Deep learning inversion method of tunnel resistivity synthetic data based on modelling data

Different water-bearing geological structures in front of the tunnel face are the main cause of tunnel water inrush disasters, affecting tunnel constructionsafety. Due to the narrow tunnel space and the limited data that can be detected, the traditional linear inversion method for detecting them has multiple solutions. In this paper, we establish a database with complex models, including water-bearing geological structures usually encountered during tunnel construction. Then we build a complex nonlinear relationship mapping between the tunnel face observation data and the resistivity model through the deep neural network algorithm (electrical resistivity inversion network tunnel, ERTInvNet-T for short). ERTInvNet-T first uses a fully connected network to extract the tunnel depth features from the observed data, from which a three-dimensional geoelectric model of the tunnel is then generated by a three-dimensional deconvolutional network. At the same time, there is a problem with the uneven spatial distribution of the sensitivity of the data to the model. Therefore, depth weighting information constraint based on the distance factor is added to the loss function, which improves the network algorithm's learning ability for different detection positions of the tunnel. The validity of the proposed method is verified by a large number of numerical simulation experiments.