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• W2913447624 abstract "Research Article| February 13, 2019 An Investigation of Rapid Earthquake Characterization Using Single‐Station Waveforms and a Convolutional Neural Network Anthony Lomax; Anthony Lomax aALomax Scientific, 320 Chemin des Indes, 06370 Mouans‐Sartoux, France, anthony@alomax.net Search for other works by this author on: GSW Google Scholar Alberto Michelini; Alberto Michelini bIstituto Nazionale di Geofisica e Vulcanologia (INGV), Via di Vigna Murata, 605, 00143 Rome, Italy, alberto.michelini@ingv.it, djozinovi@gmail.com Search for other works by this author on: GSW Google Scholar Dario Jozinović Dario Jozinović bIstituto Nazionale di Geofisica e Vulcanologia (INGV), Via di Vigna Murata, 605, 00143 Rome, Italy, alberto.michelini@ingv.it, djozinovi@gmail.com Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2019) 90 (2A): 517–529. https://doi.org/10.1785/0220180311 Article history first online: 13 Feb 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Anthony Lomax, Alberto Michelini, Dario Jozinović; An Investigation of Rapid Earthquake Characterization Using Single‐Station Waveforms and a Convolutional Neural Network. Seismological Research Letters 2019;; 90 (2A): 517–529. doi: https://doi.org/10.1785/0220180311 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 Effective early warning, emergency response, and information dissemination for earthquakes and tsunamis require rapid characterization of an earthquake’s location, size, and other parameters, usually provided by real‐time seismogram analysis using established, rule‐based, seismological procedures. Powerful, new machine learning (ML) tools analyze basic data using little or no rule‐based knowledge, and an ML deep convolutional neural network (CNN) can operate directly on seismogram waveforms with little preprocessing and without feature extraction. How a CNN will perform for rapid automated earthquake detection and characterization using short single‐station waveforms is an issue of fundamental importance for earthquake monitoring.For an initial investigation of this issue, we adapt an existing CNN for local earthquake detection and epicentral classification using single‐station waveforms (Perol et al., 2018), to form a new CNN, ConvNetQuake_INGV, to characterize earthquakes at any distance (local to far‐teleseismic). ConvNetQuake_INGV operates directly on 50‐s three‐component broadband single‐station waveforms to detect seismic events and obtain binned probabilistic estimates of the distance, azimuth, depth, and magnitude of the event. The best performance of ConvNetQuake_INGV is obtained using a last convolutional layer with fewer nodes than the number of output classifications, a form of information bottleneck.We show that ConvNetQuake_INGV detects very well (accuracy 87%) and characterizes moderately well earthquakes over a broad range of distances and magnitudes, and we analyze outlier results and indications of overfitting of the CNN training data. We find weak evidence that the CNN is performing more than high‐dimensional regression and pattern recognition, and is generalizing information or learning, to provide useful characterization of new events not represented in the training data. We expect that real‐time ML procedures such as ConvNetQuake_INGV, perhaps incorporating rule‐based knowledge, will ultimately prove valuable for rapid detection and characterization of earthquakes for earthquake response and tsunami early warning. You do not have access to this content, please speak to your institutional administrator if you feel you should have access." @default.
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• W2913447624 title "An Investigation of Rapid Earthquake Characterization Using Single‐Station Waveforms and a Convolutional Neural Network" @default.
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