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.
Citation Formats
Energy and Geoscience Institute at the University of Utah. (2024). Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation [data set]. Retrieved from https://dx.doi.org/10.15121/2441446.
Williams, Jesse. Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation. United States: N.p., 17 Sep, 2024. Web. doi: 10.15121/2441446.
Williams, Jesse. Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation. United States. https://dx.doi.org/10.15121/2441446
Williams, Jesse. 2024. "Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation". United States. https://dx.doi.org/10.15121/2441446. https://gdr.openei.org/submissions/1659.
@div{oedi_6196, title = {Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation}, author = {Williams, Jesse.}, abstractNote = {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. }, doi = {10.15121/2441446}, url = {https://gdr.openei.org/submissions/1659}, journal = {}, number = , volume = , place = {United States}, year = {2024}, month = {09}}
https://dx.doi.org/10.15121/2441446
Details
Data from Sep 17, 2024
Last updated Sep 17, 2024
Submitted Sep 17, 2024
Organization
Energy and Geoscience Institute at the University of Utah
Contact
Sean Lattice
801.581.3547
Authors
Original Source
https://gdr.openei.org/submissions/1659Research Areas
Keywords
geothermal, energy, Utah FORGE, machine learning, multi frequency, stimulation-induced seismicity, seismicity, seismicity predictor, stimulation, predictive systems, deep learning, DL, magnitude-frequency distribution, seismic, EGS, video, presentationDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
