Citation: Chodavarapu, P., Y. Ohta, B. Shibazaki (2023), Rapid determination of tsunamigenic source
parameters and real time inundation modelling for Tews, Synopsis of IISEE-GRIPS Master’s Thesis.

RAPID DETERMINATION OF TSUNAMIGENIC
SOURCE PARAMETERS AND REALTIME
INUNDATION MODELLING FOR TEWS
Supervisor: Dr. Yusaku OHTA2*
Bunichiro SHIBAZAKI3*

Patanjali Kumar CHODAVARAPU1
MEE22710
ABSTRACTINCOIS-MoES established the Global Navigation Satellite System (GNSS) - Strong Motion
Accelerometer (SMA) network of 35 stations in the Andaman and Nicobar Islands to monitor thecoseismic displacements caused by tsunamigenic earthquake occurrences. This study adopts a robust
methodology for estimating the uncertainties in coseismic fault models with the GNSS data using theMarkov Chain Monte Carlo (MCMC) method and conducts a real-time tsunami inundation modelingusing TUNAMI simulation code and ADCIRC for the assumed earthquake scenarios in the vicinity of
the Andaman and Nicobar archipelago. The results of the probability density function for a range ofsource parameters estimated by the MCMC method for a rectangular fault model, including stress dropand the Variance Reduction (VR) Index for the various assumed earthquake scenarios and tsunami
inundation results for the Port Blair and Car Nicobar regions.
Our findings emphasize the critical roles that the station density and the spatialconfiguration play in accurately determining certain fault parameters. While the fault length, strike angle,
and slip amounts are reliably recovered through the existing GNSS station network, the dip angle, depth,
and fault width estimation warrant further improvement. Our study demonstrates the efficientcomputation of tsunami inundation, achieving results within a mere 5 min computational time for a 12
h simulation of tsunami propagation and inundation modeling for the Andaman and Nicobar archipelago.
Finally, this study recommends that implementing the MCMC single rectangular fault model inversion
with the real-time tsunami inundation modeling at the ITEWC will significantly enhance the capabilityof the operational tsunami services for the Andaman and Nicobar archipelago and the Indian Mainland.
Keywords: GNSS Data, MCMC, TUNAMI, ADCIRC, Realtime Inundation.
1. INTRODUCTIONOn December 26, 2004, the Indian Ocean tsunami brought widespread devastation, highlighting theurgent need for improved disaster management and warnings. The Indian subcontinent, notably theAndaman and Nicobar Archipelago, suffered severely, pushing nations to reshape disaster preparedness.
This led India to establish the Indian Tsunami Early Warning System (ITEWS) in 2007, using real-time
seismic monitoring, tide gauges, with backbone support of Open Ocean Propagation Scenario Database(OOPSDB) of tsunami numerical simulations and following a unique Standard Operating Procedure(SOP) to handle near source and far source regions in order to predict tsunamis and provide timely
1Indian Tsunami Early Warning Center (ITEWC), INCOIS, MoES, Govt of India, India.
Associate Professor from the Graduate School of Science, Tohoku University.
3
International Institute of Seismology and Earthquake Engineering, Building Research Institute.
* Chief examiner2

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advisories by avoiding false alarms. Despite challenges like earthquake magnitude underestimation andcommunication issues, ITEWS has significantly enhanced disaster readiness by expanding its upstream
component with deployment of advanced observation network. The Global Navigation Satellite System(GNSS) - Strong Motion Accelerometer (SMA) network comprising 35 stations in the Andaman andNicobar Islands was established by INCOIS-MoES. This GNSS-SMA network monitors tsunamigenic
earthquake-induced coseismic displacements. In this context, current study is the first of its kind in thestudy area that carried out with two important research objectives: (a) adopting the Markov Chain Monte
Carlo (MCMC) method to estimate uncertainties in coseismic fault models using GNSS data as
described by Ohno et al. (2021); and (b) conducting real-time tsunami inundation modeling through theTUNAMI simulation code and ADCIRC for Andaman and Nicobar archipelago.
2. DATA
Several crucial datasets are required to perform a comprehensive analysis encompassing co-seismic
displacement, event detection, fault parameter estimation, and real-time tsunami inundation modelingin the Andaman and Nicobar Islands. Firstly, a dataset of high-precision GNSS measurements from 35stations spread across the islands is essential. This dataset captures the subtle ground movements during
seismic events, forming the foundation for co-seismic displacement analysis and event detection. The
temporal and spatial resolution of these measurements is vital for accurately assessing the dynamic
nature of seismic activity. Secondly, the co-seismic displacement data needs to be integrated to estimate
the nine source parameters of a fault model (latitude, longitude, magnitude, fault length, width, slip,strike, dip, and rake). This dataset is essential to characterize the earthquake’s underlying fault
mechanism and magnitude accurately. This information is crucial for creating realistic initial conditions
for subsequent tsunami modeling simulations.
Furthermore, the combination of GEBCO 15-arc-second bathymetry data and SRTM 15arc-second topography data is critical. These datasets provide detailed elevation information forunderwater and land areas. They enable the construction of accurate topographical models necessary toestablish modeling grids for real-time tsunami inundation simulations. The precise elevation dataensures the fidelity of the simulations and their ability to predict the potential extent of tsunami
inundation. By integrating these datasets, comprising GNSS measurements, fault parameter estimates,and detailed elevation information, a comprehensive analysis of seismic events, co-seismic
displacements, and tsunami impacts can be conducted in our study area, i.e., the Andaman and Nicobar
Islands. This holistic approach will enhance understanding of the region’s seismic behavior and improve
preparedness and response strategies for future seismic and tsunami events.
3. METHODOLOGY
In the context of our current study, we implemented a robust methodology to estimate uncertainties invarious parameters ofcoseismic fault models.
Figure 1 illustrates themethodology’s data andprocess flow outline,
which was adopted fromthe work of Kawamoto etal. (2017) and Ohta et al.
(2012).
Drawing
inspiration
fromtheresearch conducted by
Ohno et al. (2021), ourapproach involved the Figure 1. Outline of methodology for data & process flow adopted fromutilization of GNSS data Kawamoto et al. (2017) and Ohta et al. (2012)
and integrated the MCMC

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method, alongside the Metropolis-Hastings (M-H) algorithm for sampling. The goal was to gain insights
into parameters such as longitude, latitude, moment magnitude (Mw), focal depth, strike, dip, rake, fault
length, width, amount of slip, stress drop, and VR Index. Through the integration of precise GNSS
measurements and the application of the MCMC technique with the M-H algorithm, we generated adiverse set of fault models that aligned with observed data. This ensemble of fault models enabled acomprehensive analysis of parameter uncertainties with probability density functions (PDFs).
Additionally, our methodology considered geological constraints and adhered to physical principles,
bolstering the accuracy of the estimated fault parameters. In this study, we utilized the shallow waterequation in our methodology to perform real-time calculations for tsunami propagation and inundation.
Both the TUNAMI simulation code (Yanagisawa, 2022-2023) and ADCIRC (Luettich et al., 1992;Pringle, 2020) were applied to simulate tsunamis in open oceans and coastal regions, as well as to model
coastal inundation. The initial deformation conditions were determined by estimating earthquake source
parameters using MCMC methodology.
3.1. Bayesian estimation and MCMC Method
In this case, the fault model parameters, such as longitude, latitude, moment magnitude (Mw), focal
depth, strike, dip, rake, fault length, width, amount of slip, stress drop were considered as the unknowns
to be estimated based on Bayesian estimation (Ohno et al., 2021). Bayesian estimation of the unknown
model parameters conditional to the observations is based on Bayes’ theorem that described as equation(1):𝒑(𝜽|𝒅) =𝒑(𝒅|𝜽) 𝒑(𝜽)
𝒑(𝒅)

(1)

where 𝜽 is model parameter, 𝒅 is permanent displacement data, given that N is number of stations,the dimension of 𝒅 is 3N, 𝒑(𝒅) is PDF of observation, 𝒑(𝜽) is prior PDF of model parameters(Conditional PDF) represents priori information, assuming a uniform distribution in each assumed
range, 𝒑(𝒅|𝜽) is likelihood function measures the degree of fit between observed 𝒅 and calculated̂ (𝜽).
𝒅The MCMC methodology (Ohno et al., 2021) was then employed to estimate theuncertainties in the fault model parameters. MCMC method is a statistical technique that generates alarge number of samples from the parameter space, based on a given prior distribution and likelihood
function. The MCMC algorithm iteratively sampled the parameter space with MCMC sampler- the basicM-H method (Ohno et al., 2021) adopted for sampling. M-H sampler is generating a chain of samples
that approximated the posterior distribution of the parameters in three steps as described by Ohno et al.
(2021).
3.2. Tsunami simulation
For the present study, we used TUNAMI simulation code (Yanagisawa, 2022-2023) to simulate
tsunamis propagating in open oceans and coastal inundation, as well as ADCIRC (Luettich et al., 1992;Pringle, 2020) to model coastal inundation using the initial conditions of deformation computed withestimated earthquake source parameters by MCMC methodology. For real time computations, theTUNAMI simulation code is used in a standard system configuration. Here various parameters likecomputational domain size, time step size, boundary conditions, nesting grids 1,2,3, and 4, and othermodeling parameters required by TUNAMI simulation code appropriately selected based on CourantFriedrichs-Lewy (CFL) criteria as described by Yanagisawa (2022-2023). The foundational work onADCIRC and its detailed description was provided by Luettich and colleagues in 1992. For the presentstudy, simulations were conducted using the ADCIRC-2DDI version, allowing for effective analysis
and assessment.

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4. NUMERICAL SETUP AND RESULTS
4.1 Experimental Setup
Accuratelymodeling
earthquake-induced
displacements is of utmost importance when testing
the automatic uncertainty estimation of a single
rectangular fault model using the MCMC
methodology by Ohno et al. (2021) for our study
area as described in the methodology section.
However, due to the unavailability of actual
earthquake event data for the study area, analyzing
ground displacements for different historicalearthquakes presents a significant challenge. Toovercome this limitation, we propose anexperimental approach to generate synthetic
displacements for GNSS stations in the Andaman
and Nicobar Islands, simulating various
hypothetical earthquake scenarios with different
locations along the trench axis as shown in Figure 2
and combinations of other earthquake source
parameters.
The earthquake source parameters forthese simulations are selected based on historicalearthquakes and available geophysical datasets.
Accordingly, the realistic synthetic displacements
are generated by defining coseismic fault model
Figure 2. Locations of assumed earthquakes
parameters, specifying the assumed epicenter’s (yellow stars) along with GNSS-SMA network
location along the trench axis, near the trench axis, of 35 stations (red rectangles) in Andaman andand far from it for the proposed numericalNicobar archipelago (study area).
experiments. Moreover, considering the depth anddip angle of the coseismic fault is crucial as it influences the pattern of ground displacements; variations
in strike angle of fault are also considered for this study by considering depth, dip and strike angles
based on subduction zone geometries from USGS SLABs 2.0 by Hayes. (2018). The other parameterslike fault length, width and amount slip are calculated based on Wells and Coppersmith (1994) scalinglaws that relate these parameters to the earthquake magnitude, which are commonly used in seismology
and fault studies.
4.2 Uncertainty estimation for coseismic fault model parameters
For the sake of simplicity, we will focus on the Mw 8.2 scenario adjacent to the Andaman and Nicobar
Islands with parameters shown Table 1. Upon successfully producing synthetic displacements for all 35
GNSS stations using the MUDPY software (Melgar & Bock, 2015) in conjunction with the Okada
formulation (Okada, 1985), a stochastic technique was implemented to inject random noise as well.
Through the application of the MCMC algorithm, it became possible to sample from the posterior
distribution of model parameters. Upon the execution of the MCMC algorithm, PDFs for each parameterof the fault model were derived. These PDFs, as depicted in Figure 3, furnish a comprehensive
illustration of the uncertainty linked to each individual parameter. In Figure 3, sections (a) estimatedcoseismic fault models and (b) portray the Posterior PDFs, which illustrate the estimation process offault parameters. To evaluate the overall performance of the model the Variance Reduction (VR) Index
comes into play. A higher VR Index signifies a strong alignment between the synthetic displacements
and the GNSS observations.

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Table1: Assumed Earthquake
Mag(Mw)
8.2

Lat (°)Long (°)

1092

Str (°)Dip (°)

Rake(°)

360

690

Length(km)Width(km)Slip(m)21156

7.7(a)(b)
Figure 3. Results from MCMC method for the scenario of Mw 8.2 near the Andaman and Nicobar
Islands with the parameters listed in Table 1: (a). estimated coseismic fault models with varying color
gradient from light blue to yellow (0.1 to 1.0) indicate the range of solutions, with yellow representing
the most probable fault model. The estimated coseismic fault model (mode value) is represented by thered rectangle with a red vector of the slip direction for the estimated fault. The black and white vectors
denote the observations (synthetics) and calculations based on coseismic fault model respectively. and(b). posterior PDFs of nine source parameters of the estimated coseismic fault model including
Latitude, Longitude, Depth, Strike, Dip, Rake, Length, Width and Slip amount along with Mw, VR andStress drop. The ranges on horizontal axis reflect the maximum search ranges for each parameter.
Inserted values in the upper right-hand corner denote the mean, median and mode in the top, middle
and bottom positions respectively.
4.3 Real time tsunami inundation modelling
The TUNAMI simulation code,developed by Dr. Hideaki
Yanagisawa, Tohoku Gakuin
University (Yanagisawa, 20222023), was harnessed to calculate
the inundation caused by thehypothetical Mw 8.2 earthquake
near the western shoreline of theAndaman and Nicobar Islands.
The initial sea floor deformation
required for numerical simulationof tsunami propagation andinundation is calculated using theOkada formulation with thosenine fault parameters estimatedthrough the MCMC method. This
deformation is then given toTUNAMI simulation code andADCIRC,
bothemploying
numerical techniques to address
nonlinearshallow
waterequations, for computation oftsunami
propagationandinundation in our study area.

(a)(b)
Figure 4. Inundation heights for Grid - 4 of the (a) Car Nicobarand (b) Port Blair regions generated by TUNAMI.

(a)(b)
Figure 5. Inundation heights for the (a) Car Nicobar and (b)Port Blair regions generated by ADCIRC.

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Figure 4 offers a depiction of inundation heights for Grid – 4, highlighting the regions of Car Nicobar
and Port Blair within the scenario of the Mw 8.2 earthquake near the Andaman and Nicobar Islands
calculated using TUNAMI simulation code whereas Figure 5 depicts those calculated using ADCIRC.
5. DISCUSSION AND CONCLUSIONSIn the context of our numerical experiment, we meticulously designed scenarios encompassing bothinterplate and intraplate (outer rise) earthquakes. These experiments aimed to shed light on the complex
interplay between earthquake characteristics and observational constraints within the context of a given
station network.
One significant finding was the successful constraint of the most probable source area orlocation, a triumph largely attributed to the density of stations incorporated within the network. Ingeneral, these estimates fell within the predefined range of input parameters. Yet, notable exceptions
were observed in the case of dip angle, fault width, and depth. These parameters suffered fromdiscrepancies owing to the distinctive configuration of the station distribution, which was structured asa linear array extending from south to north of Andaman and Nicobar Islands. For earthquakes situatednear the network, both strike angle and fault lengths were notably well-recovered, a testament to thenetwork’s effectiveness in these cases. However, the estimation process encountered challenges whenaddressing dip angle, fault width, and depth. In particular, the estimated fault width often leaned towards
overestimation, potentially due to the constraints imposed by the linear station arrangement. A pivotal
observation pertained to instances where the station network effectively captured the fault rupture area.
To sum up, in this research, we used a strong approach to figure out uncertainties inearthquake fault models using GNSS data. We also performed simulations of how tsunamis would
spread using TUNAMI simulation code and another one ADCIRC, based on hypothetical earthquake
scenarios. Moreover, we could quickly calculate tsunami effects in just 5 minutes for a 12-hour
simulation, highlighting the efficiency. Ultimately, we suggest that using the MCMC method along withreal-time tsunami simulations at the ITEWC can greatly improve how well we can predict tsunamis andoffer better tsunami warning services.
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to my supervisors Dr. Yusaku Ohta, Dr. Bunichiro Shibazaki
for their valuable support and experience which helped me to complete my research successfully, andto Dr. Yushiro Fujii for his guidance and advice during my individual study. I would also like toacknowledge Dr. M. Ravichandran, Chairman, INCOIS, Secretary, MoES, and Dr. Tummala Srinivasa
Kumar, Director, INCOIS for nominating me as Frist Participant from India and permission to use
GNSS data from GNNS-SMA Network in Andaman and Nicobar Islands.
REFERENCES
Kawamoto, S., Ohta, Y., Hiyama, Y., Todoriki, M., Nishimura, T., Furuya, T., . . . Miyagawa, K. (2017).
Journal of Geophysical Research: Solid Earth, 122(2), 1324-1349.
Luettich, R. A., Westerink, J. J., & Scheffner, N. W. (1992). ADCIRC-2DD1 and ADCIRC-3DL
Ohno, K., Ohta, Y., Kawamoto, S., Abe, S., Hino, R., Koshimura, S., . . . Kobayashi, H. (2021). Earth,Planets and Space, 73(1), 127. https://doi.org/10.1186/s40623-021-01425-0
Ohta, Y., Kobayashi, T., Tsushima, H., Miura, S., Hino, R., Takasu, T., . . . Demachi, T. (2012). Journalof Geophysical Research: Solid Earth, 117(B2)Okada, Y. (1985). Bulletin of the seismological society of America, 75(4), 1135-1154
Pringle, W. J. (2020)., Zenodo, https://doi.org/10.5281/zenodo.3911282,. In
Yanagisawa, H. (2022-2023). Lecture Notes on "Numerical Simulation of Tsunami Inundation and Its
Application”, IISEE/BRI.
Wells, D. L., & Coppersmith, K. J. (1994). Bulletin of the seismological Society of America, 84(4), 9741002.

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