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Title: Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram-Based Machine Learning Approach

Abstract

Abstract The capability to discriminate low-magnitude earthquakes from low-yield anthropogenic sources, both detectable only at local distances, is of increasing interest to the event monitoring community. Here, we used a dataset of seismic events in Utah recorded during a 14-day period (1–14 January 2011) by the University of Utah Seismic Stations network to perform a comparative study of event classification at local scale using amplitude ratio (AR) methods and a machine learning (ML) approach. The event catalog consists of 7377 events with magnitudes M C ranging from –2 and lower up to 5.8. Events were subdivided into six populations based on location and source type: tectonic earthquakes (TEs), mining-induced events (MIEs), and mining blasts from four known mines (WMB, SMB, LMB, and CQB). The AR approach jointly exploits Pg-to-Sg phase ARs and Rg-to-Sg spectral ARs in multivariate quadratic discriminant functions and was able to classify 370 events with high signal quality from the three groups with sufficient size (TE, MIE, and SMB). For that subset of the events, the method achieved success rates between about 80% and 90%. The ML technique used trained convolutional neural network (CNN) models to classify the populations. The CNN approach was able to classify themore » subset of events with accuracies between about 91% and 98%. Because the neural network approach does not have a minimum signal quality requirement, we applied it to the entire event catalog, including the abundant extremely low-magnitude events, and achieved accuracies of about 94%–100%. Furthermore, we compare the AR and ML methodologies using a broad set of criteria and conclude that a major advantage to ML methods is their robustness to low signal-to-noise ratio data, allowing them to classify significantly smaller events.« less

Authors:
 [1];  [1];  [1];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. ENSCO, Inc., Springfield, VA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation (NA-20)
OSTI Identifier:
1575270
Report Number(s):
[SAND-2019-5288J]
[Journal ID: ISSN 0037-1106; 675434]
Grant/Contract Number:  
[AC04-94AL85000; NA0003525]
Resource Type:
Accepted Manuscript
Journal Name:
Bulletin of the Seismological Society of America
Additional Journal Information:
[ Journal Volume: 109; Journal Issue: 6]; Journal ID: ISSN 0037-1106
Publisher:
Seismological Society of America
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Tibi, Rigobert, Linville, Lisa, Young, Christopher, and Brogan, Ronald. Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram-Based Machine Learning Approach. United States: N. p., 2019. Web. doi:10.1785/0120190150.
Tibi, Rigobert, Linville, Lisa, Young, Christopher, & Brogan, Ronald. Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram-Based Machine Learning Approach. United States. doi:10.1785/0120190150.
Tibi, Rigobert, Linville, Lisa, Young, Christopher, and Brogan, Ronald. Tue . "Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram-Based Machine Learning Approach". United States. doi:10.1785/0120190150.
@article{osti_1575270,
title = {Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram-Based Machine Learning Approach},
author = {Tibi, Rigobert and Linville, Lisa and Young, Christopher and Brogan, Ronald},
abstractNote = {Abstract The capability to discriminate low-magnitude earthquakes from low-yield anthropogenic sources, both detectable only at local distances, is of increasing interest to the event monitoring community. Here, we used a dataset of seismic events in Utah recorded during a 14-day period (1–14 January 2011) by the University of Utah Seismic Stations network to perform a comparative study of event classification at local scale using amplitude ratio (AR) methods and a machine learning (ML) approach. The event catalog consists of 7377 events with magnitudes MC ranging from –2 and lower up to 5.8. Events were subdivided into six populations based on location and source type: tectonic earthquakes (TEs), mining-induced events (MIEs), and mining blasts from four known mines (WMB, SMB, LMB, and CQB). The AR approach jointly exploits Pg-to-Sg phase ARs and Rg-to-Sg spectral ARs in multivariate quadratic discriminant functions and was able to classify 370 events with high signal quality from the three groups with sufficient size (TE, MIE, and SMB). For that subset of the events, the method achieved success rates between about 80% and 90%. The ML technique used trained convolutional neural network (CNN) models to classify the populations. The CNN approach was able to classify the subset of events with accuracies between about 91% and 98%. Because the neural network approach does not have a minimum signal quality requirement, we applied it to the entire event catalog, including the abundant extremely low-magnitude events, and achieved accuracies of about 94%–100%. Furthermore, we compare the AR and ML methodologies using a broad set of criteria and conclude that a major advantage to ML methods is their robustness to low signal-to-noise ratio data, allowing them to classify significantly smaller events.},
doi = {10.1785/0120190150},
journal = {Bulletin of the Seismological Society of America},
number = [6],
volume = [109],
place = {United States},
year = {2019},
month = {10}
}

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