{"@type": "dcat:Dataset", "DOI": "10.15121/2441446", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Sean Lattice", "hasEmail": "mailto:slattis@egi.utah.edu"}, "dataQuality": true, "description": "This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by Jesse Williams. This video slide presentation discusses the development of machine learning-based predictive tools to estimate the magnitude-frequency response of stimulation-induced seismicity. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024. ", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1659/GlobalTechConnection%206-3712%20GMT20240815-143041_Recording_as_2560x1080.mp4", "description": "As part of the 2024 Utah FORGE R&D Workshop, this presentation offers the newest updates to the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks project from GTC Analytics.  The presentation follows a standard format, with a 15 minute presentation section followed by a 10 minute Q&A via Utah FORGE panelists and the presenters. ", "format": "mp4", "mediaType": "application/octet-stream", "title": "Presentation Recording.mp4"}], "identifier": "https://data.openei.org/submissions/7728", "issued": "2024-09-17T06:00:00Z", "keyword": ["DL", "EGS", "Utah FORGE", "deep learning", "energy", "geothermal", "machine learning", "magnitude-frequency distribution", "multi frequency", "predictive systems", "presentation", "seismic", "seismicity", "seismicity predictor", "stimulation", "stimulation-induced seismicity", "video"], "landingPage": "https://gdr.openei.org/submissions/1659", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2024-09-17T16:44:10Z", "programCode": ["019:006"], "projectLead": "Lauren Boyd", "projectNumber": "EE0007080", "projectTitle": "Utah FORGE", "publisher": {"@type": "org:Organization", "name": "Energy and Geoscience Institute at the University of Utah"}, "spatial": "{\"type\":\"Polygon\",\"coordinates\":[[[-112.916367,38.483935],[-112.879748,38.483935],[-112.879748,38.5148],[-112.916367,38.5148],[-112.916367,38.483935]]]}", "title": "Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation"}