Utah FORGE: Focal Mechanism Catalog from Stage 3 of the April 2022 Stimulation Test
This submission includes focal-mechanism solutions derived from the Utah FORGE April 2022 Stage-3 stimulation. Waveforms were extracted around each event (short windows bracketing origin times) from the downhole three-component arrays in wells 58-32, 78-32, and 56-32 and the surface station UU.FORK; an initial Stage-3 catalog of several thousand located events was narrowed to ~1,200 preselected events and processed to produce a final high-quality set of 717 focal mechanisms.
Methods combined automated phase picking with a noise-resistant deep-learning polarity classifier, simple amplitude-ratio measurements around arrivals, and Bayesian moment-tensor inversion using MTfit. Polarities and amplitude ratios were weighted by per-measurement confidence, posterior ensembles were sampled to quantify uncertainty, and solutions with low angular uncertainty (Kagan angle < 20 degrees) form the distributed high-quality catalog.
Citation Formats
TY - DATA
AB - This submission includes focal-mechanism solutions derived from the Utah FORGE April 2022 Stage-3 stimulation. Waveforms were extracted around each event (short windows bracketing origin times) from the downhole three-component arrays in wells 58-32, 78-32, and 56-32 and the surface station UU.FORK; an initial Stage-3 catalog of several thousand located events was narrowed to ~1,200 preselected events and processed to produce a final high-quality set of 717 focal mechanisms.
Methods combined automated phase picking with a noise-resistant deep-learning polarity classifier, simple amplitude-ratio measurements around arrivals, and Bayesian moment-tensor inversion using MTfit. Polarities and amplitude ratios were weighted by per-measurement confidence, posterior ensembles were sampled to quantify uncertainty, and solutions with low angular uncertainty (Kagan angle < 20 degrees) form the distributed high-quality catalog.
AU - Mohammadi, Ahmad
A2 - Chen, Xiaowei
A3 - Nakata, Nori
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Laboratory of the Rockies
DO -
KW - geothermal
KW - energy
KW - FORGE
KW - Utah FORGE
KW - EGS
KW - Milford
KW - Utah
KW - magnitude
KW - stage 3
KW - hydraulic stimulation
KW - strike
KW - dip
KW - Kagan angle
KW - moment-tensor inversion
KW - MTfit
KW - Bayesian inversion
KW - geophysics
KW - geophysical inversion
KW - focal-mechanisms
KW - event catalog
KW - deep-learning
KW - amplitude ratios
LA - English
DA - 2025/09/15
PY - 2025
PB - Texas A and M University
T1 - Utah FORGE: Focal Mechanism Catalog from Stage 3 of the April 2022 Stimulation Test
UR - https://data.openei.org/submissions/8518
ER -
Mohammadi, Ahmad, et al. Utah FORGE: Focal Mechanism Catalog from Stage 3 of the April 2022 Stimulation Test. Texas A and M University, 15 September, 2025, GDR. https://gdr.openei.org/submissions/1773.
Mohammadi, A., Chen, X., & Nakata, N. (2025). Utah FORGE: Focal Mechanism Catalog from Stage 3 of the April 2022 Stimulation Test. [Data set]. GDR. Texas A and M University. https://gdr.openei.org/submissions/1773
Mohammadi, Ahmad, Xiaowei Chen, and Nori Nakata. Utah FORGE: Focal Mechanism Catalog from Stage 3 of the April 2022 Stimulation Test. Texas A and M University, September, 15, 2025. Distributed by GDR. https://gdr.openei.org/submissions/1773
@misc{OEDI_Dataset_8518,
title = {Utah FORGE: Focal Mechanism Catalog from Stage 3 of the April 2022 Stimulation Test},
author = {Mohammadi, Ahmad and Chen, Xiaowei and Nakata, Nori},
abstractNote = {This submission includes focal-mechanism solutions derived from the Utah FORGE April 2022 Stage-3 stimulation. Waveforms were extracted around each event (short windows bracketing origin times) from the downhole three-component arrays in wells 58-32, 78-32, and 56-32 and the surface station UU.FORK; an initial Stage-3 catalog of several thousand located events was narrowed to ~1,200 preselected events and processed to produce a final high-quality set of 717 focal mechanisms.
Methods combined automated phase picking with a noise-resistant deep-learning polarity classifier, simple amplitude-ratio measurements around arrivals, and Bayesian moment-tensor inversion using MTfit. Polarities and amplitude ratios were weighted by per-measurement confidence, posterior ensembles were sampled to quantify uncertainty, and solutions with low angular uncertainty (Kagan angle < 20 degrees) form the distributed high-quality catalog.},
url = {https://gdr.openei.org/submissions/1773},
year = {2025},
howpublished = {GDR, Texas A and M University, https://gdr.openei.org/submissions/1773},
note = {Accessed: 2026-07-07}
}
Details
Data from Sep 15, 2025
Last updated Sep 16, 2025
Submitted Sep 15, 2025
Organization
Texas A and M University
Contact
Ahmad Mohammadi
Authors
Original Source
https://gdr.openei.org/submissions/1773Research Areas
Keywords
geothermal, energy, FORGE, Utah FORGE, EGS, Milford, Utah, magnitude, stage 3, hydraulic stimulation, strike, dip, Kagan angle, moment-tensor inversion, MTfit, Bayesian inversion, geophysics, geophysical inversion, focal-mechanisms, event catalog, deep-learning, amplitude ratiosDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
