Discrimination of Seismic Signals from Earthquakes and Tectonic Tremor by Applying a Convolutional Neural Network to Running Spectral Images

Research Article| January 03, 2019 Discrimination of Seismic Signals from Earthquakes and Tectonic Tremor by Applying a Convolutional Neural Network to Running Spectral Images Masaru Nakano; Masaru Nakano aR&D Center for Earthquake and Tsunami, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), Kanazawa, Yokohama 236‐0001, Japan, mnakano@jamstec.go.jp, horit@jamstec.go.jp Search for other works by this author on: GSW Google Scholar D. Sugiyama; D. Sugiyama bCenter for Earth Information Science and Technology, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), Kanazawa, Yokohama 236‐0001, Japan, sugiyamad@jamstec.go.jp, tsuboi@jamstec.go.jp Search for other works by this author on: GSW Google Scholar T. Hori; T. Hori aR&D Center for Earthquake and Tsunami, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), Kanazawa, Yokohama 236‐0001, Japan, mnakano@jamstec.go.jp, horit@jamstec.go.jp Search for other works by this author on: GSW Google Scholar T. Kuwatani; T. Kuwatani cDepartment of Solid Earth Geochemistry, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), Natsushima‐cho, Yokosuka 237‐0061, Japan, kuwatani@jamstec.go.jpdAlso at Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332‐0012, Japan. Search for other works by this author on: GSW Google Scholar S. Tsuboi S. Tsuboi bCenter for Earth Information Science and Technology, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), Kanazawa, Yokohama 236‐0001, Japan, sugiyamad@jamstec.go.jp, tsuboi@jamstec.go.jp Search for other works by this author on: GSW Google Scholar Author and Article Information Masaru Nakano aR&D Center for Earthquake and Tsunami, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), Kanazawa, Yokohama 236‐0001, Japan, mnakano@jamstec.go.jp, horit@jamstec.go.jp D. Sugiyama bCenter for Earth Information Science and Technology, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), Kanazawa, Yokohama 236‐0001, Japan, sugiyamad@jamstec.go.jp, tsuboi@jamstec.go.jp T. Hori aR&D Center for Earthquake and Tsunami, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), Kanazawa, Yokohama 236‐0001, Japan, mnakano@jamstec.go.jp, horit@jamstec.go.jp T. Kuwatani cDepartment of Solid Earth Geochemistry, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), Natsushima‐cho, Yokosuka 237‐0061, Japan, kuwatani@jamstec.go.jpdAlso at Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332‐0012, Japan. S. Tsuboi bCenter for Earth Information Science and Technology, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC), Kanazawa, Yokohama 236‐0001, Japan, sugiyamad@jamstec.go.jp, tsuboi@jamstec.go.jp Publisher: Seismological Society of America First Online: 03 Jan 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (2A): 530–538. https://doi.org/10.1785/0220180279 Article history First Online: 03 Jan 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Masaru Nakano, D. Sugiyama, T. Hori, T. Kuwatani, S. Tsuboi; Discrimination of Seismic Signals from Earthquakes and Tectonic Tremor by Applying a Convolutional Neural Network to Running Spectral Images. Seismological Research Letters 2019;; 90 (2A): 530–538. doi: https://doi.org/10.1785/0220180279 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 Monitoring of seismic signals generated by slow deformation at convergent and transform plate boundaries worldwide, known as tectonic tremor, might provide insights into deformation processes in the source regions of megathrust earthquakes. Tremor signals occur dominantly in the 2–8 Hz frequency band and can last for tens of seconds to several minutes, in contrast to typical earthquakes that produce seismic signals at frequencies up to several tens of hertz and last less than a minute. Because tremor is caused by stochastic processes, the resultant waveforms are represented by a stochastic function and construction of deterministic measures to discriminate tremor signals from earthquakes is very difficult. In this study, we used a convolutional neural network (CNN) to discriminate the signals of tectonic tremor from those of local earthquakes in running spectral images of these signals. We developed a method (seismic running spectra‐CNN [SRSpec‐CNN]) that is sensitive to the absolute frequency of signal appearance, which reflects the physical properties of the signal source, but is insensitive to the time of signal onset. SRSpec‐CNN has 130,211 parameters that were trained by 17,213 images of 64×64 pixels⁠. Based on simultaneous analyses of the frequency contents and durations of the signals, we achieved 99.5% accuracy for our identifications of signals from tectonic tremor, local earthquakes, and noise. Because running spectra clearly differentiate the characteristic features of these signals, we were able to achieve this high accuracy by using a CNN of simple architecture. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.