Slowness High-Resolution Tomography of Cross-Hole Radar Based on Deep Learning

Traditional cross-hole radar tomography (CRT) usually cannot obtain high-resolution imaging results due to the nonlinearity and multisolution of inversion. To cope with these challenges, we propose a scheme to achieve high-resolution CRT for complex slowness models using deep neural networks (DNNs). Given the inherent difficulty in generating complex geophysical models in batches, by series of processing some remote sensing images from the remote sensing scene classification dataset, we create a real slowness model dataset. Then, we utilize 2-D U-Net to directly construct the mapping relationship between the low-resolution slowness model from the traditional method and the real slowness model. The superiority of our scheme is verified by both synthetic data and measured data. Our scheme can significantly suppress the false anomaly of traditional CRT results and accurately reconstruct the underground target's geometry, position, and slowness value, and it has excellent accuracy and robustness. In addition, the response data of the slowness model reconstructed by our scheme are closer to the field data.