SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain

TL;DR

This paper introduces a physics-informed neural network (PINN) for seismic wave modeling in semi-infinite domains, addressing computational challenges and demonstrating high accuracy without labeled data through innovative boundary handling and training strategies.

Contribution

The study develops a novel PINN framework with boundary regularization, sequential training, and surrogate modeling for efficient seismic wave simulation in large, semi-infinite domains.

Findings

Achieves high accuracy in seismic wave forward modeling.

Effectively handles semi-infinite domain boundary conditions.

Demonstrates versatility across various material distributions.

Abstract

There has been an increasing interest in integrating physics knowledge and machine learning for modeling dynamical systems. However, very limited studies have been conducted on seismic wave modeling tasks. A critical challenge is that these geophysical problems are typically defined in large domains (i.e., semi-infinite), which leads to high computational cost. In this paper, we present a novel physics-informed neural network (PINN) model for seismic wave modeling in semi-infinite domain without the nedd of labeled data. In specific, the absorbing boundary condition is introduced into the network as a soft regularizer for handling truncated boundaries. In terms of computational efficiency, we consider a sequential training strategy via temporal domain decomposition to improve the scalability of the network and solution accuracy. Moreover, we design a novel surrogate modeling strategy for parametric loading, which estimates the wave propagation in semin-infinite domain given the seismic loading at different locations. Various numerical experiments have been implemented to evaluate the performance of the proposed PINN model in the context of forward modeling of seismic wave propagation. In particular, we define diverse material distributions to test the versatility of this approach. The results demonstrate excellent solution accuracy under distinctive scenarios.