Research on mine electrical resistivity inversion method based on U-Net model
Hu Yunbing
Wang Huricha
Yi Hongchun
Duan Tianzhu
Cui Shaobei
Abstract：To address the limitations of traditional resistivity inversion methods in mining scenarios—including initial model dependency,boundary ambiguity,and artifacts present in existing deep learning-base inversion approaches—this study proposes a physics-constrained U-Net inversion method.By integrating electrical sensitivity characteristics and depth focusing mechanisms,the method constructs a weighted cross-entropy loss function based on U-Net's multi-scale feature fusion architecture.Enhanced encoder-decoder skip connections are employed to amplify resistivity contrasts between anomalies and background fields.A parameter space for resistivity distribution was defined based on three types of typical anomalous bodies,and forward modeling was performed on 6 000 models using the finite element method.Dipole-dipole array configurations were applied to acquire apparent resistivity profiles,establishing a geoelectric model-response paired dataset for supervised training.Experimental results demonstrate a Dice coefficient of 0.950±0.018 and a reduction in inversion time from 65.2 s(least-squares method)to 1.0 s per instance,improving computational efficiency by 98.5%.The synergistic optimization of physical priors and deep learning provides an effective solution for precise detection of hidden water-conducting structures in coal mine hazard prevention.
Keywords：mine resistivity inversionU-Netdeep learning inversionmine water hazardworking facephysical constraint
Publication Date：2026-02-28
Online Publishing Date：2026-03-25(First online date of this platform, not the publication date of the document)
Pages：8( 185-192 )