3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion

TL;DR

This paper introduces 3DInvNet, a deep learning approach that efficiently reconstructs 3D subsurface permittivity maps from GPR data, overcoming traditional methods' computational and non-linearity challenges.

Contribution

The paper proposes a novel 3D deep learning framework with attention mechanisms and multi-scale features for accurate GPR data inversion, including a three-step training strategy.

Findings

Effective noise suppression in GPR C-scans

High accuracy in 3D permittivity map reconstruction

Demonstrated robustness on real and simulated data

Abstract

The reconstruction of the 3D permittivity map from ground-penetrating radar (GPR) data is of great importance for mapping subsurface environments and inspecting underground structural integrity. Traditional iterative 3D reconstruction algorithms suffer from strong non-linearity, ill-posedness, and high computational cost. To tackle these issues, a 3D deep learning scheme, called 3DInvNet, is proposed to reconstruct 3D permittivity maps from GPR C-scans. The proposed scheme leverages a prior 3D convolutional neural network with a feature attention mechanism to suppress the noise in the C-scans due to subsurface heterogeneous soil environments. Then a 3D U-shaped encoder-decoder network with multi-scale feature aggregation modules is designed to establish the optimal inverse mapping from the denoised C-scans to 3D permittivity maps. Furthermore, a three-step separate learning strategy is employed to pre-train and fine-tune the networks. The proposed scheme is applied to numerical simulation as well as real measurement data. The quantitative and qualitative results show the network capability, generalizability, and robustness in denoising GPR C-scans and reconstructing 3D permittivity maps of subsurface objects.