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Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation

Dataset ·
DOI:https://doi.org/10.15121/2441446· OSTI ID:2441446
 [1]
  1. Energy and Geoscience Institute at the University of Utah
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.
Research Organization:
DOE Geothermal Data Repository; Energy and Geoscience Institute at the University of Utah
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)
Contributing Organization:
Energy and Geoscience Institute at the University of Utah
DOE Contract Number:
EE0007080
OSTI ID:
2441446
Report Number(s):
1659
Availability:
GDRHelp@ee.doe.gov
Country of Publication:
United States
Language:
English