SAU-GAN: A Shuffle Attention U-Net Generative Adversarial Network for GPR Inversion

Abstract

Ground-penetrating radar (GPR) is widely used in geotechnical engineering investigations, construction quality assessment, and geological disaster surveys due to its high resolution, accuracy, and nondestructive testing capabilities. However, the accuracy of GPR inversion imaging is often compromised by climatic conditions (such as precipitation and temperature) and complex subsurface environments, leading to suboptimal performance. To address this issue, we propose a shuffle attention U-Net generative adversarial network (GAN) for GPR inversion imaging—SAU-GAN. This network consists of a generator and a discriminator. The generator features an encoder-decoder network enhanced with a shuffle attention mechanism, facilitating efficient feature extraction from B-scan images and aiding in the generation of permittivity models. The discriminator evaluates generated models against real ones, providing feedback to supervise the generator’s performance. Both the components use double normalization to stabilize parameters and convolutional outputs. In addition, a multiscale structural similarity (MS-SSIM) loss function enhances the existing loss function, significantly improving inversion results. Experiments with synthetic data demonstrate that SAU-GAN produces permittivity models with higher accuracy and clearer boundaries than existing methods. Even under interference, it is able to perform precise inversion, demonstrating outstanding robustness and generalization performance. We conduct a quantitative analysis of SAU-GAN using SSIM, PSNR, and MSE metrics, further validating its superior performance. When applied to real measured data, SAU-GAN also exhibits commendable performance, validating its effectiveness and practical value.