Application of Convolutional Networks for Localization and Prediction of Scalar Parameters of Fractured Geological Inclusion

Abstract

Seismic inversion is an important part of the modern geological exploration process. Novel applications of deep learning are capable of handling heterogeneous media, but require too much data for training. In this paper, we focus on the prediction of fracture inclusion location and its parameters in rock media and approach the problem in the multi-task manner. For this, several multi-task convolutional neural network (CNN) architectures are proposed and compared. The direct seismic problem is considered in the heterogeneous fractured geological model based on the well-known Marmousi2 model in a two-dimensional case. The model of the deformable solid medium containing slip planes with nonlinear slip conditions on them and explicit–implicit numerical method is applied to obtain the synthetic seismic dataset for CNN training and validation.