Estimation of Site Response for Kanto Plain by Use of the Data from
Dense Strong Motion Seismograph Array
Kenichi Tsuda1, Yoshiaki Hisada2, and Kazuki Koketsu³
Abstract: We have applied nonlinear inversion analysis to estimate the site response
for the Kanto area using data of the 852 stations constituting the SK-net (Seismic Kanto
Strong motion-network) K-NET, and KiK-net from nineteen moderate-sized earthquakes
that have occurred surrounding the Kanto Area. We have determined: source parameters
(seismic moment and corner frequency) for each event; quality factor Q(f) for the path
based on the borehole records. We also determined the frequency-dependent factors for
the borehole sites as well as for the surface sites. Our inversion scheme has the
advantage of being independent of constraints on site response at a reference station. The
resulting seismic moment values not only agree with these determined by NIED
(National Research Institute for Earth Science and Disaster Prevention), but also are
proportional to the fc-3. We found a frequency dependent quality factor for the path; Q(f )
=107 f 0.52.
The site response values, averaged over 0.5 – 1.0 Hz, correlate well with the
shear wave velocities for the upper 30m (AVS30) and the depth to the seismological
basement (shear wave velocity ≥ 3.0 km/s). These site response values also correlate
well with the areas of unconsolidated sediments, especially swampy land. The estimation
of site response based on the quantitative geophysical parameters (e.g. AVS30 or depth
to the basement) as well as on the qualitative surface geology classification can lead the
better understandings of spatial characteristics of site response around the Kanto region.
Introduction
The Kanto area, the most populated area in Japan, is situated in a zone of very complex
tectonics (Figure1), in which the Philippine Sea Plate is being subducted beneath the
North-American Plate, while the Pacific plate is being subducted beneath the Philippine Sea Plate
(Ishida, 1992, Sato et al., 2005). Because of its complexity, this area has a history of hazardous
earthquakes, such as the 1923 Kanto Earthquake and the 1703 Genroku-Kanto Earthquake
(Furumura, 2004; Kobayashi and Koketsu, 2005; Tanaka et al., 2005b). Also the existence of
thick sedimentary basin below this area could amplify ground motions drastically that causes the
damage to this populated area. Thus, the seismic hazard mitigation for the next hazardous
earthquakes is the primary issue. Because the better correlation between soil type and the degree
of damage was reported for the past hazardous events, such as 1923 Kanto earthquake, 1985
Michoacan earthquake, 1994 Northridge earthquake, and 1995 Hyogoken-Nanbu (Kobe) (e.g.,
Kawase, 2005), the estimation of site response is very important to the seismic hazard mitigation.
Seismic hazard mitigation for the next hazardous earthquakes is the primary issue for this area.
Furthermore, the huge damage on Kobe city from the recent 1995 Kobe earthquake provided the
impact for many local governments in this populated Kanto area to cooperate in the installation of
a strong-motion seismograph array for the purpose of seismic hazard mitigation. The result brings
the SK-net (Seismic Kanto Research Project, 2001).
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One of the advantages of using this kind of array is to get a better understanding of the
effects of surface geology. Recently many studies have tried to predict ground motion level for
the 1923 Kanto earthquake by using numerical simulation (Sato et al., 1998, 1999: Dan et al.,
2000) to have a better prediction for future large events. However, the estimations of the effects
of surface geology especially to the high frequency range interest to the engineers (≥ 1 Hz) are
not enough. Thus, incorporating better estimation the effects of surface geology into the ground
motion prediction is essential.
In order to quantify site effects, Borcherdt (1970) introduced the spectral ratio approach by
taking ratios of the Fourier amplitude spectrum of soil sites to a rock site. Iwata and Irikura
(1988) developed a method to derive site response from data that many researchers have applied
to estimate site response for diverse geological conditions. They also tried to find parameters to
correlate with site response values. Field and Southern California Earthquake Center Phase III
Working Group (2000) found that the average shear wave velocity in the upper 30m (AVS30) of
soil was indicative of variation in site amplification factors. They also found that the degree of
amplification was strongly related to basin depth, but cautioned that the correlation might be
spurious and connected to some other site characteristics. Uchiyama and Midorikawa (2003)
recently compiled the results of site response based on the site classification. In order to address
these problems, the accumulation of site response values from a large number of stations located
on a variety of soil conditions is necessary.
In this study, we have tried to estimate site response for the whole Kanto area. The initial
step was the isolation of source and path parameters by nonlinear inversion analysis (Tsuda et al.,
2006). We used the S wave amplitudes. The absolute site response (response relative to the
seismological basement, usually VS: 3 km/sec) is estimated because the estimates of borehole
response derived from the inversion are independent of the reference site. After the source and
path parameters are determined by inversion, the borehole site response is simply the difference
between the observed spectrum and the predicted spectrum—based on the source and path
parameters. Once stable source and path parameters have been established, a spectrum for each
event is computed for each surface site. The ratio between the observed spectrum and computed
spectrum is the surface site response. We compute an average surface site response based on all
events that are analyzed. Having obtained this average site response, we look for correlations
between site response and surface geology. While the classification of surface geology is a
qualitative estimation, the data is useful for analyzing the gross characteristics of spatial variation
of site response. The average site responses are compared with average shear wave velocities in
the upper 30m of surface soil, with depths to the seismological basement and with surface
geology. Because our site responses represent the relative response from seismological basement
to the surface i.e. independent of a reference site, they can bring a better understanding of spatial
characteristics of site responses and be useful for the ground motion prediction for future
earthquakes.
Data Set
The SK-net began operation after 1995 Kobe earthquakes for the purpose of seismic hazard
mitigation around Kanto area. The eleven local governments and three institutions (listed in the
Table 1) developed the seismic array; ERI (Earthquake Research Institute, University of Tokyo)
collects the data. The array consists of three-component accelerometers for more than 800 surface
stations and more than 70 borehole stations (Seismic Kanto Research Project, 2001, Table 1).
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Since 1997, this array has recorded numerous earthquakes over a broad range of magnitudes.
Each accelerometer is digitized at 200 samples /sec for the KiK-net and the Yokohama City
Strong-Motion Network, and at 100 samples /sec for other networks. The K-NET and KiK-net
arrays are managed by the National Institute for Earth Science and Disaster Prevention (NIED).
Each station has been logged for P-wave velocity, S-wave velocity, and density to the depth range
up to 10-100m. For this study, we selected 19 events that occurred around Kanto area. Each event
was recorded on almost all of the KiK-net borehole accelerographs as well as most surface
stations (Table 2). Because we used only two horizontal components of the record, the total
number of records is 10220 (20440 components). The epicenters, shown as solid red circles, and
the station distribution are shown in Figure 2. The solid blue triangle denotes the station used to
determine source and path parameters. The focal depths are greater than 50 km; magnitudes range
between 4.5 - 5.8. Average hypocentral distances to the array vary from 50 to 130 km (Figure 2).
In Table 2, we give the source location, focal depth and magnitude determined by the NIED for
each event.
For our analysis, we used the two horizontal components at each site. A 10 s time window
beginning 1.0 sec before the first S-wave arrival was picked for all events. We applied a cosine
window taper of 0.5 sec to both ends of the record. After the Fourier amplitude spectrum of each
component was calculated, we used vector summation of the two horizontal components as the
amplitude spectrum in the frequency range of 0.5–20 Hz.
For the events occurring in the Kanto area, Kinoshita (1992) showed that f max (Hanks, 1982)
is usually greater than 25 Hz; thus the effects from f max are likely to be small within our frequency
range. Since most level of ground shaking, such as peak ground acceleration (PGA) is smaller
than 200 gal, the effects of nonlinear site response are likely to be negligible (Beresvev, 2002).
Method
The observed ground motion—for linear response—can be expressed as a convolution of the
source, path and site. In the frequency domain, we can write this convolution relation as a
multiplication:
A( f )  S( f ) Site( f ) R1efR /Q( f )
(1)
where f is frequency, A( f ) is the acceleration amplitude spectrum of the recorded ground

motion, S( f ) is the source spectrum, Q ( f ) is the quality factor, which is assumed to be


frequency dependent, Q( f )  Qo f  , Site( f ) is the site response amplitude, R is the distance

from source (Table 2) to site, and  is the average shear wave velocity of the medium (3.7 km/s,

Yamanaka et 
al., 1998). To estimate
the site response, it is necessary to isolate these three factors
from observed records. In general, isolating each element requires constraints to avoid tradeoffs
 A standard constraint is to impose a condition of Site( f ) at a rock
among these three elements.
-3-

station. This method has already been applied extensively in many studies (e.g., Iwata and Irikura,
1988; Bonilla et al., 1997; Yamanaka et al., 1998; Kinoshita and Ohike, 2002; Kawakami et al.,
2005). Another approach is to define a source spectrum S( f ) for a specific ‘reference’ event
(Moya et al., 2000 and Moya and Irikura, 2003). Because some shallow borehole records
(reference station) may be contaminated the borehole response itself, we used a method to

separate the source, path and site effect that is independent
of a reference station (Tsuda et al.,
2006).
Equation 2 uses Boatwright’s (1978) representation of a  2 source spectrum (Brune, 1970),
in which the amplitude of the source spectrum has a nonlinear dependence on the corner
frequency:
S( f )  CM o (2f ) 2 f c2
where

C
f
4
G0  F rad
4πVs 3
 f c4 
0.5
(2)
(3)
C depends on the radiation parameter of the source: Frad [we used the average S-wave radiation
pattern coefficient of 0.63, (Boore and Boatwright, 1984)], the material parameters for source
area (3000 [kg/m³] for density and 4.5 [km/s] for shear wave velocity) and the free surface effect
Go is included. The seismic moment Mo and corner frequency fc are determined from the
spectrum (Brune, 1970). Including the path effect introduces a nonlinear dependence on
frequency when the attenuation parameter Q(f) is assumed to have a power law dependence on
frequency: Q(f)=Qo f .
Because of the nonlinear relationship between the data and source and path parameters, we
use a Heat Bath algorithm (Sen and Stofa, 1995) to invert for the four parameters: Mo, fc, Qo, and
. Having these parameters, we derive a frequency-dependent site response. We start by using
data from 10 events of similar size (Mw: 4.0-5.3) having been recorded on more than 50 borehole
accelerometers of KiK-net, plus three ERI basement stations. We assume that the initial borehole
response is independent of frequency and then invert the data for all 10 events to determine Mo, fc,
Qo, and. Our first estimate of borehole response is the difference between observed and
predicted spectrum. This estimate is used to invert for Mo, but only for f ≤ 1.0 Hz, which yields a
new estimate of seismic moment by using initial values for other path parameters (Kinoshita and
Ohike, 2002). We again invert all of the data to find fc, Qo, and . This produces a second estimate
of the site response: the difference between the observed spectrum and the predicted spectrum,
based on the current values of Mo, fc, Qo, and. This process is repeated until the difference
between observed and predicted spectra (borehole response) converges. In this way we derive a
borehole response that depends on frequency and simultaneously solve for the path
parameters Qo, . Once the stable values for both borehole site response and path parameters are
obtained, we invert the borehole data for all 19 events to find Mo and fc (Table 2).
Having the source and path parameters for all 19 events, we use Equation 1 to predict the

spectrum at all surface stations for all the events. The difference between the predicted spectrum
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and the observed spectrum is the surface site response |Site (f) |. We average the results from the
19 events to determine the final estimate of the site response at each surface station.
Results
Source and Path parameters
In Figure 3a, we compare seismic moment for the deep events found by our inversion
between our results and those obtained by NIED based on the teleseismic data (Fukuyama et al.,
1998). We also show the values of seismic moment and corner frequency for each event in Table
2. The two estimates of seismic moment are generally within a factor of two. In Figure 3b, we
plot seismic moment versus corner frequency for 19 events. The three lines correspond to the
Brune stress drops of 1 MPa, 10 MPa and 100 MPa, respectively. Overall, seismic moment scales
with the inverse corner frequency cubed, indicating a constant stress drop about 10 MPa. Overall,
the stress drop varies by a factor 10, which is similar to the results produced by other studies (e.g.,
Hanks, 1978; Archuleta, et al., 1982).
Our results for the quality factor; Q( f )  107 f 0.52 is plotted in Figure 3c, along with
quality factors obtained by others for the Kanto area (Yamanaka et al., 1998; Kinoshita and
Ohike, 2002; Kawakami et al., 2005). Even though our study deals with the larger area with
broad hypocentral distance ranges, our average attenuation values for the Kanto area agree in
general with the other studies.
Because we assume the path effect (Q) is common to each event and each site, the derived
attenuation parameters are the averaged value for the whole Kanto area. As Kato (2005) pointed
out, if the data set has stations with a broad range of hypocentral distances, the effects of
attenuation could be different across the array. Because the hypocentral distances to the stations
at northern area are large, the high attenuation (larger Q) values may be expected. However, this
area is also located close to the volcanic area that is expected to have lower Q values. Thus, the
more attention is necessary when ground motion has been predicted incorporating Q values for
the northern area.
Site response
To interpret the spatial variation of the site response, we first contoured the average site
responses for three frequency ranges: 0.5–1.0 Hz (a), 1.0-3.0 Hz (b), 3.0-10.0 Hz (c) in Figure 5.
While the averaged site response values show huge variations even for low frequency range
0.5–1.0 Hz, there seem to have some trends of spatial distribution of site response. For example,
the area in the northern part of Tokyo, which recorded high seismic intensity values by the Kanto
earthquakes (Koketsu and Miyake, 2005), shows noticeably large site response values for 0.5–1.0
Hz. Furthermore, southwestern area (west Yokohama) with large site response values correlates
with the area also heavily damage from the 1923 Kanto earthquake (Dan et al., 2000). These
results indicate that understanding the characteristics of spatial distribution of site response is
useful to consider the potential damage from the future hazardous events.
In the following section, we will discuss the relationship between the averaged site response
values and the possible quantitative geophysical and qualitative geological parameters (AVS30,
depth to the basin, and category of the surface geology) for the possibilities to explain the trend
of spatial distribution of site responses.
Before moving on to the discussion about the relationships of site response values and some
-5-
parameters, we here have tried to validate the resultant site response values. A recent and
extensive reflection, refraction, and gravity investigation, the Dai-Dai Toku geophysical survey,
examined structure in the Kanto basin (Sato et al., 2005; Tanaka et al., 2005). Tanaka et al.
(2005) developed a velocity structure by incorporating new data obtained by the Dai-Dai Toku
project and the result is shown in Figure 6. Using three layers (Shimousa, Kazusa, Miura) to
represent sediments overlying a basement layer (Table 4), we have tried to calculate theoretical
1D-transfer functions based on data from 20 KiK-net stations located inside the area (Figure 6).
By using the velocity data for these KiK-net stations, we calculated 1D theoretical transfer
functions. In Figure 7a, the theoretical transfer functions and the derived site responses on
KiK-net surface station are shown. The theoretical transfer functions agree fairly well with the
derived responses in the low frequency range (0.5–1.0 Hz) for many stations. However, the
derived site responses for frequencies greater than 1.0 Hz range are larger than the theoretical
estimates of site response. This difference may come from the incorporation of
frequency-dependent Q in the theoretical estimates (Uetake and Kudo, 2005) or other complex
effects, such as lenses or dipping subsurface topography are missing in the plane layer
approximation used to compute the theoretical estimates. The comparison shows that the
amplification agrees in the low frequency range.
Because some boreholes seismographs are situated on very stiff material (shear wave
velocity ≥ 3000 m/s) for these KiK-net stations, we can also compare the derived site responses
with the observed (borehole-surface) spectral ratio as another validation (Figure 7b). For these
borehole-surface station pairs, the derived (surface) site response compares well with the
observed spectral ratio over a broad frequency range. As already mentioned, most borehole
accelerometers are deployed at depths where the sensor is located within a very stiff layer that
can be used as an approximation to the seismological basement. This accounts for the better
correlation between spectral ratio estimates and the site response estimates based on the inversion
results.
Discussion
For the Kanto area, geotechnical and geomophological data are complied by Wakamatsu and
Matsuoka (2005). In this section, we discuss the trend of spatial variation of site response based
on those data. First, we show the map of the surface geology (Figure 7a). This classification is
based on the GIS data of 250m-mesh of the surface (Wakamatsu and Matsuoka, 2005). Originally
Wakamatsu and Matsuoka (2005) classified surface geology into 25 groups based on the
geomorphologic data (Table 4). However, the number of group (25) looks too many to make use
of this information for estimation of spatial variation of site response, we coalesced their data into
9 groups (Table 4). The new groups are:
Group1: Mountain Range
Group2: Volcanic Range
Group3: Plateau
Group4: Lowland
Group5: Swampy Area
Group6: Sandy Beach
Group7: Sandy Area
Group8: Reclaimed Land
Group9: Coast Line (River Bed)
-6-
A better correlation based on the detail clustering between site response values and the category
of surface geology is usually not expected (Rogers et al., 1985). However, the sites having high
response values for low frequency range (Figure 4a) correlate very well with surface geology
such as swampy areas or sediment areas near rivers. The surface geology is useful to understand
the gross feature of the spatial distribution of site responses, even though it is only a qualitative
estimation.
Next we show a map of the average shear wave velocities in the upper 30m (AVS30) (Figure
7b). Compared to the contour map of site response (Figure 4), sites having high response values
correlate with basins of significant sediment depth and with small AVS30 values in the frequency
range 0.5–1.0 Hz. This spatial interpretation indicates that geophysical parameters (such as
AVS30) can be also used to get rough estimation of site response for low frequency range. We
plotted the averaged site response as a function of AVS30 in Figure 7c. Apparently, the averaged
site response for the low frequency range shows a negative correlation with AVS30 (The
correlation coefficient: γ is -0.63); a functional form of least square fitting is as follows;
log( Site ( 0.51.0 Hz) )  0.977  log( AVS30)  2.97  1.6
(3)
The middle and high frequency distributions appear to be randomly related to AVS30 and with
many large site response values. Almost all stations show site response values being larger than
1.0—most surface stations are amplified. The large site response values in the high frequency
range may come from the effects of very thin weathered layers. This is sometimes reported by
some recent earthquakes, such as 2003 Miyagi-oki earthquake and 2003 Northern-Miyagi
earthquake sequences (Tsuda et al., 2006b).
Finally we compared the site response distribution with the basin depth determined by
Tanaka et al. (2005b). We show the depth to the Kazusa-layer (Figure 8a) and the Miura-layer
(Figure 8b) in addition to the basement (Figure 8c). The area around Yokohama (KiK-net,
KNGH10 station, Figure 5) with deep sediment layer (Miura) shows large site response values
for both low and middle (1Hz-5Hz) frequency ranges. The depth to the seismological basement is
also large around this area (Figure 8c). The area with very soft material (low AVS30 and swampy
surface geology) around middle Kanto area (northern part of Tokyo-Bay) corresponds to the area
of thick sediment layers.
Conclusions
In this study we have used a nonlinear inversion method to solve for both source and path
parameters. The trend of our attenuation parameters represented by Q(f) is similar to previous
studies. Seismic moments agree well with values from NIED and scale with corner frequency f c3 .
The site response values are supported by the correlation of the 1D theoretical transfer functions
and observed spectral amplitude ratio between borehole to the surface with derived site response
for low frequency ranges. The average site response values are in agreement with depth to the
seismological basement and AVS30 in low frequency range 0.5–1.0 Hz. These results indicate
that geophysical parameters, such as AVS30 and depth to the seismological basement are useful
to estimate spatial variation of site response, especially for low frequencies. The areas that show
large site response (in the low frequency range) coincide with swamps or areas of soft soil.
-7-
Because of the large population and many urban facilities in the Kanto area, a huge amount
of the physical damage as well as loss of life and injured is expected from future large
earthquakes. In order to mitigate the damage from these earthquakes, the prediction of ground
motion for broad frequency range (Kamae et al., 1998; Liu et al., 2006) is important. The results
of site response can be applicable to the ground motion prediction for future large events based
on the combination with source and path modeling (Tsuda, 2007). Thus, the accumulation of
observed data recorded on this array is very important for the better estimation of site response.
Acknowledgements
We are indebted to SK-net for allowing us access to this unique data set. This study is
supported by Dai-Dai toku project, supported by the Ministry of Education, Culture, Sports,
Science and Technology of Japan. We also appreciate the data provided by Drs. M. Matsuoka and
K. Wakamatsu at NIED, and Mr. Y. Tanaka at ERI, University of Tokyo. The comments by Drs K
Wakamatsu and H. Miyake were useful. We also appreciated the comments by Prof. Archuleta
and Dr Steidl at UCSB and help for improving the English of this manuscript. The comments by
Dr. Cassidy, anonymous reviewer and Editor Prof. Kawase were useful to develop the
manuscript.
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strong motions during the great 1923 Kanto, Japan, Earthquake (Ms 8.2). Part 2: Forward
simulation of seismograms using variable-slip rupture models and estimation of near-fault
long-period ground motion, Bull. Seism. Soc. Am. 88, 206-227.
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http://www.sknet.eri.u-tokyo.ac.jp/ (in Japanese).
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Elsevier, New York.
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Tanaka, Y., K.Koketsu, H. Miyake, T. Furumura, H. Sato, N. Hirata, H. Suzuki, and T. Masuda
(2005a). The Daidaitoku community model of the velocity structure beneath the Tokyo
metropolitan area (1), Programme and abstracts, Joint meeting for Earth and Planetary
Science.
Tanaka, Y., Y. Ikegami, R. Kobayashi, H. Miyake, and K. Koketsu. (2005b). Strong motion
validation in the Tokyo metropolitan area: ground motion simulation of the 1923 Kanto
earthquakes, Programme and abstracts, Seismological Society of Japan, Fall meeting (in
Japanese).
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prediction, Ph.D thesis, University of California, Santa Barbara, 199pp.
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response by use of the Yokohama High-Density Strong Motion Network, Bull. Seism. Soc.
Am. 96, 926-942.
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Butsuri-Tansa, 55, 53-65. ( in Japanese with English abstract).
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1. K. Tsuda, Department of Earth Science and Institute for Crustal Studies, University of
California, Santa Barbara, CA 93106-1100, U.S.A kenichi@crustal.ucsb.edu
Current Address: Shimizu Corporation, 3-4-17, Ethujima, Koto-ku, Tokyo, 135-8530, Japan
2. Y. Hisada, Kogakuin University, Shinjuku, Tokyo 113-0032, Japan.
3. K Koketsu, Earthquake Research Institute, University of
Tokyo, Tokyo, Japan.
Figure Caption
Figure 1: Plate geometry surrounding Japanese arc-Islands (after Ishida, 1992). The red and black
numbers correspond to the depth of subducting the Philippine Sea Plate and the Pacific Plate,
respectively.
Figure 2: Geography of the Kanto region. Epicenters of the events listed in the Table 2 are plotted
in map view by solid circles. Solid triangles colored by blue denote the reference station used to
determined source and path parameters. Open square means the surface station used in this study.
Figure 3: Source and path parameters obtained by inversion. (a) Seismic moments from this study
are compared with those from NIED which uses three stations in a regional broadband network.
Solid circles correspond to the deep events, and open circles—shallow events. Dashed lines show
a factor of two. (b) Seismic moment is plotted versus corner frequency for the 19 events. Lines of
constant stress drops (Brune) are plotted. Within a factor of two the stress drops are ~10 MPa. (c)
Comparison of Q 1 including our model of Q( f )  107 f 0.52 .We also plotted other Q model
obtained by data in Kanto area (Yamanaka et al., 1998; Kinoshita and Ohike, 2002; Kawakami et
al., 2005)
Figure 4: The contour map site responses are averaged for over frequency bands of 0.5 -1.0 Hz
(a), 1.0-5.0 Hz (b) and 5.0 - 10 Hz (c).
- 11 -
Figure 5: Velocity structure around Kanto area determined by Tanaka et al. (2005b). Each
contour line corresponds to the elevation of seismological basement (Vs=3.0 km/s).
Figure 6: (a) Comparison of site response by 1D transfer function based on four-layered
model (red) and with derived surface site response (blue lines, average ± 1 σ) at KiK-net
stations located inside Fig 6. (b) The observed spectrum amplitude ratio between the
borehole to the surface is also shown by black traces (average ± 1 σ).
Figure 7: (a) The map of surface geology classified for each 250 m mesh (originally based on
Wakamatsu and Matsuoka, 2005). The area with purple corresponds to the reclaimed land; green
is swamp area and brown is mountainous area. (b) The map of averaged shear wave velocities for
upper 30m (AVS30) for each 250 m mesh (Wakamatsu and Matsuoka, 2005). Averaged site
response for over frequency bands of 0.5 -1.0 Hz (c1), 1.0-5.0 Hz (c2) and 5.0 - 10 Hz (c3) as a
function of averaged shear wave velocities for upper 30m(AVS30) (Wakamatsu and Matsuoka,
2005). The solid line in (c1) corresponds to the least square fittings.
Figure 8: The contour map of the sediment layers to (a) Miura-layer (Vs = 900 [m/sec]), (b)
Kazusa-layer (Vs = 1500 [m/sec]), and (c) Seismological basement (Vs = 3000 [m/sec]),
respectively. All the depths are determined by Tanaka et al. (2005b).
- 12 -
Network
(NIED) K-NET
(NIED) KiK-net
ERI
ERI (Basement)
Gunma Pref
Tochigi Pref
Ibaraki Pref.
Saitama Pref
Number of
Stations
135
61
25
3
59
47
79
92
Chiba Pref.
Tokyo Metropolitan Office
Tokyo Fire Department
Kanagawa Pref
Yokohama City
Yokohama (Borehole)
Number of
Stations
74
42
40
37
150
8
Total
852
Network
Table1: Number of stations deployed by local government and institutions collected by SK-net.
Event
Lat.† (N)
Long. † (E)
Depth†
(km)
MW
Moment†
[Nm]
Moment§
[Nm]
fc§ [Hz]
1
3/13/03
36.09
139.87
50
4.9
2.34E+16
2.41E+16
1.95
2
4/8/2003
36.07
139.91
44
4.8
2.11E+16
1.58E+16
1.69
3
5/10/2003
35.81
140.11
65
4.7
1.37E+16
2.00E+16
1.41
4
5/12/2003
35.87
140.09
50
5.2
7.07E+16
3.87E+16
1.28
5
5/17/03
35.74
140.7
53
5.3
1.13E+17
1.03E+17
0.71
6
8/18/03
35.8
140.11
71
4.8
1.92E+16
2.55E+16
1.55
7
9/20/03
35.22
140.3
56
5.7
3.53E+17
2.22E+17
0.89
8
10/15/03
35.61
140.05
68
5.1
5.15E+16
5.32E+16
1.60
9
7/10/04
36.08
139.89
50
4.7
1.21E+16
1.34E+16
2.10
10
7/17/04
34.83
140.36
59
5.6
2.39E+17
1.40E+17
1.34
11
8/6/04
35.61
140.06
71
4.7
1.27E+16
1.92E+16
1.98
12
10/6/04
35.99
140.09
65
5.7
4.52E+17
2.80E+17
0.78
13
2/8/05
36.14
140.09
65
4.9
2.20E+16
1.88E+16
2.11
14
2/16/05
36.04
139.9
53
5.4
1.33E+17
7.44E+16
1.52
15
4/10/05
35.73
140.62
50
5.8
4.95E+17
5.52E+17
0.42
16
6/20/05
35.73
140.69
51
5.5
1.94E+17
1.26E+17
0.81
17
7/23/05
35.58
140.14
73
4.7
4.74E+17
4.64E+17
0.84
18
7/28/05
36.13
139.85
51
4.7
1.17E+16
9.58E+15
3.00
19
8/7/05
35.56
140.11
73
4.5
6.88E+15
1.30E+16
2.35
†These parameters were determined by National Institute for Earth Science and Disaster Prevention (NIED),
Tsukuba, Japan.
§ Resultant parameters
Table 2: List of the earthquakes used in this study. The 'lat' and 'lon' are the northern latitude and
eastern longitude of an earthquake epicenter, respectively. Mo and fc denote the resultant seismic
moments and corner frequencies obtained by our inversion: Mo (NIED) are those determined by
NIED with the centroid moment tensor inversion.
- 13 -
Layer No.
1
2
3
4
Vs [m/s]
450
900
1500
3000
Density [kg/m³]
1.85
2.4
3.2
3.3
Q
30
50
80
150
Table 3: Velocity model used to calculate synthetic transfer functions (Sato et al., 1998, 1999:
Tanaka et al., 2005b).
Grouping by
this study
Grouping by Wakamatsu and Matsuoka (2005)
Mountain Range
Volcanic Range
Plateau
Mountain, Mountain footslope, Hill
Volcano, Volcnic footslope, Volcanic hill
Rocky strath terrace, Gravely terrace,
Terrace covered with volcanic ash soil
Valley bottom lowland, Alluvium fan, Natural levee
Back marsh, Abandoned river channels
Delta and coastal lowland, Marine sand and gravel bars
Sand dune, Lowland between coastal dunes and /or bars
Reclaimed land, Filled land
Rocky shore, Rock reef, Dry riverbed, Riverbed
Lowland
Swampy Area
Sandy Beach
Sandy Area
Reclaimed Land
Coast Line
(River bed)
Table 4: Geological classification by Wakamatsu and Matsuoka (2005)
- 14 -
Figure 1:
- 15 -
Figure 2:
- 16 -
Figure 3:
- 17 -
Figure 4
- 18 -
Figure 5
- 19 -
Figure 6
- 20 -
Figure 6 (Continued)
- 21 -
Figure 7
- 22 -
Figure 8:
- 23 -