An attempt to use convolutional neural network to recover layered-earth structure from electrical resistivity tomography survey

Abstract

Abstract The Electrical Resistivity Tomography (ERT) method is a geophysical prospecting technique for determining subsurface structure in terms of electrical resistivity distribution. Direct current is injected into the ground through a pair of electrodes, and the potential difference is measured using another pair of electrodes. The estimated resistivity model of the subsurface is then obtained using a mathematical optimization algorithm called the inversion algorithm, which incorporates multiple measured potential differences from different electrode configurations as the input data. The inverted resistivity model serves as a resource for the interpretation process of the survey. Specialists employ the model and the prior geological information in the area to extract the subsurface geological structure under the data measurement profile. The inverted model can be treated as a two-dimensional image in which each element represents resistivity. The image can be visualized as a blurred or distorted version of the actual resistivity image of the subsurface. We, therefore, developed a post-inversion image enhancement code using a neural network aiming to recover the true subsurface model with predetermined features. We employed the convolutional neural network (CNN) that uses the inverted models as input and returns an enhanced model as output. We tested our developed neural network with a case study that utilized the proposed method to recover the layered earth structure which is often found in real surveys. The electrode spacing was 5 meters in the Schlumberger array configuration. The true subsurface resistivity models of training data and validating data for evaluating the neural network were randomly generated based on the predetermined feature of the layered structure. We then generated their respective inverted models from those data through simulated inversion routine. As a result, 94.75% of the enhanced models in the validation dataset exhibited a lower loss metric compared to the original inverted models in the same validation dataset. We also applied the proposed method to practical field data to recover the subsurface layers and compared them with stratigraphic information from a borehole in each area. The result showed that the proposed method gives well-resolved structures in the enhancement process after the typical inversion.