Quantifying Uncertainties in Fault Slip Distribution during the T\=ohoku Tsunami using Polynomial Chaos

TL;DR

This paper develops a method to infer fault slip distribution during the Tōhoku tsunami using polynomial chaos surrogates and water surface data, demonstrating the approach's effectiveness despite initial challenges.

Contribution

It introduces an alternative polynomial chaos construction method for fault slip inversion, enabling accurate inference from water surface elevation data.

Findings

Fault slip distribution can be inferred from water surface data.

An alternative basis pursuit approach improved surrogate convergence.

The method minimizes error between observations and model predictions.

Abstract

An efficient method for inferring Manning's $n$ coefficients using water surface elevation data was presented in Sraj et al. (2014) focusing on a test case based on data collected during the $T\=ohoku$ earthquake and tsunami. Polynomial chaos expansions were used to build an inexpensive surrogate for the numerical model Geoclaw, which were then used to perform a sensitivity analysis in addition to the inversion. In this paper, a new analysis is performed with the goal of inferring the fault slip distribution of the $T\=ohoku$ earthquake using a similar problem setup. The same approach to constructing the PC surrogate did not lead to a converging expansion, however an alternative approach based on Basis-Pursuit DeNoising was found to be suitable. Our result shows that the fault slip distribution can be inferred using water surface elevation data whereas the inferred values minimizes the error between observations and the numerical model. The numerical approach and the resulting inversion are presented in this work.