Leveraging Envelope Data in cGAN for Robust GPR Inversion

Although deep learning techniques for ground-penetrating radar (GPR) inversion offer significant advantages, such as high accuracy and computational efficiency, they still face challenges related to limited robustness and generalization. Using envelope radar data in inversion helps mitigate some of these issues. This data emphasizes amplitude characteristics, reducing the impact of high-frequency noise and phase-related problems on the inversion results. In this article, we propose a GPR inversion method based on a conditional generative adversarial network (cGAN), incorporating envelope radar data as conditional input for both the generator and the discriminator. We also use double normalization to adjust the discriminator’s convergence speed, ensuring the adversarial relationship is maintained, which enhances model robustness without sacrificing inversion accuracy. For the loss function, we introduce a hybrid of mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM) loss, guiding the generator to produce results closer to the true model. To evaluate the inversion performance of our proposed method, we designed three synthetic data experiments: a comparison experiment with varying degrees of low-frequency component depletion, a comparison experiment with different noise levels, and a comparison experiment with varying central frequencies. Results show high resolution, clear inversion boundaries, and well-defined anomalous structures, demonstrating high inversion accuracy and robustness. Furthermore, our method shows superior performance and practical value with real-world data.