Generative adversarial networks for reconstruction of three-dimensional porous media from two-dimensional slices
Denis Volkhonskiy, Ekaterina Muravleva, Олег Валериевич Судаков, Denis Orlov, Evgeny Burnaev, Dmitry Koroteev, Boris Belozerov, Vladislav Krutko
Abstract
In many branches of earth sciences, the problem of rock study on the microlevel arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes actual. In this paper we propose a deep learning architecture for three-dimensional porous medium reconstruction from two-dimensional slices. We fit a distribution on all possible three-dimensional structures of a specific type based on the given data set of samples. Then, given partial information (central slices), we recover the three-dimensional structure around such slices as the most probable one according to that constructed distribution. Technically, we implement this in the form of a deep neural network with encoder, generator, and discriminator modules. Numerical experiments show that this method provides a good reconstruction in terms of Minkowski functionals.