{"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/parametrization-and-generation-of-geological","title":"Parametrization and generation of geological models with generative adversarial networks","arxiv_id":"1708.01810","date":"2017-08-05","proceeding":null,"authors":["Shing Chan","Ahmed H. Elsheikh"],"abstract":"One of the main challenges in the parametrization of geological models is the\nability to capture complex geological structures often observed in the\nsubsurface. In recent years, generative adversarial networks (GAN) were\nproposed as an efficient method for the generation and parametrization of\ncomplex data, showing state-of-the-art performances in challenging computer\nvision tasks such as reproducing natural images (handwritten digits, human\nfaces, etc.). In this work, we study the application of Wasserstein GAN for the\nparametrization of geological models. The effectiveness of the method is\nassessed for uncertainty propagation tasks using several test cases involving\ndifferent permeability patterns and subsurface flow problems. Results show that\nGANs are able to generate samples that preserve the multipoint statistical\nfeatures of the geological models both visually and quantitatively. The\ngenerated samples reproduce both the geological structures and the flow\nstatistics of the reference geology.","url_abs":"http://arxiv.org/abs/1708.01810v2","url_pdf":"http://arxiv.org/pdf/1708.01810v2.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":"parametrization-and-generation-of-geological","repo_url":"https://github.com/akshaysubr/TEGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"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}