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

Li, Zefeng and Peng, Zhigang and Hollis, Dan and Zhu, Lijun and McClellan, James (2018) High-resolution seismic event detection using local similarity for Large-N arrays. Scientific Reports, 8 (1). Art. No. 1646. ISSN 2045-2322. PMCID PMC5786042. https://resolver.caltech.edu/CaltechAUTHORS:20180131-133234791

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Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41598-018-19728-wDOIArticle
http://www.ncbi.nlm.nih.gov/pmc/articles/pmc5786042/PubMed CentralArticle
ORCID:
AuthorORCID
Li, Zefeng0000-0003-4405-8872
Peng, Zhigang0000-0002-0019-9860
Zhu, Lijun0000-0002-3046-5824
McClellan, James0000-0003-0647-9561
Additional Information:© 2018 The Author(s). 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/. Received: 23 February 2017; Accepted: 08 January 2018; Published online: 26 January 2018. The seismic data analyzed in this study are owned by Signal Hill Petroleum, Inc. and acquired by NodalSeismic LLC We thank NodalSeismic LLC for making the one-week data available in this study. The manuscript benefits from useful comments by Clara Daniels, Xiaofeng Meng and Hongfeng Yang. We thank Asaf Inbal for making his backprojection catalog available for comparison. This work is supported by National Science Foundation grant (EAR-1551022). Author Contributions: Z.L. and Z.P. developed the method, conducted data analysis, and wrote the paper. D.H. helped collect the data and contributed to writing. L.Z. and J.M. analyzed the event location and contributed to writing. The authors declare that they have no competing interests.
Group:Seismological Laboratory
Funders:
Funding AgencyGrant Number
NSFEAR-1551022
Issue or Number:1
PubMed Central ID:PMC5786042
Record Number:CaltechAUTHORS:20180131-133234791
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180131-133234791
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:84605
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:31 Jan 2018 22:14
Last Modified:06 Jul 2020 22:01

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