Utah FORGE 6-3712: Report on Building a Recurrent Neural Network Framework for Induced Seismicity - October, 2025
This is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of designing a recurrent neural network (RNN) to predict induced seismicity. Background material is included to inform non-subject matter experts about the types of architectures available. The exact architectures (layers) of three models are discussed, which are being used to predict induced seismicity.
Citation Formats
TY - DATA
AB - This is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of designing a recurrent neural network (RNN) to predict induced seismicity. Background material is included to inform non-subject matter experts about the types of architectures available. The exact architectures (layers) of three models are discussed, which are being used to predict induced seismicity.
AU - Williams, Jesse
A2 - Peng, Zhigang
A3 - Si, Xu
A4 - Dai, Sheng
A5 - Jin, Wencheng
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Laboratory of the Rockies
DO -
KW - geothermal
KW - energy
KW - Deep learning
KW - Induced Seismicity
KW - predictive
KW - magnitude
KW - artificial intelligence
KW - AI
KW - machine learning
KW - ML
KW - DL
KW - physics-based
KW - modeling
KW - Utah FORGE
KW - EGS
KW - technical report
KW - seismic data
KW - injection parameters
KW - geophysical models
KW - probabilistic
LA - English
DA - 2025/10/13
PY - 2025
PB - Global Technology Connection, Inc.
T1 - Utah FORGE 6-3712: Report on Building a Recurrent Neural Network Framework for Induced Seismicity - October, 2025
UR - https://data.openei.org/submissions/11626
ER -
Williams, Jesse, et al. Utah FORGE 6-3712: Report on Building a Recurrent Neural Network Framework for Induced Seismicity - October, 2025. Global Technology Connection, Inc., 13 October, 2025, GDR. https://gdr.openei.org/submissions/1797.
Williams, J., Peng, Z., Si, X., Dai, S., & Jin, W. (2025). Utah FORGE 6-3712: Report on Building a Recurrent Neural Network Framework for Induced Seismicity - October, 2025. [Data set]. GDR. Global Technology Connection, Inc.. https://gdr.openei.org/submissions/1797
Williams, Jesse, Zhigang Peng, Xu Si, Sheng Dai, and Wencheng Jin. Utah FORGE 6-3712: Report on Building a Recurrent Neural Network Framework for Induced Seismicity - October, 2025. Global Technology Connection, Inc., October, 13, 2025. Distributed by GDR. https://gdr.openei.org/submissions/1797
@misc{OEDI_Dataset_11626,
title = {Utah FORGE 6-3712: Report on Building a Recurrent Neural Network Framework for Induced Seismicity - October, 2025},
author = {Williams, Jesse and Peng, Zhigang and Si, Xu and Dai, Sheng and Jin, Wencheng},
abstractNote = {This is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of designing a recurrent neural network (RNN) to predict induced seismicity. Background material is included to inform non-subject matter experts about the types of architectures available. The exact architectures (layers) of three models are discussed, which are being used to predict induced seismicity. },
url = {https://gdr.openei.org/submissions/1797},
year = {2025},
howpublished = {GDR, Global Technology Connection, Inc., https://gdr.openei.org/submissions/1797},
note = {Accessed: 2026-11-10}
}
Details
Data from Oct 13, 2025
Last updated Oct 13, 2025
Submitted Oct 13, 2025
Organization
Global Technology Connection, Inc.
Contact
Jesse Williams
770.803.3001
Authors
Original Source
https://gdr.openei.org/submissions/1797Research Areas
Keywords
geothermal, energy, Deep learning, Induced Seismicity, predictive, magnitude, artificial intelligence, AI, machine learning, ML, DL, physics-based, modeling, Utah FORGE, EGS, technical report, seismic data, injection parameters, geophysical models, probabilisticDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
