Migration-Supervised Learning for Ground-Penetrating Radar Denoising and Imaging

Abstract

Ground-penetrating radar (GPR) denoising is typically optimized in the radargram domain, whereas target interpretation is often performed after migration. This separation causes an imaging-inconsistency problem: a denoiser may improve visual quality or data-domain metrics while attenuating weak diffractions and phase-coherent events required by reverse time migration (RTM). To address this problem, this letter proposes MigSup-Net, a migration-supervised learning framework that couples radargram restoration with image-domain consistency. Clean synthetic observations are generated by wave-equation forward modeling and corrupted by mixed structured–Gaussian noise to emulate both random and field-like disturbances. Smooth-background RTM of the clean scattered data is then used to construct migration-domain supervision. MigSup-Net adopts a shared encoder–decoder backbone with two prediction heads for denoised radargrams and migration-domain images, and is trained using radargram fidelity, migration fidelity, and total variation (TV) regularization. At inference, the trained network directly outputs both a restored radargram and a migration-domain image in a single forward pass. Experiments on held-out synthetic models show that MigSup-Net achieves the highest output signal-to-noise ratio (SNR), peak SNR (PSNR), radargram structural similarity index measure (SSIM), and migration SSIM among the tested conventional and learning-based baselines. A measured pipe-profile example further indicates that the proposed migration supervision suppresses incoherent field noise while preserving migration-relevant responses. These results demonstrate that migration supervision provides an effective imaging-aware constraint for GPR denoising.