GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining

A DNN architecture called GPRInvNet is proposed to tackle the challenge of mapping Ground Penetrating Radar (GPR) B-Scan data to complex permittivity maps of subsurface structure. GPRInvNet consists of a trace-to-trace encoder and a decoder. It is specially designed to take account of the characteristics of GPR inversion when faced with complex GPR B-Scan data as well as addressing the spatial alignment issue between time-series B-Scan data and spatial permittivity maps. It fuses features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. The sensitive zone on the permittivity map spatially aligned to the enhanced trace is reconstructed accurately. GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel lining containing complex defects has been reconstructed using GPRInvNet, and results demonstrate that GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrate the superiority of the GPRInvNet. To generalize GPRInvNet to real GPR data, we integrated background noise patches recorded form a practical model testing into synthetic GPR data to train GPRInvNet. The model testing has been conducted for validation, and experimental results show that GPRInvNet achieves satisfactory results on real data.