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Title: Evaluation of a pair-based, joint-likelihood association approach for regional infrasound event identification

Abstract

SUMMARY A Bayesian framework for the association of infrasonic detections is presented and evaluated for analysis at regional propagation scales. A pair-based, joint-likelihood association approach is developed that identifies events by computing the probability that individual detection pairs are attributable to a hypothetical common source and applying hierarchical clustering to identify events from the pair-based analysis. The framework is based on a Bayesian formulation introduced for infrasonic source localization and utilizes the propagation models developed for that application with modifications to improve the numerical efficiency of the analysis. Clustering analysis is completed using hierarchical analysis via weighted linkage for a non-Euclidean distance matrix defined by the negative log-joint-likelihood values. The method is evaluated using regional synthetic data with propagation distances of hundreds of kilometres in order to study the sensitivity of the method to uncertainties and errors in backazimuth and time of arrival. The method is found to be robust and stable for typical uncertainties, able to effectively distinguish noise detections within the data set from those in events, and can be made numerically efficient due to its ease of parallelization.

Authors:
 [1];  [1];  [1];  [1]
  1. Los Alamos National Laboratory, Earth & Environmental Science Division, Los Alamos, NM 87545, USA
Publication Date:
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1606270
Resource Type:
Published Article
Journal Name:
Geophysical Journal International
Additional Journal Information:
[Journal Name: Geophysical Journal International Journal Volume: 221 Journal Issue: 3]; Journal ID: ISSN 0956-540X
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Blom, Philip, Euler, Garrett, Marcillo, Omar, and Dannemann Dugick, Fransiska. Evaluation of a pair-based, joint-likelihood association approach for regional infrasound event identification. United Kingdom: N. p., 2020. Web. doi:10.1093/gji/ggaa105.
Blom, Philip, Euler, Garrett, Marcillo, Omar, & Dannemann Dugick, Fransiska. Evaluation of a pair-based, joint-likelihood association approach for regional infrasound event identification. United Kingdom. doi:10.1093/gji/ggaa105.
Blom, Philip, Euler, Garrett, Marcillo, Omar, and Dannemann Dugick, Fransiska. Mon . "Evaluation of a pair-based, joint-likelihood association approach for regional infrasound event identification". United Kingdom. doi:10.1093/gji/ggaa105.
@article{osti_1606270,
title = {Evaluation of a pair-based, joint-likelihood association approach for regional infrasound event identification},
author = {Blom, Philip and Euler, Garrett and Marcillo, Omar and Dannemann Dugick, Fransiska},
abstractNote = {SUMMARY A Bayesian framework for the association of infrasonic detections is presented and evaluated for analysis at regional propagation scales. A pair-based, joint-likelihood association approach is developed that identifies events by computing the probability that individual detection pairs are attributable to a hypothetical common source and applying hierarchical clustering to identify events from the pair-based analysis. The framework is based on a Bayesian formulation introduced for infrasonic source localization and utilizes the propagation models developed for that application with modifications to improve the numerical efficiency of the analysis. Clustering analysis is completed using hierarchical analysis via weighted linkage for a non-Euclidean distance matrix defined by the negative log-joint-likelihood values. The method is evaluated using regional synthetic data with propagation distances of hundreds of kilometres in order to study the sensitivity of the method to uncertainties and errors in backazimuth and time of arrival. The method is found to be robust and stable for typical uncertainties, able to effectively distinguish noise detections within the data set from those in events, and can be made numerically efficient due to its ease of parallelization.},
doi = {10.1093/gji/ggaa105},
journal = {Geophysical Journal International},
number = [3],
volume = [221],
place = {United Kingdom},
year = {2020},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
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DOI: 10.1093/gji/ggaa105

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