Traditional survey methods for finding surface re- sistivity are time consuming and labor intensive. Very few studies have focused on finding the resistivity / conductivity using remote sensing data and deep learning techniques. In this line of work, we assessed the correlation between surface resistivity and Synthetic Aperture Radar (SAR) by applying various deep learning methods and tested our hypothesis in the Coso Geothermal Area, USA. For detecting the resistivity, L- band full polarimetric SAR data acquired by UAVSAR were used, and MT (Magnetotellurics) inverted resistivity data of the area were used as the ground truth. We conducted experiments to compare various deep learning architectures, and suggest the use of Dual Input UNet (DI-UNet) architecture. DI-UNet uses a deep learning architecture to predict the resistivity using full polarimetric SAR data by promising a quick survey addition to the traditional method. Our proposed approach accomplished improved outcomes for the mapping of MT resistivity from SAR data.

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 paper proposes a Variaional Encoder-Decoder (VED) based network to obtain resistivity model, which maps the apparent resistivity data(input) to true resistivity data(output).  Since deep learning models are highly dependent on training sets and providing a meaningful geological resistivity model is complex, we have first developed a method 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 deep learning 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 networks considered.