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  • 1
    Publication Date: 2019
    Description: 〈span〉〈div〉ABSTRACT〈/div〉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 (〈a href="https://pubs.geoscienceworld.org/srl#rf24"〉Perol 〈span〉et al.〈/span〉, 2018〈/a〉), 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.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
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  • 2
    Publication Date: 2019
    Description: 〈span〉〈div〉ABSTRACT〈/div〉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 (〈a href="https://pubs.geoscienceworld.org/srl#rf24"〉Perol 〈span〉et al.〈/span〉, 2018〈/a〉), 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.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
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  • 3
    Publication Date: 2018
    Description: 〈span〉〈div〉Abstract〈/div〉A detailed study, based on ocean‐bottom seismometers (OBSs) recordings from two recording periods (3.5 months in 2011 and 2 months in 2014) and on a high‐resolution, 3D velocity model, is presented here, which provides an alternative view of the microseismicity along the submerged section of the North Anatolian fault (NAF) within the western Sea of Marmara (SoM). The nonlinear probabilistic software packages of NonLinLoc and NLDiffLoc were used for locating earthquakes. Only earthquakes that comply with the following location criteria (e.g., representing 20% of the total amount of events) were considered for analysis: (1) number of stations≥5; (2) number of phases≥6, including both 〈span〉P〈/span〉 and 〈span〉S〈/span〉; (3) root mean square (rms) location error≤0.5  s; and (4) azimuthal gap≤180°. 〈span〉P〈/span〉 and 〈span〉S〈/span〉 travel times suggest that there are strong velocity anomalies along the Western High, with low Vp, low Vs, and ultra‐high Vp/Vs in areas where mud volcanoes and gas‐prone sediment layers are known to be present. The location results indicate that not all earthquakes occurred as strike‐slip events at crustal depths (〉8  km) along the axis of the Main Marmara fault (MMF). In contrast, the following features were observed: (1) a significant number of earthquakes occurred off‐axis (e.g., 24%), with predominantly normal focal mechanisms, at depths between 2 and 6 km, along tectonically active, structural trends oriented east–west or southwest–northeast, and (2) a great number of earthquakes was also found to occur within the upper sediment layers (at depths〈2  km), particularly in the areas where free gas is suspected to exist, based on high‐resolution 3D seismics (e.g., 28%). Part of this ultra‐shallow seismicity appears to occur in response to deep earthquakes of intermediate (ML∼4–5) magnitude. Resolving the depth of the shallow seismicity requires adequate experimental design ensuring source–receiver distances of the same order as hypocentral depths. To reach this objective, deep‐seafloor observatories with a sufficient number of geophone sensors near the fault trace are needed.〈/span〉
    Print ISSN: 0037-1106
    Electronic ISSN: 1943-3573
    Topics: Geosciences , Physics
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  • 4
    Publication Date: 2017-05-31
    Description: On 27 August 1904, seismic stations from around the globe recorded an M 〉7 earthquake originating from central Alaska. Very little was known about this earthquake. One felt report from Rampart, Alaska, had been attributed to the notes of Harry Fielding Reid, yet its original source was unknown. Here, we present five felt reports for the 1904 earthquake that show evidence of felt shaking across most of central Alaska. Using the 1904 arrival-time data, we estimate an epicentral location near Lake Minchumina at the northeastern extent of the Iditarod–Nixon fault. Our preferred fault for the 1904 earthquake is the right-lateral Iditarod–Nixon fault, which, though relatively seismically quiet, generated an M  6.2 earthquake in 1935. Paleoseismic investigations are needed to search for evidence of fault activity, including the 1904 earthquake rupture, in the tectonically complex region of the 1904 earthquake. Electronic Supplement: Tables of arrival time, figures of station registers, visualization of NonLinLoc (NLL) solution for the 1904 Alaska earthquake, distribution of depths of the posterior probability for the 1904 and 1935 events, epicenter and samples of the posterior probability distribution for the 1904 and 1935 earthquakes, map of southward shift of epicenters, and estimated epicenters for the 3 February 2000 Kaltag earthquake.
    Print ISSN: 0037-1106
    Electronic ISSN: 1943-3573
    Topics: Geosciences , Physics
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  • 5
    Publication Date: 2017-04-04
    Description: We image the rupture history of the 2009 L’Aquila (Central Italy) earthquake using a nonlinear joint inversion of strong motion and GPS data. This earthquake ruptured a normal fault striking along the Apennines axis and dipping to the SW. The inferred slip distribution is heterogeneous and characterized by a small, shallow slip patch located up-dip from the hypocenter (9.5 km depth) and a large, deeper patch located southeastward. The rupture velocity is larger in the up-dip than in the along-strike direction. This difference can be partially accounted by the crustal structure, which is characterized by a high velocity layer above the hypocenter and a lower velocity below. The latter velocity seems to have affected the along strike propagation since the largest slip patch is located at depths between 9 and 14 km. The imaged slip distribution correlates well with the on-fault aftershock pattern as well as with mapped surface breakages.
    Description: Published
    Description: L19304
    Description: 3.1. Fisica dei terremoti
    Description: JCR Journal
    Description: reserved
    Keywords: 2009 L'Aquila earthquake ; kinematic inversion ; joint inversion ; 04. Solid Earth::04.06. Seismology::04.06.03. Earthquake source and dynamics
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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