Enhanced Ground-Penetrating Radar Inversion With Closed-Loop Convolutional Neural Networks

Abstract

Traditional ground-penetrating radar (GPR) inversion techniques, while capable of providing high-resolution subsurface imaging, suffer from issues, such as heavy reliance on initial models, high computational demands, and sensitivity to noise and data incompleteness. In contrast, deep-learning-based methods excel in feature extraction and model fitting. However, as a data-driven algorithm, the practical application of convolutional neural networks (CNNs) is limited by the quantity of labeled samples. To reduce the dependence of CNN-based GPR inversion methods on observational data and labels, this project proposes an inversion method based on closed-loop CNNs (CL-CNNs). This approach improves inversion accuracy and reduces the ill-posedness of GPR inversion by modeling both the forward and inverse GPR processes. The CL structure increases the number of features that CNNs can learn from limited labeled samples, while the mutual inversion constraints between the forward and inverse subnetworks help alleviate the ill-posedness of the inversion problem, making the inversion results more consistent with geological principles. Research using synthetic data demonstrates that this method outperforms traditional approaches, as evidenced by enhanced structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), and a significantly lower mean-squared error (mse), highlighting its advanced performance compared with traditional open-loop CNNs (OL-CNNs). Furthermore, applying this method to real measurement data further validates its effectiveness and practical applicability in engineering contexts, emphasizing its significant practical value.