In this study, we propose a new approach to borehole resistivity images interpretation, based on combination of 3D finite element simulation and convolutional neural network (CNN) algorithms. The CNN is trained on the results of 3D numerical simulation to detect geoelectric boundaries. High-performance parallel computing and data augmentation are used in order to minimize the time needed to obtain a set of images sufficient for CNN training. Despite the time-consuming processes of synthetic data obtaining and CNN training, the algorithm application does not require serious computing resources and takes seconds. The advantage of the developed algorithm is the ability to process images of an arbitrary length, due to the absence of fully connected layers in the CNN architecture.


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