{"@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Jesse Williams", "hasEmail": "mailto:jwilliams@gtcanalytics.com"}, "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 Dr. 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 at the Utah FORGE R&D Annual Workshop on September 9, 2025. The workshop offered a valuable opportunity to review the progress of Research and Development projects funded under Solicitation 2022-2, which aim to improve our understanding of the key factors influencing Enhanced Geothermal System (EGS) reservoir and resource development.", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1785/6-3712-GTC%202025%20Annual%20Workshop%20Presentation.pdf", "description": "These are the slides presented at the 2025 Utah FORGE annual workshop for project 6-3712.", "format": "pdf", "mediaType": "application/pdf", "title": "Presentation Slides.pdf"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1785/6-3712-GTC%202025%20Annual%20Report.pdf", "description": "This 2025 report summarizes the progress of the Utah FORGE project 6-3712.", "format": "pdf", "mediaType": "application/pdf", "title": "6-3712 - 2025 Annual Report.pdf"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1785/GTC_6-3712%202025%20Annual%20Workshop%20Recording.mp4", "description": "This is a presentation recording from the 2025 Utah FORGE annual workshop for project 6-3712.", "format": "mp4", "mediaType": "application/octet-stream", "title": "Presentation Recording.mp4"}], "identifier": "https://data.openei.org/submissions/8529", "issued": "2025-09-18T06:00:00Z", "keyword": ["geothermal", "energy", "Utah FORGE", "EGS", "2025 Annual Workshop", "induced seismicity", "machine learning", "recurrent neural networks", "probabilistic modeling", "seismic response prediction", "magnitude-frequency analysis", "physics-informed ai", "presentation", "presentation recording", "presentation slides", "report"], "landingPage": "https://gdr.openei.org/submissions/1785", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2025-09-21T20:28:24Z", "programCode": ["019:006"], "projectLead": "Lauren Boyd", "projectNumber": "EE0007080", "projectTitle": "Utah FORGE", "publisher": {"@type": "org:Organization", "name": "GTC Analytics"}, "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 - 2025 Workshop Presentation"}