Citation: Harvan, M., Y. Tanioka (2023), Real-time tsunami inundation forecasting for Mentawai Islands,Indonesia, Synopsis of IISEE-GRIPS Master’s Thesis.

REAL-TIME TSUNAMI INUNDATION FORECASTING
FOR MENTAWAI ISLANDS, INDONESIA
Muhammad HARVAN1
MEE22714

Supervisor: Yuichiro TANIOKA2
ABSTRACTThis study evaluated the implementation of the Near-Field Tsunami Inundation Forecasting (NearTIF)
method to enhance the operational capability of the tsunami early warning system in Mentawai Islands,
Indonesia. This study focused on testing the method’s reliability by conducting a retrospective test in
forecasting the tsunami inundation during the 2010 Mentawai tsunami event.
The NearTIF method utilizes a pre-computed database containing pairs of inundation model andtsunami waveforms, an input fault model, and a NearTIF search engine. The pre-computed database wasconstructed using 462 unique fault scenarios. Linear and Non-Linear tsunami simulation was done tosimulate tsunami propagation and inundation model. This study also utilized w-phase inversion togenerate input fault models for the NearTIF method. A search engine was applied to choose the best
inundation model for a specific site.
The NearTIF method’s reliability was analyzed by comparing the simulated tsunami height fromforecasting results and direct numerical forward modeling results (NFM) using input fault parameterswith field observation data collected from another study. The computation speed to obtain each
inundation model is also being compared. Analysis results in this study indicated that the NearTIF
method is reliable and practical to be implemented in real-time tsunami early warning systems.
Keywords: Tsunami, Inundation, Early Warning System, Mentawai.
1. INTRODUCTION
The Mentawai islands are located above a seismically active megathrust zone and have experienced
significant earthquakes and tsunamis. The deadliest event occurred in 2010 when an M7.8 earthquake
struck the Island, triggering a big tsunami that caused over 700 people to die, 20,000 were displaced,and 4,000 households were affected (Ismoyo, 2010).
To mitigate the tsunami risk, the Indonesia Tsunami Early Warning System (InaTEWS) wasestablished. The system provides crucial tsunami information to all affected communities, such as thetsunami’s Estimated Time of Arrival (ETA) and Estimated Tsunami Height (ETH). However, thequalitative nature of the tsunami warning provided by InaTEWS does not currently provide detailed
information about tsunami-affected areas on land.
This study aims to evaluate the implementation of the Near-field tsunami inundation forecasting(NearTIF) method. This method was developed by Gusman et al. (2014), which allows the generationof a detailed forecast of tsunami inundation, offering valuable information about the potential areas that
may be affected by the tsunami's inundation. By integrating this forecasting method into the existing
warning system, we aim to enhance the reliability of tsunami warnings in the Mentawai islands region.
1 Agency for Meteorology Climatology and Geophysics (BMKG), Indonesia.
2

Professor, Institute of Seismology and Volcanology (ISV), Faculty of Science, Hokkaido Univ.

12. DATA
2.1. Bathymetry and topography
This study used BATNAS 6 arc-seconds bathymetry dataset and interpolation results of elevation
contour lines from a 1:50,000 m Mentawai islands topography map with 12.5 m interval as a topography
grid dataset. Both datasets were obtained from the Indonesia Geospatial Agency (BIG).
2.2. Broadband seismic waveform
This study utilized the vertical component of IRIS broadband stations with an epicentral distance
between 5o to 50o to conduct the W-phase inversion to obtain the fault parameter. The broadband seismic
data used in the W-phase inversion were divided into several datasets, which are 10 min, 20 min, and 30
min recording data after the origin time.
2.3. Tsunami field study
This study validated the simulation results from numerical forward modeling and the forecasted resultsfrom the NearTIF method with the 2010 Mentawai tsunami field study data collected by Satake et al.
(2012). The data was collected from 27 observation points in 6 different areas in Mentawai islands.
3. METHODOLOGY
3.1. NearTIF method
This method was developed based on the idea that if different earthquakes generate the same tsunami
waveform at nearshore locations, the resulting tsunami inundation in the coastal areas from thosetsunamis will exhibit similar characteristics. Therefore, the inundation model at a specific site can beforecasted without requiring a precise earthquake source model for a given event. The NearTIF method
relies on three components to work: (1) database containing pairs of pre-computed inundation scenariosand pre-computed tsunami waveforms at nearshore locations, (2) an input fault or tsunami source model,and (3) a “best-scenario” search engine.
3.2. Tsunami simulation
Non-linear tsunami simulation was performed to compute the inundation extent and tsunami height
inland. This simulation used seven computational layers assigned to four-level nested grid domains(Table 1). Inundation models were computed in the finest layers (Domains 4A to 4D). On the other hand,linear tsunami simulation was done to simulate tsunami waveforms in specific observation points forforecasting processes. Linear tsunami simulation only incorporated one coarsest layer assigned to thelargest domain (Domain 1) to generate tsunami waveform simulation results rapidly and efficiently.
Table 1. Computational domains for the nested grid configuration in tsunami simulation.
Dom.

Lon. Min (o)Lon. Max (o)Lat. Min (o)Lat. Max (o)Grid size (')

Grid size (m)

1

96.17

103.41

-6.86

0.38

11847.5

298.41102.00

-4.85

-1.24

0.25

461.9

399.83100.73

-3.33

-2.43

0.083

154.0

4A

99.95

100.01

-2.64

-2.58

0.016

30.9

2

4B

100.02100.09

-2.86

-2.79

0.016

30.9

4C

100.17100.24

-3.09

-3.02

0.016

30.9

4D

100.30100.37

-3.24

-3.17

0.016

30.9

3.3. Fault model scenarios and virtual observation points
The inundation database in this study was created from 462
rectangular fault scenarios, accommodating three types ofearthquakes: (1) megathrust, (2) tsunami earthquake, and (3) outerrise. Figure 1 shows the location of fault scenarios included in thedatabase.
Twelve virtual observation points were employed, at whichtsunami waveforms were simulated for the input fault model andevery fault scenario in the pre-computed database. Eachobservation point is associated with a computation domain (Figure2). Waveforms from the input model will be compared towaveforms from the database at the same observation points, andthe most similar waveform based on Root Mean Square Error
Figure 1. fault model scenarios(RMSE) analysis will be chosen as the forecasting result for thatlocation. Colored dots are thecomputational domain.
top center position of each fault.
3.4. W-phase inversion
The w-phase inversion was utilized to obtain fault parameters ofthe 2010 Mentawai tsunami event. 10-min, 20-min, and 30-min
broadband recording datasets were used to simulate earthquake andfault parameter determination during real-time tsunami monitoring
and to test the quality of the inversion results for each elapsed timeafter the earthquake’s origin time.
3.5. Model validation
K and κ parameters by Aida (1978) were used in this study tovalidate the accuracy of tsunami height from the simulation
results. This study considers the acceptable accuracy for
simulated tsunami inundation height when the K parameter isbetween 0.6 – 1.4.

Figure 2. The location of virtual
observation points assigned toeach computation domain.

4. RESULTS AND DISCUSSION
4.1. Comparison of the forecasted tsunami inundation scenarios with each input fault model
4.1.1. Case 1: Input fault model from 10-min W-Phase solution
The solution from the 10-min broadband dataset provided a fault
model with a seismic moment of 5.95 x 1020 Nm or equivalent toan Mw 7.78 earthquake. The fault has an area of 104.7 x 34.9 km2,
thrust mechanism (i.e., strike = 336o, dip = 7o, rake = 110o),centroid location of 98.81o E and 3.51o S, depth of 25.5 km, andslip amount of 8.06 m. Figure 3 illustrates the inundation model(NFM) and the scenario forecasted using the NearTIF method.
NearTIF search engine chose Scenario 290 for this domain.

3This scenario has a magnitude of Mw 7.9, an area of 116.1 x 38.7
km2, thrust mechanism (i.e., strike = 320o, dip = 10o, rake = 90o),
centroid location of 99.66oE and 3.82oS, depth of 10 km, and slip
amount of 19.84 m.
Both models show similar inundation characteristics atmost locations. The maximum inundation distance is 266 m forthe simulation result using NFM and 268 m for the forecasted
result from the NearTIF method. Tsunami heights from bothmodels generally agree with each other in most areas. However,
the maximum tsunami height simulated by both models issomewhat different. Maximum tsunami height is 10.1 m for theNFM result and 12.1 m for the chosen scenario model.
Figure 4 compares the tsunami waveform from NFM (red)and chosen scenario from the NearTIF database (blue) for Domain
4A in this case. Both waveforms are relatively similar, as indicatedby low RMSE.

Figure 3. Tsunami inundation
model for Domain 4A. (top)
NFM
results,(bottom)
forecasted model.

Figure 4. Comparison between tsunami waveform simulated from input model in Case 1(blue) and from best site-specific scenario selected by NearTIF search engine (red).
4.1.2. Case 2: Input fault model from 20-min W-Phase solution
The solution using the 20-min broadband dataset provided a fault
model with a seismic moment equivalent to an Mw 7.85
earthquake. The fault has an area of 106.5 x 35.5 km2, thrustmechanism (i.e., strike = 341o, dip = 7.1o, rake = 125o), centroid
location of 98.72oE and 3.51oS, hypocenter of 25.5 km depth, anda slip amount of 9.58 m.
Figure 5 depicts the inundation model for Domain 4B.
Scenario 266 was selected by the NearTIF search engine for thisdomain. This scenario has a magnitude of Mw 7.7, an area of 97.7
x 32.6 km2, thrust mechanism (i.e., strike = 320o, dip = 10o, rake= 90o), centroid location of 99.06oE and 3.02oS, hypocenter at 10km depth, and a slip amount of 14.04 m.
Both models from NFM and the chosen scenario exhibit
similar inundation characteristics at most locations. The
maximum inundation distance is 1.393 m for the NFM simulationand 1.362 m for the forecasted result. Tsunami heights from bothmodels generally agree in most areas, although the maximum
tsunami height differs slightly: 7.58 m for the NFM result and 7.05
m for the chosen scenario model.
Figure 6 compares tsunami waveforms from the NFM
result, and the chosen NearTIF scenario result for Domain 4B.
Both waveforms are relatively similar, indicated by the small
RMSE value.

4

Figure 5. Tsunami inundation
model for Domain 4B. (top)
NFM
results,(bottom)
forecasted model.

Figure 6. Comparison between tsunami waveform simulated from input model in Case 2(blue), and from best site-specific scenario selected by NearTIF search engine (red).
1.3. Case 3: Input fault model from 30-min W-Phase solution
The solution using the 30-min broadband dataset provided a fault
model with a seismic moment equivalent to an Mw 7.91
earthquake. The fault has an area of 106.5 x 35.5 km2, thrustmechanism (i.e., strike = 321o, dip = 5.1o, rake = 82.7o), centroid
location of 99.52oE and 4.01oS, hypocenter of 25.5 km depth, anda slip amount of 10.81 m.
Figure 7 compares the inundation model between theinput model for this case with the forecasted model. Scenario 298
was selected as the best site-specific inundation model for domain
4d. This scenario has a magnitude of Mw 7.6, an area of 89.6 x
29.9 km2, thrust mechanism (i.e., strike = 320o, dip = 10o, rake =90o), centroid location of 99.966oE and 4.22oS, hypocenter of 10km depth, and a slip amount of 11.82 m.
Both models show similarities to each other in terms ofinundation extent and tsunami height. Both models have amaximum inundation distance of 179 m in the same location.
Tsunami heights from both models are matched in most areas,
although the maximum tsunami height differs slightly. Themaximum tsunami height for the current domain is 9.29 m for theinput model and 10.31 m for the best-forecasted model.
Figure 8 illustrates the comparison between tsunami
waveforms from the NFM result and the forecasted scenario
model by the NearTIF method for Domain 4D.

Figure 7. Tsunami inundation
model for Domain 4D. (top)
NFM
results,(bottom)
forecasted model.

Figure 8. Comparison between tsunami waveform simulated from input model in Case3 (blue), and from best site-specific scenario selected by NearTIF search engine (red).
4.2. Model validation with tsunami field observation data
The calculation results of K and κ parameters for all input model cases in 6 field study areas are shownin Figure 9. Results from Cases 1 and 2 show that simulated tsunami height from both models has
acceptable accuracy in most areas, except the Sabeu area in Domain 4B. This is probably because the5fault solution from the 10- or 20-min broadband data still contains errors. The results are better for Case
3. However, the overestimated tsunami height might be due to an inappropriate assumed rigidity value
for estimating the slip amount.

Figure 9. K and κ parameters for Case 1 (left), 2 (middle), and 3 (right). Blue diamonds
and lines are for the input model, and orange ones are for the forecasted model.
4.3. Computation Speed
On average, it required at least 44 min to obtain the inundation model for four domains in this study byusing the NFM method. As for the NearTIF method, the average run time was merely 48 seconds.
Therefore, by utilizing the NearTIF method and w-phase inversion, a reliable inundation model forecast
for Mentawai islands can be obtained within 11 minutes after the earthquake happened (10 minutes toobtain inversion results and less than 1 minute to forecast the inundation model).
5. CONCLUSIONSThis study successfully evaluated the reliability and the practical aspect of the NearTIF method
application in tsunami early warning systems. The results indicated that if an appropriate fault or tsunamisource model can be obtained, the NearTIF method can forecast reliable inundation models with highaccuracy within a very efficient run-time. Therefore, the NearTIF method is very practical to be
implemented to enhance the tsunami early warning system.
ACKNOWLEDGEMENTS
This research was required to finish the “Seismology, Earthquake Engineering, and Tsunami Disaster
Mitigation” training course by GRIPS, JICA, and IISEE/BRI. I would like to give my deepest gratitude
to Prof. Tanioka and Dr. Fujii for their guidance, support, and encouragement throughout the completionof this research.
REFERENCES
Aida, I. (1978). Reliability of a tsunami source model derived from fault parameters. Journal of Physics
of the Earth, 26(1), 57-73.
Ismoyo, B. (2010). Indonesia Battles Disasters on Two Fronts. Jakarta, Indonesia: The Jakarta Globe.
Retrieved April 15, 2023
Gusman, A. R., Tanioka, Y., Macinnes, B. T., & Tsushima. (2014). A methodology for near‐fieldtsunami inundation forecasting: Application to the 2011 Tohoku tsunami. Journal of GeophysicalResearch: Solid Earth, 119(11), 8186-8206.
Satake, K., Nishimura, Y., Putra, P. S., Gusman, A. R., Sunendar, H., Fujii, Y., . . . Yulianto, E. (2012).
Tsunami Source of the 2010 Mentawai, Indonesia Earthquake Inferred from Tsunami Field Surveyand Waveform Modeling. Pure Appl. Geophys., 170(2013).

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