Appraisal of Resistivity Inversion Models With Convolutional Variational Encoder–Decoder Network

Recovering the actual subsurface electrical resistivity properties from the electrical resistivity tomography data is challenging because the inverse problem is nonlinear and ill-posed. This article proposes a variational encoder-decoder (VED)-based network to obtain resistivity model, which maps the apparent resistivity data (input) to true resistivity data (output). Since deep learning (DL) models are highly dependent on training sets and providing a meaningful geological resistivity model is complex, we have first developed an algorithm to construct many realistic resistivity synthetic models. Our algorithm automatically constructs an apparent resistivity pseudo-section from these resistivity models. We further computed the resistivity from two different neural architectures for comparison–UNet, and attention UNet with and without input depth encoding apparent data. In the end, we have compared our DL results with traditional inversion and borewell data on apparent resistivity datasets collected for aquifer mapping in the hard rock terrain of the West Medinipur district of West Bengal, India. A detailed qualitative and quantitative evaluation reveals that our VED approach is the most effective for the inversion compared to other approaches considered.