Monte Carlo Full-Waveform Inversion of Cross-Hole Ground-Penetrating Radar Data Based on Improved Residual Network

Abstract

A full-waveform inversion (FWI) of ground-penetrating radar (GPR) data can be used to effectively obtain the parameters of a shallow subsurface. Introducing the Markov chain Monte Carlo (MCMC) algorithm into the FWI can reduce the dependence on the initial model and obtain the global optimal solution, but it requires a large number of computations. In order to better detect underground targets based on ground-penetrating radar data, this paper proposes a joint scheme of an improved ResNet and an MCMC full-waveform inversion. This scheme combines the Bayesian MCMC algorithm and an improved ResNet to accurately invert the target dielectric permittivity. The introduction of deep learning networks into the forward calculation part of the MCMC inversion algorithm replaced the complex forward simulation process, greatly improving the inversion speed. It is worth noting that the neural network model was an approximation of complex forward modeling, and, therefore, it contained modeling errors. The MCMC method could quantify and explain modeling errors during the inversion process, reducing the impact of modeling errors on the inversion results. Finally, a simulation dataset was constructed for training and testing, and the errors were statistically analyzed. The results showed that this method can accurately reconstruct underground medium targets, and it has strong robustness and efficiency.