T187 Mathematics, Statistics, and Machine Learning in the Geosciences. How Can We Solve Today’s Challenges through Data Mining and Artificial Intelligence?

Tuesday, 11 October 2022
1:30 PM - 5:30 PM
502

MARKOV-CHAIN MONTE CARLO METHODS FOR RECONSTRUCTING HISTORICAL EARTHQUAKE-INDUCED TSUNAMIS

Paper number: T187-14
Program: Technical Sessions
Time: 4:45 PM – 5:00 PM

Authors

Abstract

In this work we explore methods for reconstructing historical tsunami events from anecdotal data. We use Markov chain Monte-Carlo (MCMC) methods in combination with various statistical models to construct a probability distribution on tsunami parameters for an event that occurred near south Sulawesi, Indonesia in 1820. After collecting nearly 80,000 samples from this distribution, we find that the Walanae fault best matches the anecdotal data, although we postulate that this particular earthquake and tsunami were caused by a simultaneous rupture of both the Walane fault and Flores thrust.

Exploring the posterior distribution for this scenario is inherently computationally expensive. To lessen this computational expense, we propose implementing a higher order MCMC sampler (Hamiltonian Monte Carlo) that uses the gradient of the tsunami models to determine where next to sample the distribution, theoretically speeding up convergence of the algorithm and yielding a better exploration of the sample space. In our current setting, computing the gradient is prohibitively expensive, so we discuss methods for using surrogate models with similar but easier-to-compute gradients.

Geological Society of America Abstracts with Programs. Vol. 54, No. 6

doi: 10.1130/abs/2022AM-382789

© Copyright 2022 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.