Unsupervised Clustering of Microseismic Events and Focal Mechanism Analysis at the CO2 Injection Site in Decatur, Illinois
Name
Journal of Geophysical Research Machine Learning and Computation - 2025 - Willis - Unsupervised Clustering of Microseismic.pdf
Description
Published version
Size
5.83 MB
Format
Adobe PDF
Checksum (MD5)
638c6b0b1ca5c60663656499b028ee71
Author(s)
Willis, Rachel M
Yoon, Hongkyu
Silva, Josimar A
Juanes, Ruben
Williams‐Stroud, Sherilyn
Frailey, Scott M
Date Issued
July 9, 2025
Journal
Journal of Geophysical Research: Machine Learning and Computation
Publisher
American Geophysical Union
Citation
Willis, R. M., Yoon, H., Silva, J. A., Juanes, R., Williams-Stroud, S., & Frailey, S. M. (2025). Unsupervised clustering of microseismic events and focal mechanism analysis at the CO2 injection site in Decatur, Illinois. Journal of Geophysical Research: Machine Learning and Computation, 2, e2024JH000544.
Version
Final published version
Abstract
Characterization of induced microseismicity at a carbon dioxide () storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin‐Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML‐based clustering with focal mechanism analysis, we resolve small‐scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio‐temporal evolution of fault reactivation during injection. This approach enhances seismic monitoring capabilities at injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.
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Creative Commons Attribution-NonCommercial
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DOI of Published Version
https://doi.org/10.1029/2024JH000544
