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

TL;DR

GPRInvNet is a deep learning model designed to accurately invert GPR B-Scan data into detailed permittivity maps of tunnel linings, effectively handling complex defects and noise.

Contribution

The paper introduces GPRInvNet, a novel deep neural network architecture specifically tailored for GPR data inversion in tunnel linings, improving accuracy and robustness over existing methods.

Findings

Successfully reconstructed complex tunnel lining defects with clear boundaries.

Outperformed baseline methods in GPR data inversion accuracy.

Achieved satisfactory results on real GPR data with noise integration.

Abstract

A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data.