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High-resolution seismic event detection using local similarity for Large-N arrays

We develop a novel method for seismic event detection that can be applied to large-N arrays. The method is based on a new detection function named local similarity, which quantifies the signal consistency between the examined station and its nearest neighbors. Using the 5200-station Long Beach nodal...

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Detalles Bibliográficos
Autores principales: Li, Zefeng, Peng, Zhigang, Hollis, Dan, Zhu, Lijun, McClellan, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5786042/
https://www.ncbi.nlm.nih.gov/pubmed/29374191
http://dx.doi.org/10.1038/s41598-018-19728-w
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author Li, Zefeng
Peng, Zhigang
Hollis, Dan
Zhu, Lijun
McClellan, James
author_facet Li, Zefeng
Peng, Zhigang
Hollis, Dan
Zhu, Lijun
McClellan, James
author_sort Li, Zefeng
collection PubMed
description We develop a novel method for seismic event detection that can be applied to large-N arrays. The method is based on a new detection function named local similarity, which quantifies the signal consistency between the examined station and its nearest neighbors. Using the 5200-station Long Beach nodal array, we demonstrate that stacked local similarity functions can be used to detect seismic events with amplitudes near or below noise levels. We apply the method to one-week continuous data around the 03/11/2011 Mw 9.1 Tohoku-Oki earthquake, to detect local and distant events. In the 5–10 Hz range, we detect various events of natural and anthropogenic origins, but without a clear increase in local seismicity during and following the surface waves of the Tohoku-Oki mainshock. In the 1-Hz low-pass-filtered range, we detect numerous events, likely representing aftershocks from the Tohoku-Oki mainshock region. This high-resolution detection technique can be applied to both ultra-dense and regular array recordings for monitoring ultra-weak micro-seismicity and detecting unusual seismic events in noisy environments.
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spelling pubmed-57860422018-02-07 High-resolution seismic event detection using local similarity for Large-N arrays Li, Zefeng Peng, Zhigang Hollis, Dan Zhu, Lijun McClellan, James Sci Rep Article We develop a novel method for seismic event detection that can be applied to large-N arrays. The method is based on a new detection function named local similarity, which quantifies the signal consistency between the examined station and its nearest neighbors. Using the 5200-station Long Beach nodal array, we demonstrate that stacked local similarity functions can be used to detect seismic events with amplitudes near or below noise levels. We apply the method to one-week continuous data around the 03/11/2011 Mw 9.1 Tohoku-Oki earthquake, to detect local and distant events. In the 5–10 Hz range, we detect various events of natural and anthropogenic origins, but without a clear increase in local seismicity during and following the surface waves of the Tohoku-Oki mainshock. In the 1-Hz low-pass-filtered range, we detect numerous events, likely representing aftershocks from the Tohoku-Oki mainshock region. This high-resolution detection technique can be applied to both ultra-dense and regular array recordings for monitoring ultra-weak micro-seismicity and detecting unusual seismic events in noisy environments. Nature Publishing Group UK 2018-01-26 /pmc/articles/PMC5786042/ /pubmed/29374191 http://dx.doi.org/10.1038/s41598-018-19728-w Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Li, Zefeng
Peng, Zhigang
Hollis, Dan
Zhu, Lijun
McClellan, James
High-resolution seismic event detection using local similarity for Large-N arrays
title High-resolution seismic event detection using local similarity for Large-N arrays
title_full High-resolution seismic event detection using local similarity for Large-N arrays
title_fullStr High-resolution seismic event detection using local similarity for Large-N arrays
title_full_unstemmed High-resolution seismic event detection using local similarity for Large-N arrays
title_short High-resolution seismic event detection using local similarity for Large-N arrays
title_sort high-resolution seismic event detection using local similarity for large-n arrays
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5786042/
https://www.ncbi.nlm.nih.gov/pubmed/29374191
http://dx.doi.org/10.1038/s41598-018-19728-w
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