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  • Articles  (54)
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  • 1
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    The cape mendocino, california, earthquakes of april 1992: subduction at the triple junction (1993)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 1993-07-23
    Description: The 25 April 1992 magnitude 7.1 Cape Mendocino thrust earthquake demonstrated that the North America-Gorda plate boundary is seismogenic and illustrated hazards that could result from much larger earthquakes forecast for the Cascadia region. The shock occurred just north of the Mendocino Triple Junction and caused strong ground motion and moderate damage in the immediate area. Rupture initiated onshore at a depth of 10.5 kilometers and propagated up-dip and seaward. Slip on steep faults in the Gorda plate generated two magnitude 6.6 aftershocks on 26 April. The main shock did not produce surface rupture on land but caused coastal uplift and a tsunami. The emerging picture of seismicity and faulting at the triple junction suggests that the region is likely to continue experiencing significant seismicity.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Oppenheimer, D -- Eaton, J -- Jayko, A -- Lisowski, M -- Marshall, G -- Murray, M -- Simpson, R -- Stein, R -- Beroza, G -- Magee, M -- Carver, G -- Dengler, L -- McPherson, R -- Gee, L -- Romanowicz, B -- Gonzalez, F -- Li, W H -- Satake, K -- Somerville, P -- Valentine, D -- New York, N.Y. -- Science. 1993 Jul 23;261(5120):433-8.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/17770022" target="_blank"〉PubMed〈/a〉
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 2
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    Seismicity remotely triggered by the magnitude 7.3 landers, california, earthquake (1993)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 1993-06-11
    Description: The magnitude 7.3 Landers earthquake of 28 June 1992 triggered a remarkably sudden and widespread increase in earthquake activity across much of the western United States. The triggered earthquakes, which occurred at distances up to 1250 kilometers (17 source dimensions) from the Landers mainshock, were confined to areas of persistent seismicity and strike-slip to normal faulting. Many of the triggered areas also are sites of geothermal and recent volcanic activity. Static stress changes calculated for elastic models of the earthquake appear to be too small to have caused the triggering. The most promising explanations involve nonlinear interactions between large dynamic strains accompanying seismic waves from the mainshock and crustal fluids (perhaps including crustal magma).〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Hill, D P -- Reasenberg, P A -- Michael, A -- Arabaz, W J -- Beroza, G -- Brumbaugh, D -- Brune, J N -- Castro, R -- Davis, S -- Depolo, D -- Ellsworth, W L -- Gomberg, J -- Harmsen, S -- House, L -- Jackson, S M -- Johnston, M J -- Jones, L -- Keller, R -- Malone, S -- Munguia, L -- Nava, S -- Pechmann, J C -- Sanford, A -- Simpson, R W -- Smith, R B -- Stark, M -- Stickney, M -- Vidal, A -- Walter, S -- Wong, V -- Zollweg, J -- New York, N.Y. -- Science. 1993 Jun 11;260(5114):1617-23.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/17810202" target="_blank"〉PubMed〈/a〉
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 3
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    PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method (2018)
    Oxford University Press
    Details
    Publication Date: 2018
    Description: 〈span〉〈div〉Summary〈/div〉As the number of seismic sensors grows, it is becoming increasingly difficult for analysts to pick seismic phases manually and comprehensively, yet such efforts are fundamental to earthquake monitoring. Despite years of improvements in automatic phase picking, it is difficult to match the performance of experienced analysts. A more subtle issue is that different seismic analysts may pick phases differently, which can introduce bias into earthquake locations. We present a deep-neural-network-based arrival-time picking method called ”PhaseNet” that picks the arrival times of both P and S waves. Deep neural networks have recently made rapid progress in feature learning, and with sufficient training, have achieved super-human performance in many applications. PhaseNet uses three-component seismic waveforms as input and generates probability distributions of P arrivals, S arrivals, and noise as output. We engineer PhaseNet such that peaks in the probability distributions provide accurate arrival times for both P and S waves. PhaseNet is trained on the prodigious available data set provided by analyst-labeled P and S arrival times from the Northern California Earthquake Data Center. The dataset we use contains more than seven hundred thousand waveform samples extracted from over thirty years of earthquake recordings. We demonstrate that PhaseNet achieves much higher picking accuracy and recall rate than existing methods when applied to the waveforms of known earthquakes, which has the potential to increase the number of S-wave observations dramatically over what is currently available. This will enable both improved locations and improved shear wave velocity models.〈/span〉
    Print ISSN: 2051-1965
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Deutsche Geophysikalische Gesellschaft (DGG) and the Royal Astronomical Society (RAS).
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  • 4
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    Machine learning for data-driven discovery in solid Earth geoscience (2019)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2019
    Description: 〈p〉Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth’s behavior and by the inaccessibility of nearly all of Earth’s subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging. Machine learning will play a key role in this effort. We review the state of the field and make recommendations for how progress might be broadened and accelerated.〈/p〉
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 5
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    How many great earthquakes should we expect? (2012)
    National Academy of Sciences
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    Publication Date: 2012-01-17
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 6
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    Beyond basin resonance: characterizing wave propagation using a dense array and the ambient seismic field (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-03
    Description: Seismic wave resonance in sedimentary basins is a well-recognized seismic hazard; however, concentrated areas of earthquake damage have been observed near basin edges, where wave propagation is particularly complex and difficult to understand with sparse observations. The Tokyo metropolitan area is densely populated, subject to strong shaking from a diversity of earthquake sources, and sits atop the deep Kanto sedimentary basin. It is also instrumented with two seismic arrays: the dense MEtropolitan Seismic Observation network (MeSO-net) within the basin, and the High sensitivity seismograph network (Hi-net) surrounding it. In this study, we explore the 3-D seismic wavefield within and throughout the Kanto basin, including near and across basin boundaries, using cross-correlations of all components of ambient seismic field between the stations of these two arrays. Dense observations allow us to observe clearly the propagation of three modes of both Rayleigh and Love waves. They also show how the wavefield behaves in the vicinity of sharp basin edges with reflected/converted waves and excitation of higher modes.
    Keywords: Seismology
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Deutsche Geophysikalische Gesellschaft (DGG) and the Royal Astronomical Society (RAS).
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  • 7
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    Emerging challenges to the oil and gas industry from induced seismicity -- An introduction to this special section: Injection-induced seismicity (2015)
    Society of Exploration Geophysicists (SEG)
    Details
    Publication Date: 2015-06-03
    Description: The phenomenon of induced seismicity has been recognized for decades; however, it has commanded significant new attention in the last five years. This is particularly true as it is related to unconventional hydrocarbon recovery methods that are seeing increasingly wide use in the oil and gas industry. Early attention on the topic was brought into focus by a U. S. Department of Energy (DOE)-sponsored study undertaken by the National Research Council (issued in 2012), well-attended SEG technical workshops, and interest from both the public and the press about the cause of earthquakes in areas that are unaccustomed to having them.
    Print ISSN: 1070-485X
    Electronic ISSN: 1938-3789
    Topics: Geosciences
    Published by Society of Exploration Geophysicists (SEG)
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  • 8
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    Characterizing and Responding to Seismic Risk Associated with Earthquakes Potentially Triggered by Fluid Disposal and Hydraulic Fracturing (2015)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2015-07-02
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
    Published by Seismological Society of America (SSA)
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  • 9
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    Stable Stress-Drop Measurements and their Variability: Implications for Ground-Motion Prediction (2013)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2013-02-07
    Description: We estimate the a rms -stress drop, , ( Hanks, 1979 ) using acceleration time records of 59 earthquakes from two earthquake sequences in eastern Honshu, Japan. These acceleration-based static stress drops compare well to stress drops calculated for the same events by Baltay et al. (2011) using an empirical Green’s function (eGf) approach. This agreement supports the assumption that earthquake acceleration time histories in the bandwidth between the corner frequency and a maximum observed frequency can be considered white, Gaussian, noise. Although the is computationally simpler than the eGf-based -stress drop, and is used as the "stress parameter" to describe the earthquake source in ground-motion prediction equations, we find that it only compares well to the at source-station distances of ~20 km or less because there is no consideration of whole-path anelastic attenuation or scattering. In these circumstances, the correlation between the and is strong. Events with high and low stress drops obtained through the eGf method have similarly high and low . We find that the inter-event standard deviation of stress drop, for the population of earthquakes considered, is similar for both methods, 0.40 for the method and 0.42 for the , in log 10 units, provided we apply the ~20 km distance restriction to . This indicates that the observed variability is inherent to the source, rather than attributable to uncertainties in stress-drop estimates. Online Material: Earthquake catalog including additional source parameters.
    Print ISSN: 0037-1106
    Electronic ISSN: 1943-3573
    Topics: Geosciences , Physics
    Published by Seismological Society of America (SSA)
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  • 10
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    Earthquake detection through computationally efficient similarity search (2015)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2015-12-06
    Description: Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection—identification of seismic events in continuous data—is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity that overcomes the disadvantages of existing detection methods. Our method, called Fingerprint And Similarity Thresholding (FAST), can analyze a week of continuous seismic waveform data in less than 2 hours, or 140 times faster than autocorrelation. FAST adapts a data mining algorithm, originally designed to identify similar audio clips within large databases; it first creates compact "fingerprints" of waveforms by extracting key discriminative features, then groups similar fingerprints together within a database to facilitate fast, scalable search for similar fingerprint pairs, and finally generates a list of earthquake detections. FAST detected most (21 of 24) cataloged earthquakes and 68 uncataloged earthquakes in 1 week of continuous data from a station located near the Calaveras Fault in central California, achieving detection performance comparable to that of autocorrelation, with some additional false detections. FAST is expected to realize its full potential when applied to extremely long duration data sets over a distributed network of seismic stations. The widespread application of FAST has the potential to aid in the discovery of unexpected seismic signals, improve seismic monitoring, and promote a greater understanding of a variety of earthquake processes.
    Electronic ISSN: 2375-2548
    Topics: Natural Sciences in General
    Published by American Association for the Advancement of Science (AAAS)
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