{"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/generating-realistic-geology-conditioned-on","title":"Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks","arxiv_id":"1802.03065","date":"2018-02-08","proceeding":null,"authors":["Emilien Dupont","Tuanfeng Zhang","Peter Tilke","Lin Liang","William Bailey"],"abstract":"An important problem in geostatistics is to build models of the subsurface of\nthe Earth given physical measurements at sparse spatial locations. Typically,\nthis is done using spatial interpolation methods or by reproducing patterns\nfrom a reference image. However, these algorithms fail to produce realistic\npatterns and do not exhibit the wide range of uncertainty inherent in the\nprediction of geology. In this paper, we show how semantic inpainting with\nGenerative Adversarial Networks can be used to generate varied realizations of\ngeology which honor physical measurements while matching the expected\ngeological patterns. In contrast to other algorithms, our method scales well\nwith the number of data points and mimics a distribution of patterns as opposed\nto a single pattern or image. The generated conditional samples are state of\nthe art.","url_abs":"http://arxiv.org/abs/1802.03065v3","url_pdf":"http://arxiv.org/pdf/1802.03065v3.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":"generating-realistic-geology-conditioned-on","repo_url":"https://github.com/amoodie/StratGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"spatial-interpolation","task_name":"Spatial Interpolation"}],"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}