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  • 1
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    Resolving the Kinematics and Moment Release of Early Afterslip Within the First Hours Following the 2016 M w 7.1 Kumamoto Earthquake: Implications for the Shallow Slip Deficit and Frictional Behavior of Aseismic Creep (2020)
    American Geophysical Union
    Details
    Publication Date: 2020-09-01
    Print ISSN: 2169-9313
    Electronic ISSN: 2169-9356
    Topics: Geosciences , Physics
    Published by American Geophysical Union
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  • 2
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    Sources of Long‐Range Anthropogenic Noise in Southern California and Implications for Tectonic Tremor Detection (2018)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2018
    Description: 〈span〉〈div〉Abstract〈/div〉We study anthropogenic noise sources seen on seismic recordings along the central section of the San Jacinto fault near Anza, southern California. The strongest signals are caused by freight trains passing through the Coachella Valley north of Anza. Train‐induced transients are observed at distances of up to 50 km from the railway, with durations of up to 20 min, and spectra that are peaked between 3 and 5 Hz. Additionally, truck traffic through the Coachella Valley generates a sustained hum with a similar spectral signature as the train transients but with lower amplitude. We also find that wind turbine activity in northern Baja California introduces a seasonal modulation of 1– to 5‐Hz energy across the Anza network. We show that the observed train‐generated transients can be used to constrain shallow attenuation structure at Anza. Using the results from this study as well as available borehole data, we further evaluate the performance of approaches that have been used to detect nonvolcanic tremor at Anza. We conclude that signals previously identified as spontaneous tremor (〈a href="https://pubs.geoscienceworld.org/bssa#rf21"〉Hutchison and Ghosh, 2017〈/a〉) were probably generated by other nontectonic sources such as trains.〈/span〉
    Print ISSN: 0037-1106
    Electronic ISSN: 1943-3573
    Topics: Geosciences , Physics
    Published by Seismological Society of America (SSA)
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  • 3
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    Machine Learning Aspects of the MyShake Global Smartphone Seismic Network (2018)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2018
    Description: 〈span〉〈div〉ABSTRACT〈/div〉This article gives an overview of machine learning (ML) applications in MyShake—a crowdsourcing global smartphone seismic network. Algorithms from classification, regression, and clustering are used in the MyShake system to address various problems, such as artificial neural network (ANN) and convolutional neural network (CNN) to distinguish earthquake motions, spatial–temporal clustering using density‐based spatial clustering of applications with noise (DBSCAN) to detect earthquakes from phone aggregated information, and random forest regression to learn from existing physics‐based relationships. Beyond existing efforts, this article also presents a vision of the role of ML in some new directions and challenges. Using MyShake as an example, this article demonstrates the promising combination of ML and seismology.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
    Published by Seismological Society of America (SSA)
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  • 4
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    On the Feasibility of Using the Dense MyShake Smartphone Array for Earthquake Location (2019)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2019
    Description: 〈span〉〈div〉ABSTRACT〈/div〉MyShake is a growing smartphone‐based network for seismological research applications. We study how dense array analysis of the seismic wavefield recorded by smartphones may enhance microearthquake monitoring in urban environments. In such areas, the microearthquake signal‐to‐noise ratio on smartphones is not well constrained. We address this issue by compiling a seismic noise model for the Los Angeles (LA) metropolitan area using over 500,000 seismograms recorded by stationary phones running MyShake. We confirm that smartphone noise level is reduced during nighttime, and identify strong noise sources such as major traffic highways, the LA airport, and the Long Beach seaport. The noise analysis shows that stationary smartphones are sensitive to human‐induced ground motions, and therefore smartphone‐derived seismograms may be used to infer the elastic properties of the shallow subsurface. We employ array backprojection analysis on synthetic data to estimate what fraction of LA’s smartphone user population is required to install MyShake to enable the location of events whose induced ground motions are below the smartphone noise level. We find that having 0.5% of LA’s population download the MyShake app would be sufficient to accurately locate M〉1 events recorded during nighttime by stationary phones located at epicentral distances 〈5  km. Currently, the MyShake user coverage in LA is approaching a value that will allow us to locate events whose magnitude is near the regional catalog’s magnitude of completeness.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
    Published by Seismological Society of America (SSA)
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  • 5
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    Machine Learning Aspects of the MyShake Global Smartphone Seismic Network (2018)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2018
    Description: 〈span〉〈div〉ABSTRACT〈/div〉This article gives an overview of machine learning (ML) applications in MyShake—a crowdsourcing global smartphone seismic network. Algorithms from classification, regression, and clustering are used in the MyShake system to address various problems, such as artificial neural network (ANN) and convolutional neural network (CNN) to distinguish earthquake motions, spatial–temporal clustering using density‐based spatial clustering of applications with noise (DBSCAN) to detect earthquakes from phone aggregated information, and random forest regression to learn from existing physics‐based relationships. Beyond existing efforts, this article also presents a vision of the role of ML in some new directions and challenges. Using MyShake as an example, this article demonstrates the promising combination of ML and seismology.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
    Published by Seismological Society of America (SSA)
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  • 6
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    On the Feasibility of Using the Dense MyShake Smartphone Array for Earthquake Location (2019)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2019
    Description: 〈span〉〈div〉ABSTRACT〈/div〉MyShake is a growing smartphone‐based network for seismological research applications. We study how dense array analysis of the seismic wavefield recorded by smartphones may enhance microearthquake monitoring in urban environments. In such areas, the microearthquake signal‐to‐noise ratio on smartphones is not well constrained. We address this issue by compiling a seismic noise model for the Los Angeles (LA) metropolitan area using over 500,000 seismograms recorded by stationary phones running MyShake. We confirm that smartphone noise level is reduced during nighttime, and identify strong noise sources such as major traffic highways, the LA airport, and the Long Beach seaport. The noise analysis shows that stationary smartphones are sensitive to human‐induced ground motions, and therefore smartphone‐derived seismograms may be used to infer the elastic properties of the shallow subsurface. We employ array backprojection analysis on synthetic data to estimate what fraction of LA’s smartphone user population is required to install MyShake to enable the location of events whose induced ground motions are below the smartphone noise level. We find that having 0.5% of LA’s population download the MyShake app would be sufficient to accurately locate M〉1 events recorded during nighttime by stationary phones located at epicentral distances 〈5  km. Currently, the MyShake user coverage in LA is approaching a value that will allow us to locate events whose magnitude is near the regional catalog’s magnitude of completeness.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
    Published by Seismological Society of America (SSA)
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  • 7
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    Sources of Long‐Range Anthropogenic Noise in Southern California and Implications for Tectonic Tremor Detection (2018)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2018
    Description: 〈span〉〈div〉Abstract〈/div〉We study anthropogenic noise sources seen on seismic recordings along the central section of the San Jacinto fault near Anza, southern California. The strongest signals are caused by freight trains passing through the Coachella Valley north of Anza. Train‐induced transients are observed at distances of up to 50 km from the railway, with durations of up to 20 min, and spectra that are peaked between 3 and 5 Hz. Additionally, truck traffic through the Coachella Valley generates a sustained hum with a similar spectral signature as the train transients but with lower amplitude. We also find that wind turbine activity in northern Baja California introduces a seasonal modulation of 1– to 5‐Hz energy across the Anza network. We show that the observed train‐generated transients can be used to constrain shallow attenuation structure at Anza. Using the results from this study as well as available borehole data, we further evaluate the performance of approaches that have been used to detect nonvolcanic tremor at Anza. We conclude that signals previously identified as spontaneous tremor (〈a href="https://pubs.geoscienceworld.org/bssa#rf21"〉Hutchison and Ghosh, 2017〈/a〉) were probably generated by other nontectonic sources such as trains.〈/span〉
    Print ISSN: 0037-1106
    Electronic ISSN: 1943-3573
    Topics: Geosciences , Physics
    Published by Seismological Society of America (SSA)
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  • 8
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    Bayesian inversion for finite fault earthquake source models - II: the 2011 great Tohoku-oki, Japan earthquake (2014)
    Oxford University Press
    Details
    Publication Date: 2014-06-28
    Description: We present a fully Bayesian inversion of kinematic rupture parameters for the 2011 M w 9 Tohoku-oki, Japan earthquake. Albeit computationally expensive, this approach to kinematic source modelling has the advantage of producing an ensemble of slip models that are consistent with physical a priori constraints, realistic data uncertainties, and realistic but simplistic uncertainties in the physics of the kinematic forward model, all without being biased by non-physical regularization constraints. Combining 1 Hz kinematic GPS, static GPS offsets, seafloor geodesy and near-field and far-field tsunami data into a massively parallel Monte Carlo simulation, we construct an ensemble of samples of the posterior probability density function describing the evolution of fault rupture. We find that most of the slip is concentrated in a depth range of 10–20 km from the trench, and that slip decreases towards the trench with significant displacements at the toe of wedge occurring in just a small region. Estimates of static stress drop and rupture velocity are ambiguous. Due to the spatial compactness of the fault rupture, the duration of the entire rupture was less than approximately 150 s.
    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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  • 9
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    Assessing the Sensitivity and Accuracy of the MyShake Smartphone Seismic Network to Detect and Characterize Earthquakes (2019)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2019
    Description: 〈span〉〈div〉Abstract〈/div〉MyShake harnesses private and personal smartphones to build a global seismic network. It uses the accelerometers embedded in all smartphones to record ground motions induced by earthquakes, returning recorded waveforms to a central repository for analysis and research. A demonstration of the power of citizen science, MyShake expanded to six continents within days of being launched and has recorded 757 earthquakes in the first 2 yr of operation. The data recorded by MyShake phones have the potential to be used in scientific applications, thereby complementing current seismic networks. In this article, we (1) report the capabilities of smartphone sensors to detect earthquakes by analyzing the earthquake waveforms collected by MyShake; (2) determine the maximum epicentral distance at which MyShake phones can detect earthquakes as a function of magnitude; and (3) then determine the capabilities of the MyShake network to estimate the location, origin time, depth, and magnitude of earthquakes. In the case of earthquakes for which MyShake has provided four or more phases (〈span〉P〈/span〉‐ or 〈span〉S〈/span〉‐wave signals) and an azimuthal gap 〈180° (21 events), the median (± standard deviations) of the location, origin time, and depth errors are 2.7 (±2.8) km, 0.2 (±1.2) s, and 0.1 (±4.9) km, respectively, relative to the U.S. Geological Survey global catalog locations. Magnitudes are also estimated and have a mean error of 0.0 and standard deviation of 0.2. These preliminary results suggest that MyShake could provide basic earthquake catalog information in regions that currently have no traditional networks. With an expanding MyShake network, we expect the event detection capabilities to improve and provide useful data on seismicity and hazards.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
    Published by Seismological Society of America (SSA)
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  • 10
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    Assessing the Sensitivity and Accuracy of the MyShake Smartphone Seismic Network to Detect and Characterize Earthquakes (2019)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2019
    Description: 〈span〉〈div〉Abstract〈/div〉MyShake harnesses private and personal smartphones to build a global seismic network. It uses the accelerometers embedded in all smartphones to record ground motions induced by earthquakes, returning recorded waveforms to a central repository for analysis and research. A demonstration of the power of citizen science, MyShake expanded to six continents within days of being launched and has recorded 757 earthquakes in the first 2 yr of operation. The data recorded by MyShake phones have the potential to be used in scientific applications, thereby complementing current seismic networks. In this article, we (1) report the capabilities of smartphone sensors to detect earthquakes by analyzing the earthquake waveforms collected by MyShake; (2) determine the maximum epicentral distance at which MyShake phones can detect earthquakes as a function of magnitude; and (3) then determine the capabilities of the MyShake network to estimate the location, origin time, depth, and magnitude of earthquakes. In the case of earthquakes for which MyShake has provided four or more phases (〈span〉P〈/span〉‐ or 〈span〉S〈/span〉‐wave signals) and an azimuthal gap 〈180° (21 events), the median (± standard deviations) of the location, origin time, and depth errors are 2.7 (±2.8) km, 0.2 (±1.2) s, and 0.1 (±4.9) km, respectively, relative to the U.S. Geological Survey global catalog locations. Magnitudes are also estimated and have a mean error of 0.0 and standard deviation of 0.2. These preliminary results suggest that MyShake could provide basic earthquake catalog information in regions that currently have no traditional networks. With an expanding MyShake network, we expect the event detection capabilities to improve and provide useful data on seismicity and hazards.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
    Published by Seismological Society of America (SSA)
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