(JUICE): Crustal seismicity beneath the Japanese ...

Tectonophysics 702 (2017) 19–28

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Japan unified hIgh-resolution relocated catalog for earthquakes (JUICE): Crustal seismicity beneath the Japanese Islands Tomoko E. Yano ⁎, Tetsuya Takeda, Makoto Matsubara, Katsuhiko Shiomi National Research Institute for Earth Science and Disaster Resilience, 3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan

a r t i c l e

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Article history: Received 31 March 2016 Received in revised form 18 January 2017 Accepted 20 February 2017 Available online 24 February 2017 Keywords: Seismicity Active fault Hypocenter relocation Seismic hazard Seismogenic depth

a b s t r a c t We have generated a high-resolution catalog called the “Japan Unified hIgh-resolution relocated Catalog for Earthquakes” (JUICE), which can be used to evaluate the geometry and seismogenic depth of active faults in Japan. We relocated N 1.1 million hypocenters from the NIED Hi-net catalog for events which occurred between January 2001 and December 2012, to a depth of 40 km. We apply a relative hypocenter determination method to the data in each grid square, in which entire Japan is divided into 1257 grid squares to parallelize the relocation procedure. We used a double-difference method, incorporating cross-correlating differential times as well as catalog differential times. This allows us to resolve, in detail, a seismicity distribution for the entire Japanese Islands. We estimated location uncertainty by a statistical resampling method, using Jackknife samples, and show that the uncertainty can be within 0.37 km in the horizontal and 0.85 km in the vertical direction with a 90% confidence interval for areas with good station coverage. Our seismogenic depth estimate agrees with the lower limit of the hypocenter distribution for a recent earthquake on the Kamishiro fault (2014, Mj 6.7), which suggests that the new catalog should be useful for estimating the size of future earthquakes for inland active faults. © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

1. Introduction After the 1995 Hyogoken-Nanbu (Kobe) earthquake, the Headquarters for Earthquake Research Promotion (HERP) was established by the Japanese Government. Among other achievements, HERP developed a list of the principal 100 active faults in Japan and documented their seismic activity using information available. However, since the HERP list was compiled, some disastrous inland earthquakes (M N 6) occurred at faults that were not listed. Thus, it became imperative to develop a more complete accounting of active faults, both known and unknown. As a supplement to existing geological studies, a high resolution earthquake catalog can provide information about the location, seismogenic depth, and geometry of active faults through a community-based three-dimensional fault model (CFM), like that made for southern California by scientists of the Southern California Earthquake Center (SCEC) (Plesch et al., 2007). This CFM is constrained by a variety of data including surface traces (e.g., Jennings, 1994), earthquake focal mechanisms and hypocentral distributions (Hauksson, 2000; Hauksson and Shearer, 2005; Richards-Dinger and Shearer, 2000; Shearer et al., 2005), well penetrations, seismic reflection profiles, and geological cross sections. A unified hypocenter catalog for Japan has already been made available to the public by the Japan Meteorological Agency (JMA). In this ⁎ Corresponding author. E-mail address: [email protected] (T.E. Yano).

catalog, hypocenters are determined using high-density seismic data collected by Japanese institutes and universities. However, the JMA hypocenter locations are based upon P- and S-wave arrival picks, which give insufficient resolution for detailed study of active faults at the local scale. The catalog of relocated hypocenters provides a more accurate understanding of the source fault geometry for specific events, such as the 2004 Niigata Chuetsu earthquake (e.g. Aoki et al., 2005; Shibutani et al., 2005). Waldhauser and Ellsworth (2000) developed a Double-difference (DD) location method for relocating hypocenters with greater precision. This method is a relative location method which optimizes the residuals of travel time differences to the common observation point for pair of events. With a relative timing precision of about 1 ms by accounting for travel time data with highly precision by waveform cross-correlations, it can improve the relative location between earthquakes with errors of only meters to a few tens of meters. Since 2000, the National Research Institute for Earth Science and Disaster Resilience (NIED) has deployed and been operating a high-sensitivity seismograph network (Hi-net). Seismometers are located in quiet locations nationwide, at the bottom of boreholes with depths of over 100 m, in order to detect non-human-sensitive weak ground shaking by micro-earthquakes (Okada et al., 2004). The Hi-net includes about 800 stations laid across a 20 km mesh grid throughout Japan. The network can detect events as small as M0.9, and has been instrumental in the discovery of non-volcanic tremor (Obara and Ito, 2005), lithospheric slab segmentation, dehydration, and anisotropic rocks (Shiomi and

http://dx.doi.org/10.1016/j.tecto.2017.02.017 0040-1951/© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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T.E. Yano et al. / Tectonophysics 702 (2017) 19–28

Park, 2008), detailed spatial distribution of the centroid moment tensors (Asano et al., 2011), and underground seismic velocity structure (Matsubara and Obara, 2011), and in the development of an earthquake early warning system (Horiuchi, et al., 2005; Tsukada, et al., 2004). The accumulation of over a decade of high quality seismic data makes it desirable to revisit existing hypocenter catalog with the goal of developing a higher-resolution catalog. Our purpose in this study has been to construct a relocated high-resolution earthquake catalog (hereinafter referred to as “JUICE catalog”) to aid in the investigation of earthquake phenomena and active faults and improve assessment of regional earthquake hazard. Here, we explain the methods we have adopted, and the datasets we have used. We also investigate location error in order to evaluate (1) the robustness of the catalog, (2) the impact of the relocation gridding method on our results, and (3) the possibility of detecting unknown faults. In an effort to assess the usefulness of the new catalog for determining the seismogenic depth for an active fault, we look at its application to the case of the 2014 Kamishiro Earthquake (Mj 6.7). 2. Data In October 2000, the NIED Hi-net network started operation (Okada et al., 2004). A Hi-net routine catalog is generated from the picked data from Hi-net stations, combined with data from networks operated by other organizations in Japan, for a total of 1400 stations (Fig. 1). We obtained Hi-net event catalog data for the period January 1, 2001 to December 31, 2012 for events of magnitude M0–M6.5, from depths shallower than 40 km. Relocation is a three-step process (Fig. 2). Step 1 is to obtain two datasets. The first dataset contains routinely-determined Hi-net event

Fig. 2. Flowchart for generation of JUICE catalog. Step 1: we obtain routinely-determined Hi-net event catalog and corresponding waveforms. Step 2: we prepare two data sets: (1) differential travel time data produced from routinely-picked arrival times (CT data), and (2) differential travel time data measured by waveform cross-correlation (CC data). Step 3: we apply the hypoDD code (Waldhauser and Ellsworth, 2000) to the data sets from Step 2, evaluate the results, and compile the results into the relocated event catalog.

catalog data, including hypocentral parameters for earthquakes (e.g. hypocenter, magnitude, phase arrival picks and maximum amplitudes) from all stations (Fig. 1). The second dataset contains event waveforms observed at seismic stations from the Hi-net network and from networks operated by other Japanese institutes and universities. Step 2 is to determine differential travel times observed at a common station for different earthquake pairs. The catalog (hereinafter referred to as “CT”) data are produced from picked phase arrival times. The cross-correlation (hereinafter referred to as “CC”) data are measured by crosscorrelating the event waveform data at each common station. Finally, Step 3 is to apply these CT data and CC data to the hypoDD algorithm (Waldhauser & Ellsworth, 2000). More details for Steps 1 and 2 are explained later in this section. 2.1. Differential travel times (CT data and CC data) Fig. 1. Distribution of seismic stations from which we obtained waveform data for this study. Different networks and their operating agencies, Japan Meteorological Agency (JMA), Disaster Prevention Research Institute (DPRI), Earthquake Research Institute (ERI), National Institute of Advanced Industrial Science and Technology (AIST), Nagoya university (NAGOYA), Tohoku university (TOHOKU), Hirosaki university (HIROSAKI), Kagoshima university (KAGOSHIMA), Aomori prefecture (AOMORI), Hokkaido university (HOKKAIDO), Kochi university (KOCHI), Kyushu university (KYUSHU), and Tokyo metropolitan (TOKYO), identified by colour. Solid-square markers indicate the stations from which we obtained both phase-pick data (CT) and waveform crosscorrelation data: others provided only travel time information picked by NIED Hi-net. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

The reliability of an earthquake catalog increases when the earthquakes are relocated using as many data points as possible. For hypocenter relocations for our JUICE catalog, we identified routinelylocated events within each grid square whose locations were determined from eight or more phase-picked data from surrounding stations. The station search area for each grid square was larger than the grid square itself. We explain our method for defining the station search area for different grid squares in Section 3.2. Both CT and CC differential travel times, from a total of 1,175,672 events, were collected.


The travel time data were transformed into differential travel times for pairs of neighboring earthquakes. During this process, parameters could be customized to optimize the strength of connectedness between events, for example we adopted a maximum hypocentral separation of three km for CT data and 10 km for CC data. We used the same parameters consistently for grid areas throughout Japan. For CC data, we applied a Butterworth-type band pass filter to each waveform, specifying a low frequency cutoff of 3 Hz and a high frequency cutoff of 20 Hz, with a filter order of two and zero-phase filtering. Time-windows for the waveform data were 0.29 s before to 1.20 s after the picked onset containing the P-wave or S-wave train. In order to get robust travel times, we only included CC data for which the correlation coefficient N 0.8. 3. Method The purpose of the DD method is to improve a relative location within a cluster or among nearby events. This method aims to reduce the effect of unknown velocity heterogeneities along the source-receiver paths. We employ the hypocenter determination method called hypoDD (Waldhauser and Ellsworth, 2000) adopting the DD algorithm that refines the event locations by reducing the differential travel-time residual between pairs of events recorded at a common station. The hypoDD method is a well-tested code. Therefore, we apply our differential travel time data, including the waveform correlation data, and also ordinal phase pick data to create our high-resolution earthquake catalog. 3.1. Location error consideration We attempt to reduce location errors due to the time picking errors and heterogeneity of velocity models by (i) choosing reliable event locations that are determined by eight or more phase pick data, and (ii) in addition to the phase pick data, introducing the travel-time differences estimated by cross-correlation of waveforms in which their correlation coefficient are 0.8 or larger, and (iii) acquiring data from multiple institutions to improve the station coverage, and (iv) limiting the hypocentral separation to be three km for CT data and 10 km for CC data to make less affection due to heterogeneity of velocity structure since it is closer to the assumption in which the ray paths between the source region and a common station are similar along almost the entire ray path (Fre'chet, 1985; Got et al., 1994). 3.2. Grid spaces and partition We need to define a grid space for parallelizing the relocation procedure to meet acceptable computation efficiency since our study range and size of data are too large to process at once. Any relative location method may need to sacrifice connections between some nearby events if a single cluster is separated as two clusters due to gridding. We investigate that how the gridding impacts on the result. We show in the later section that the effects by gridding are relatively small. However, user should pay attention to the edge of grid if there are any sharp offset of seismicity. We adopt the method of partitioning into smaller grid area to parallelize the relocation computation since computing over 1,000,000 events can be overburdened. First, area over Japan Island is parameterized by a size of grid as latitude of 1° x longitude of 1°. Then, the grid space is repeatedly divided into four smaller rectangles until the number of pre-relocated events in the rectangle box falls ≤ 3000. We should note that this reference number of events contains a complete collection of events before selecting for reliable event locations. Finally, we generate 1257 rectangular grid spaces over Japan Island (Fig. 3). Each grid rectangle does not overlap any area of its neighboring rectangle(s). A discussion on at the edges of grid space is in Section 5.1.

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The station range is set to be a large enough to cover its whole grid space. For most cases, the station range is defined by its grid dimension extended by latitude of 0.5° and longitude of 0.5°. However for some cases such as events are being far from the land stations, station range is needed to extend to even greater area in order to obtain a sufficient amount of data for relocation procedure. 3.3. Velocity structure To generate the relocation catalog for the first version, we use a 1D velocity model as shown in Fig. 4 (blue solid line) simplified from the model used in determination of hypocenter within the NIED Hi-net (Ukawa et al., 1984) in Fig. 4 (red dotted line). The velocity model by Ukawa et al. (1984) is one of velocity models representing the wide range from Kanto to Tokai regions. Despite the region has complicated structures, Ukawa et al. (1984) concluded that the upper crust has 6.0 km/s, lower crust has 6.5–7.0 km/s, and depth of Moho is 30–35 km with 7.8–8.0 km/s from previous studies (Aoki et al., 1972; Horie and Shibuya, 1979; Ikami, 1978; Mikumo, 1966; Ukawa and Fukao, 1982). We simplify Ukawa's model while keeping its key features to make the computational performance to be improved. As for the first version of JUICE catalog, we apply this simplified 1D velocity model while we are planning to use more realistic velocity model in later version of JUICE catalog. 3.4. 3.4. Weighting and re-weighting A priori weight is assigned to CT data relative to the pick quality factor as 1.0, 0.75, 0.25, 0.1, 0.05, and 0.01 according to the Hi-net picking quality. A priori weight is assigned the CC data relative to correlation coefficient. In addition to priori weights, we also applied a weighting/ reweighting scheme (parameters given in Table 1) after Waldhauser and Ellsworth (2000). This scheme assures that CT data and CC data are both used to constrain a hypocenter location, at two different scales. The CT data mainly constrain the relative position of events on a large scale, while CC data, which is of high accuracy, mainly constrain the locations of nearby events on a small scale. We performed a total of 30 iterations for each grid space, consisting of five sets of inversions, each containing six iterations. The first two sets (labeled as “6th” and “12th”) were mainly weighted using CT data. The rest (labeled as “18th”, “24th”, and “30th”) were mainly weighted using CC data. In this section, we show how the weighting and re-weighting scheme is successively applied in our relocation procedure. We chose a small region with good station coverage in order to obtain sufficient data to demonstrate the impact of the weighting/reweighting scheme the seismicity distribution pattern. In Fig. 5, the original catalog (labeled “LOC”) for this small region can be seen as a broad linear pattern with width N 2 km from northwest to southeast. At the end of the first inversion, 6th iteration, the broad linear seismicity distribution has separated into two distinct groups, northwest (black dots) and southeast (red dots). The width of both groups combined, as shown by the distribution of black and red hypocenter locations, becomes narrows to about 1.5 km, as shown in cross-section. For the second inversion, we weighted using CT data like in the first inversion, but we underweighted outliers in two ways: (1) by introducing misfit weights to account for the misfit of the data from the previous iteration and (2) by introducing distance weights to account for the offset distance between events. At the end of 12th iteration, the RMS of CT data, 80.9 ms, reduced for 18.4 ms from the RMS of 6th iteration, 99.3 ms. Otherwise, the width of the seismicity distribution is similar to the result after 6th iterations. For the third, fourth, fifth, and sixth inversions (labeled “18th”, “24th”, and “30th” iterations, respectively), we weight using CC data, focusing in particular on event pairs with separations b 2 km. The results of the 18th, 24th and 30th iterations preserved the general seismicity pattern of the previous iterations (Fig. 5), but the

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Fig. 3. Map of grid employed for the JUICE catalog (rectangles), original hypocenter locations from Hi-net event catalog (gray dots), and relocation results (red dots). Total number of grid squares is 1257 and that of relocated hypocenters is 1,091,636. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

pattern became finer and more detailed. For example, the red hypocenter group of iterations 1 and 2 further separated into two smaller linear distributions (Fig. 5, 18th iteration, black circle). We continued our inversions to the 30th iteration inversion to eventually constrain event pairs with separations smaller than 500 m.

4. Result The JUICE catalog (Fig. 3), contains hypocenters (M ≦ 6.5) relocated by a DD algorithm (Waldhauser & Ellsworth, 2000) from 1,091,636 events of M ≦ 6.5 that occurred between January 2001 and December 2012, at depths shallower than 40 km. We successfully restored 93% of events recorded at eight or more stations. Here, we show an example of how the new locations are constrained by CT data and CC and tested for robustness by statistically resampling using a jackknife sample test. 4.1. Error analysis

Fig. 4. P-wave velocity structure used in this study. Red broken line shows the model by Ukawa et al., 1984. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

In order to estimate the robustness of the new locations and to estimate uncertainties, we performed a “delete-half” jackknife resampling test (Tichelaar and Ruff, 1989). Jackknife resampling is a non-parametric method for uncertainty estimation. A total of 100 jackknife resample sets are randomly extracted from half of the original differential travel time data. A relocation process is performed on each set of jackknife samples. Since there are 100 jackknife resampling sets, this step is repeated 100 times to generate 100 possible solutions. Finally, for each event, we calculated the standard deviation of the distance between the original relocated hypocenter location and about 100 sets of relocated hypocenter location due to jackknife sample data. We calculated a standard deviation for each event in two different regions: Region 1 where station distribution is excellent, and Region 2 where station distribution is relatively poor and sparse. The spatial distribution


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Table 1 Parameters for weighting scheme employed for JUICE catalog. Set Iterations Cross correlation data

1 2 3 4 5

1–6 7–12 13–18 19–24 25–30

Catalog data

A priori, P-wave WTCCP

A priori, S-wave WTCCS

Misfit weight (residual cutoff. Factor times SD) WRCC

Dist. Weight (separation in km) WDCC

A priori, P-wave WTCTP

A priori, S-wave WTCTS

Misfit weight (residual cutoff. Factor times SD) WRCT

Dist. Weigh (separation in km) WDCT

0.01 0.01 1.0 1.0 1.0

0.01 0.01 0.5 0.5 0.5

−9 −9 −9 6 6

−9 −9 2 2 0.5

1.00 1.00 0.01 0.01 0.01

0.3 0.5 0.005 0.005 0.005

−9 10 8 6 6

−9 10 5 4 4

of standard deviations for both regions is plotted in Fig. 6. The shape of the distribution is shown in the histogram plots of Fig. 7, along with the 90% confidence limits. Station distribution affects the robustness of results. As expected, increasing distance between the epicenter and the recording station is accompanied by an increase in the standard deviation for the relocated hypocenters for each event, as seen in Fig. 6. For Region 1, the standard deviations are evenly distributed, but for Region 2, the standard deviations become larger with increasing distance from the recording stations on land or offshore. Station distribution also affects the tightness of the 90% confidence limits for the standard deviations of the hypocenter relocations for

each event. In Fig. 7, the 90% confidence limits for the horizontal and vertical components of relocation are 0.37 km and 0.83 km in Region 1, and 1.21 km and 1.58 km in Region 2. Hence, the 90% confidence limits are smaller in Region 1 than those in Region 2 for both horizontal and vertical components. It is possible that 100 jackknife resample sets are not a sufficient number to represent all possible combinations of data. Moreover, the possibility of a complex velocity structure is not addressed in the resampling. Nevertheless, we feel confident about the robustness of our relocations, particularly for those on-land events occurring in areas with good station coverage. In addition, the DD method itself is can tolerate complex velocity structures to some extent.

Fig. 5. (a) Location map for seismic stations. (b) Original hypocenter locations (labeled as “LOC”), and (c–g) relocated results for 5 inversions, labeled by final iteration number in inversion set: 6th, 12th, 18th, 24th, and 30th iterations. The symbols in the station map (a) are in the same as in Fig. 1. The results of each inversion are displayed in two panels: (1) top panel shows the spatial distribution of relocated hypocenters for 5 inversions, and (2) bottom panel shows the depth distribution along cross-section line A – A′ with width of 7 km, indicating as a thin black line in the panel (b)(1). Black circle in (e) 18th iteration marks first appearance of branched seismicity distribution pattern (Section 3.4). Red dots are hypocenter and its pattern is described in detail in the text. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 6. Results of Jackknife test for two different regions. Station distribution is shown in the left panel; small rectangles indicate the location of stations used to relocate hypocenters for each region (see Fig. 1). Region 1(top panels) is an excellent example of hypocenter distribution in an area with good station coverage. Region 2 (bottom panels) is an example in an area where station coverage is relatively poor. Standard deviations of different components, horizontal and vertical are shown from left and right. Colour key indicates values for standard deviations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

4.2. Fitness to the data Our relocation after 30 iterations improved the fitness to the data. To evaluate fitness, we compared the standard deviation for the root mean square (RMS) of the travel time residuals for the original Hi-net hypocenter catalog to the RMS of the travel time residuals for the relocated hypocenter (JUICE) catalog, after the 30th iteration using the following method. First, we collected initial and final RMS residuals for CT data from all of the grid squares (n = 1257). We assumed that these RMS residuals follow a folded normal distribution (Fig. 7c) and we calculated a standard deviation for each of the two sets. The standard deviation for the initial RMS was 0.087 ms and for the final RMS was 0.058 ms, showing that the final relocation was more compact about 0 ms, hence a better fit to the data.

5. Discussion

In order to test the effect of grid placement on the resulting seismicity distribution, we obtained relocated hypocenter locations for two cases: (1) events relocated in a single grid space (N = 1224), and (2) events relocated in two grid spaces (N = 1229) within the same area to see if a continuous linear feature spanning adjacent grid squares is affected by border placement (Fig. 8). The locations for events within 0.2 degrees of the new grid border generated in case (2) (Fig. 8, blue rectangle) differ from those generated in case (1) with an average of 0.1 km horizontally and 0.2 km vertically. Since these values are smaller than the uncertainties estimated by the jackknife test in Section 4.2, we conclude that the relocation results are not significantly influenced by the grid placement. This is most likely due to the overlap of station ranges among neighboring grid squares, such that data from the same station may constrain relocations in more than one grid square. Based on the results of our border test, we were comfortable with a simpler nonoverlapping grid approach for our final solutions to simply explain the data. 5.2. Linear seismicity in Tottori area

5.1. Grid border Since each grid rectangle does not overlap with any area of its neighboring grid rectangle(s), it is important to investigate if the border of grid can strongly impact on shapes of clusters. The grid spaces are systematically defined as explained in the Section 2.2. One way to avoid a possible offset around the grid borders, Waldhauser and Schaff (2008) made their neighboring boxes to overlap by 50%. And their final solutions are carefully represented by taking a linear weighted location average when combining into a catalog. Since our grid areas do not overlap to each other, the shape of clusters at the grid boarder may remind an ambiguous due to its connectedness of event pairs and its center of mass can be totally different from neighboring grids. Thus, it is important for us to notify this issue particularly along the vicinity of the grid boarders.

The San'in region, including Tottori and Shimane prefectures, is known to have distinct seismic active areas and seismic gaps (Fig. 9). Seismic activity trends NW-SE particularly in the area of the 2000 Western Tottori earthquake (M7.3). A conjugate trend running NE-SW is also recognized around the area of the 1943 Tottori earthquake (M7.2). Both trend directions were identified by Takada et al. (2003) who investigated active faults and their lineaments, in the context of their topographical, geological, seismological, and tectonic settings. The NE-SW direction is identified for 50% of their faults and lineaments, while the conjugate direction (NW-SE) is identified for another 30%. Seismic gaps like that around Daisen volcano are also apparent (Fig. 9, gray circle). Our JUICE catalog of relocated hypocenters is particularly useful in identifying active faults in an area like Tottori. GEONET data shows an


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Fig. 7. (a) and (b): Histograms of standard deviations (in blue for jackknife test in Regions 1 and 2 (Fig. 6)). Horizontal and vertical components are shown on the left and right panels, respectively. The 90% confidence limit is indicated by a vertical red line, with values of 0.37 km horizontal and 0.83 km vertical for Region 1, and 1.21 km horizontal and 1.58 km vertical for the Region 2. (c): Histograms of the root mean square (rms) of the travel time residuals for the original hypocenter catalog (initial rms) and for the JUICE hypocenter catalog (final rms) shown in top and bottom panels. Red vertical lines mark standard deviations of the folded normal distribution. The initial and final standard deviations are 0.087 ms and 0.058 ms. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

area (Fig. 9, red circle) where right-lateral strike-slip movement of 2 mm/year is observed (Nishimura, 2014), which is one of the largest displacement rates in inland Japan. No active fault has been mapped in that location, implying that stress is accumulating on an unidentified fault. The orientation of the stress accumulation due to the displacement is consistent with a linear earthquake distribution trending NE-SW. Our JUICE catalog reveals a major NE-SW trending lineament that comprises a series of small NW-SE lineaments many of which occur in areas with no known faults. We believe that a detailed and accurate map of seismic activity, like our JUICE catalog, will prove indispensable to future active fault research in areas like Tottori, where conjugate structural trends (NE-SW, NW-SE) coexist, stress accumulation is observed in GEONET data but not associated with any surface feature, and seismicity shows linear patterns where no active faults have been mapped.

5.3. D95 (2014 Kamishiro Fault earthquake area) Given a well-constrained hypocenter depth distribution, researchers has suggested that the base of the seismogenic zone can be estimated as lying near the deepest and shallowest of the observed hypocenter depths. The depth where 300 °C isotherm is, corresponding to the onset of crystal plasticity of quartz, interpreted to control the seismic/ aseismic transition zone (Scholz, 1998). The heat flow or geothermal gradient and D90, the depth above which 90% of earthquakes occur, correlated with each other. Thus, the temperatures for D90 range could be evaluated to be between 250 °C and 450 °C, which falls within the range for defining the seismogenic zone (e.g., Fagereng and Toy, 2011). In this study we estimate D95, the depth above which 95% of earthquakes occur, because more precise and reliable catalog is now available by the JUICE catalog. In order to calculate the bottom of the seismogenic

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Fig. 8. Comparison of the relocation result processed in a single grid area to the relocation result processed in two separate grid areas; a. Station distribution used for this example (colour and notations for stations are the same as Fig. 1); b. relocation result processed in a single grid area (left) and its depth distribution along the cross-section line A-A′ shown in map a.; c. Relocation results independently processed in two divided grid areas and its depth distribution. The green lines are the known active faults (the Research Group for Active Faults of Japan, 1991). The blue rectangle indicates the area within ±0.2° of the grid boundary. The average difference in depth for the relocated hypocenters between the two test regions is 0.1 km vertical and 0.2 km horizontal components. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

zone from the JUICE catalog, we define a grid size of 0.1° × 0.1° (longitude x latitude), which is greater than the uncertainty of the catalog, and establish a set of depth columns extending down from each grid square. We then calculate D95 by progressively shifting the grid by 0.02° northwards and then eastwards, and determining an average D95 for each 0.1° by 0.1° column, using only columns which contain at least 50 events. The JUICE catalog contains events with a depth down to 40 km. This means that hypocenters occurring in/along the subducting Pacific and Philippine Sea plates will be partially included in the catalog. In fact, the D95 results for the Pacific Ocean side of Japan tend to be unreasonably deep due to these hypocenters occurring in relate to the subduction plates are included in the catalog. Otherwise,

Fig. 9. Enlarged view of the San'in region including Tottori and Shimane prefectures. The relocated hypocenter distribution (depth 8–10 km) from the JUICE catalog (black dots) and the location of known active faults (green line, the Research Group for Active Faults of Japan, 1991) are shown. Dark red circle marks the approximate location where Nishimura (2014) observed a crustal movement of 2 mm/year eastward from GEONET data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

the general pattern in SW Japan is consistent with that of previous D90 studies (e.g., Omuralieva et al., 2012; Tanaka, 2004). The Kamishiro fault has been recognized as the northern portion of a major tectonic line, the Itoigawa-Shizuoka Tectonic Line, which lies in a structurally complex area, and is bounded by a localized zone of higher heat flow, higher geothermal gradient (Tanaka, 2004), and higher temperature at D90 depth (Cho and Kuwahara, 2013). D90 here is at least five km shallower than in surrounding areas (Cho and Kuwahara, 2013; Omuralieva et al., 2012; Tanaka, 2004). The 2014 Kamishiro Fault earthquake (Mj6.7) resulted from activation of the northern part of the Kamishiro fault and its northern extension. An important event, the 2014 earthquake was the first destructive inland event on one of the known inland faults, which has been evaluated by HERP. In 1996, HERP estimated the seismogenic depth for the northern portion of Itoigawa-Shizuoka fault zone to be approximately from 0 to 10 km (Headquarters for Earthquake Research Promotion, 2015, Headquarters for Earthquake Research Promotion, 1996). The D95 inferred by the JUICE catalog is between 15 km and 18 km (Fig. 10) which is slightly deeper than the initial estimate of HERP. D95 varies along the fault from about 15 km in the hypocentral region to around 18 km elsewhere. Since the JUICE catalog is not conflated by inter/ intra plate type events in this region, D95 of this region estimated by the inland crustal earthquakes. The source inversion using near source strong motion data indicates that rupture travels down to 12 km in depth at most with the maximum slip to be 2.8 m (Japan Meteorogical Agency, 2015). The resulting source inversion indicates that rupture traveled down to a maximum of 12 km depth with a maximum slip of 2.8 m. After-shock distribution penetrated to a depth of about 15 km in the hypocentral region. Since neither initial rupture nor aftershock distribution extends below the depth of the JUICE-determined D95, we believe that the bottom of the seismogenic zone is successfully estimated by our JUICE catalog. This suggests that the JUICE relocated hypocenters catalog can be a useful tool for understanding the location and geometry of active faults, and by extension, for estimating the depth and magnitude of future earthquakes for earthquake hazard assessment.


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Fig. 10. (a) Relocated hypocenters including the main shock of the November 22, 2014 Kamishiro Fault earthquake in Nagano, Japan and before/after events of M ≥ 1.0 for a 2-month period from October 1 to November 30, 2014. Active faults shown in pink. (b) D95 spatial distribution inferred by the JUICE catalog is colour coded by its depth. The surface positions of relocated hypocenters from the 2-month Kamishiro earthquake window (black) and from general background seismicity (the JUICE catalog, gray) are superimposed on the D95 depth distribution. (c) Cross sectional plot of seismogenic depth estimated by HERP (blue line) and D95 depth by JUICE (red line) along line A – A′, an approximation of the Kamishiro fault. Gray dots indicate background seismicity from the JUICE catalog; black dots are relocated hypocenters from the 2-month Kamishiro earthquake window, same as those in (a). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

6. Conclusions

Acknowledgements

We had created an initial version of the JUICE hypocenter catalog for seismic events occurring between January 1, 2001 and December 31, 2012, at depths shallower than 40 km, with magnitudes ≤ M6.5, in and around the Japan Islands. Events were relocated using cross-correlation data and a double-difference method for high-resolution earthquake location. We collected P and S phase arrival times and event waveforms from the NIED Hi-net hypocenter catalog over the interval January 2001 to December 2012, for a total of 1,175,672 events before relocation, of which 1,091,636 have been relocated. In order to make the relocation procedure efficient, we adopted 1257 rectangle grids to parallelize the computation. Since our grid squares did not overlap, we analyzed the effect of grid placement on hypocenter relocations, in particular, near the boundaries of our grid squares. We examined any positional changes for relocated hypocenters near the edges of the grid squares by comparing two cases of single and two independent rectangle grids in the same area. In other words, cases where the relocations either included or did not include grid square boundaries. Our analysis implied that hypocenter locations near the edge of a grid square are not significantly affected by the boundary since the average change in the position of relocated hypocenters between the two cases is less than the uncertainty error for the relocation procedure. Our new hypocenter catalog exhibits tighter and finer clusters, therefore, linear seismicity patterns may be more easily discriminated around known active faults. Using the new JUICE catalog, we estimated the seismogenic depth for the Kamishiro fault. In case of the 2014 Kamishiro Fault earthquake, co-seismic slip and aftershock distribution for a two month period following the event was consistent with the estimated seismogenic depth from the JUICE catalog.

We wish to thank all of the data providers who made this study possible, and in particular Japan Meteorological Agency (JMA), Disaster Prevention Research Institute (DPRI), Earthquake Research Institute (ERI), National Institute of Advanced Industrial Science and Technology (AIST), Nagoya university (NAGOYA), Tohoku university (TOHOKU), Hirosaki university (HIROSAKI), Kagoshima university (KAGOSHIMA), Aomori prefecture (AOMORI), Hokkaido university (HOKKAIDO), Kochi university (KOCHI), Kyushu university (KYUSHU), and Tokyo metropolitan (TOKYO). Some Figures were drawn with Generic Mapping Tools (Wessel and Smith, 1998). We thank Dr. Anne Van Horne for language editing and anonymous reviewers for providing helpful comments and improve this manuscript.

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