Citation: Putra Y. M. K., Y. Fujii (2022), Slip distribution of the 2006 West Java earthquake by inversionof tide gauge data using phase-corrected Green’s functions, Synopsis of IISEE-GRIPS Master’s Thesis.

SLIP DISTRIBUTION OF THE 2006 WEST JAVA
EARTHQUAKE BY INVERSION OF TIDE GAUGEDATA USING PHASE-CORRECTED GREEN’S
FUNCTIONSYogha Mahardikha Kuncoro Putra1
MEE21721

Supervisor:Yushiro FUJII2
ABSTRACTThe 2006 West Java earthquake was one of the devastated tsunami events in Indonesia. In this study,
we re-estimated the slip distribution of the 2006 earthquake by tsunami waveform inversions of tidegauge data using phase-corrected Green’s functions. Then, to evaluate our slip models, we performed
tsunami inundation simulations and computed the K and 𝜅 numbers.
The slip distribution obtained with an assumed rupture velocity of 1.25 km/s shows that themaximum slip was around 5.9 to 11.8 m in the shallower part near the trench. The total source length
was 300 km, while the seismic moment calculated from this source was 6.4 x 1020 Nm (Mw = 7.8). The
dominant shallow slips in our slip models support the previous study that classified the 2006 earthquake
as a tsunami earthquake event.
We successfully updated the source model based on the previous study, although we foundthat the K and 𝜅 numbers of our slip models were unsatisfied with recommended standard values. We
also found that the tsunami inundation simulation results were still underestimated around thePangandaran, Cilacap, and Binangun. One possible reason for the underestimation at some survey points
may be local (near coasts) bathymetry effects. Furthermore, we tried to assess the possible locations ofthe landslide source in front of the Permisan region. We found that a near-coast landslide source looks
better to reproduce the extreme tsunami heights in the Permisan region. However, a landslide source far
from the coastline was preferable to reproduce the tsunami heights for the western and eastern sides.
Nevertheless, further studies are needed to determine more accurate landslide sources.
Keywords: 2006 West Java earthquake, Tsunami waveform inversion, Tsunami inundation simulation.
1. INTRODUCTION
Indonesia is a country that has quite complex tectonic conditions and lies on the boundaries of major
tectonic plates; at least five large tectonic plates interact and drive seismotectonic activity aroundIndonesian territory. One Indonesia region with a historical record of big earthquakes and tsunamis isJava Island. According to the BMKG tsunami catalog, in the last 30 years, there have been four
earthquake events that caused tsunamis around Java Island. Among the four tsunami events, the one thatcaused the most casualties was the 17 July 2006 West Java earthquake and tsunami. According to theUnited States Geological Survey (USGS) catalog, the epicenter of the 2006 earthquake was located at
9.284o S and 107.419o E, and the depth was 20 km. After this earthquake and tsunami event, Fritz et al.
(2007) and Tsuji et al. (2021) conducted field surveys by interviewing local people along 200 km ofcoast northeast of the epicenter, and they reported that the residents felt slight or no shaking. The large
tsunami having slight, or no ground shaking was typical of a tsunami earthquake. Using six tide gauge
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2

Indonesian Agency for Meteorology, Climatology, and Geophysics-BMKG.
International Institute of Seismology and Earthquake Engineering (IISEE), Building Research Institute (BRI).

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data, Fujii and Satake (2006) estimated the slip distributions on the faults of the 2006 event. However,their slip distribution was underestimated to reproduce the measured tsunami heights.
There was a systematic delay in travel times and the polarity reversals of the first waves
between the observed tsunami waveforms and simulation results at far-field stations since the case of
the 1960 Chile earthquake. Watada et al. (2014) noticed that the effects of solid earth's elasticity,seawater compressibility, and potential gravitation variation associated with mass motion during thetsunami propagation caused these problems. They proposed a phase-correction method for Green’sfunctions to solve the problems. After this study, there was an improvement to this method by adding
the effects of ocean density stratification and actual ray path (Ho et al., 2017). This phase correction
method has been applied to conduct tsunami waveform inversions using far-field data to construct source
models. In this study, we re-estimate the source of the 2006 earthquake by tsunami waveform inversionsof tide gauge data including far-field stations using the phase-corrected Green’s functions.
2. DATA
We collected the tide gauge data at 19stations around the Indian Ocean. We
obtained the data by sending requests
and downloading the data from thewebsite. The locations of tide gauge
stations are shown in Figure 1. The
tide gauge records contain tsunami
signals and ocean tides. In this study,
to prepare the observed tsunami
waveforms we used a fitted
polynomial function to estimate theeffects of ocean tide and then remove
them from the original waveform.
To prepare the bathymetry
data for tsunami simulations, we usedbathymetry data from GEBCO 2021
with a spatial resolution of 15 arc-sec,
about 0.463 km and anotherbathymetry data from BATNAS with aFigure 1. Computation area used to calculate Green’s
resolution of 6 arc-sec, about 180 m. We
functions and location of tide gauge stations (yellowalso used topography data fromtriangles). The black outline and red line show the areas that
FABDEM with a resolution of 1 arc-sec,
we used GEBCO 2021 and BATNAS data, respectively.
about 30 m. In order to perform
numerical simulations, we need to convert bathymetry data to grid files using GMT software. However,the processed bathymetry grid data from GMT coastline database could not reproduce the actual
coastline's shape well. Because of that, we slightly modified the bathymetry grid values to express theshoreline's shape for obtaining more accurate locations of the tide gauges, referring to Prototype Global
Shoreline Data from the National Oceanic and Atmospheric Administration (NOAA) and Google
Satellite Data.
We also used field survey data to validate the source model obtained from this study. We
referred to four different results of field survey data (Fritz et al., 2007; Kato et al., 2007; Indonesia
Survey Team; and Tsuji et al., 2021) and combined those data. According to Fritz et al. (2007), there
were extreme run up heights recorded up to 21 m around the Permisan region, Nusa Kambangan Island.
3. METHODOLOGY
In this study, to estimate the slip distribution on the fault of the 2006 West Java earthquake, we used thefault model from Fujii and Satake (2006) as a base model. Then, we modified the fault model by halving2the width of the shallow sub faults and extending the fault to the east (a length of 300 km and a widthof 100 km). We divided the fault plane into 18 subfaults with the length and width equal to 50 km eachfor the deeper part and 50 km x 25 km for the shallower part (Figure 2a). Then, by referring to Slab2
model, we determined the depths and the dip angles of the subfaults. For the shallow subfaults, we used
the top depths of 3 km and 4.9 km, shown by odd numbers, while for the deep subfaults, we used 7.6km, shown by even numbers. For the focal mechanism, we used the dip values of 4.4°, 6.2°, and 9.5°
from shallow to deep subfaults. For the value of strike and rake angles, we used 289° and 95°,
respectively.
Then, we calculated the tsunami propagation from each subfault to the tide gauge stations toobtain Green’s functions. The computation area ranges 40°S - 15°N and 55°E - 130°E as shown in Figure
1. For the bathymetry data, we merged the GEBCO 2021 and BATNAS data and resampled it to 24 arcsec. We performed the numerical simulation using the GPGPU code from Satake et al. (2017), which isbased on linear long wave equations in a spherical coordinate system. The computation time was about
6 hours for the tsunami propagation of 10 hours.
We calculated the tsunami waveforms (Green's functions) based on the phase correction
method proposed by Watada et al. (2014). The computation of phase-corrected Green's function isgreatly simplified as follows: for applying the phase correction to the Green's functions from linear longwave simulations, we needed to use Fast Fourier Transform (FFT) which transforms the synthetic
waveform from the time domain to Fourier spectra in the frequency domain. Then, we applied the phasedifferences correction using the table of Ho et al. (2017). Next, the spectra were converted to the time
domain by conducting Inverse FFT.
In this study, we estimated the slip amount for each subfault and its error by using the nonnegative least square method to avoid a negative slip and the delete-half jack knife method, respectively.
We used different assumed rupture velocities such as instantaneous rupture, 1 km/s, 1.25 km/s, 1.5 km/s,
and 2 km/s. We set five times larger weights to the Rodrigues station and we included the effects ofrupture time delays in the tsunami waveform inversions.
After obtaining the slip distributions, we performed tsunami inundation simulations. We used
the TUNAMI code by Yanagisawa (2022). The governing equations are based on the non-linear longwave theory with a spherical coordinate system. We used the merged data from FABDEM and BATNAS.
We used nested grid systems which consist of three layers of 27 arc-sec, 9 arc-sec, and 3 arc-sec fornumerical simulation.
To confirm the reliability of the obtained tsunami source models, we calculated the K and 𝜅numbers (Aida, 1978). K is the ratio between the measured tsunami heights and the simulated ones,
while 𝜅 is the standard deviation. The recommended values of K and κ are 0.8 < 𝐾 < 1.2 and κ < 1.45,respectively.
4. RESULTS AND DISCUSSION
The slip distributions of the 2006 West Java earthquake by the tsunami waveform inversions of tidegauge data using phase-corrected Green’s functions are shown in Figure 2. We found that the larger slip
located in shallow region of the slip models regardless of the assumed rupture velocities. Among those
five slip model results, we preferred to choose the slip model with an assumed rupture velocity 1.25
km/s (Figure 2a.). The slip model shows that the large slip amount is 5.9 to 11.8 m located in the easternpart of the epicenter near the trench with the top depth of 4.9 km. The variance reduction is 0.56 fromthe comparison of the observed tsunami waveforms and the synthetic ones (Figure 3). The seismic
moment calculated from the slip model is 6.4 x 1020 Nm (Mw = 7.8), assuming a rigidity of 1.0 x 1010
N/m2. Our slip models are relatively consistent with other source models that were obtained by seismic
waveform analyses such as Ammon et al. (2006), Yagi and Fukahata (2011), Bilek and Enghdal (2007),and the source model from a tsunami waveform inversion (Fujii and Satake, 2006).

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Figure 2. Slip distributions estimated for the 2006 West Java earthquake by tsunami waveform
inversions with an assumed rupture velocity of (a) 1.25 km/s and different rupture velocities (b)instantaneous rupture, (c) 1.0 km/s, (d) 1.5 km/s, and (e) 2.0 km/s.

Figure 3. Observed tsunami waveforms (black) and synthetic ones with (red) and without (blue) phasecorrection in case of the rupture velocity 1.25 km/s.
We performed tsunami inundation simulations to evaluate our source models using threedifferent slip models and computed K and 𝜅 numbers. We used slip models with an assumed rupture
velocity of 1.25 km/s, which were determined using the Green's functions with phase correction (ourpreferred model) and without phase correction, and the one from Fujii and Satake (2006). We removed
the data in the Permisan region to calculate the K and 𝜅 numbers. For our preferred slip model, weobtained the K number of 1.21, and the 𝜅 number of 2.12, while for the slip model without phasecorrection, we found the K number of 1.46 and the 𝜅 number of 2.37. For the source model from Fujiiand Satake (2006), the K value is 2.14 while the 𝜅 is 2.87. According to the results, we successfully
updated the source model by providing a better ratio of measured tsunami heights and simulation results,
and by providing a smaller value of geometric standard deviation. However, we found that the K and 𝜅
numbers of our slip models were unsatisfied with the recommended standard values. The comparison4

of the measured tsunami height data and simulation results isshown in Figure 4. We also found that the simulation results
were still underestimated around the Pangandaran, Cilacap,and Binangun. One possible reason for the underestimation
at some survey points may be local (near coasts) bathymetryeffects.
Furthermore, we also tried to assess the possible
locations of the landslide source in front of the Permisan
region, which had extreme runup heights. We assumed twocandidates for the landslide source near and far from thecoast (Figure 5a). We performed tsunami inundation
simulations by combining our preferred source model withthe two candidate locations of landslide sources. The results
showed that the near-coast landslide source (Source 1) looks
better to reproduce the extreme tsunami height in Permisan
region than the far-coast landslide source (Source 2).
However, a landslide source far from the coastline ispreferable to increase the tsunami heights for the western andeastern sides (Figure 5b). In addition, we also computed K
Figure 4. Comparison of measured tsunami
and 𝜅 numbers using all the survey points, including the data
height data from four different sources andin the Permisan region. We obtained the K number of 1.18
tsunami inundation simulation results using
and the 𝜅 number of 1.98 for the near-coast landslide source,the three different slip models.
and the K number of 1.19 and the 𝜅 number of 2.10 for farcoast landslide source. The K number from using these twosources satisfied the recommended values. For the 𝜅 number, our result is still unsatisfied with therecommended standard value.

Figure 5. (a) Two candidate locations of landslide source in front of Permisan region. We assumed thetwo sources near the coast (Source 1) and far from coast (Source 2). (b) Comparison of measured
tsunami heights and tsunami inundation simulation results by using combinations of the near-coast
landslide source (Source 1) with our slip model and far-coast landslide source (Source 2) with our slip
model.
5. CONCLUSIONSWe re-estimated the slip distribution of the 2006 West Java earthquake by tsunami waveform inversionsof tide gauge data using phase-corrected Green’s functions. The slip distribution obtained with anassumed rupture velocity of 1.25 km/s shows that the large slips were around 5.9 to 11.8 m in theshallower part near the trench. The total source length was 300 km, while the seismic moment calculated

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from this source was 6.4 x 1020 Nm (Mw = 7.8). The obtained slip distributions were relatively consistent
with other source models. The dominant shallow slips in our slip models support the previous study thatclassified the 2006 West Java earthquake as a tsunami earthquake event.
We performed tsunami inundation simulations to evaluate our slip models and computed theK and 𝜅 numbers. We compared the K and 𝜅 numbers of the slip models with an assumed rupture
velocity of 1.25 km/s which were constructed in this study using the Green's functions with phasecorrection and without phase correction, and the one from the previous study of a tsunami waveform
inversion. The results showed that our preferred slip model provided better K and 𝜅 numbers, although
we found that the K and 𝜅 numbers were unsatisfied with the recommended standard values. One
possible reason for the underestimation at some survey points may be local (near coasts) bathymetryeffects.
Furthermore, we also tried to assess the possible location of the landslide source in front of thePermisan region. The results showed that the near-coast landslide source looks better to reproduce theextreme tsunami heights in the Permisan region than the far-coast landslide source. However, a landslide
source far from the coastline is preferable to increase the tsunami heights for the western and easternsides. Nevertheless, further studies are needed to determine more accurate landslide sources.
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to my supervisor Dr. Yushiro Fujii for his valuable advice
and support which helped me to complete my research successfully, and to Prof. Kenji Satake for hisvaluable comments to improve this study.
REFERENCES
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