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Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events

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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 -
Export Citation to RIS
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

Jesse Williams

Global Technology Connection Inc.

Zhigang Peng

Georgia Institute of Technology

Sheng Dai

Georgia Institute of Technology

Wencheng Jin

Idaho National Laboratory

Research Areas

DOE Project Details

Project Name Utah FORGE

Project Lead Lauren Boyd

Project Number EE0007080

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