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.

翻译：估计地球内部介质分布是地震学与地震工程中的一项挑战性任务。物理信息神经网络（PINN）的最新发展为地震反演带来了新的思路。本文提出了一种适用于层状（一维）半无限域地震波反演的PINN框架。通过将吸收边界条件作为软正则化项引入网络，避免了过度计算。具体而言，我们设计了一个轻量级网络来学习未知的介质分布，并利用深度神经网络近似求解变量。整个网络采用端到端架构，同时受稀疏观测数据与潜在物理定律（即控制方程及初始/边界条件）的约束。通过多项实验验证了该方法在一维半无限域地震波传播反演建模中的有效性。