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A unified wavefield-partitioning approach for distributed acoustic sensing

Atterholt, James and Zhan, Zhongwen and Shen, Zhichao and Li, Zefeng (2022) A unified wavefield-partitioning approach for distributed acoustic sensing. Geophysical Journal International, 228 (2). pp. 1410-1418. ISSN 0956-540X. doi:10.1093/gji/ggab407.

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While distributed acoustic sensing (DAS) has been demonstrated to have great potential in seismology, DAS data often have much higher levels of stochastic and coherent noise (e.g. instrument noise, traffic vibrations) than data collected by traditional seismometers. The linearly, densely spaced nature of DAS arrays presents a suite of opportunities for more innovative processing techniques that can be used to address this issue. One way to take advantage of DAS’s array architecture is through the use of curvelets. Curvelets have a non-uniform scaling property that makes them an excellent tool for representing images with discontinuities along piecewise, twice continuously differentiable curves. This anisotropic scaling property makes curvelets an ideal processing tool for DAS data, for which the measured wavefield can be represented as an image composed of curved features. Here, we use the curvelet frame as a tool for the manipulation of DAS signal and demonstrate how this manipulation can improve our ability to identify important features in DAS data sets. We use the curvelet representation to partition the measured wavefield using DAS data collected near Ridgecrest, CA, following the 2019 M_w7.1 Ridgecrest earthquake. Here, we isolate the earthquake-induced wavefield from coherent and stochastic noise using the curvelet frame in an effort to improve the results of template matching of the Ridgecrest aftershock sequence. We show that our wavefield-partitioning technique facilitates the identification of over 30 per cent more aftershocks and greatly reduces the magnitude of diurnal depressions in the aftershock catalogue due to cultural noise.

Item Type:Article
Related URLs:
URLURL TypeDescription ItemCode ItemCurveLab toolbox
Atterholt, James0000-0003-1603-5518
Zhan, Zhongwen0000-0002-5586-2607
Shen, Zhichao0000-0003-0458-5264
Li, Zefeng0000-0003-4405-8872
Additional Information:© The Author(s) 2021. Published by Oxford University Press on behalf of The Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( Accepted 2021 October 1. Received 2021 September 30; in original form 2021 August 27. Published: 07 October 2021. This work was partially funded by the National Science Foundation’s (NSF) Graduate Research Fellowship Program (GRFP) under grant number DGE-1745301. We would like to thank the teams at Caltech and OptaSense involved in the deployment and maintenance of the Ridgecrest DAS array. This manuscript benefitted greatly from the useful comments and suggestions of Dr Charles Langston and Dr Itzhak Lior. We would also like to thank the editors Dr Jörg Renner, Dr Ian Barstow and Dr Andrew Valentine for the facilitation of the publishing of this manuscript. Data Availability: Earthquake record sections used in Figs 1, 3, and 4 are available for download at Caltech’s research data repository ( Code for performing this wavefield-partitioning technique with a working example is available on Github ( This code makes use of the CurveLab toolbox that is available on the website (
Group:Seismological Laboratory
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
Subject Keywords:Instrumental noise, Image processing, Time-series analysis, Seismicity and tectonics
Issue or Number:2
Record Number:CaltechAUTHORS:20211202-152847906
Persistent URL:
Official Citation:James Atterholt, Zhongwen Zhan, Zhichao Shen, Zefeng Li, A unified wavefield-partitioning approach for distributed acoustic sensing, Geophysical Journal International, Volume 228, Issue 2, February 2022, Pages 1410–1418,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112159
Deposited By: Tony Diaz
Deposited On:02 Dec 2021 16:41
Last Modified:02 Dec 2021 16:41

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