Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2025 Workshop Presentation
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.
Resources
3 resources available
-
6-3712 - 2025 Annual Report.pdf
PDF -
Presentation Slides.pdf
PDF -
Presentation Recording.mp4
MP4
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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 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", "title": "6-3712 - 2025 Annual Report.pdf", "format": "pdf", "accessURL": "https://gdr.openei.org/files/1785/6-3712-GTC%202025%20Annual%20Report.pdf", "mediaType": "application/pdf", "description": "This 2025 report summarizes the progress of the Utah FORGE project 6-3712." }, { "@type": "dcat:Distribution", "title": "Presentation Slides.pdf", "format": "pdf", "accessURL": "https://gdr.openei.org/files/1785/6-3712-GTC%202025%20Annual%20Workshop%20Presentation.pdf", "mediaType": "application/pdf", "description": "These are the slides presented at the 2025 Utah FORGE annual workshop for project 6-3712." }, { "@type": "dcat:Distribution", "title": "Presentation Recording.mp4", "format": "mp4", "accessURL": "https://gdr.openei.org/files/1785/GTC_6-3712%202025%20Annual%20Workshop%20Recording.mp4", "mediaType": "application/octet-stream", "description": "This is a presentation recording from the 2025 Utah FORGE annual workshop for project 6-3712." } ] |
| identifier | https://data.openei.org/submissions/8529 |
| issued | 2025-09-18T06:00:00Z |
| keyword |
[ "2025 Annual Workshop", "EGS", "Utah FORGE", "energy", "geothermal", "induced seismicity", "machine learning", "magnitude-frequency analysis", "physics-informed ai", "presentation", "presentation recording", "presentation slides", "probabilistic modeling", "recurrent neural networks", "report", "seismic response prediction" ] |
| 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 |
{ "name": "GTC Analytics", "@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: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2025 Workshop Presentation |
