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arXiv · 1901.10233

Reconstruction of 3D Porous Media From 2D Slices

Denis Volkhonskiy·Ekaterina Muravleva·Oleg Sudakov·Denis Orlov·Boris Belozerov·Evgeny Burnaev·Dmitry Koroteev
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Abstract

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.

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Denis Volkhonskiy, Ekaterina Muravleva, Oleg Sudakov, Denis Orlov, Boris Belozerov, Evgeny Burnaev, Dmitry Koroteev. 2019-01-29. Reconstruction of 3D Porous Media From 2D Slices. https://arxiv.org/abs/1901.10233

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