Joint Physics and Data Driven Full-Waveform Inversion for Underground Dielectric Targets Imaging

To better reconstruct underground targets based on ground-penetrating radar (GPR) data, this article proposes a joint physics and data driven full-waveform inversion (PDD-FWI) scheme. This scheme combines a physics-based noniterative approach and a data driven deep neural network (DNN) to reconstruct target location, shape, and permittivity accurately. First, the normalized range migration algorithm (RMA) is introduced to extract the target contour and location information, which not only improves the robustness of the proposed scheme but also ensures adaptability to different GPR equipment. Then, the GPR dielectric target reconstruction network (GPRDtrNet) is developed based on the improved U-net structure, including reducing network layers and adding multiscale additive spatial attention gates and skip-connection structures. Compared with previous DNN-based reconstruction methods, GPRDtrNet has the advantages of small data requirement, high accuracy, strong generalization, and noise tolerance. Finally, the simulated and real dataset containing kinds of targets is constructed to train and test GPRDtrNet. The results show that the proposed method can reconstruct underground dielectric targets accurately with high robustness and noise tolerance.

Automated summary

This article proposes a joint physics and data driven full-waveform inversion scheme to better reconstruct underground targets based on ground-penetrating radar (GPR) data. It combines a noniterative physics-based approach with a data driven deep neural network (DNN) to accurately reconstruct target location, shape, and permittivity. The proposed method shows high accuracy, robustness, and noise tolerance in reconstructing underground dielectric targets.