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  • 1
    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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  • 2
    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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  • 3
    Publication Date: 2015-11-05
    Description: Earth's seismic velocity structure is heterogeneous at all scales, and mapping that heterogeneity provides insight into the processes that create it. At large scale lengths, seismic tomography is used to map Earth structure deterministically. At small scale lengths, structure can be imaged deterministically, but because it is impractical to image short-wavelength heterogeneity everywhere, we often resort to statistical methods to depict its variability. In this study, we develop random-field model representations of a 3-D P -wave velocity model at Long Beach, California, estimated from dense-array recordings of the ambient seismic wavefield. We focus on heterogeneity at the mesoscale, which is smaller than 10+ km scale of regional tomography but larger than the micro scale of borehole measurements. We explore four ellipsoidally anisotropic heterogeneity models, including von Kármán, Gaussian, self-affine and Kummer models, based on their autocorrelation functions. We find that the von Kármán model fits the imaged velocity model best among these options with a correlation length in the horizontal direction about five times greater than in the vertical direction, and with strong small-scale length variations. We validate our results by showing that our model accurately predicts the observed decay of scattered waves in the coda of a nearby earthquake, suggesting that quantitative measures of velocity variability will be useful for predicting high-frequency ground motion in earthquakes.
    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).
    Location Call Number Expected Availability
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