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Title: Rapid and Robust Cross-Correlation-Based Seismic Signal Identification Using an Approximate Nearest Neighbor Method

Abstract

The matched filtering technique that uses the cross correlation of a waveform of interest with archived signals from a template library has proven to be a powerful tool for detecting events in regions with repeating seismicity. However, waveform correlation is computationally expensive and therefore impractical for large template sets unless dedicated distributed computing hardware and software are used. In this paper, we introduce an approximate nearest neighbor (ANN) approach that enables the use of very large template libraries for waveform correlation. Our method begins with a projection into a reduced dimensionality space, based on correlation with a randomized subset of the full template archive. Searching for a specified number of nearest neighbors for a query waveform is accomplished by iteratively comparing it with the neighbors of its immediate neighbors. We used the approach to search for matches to each of ~2300 analyst-reviewed signal detections reported in May 2010 for the International Monitoring System station MKAR. The template library in this case consists of a data set of more than 200,000 analyst-reviewed signal detections for the same station from February 2002 to July 2016 (excluding May 2010). Of these signal detections, 73% are teleseismic first P and 17% regional phases (Pn,more » Pg, Sn, and Lg). Finally, the analyses performed on a standard desktop computer show that the proposed ANN approach performs a search of the large template libraries about 25 times faster than the standard full linear search and achieves recall rates greater than 80%, with the recall rate increasing for higher correlation thresholds.« less

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1372350
Report Number(s):
SAND-2017-0268J
Journal ID: ISSN 0037-1106; 650314
Grant/Contract Number:
AC04-94AL85000
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Bulletin of the Seismological Society of America
Additional Journal Information:
Journal Volume: 107; Journal Issue: 3; Journal ID: ISSN 0037-1106
Publisher:
Seismological Society of America
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Tibi, Rigobert, Young, Christopher, Gonzales, Antonio, Ballard, Sanford, and Encarnacao, Andre. Rapid and Robust Cross-Correlation-Based Seismic Signal Identification Using an Approximate Nearest Neighbor Method. United States: N. p., 2017. Web. doi:10.1785/0120170011.
Tibi, Rigobert, Young, Christopher, Gonzales, Antonio, Ballard, Sanford, & Encarnacao, Andre. Rapid and Robust Cross-Correlation-Based Seismic Signal Identification Using an Approximate Nearest Neighbor Method. United States. doi:10.1785/0120170011.
Tibi, Rigobert, Young, Christopher, Gonzales, Antonio, Ballard, Sanford, and Encarnacao, Andre. Tue . "Rapid and Robust Cross-Correlation-Based Seismic Signal Identification Using an Approximate Nearest Neighbor Method". United States. doi:10.1785/0120170011. https://www.osti.gov/servlets/purl/1372350.
@article{osti_1372350,
title = {Rapid and Robust Cross-Correlation-Based Seismic Signal Identification Using an Approximate Nearest Neighbor Method},
author = {Tibi, Rigobert and Young, Christopher and Gonzales, Antonio and Ballard, Sanford and Encarnacao, Andre},
abstractNote = {The matched filtering technique that uses the cross correlation of a waveform of interest with archived signals from a template library has proven to be a powerful tool for detecting events in regions with repeating seismicity. However, waveform correlation is computationally expensive and therefore impractical for large template sets unless dedicated distributed computing hardware and software are used. In this paper, we introduce an approximate nearest neighbor (ANN) approach that enables the use of very large template libraries for waveform correlation. Our method begins with a projection into a reduced dimensionality space, based on correlation with a randomized subset of the full template archive. Searching for a specified number of nearest neighbors for a query waveform is accomplished by iteratively comparing it with the neighbors of its immediate neighbors. We used the approach to search for matches to each of ~2300 analyst-reviewed signal detections reported in May 2010 for the International Monitoring System station MKAR. The template library in this case consists of a data set of more than 200,000 analyst-reviewed signal detections for the same station from February 2002 to July 2016 (excluding May 2010). Of these signal detections, 73% are teleseismic first P and 17% regional phases (Pn, Pg, Sn, and Lg). Finally, the analyses performed on a standard desktop computer show that the proposed ANN approach performs a search of the large template libraries about 25 times faster than the standard full linear search and achieves recall rates greater than 80%, with the recall rate increasing for higher correlation thresholds.},
doi = {10.1785/0120170011},
journal = {Bulletin of the Seismological Society of America},
number = 3,
volume = 107,
place = {United States},
year = {Tue Jul 04 00:00:00 EDT 2017},
month = {Tue Jul 04 00:00:00 EDT 2017}
}

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