{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stochastic-reconstruction-of-an-oolitic","title":"Stochastic reconstruction of an oolitic limestone by generative adversarial networks","arxiv_id":"1712.02854","date":"2017-12-07","proceeding":null,"authors":["Lukas Mosser","Olivier Dubrule","Martin J. Blunt"],"abstract":"Stochastic image reconstruction is a key part of modern digital rock physics\nand materials analysis that aims to create numerous representative samples of\nmaterial micro-structures for upscaling, numerical computation of effective\nproperties and uncertainty quantification. We present a method of\nthree-dimensional stochastic image reconstruction based on generative\nadversarial neural networks (GANs). GANs represent a framework of unsupervised\nlearning methods that require no a priori inference of the probability\ndistribution associated with the training data. Using a fully convolutional\nneural network allows fast sampling of large volumetric images.We apply a GAN\nbased workflow of network training and image generation to an oolitic Ketton\nlimestone micro-CT dataset. Minkowski functionals, effective permeability as\nwell as velocity distributions of simulated flow within the acquired images are\ncompared with the synthetic reconstructions generated by the deep neural\nnetwork. While our results show that GANs allow a fast and accurate\nreconstruction of the evaluated image dataset, we address a number of open\nquestions and challenges involved in the evaluation of generative network-based\nmethods.","url_abs":"http://arxiv.org/abs/1712.02854v1","url_pdf":"http://arxiv.org/pdf/1712.02854v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stochastic-reconstruction-of-an-oolitic","repo_url":"https://github.com/LukasMosser/geogan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}