While full-waveform inversion (FWI) offers high-resolution ground penetrating radar (GPR) imaging, it is hampered by prohibitive computational costs and intrinsic sensitivity to initial models. Conversely, deep learning provides efficiency but often lacks physical constraints, leading to structural artifacts under noise. To reconcile high fidelity and robustness with computational efficiency, we propose a physics-consistent inversion framework driven by envelope data and a feature-enhanced forward module. A key innovation of our approach is shifting physical constraints from the noisy pixel domain to the robust latent feature manifold. Specifically, we construct a pretrained differentiable forward modeling engine and introduce a Latent Feature Injection mechanism. This strategy transfers multiscale structural semantics from the inversion network to the forward engine, enforcing strict physical compliance guided by the global geological context. This effectively compensates for the information bottleneck in traditional parameter prediction. Extensive experiments on synthetic and measured datasets demonstrate that our method significantly outperforms traditional baselines while maintaining the computational efficiency of deep learning. It exhibits superior inversion accuracy, robust noise immunity, and strong generalization capabilities.
Meijia Huang, Xiang Qiu, Yanqi Wu et al.· IEEE Transactions on Geoscie...· 0 citations
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.
