UNDERGROUND VELOCITY STRUCTURE
EXPLORATION USING SURFACE WAVESIN IWAKI CITY HALL, JAPANChiko Bhakti Mulia WIRADIKARTAMEE12603

Supervisor:

Toshiaki YOKOI

ABSTRACT
We performed microtremor array survey in the Iwaki City Hall in Fukushima prefecture, Japan inorder to estimate underground structure beneath the site and to understand the effectiveness,
limitations, and advantages of surface-wave array measurements as a tool for seismic microzonationand earthquake disaster mitigation. Two methods were applied to microtremor and surface wave data:
the spatial auto-correlation (SPAC) method and the multi-channel analysis of surface waves (MASW)
method with common mid-point cross correlation (CMPCC) analysis. The SPAC method performed
well in relatively low frequency range (0-5Hz) that covers deep shear wave velocity (Vs) structure andthe results show that the engineering bedrock (Vs > 400m/s) depths are deeper than 40 m. The MASW
method has advantages in higher frequency range (5-20Hz) that covers lateral variations of shallow
underground structure and the results show that the subsurface soft layer (Vs < 150m/s) varies between
15-40m. These results indicate that the soft sediment layers are several tens of meters in thickness andcharacteristics of the estimated Vs structure were comparable to those from the reference borehole data,
indicating the soft sediment bottom declines more deeply from east to west. The study has shown thatthe combination of the SPAC and the MASW methods is an effective tool for seismic microzonation
with more stable resolution.
Keywords: Microzonation, SPAC, MASW-CMPCC, Vs structure.

1. INTRODUCTION
Indonesia is one of the most seismically active countries in the world due to a complex tectonic
environment. In order to minimize the damage caused by earthquakes, establishment of the seismic
microzonation database can be a crucial factor. The surface-wave survey method including
microtremor survey plays an important role in the seismic microzonation and has advantages in termsof its convenience, cost and environment-friendliness. We selected Iwaki City Hall in Fukushima
prefecture, Japan as the study area because abundant strong-motion records have been observed in thepremise after the 2011 Tohoku earthquake (Mw9.0) and the site is suited for investigating the relation
between underground velocity structure and local site effects. The purpose of this study is to estimate
shear-wave (Vs) structure beneath the site and to understand the effectiveness, limitations, andadvantages of surface-wave array measurements as a tool for earthquake disaster mitigation.
2. METHODOLOGYThis study uses two kinds of method: the spatial auto-correlation (SPAC) method and multi-channel
analysis of surface waves (MASW) method. Li (2009) has shown that the MASW method has
advantages in higher frequency range while the SPAC method has good performance in relatively lowResearch Institute for Human Settlements (RIHS), Ministry of Public Works, Indonesia.
International Institute of Seismology and Earthquake Engineering, Building Research Institute, Japan.

1frequency range. This indicates that the combination of these two can provide better constraint on Vs
structure.
2.1. SPAC Method
The SPAC method is a method to estimate phase velocity of surface waves that come from various
directions using bi-dimensionally deployed array of seismographs. The concept of the SPAC method
is based on assumptions that microtremors are a spatiotemporally stationary stochastic process, andalso the wavefield consists of dispersive waves propagating along the free surface (Aki 1957, 1965,
Okada 2003, Yokoi and Margayan 2008). The SPAC method requires a circular array that consists ofevenly spaced sensors (usually three sensors) on the circumference of a circle and one sensor at thecenter point. The SPAC coefficient is obtained by the azimuthal average of coherency between
microtremor records observed by two sensors. According to Okada (2003), the SPAC coefficient atangular frequency ω between microtremors observed by two sensors in different locations A(0,0) andB(r,0) is written as:(1)
where E[･] and Re in the third member of the equation denotes the ensemble average over time blockand the real part of a complex, respectively. CA,A(ω) ) is the power spectrum of sensor at A and
CA,B(ω) is the cross-spectrum between sensors at A and B. J0(kr) in the fourth member of the equationis the Bessel function of the first kind of zero order.
The Vs profile is finally estimated using the derived dispersion curve in the inversion
analysis. In this study we combined two different kinds of inversion method (Yokoi, 2005): thedownhill simplex method (DHSM, e.g., Press et al. 2007) and the very fast simulated annealing(VFSA) method (Ingber, 1989) to find optimal values of Vs and thickness of each sedimentary layer.
The P-wave velocity (Vp, m/s) of each layer is estimated using the empirical relationshipbetween Vp and Vs for sedimentary layers saturated by underground water (Ludwig et al., 1970):(2)
The density (ρ, g/cm3) for each layer is estimated by the following relationship (Kitsunezaki et al.,1990):(3)
2.2. MASW Method
The MASW method was first introduced in geophysics (Park et al., 1999) as an evolution of thespectral analysis of surface waves (SASW) for one-dimensional (1D) shear wave velocity structure(Nazarian et al., 1983). The method is a kind of seismic exploration survey that directly converts
time-distance domain waveform data into an image of phase velocity versus frequency. The method
utilizes the dispersion property of surface waves for the purpose of Vs profiling in 1D (depth) or 2D(depth and surface location) format.
Data processing of the MASW method generally consists of three stages: 1) measurement ofseismic surface waves generated from various types of seismic sources (such as sledge hammer), 2)
analysis of the surface wave phase velocities (extracting the fundamental-mode dispersion curves, one
curve from each record), and 3) inverting these curves to obtain 1D (depth) Vs profiles (one profile
from one curve) (Park et al., 1999).
We also use the common mid-point cross correlation (CMPCC) analysis that can improves
the accuracy and resolution of the MASW method, and also enables the SASW method to perform a2

pseudo multi-channel analysis in order to distinguish a fundamental mode from higher modes visually(Hayashi and Suzuki, 2004).
The CMPCC analysis utilizes multi-channel surface wave data from moving-source andcorresponding moving-receiver array observations. According to Hayashi and Suzuki (2004), dataprocessing of the CMPCC analysis consists of the following steps: 1) calculations of cross-correlations
for every pair of traces for each shot gather, 2) grouping of the cross-correlation traces for different
sensor spacing, 3) stacking of cross-correlation traces that have equal sensor spacing in the time
domain, 4) arrangement of the stacked cross-correlations with respect to their spacing at each CMP,
like shot gathers, 5) application of multi-channel analysis to the CMP cross-correlation gathers forcalculating phase velocities of surface waves, and 6) construction of 2D Vs structure image throughthe non-linear least squares inversion.

3. DATAThe microtremor measurements were performed with OYO Company during the daytime from
December 22 to 23, 2012. The arrays for the SPAC method were deployed in two different sizes (seeFigure 1); large (L- and M-arrays) and small (SS-arrays) ones. Each array consists of two sizes oftriangular array (r=75m and 150m for large arrays, r=40m and 50m for small arrays). For the area
where small triangular array cannot be deployed due to the limited space, we introduced an L-shapearray (SS6). Data acquisition for the MASW-CMPCC method is similar to acquisition for a 2D
seismic reflection survey. The geometry is based on the end-on spread, and both source and receivers
move up along survey line. Receivers are fixed at the end of survey line. Here we deployed a line array(see Figure 1) consisting of 24 receivers. The interval of sources and receivers are 1 meter each. Total
numbers of data acquisition for line SW-1 through SW-3 are 68, 65, and 57, respectively.

Figure 1. Array deployment for the SPAC (grey lines) and MASW (red lines) methods. Red star andyellow dots (No. 1-8) indicate the location of boreholes for PS-logging and standard penetration test
(SPT), respectively (based on Guerra-Carballo, 2012).

4. ANALYSIS AND RESULTS
The SPAC and the MASW-CMPCC methods were applied to multi-channel microtremor and surface
wave data observed at Iwaki City Hall. All data processing for the SPAC and MASW methods werecarried out using FORTRAN programs developed by Dr. Toshiaki Yokoi.

34.1. Data Analysis for the SPAC Method
Data analysis for the SPAC method mainly consists of four steps; 1) multiplexing and resampling, 2)
calculation of SPAC coefficient, 3) determination of Rayleigh-wave dispersion curve, and 4) heuristic
search of Vs structure (after Yokoi, 2011).
The microtremor data was originally stored in the SEG2 format (.sg2) and then the data files
was converted to single channel ASCII text format files. The single-channel data files of an array (UDcomponent) were converted into a multi-channel data file of the time-sequential format, in themultiplexing procedure. Next the data was resampled to reduce it in size. This can cause an aliasing
effect so it is necessary to apply a digital anti-aliasing filter that has high cut characteristics before
thinning out.
The waveform data derived in the previous step were used to calculate the SPAC coefficient,which is an azimuthal average of the coherency between microtremor records observed by two sensors(Figure 2). The frequency range of analysis was set
from 0.1Hz to 10Hz. Smoothing was carried out on
the averaged cross- and auto-correlations by theParzen window with 0.4Hz for the L- and Marrays, and with 0.7Hz for SS-arrays.
The next step is to estimate dispersion
curve of Rayleigh waves from the derived SPAC
coefficients. The SPAC coefficients wereconverted to the value of kr in Eq. (1) by applyinga fifth order polynomial equation thatapproximates the inverse function of J0(x). We
combined small and large sized array data toestimate the dispersion curves in a broad frequency
Figure 2. Derived SPAC coefficients
range.
L1 and M1 arrays.
For the heuristic search of Vs structure,
we set the search range of each layer thickness and Vs values considering estimated physicalproperties of the existing borehole data. The optimum values of Vs and thickness of each layer were
estimated to explain phase velocity for the given dispersion curve of the Rayleigh wave (result shownin Figure 3). The values of Vp and ρ are calculated from the estimated Vs after each iteration using
Eqs. (2) and (3), respectively.

Figure 3. (a) Comparison between observed and theoretical dispersion curves. (b) Vs structure and its
standard deviation estimated by the inversion method for a combination of L1, M1, and SS3 arrays.

44.2. Data Analysis for the MASW Method
Data analysis for the MASW method in this study mainly consist of four steps; 1) data acquisition, 2)
CMP gathering, 3) velocity analysis and 1D inversion of dispersion curve for all CMP, and 4) plotting
2D velocity structures (after Yokoi, 2012).
Fast Fourier Transform (FFT) was applied to all waveforms of common shot gather andcross spectra were calculated for all pairs of channels to obtain cross-correlograms. The
cross-correlograms were then sorted over various
shot gathers to form CMP gathers. The CMP
gathers (distance-time space) were againtransformed into the frequency domain by FFT.
The frequency domain data was integrated overspacing with respect to apparent phase velocity
to derive the velocity-frequency (c-f) domain
image. The maximum amplitude of stacked CMP
gathers at each frequency was used to determine
the phase velocity dispersion curve. The
dispersion curve in the lower phase velocity wasrecognized as the fundamental mode and thenFigure 4. The c-f images from the CMPCC
extracted (Figure 4). For the heuristic search ofgathers.
Red crosses indicate the estimated phase
Vs structure (see Figure 5), we used the same
velocities
inversion method that we adopted in the SPAC
method.

Figure 5. (a) Comparison between observed and theoretical dispersion curves of line
SW-1 at a lateral distance of 14.5 m. (b) Estimated 2D Vs profile along line SW-1.
5. DISCUSSION
The Vs structures obtained through the SPAC and MASW methods were compared well to thereference borehole data. It is noteworthy that all the analysis procedures were critically checked withthe help of available standard tools. However, as for the MASW results, there is a difference in Vs
value trend between the PS-logging data and the estimated model. In the PS-logging data, Vs values inthe second top layer is larger than those in top and third layers, while the estimated Vs value in thesecond layer is substantially smaller than those of top and third layers. It is speculated that thedifferent trend is due to geological irregularities at the site. In addition, the fourth layer of the MASW
results has low Vs values, indicating the MASW method provides information of the sediment at ashallower level than that from the SPAC method.
Figure 6 shows estimated contour lines for the engineering bedrock depth inferred from the
SPAC results and borehole data. Figure 7 shows estimated contour lines for the bottom subsurface
layer inferred from the MASW results and borehole data. The results indicate that the bottom of thesoft sediment and bedrock are inclined toward the west.

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Figure 6. Estimated contour map for theengineering bedrock depth inferred from the
SPAC results and existing borehole data.

Figure 7. Estimated contour map for the bottom
subsurface layer inferred from the MASW
results and existing borehole data.

6. CONCLUSIONSThe estimated Vs structures from the SPAC and MASW methods indicate the existence of soft
sediment layers that have thickness of several tens of meters. The structural characteristics are notlargely deviated from those of the existing borehole data, indicating the combined use of two kinds of
inversion methods provided reasonable solutions. Considering our results and existing survey data, thedepth to the engineering bedrock becomes gradually deeper in a westerly. And it is my intention to
apply the knowledge I have gained to help my institution on the microzonation program, specificallyusing microtremor array observation.
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to Dr. Takumi HAYASHIDA who helped me a great deal
during the writing of this thesis and during my research. Also, I would like to thank the OYO
Corporation for their help during the field work.
REFERENCES
Aki, K., 1957, Bulletin of the Earthquake Research Institute, 35, 415–456.
Aki, K., 1965, Geophysics, 30, 665–666.
Guerra-Carballo, José Roberto, 2012, Master Paper of Seismology DMP Program, BRI & GRIPS.
Hayashi, K., and H. Suzuki, 2004, Exploration Geophysics, 35, 7–13.
Ingber, L., 1989, Mathl. Comput. Modelling, Vol. 12 No. 8, 967-973.
Kitsunezaki, C., N. Goto, Y. Kobayashi, T. Ikawa, M. Horike, T. Saito, T. Kuroda, K. Yamane, and K.
Okuzumi, 1990, Journal of Japan Society for Natural Disaster Science, 9, 1–17.
Li, J, 2009, Master Paper of Seismology DMP Program, BRI & GRIPS.
Ludwig, W. J., Nafe, J. E., and Drake, C. L., 1970, The Sea, Vol. 4, Wiley-Interscience, NY, 53-84.
Nazarian, S., Stokoe II, K.H., and Hudson, W.R., 1983, Transportation Research Record No. 930,
38-45.
Okada, H., 2003, Geophysical Monograph Series, No.12, Society of Exploration Geophysicists.
Park, C.B., Miller, R.D., and Xia, J., 1999, Geophysics, 64, 800-808.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P., 2007, Cambridge Univ. Press.
Yokoi, T., 2005, Seismological Society of Japan Fall Meeting, Program and Abstract B049.
Yokoi, T., 2011, Instruction Analysis of SPAC Method, IISEE, BRI.
Yokoi, T,. 2012, MASW Instruction Manual, IISEE, BRI.
Yokoi. T., and Margaryan, S., 2008, Geophysical Prospecting, 56, 435-451.

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