Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation
This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by 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 in the Utah FORGE R&D Annual Workshop on August 15, 2024.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "019:20" ] |
| contactPoint |
{ "fn": "Sean Lattice", "@type": "vcard:Contact", "hasEmail": "mailto:slattis@egi.utah.edu" } |
| 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 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 in the Utah FORGE R&D Annual Workshop on August 15, 2024. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Presentation Recording.mp4", "format": "mp4", "accessURL": "https://gdr.openei.org/files/1659/GlobalTechConnection%206-3712%20GMT20240815-143041_Recording_as_2560x1080.mp4", "mediaType": "application/octet-stream", "description": "As part of the 2024 Utah FORGE R&D Workshop, this presentation offers the newest updates to the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks project from GTC Analytics. The presentation follows a standard format, with a 15 minute presentation section followed by a 10 minute Q&A via Utah FORGE panelists and the presenters. " } ] |
| DOI | 10.15121/2441446 |
| identifier | https://data.openei.org/submissions/7728 |
| issued | 2024-09-17T06:00:00Z |
| keyword |
[ "DL", "EGS", "Utah FORGE", "deep learning", "energy", "geothermal", "machine learning", "magnitude-frequency distribution", "multi frequency", "predictive systems", "presentation", "seismic", "seismicity", "seismicity predictor", "stimulation", "stimulation-induced seismicity", "video" ] |
| landingPage | https://gdr.openei.org/submissions/1659 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2024-09-17T16:44:10Z |
| programCode |
[ "019:006" ] |
| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Utah FORGE |
| publisher |
{ "name": "Energy and Geoscience Institute at the University of Utah", "@type": "org:Organization" } |
| spatial |
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|
| title | Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation |
