Physics-informed neural network for seismic wave inversion in layered semi-infinite domain
- Resource Type
- Working Paper
- Authors
- Ren, Pu; Rao, Chengping; Sun, Hao; Liu, Yang
- Source
- Subject
- Physics - Geophysics
Computer Science - Machine Learning
Mathematics - Numerical Analysis
- Language
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.