In many branches of earth sciences, the problem of rock study on the micro-level 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 novel deep learning architecture for three-dimensional porous media reconstruction from two-dimensional slices. We fit a distribution on all possible three-dimensional structures of a specific type based on the given dataset 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.
@article{arxiv.1901.10233,
title = {Reconstruction of 3D Porous Media From 2D Slices},
author = {Denis Volkhonskiy and Ekaterina Muravleva and Oleg Sudakov and Denis Orlov and Boris Belozerov and Evgeny Burnaev and Dmitry Koroteev},
journal= {arXiv preprint arXiv:1901.10233},
year = {2021}
}