Bayesian inference of earthquake rupture models using polynomial chaos expansion

by Hugo Cruz-Jiménez, Guotu Li, P. Martin Mai, Ibrahim Hoteit, Omar M. Knio
Year: 2018

Extra Information

Geoscientific Model Development. July 2018, Vol. 11, Issue 7, Pages 3071-3088.


In this paper, we employed polynomial chaos (PC) expansions to understand earthquake rupture model responses to random fault plane properties. A sensitivity analysis based on our PC surrogate model suggests that the hypocenter location plays a dominant role in peak ground velocity (PGV) responses, while elliptical patch properties only show secondary impact. In addition, the PC surrogate model is utilized for Bayesian inference of the most likely underlying fault plane configuration in light of a set of PGV observations from a ground-motion prediction equation (GMPE). A restricted sampling approach is also developed to incorporate additional physical constraints on the fault plane configuration and to increase the sampling efficiency.