Physics-Consistent GPR Inversion via Feature-Enhanced Forward Module and Envelope Data

Abstract

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.