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 - Geothermal Data Repository
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://gdr.openei.org/submissions/1785
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, Geothermal Data Repository. 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]. Geothermal Data Repository. 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 Geothermal Data Repository. https://gdr.openei.org/submissions/1785
@misc{GDR_Dataset_1785,
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 = {Geothermal Data Repository, GTC Analytics, https://gdr.openei.org/submissions/1785},
note = {Accessed: 2026-08-20}
}
Details
Data from Sep 18, 2025
Last updated Sep 21, 2025
Submitted Sep 18, 2025
Organization
GTC Analytics
Contact
Jesse Williams
Authors
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
