Ground-Penetrating Radar Inversion via Steady-State Diffusion Processes

Ground-penetrating radar (GPR) inversion typically relies on iterative methods, which often involve high computational complexity and challenges in noise handling. These limitations affect the robustness and generalization of traditional approaches. To address these issues, we propose an innovative inversion method using diffusion models (DGPRI-Net) tailored for GPR. Diffusion models inherently capture signal characteristics through progressive noise addition and subtraction, reducing noise impact and enhancing robustness. This approach effectively overcomes the noise management weaknesses of conventional methods. In the reverse generation process, we use the UNet++ network architecture, enhanced with vision transformer (ViT) structures and a simple parameter-free attention module (SimAM). This combination improves multiscale feature extraction and contextual understanding, increasing robustness and enabling high-precision permittivity models. To further evaluate the robustness of the model, we prepared three dedicated test sets: one with added noise, one without low-frequency signals, and one with 30% of the columns missing. Comparative experiments with synthetic data showed exceptional inversion accuracy, superior noise management, and enhanced robustness and generalization. We also validated performance with metrics such as structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (mse). Applied to measured data, our method continued to yield impressive results, confirming its practical value and effectiveness. This study highlights the potential of diffusion models in advancing GPR inversion applications.