Seismic anisotropy of the upper crust around Mount Fuji, JapanSeismic anisotropy of the upper crust...

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Seismic anisotropy of the upper crust around Mount Fuji, Japan Kohtaro R. Araragi 1 , Martha K. Savage 2 , Takao Ohminato 1 , and Yosuke Aoki 1 1 Earthquake Research Institute, University of Tokyo, Tokyo, Japan, 2 Institute of Geophysics, Victoria University of Wellington, Wellington, New Zealand Abstract We measure shear wave splitting and estimate stresses of Mount Fuji, Japan, to interpret anisotropic structure and its implication for geologic processes using local crustal earthquake seismograms from 2009 to 2012. The measured fast polarizations have preferred orientations at each station with mean values of delay times <0.15 s. We infer that the anisotropic structure is located at shallow depths (<4 km) from a lack of focal 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 summit exhibit fast polarization directions approximately parallel to the NW-SE compressional regional stress eld. 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 reect interactions of the gravitational stresses and regional tectonics. Assuming stress control only, the spatial pattern of anisotropy can be t 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. 1. Introduction Constraining subsurface structure by geophysical approaches is critical for understanding geologic processes. Measurements of seismic anisotropy have recently been used to interpret the subsurface structure and its relationship to the stress eld. Shear wave splitting (splitting), in which a linearly polarized S wave is split into two S waves with mutually perpendicular orientations in anisotropic media, is a good indicator of seismic 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 ips 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 ARARAGI ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2739 PUBLICATION S Journal of Geophysical Research: Solid Earth RESEARCH ARTICLE 10.1002/2014JB011554 Special Section: Stress, Strain and Mass Changes at Volcanoes Key Points: Fast directions around Mount Fuji have two distinct patterns The gravitational effects have inuence on the seismic anisotropy of Mount Fuji Seismic anisotropy of Mount Fuji is at shallow depths Supporting Information: Figures S1S10 Correspondence to: K. R. Araragi, [email protected] 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, 27392751, doi:10.1002/2014JB011554. Received 21 AUG 2014 Accepted 3 MAR 2015 Accepted article online 9 MAR 2015 Published online 22 APR 2015

Transcript of Seismic anisotropy of the upper crust around Mount Fuji, JapanSeismic anisotropy of the upper crust...

Page 1: Seismic anisotropy of the upper crust around Mount Fuji, JapanSeismic anisotropy of the upper crust around Mount Fuji, Japan Kohtaro R. Araragi1, Martha K. Savage2, Takao Ohminato1,

Seismic anisotropy of the upper crustaround Mount Fuji, JapanKohtaro R. Araragi1, Martha K. Savage2, Takao Ohminato1, and Yosuke Aoki1

1Earthquake Research Institute, University of Tokyo, Tokyo, Japan, 2Institute of Geophysics, Victoria University ofWellington, Wellington, New Zealand

Abstract Wemeasure shear wave splitting and estimate stresses of Mount Fuji, Japan, to interpret anisotropicstructure and its implication for geologic processes using local crustal earthquake seismograms from 2009 to2012. The measured fast polarizations have preferred orientations at each station with mean values of delaytimes <0.15 s. We infer that the anisotropic structure is located at shallow depths (<4 km) from a lack of focaldepth dependence of delay times. The fast polarization directions for stations within approximately 15 kmof the summit of Mount Fuji show a radial pattern pointing toward the summit, while stations far from thesummit exhibit fast polarization directions approximately parallel to the NW-SE compressional regional stressfield. We infer that the symmetrical seismic anisotropic structure around the summit and the fast directionsparallel to the regional compression observed at distant stations from the summit reflect interactions of thegravitational stresses and regional tectonics. Assuming stress control only, the spatial pattern of anisotropy canbe fit by the interaction of gravitational with regional stresses if the regional maximum horizontal stress is 1.02times lithostatic pressure (51.9MPa at a depth of 2.0 km). If structural anisotropy also contributes to the radialpattern, then the regional maximum horizontal stress magnitude is not constrained.

1. Introduction

Constraining subsurface structure by geophysical approaches is critical for understanding geologic processes.Measurements of seismic anisotropy have recently been used to interpret the subsurface structure and itsrelationship to the stress field. Shear wave splitting (splitting), in which a linearly polarized S wave is split intotwo S waves with mutually perpendicular orientations in anisotropic media, is a good indicator of seismicanisotropy 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 tidaleffects [e.g., Teanby et al., 2004]. Temporal changes of hydrothermal activity and gas release are also consideredas a cause of splitting changes in volcanic areas [e.g., Unglert et al., 2011; Johnson and Poland, 2013]. Basedon these observations, splitting is now regarded as an indicator of various scales of stress-related geologic eventsin the shallow crust.

Splittingmeasurements and other geophysical data are often simultaneously used to better constrain subsurfaceprocesses. There are two origins of seismic anisotropy: intrinsic or structural anisotropy and stress-inducedanisotropy. In some cases, structural anisotropy and stress-induced anisotropy can be differentiated bycomparing 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 andZoback, 2000]. When seismic anisotropy is caused by strata, relatively large parallel fractures, or alignment ofmineral fabric, we can consider this to be intrinsic anisotropy or structural anisotropy. This type of anisotropy isinsensitive to stress changes. Stress-induced anisotropy originates from opening and closure of preexistingmicrocracks and thus is distinct in that it is sensitive to stress changes. Analysis of regional stresses and thedistribution of anisotropy around a volcanic area, coupled with fault plane solution analyses, succeeded inexplaining precursory stress change caused by diking [e.g., Roman et al., 2011]. Changes in delay times or flipsof 90° of fast directions can occur for various reasons such as increasing crack density and increase of porepressure after a main shock near the target area [e.g., Saiga et al., 2003]. However, in addition to these geologicfactors, the path effect caused by variation of hypocenters or characteristics of structural anisotropy sometimes

ARARAGI ET AL. ©2015. American Geophysical Union. All Rights Reserved. 2739

PUBLICATIONSJournal of Geophysical Research: Solid Earth

RESEARCH ARTICLE10.1002/2014JB011554

Special Section:Stress, Strain and MassChanges at Volcanoes

Key Points:• Fast directions around Mount Fujihave two distinct patterns

• The gravitational effects haveinfluence on the seismic anisotropyof Mount Fuji

• Seismic anisotropy of Mount Fuji is atshallow depths

Supporting Information:• Figures S1–S10

Correspondence to:K. R. Araragi,[email protected]

Citation:Araragi, K. R., M. K. Savage, T. Ohminato,and Y. Aoki (2015), Seismic anisotropy ofthe upper crust around Mount Fuji,Japan, J. Geophys. Res. Solid Earth, 120,2739–2751, doi:10.1002/2014JB011554.

Received 21 AUG 2014Accepted 3 MAR 2015Accepted article online 9 MAR 2015Published online 22 APR 2015

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result in apparent temporal changes [e.g., Aster et al., 1990]. For example, a 90° flip of fast polarization directioncan occur by raypaths changing near inclined faults [e.g., Elkibbi et al., 2005] or through varying incidence angleswith 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 ofdikes [Nakamura, 1977] and focal mechanisms of local earthquakes (Figure 2) show that the principalcompressional axis around Mount Fuji is oriented NW-SE. While there are few fumaroles or geothermalmanifestations that suggest magmatic activity, a magnetotelluric survey indicates that the volcano still has anactive 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 for100,000 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 contrastto 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 depthsaround 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 thelocation of LFEs may suggest that supercritical volatile fluid, such as H2O and CO2, is abundant in the velocityanomaly [Nakamichi et al., 2007]. The LFE activity increased for 2 years in Mount Fuji after a dike intrusion

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 thePhilippine 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 hypocenterof 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.

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event in 2000 in the Miyake-jima and Kozu-shima regions (Figure 1a), 130–160 km to the southeast of MountFuji [Ukawa, 2005].

Two large earthquakes have recently perturbed the seismic activity andmagmatic system aroundMount Fuji:the Mw 9.0 Tohoku-Oki earthquake on 11 March 2011 (Figure 1a) and the Mw 5.9 earthquake beneathsouthern flank of Mount Fuji on 15 March 2011, which was apparently itself triggered by the Mw 9.0 event4 days before. Tectonic earthquake seismicity at Mount Fuji was low before the Mw 5.9 event and itsaftershocks (Figure 3). Fujita et al. [2013] calculated that the Tohoku-Oki earthquake and this aftershockperturbed the static stress field to increase the overpressure of the magma reservoir at a depth of 15 kmbeneathMount Fuji by 0.1–1MPa. Considering that static [e.g.,Nostro et al., 1998] and dynamic [e.g.,Manga and

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

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

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Brodsky, 2006] stress perturbations can trigger eruptions and that the last eruption of Mount Fuji in 1707 wasapparently triggered by a nearbyM 8.5 earthquake 49days before the eruption [e.g., Chesley et al., 2012], thesestress 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 influencedthe Fuji magmatic system at depth. The effect was manifested by increased LFE activity beneath MountFuji [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 seismicvelocity at depths shallower than 10 km by 0.1% [Brenguier et al., 2014]. The Mount Fuji area significantlymarks the highest susceptibility of velocity drop to stress changes, suggesting a high pressure of volcanicfluid at shallow depths beneath Mount Fuji. Yet despite the increase in LFEs in 2000, after the Tohoku-Okiearthquake and an induced local Mw 5.9 event, neither magmatic activity nor a significant increase of LFEswere 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 databefore and after the 2011 Tohoku-Oki earthquake and its aftershocks to understand the geologic structureand stress. An automated splitting analysis code MFAST [Savage et al., 2010b] is used for processing this largeamount of data.

2. Method

Polarizations of seismic shear waves were measured by minimizing the eigenvalue of the covariance matrixof 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 automatedmethodology 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 theenvironment 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] with modifications to enable automaticquality classification. Specification of a measurement window is necessary to calculate the covariancematrix. MFAST first uses a set of 14 filters over wide windows before and after the S arrival to determine theproduct of the signal-to-noise ratio and the filter bandwidth. The three filters with the largest suchproducts are used to make measurements. For each of these three filters, the Silver and Chan method isapplied over multiple windows, whose limits are determined based on the frequency at the maximumspectral amplitude, thus avoiding subjective criteria or inappropriate settings by the definition of fixedlengths of measurement windows.

From the measurements in each of these windows, the best measurement is chosen by cluster analysis. Ifwaveforms cannot return stable results, these results are rejected by evaluation of splitting clusters, basedon the number of measurements in each cluster and the variation between measurements for clusterswith similar numbers (Figure S1 in the supporting information). In addition to the criteria of the qualityof clustering, we reject measurement results with waveforms that have unclear linearity in incomingpolarization by rejecting measurements with little difference between the smallest and largest eigenvaluesin the grid search for the chosen measurement. These processes reject most of the measurement resultsfrom noisy data.

Measurement results may also be disturbed by additional causes. If delay times are too close to (80% of) themaximum delay time, they are likely to be mismeasured. If the incoming polarization direction is close tomeasured fast directions, there will be too little energy on one of the components to measure splittingproperly. Therefore, MFAST rejects all of these measurement results. Finally, it compares the results from thethree 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 shearwave splitting studies [e.g., Liu et al., 2008; Peng and Ben-Zion, 2004], some scattering of measurementsremains, which may be caused by varying raypaths through the medium and by heterogeneous anisotropy.

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3. Data

We choose data from seismic stations operated by the Earthquake Research Institute (ERI), University ofTokyo, the National Research Institute for Earth Science and Disaster Prevention (NIED), and the JapanMeteorological Agency (JMA) (Figure 1b). The stations include Hi-net, a high-sensitivity seismographnetwork, 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 equippedwith short-period sensors or broadband seismic sensors with a sampling interval of 0.01 s. We use datafrom 26 stations in total. Eighteen stations are located in the vicinity of the volcanic edifice, and 8 Hi-netstations 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 17March 2011 to October 2011, and the data fromthe other 11 stations are available from January 2009 to October 2012. Data from the 8 Hi-net stations are

Figure 4. Equal area projections with lines oriented parallel to the fast direction at selected stations. The circles withdashed lines correspond to straight line incidence angle of 22.5°, and the ones with solid lines indicate incidence angleof 45°. This allows comparison of fast orientation as a function of incidence angle and back azimuth. S-P conversions easilyoccur at shallower incidence angles. Therefore, we compare the incidence angles and fast polarization directions. If S-Pconversion occurs, themeasured fast polarization directions may be biased owing to the polarized phase. Sincemost of theevents are from the area of the aftershock of the Mw 5.9 earthquake on 15 March 2011, we verify the fast polarizationdirections measured at northeast of the volcanic edifice by manually checking the selected data. We did not find anysignificant variations of fast polarization directions as a function of back azimuth and incidence angle. Larger amplitude ofthe 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.

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available from January 2011 to June 2011. The data from the 18 stations near the summit were processed withthe WIN system for automatic earthquake picking and location [Urabe and Tsukada, 1992], while data from theHi-net stations were picked manually by JMA. Approximately 3500 events occurred at depths shallower than20 km during our measurement period. Although the Mount Fuji region was largely aseismic before the 2011Tohoku-Oki earthquake, we were able to analyze numerous aftershocks from the Mw 5.9 earthquake on thesouthwestern flank.

The accuracy of automated splittingmeasurements depends on the accuracy of phase picks. In order to verifythe 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 withmanual picks. The epicentral distances between the locations of the same earthquake in the two databasesare less than ~2 km. We also verified manually that the phase picks have adequate accuracy for clusteringanalysis of splitting. Detailed comparison of the MFAST method on phases picked with a somewhat differentautomatic code versus manual S arrivals used with the MFAST method also shows high consistency betweenthe two data sets at another volcano (C. Castellazi et al., Shear wave automatic picking and splittingmeasurement 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 haveacceptable accuracy for measurement of splitting and spatial analysis.

Splitting measurements can be contaminated by S- to P-converted phases from subsurface layers. Theconverted phase polarizes in the direction that aligns along the raypath. We verify that this is not affectingour results by checking waveforms (Figure S2 in the supporting information) and by the consistency of fastdirections over the incidence angles and back azimuths (Figure 4).

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 themap 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 fromthe 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 thesummit. 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 showstations that were operating at the same time.

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4. Results and Discussion4.1. Structural Anisotropy

Seismic anisotropy is obtained from earthquakes at depths shallower than 20 km to focus on the shallowanisotropic structure. We show examples of measured particle motions in Figure S3 in the supportinginformation. The strongly aligned fast orientations observed in the circular histograms in Figure 5 and the meanresultant length R at each station (>~0.8) in Table 1 indicate that the fast polarization directions are wellconstrained at most of the stations. To compare consistent data sets, we restrict the data in Figure 5 to the periodof time between 17 March 2011 and 31 October 2012, in which all stations were operating. The fast directionsat most of the stations close to the summit are radial to the summit with an offset less than ~30° (Table 1 andFigure 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 thatalign 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. Delaytimes are also nearly independent of angles of incidence and back azimuth (Figures S4–S6 in the supportinginformation). We plot back azimuth and delay times by a scatterplot and a contour map (Figure S4 in thesupporting information). We plot contour map of delay times with incident angles in Figure S5 in thesupporting information. Contour map of back azimuth and incident angles are shown in Figure S6 inthe supporting information. Figures S5 and S6 in the supporting information indicate that events comefrom narrow ranges of angles (~10° in incident angles or back azimuths). Events of Group 3 come from anglesthat 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 delaytimes and large scatter are common for shear wave splitting of local earthquakes in tectonic and in volcanic

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

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regions. For example, Johnson et al. [2010] found delay time averaged 0.10–0.27 s for local earthquakes atOkmok Volcano, Savage et al. [2010a] found that the average delay times from local earthquakes at AsamaVolcano were between 0.07 ± 0.2 s and 0.16 ± 0.03 s, Vavryčuk [1993] obtained the maximum delay time as0.15 s in West Bohemia, Yang et al. [2011] reported average delays of 0.09 ± 0.05 s in Southern California, andPeng 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 multiplepopulations have recently been observed at other volcanic areas, particularly for closely spaced clusters ofearthquakes [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 cycleskipping (C. Castellazi et al., submitted manuscript, 2015) or possibly by some scattered energy arrivingafter the S arrival [Johnson et al., 2010]. In any case, the average anisotropy at shallow depths at Mount Fuji isnot strong if we assume that the delay times accumulate along the entire path. However, the observedindependence of delay times with depth strongly suggests that the anisotropy occurs not along the entire pathbut at depths shallower than the 4km depth of the shallowest measurements. We infer that the measured lowmagnitude of anisotropy is caused by an absence of anisotropy between the anisotropic structure concentrated atshallower depths and the depths of the seismic events. If we consider that it all occurs above the shallowestearthquakes around 4km, then themagnitude of anisotropy at depths less than 4km (Figure S7 in the supportinginformation) is calculated to be up to ~5%, which could reach fracture criticality and the rocks can be categorizedas highly fractured rocks [Crampin, 1994].

Anisotropy orientation at shallow depths in Mount Fuji is consistent with the geologic evidence of repeatingdike intrusions evidenced by the orientation of radially oriented surface fissures on the edifice [e.g., Takadaet al., 2007]. The number of dikes and vents near the summit around Mount Fuji suggests repeating dike

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 andstandard deviations at every 1 km depths from 4 km to 12 km.

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intrusions into the volcanic edifice [e.g.,Nakamura, 1977]. In addition, smallerdensity of shallow rocks deduced from agravity survey also supports higherporosity or fractured structure at shallowdepths [Komazawa, 2003].

Seismic anisotropy measured by splittingshows that the radial pattern of fastdirections spreading out from the summitto the flank of the volcanic edifice is similarto the distribution of the diking [Takadaet al., 2007] around the summit. Consistentfast polarization directions are located atclose locations within each group ofstations that align on the radial line fromthe summit (Figures 5 and 7). The radialpattern extends around 10 km from thesummit, and this agrees with the area ofradial dikes of Mount Fuji out to severalkilometers away from the summit [Takadaet al., 2007]. The consistency of the patternsuggests that the stress causing the radialdikes extends to the depths sampled bythe seismic waves.

Splitting analysis including a wider areashows two different patterns of fastpolarization 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>15km fromthe summit are parallel to the direction of the regional compression. Fast polarization directions parallel to theregional compression were also obtained 50 km southeast of Mount Fuji [Honda and Tanada, 1991], consistentwith our results. Nakamura [1977] indicates that the regional maximum compression can cause the radialpattern of dike orientations in volcanoes. Acocella and Neri [2009] find a semiquantitative relationship amongtopography of a volcano, tectonic setting, and magma composition. Their application of these relationshipsto the relative length of dike and the height of Mount Fuji suggests that the influence of both gravitationaleffects of volcanic edifice and regional stresses is dominant in the formation of dike orientations. Thesestudies suggest that a change of spatial pattern of splitting can be interpreted by the interaction of stresses. Inthe 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 themto the shear wave splitting to get an estimate of the horizontal stress magnitude. We solve this as aneigenvalue problem to obtain the eigenvector of a stress tensor. We make grids with 2 km interval aroundMount Fuji and calculate stress tensors at each grid point due to gravitational stresses and to regionalstresses. Then we sum these tensors and calculate the horizontal eigenvectors of each grid point that areequivalent to the direction of maximum horizontal compression. We calculate the gravitational stresstensors from the weight of the volcanic edifice assuming a Boussinesq’s problem [Jaeger et al., 2007] with apoint load equal to 1.13 × 1013 N. We estimate regional compression from the average ratio of principalstresses R= (σ1� σ2)/(σ1� σ3) of R=0.57 deduced from the analysis of focal mechanisms (Figure 2) assumingthe intermediate principal stress σ2 as the lithostatic pressure as a function of depth. To calculate lithostaticpressure, we use 2650 kg/m3, the average of granite, as the density. Rigidity μ and Lame constant λ are estimatedfrom Vp (6.53 km/s) and Vs (3.18 km/s) [Ukawa, 2005] to be μ=26.8GPa and λ=59.4GPa. We assume the regionalhorizontal stress (SHmax) to be parallel to the regional NW-SE direction and a magnitude σ1 as σ2 times a

Figure 7. The measured horizontal distribution of fast polarizationdirections compared to that expected for a symmetrical radial patternwith its center beneath the summit of Mount Fuji. The colored trianglesshow stations. Each line indicates the fast polarization direction, andthe color indicates the station on which it was measured. The linesshowing the fast polarization directions are plotted at the piercingpoints for rays intersecting depths of 2.5 km. Its latitude (longitude) isgiven by X D�dð Þþxd

D Here X, x, D, and d are a station latitude (longitude),a hypocenter latitude (longitude), a hypocenter depth, and a plotteddepth (2.5 km), respectively.

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factor (A). The distribution of the directions of the eigenvectors depends on this assumption of A and thedepth 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 compressionchange. We determine the appropriate factor and depth by a grid search. For each factor and depth in oursearch, we add the gravitational and regional stress tensors to obtain principal stress directions and comparethem to the mean values of fast polarization directions at each station, which are assumed to represent themaximum stress direction. The factor and depth giving the minimum difference between the calculatedandmeasured directions are considered to be the best measurement. The grid search was calculated for factorsfrom 1.01 to 1.05 with the interval of 0.001 and for depths from 1.5 km to 3.5 kmwith 0.5 km interval. In this gridsearch, 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 thebest-fit depth of 2.0 km and factor of 1.02 are plotted. The data period starts 3months before the 2011Tohoku-Oki earthquake and ends 3months after the earthquake. Since we did not observe any significantchanges of splitting with time, using half a year is enough to represent the regional distribution of seismicanisotropy. The calculated direction of maximum compression agrees well with the distribution of fastpolarizations. We infer that the combination of the regional stresses and gravitational effects can cause theobserved contrast of radial pattern of fast directions close to the summit and those parallel to the regionalstresses farther away. Note that the influence of gravitational effects dominates only near the summit.

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 ofstations 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.

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Polarizations at three northern stations, N.SMBH, N.KKKH, and N.TR2H, are not parallel to the regionalcompression. Faults trending ENE-WSWare dominant in this region [Ozaki et al., 2002]. The splitting polarizationsat these stations may be affected more by local fracture systems than by the regional stresses. The existence ofboth stress-controlled anisotropy and structurally controlled anisotropy in the close proximity to each other iscomparable with anisotropic structure obtained in the upper crust of the West Bohemia/Vogtland seismicallyactive 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 areany changes in the subsurface area. A sudden decrease in b value after 11 March 2011 (Figure S9 in thesupporting 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, seismicanisotropy shows little significant temporal change (Figure S10 in the supporting information). The b valuechanges mostly come from the aftershock sequence, which dominate the catalog after the 15 March mainshock. The isotropic velocity change [Brenguier et al., 2014] detected around Mount Fuji may be related tothe b value changes. However, the magnitude of the velocity change is only 0.1%, and if anisotropy alsochanged by 0.1%, we would not be able to distinguish it due to the high scatter of our measurements (FigureS10 in the supporting information). A limited influence of the earthquakes on microcrack distribution isconsistent with the lack of significant increase of the number of LFEs. But since our measurements of splittingand LFE numbers do not have adequate time resolution, this interpretation may change if we increase thetime 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 todiscriminate whether observed anisotropy is structural or stress induced. However, since these two directionscoincide with each other in Mount Fuji, differentiation is very difficult, especially since stresses are animportant factor to form the radial dike and fissure structures. Near the edifice of Mount Fuji, both the fastpolarization directions and the lineament of the geologic structure in the volcanic edifices align parallel to thestresses caused by both the gravitational effects and the regional stress field. Although dikes orient parallel tothe directions of maximum compression, dikes are sporadically distributed [e.g., Takada et al., 2007] andseismic stations are not always placed just above the dikes. The calculated 5% magnitude of anisotropy isnear the fracture criticality (Figure S7 in the supporting information), and thus, structural anisotropy may notbe as significant as that caused by heavily fractured faults or dike structures [Crampin, 1994].

5. Conclusions

Wemeasure shear wave splitting in the vicinity of Mount Fuji with seismic data from 2009 to 2012. At stationsclose to Mount Fuji, fast directions are radial with respect to the summit while those far from the summitare parallel to the direction of regional compression or nearby faults. We also interpret the spatial distributionof 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 fastpolarization directions observed at stations more than ~15 km from the summit suggest that the radiallysymmetric topography of the volcano mainly influences nearby stations and regional stresses aredominant 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 volcanicedifice. Assuming stress control only, the pattern of anisotropy can be fit by the interaction of gravitationalwith regional stresses. If structural anisotropy also contributes to the radial pattern, then the regionalmaximum horizontal stress magnitude is not constrained.

2. The lack of depth dependence of splitting suggests a shallow anisotropic structure around Mount Fuji. This isalso consistent with the fast directions being parallel to dike orientations. We thus suggest that the measuredlow magnitude of splitting, when averaged over the entire path, is caused by effectively isotropic materialat depths below 4km. If we assume an average depth of anisotropy as 1.5–3.5 km, the regional compressionranges from 1.02 to 1.04 times lithostatic pressure, leading to a maximum compression of 51.9MPa.

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

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

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Acknowledgments

K. A. thanks Gaku Kimura, SawakoKinoshita, Setsuya Nakada, and FlorentBrenguier for their helpful discussionand Kiwamu Nishida for helping him toprocess Hi-net data. M.K.S. thanksSimon Lamb for the helpful discussionsand the New Zealand Marsdsen Fundand an ERI Visiting Scholar Fellowshipfor the support. Y.A. is supported by theGrand-in-Aid for Scientific Research(25800244) from Japan Society for thePromotion of Science. We thank theAssociate Editor, the anonymousreviewer, and Václav Vavryčuk for theirconstructive remarks and suggestions.We thank JMA for allowing us to useFUJ2 data. We use the JMA earthquakecatalog and waveform data obtained byNIED Hi-net data server.

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