Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2025 Workshop Presentation
This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by Dr. 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 at the Utah FORGE R&D Annual Workshop on September 9, 2025. The workshop offered a valuable opportunity to review the progress of Research and Development projects funded under Solicitation 2022-2, which aim to improve our understanding of the key factors influencing Enhanced Geothermal System (EGS) reservoir and resource development.
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 Dr. 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 at the Utah FORGE R&D Annual Workshop on September 9, 2025. The workshop offered a valuable opportunity to review the progress of Research and Development projects funded under Solicitation 2022-2, which aim to improve our understanding of the key factors influencing Enhanced Geothermal System (EGS) reservoir and resource development.
AU - Williams, Jesse
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Laboratory of the Rockies
DO -
KW - geothermal
KW - energy
KW - Utah FORGE
KW - EGS
KW - 2025 Annual Workshop
KW - induced seismicity
KW - machine learning
KW - recurrent neural networks
KW - probabilistic modeling
KW - seismic response prediction
KW - magnitude-frequency analysis
KW - physics-informed ai
KW - presentation
KW - presentation recording
KW - presentation slides
KW - report
LA - English
DA - 2025/09/18
PY - 2025
PB - GTC Analytics
T1 - Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2025 Workshop Presentation
UR - https://data.openei.org/submissions/8529
ER -
Williams, Jesse. Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2025 Workshop Presentation. GTC Analytics, 18 September, 2025, GDR. https://gdr.openei.org/submissions/1785.
Williams, J. (2025). Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2025 Workshop Presentation. [Data set]. GDR. GTC Analytics. https://gdr.openei.org/submissions/1785
Williams, Jesse. Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2025 Workshop Presentation. GTC Analytics, September, 18, 2025. Distributed by GDR. https://gdr.openei.org/submissions/1785
@misc{OEDI_Dataset_8529,
title = {Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2025 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 Dr. 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 at the Utah FORGE R\&D Annual Workshop on September 9, 2025. The workshop offered a valuable opportunity to review the progress of Research and Development projects funded under Solicitation 2022-2, which aim to improve our understanding of the key factors influencing Enhanced Geothermal System (EGS) reservoir and resource development.},
url = {https://gdr.openei.org/submissions/1785},
year = {2025},
howpublished = {GDR, GTC Analytics, https://gdr.openei.org/submissions/1785},
note = {Accessed: 2026-04-08}
}
Details
Data from Sep 18, 2025
Last updated Sep 21, 2025
Submitted Sep 18, 2025
Organization
GTC Analytics
Contact
Jesse Williams
Authors
Original Source
https://gdr.openei.org/submissions/1785Research Areas
Keywords
geothermal, energy, Utah FORGE, EGS, 2025 Annual Workshop, induced seismicity, machine learning, recurrent neural networks, probabilistic modeling, seismic response prediction, magnitude-frequency analysis, physics-informed ai, presentation, presentation recording, presentation slides, reportDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
