Three-dimensional crustal deformation analysis using physics-informed deep learning

TL;DR

This paper explores the use of physics-informed neural networks (PINNs) to model 3-D crustal deformation caused by earthquakes, demonstrating high accuracy in internal deformation and potential for large-scale earthquake analysis.

Contribution

The study introduces a novel PINN-based method for 3-D crustal deformation modeling, including fault slip estimation from observational data, advancing earthquake simulation techniques.

Findings

High accuracy in internal deformation modeling

Successful fault slip estimation from real data

Identified challenges in modeling rigid motions

Abstract

Earthquake-related phenomena such as seismic waves and crustal deformation impact broad regions, requiring large-scale modeling with careful treatment of artificial outer boundaries. Physics-informed neural networks (PINNs) have been applied to analyze wavefront propagation, acoustic and elastic waveform propagations, and crustal deformation in semi-infinite domains. In this study, we investigated the capability of PINNs for modeling earthquake crustal deformation in 3-D structures. To improve modeling accuracy, four neural networks were constructed to represent the displacement and stress fields in two subdomains divided by a fault surface and its extension. Forward simulations exhibited high accuracy for internal deformation but yielded errors for rigid motions, underscoring the inherent difficulty in constraining static deformation at an infinite distance. In the inversion analysis, fault slip distributions were estimated using surface observational data. Application to real data from the 2008 Iwate-Miyagi inland earthquake showed a fault slip consistent with previous studies, despite underestimation of the magnitude. This study demonstrates the capability of PINNs to analyze 3-D crustal deformation, thereby offering a flexible approach for large-scale earthquake modeling using real-world observations and crustal structures.