GPR-TransUNet: An improved TransUNet based on self-attention mechanism for ground penetrating radar inversion

Abstract

Convolutional Neural Networks (CNN) are widely applied to Ground Penetrating Radar (GPR) inversion because they have strong data-driven capabilities and are suitable for the data structure form of GPR. For CNN, the computation increases with the distance that the convolutional block moves from one region to another when it calculates the relationship between two regions. For GPR data, the target reflection exists in the surrounding traces and full time-window of the target, which leads to high degree of remote relationship. In this paper, we propose GPR-TransUNet, a deep-learning based inversion network which use self-attention mechanism. According to the characteristics of GPR data, regression network and GPR-Loss mechanism were used. Both numerical and model experiments were arranged to test the performance of the network, and the result as well as comparative analysis demonstrate the superiority of GPR-TransUNet. Finally, we applied this method to the field GPR data of Guangxi as an attempt.