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• W2912600959 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 good reconstruction in terms of Minkowski functionals." @default.
• W2912600959 created "2019-02-21" @default.
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• W2912600959 date "2019-01-29" @default.
• W2912600959 modified "2023-09-22" @default.
• W2912600959 title "Reconstruction of 3D Porous Media From 2D Slices." @default.
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• W2912600959 hasPublicationYear "2019" @default.
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