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 - Geothermal Data Repository
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://gdr.openei.org/submissions/1797
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, Geothermal Data Repository. 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]. Geothermal Data Repository. 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 Geothermal Data Repository. https://gdr.openei.org/submissions/1797
@misc{GDR_Dataset_1797,
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 = {Geothermal Data Repository, Global Technology Connection, Inc., https://gdr.openei.org/submissions/1797},
note = {Accessed: 2026-01-08}
}
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
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
