Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-Concept

TL;DR

This paper demonstrates how Gaussian process regression can fuse multiple seismic velocity models to quantify uncertainty in earthquake ground motion predictions, highlighting the importance of probabilistic approaches in seismic hazard analysis.

Contribution

It introduces a novel workflow that incorporates model uncertainty into ground motion prediction using Gaussian processes, a first step towards more comprehensive seismic hazard assessments.

Findings

Predictive uncertainty captures differences between velocity models.

Distribution of ground motion amplitudes is wider when accounting for model uncertainty.

Probabilistic methods improve physics-based seismic hazard analysis.

Abstract

Estimates of seismic wave speeds in the Earth (seismic velocity models) are key input parameters to earthquake simulations for ground motion prediction. Owing to the non-uniqueness of the seismic inverse problem, typically many velocity models exist for any given region. The arbitrary choice of which velocity model to use in earthquake simulations impacts ground motion predictions. However, current hazard analysis methods do not account for this source of uncertainty. We present a proof-of-concept ground motion prediction workflow for incorporating uncertainties arising from inconsistencies between existing seismic velocity models. Our analysis is based on the probabilistic fusion of overlapping seismic velocity models using scalable Gaussian process (GP) regression. Specifically, we fit a GP to two synthetic 1-D velocity profiles simultaneously, and show that the predictive uncertainty accounts for the differences between the models. We subsequently draw velocity model samples from the predictive distribution and estimate peak ground displacement using acoustic wave propagation through the velocity models. The resulting distribution of possible ground motion amplitudes is much wider than would be predicted by simulating shaking using only the two input velocity models. This proof-of-concept illustrates the importance of probabilistic methods for physics-based seismic hazard analysis.