Quantification of Uncertainty and Its Propagation in Seismic Velocity Structure and Earthquake Source Inversion

TL;DR

This paper discusses methods to quantify and propagate uncertainties in seismic velocity models into earthquake source inversion, improving the accuracy and reliability of hypocenter determination using Bayesian and machine learning techniques.

Contribution

It introduces a Bayesian multi-model inversion and a physics-informed neural network approach to account for velocity uncertainties in seismic source analysis.

Findings

Bayesian multi-model inversion effectively incorporates uncertainty.

PINN-based tomography quantifies velocity model uncertainties.

Application to Nankai Trough improves hypocenter accuracy.

Abstract

In earthquake source inversions aimed at understanding diverse fault activities on earthquake faults using seismic observation data, uncertainties in velocity structure models are typically not considered. As a result, biases and underestimations of uncertainty can occur in source inversion. This article provides an overview of the author's efforts to address this issue by quantitatively evaluating the uncertainty in velocity structure models and appropriately accounting for its propagation into source inversion. First, the Bayesian multi-model source inversion method that can incorporate such uncertainties as probability distributions in the form of ensembles is explained. Next, a Bayesian traveltime tomography technique utilizing physics-informed neural networks (PINN) to quantify uncertainties in velocity structure models is introduced. Furthermore, the author's recent efforts to integrate these methods and apply them to hypocenter determination in the Nankai Trough region are briefly discussed. The article also outlines future prospects of source inversions considering uncertainties in velocity structure models and the anticipated role of the emerging scientific machine learning (SciML) methods such as PINN.