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
TY - DATA
AB - 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.
AU - Williams, Jesse
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/2441446
KW - geothermal
KW - energy
KW - Utah FORGE
KW - machine learning
KW - multi frequency
KW - stimulation-induced seismicity
KW - seismicity
KW - seismicity predictor
KW - stimulation
KW - predictive systems
KW - deep learning
KW - DL
KW - magnitude-frequency distribution
KW - seismic
KW - EGS
KW - video
KW - presentation
LA - English
DA - 2024/09/17
PY - 2024
PB - Energy and Geoscience Institute at the University of Utah
T1 - Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation
UR - https://doi.org/10.15121/2441446
ER -
Williams, Jesse. Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation. Energy and Geoscience Institute at the University of Utah, 17 September, 2024, Geothermal Data Repository. https://doi.org/10.15121/2441446.
Williams, J. (2024). Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation. [Data set]. Geothermal Data Repository. Energy and Geoscience Institute at the University of Utah. https://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. Energy and Geoscience Institute at the University of Utah, September, 17, 2024. Distributed by Geothermal Data Repository. https://doi.org/10.15121/2441446
@misc{GDR_Dataset_1659,
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. },
url = {https://gdr.openei.org/submissions/1659},
year = {2024},
howpublished = {Geothermal Data Repository, Energy and Geoscience Institute at the University of Utah, https://doi.org/10.15121/2441446},
note = {Accessed: 2025-04-22},
doi = {10.15121/2441446}
}
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
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
