Generative Adversarial Network Based Inversion of Cross-hole Radar Data

Abstract

Cross-hole radar is a popular method to characterize subsurface structures, yet traditional data interpretation methods have limitations: tomography method has poor inversion accuracy, while the full waveform inversion method costs huge computing time. In order to solve the above problems, an automatic inversion algorithm based on generative adversarial networks (GAN) is developed in this paper to interpret the permittivity from cross-hole radar B-scan images. This algorithm uses a low-resolution GAN to extract the global features in the cross-hole radar data to reconstruct the dielectric constant distribution map with low resolution, and then adopts a high-resolution GAN to enhance the resolution of the inversion results. The algorithm is trained on 1000 pairs of cross-hole radar data obtained from the finite-difference time-domain (FDTD) method. Finally, 100 pairs of similar data which has never been shown in the network are used to verify the inversion performance of the algorithm. The results show that the inversion accuracy of is greater than 90%, and the structural similarity index measure (SSIM) of the reconstructed image reaches 0.9. In addition, the proposed method also has rapid computing speed.