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
Electrical resistivity tomography (ERT) inversion is inherently nonlinear and ill-posed, and conventional algorithms often suffer from limited resolution due to their dependence on initial models and regularization. Recent deep learning (DL)-based approaches have shown potential for resolution enhancement but purely data-driven methods lack physical interpretability, while physics-constrained networks typically provide limited improvement and incur high computational cost. To address these challenges, we propose a physics-driven resolution refinement framework for ERT. The core idea is to learn a differentiable surrogate forward model from multiarray observations and use it as a physical guide to refine conventional inversion results. Specifically, a multiarray forward network is trained to jointly predict responses from Wenner- $\alpha $ , dipole–, and Schlumberger (SLM) arrays, capturing their complementary sensitivities through shared representation learning. Based on this learned forward mapping, the conventional inversion model is iteratively refined by enforcing cross-array response consistency, while an adaptively weighted supervision term is incorporated to stabilize the update. A Vision Mamba backbone is adopted to enhance feature representation and improve the recovery of fine-scale structures. Experiments on both synthetic and field data demonstrate that the proposed method improves anomaly delineation and structural detail recovery under the tested synthetic and field settings. Meanwhile, it enhances cross-array physical consistency and avoids the computational overhead associated with repeated numerical forward modeling. These results highlight the effectiveness of integrating learned forward physics with data-driven refinement for high-resolution ERT imaging.
Yinpeng Li, Xianghao Liu, Yanqi Wu et al.· IEEE Transactions on Geoscie...· 0 citations
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