512.Approximately 1000 events occurred due to hydraulic fracturingat each well except for M1: the number of events associatedwith M1 is only 189. At well M1, three clusters were found, andall strikes are different from the local maximum stress directionof N60°E. Since there are only three clusters and the eventlocation data are of low quality, we are unable to reach anyconclusions for this well. However, the results from the otherthree wells provide more information. The most prominentfeature of clusters for wells M2, 3, and 4 is that the pre-existingfaults seem to control the spatial occurrence pattern of the events.Figure 7 shows that many clusters are located at the south-eastern and north-western parts of the site, with few clusters inthe south-western part bounded by faults. This feature is alsorecognized in the map showing the distribution of individualepicenters (Fig. 6). Although it is likely that faults can affectthe spatial pattern of hypocenters, most strikes in the Muskwaclusters are well matched with the local maximum stress directionof N60°E. Quantitatively, 61.1% of clusters have strikes rangingfrom N40°E to N80°E.

3.2. The Evie Formation

The Evie Formation contains three horizontal wells, termed(from northeast to southwest) E1, 2, and 3 (Fig. 1). In thisformation, 3512 events were recorded (2100, 1975, and 910 atwells E1, 2, and 3, respectively). The total number of clustersfor the Evie Formation is 120, which is more than at the MuskwaFormation. The number of events associated with E1 (locateddirectly below M1) exceeds that of M1 by more than one orderof magnitude. The E1 clusters are uniformly distributed fromtoe to heel, and most of their strikes are consistent with thelocal stress maximum direction, similarly to the clusters ofthe Muskwa Formation. However, some clusters have strikesof about N45°E. For wells E2 and E3, the cluster locations areuneven, and variations in strike are not negligible. Particularly,we found a group of clusters showing NW–SE trend, withlocations several hundred meters away from E3. Clusters withstrikes ranging from N40°E to N80°E account for 52.2% ofcases, which is less than that for the Muskwa Formation.

3.3. Other Characteristics of Microseismic Cluster

To quantify the size of the fracture hosting microseismicevents for each cluster, we calculated the length and area ofthe cluster. The length is calculated by (1) finding two hypocentersin each cluster, which maximize the apparent distance in directionof the vector w1, (2) projecting corresponding two hypocenters

on the surface, and (3) measuring the distance between thetwo projected points on the surface. The lengths of the clustersare plotted as bars in Figure 7. The average lengths of clusters inthe Muskwa and Evie formations are 381 m and 201 m, respectively.This indicates that hydraulic fracturing generated longer fracturesin the Muskwa Formation, if the powers of hydraulic fracturingare uniform. A similar trend is observed for the projected area,which is defined as an area of the polygon containing the projectedhypocenters on the fitting plane (Woo et al., 2016). The average

Fig. 7. PCA analysis for each cluster of the Muskwa (a) and Evie (b)formations. The length of each rectangle represents the range of theprincipal component in the map view, such that it matches the lineartrend of each cluster. White bars denote clusters with strike betweenN40°E and N80°E; other clusters are denoted as black bars. All othersymbols match those described in Figure 1.



projected areas for the Muskwa and Evie formations are3.93 × 106 m2 and 2.03 × 106 m2, respectively, i.e., the averageprojected area for the Evie Formation is only about 51% ofthat for the Muskwa Formation.

4. DISCUSSION

Several previous studies have used the same data. RahimiZeynal et al. (2014) applied the beam-steering processingtechnique of Lakings et al. (2006) as an imaging tool based onbeam forming, which is applicable to the surface array. Theirmethod stacks highly attenuated signals generated by microseismicevents, and they detected more than 11200 events at the samesite. Snelling et al. (2013b) discussed the uncertainty of detectedmicroseismic events: approximately 10 m horizontally and 20 mvertically. For each hydro-fracking well, Snelling et al. (2013a)analyzed the b-value, which shows the relationship betweenthe event frequency and magnitude, and focal mechanism solutions.Since the Muskwa and Evie formations are dominated by non-double-couple and double-couple events respectively, theyreported that the events in the Muskwa Formation might beinduced by hydro-fracturing, whereas those of the Evie mightbe related to the structure-controlled rock failure due tochange of stress. In addition, they insisted that the generationof higher-magnitude events is related to fault reactivation, becausemost occur near known faults. With regard to the fault reactivation,Davies et al. (2006) suggest that fluid flow can reach pre-existingfaults by various ways such as a direct connection of injectionwell, permeable strata, and fractures.

The number of events located in this study is approximately8500, which is slightly less than in the previous studies discussedin the above paragraph. This is because we applied conservativethresholds for event detection and association. Especially, wedeclared event detection only if both P- and S-arrival data wereavailable. In doing so, we attempted to obtain more reliableimages of microseismic hypocenters, free of distortion by falseevents. Although we used a different method from previousstudies for locating events, our results are consistent with thoseof Snelling and de Groot (2014), thereby validating our result.In addition, since the double-difference method applied inour study can precisely determine the relative locations of theevents, our results well depict the nature of the microseismicevents.

To investigate the spatial variations in strike and the possibleeffects of natural fractures, we divided the clusters into twogroups. Clusters with strikes ranging between N40°E and N80°Eare represented as white bars and others are indicated withblack bars (Fig. 7). For both formations, it is clear that theblack bars are located near the known natural faults whereas

the white bars are more scattered across the entire area (Fig. 7).This observation is natural, since we would expect that thelocations of clusters directly induced by hydraulic fracturingwill be less affected by the faults or fractures compared withthose clusters associated with re-activation of the faults. Someblack bars, showing a linear trend in the map view, are locatedin the north-west part of the site. Although there is no knownfault in this area, the trend of clusters is completely differentfrom the direction of local stress maximum. Therefore, it islikely that they are related to unknown local faults. Our resultsindicate that the proposed cluster analysis might distinguishbetween events directly induced by fracture generation andthose related to re-activation of the faults even in cases wherethere is no information on the natural faults.

We also investigate whether there are characteristic migrationpatterns of microseismic events. For each cluster, we examinethe relationship between relative origin times and locations.Relative origin time is measured from the origin time of thefirst event in each cluster, and the relative location (L) of eachhypocenter is measured by inner product of the vector, definedfrom the center of the cluster to the hypocenter, and anothervector w1 defined in Equation (7a). After investigating all clusters,we found two distinct unilateral and bilateral migration patterns(Fig. 8). As shown in Figure 8, in some clusters the hypocentersseem to propagate in one direction with increasing time, whereasothers propagate in two directions. These phenomena have beenreported by several case studies (Maxwell and Cipolla, 2011;Neuhaus et al., 2012). In Neuhaus et al. (2012), sequential andlateral migration of events usually appeared in the initial stateof hydro-fracturing, when there were only a few cracks nearoperated wells. As the fracture system becomes more complexdue to hydro-fracking, the sequence of hypocenters becomesdisorderly. We found only 23 and 6 clusters clearly showingunilateral and bilateral patterns, respectively, representing only asmall fraction of all clusters.

Figure 9 plots the hypocenters with their relative origin timeon a color scale. For comparison, the relative origin times ofall clusters are normalized such that 0 and 1 indicate the origintimes of the first and last events in each cluster, respectively.The light-gray and dark-gray circles represent the clusters ofunilateral propagation and bilateral propagation, respectively.The clusters with unilateral propagation seem to be found nearthe well, meaning that the unilateral pattern may be mainlyrelated to hydro fracturing rather than regional effects, whereasthose with bilateral pattern are located in limited regions,suggesting stronger association with regional environments.It is noteworthy that the migration velocities of the four casesin Figure 8 are approximately uniform at 0.16 meters per minute(see the red lines in Fig. 8). Although this value does not apply



to all clusters and the values differ case by case, this may reflectthe physics of stress transfer or fluid flow, which are not wellunderstood.

5. CONCLUSION

We located more than 8000 microseismic events within oneshale gas play in the Horn River Basin by using a series of automaticprocesses including STA/LTA algorithm and linear inversionmethods. We then analyzed the spatial and temporal characteristicsof hypocenters, and the influences of local stress environmentand pre-existing faults. For quantitative investigation of the

effects of the two different mechanisms, we grouped hypocentersinto clusters and extracted parameters characterizing eachcluster. In addition, we divided the clusters into two groups:one directly induced by hydraulic fracturing, the other relatedto reactivation of the faults. The distributions of the two groupsof clusters show specific patterns, which can be predicted byhydro-fracking and fault reactivation theories. We also observedinteresting unilateral and bilateral migration patterns of theevents, and the migration velocity is estimated at approximately0.16 m/min. At this stage, it is difficult to associate this velocitywith any specific physical mechanism. Additional case studiesand various models of fracture generation will be necessary to

Fig. 8. Four examples showing relationships between the relative origin time and the relative location, L (the score of principal component,w1 of Eq. 7a). Open circles shown in each window represent the events of each cluster. The origin of each window is based on the first origintime and the mean location of the events. The top panels show unilaterally propagating migration patterns and the bottom panels show bilat-erally propagating migration patterns. The red lines approximate the migration velocity (0.16 m/min). The sites for the four examples areshown in Figure 9.



Fig. 9. Map view for the relative origin time in each cluster. For every cluster, the event origin times are linearly normalized based on thefirst and last event origin times. All the basic symbols are equal to those in Figure 1. Clusters with unilateral- and bilateral-propagating migra-tion patterns are indicated as light and dark circles, respectively. The four sites (a–d) are denoted for the description of Figure 8.



better understand the general characteristics, including thisinteresting observation, of microseismic events.

ACKNOWLEDGMENTS

This work was supported by the Korea Institute of EnergyTechnology Evaluation and Planning (KETEP) and the Ministryof Trade, Industry & Energy (MOTIE) of the Republic of Korea(No. 20132510100060).

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