Earthquake Detection in 1D Time‐Series Data with Feature Selection and Dictionary Learning

Research Article| January 30, 2019 Earthquake Detection in 1D Time‐Series Data with Feature Selection and Dictionary Learning Zheng Zhou; Zheng Zhou aEarth and Environment Sciences Division, Los Alamos National Laboratory, Bikini Atoll Road, SM 30, Los Alamos, New Mexico 87545 U.S.A., ylin@lanl.gov Search for other works by this author on: GSW Google Scholar Youzuo Lin; Youzuo Lin aEarth and Environment Sciences Division, Los Alamos National Laboratory, Bikini Atoll Road, SM 30, Los Alamos, New Mexico 87545 U.S.A., ylin@lanl.gov Search for other works by this author on: GSW Google Scholar Zhongping Zhang; Zhongping Zhang aEarth and Environment Sciences Division, Los Alamos National Laboratory, Bikini Atoll Road, SM 30, Los Alamos, New Mexico 87545 U.S.A., ylin@lanl.gov Search for other works by this author on: GSW Google Scholar Yue Wu; Yue Wu aEarth and Environment Sciences Division, Los Alamos National Laboratory, Bikini Atoll Road, SM 30, Los Alamos, New Mexico 87545 U.S.A., ylin@lanl.gov Search for other works by this author on: GSW Google Scholar Paul Johnson Paul Johnson aEarth and Environment Sciences Division, Los Alamos National Laboratory, Bikini Atoll Road, SM 30, Los Alamos, New Mexico 87545 U.S.A., ylin@lanl.gov Search for other works by this author on: GSW Google Scholar Author and Article Information Zheng Zhou aEarth and Environment Sciences Division, Los Alamos National Laboratory, Bikini Atoll Road, SM 30, Los Alamos, New Mexico 87545 U.S.A., ylin@lanl.gov Youzuo Lin aEarth and Environment Sciences Division, Los Alamos National Laboratory, Bikini Atoll Road, SM 30, Los Alamos, New Mexico 87545 U.S.A., ylin@lanl.gov Zhongping Zhang aEarth and Environment Sciences Division, Los Alamos National Laboratory, Bikini Atoll Road, SM 30, Los Alamos, New Mexico 87545 U.S.A., ylin@lanl.gov Yue Wu aEarth and Environment Sciences Division, Los Alamos National Laboratory, Bikini Atoll Road, SM 30, Los Alamos, New Mexico 87545 U.S.A., ylin@lanl.gov Paul Johnson aEarth and Environment Sciences Division, Los Alamos National Laboratory, Bikini Atoll Road, SM 30, Los Alamos, New Mexico 87545 U.S.A., ylin@lanl.gov Publisher: Seismological Society of America First Online: 30 Jan 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (2A): 563–572. https://doi.org/10.1785/0220180315 Article history First Online: 30 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 Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Paul Johnson; Earthquake Detection in 1D Time‐Series Data with Feature Selection and Dictionary Learning. Seismological Research Letters 2019;; 90 (2A): 563–572. doi: https://doi.org/10.1785/0220180315 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 Earthquakes can be detected by matching spatial patterns or phase properties from 1D seismic waves. Current earthquake detection methods such as waveform correlation and template matching (TM) have difficulty detecting anomalous earthquakes that are not similar to other earthquakes. In recent years, machine‐learning techniques for earthquake detection have been emerging as a new active research direction. In this article, we develop a novel earthquake detection method based on dictionary learning. Our detection method first generates rich features via signal processing and statistical methods, and further employs feature selection techniques to choose features that carry the most significant information. Based on these selected features, we build a dictionary for classifying earthquake events from nonearthquake events. To evaluate the performance of our dictionary‐based detection methods, we test our method on a labquake dataset, which contains 3,357,566 time‐series data points with a 400 MHz sampling rate. A total of 1000 earthquake events are manually labeled, and the length of these earthquake events varies from 74 to 7151 data points. Through comparison with other detection methods, we show that our feature selection and dictionary‐learning incorporated earthquake detection method achieves an 80.1% prediction accuracy and outperforms the baseline methods in earthquake detection, including TM and support vector machine (SVM). You do not have access to this content, please speak to your institutional administrator if you feel you should have access.