Seismic Anisotropy of the Upper Crust Around Mount Fuji, Japan, J

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Journal of Geophysical Research: Solid Earth

RESEARCH ARTICLE Seismic anisotropy of the upper crust 10.1002/2014JB011554 around Mount Fuji, Japan

Special Section: Kohtaro R. Araragi1, Martha K. Savage2, Takao Ohminato1, and Yosuke Aoki1 Stress, Strain and Mass Changes at Volcanoes 1Earthquake Research Institute, University of Tokyo, Tokyo, Japan, 2Institute of Geophysics, Victoria University of Wellington, Wellington, New Zealand

Key Points: • Fast directions around Mount Fuji have two distinct patterns Abstract We measure shear wave splitting and estimate stresses of Mount Fuji, Japan, to interpret anisotropic • The gravitational effects have structure and its implication for geologic processes using local crustal earthquake seismograms from 2009 to influence on the seismic anisotropy 2012. The measured fast polarizations have preferred orientations at each station with mean values of delay of Mount Fuji < < • Seismic anisotropy of Mount Fuji is at times 0.15 s. We infer that the anisotropic structure is located at shallow depths ( 4 km) from a lack of focal shallow depths depth dependence of delay times. The fast polarization directions for stations within approximately 15 km of the summit of Mount Fuji show a radial pattern pointing toward the summit, while stations far from the Supporting Information: summit exhibit fast polarization directions approximately parallel to the NW-SE compressional regional stress • Figures S1–S10 field. We infer that the symmetrical seismic anisotropic structure around the summit and the fast directions parallel to the regional compression observed at distant stations from the summit reflect interactions of the Correspondence to: gravitational stresses and regional tectonics. Assuming stress control only, the spatial pattern of anisotropy can K. R. Araragi, [email protected] be fit by the interaction of gravitational with regional stresses if the regional maximum horizontal stress is 1.02 times lithostatic pressure (51.9 MPa at a depth of 2.0 km). If structural anisotropy also contributes to the radial pattern, then the regional maximum horizontal stress magnitude is not constrained. Citation: Araragi, K. R., M. K. Savage, T. Ohminato, and Y. Aoki (2015), Seismic anisotropy of the upper crust around Mount Fuji, Japan, J. Geophys. Res. Solid Earth, 120, 1. Introduction 2739–2751, doi:10.1002/2014JB011554. Constraining subsurface structure by geophysical approaches is critical for understanding geologic processes.

Received 21 AUG 2014 Measurements of seismic anisotropy have recently been used to interpret the subsurface structure and its Accepted 3 MAR 2015 relationship to the stress ﬁeld. Shear wave splitting (splitting), in which a linearly polarized S wave is split into Accepted article online 9 MAR 2015 two S waves with mutually perpendicular orientations in anisotropic media, is a good indicator of seismic Published online 22 APR 2015 anisotropy resulting from crack opening and fractures in the upper crust [e.g., Crampin, 1999]. In volcanic areas, temporal changes of seismic anisotropy have often been observed [e.g., Gerst and Savage, 2004; Bianco et al., 2006]. These observations are often interpreted as stress changes due to magma intrusion at shallow depths. Consistency of ground deformation inferred from GPS measurements and delay time changes was observed [e.g., Savage et al., 2010a]. Splitting measurements can also detect subtle changes such as those caused by tidal effects [e.g., Teanby et al., 2004]. Temporal changes of hydrothermal activity and gas release are also considered as a cause of splitting changes in volcanic areas [e.g., Unglert et al., 2011; Johnson and Poland, 2013]. Based on these observations, splitting is now regarded as an indicator of various scales of stress-related geologic events in the shallow crust. Splitting measurements and other geophysical data are often simultaneously used to better constrain subsurface processes. There are two origins of seismic anisotropy: intrinsic or structural anisotropy and stress-induced anisotropy. In some cases, structural anisotropy and stress-induced anisotropy can be differentiated by comparing fast polarization directions of splitting, maximum horizontal compressive stress, and fault strikes [e.g., Boness and Zoback, 2006; Johnson et al., 2010; Vavryčuk, 1993; Vavryčuk and Boušková, 2008; Zinke and Zoback, 2000]. When seismic anisotropy is caused by strata, relatively large parallel fractures, or alignment of mineral fabric, we can consider this to be intrinsic anisotropy or structural anisotropy. This type of anisotropy is insensitive to stress changes. Stress-induced anisotropy originates from opening and closure of preexisting microcracks and thus is distinct in that it is sensitive to stress changes. Analysis of regional stresses and the distribution of anisotropy around a volcanic area, coupled with fault plane solution analyses, succeeded in explaining precursory stress change caused by diking [e.g., Roman et al., 2011]. Changes in delay times or ﬂips of 90° of fast directions can occur for various reasons such as increasing crack density and increase of pore pressure after a main shock near the target area [e.g., Saiga et al., 2003]. However, in addition to these geologic factors, the path effect caused by variation of hypocenters or characteristics of structural anisotropy sometimes

Figure 1. (a) Tectonic setting around Mount Fuji (modiﬁed from Bird [2003]). The thick black lines indicate plate boundaries. PHS, AMU, OKH, and PAC indicate the Philippine Sea Plate, the Amurian Plate, the Okhotsk Plate, and the Paciﬁc Plate, respectively. The location of Mount Fuji is shown by a triangle, and the hypocenter of the 2011 Tohoku-Oki earthquake is shown by a star. A closed circle shows the location of a dike intrusion between Miyake-jima and Kozu-shima in 2000. (b) Topography of Mount Fuji. The solid circles are the seismic stations installed by JMA, NIED, or ERI. The open rectangles are the Hi-net stations installed by NIED.

result in apparent temporal changes [e.g., Aster et al., 1990]. For example, a 90° flip of fast polarization direction can occur by raypaths changing near inclined faults [e.g., Elkibbi et al., 2005] or through varying incidence angles with respect to crack planes [e.g., Savage et al., 2010a]. Mount Fuji, a stratovolcano with a symmetrical shape, is located on the triple junction of the Amurian, Okhotsk, and Philippine Sea plates (Figure 1). The locations of flank eruption vents and the orientation of dikes [Nakamura, 1977] and focal mechanisms of local earthquakes (Figure 2) show that the principal compressional axis around Mount Fuji is oriented NW-SE. While there are few fumaroles or geothermal manifestations that suggest magmatic activity, a magnetotelluric survey indicates that the volcano still has an active hydrothermal system beneath the summit crater [Aizawa et al., 2005]. Mount Fuji is geologically unique in its size and rock chemistry. While Mount Fuji has been active only for 100,000 years [Tsuya, 1968], it forms a much larger edifice than other volcanoes in the Japanese islands, indicating a high effusion rate. Also, the edifice of Mount Fuji is composed mostly of basaltic rocks, in contrast to other arc volcanoes whose rocks are generally more felsic [Tsuya, 1971]. Seismicity and magmatism are sensitive to stress changes in the area [e.g., Ukawa, 2005; Nakamichi et al., 2004]. At Mount Fuji, deep low-frequency earthquakes (LFEs) occur beneath the northeast flank at depths around 10 to 15 km. One of the possible source models for LFEs is the contribution of magmatic fluid, deduced from a large compensated linear vector dipole and volumetric components of a large LFE

[Nakamichi et al., 2004]. The coincidence of a low-Vp/Vs zone deduced from seismic tomography and the location of LFEs may suggest that supercritical volatile ﬂuid, such as H2O and CO2, is abundant in the velocity anomaly [Nakamichi et al., 2007]. The LFE activity increased for 2 years in Mount Fuji after a dike intrusion

Figure 2. The regional stress ﬁeld around Mount Fuji. (a) Focal mechanism from NIED catalog. (b) Directions of SHmax using the method of Hardebeck and Michael [2006] on these earthquakes and a grid of 10 min. The solid bars indicate thrusting, and the dashed bars indicate strike-slip mechanisms.

event in 2000 in the Miyake-jima and Kozu-shima regions (Figure 1a), 130–160 km to the southeast of Mount Fuji [Ukawa, 2005]. Two large earthquakes have recently perturbed the seismic activity and magmatic system around Mount Fuji:

the Mw 9.0 Tohoku-Oki earthquake on 11 March 2011 (Figure 1a) and the Mw 5.9 earthquake beneath southern ﬂank of Mount Fuji on 15 March 2011, which was apparently itself triggered by the Mw 9.0 event 4 days before. Tectonic earthquake seismicity at Mount Fuji was low before the Mw 5.9 event and its aftershocks (Figure 3). Fujita et al. [2013] calculated that the Tohoku-Oki earthquake and this aftershock perturbed the static stress ﬁeld to increase the overpressure of the magma reservoir at a depth of 15 km beneath Mount Fuji by 0.1–1 MPa. Considering that static [e.g., Nostro et al., 1998] and dynamic [e.g., Manga and

Figure 3. Earthquakes in the Mount Fuji volcanic area reported by the JMA catalog. The black dots indicate volcano tectonic earthquakes, and the gray circles are deep low-frequency earthquakes (LFEs). (a) Hypocenters from 1 January 2009 to 14 March 2011. (b) Hypocenters from 15 March 2011 to 31 December 2012. The hypocenter of Mw 5.9 event on 15 March 2011 is shown by a star.

Brodsky, 2006] stress perturbations can trigger eruptions and that the last eruption of Mount Fuji in 1707 was apparently triggered by a nearby M 8.5 earthquake 49 days before the eruption [e.g., Chesley et al., 2012], these stress perturbations quantitatively estimated by Fujita et al. [2013] could have caused volcanic unrest. The dike intrusion events in the Miyake-jima and Kozu-shima regions in 2000 (Figure 1a) may have influenced the Fuji magmatic system at depth. The effect was manifested by increased LFE activity beneath Mount Fuji [Ukawa, 2005]. The Tohoku-Oki earthquake also affected the velocity structure of the Mount Fuji area. Cross correlations of seismic ambient noise revealed that the Tohoku-Oki earthquake dropped the seismic velocity at depths shallower than 10 km by 0.1% [Brenguier et al., 2014]. The Mount Fuji area significantly marks the highest susceptibility of velocity drop to stress changes, suggesting a high pressure of volcanic fluid at shallow depths beneath Mount Fuji. Yet despite the increase in LFEs in 2000, after the Tohoku-Oki

earthquake and an induced local Mw 5.9 event, neither magmatic activity nor a signiﬁcant increase of LFEs were observed. We need to evaluate the effect of these earthquakes in 2011 quantitatively. In this paper, we determine the seismic anisotropic structure in the vicinity of Mount Fuji from seismic data before and after the 2011 Tohoku-Oki earthquake and its aftershocks to understand the geologic structure and stress. An automated splitting analysis code MFAST [Savage et al., 2010b] is used for processing this large amount of data.

2. Method Polarizations of seismic shear waves were measured by minimizing the eigenvalue of the covariance matrix of horizontal particle motions based on a grid search over possible values of fast direction and delay times (up to 0.4 s in this study) [Silver and Chan, 1991]. We used the MFAST software package, an automated methodology that is based on the method of Silver and Chan [1991] to measure splitting [Savage et al., 2010b], as summarized below. For details, please see the original papers. Measurement of shear wave splitting often has to deal with noise and scattering of waves due to the environment or to heterogeneous geologic structure. In order to deal with the difficulty of measuring splitting, MFAST has multiple verification processes and rejects bad data based on several quantitative criteria. MFAST uses at its base the methodology of Teanby et al.[2004]withmodifications to enable automatic quality classification. Specification of a measurement window is necessary to calculate the covariance matrix. MFAST first uses a set of 14 filters over wide windows before and after the S arrival to determine the product of the signal-to-noise ratio and the filter bandwidth. The three filters with the largest such products are used to make measurements. For each of these three filters, the Silver and Chan method is applied over multiple windows, whose limits are determined based on the frequency at the maximum spectral amplitude, thus avoiding subjective criteria or inappropriate settings by the definition of fixed lengths of measurement windows. From the measurements in each of these windows, the best measurement is chosen by cluster analysis. If waveforms cannot return stable results, these results are rejected by evaluation of splitting clusters, based on the number of measurements in each cluster and the variation between measurements for clusters with similar numbers (Figure S1 in the supporting information). In addition to the criteria of the quality of clustering, we reject measurement results with waveforms that have unclear linearity in incoming polarization by rejecting measurements with little difference between the smallest and largest eigenvalues in the grid search for the chosen measurement. These processes reject most of the measurement results from noisy data. Measurement results may also be disturbed by additional causes. If delay times are too close to (80% of) the maximum delay time, they are likely to be mismeasured. If the incoming polarization direction is close to measured fast directions, there will be too little energy on one of the components to measure splitting properly. Therefore, MFAST rejects all of these measurement results. Finally, it compares the results from the three chosen filters. If these measurements are not consistent, the measurement is rejected. These criteria replace subjective manual grading criteria to avoid unintentional bias. As in most crustal shear wave splitting studies [e.g., Liu et al., 2008; Peng and Ben-Zion, 2004], some scattering of measurements remains, which may be caused by varying raypaths through the medium and by heterogeneous anisotropy.

Figure 4. Equal area projections with lines oriented parallel to the fast direction at selected stations. The circles with dashed lines correspond to straight line incidence angle of 22.5°, and the ones with solid lines indicate incidence angle of 45°. This allows comparison of fast orientation as a function of incidence angle and back azimuth. S-P conversions easily occur at shallower incidence angles. Therefore, we compare the incidence angles and fast polarization directions. If S-P conversion occurs, the measured fast polarization directions may be biased owing to the polarized phase. Since most of the events are from the area of the aftershock of the Mw 5.9 earthquake on 15 March 2011, we verify the fast polarization directions measured at northeast of the volcanic edifice by manually checking the selected data. We did not find any significant variations of fast polarization directions as a function of back azimuth and incidence angle. Larger amplitude of the vertical components than horizontal ones was not also observed in Figure S2 in the supporting information. Consequently, we conclude that S-P conversion did not affect our results systematically.

3. Data We choose data from seismic stations operated by the Earthquake Research Institute (ERI), University of Tokyo, the National Research Institute for Earth Science and Disaster Prevention (NIED), and the Japan Meteorological Agency (JMA) (Figure 1b). The stations include Hi-net, a high-sensitivity seismograph network, operated by NIED. Most of the stations started their operation before the Tohoku-Oki earthquake, but data at some stations were only available after the 2011 Tohoku-Oki earthquake. All sites are equipped with short-period sensors or broadband seismic sensors with a sampling interval of 0.01 s. We use data from 26 stations in total. Eighteen stations are located in the vicinity of the volcanic ediﬁce, and 8 Hi-net stations are located relatively far from the summit. For the 18 nearby stations, the data from 7 stations (FY1, FJ5V, FJ6V, FJHV, FJNV, FJY2, and FJSV) are available from 17 March 2011 to October 2011, and the data from the other 11 stations are available from January 2009 to October 2012. Data from the 8 Hi-net stations are

Figure 5. Rose diagrams of fast polarization directions after the 2011 Tohoku-Oki earthquake from 17 March 2011 to 31 October 2012. We plot fast directions on the map for each station with the number of events at a speciﬁc range of directions as the lengths of bins on the diagram. Dashed lines indicate radial directions from the summit of Mount Fuji. Group 1 includes stations HSO and MMS located to the NE of the summit. Group 2 includes stations FJ5V and MTS located to the NW of the summit. Group 3 includes southern stations of FJ6V and FJY2. Group 4 includes stations OSWA and FJHV located in the west. We deﬁned the data period to show stations that were operating at the same time.

available from January 2011 to June 2011. The data from the 18 stations near the summit were processed with the WIN system for automatic earthquake picking and location [Urabe and Tsukada, 1992], while data from the Hi-net stations were picked manually by JMA. Approximately 3500 events occurred at depths shallower than 20 km during our measurement period. Although the Mount Fuji region was largely aseismic before the 2011

Tohoku-Oki earthquake, we were able to analyze numerous aftershocks from the Mw 5.9 earthquake on the southwestern ﬂank. The accuracy of automated splitting measurements depends on the accuracy of phase picks. In order to verify the accuracy of automated picks by the WIN system, we compared hypocenters of larger-magnitude events (>M 3.5 at the WIN catalog) from the automated picks by the WIN system and from the JMA catalog with manual picks. The epicentral distances between the locations of the same earthquake in the two databases are less than ~2 km. We also veriﬁed manually that the phase picks have adequate accuracy for clustering analysis of splitting. Detailed comparison of the MFAST method on phases picked with a somewhat different automatic code versus manual S arrivals used with the MFAST method also shows high consistency between the two data sets at another volcano (C. Castellazi et al., Shear wave automatic picking and splitting measurement at Ruapehu Volcano, New Zealand, submitted to Journal of Geophysics Research: Solid Earth, 2015). Therefore, we consider that quality of automatic picks and hypocenters of the WIN catalog have acceptable accuracy for measurement of splitting and spatial analysis. Splitting measurements can be contaminated by S-toP-converted phases from subsurface layers. The converted phase polarizes in the direction that aligns along the raypath. We verify that this is not affecting our results by checking waveforms (Figure S2 in the supporting information) and by the consistency of fast directions over the incidence angles and back azimuths (Figure 4).

Table 1. Splitting Parameters at Stations Measured in this Studya Mean Period: 1/2009–10/2012, N stations: 1/2011–6/2011, Stations FY1-FJSV: 15/3/2011–31/10/2012

(1) Group (2) Deg From Summit (3) Mean Delay (4) Std of delay (5) Mean Fast (6) Std of fast (7) R (8) Num

FJZ - 13.0 0.085 0.042 11.2 34.2 0.84 206 FUJ - 221.9 0.050 0.026 13.2 35.6 0.83 730 FUJ2 - 309.5 0.089 0.052 11.9 33.2 0.86 14 MMS 1 35.5 0.054 0.033 22.2 40.4 0.78 421 MTS 2 313.8 0.080 0.039 45.4 36.0 0.84 448 OIS - 16.5 0.071 0.032 7.2 21.7 0.93 716 HSO 1 37.6 0.064 0.040 20.2 37.5 0.81 251 OSWA 4 266.3 0.049 0.025 79.8 32.5 0.85 727 SBSR - 82.7 0.061 0.037 46.4 42.5 0.79 448 FJO - 87.6 0.059 0.049 17.1 57.0 0.57 149 NHOW - 152.2 0.084 0.043 28.5 49.5 0.68 149 FY1 - 39.6 0.059 0.044 13.2 54.1 0.61 82 FJ5V 2 312.9 0.073 0.025 30.9 18.0 0.95 749 FJ6V 3 179.8 0.077 0.029 7.8 20.2 0.94 443 FJHV 4 266.4 0.040 0.026 87.7 30.2 0.87 745 FJNV - 340.4 0.072 0.037 2.5 30.8 0.87 250 FJY2 3 185.7 0.097 0.045 3.1 23.4 0.92 510 FJSV - 79.1 0.067 0.051 29.7 54.9 0.62 272 N.TR2H - 39.4 0.043 0.031 18.7 46.5 0.72 71 N.KKKH - 329.8 0.061 0.043 14.4 27.5 0.89 72 N.SSNH - 145.7 0.089 0.048 20.5 37.7 0.82 244 N.SMBH - 286.2 0.052 0.019 55.1 37.1 0.85 237 N.TU2H - 52.3 0.061 0.035 20.9 36.7 0.83 30 N.YM2H - 77.5 0.063 0.033 19.8 38.1 0.81 52 N.ASGH - 100.2 0.045 0.029 29.4 27.0 0.90 25 N.NMZH - 154.7 0.117 0.038 17.6 23.7 0.92 71 a(1) Group: grouping shown in Figure 5, (2) deg from summit: azimuth between the summit and stations measured from north (deg), (3)mean delay: mean delay time (s), (4) std of delay: standard deviation of delay time, (5) mean fast: mean fast direction (deg) (see also Figure 8), (6) std of fast: standard deviation of fast direction, (7) the mean resultant length R [Mardia and Jupp, 2000], and (8) num: number of event at each station.

4. Results and Discussion 4.1. Structural Anisotropy Seismic anisotropy is obtained from earthquakes at depths shallower than 20 km to focus on the shallow anisotropic structure. We show examples of measured particle motions in Figure S3 in the supporting information. The strongly aligned fast orientations observed in the circular histograms in Figure 5 and the mean resultant length R at each station (>~0.8) in Table 1 indicate that the fast polarization directions are well constrained at most of the stations. To compare consistent data sets, we restrict the data in Figure 5 to the period of time between 17 March 2011 and 31 October 2012, in which all stations were operating. The fast directions at most of the stations close to the summit are radial to the summit with an offset less than ~30° (Table 1 and Figure 5). The fast directions of three stations far from the summit of Mount Fuji (stations FY1, FJSV, and FJO; Figure 5) do not follow the radial pattern and are subparallel to the direction of the NW-SE regional compression. We divide seismic stations into four groups (Figure 5 and Table 1), each of which consists of two stations that align on a radial line from the summit. The delay times do not depend strongly on earthquake depths (Figure 6). We obtain depth independence of delay times and consistent delay times over at least ~5 km or more. Delay times are also nearly independent of angles of incidence and back azimuth (Figures S4–S6 in the supporting information). We plot back azimuth and delay times by a scatterplot and a contour map (Figure S4 in the supporting information). We plot contour map of delay times with incident angles in Figure S5 in the supporting information. Contour map of back azimuth and incident angles are shown in Figure S6 in the supporting information. Figures S5 and S6 in the supporting information indicate that events come from narrow ranges of angles (~10° in incident angles or back azimuths). Events of Group 3 come from angles that spread perpendicular to the symmetry axis of anisotropy, and the inﬂuence of raypaths is not signiﬁcant. The majority of measured delay times are less than ~0.15 s but also exhibit a large scatter. Such small delay times and large scatter are common for shear wave splitting of local earthquakes in tectonic and in volcanic

Figure 6. Depths and delay times at each group of Figure 5. Stations at the same subregion are placed on the same row. The black dots show individual measurement. The symbols with error bars indicate mean values of delay times and standard deviations at every 1 km depths from 4 km to 12 km.

regions. For example, Johnson et al. [2010] found delay time averaged 0.10–0.27 s for local earthquakes at Okmok Volcano, Savage et al. [2010a] found that the average delay times from local earthquakes at Asama Volcano were between 0.07 ± 0.2 s and 0.16 ± 0.03 s, Vavryčuk [1993] obtained the maximum delay time as 0.15 s in West Bohemia, Yang et al. [2011] reported average delays of 0.09 ± 0.05 s in Southern California, and Peng and Ben-Zion [2004] found that delay times near the Anatolian Fault in Turkey ranged 0.043–0.085 s. Several stations have two or more populations of delay times (e.g., FJY2 and MTS; Figure 6). Such multiple populations have recently been observed at other volcanic areas, particularly for closely spaced clusters of earthquakes [e.g., Johnson et al., 2010; C. Castellazi et al., submitted manuscript, 2015; M. K. Savage et al., Seismic anisotropy and its precursory change before eruptions at Piton de la Fournaise volcano, La Réunion, submitted to Journal of Geophysical Research: Solid Earth, 2014]. They might be caused by a form of cycle skipping (C. Castellazi et al., submitted manuscript, 2015) or possibly by some scattered energy arriving after the S arrival [Johnson et al., 2010]. In any case, the average anisotropy at shallow depths at Mount Fuji is not strong if we assume that the delay times accumulate along the entire path. However, the observed independence of delay times with depth strongly suggests that the anisotropy occurs not along the entire path but at depths shallower than the 4 km depth of the shallowest measurements. We infer that the measured low magnitude of anisotropy is caused by an absence of anisotropy between the anisotropic structure concentrated at shallower depths and the depths of the seismic events. If we consider that it all occurs above the shallowest earthquakes around 4 km, then the magnitude of anisotropy at depths less than 4 km (Figure S7 in the supporting information) is calculated to be up to ~5%, which could reach fracture criticality and the rocks can be categorized as highly fractured rocks [Crampin, 1994]. Anisotropy orientation at shallow depths in Mount Fuji is consistent with the geologic evidence of repeating dike intrusions evidenced by the orientation of radially oriented surface ﬁssures on the ediﬁce [e.g., Takada et al., 2007]. The number of dikes and vents near the summit around Mount Fuji suggests repeating dike

intrusions into the volcanic edifice [e.g., Nakamura, 1977]. In addition, smaller density of shallow rocks deduced from a gravity survey also supports higher porosity or fractured structure at shallow depths [Komazawa, 2003]. Seismic anisotropy measured by splitting shows that the radial pattern of fast directions spreading out from the summit to the flank of the volcanic edifice is similar to the distribution of the diking [Takada et al., 2007] around the summit. Consistent fast polarization directions are located at close locations within each group of stations that align on the radial line from the summit (Figures 5 and 7). The radial pattern extends around 10 km from the summit, and this agrees with the area of Figure 7. The measured horizontal distribution of fast polarization radial dikes of Mount Fuji out to several directions compared to that expected for a symmetrical radial pattern kilometers away from the summit [Takada with its center beneath the summit of Mount Fuji. The colored triangles show stations. Each line indicates the fast polarization direction, and et al., 2007]. The consistency of the pattern the color indicates the station on which it was measured. The lines suggests that the stress causing the radial showing the fast polarization directions are plotted at the piercing dikes extends to the depths sampled by points for rays intersecting depths of 2.5 km. Its latitude (longitude) is XDðÞþd xd the seismic waves. given by D Here X, x, D,andd are a station latitude (longitude), a hypocenter latitude (longitude), a hypocenter depth, and a plotted Splitting analysis including a wider area depth (2.5 km), respectively. shows two different patterns of fast polarization directions for those stations close to the summit and those far from the summit. Figure 8 indicates that most of the seismic stations within ~15 km of the summit show a radial pattern to the summit while the fast polarization directions at >15 km from the summit are parallel to the direction of the regional compression. Fast polarization directions parallel to the regional compression were also obtained 50 km southeast of Mount Fuji [Honda and Tanada, 1991], consistent with our results. Nakamura [1977] indicates that the regional maximum compression can cause the radial pattern of dike orientations in volcanoes. Acocella and Neri [2009] find a semiquantitative relationship among topography of a volcano, tectonic setting, and magma composition. Their application of these relationships to the relative length of dike and the height of Mount Fuji suggests that the influence of both gravitational effects of volcanic edifice and regional stresses is dominant in the formation of dike orientations. These studies suggest that a change of spatial pattern of splitting can be interpreted by the interaction of stresses. In the next section, we estimate gravitational effects on seismic anisotropy in the Mount Fuji volcanic area. 4.2. Stress-Induced Anisotropy We calculate the combined effect of both gravitational stresses and regional stresses and compare them to the shear wave splitting to get an estimate of the horizontal stress magnitude. We solve this as an eigenvalue problem to obtain the eigenvector of a stress tensor. We make grids with 2 km interval around Mount Fuji and calculate stress tensors at each grid point due to gravitational stresses and to regional stresses. Then we sum these tensors and calculate the horizontal eigenvectors of each grid point that are equivalent to the direction of maximum horizontal compression. We calculate the gravitational stress tensors from the weight of the volcanic edifice assuming a Boussinesq’sproblem[Jaeger et al., 2007] with a point load equal to 1.13 × 1013 N. We estimate regional compression from the average ratio of principal

stresses R =(σ1 σ2)/(σ1 σ3)ofR = 0.57 deduced from the analysis of focal mechanisms (Figure 2) assuming the intermediate principal stress σ2 as the lithostatic pressure as a function of depth. To calculate lithostatic pressure, we use 2650 kg/m3, the average of granite, as the density. Rigidity μ and Lame constant λ are estimated

from Vp (6.53 km/s) and Vs (3.18 km/s) [Ukawa, 2005] to be μ = 26.8 GPa and λ =59.4GPa.Weassumetheregional horizontal stress (SHmax) to be parallel to the regional NW-SE direction and a magnitude σ1 as σ2 times a

Figure 8. Rose diagrams of fast polarization directions from 1 January 2011 to 30 June 2011. The periods of data cover before and after the 2011 Tohoku-Oki earthquake. Since we found that fast directions are time independent, we plot results of half a year that is shorter than the entire data set. Owing to availability of data, results of stations at FY1, FJ5V, FJ6V, FJNV, FJY2, and FJSV are from 17 March 2011. Hi-net stations are also used to show fast polarization directions at stations far from the summit. The gray lines indicate directions of the best-ﬁt maximum compression at a depth of 2.0 km by a point load at the origin of the coordinate as described in the text. Dashed circles indicate distances from the summit of Mount Fuji.

factor (A). The distribution of the directions of the eigenvectors depends on this assumption of A and the depth at which it is calculated. In Figure S8 in the supporting information, we show examples of A with 1.01, 1.05, 1.5, and 3 at depths of 2.0 km and 4.0 km to show how directions of maximum horizontal compression change. We determine the appropriate factor and depth by a grid search. For each factor and depth in our search, we add the gravitational and regional stress tensors to obtain principal stress directions and compare them to the mean values of fast polarization directions at each station, which are assumed to represent the maximum stress direction. The factor and depth giving the minimum difference between the calculated and measured directions are considered to be the best measurement. The grid search was calculated for factors from 1.01 to 1.05 with the interval of 0.001 and for depths from 1.5 km to 3.5 km with 0.5 km interval. In this grid search, the best factors ranged between 1.02 and 1.04. In Figure 8, rose diagrams of all stations and the calculated directions of maximum compression for the best-fit depth of 2.0 km and factor of 1.02 are plotted. The data period starts 3 months before the 2011 Tohoku-Oki earthquake and ends 3 months after the earthquake. Since we did not observe any significant changes of splitting with time, using half a year is enough to represent the regional distribution of seismic anisotropy. The calculated direction of maximum compression agrees well with the distribution of fast polarizations. We infer that the combination of the regional stresses and gravitational effects can cause the observed contrast of radial pattern of fast directions close to the summit and those parallel to the regional stresses farther away. Note that the influence of gravitational effects dominates only near the summit.

Polarizations at three northern stations, N.SMBH, N.KKKH, and N.TR2H, are not parallel to the regional compression. Faults trending ENE-WSW are dominant in this region [Ozaki et al., 2002]. The splitting polarizations at these stations may be affected more by local fracture systems than by the regional stresses. The existence of both stress-controlled anisotropy and structurally controlled anisotropy in the close proximity to each other is comparable with anisotropic structure obtained in the upper crust of the West Bohemia/Vogtland seismically active area [Vavryčuk, 1993; Vavryčuk and Boušková, 2008] or Mount Ruapehu Volcano [Johnson et al., 2011]. We calculated b values following the methodology of Wiemer and Wyss [2000] to see whether there are any changes in the subsurface area. A sudden decrease in b value after 11 March 2011 (Figure S9 in the supporting information) may indicate changes of pore pressure or heterogeneity in the source region [e.g., Mori and Abercrombie, 1997; Schorlemmer et al., 2005]. In spite of this change in the subsurface, seismic anisotropy shows little significant temporal change (Figure S10 in the supporting information). The b value changes mostly come from the aftershock sequence, which dominate the catalog after the 15 March main shock. The isotropic velocity change [Brenguier et al., 2014] detected around Mount Fuji may be related to the b value changes. However, the magnitude of the velocity change is only 0.1%, and if anisotropy also changed by 0.1%, we would not be able to distinguish it due to the high scatter of our measurements (Figure S10 in the supporting information). A limited influence of the earthquakes on microcrack distribution is consistent with the lack of significant increase of the number of LFEs. But since our measurements of splitting and LFE numbers do not have adequate time resolution, this interpretation may change if we increase the time resolution of these measurements by improving our analysis techniques. When the orientation of the stress fields and surface structures such as fault strikes is different, it is easy to discriminate whether observed anisotropy is structural or stress induced. However, since these two directions coincide with each other in Mount Fuji, differentiation is very difficult, especially since stresses are an important factor to form the radial dike and fissure structures. Near the edifice of Mount Fuji, both the fast polarization directions and the lineament of the geologic structure in the volcanic edifices align parallel to the stresses caused by both the gravitational effects and the regional stress field. Although dikes orient parallel to the directions of maximum compression, dikes are sporadically distributed [e.g., Takada et al., 2007] and seismic stations are not always placed just above the dikes. The calculated 5% magnitude of anisotropy is near the fracture criticality (Figure S7 in the supporting information), and thus, structural anisotropy may not be as significant as that caused by heavily fractured faults or dike structures [Crampin, 1994].

5. Conclusions We measure shear wave splitting in the vicinity of Mount Fuji with seismic data from 2009 to 2012. At stations close to Mount Fuji, fast directions are radial with respect to the summit while those far from the summit are parallel to the direction of regional compression or nearby faults. We also interpret the spatial distribution of splitting in terms of the stress regime of the area. We draw the following conclusions: 1. Radially symmetric fast directions at stations within ~15 km from the summit and NW-SE trending fast polarization directions observed at stations more than ~15 km from the summit suggest that the radially symmetric topography of the volcano mainly influences nearby stations and regional stresses are dominant far from the summit. This conclusion is endorsed by calculating stresses due to a point load. The radial pattern of fast polarization directions is consistent with the strikes of dikes around the volcanic edifice. Assuming stress control only, the pattern of anisotropy can be fit by the interaction of gravitational with regional stresses. If structural anisotropy also contributes to the radial pattern, then the regional maximum horizontal stress magnitude is not constrained. 2. The lack of depth dependence of splitting suggests a shallow anisotropic structure around Mount Fuji. This is also consistent with the fast directions being parallel to dike orientations. We thus suggest that the measured low magnitude of splitting, when averaged over the entire path, is caused by effectively isotropic material at depths below 4 km. If we assume an average depth of anisotropy as 1.5–3.5 km, the regional compression ranges from 1.02 to 1.04 times lithostatic pressure, leading to a maximum compression of 51.9 MPa.

3. Even if the Mw 9.0 Tohoku-Oki earthquake and its Mw 5.9 aftershock near Mount Fuji changed the static stress on the order of 0.1–1.0 MPa at the boundary of magma reservoir at depths of ~15–20 km [Fujita et al., 2013], we did not obtain signiﬁcant changes in splitting parameters due to these earthquakes. The lack of splitting change is qualitatively consistent with the lack of increase in the activity of LEFs.

We show semiquantitatively that local gravitational effects and tectonic stresses could cause the anisotropic structure at shallow depths around Mount Fuji. If we quantify the influence of these geologic processes on the seismic anisotropy, the splitting and a symmetrical structure of Mount Fuji may be used as a reference model of stress field under volcanoes for interpretation of temporal changes after giant earthquakes and/or dike intrusions that cause stress changes beneath the volcanic edifice.

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