Physics-informed neural network for seismic wave inversion in layered semi-infinite domain
Abstract
Estimating the material distribution of Earth's subsurface is a challenging task in seismology and earthquake engineering. The recent development of physics-informed neural network (PINN) has shed new light on seismic inversion. In this paper, we present a PINN framework for seismic wave inversion in layered (1D) semi-infinite domain. The absorbing boundary condition is incorporated into the network as a soft regularizer for avoiding excessive computation. In specific, we design a lightweight network to learn the unknown material distribution and a deep neural network to approximate solution variables. The entire network is end-to-end and constrained by both sparse measurement data and the underlying physical laws (i.e., governing equations and initial/boundary conditions). Various experiments have been conducted to validate the effectiveness of our proposed approach for inverse modeling of seismic wave propagation in 1D semi-infinite domain.
Cite
@article{arxiv.2305.05150,
title = {Physics-informed neural network for seismic wave inversion in layered semi-infinite domain},
author = {Pu Ren and Chengping Rao and Hao Sun and Yang Liu},
journal= {arXiv preprint arXiv:2305.05150},
year = {2023}
}