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Recursive Entropy Method of Segmentation for Seismic Signals

2019; Seismological Society of America; Linguagem: Inglês

10.1785/0220180317

1938-2057

Ángel Bueno, Alejandro Díaz‐Moreno, Silvio De Angelis, Carmen Benı́tez, Jesús M. Ibáñez,

Seismic Waves and Analysis

Research Article| May 22, 2019 Recursive Entropy Method of Segmentation for Seismic Signals Angel Bueno; Angel Bueno Corresponding Author aDepartment of Signal Processing, Telematic and Communications, ETS Ingenierías Informática y de Telecomunicación, University of Granada, Calle Periodista Daniel Saucedo Aranda, 18071 Granada, Spain, angelbueno@ugr.es Search for other works by this author on: GSW Google Scholar Alejandro Díaz‐Moreno; Alejandro Díaz‐Moreno bDepartment of Earth, Ocean and Ecological Sciences, University of Liverpool, Jane Herdman Building, 4 Brownlow Street, Liverpool L69 3GP, United Kingdom Search for other works by this author on: GSW Google Scholar Silvio De Angelis; Silvio De Angelis bDepartment of Earth, Ocean and Ecological Sciences, University of Liverpool, Jane Herdman Building, 4 Brownlow Street, Liverpool L69 3GP, United Kingdom Search for other works by this author on: GSW Google Scholar Carmen Benítez; Carmen Benítez aDepartment of Signal Processing, Telematic and Communications, ETS Ingenierías Informática y de Telecomunicación, University of Granada, Calle Periodista Daniel Saucedo Aranda, 18071 Granada, Spain, angelbueno@ugr.es Search for other works by this author on: GSW Google Scholar Jesús M. Ibañez Jesús M. Ibañez cInstituto Universitario de Investigación Andaluz de Geofísica y Prevención de Desastres Sísmicos, University of Granada, Calle Profesor Clavera, Number 12, 18071 Granada, Spain Search for other works by this author on: GSW Google Scholar Author and Article Information Angel Bueno Corresponding Author aDepartment of Signal Processing, Telematic and Communications, ETS Ingenierías Informática y de Telecomunicación, University of Granada, Calle Periodista Daniel Saucedo Aranda, 18071 Granada, Spain, angelbueno@ugr.es Alejandro Díaz‐Moreno bDepartment of Earth, Ocean and Ecological Sciences, University of Liverpool, Jane Herdman Building, 4 Brownlow Street, Liverpool L69 3GP, United Kingdom Silvio De Angelis bDepartment of Earth, Ocean and Ecological Sciences, University of Liverpool, Jane Herdman Building, 4 Brownlow Street, Liverpool L69 3GP, United Kingdom Carmen Benítez aDepartment of Signal Processing, Telematic and Communications, ETS Ingenierías Informática y de Telecomunicación, University of Granada, Calle Periodista Daniel Saucedo Aranda, 18071 Granada, Spain, angelbueno@ugr.es Jesús M. Ibañez cInstituto Universitario de Investigación Andaluz de Geofísica y Prevención de Desastres Sísmicos, University of Granada, Calle Profesor Clavera, Number 12, 18071 Granada, Spain Publisher: Seismological Society of America First Online: 22 May 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (4): 1670–1677. https://doi.org/10.1785/0220180317 Article history First Online: 22 May 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Angel Bueno, Alejandro Díaz‐Moreno, Silvio De Angelis, Carmen Benítez, Jesús M. Ibañez; Recursive Entropy Method of Segmentation for Seismic Signals. Seismological Research Letters 2019;; 90 (4): 1670–1677. doi: https://doi.org/10.1785/0220180317 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 A wealth of data collected over the past three decades has demonstrated that volcanic unrest is often associated with elevated levels of seismicity. Volcano seismic networks commonly record intense swarms of earthquakes in the weeks to months before eruptions; peak rates of more than one event per minute are common. The ability to readily detect and classify these signals is crucial to effective monitoring operations and hazard assessment. The sheer volume of information collected, however, poses a challenge to volcano observatories because of the unrealistically large number of staffs required for manual inspection of these data. Here, we present Recursive Entropy Method of Segmentation (REMOS), a computationally efficient Python workflow used to detect, extract, and classify volcanic earthquakes starting from raw continuous waveform data. Within REMOS, seismograms are first analyzed using the well‐established short‐term average/long‐term average method to identify trigger times of candidate earthquakes. A new algorithm based on measurements of seismic energy and minimum entropy is then used to investigate large amounts of earthquake triggers and to discriminate and parse events into individual waveforms for further analyses. REMOS also includes a facility for classification of the extracted waveforms based on simple frequency‐domain metrics. Finally, the results can be visualized using t‐distributed stochastic neighbor embedding, a technique for dimensionality reduction that is particularly well suited to inspection of high‐dimensional datasets. In this work, we demonstrate the use of REMOS with seismic data recorded in 2007 during a period of unrest and eruption at Bezyminany Volcano. Our results show that REMOS can efficiently detect, segment, and classify earthquakes at scale and at very low computational cost. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.