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  • 1
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    A Stochastic Earthquake Ground-Motion Prediction Model for the United Kingdom (2013)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2013-02-07
    Description: Low-seismicity regions such as the United Kingdom (UK) pose a challenge for seismic hazard analysis in view of the limited amount of locally recorded data available. In particular, ground-motion prediction is faced with the problem that most of the instrumental observations available have been recorded at large distances from small earthquakes. Direct extrapolation of the results of regression on these data to the range of magnitudes and distances relevant for the seismic hazard analysis of engineered structures generally leads to unsatisfactory predictions. The present study presents a new ground-motion prediction equation (GMPE) for the UK in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped pseudospectral acceleration (PSA), based on the results of numerical simulations using a stochastic point-source model calibrated with parameters derived from local weak-motion data. The predictions from this model are compared with those of previous GMPEs based on UK data, other GMPEs derived for stable continental regions (SCRs), as well as recent GMPEs developed for the wider European area.
    Print ISSN: 0037-1106
    Electronic ISSN: 1943-3573
    Topics: Geosciences , Physics
    Published by Seismological Society of America (SSA)
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  • 2
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    Convolutional Neural Network for Seismic Phase Classification, Performance Demonstration over a Local Seismic Network (2019)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2019
    Description: 〈span〉〈div〉ABSTRACT〈/div〉Over the past two decades, the amount of available seismic data has increased significantly, fueling the need for automatic processing to use the vast amount of information contained in such data sets. Detecting seismicity in temporary aftershock networks is one important example that has become a huge challenge because of the high seismicity rate and dense station coverage. Additionally, the need for highly accurate earthquake locations to distinguish between different competing physical processes during the postseismic period demands even more accurate arrival‐time estimates of seismic phase. Here, we present a convolutional neural network (CNN) for classifying seismic phase onsets for local seismic networks. The CNN is trained on a small dataset for deep‐learning purposes (411 events) detected throughout northern Chile, typical for a temporary aftershock network. In the absence of extensive training data, we demonstrate that a CNN‐based automatic phase picker can still improve performance in classifying seismic phases, which matches or exceeds that of historic methods. The trained network is tested against an optimized short‐term average/long‐term average (STA/LTA) based method (〈a href="https://pubs.geoscienceworld.org/srl#rf25"〉Rietbrock 〈span〉et al.〈/span〉, 2012〈/a〉) in classifying phase onsets for a separate dataset of 3878 events throughout the same region. Based on station travel‐time residuals, the CNN outperforms the STA/LTA approach and achieves location residual distribution close to the ones obtained by manual inspection.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
    Published by Seismological Society of America (SSA)
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  • 3
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    Convolutional Neural Network for Seismic Phase Classification, Performance Demonstration over a Local Seismic Network (2019)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2019
    Description: 〈span〉〈div〉ABSTRACT〈/div〉Over the past two decades, the amount of available seismic data has increased significantly, fueling the need for automatic processing to use the vast amount of information contained in such data sets. Detecting seismicity in temporary aftershock networks is one important example that has become a huge challenge because of the high seismicity rate and dense station coverage. Additionally, the need for highly accurate earthquake locations to distinguish between different competing physical processes during the postseismic period demands even more accurate arrival‐time estimates of seismic phase. Here, we present a convolutional neural network (CNN) for classifying seismic phase onsets for local seismic networks. The CNN is trained on a small dataset for deep‐learning purposes (411 events) detected throughout northern Chile, typical for a temporary aftershock network. In the absence of extensive training data, we demonstrate that a CNN‐based automatic phase picker can still improve performance in classifying seismic phases, which matches or exceeds that of historic methods. The trained network is tested against an optimized short‐term average/long‐term average (STA/LTA) based method (〈a href="https://pubs.geoscienceworld.org/srl#rf25"〉Rietbrock 〈span〉et al.〈/span〉, 2012〈/a〉) in classifying phase onsets for a separate dataset of 3878 events throughout the same region. Based on station travel‐time residuals, the CNN outperforms the STA/LTA approach and achieves location residual distribution close to the ones obtained by manual inspection.〈/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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    Along‐Arc Heterogeneity in Local Seismicity across the Lesser Antilles Subduction Zone from a Dense Ocean‐Bottom Seismometer Network (2019)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2019
    Description: 〈span〉〈div〉Abstract〈/div〉The Lesser Antilles arc is only one of two subduction zones where slow‐spreading Atlantic lithosphere is consumed. Slow‐spreading may result in the Atlantic lithosphere being more pervasively and heterogeneously hydrated than fast‐spreading Pacific lithosphere, thus affecting the flux of fluids into the deep mantle. Understanding the distribution of seismicity can help unravel the effect of fluids on geodynamic and seismogenic processes. However, a detailed view of local seismicity across the whole Lesser Antilles subduction zone is lacking. Using a temporary ocean‐bottom seismic network we invert for hypocenters and 1D velocity model. A systematic search yields a 27 km thick crust, reflecting average arc and back‐arc structures. We find abundant intraslab seismicity beneath Martinique and Dominica, which may relate to the subducted Marathon and/or Mercurius Fracture Zones. Pervasive seismicity in the cold mantle wedge corner and thrust seismicity deep on the subducting plate interface suggest an unusually wide megathrust seismogenic zone reaching ∼65  km depth. Our results provide an excellent framework for future understanding of regional seismic hazard in eastern Caribbean and the volatile cycling beneath the Lesser Antilles arc.〈/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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    Along‐Arc Heterogeneity in Local Seismicity across the Lesser Antilles Subduction Zone from a Dense Ocean‐Bottom Seismometer Network (2019)
    Seismological Society of America (SSA)
    Details
    Publication Date: 2019
    Description: 〈span〉〈div〉ABSTRACT〈/div〉The Lesser Antilles arc is only one of two subduction zones where slow‐spreading Atlantic lithosphere is consumed. Slow‐spreading may result in the Atlantic lithosphere being more pervasively and heterogeneously hydrated than fast‐spreading Pacific lithosphere, thus affecting the flux of fluids into the deep mantle. Understanding the distribution of seismicity can help unravel the effect of fluids on geodynamic and seismogenic processes. However, a detailed view of local seismicity across the whole Lesser Antilles subduction zone is lacking. Using a temporary ocean‐bottom seismic network we invert for hypocenters and 1D velocity model. A systematic search yields a 27 km thick crust, reflecting average arc and back‐arc structures. We find abundant intraslab seismicity beneath Martinique and Dominica, which may relate to the subducted Marathon and/or Mercurius Fracture Zones. Pervasive seismicity in the cold mantle wedge corner and thrust seismicity deep on the subducting plate interface suggest an unusually wide megathrust seismogenic zone reaching ∼65  km depth. Our results provide an excellent framework for future understanding of regional seismic hazard in eastern Caribbean and the volatile cycling beneath the Lesser Antilles arc.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
    Published by Seismological Society of America (SSA)
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