Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events
This submission is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of extracting events from the borehole seismic sensors. To be effective once deployed, the process must be done in real-time. A summary of the methodology is as follows: bandpass filter, shift (via cross-correlation) and stack signals, envelope function, peak detection, transfer function from amplitude to magnitude, creation of magnitude-frequency distribution, and finally, extract MFD "a" and "b" parameters. The datasets used in this work are linked below and include the raw waveform data and the seismic event catalog used for magnitude calibration, also hosted on the GDR.
Citation Formats
TY - DATA
AB - This submission is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of extracting events from the borehole seismic sensors. To be effective once deployed, the process must be done in real-time. A summary of the methodology is as follows: bandpass filter, shift (via cross-correlation) and stack signals, envelope function, peak detection, transfer function from amplitude to magnitude, creation of magnitude-frequency distribution, and finally, extract MFD "a" and "b" parameters. The datasets used in this work are linked below and include the raw waveform data and the seismic event catalog used for magnitude calibration, also hosted on the GDR.
AU - Williams, Jesse
A2 - Peng, Zhigang
A3 - Dai, Sheng
A4 - Jin, Wencheng
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - geothermal
KW - energy
KW - Utah FORGE
KW - data processing
KW - machine learning
KW - induced seismicity
KW - technical report
KW - event detection
KW - ML
KW - artificial intelligence
KW - AI
KW - real-time
KW - physics informed
KW - recurrent neural networks
KW - borehole seismic
KW - seismic data
KW - microseismic
KW - event catalog
KW - magnitude-frequency distribution
KW - geophysics
KW - EGS
LA - English
DA - 2025/01/21
PY - 2025
PB - Global Technology Connection, Inc.
T1 - Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events
UR - https://data.openei.org/submissions/8318
ER -
Williams, Jesse, et al. Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events. Global Technology Connection, Inc., 21 January, 2025, GDR. https://gdr.openei.org/submissions/1705.
Williams, J., Peng, Z., Dai, S., & Jin, W. (2025). Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events. [Data set]. GDR. Global Technology Connection, Inc.. https://gdr.openei.org/submissions/1705
Williams, Jesse, Zhigang Peng, Sheng Dai, and Wencheng Jin. Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events. Global Technology Connection, Inc., January, 21, 2025. Distributed by GDR. https://gdr.openei.org/submissions/1705
@misc{OEDI_Dataset_8318,
title = {Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events},
author = {Williams, Jesse and Peng, Zhigang and Dai, Sheng and Jin, Wencheng},
abstractNote = {This submission is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of extracting events from the borehole seismic sensors. To be effective once deployed, the process must be done in real-time. A summary of the methodology is as follows: bandpass filter, shift (via cross-correlation) and stack signals, envelope function, peak detection, transfer function from amplitude to magnitude, creation of magnitude-frequency distribution, and finally, extract MFD "a" and "b" parameters. The datasets used in this work are linked below and include the raw waveform data and the seismic event catalog used for magnitude calibration, also hosted on the GDR.},
url = {https://gdr.openei.org/submissions/1705},
year = {2025},
howpublished = {GDR, Global Technology Connection, Inc., https://gdr.openei.org/submissions/1705},
note = {Accessed: 2025-04-24}
}
Details
Data from Jan 21, 2025
Last updated Jan 22, 2025
Submitted Jan 22, 2025
Organization
Global Technology Connection, Inc.
Contact
Jesse Williams
770.803.3001
Authors
Original Source
https://gdr.openei.org/submissions/1705Research Areas
Keywords
geothermal, energy, Utah FORGE, data processing, machine learning, induced seismicity, technical report, event detection, ML, artificial intelligence, AI, real-time, physics informed, recurrent neural networks, borehole seismic, seismic data, microseismic, event catalog, magnitude-frequency distribution, geophysics, EGSDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080