Generative adversarial networks for reconstruction of three-dimensional porous media from two-dimensional slices

Denis Volkhonskiy, Ekaterina Muravleva, Oleg Sudakov, Denis Orlov, Evgeny Burnaev, Dmitry Koroteev, Boris Belozerov, Vladislav Krutko

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number025304
JournalPhysical Review E
Volume105
Issue number2
DOIs
Publication statusPublished - Feb 2022

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