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Super‐Efficient Cross‐Correlation (SEC‐C): A Fast Matched Filtering Code Suitable for Desktop Computers

2018; Seismological Society of America; Volume: 90; Issue: 1 Linguagem: Inglês

10.1785/0220180122

1938-2057

Nader Shakibay Senobari, G. J. Funning, Eamonn Keogh, Yan Zhu, Chin‐Chia Michael Yeh, Zachary Zimmerman, Abdullah Mueen,

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Research Article| November 07, 2018 Super‐Efficient Cross‐Correlation (SEC‐C): A Fast Matched Filtering Code Suitable for Desktop Computers Nader Shakibay Senobari; Nader Shakibay Senobari aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Search for other works by this author on: GSW Google Scholar Gareth J. Funning; Gareth J. Funning aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Search for other works by this author on: GSW Google Scholar Eamonn Keogh; Eamonn Keogh aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Search for other works by this author on: GSW Google Scholar Yan Zhu; Yan Zhu aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Search for other works by this author on: GSW Google Scholar Chin‐Chia Michael Yeh; Chin‐Chia Michael Yeh aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Search for other works by this author on: GSW Google Scholar Zachary Zimmerman; Zachary Zimmerman aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Search for other works by this author on: GSW Google Scholar Abdullah Mueen Abdullah Mueen bUniversity of New Mexico, Albuquerque, New Mexico 87131 U.S.A. Search for other works by this author on: GSW Google Scholar Author and Article Information Nader Shakibay Senobari aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Gareth J. Funning aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Eamonn Keogh aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Yan Zhu aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Chin‐Chia Michael Yeh aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Zachary Zimmerman aUniversity of California, Riverside, 900 University Avenue, Riverside, California 92521 U.S.A., nshak006@ucr.edu Abdullah Mueen bUniversity of New Mexico, Albuquerque, New Mexico 87131 U.S.A. Publisher: Seismological Society of America First Online: 07 Nov 2018 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (1): 322–334. https://doi.org/10.1785/0220180122 Article history First Online: 07 Nov 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Nader Shakibay Senobari, Gareth J. Funning, Eamonn Keogh, Yan Zhu, Chin‐Chia Michael Yeh, Zachary Zimmerman, Abdullah Mueen; Super‐Efficient Cross‐Correlation (SEC‐C): A Fast Matched Filtering Code Suitable for Desktop Computers. Seismological Research Letters 2018;; 90 (1): 322–334. doi: https://doi.org/10.1785/0220180122 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT We present a new method to accelerate the process of matched filtering (template matching) of seismic waveforms by efficient calculation of (cross‐) correlation coefficients. The cross‐correlation method is commonly used to analyze seismic data, for example, to detect repeating or similar seismic waveform signals, earthquake swarms, foreshocks, aftershocks, low‐frequency earthquakes (LFEs), and nonvolcanic tremor. Recent growth in the density and coverage of seismic instrumentation demands fast and accurate methods to analyze the corresponding large volumes of data generated. Historically, there are two approaches used to perform matched filtering; one using the time domain and the other the frequency domain. Recent studies reveal that time domain matched filtering is memory efficient and frequency domain matched filtering is time efficient, assuming the same amount of computational resources.We show that our super‐efficient cross‐correlation (SEC‐C) method—a frequency domain method that optimizes computations using the overlap–add method, vectorization, and fast normalization—is not only more time efficient than existing frequency domain methods when run on the same number of central processing unit (CPU) threads but also more memory efficient than time domain methods in our test cases. For example, using 30 channels of data with a sample rate of 50 Hz and 30 templates, each with durations of 8 s, SEC‐C uses only 2.3 GB of memory whereas other frequency domain codes use three times more and parallelized time‐domain codes use ∼30% more. We have implemented a precise, fully normalized version of SEC‐C that removes the mean of the data in each sliding window, and thus can be applied to raw seismic data. Another strength of the SEC‐C method is that it can be used to search for repeating seismic events in a concatenated stack of individual event waveforms. In this use case, our method is more than one order of magnitude faster than conventional methods. The SEC‐C method does not require specialized hardware to achieve its computation speed; instead it exploits algorithmic ideas that are both time‐ and memory‐efficient and are thus suitable for use on off‐the‐shelf desktop machines. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.