{"@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 technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of designing a recurrent neural network (RNN) to predict induced seismicity. Background material is included to inform non-subject matter experts about the types of architectures available. The exact architectures (layers) of three models are discussed, which are being used to predict induced seismicity. ", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1797/FORGE_milestone_3p1_13_10_2025.pdf", "description": "Technical report describing three deep learning architectures developed to predict induced seismicity at Utah FORGE.", "format": "pdf", "mediaType": "application/pdf", "title": "Technical Report.pdf"}], "identifier": "https://data.openei.org/submissions/8550", "issued": "2025-10-13T06:00:00Z", "keyword": ["geothermal", "energy", "Deep learning", "Induced Seismicity", "predictive", "magnitude", "artificial intelligence", "AI", "machine learning", "ML", "DL", "physics-based", "modeling", "Utah FORGE", "EGS", "technical report", "seismic data", "injection parameters", "geophysical models", "probabilistic"], "landingPage": "https://gdr.openei.org/submissions/1797", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2025-10-13T19:39:01Z", "programCode": ["019:006"], "projectLead": "Lauren Boyd", "projectNumber": "EE0007080", "projectTitle": "Utah FORGE", "publisher": {"@type": "org:Organization", "name": "Global Technology Connection, Inc."}, "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: Report on Building a Recurrent Neural Network Framework for Induced Seismicity - October, 2025"}