Ground-penetrating radar wavefield simulation via physics-informed neural network solver

Abstract

The traditional methods for ground-penetrating radar (GPR) wavefield simulation suffer from numerical dispersion phenomena and difficulties for models with complex geometry. We propose to use the physics-informed neural networks (PINNs) to solve the GPR wave equation and model the wavefield propagation. Deep fully connected networks are used to approximate the solution to the equations. Automatic differentiation with back propagation is taken to calculate the partial derivatives. The loss function combining the wave equation, boundary condition, and temporal constraints is constructed and minimized to train the PINNs. As the solver is mesh-free, this simulation method avoids numerical dispersion artifacts and is very flexible in implementation for inversion. The numerical examples indicate that the PINNs solver for GPR wavefield simulation has high accuracy and efficiency. We also explore the possibility of inverting the electrical parameters from the observed wavefields. The results demonstrate the promising potential of the proposed approach in GPR forward modeling.