Return to search results
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.
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 |
