Physics-informed neural network for seismic wave inversion in layered semi-infinite domain

• URL: http://arxiv.org/abs/2305.05150v1
• Date: Tue, 9 May 2023 03:30:06 GMT
• Title: Physics-informed neural network for seismic wave inversion in layered semi-infinite domain
• Authors: Pu Ren, Chengping Rao, Hao Sun, Yang Liu
• Abstract summary: Estimating the material distribution of Earth's subsurface is a challenging task in seismology and earthquake engineering. 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.
• Score: 11.641708412097659
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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