{"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/including-physics-in-deep-learning-an-example","title":"Including Physics in Deep Learning -- An example from 4D seismic pressure saturation inversion","arxiv_id":"1904.02254","date":"2019-04-03","proceeding":null,"authors":["Jesper Sören Dramsch","Gustavo Corte","Hamed Amini","Colin MacBeth","Mikael Lüthje"],"abstract":"Geoscience data often have to rely on strong priors in the face of\nuncertainty. Additionally, we often try to detect or model anomalous sparse\ndata that can appear as an outlier in machine learning models. These are\nclassic examples of imbalanced learning. Approaching these problems can benefit\nfrom including prior information from physics models or transforming data to a\nbeneficial domain. We show an example of including physical information in the\narchitecture of a neural network as prior information. We go on to present\nnoise injection at training time to successfully transfer the network from\nsynthetic data to field data.","url_abs":"http://arxiv.org/abs/1904.02254v1","url_pdf":"http://arxiv.org/pdf/1904.02254v1.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":"including-physics-in-deep-learning-an-example","repo_url":"https://github.com/JesperDramsch/4D-seismic-neural-inversion","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}