INCOIS-MoES
established the Global Navigation Satellite System (GNSS) - Strong Motion
Accelerometer (SMA) network of 35 stations in the Andaman and Nicobar Islands
to monitor the coseismic 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 the Markov
Chain Monte Carlo (MCMC) method and conducts a real-time tsunami inundation
modeling using 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 of source parameters estimated
by the MCMC method for a rectangular fault model, including stress drop and 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 spatial configuration 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
efficient computation 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 capability of the operational tsunami services for
the Andaman and Nicobar archipelago and the Indian Mainland.
Keywords: GNSS Data, MCMC, TUNAMI, ADCIRC, Realtime Inundation.