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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.
Complete Metadata
| bureauCode |
[ "019:20" ] |
|---|---|
| dataQuality | true |
| DOI | 10.15121/2441446 |
| identifier | https://data.openei.org/submissions/7728 |
| issued | 2024-09-17T06:00:00Z |
| landingPage | https://gdr.openei.org/submissions/1659 |
| programCode |
[ "019:006" ] |
| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Utah FORGE |
| spatial | {"type":"Polygon","coordinates":[[[-112.916367,38.483935],[-112.879748,38.483935],[-112.879748,38.5148],[-112.916367,38.5148],[-112.916367,38.483935]]]} |
