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Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study

Ambient-noise-based seismic monitoring of the near surface often has limited spatiotemporal resolutions because dense seismic arrays are rarely sufficiently affordable for such applications. In recent years, however, distributed acoustic sensing (DAS) techniques have emerged to transform telecommuni...

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Autores principales: Dou, Shan, Lindsey, Nate, Wagner, Anna M., Daley, Thomas M., Freifeld, Barry, Robertson, Michelle, Peterson, John, Ulrich, Craig, Martin, Eileen R., Ajo-Franklin, Jonathan B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5599533/
https://www.ncbi.nlm.nih.gov/pubmed/28912436
http://dx.doi.org/10.1038/s41598-017-11986-4
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author Dou, Shan
Lindsey, Nate
Wagner, Anna M.
Daley, Thomas M.
Freifeld, Barry
Robertson, Michelle
Peterson, John
Ulrich, Craig
Martin, Eileen R.
Ajo-Franklin, Jonathan B.
author_facet Dou, Shan
Lindsey, Nate
Wagner, Anna M.
Daley, Thomas M.
Freifeld, Barry
Robertson, Michelle
Peterson, John
Ulrich, Craig
Martin, Eileen R.
Ajo-Franklin, Jonathan B.
author_sort Dou, Shan
collection PubMed
description Ambient-noise-based seismic monitoring of the near surface often has limited spatiotemporal resolutions because dense seismic arrays are rarely sufficiently affordable for such applications. In recent years, however, distributed acoustic sensing (DAS) techniques have emerged to transform telecommunication fiber-optic cables into dense seismic arrays that are cost effective. With DAS enabling both high sensor counts (“large N”) and long-term operations (“large T”), time-lapse imaging of shear-wave velocity (V (S)) structures is now possible by combining ambient noise interferometry and multichannel analysis of surface waves (MASW). Here we report the first end-to-end study of time-lapse V (S) imaging that uses traffic noise continuously recorded on linear DAS arrays over a three-week period. Our results illustrate that for the top 20 meters the V (S) models that is well constrained by the data, we obtain time-lapse repeatability of about 2% in the model domain—a threshold that is low enough for observing subtle near-surface changes such as water content variations and permafrost alteration. This study demonstrates the efficacy of near-surface seismic monitoring using DAS-recorded ambient noise.
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spelling pubmed-55995332017-09-15 Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study Dou, Shan Lindsey, Nate Wagner, Anna M. Daley, Thomas M. Freifeld, Barry Robertson, Michelle Peterson, John Ulrich, Craig Martin, Eileen R. Ajo-Franklin, Jonathan B. Sci Rep Article Ambient-noise-based seismic monitoring of the near surface often has limited spatiotemporal resolutions because dense seismic arrays are rarely sufficiently affordable for such applications. In recent years, however, distributed acoustic sensing (DAS) techniques have emerged to transform telecommunication fiber-optic cables into dense seismic arrays that are cost effective. With DAS enabling both high sensor counts (“large N”) and long-term operations (“large T”), time-lapse imaging of shear-wave velocity (V (S)) structures is now possible by combining ambient noise interferometry and multichannel analysis of surface waves (MASW). Here we report the first end-to-end study of time-lapse V (S) imaging that uses traffic noise continuously recorded on linear DAS arrays over a three-week period. Our results illustrate that for the top 20 meters the V (S) models that is well constrained by the data, we obtain time-lapse repeatability of about 2% in the model domain—a threshold that is low enough for observing subtle near-surface changes such as water content variations and permafrost alteration. This study demonstrates the efficacy of near-surface seismic monitoring using DAS-recorded ambient noise. Nature Publishing Group UK 2017-09-14 /pmc/articles/PMC5599533/ /pubmed/28912436 http://dx.doi.org/10.1038/s41598-017-11986-4 Text en © The Author(s) 2017 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
Dou, Shan
Lindsey, Nate
Wagner, Anna M.
Daley, Thomas M.
Freifeld, Barry
Robertson, Michelle
Peterson, John
Ulrich, Craig
Martin, Eileen R.
Ajo-Franklin, Jonathan B.
Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study
title Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study
title_full Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study
title_fullStr Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study
title_full_unstemmed Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study
title_short Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study
title_sort distributed acoustic sensing for seismic monitoring of the near surface: a traffic-noise interferometry case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5599533/
https://www.ncbi.nlm.nih.gov/pubmed/28912436
http://dx.doi.org/10.1038/s41598-017-11986-4
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