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Utah FORGE 6-3712: Report on Building a Recurrent Neural Network Framework for Induced Seismicity - October, 2025
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.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "019:20" ] |
| contactPoint |
{ "fn": "Jesse Williams", "@type": "vcard:Contact", "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", "title": "Technical Report.pdf", "format": "pdf", "accessURL": "https://gdr.openei.org/files/1797/FORGE_milestone_3p1_13_10_2025.pdf", "mediaType": "application/pdf", "description": "Technical report describing three deep learning architectures developed to predict induced seismicity at Utah FORGE." } ] |
| identifier | https://data.openei.org/submissions/8550 |
| issued | 2025-10-13T06:00:00Z |
| keyword |
[ "AI", "DL", "Deep learning", "EGS", "Induced Seismicity", "ML", "Utah FORGE", "artificial intelligence", "energy", "geophysical models", "geothermal", "injection parameters", "machine learning", "magnitude", "modeling", "physics-based", "predictive", "probabilistic", "seismic data", "technical report" ] |
| 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 |
{ "name": "Global Technology Connection, Inc.", "@type": "org:Organization" } |
| 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 |
