Physics-Driven Resolution Refinement for Electrical Resistivity Tomography Using Multiarray Learning and Vision Mamba

Abstract

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.