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Data Publication
New 2D-to-3D Reconstruction of Heterogenous Porous Media via Deep Generative Adversarial Networks (GANs)
Vogel, Hannah | Amiri, Hamed
YoDa Data Repository, Utrecht University, Netherlands
(2024)
We imaged samples of Berea sandstone from Ohio (USA) using two 2D imaging techniques, backscattered electron (BSE) and optical microscopy, as well as one 3D imaging technique, X-ray (micro-)computed tomography (XCT). The goal is to employ a deep-learning-based generative model called a generative adversarial network (GAN) to reconstruct statistically equivalent microstructures in 3D from exclusively 2D training images. To evaluate the reconstruction accuracy, we conduct a visual and statistical analysis comparing reconstructions with a 3D X-ray tomography of the same sample. Our method uniquely uses true 2D images from three orthogonally oriented planes for training the model. The data are organized into 13 folders: three contain the original segmented (binary) images of Berea sandstone samples, and the other 10 folders contain data and individual figures used to create figures in the main publication. Link to GitHub containing codes: https://github.com/hamediut/2D-to3D-recon
Keywords
Originally assigned keywords
Corresponding MSL vocabulary keywords
MSL enriched keywords
MSL enriched sub domains i
Source publisher
YoDa Data Repository, Utrecht University, Netherlands
DOI
10.24416/uu01-2l689l
Authors
Vogel, Hannah
0009-0004-5393-0866
Utrecht University;
Amiri, Hamed
0000-0002-2981-1398
Utrecht University;
Contributers
Vogel, Hannah
Researcher
0009-0004-5393-0866
Utrecht University;
Amiri, Hamed
Researcher
0000-0002-2981-1398
Utrecht University;
Plümper, Oliver
Supervisor
0000-0001-9726-0885
Utrecht University;
References
Amiri, H., Vogel, H., & Plümper, O. (2024). New 2D to 3D Reconstruction of Heterogeneous Porous Media via Deep Generative Adversarial Networks (GANs). Journal of Geophysical Research: Machine Learning and Computation, 1(3). Portico. https://doi.org/10.1029/2024jh000178
10.1029/2024JH000178
IsSupplementTo
IsSupplementTo
Vogel, H., & Amiri, H. (2024). <i>True 2D-to-3D Reconstruction of Heterogenous Porous Media via Deep Generative Adversarial Networks (GANs)</i> (Version 1.0) [Data set]. Utrecht University. https://doi.org/10.24416/UU01-DO6LT4
10.24416/UU01-DO6LT4
IsNewVersionOf
Citiation
Vogel, H., & Amiri, H. (2024). New 2D-to-3D Reconstruction of Heterogenous Porous Media via Deep Generative Adversarial Networks (GANs) (Version 1.1) [Data set]. Utrecht University. https://doi.org/10.24416/UU01-2L689L
Collection Period
2022-03-01 - 2024-05-14