Physics-informed neural network for seismic wave inversion in layered
  semi-infinite domain
Pu Ren Chengping Rao Hao Sun Yang Liu
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.