Fast Matched Filter (FMF): An Efficient Seismic Matched‐Filter Search for Both CPU and GPU Architectures

Research Article| December 20, 2017 Fast Matched Filter (FMF): An Efficient Seismic Matched‐Filter Search for Both CPU and GPU Architectures Eric Beaucé; Eric Beaucé aDepartment of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 54‐527, Cambridge, Massachusetts 02139 U.S.A., ebeauce@mit.edu Search for other works by this author on: GSW Google Scholar William B. Frank; William B. Frank aDepartment of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 54‐527, Cambridge, Massachusetts 02139 U.S.A., ebeauce@mit.edu Search for other works by this author on: GSW Google Scholar Alexey Romanenko Alexey Romanenko bChair of Informatics Systems, Novosibirsk State University, Pirogova, 2, Novosibirsk 630090, Russia Search for other works by this author on: GSW Google Scholar Author and Article Information Eric Beaucé aDepartment of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 54‐527, Cambridge, Massachusetts 02139 U.S.A., ebeauce@mit.edu William B. Frank aDepartment of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 54‐527, Cambridge, Massachusetts 02139 U.S.A., ebeauce@mit.edu Alexey Romanenko bChair of Informatics Systems, Novosibirsk State University, Pirogova, 2, Novosibirsk 630090, Russia Publisher: Seismological Society of America First Online: 20 Dec 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2018) 89 (1): 165–172. https://doi.org/10.1785/0220170181 Article history First Online: 20 Dec 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Eric Beaucé, William B. Frank, Alexey Romanenko; Fast Matched Filter (FMF): An Efficient Seismic Matched‐Filter Search for Both CPU and GPU Architectures. Seismological Research Letters 2017;; 89 (1): 165–172. doi: https://doi.org/10.1785/0220170181 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 Matched‐filter searches are an important tool in modern seismology to detect seismic events. They operate via an algorithm that computes the correlation coefficient between a template event and a sliding window of continuous seismic records. A detection is recorded when the correlation coefficient crosses an established threshold. We present an optimized program, called Fast Matched Filter (FMF), that efficiently runs a network‐based matched‐filter search with either central processing units (CPUs) or Nvidia graphics processing units (GPUs). Wrappers for both Python and MATLAB (CPU only) are provided to easily run FMF on a wide range of computational resources, from multicore laptops to specialized computing clusters with GPUs. Both implementations leverage a significantly similar structure when it comes to the continuous computation of correlation coefficients in the time domain to achieve rapid performance. The highly parallel architecture of GPUs lends itself perfectly to the matched‐filter algorithm, and we achieve the fastest run times with our GPU implementation. FMF allows for seismic network‐based matched‐filtering between a large set of template waveforms and a large continuous dataset in a reasonable amount of time. Such fast run times are an important step in expanding the scope of earthquake detection and fostering the reproducibility of such studies. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.