Abstract

We build on the method introduced by Ringer, et al., applying it to an 1820 event that happened near South Sulawesi, Indonesia. We utilize other statistical models to aid our Metropolis-Hastings sampler, including a Gaussian process which informs the prior. We apply the method to multiple possible fault zones to determine which fault is the most likely source of the earthquake and tsunami. After collecting nearly 80,000 samples, we find that between the two most likely fault zones, the Walanae fault zone matches the anecdotal accounts much better than Flores. However, to support the anecdotal data, both samplers tend toward powerful earthquakes that may not be supported by the faults in question. This indicates that even further research is warranted. It may indicate that some other type of event took place, such as a multiple-fault rupture or landslide tsunami.

Degree

MS

College and Department

Physical and Mathematical Sciences; Mathematics

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2022-07-13

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd12849

Keywords

mathematics, uncertainty quantification, tsunamis, geology, Markov chain Monte Carlo, Bayesian inference, Gaussian processes, machine learning, statistics, inverse problems

Language

english

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