Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783295721113387008 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5786042 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lizefeng highresolutionseismiceventdetectionusinglocalsimilarityforlargenarrays AT pengzhigang highresolutionseismiceventdetectionusinglocalsimilarityforlargenarrays AT hollisdan highresolutionseismiceventdetectionusinglocalsimilarityforlargenarrays AT zhulijun highresolutionseismiceventdetectionusinglocalsimilarityforlargenarrays AT mcclellanjames highresolutionseismiceventdetectionusinglocalsimilarityforlargenarrays |