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

Abstract

Recently, there has been an increasing interest in leveraging physics-informed neural networks (PINNs) for modeling dynamical systems. However, limited studies have been conducted along this horizon 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 costs. We present a new PINN model for seismic wave modeling in semi-infinite domain without the need for labeled data. Specifically, the absorbing boundary condition is introduced into the network as a soft regularizer for handling truncated boundaries. To scale up, 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 to account for parametric loading, which estimates the wave propagation in semi-infinite domain given the seismic loading at different locations. Various numerical experiments are implemented to evaluate the performance of the proposed PINN model in the context of forward modeling of seismic wave propagation. In particular, we use diverse material distributions to test the versatility of this approach. The results demonstrate excellent solution accuracy under distinctive scenarios.