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.

本文提出一种基于物理信息神经网络（PINN）的地震波反演框架，以求解半无限域的介质分布问题。通过设计轻量网络学习未知介质分布和深度神经网络近似求解变量来验证该方法的有效性。