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    Automatic seismic phase picking based on unsupervised machine learning classification and content information analysis

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    Name:
    Automatic_geo2020-0308.1.pdf
    Size:
    11.63Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Valero Cano, Eduardo cc
    Akram, Jubran
    Peter, Daniel cc
    KAUST Department
    Earth Science and Engineering Program
    Extreme Computing Research Center
    Physical Science and Engineering (PSE) Division
    Date
    2021-06-30
    Online Publication Date
    2021-06-30
    Print Publication Date
    2021-07-01
    Submitted Date
    2020-05-31
    Permanent link to this record
    http://hdl.handle.net/10754/668383
    
    Metadata
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    Abstract
    Accurate identification and picking of P- and S-wave arrivals is important in earthquake and exploration seismology. Often, existing algorithms lack in automation, multi-phase classification and picking, as well as performance accuracy. A new fully-automated fourth-step workflow for efficient classification and picking of P- and S-wave arrival times on microseismic datasets is presented. First, time intervals with possible arrivals on the waveform recordings are identified using the fuzzy c-means clustering algorithm. Second, these signal intervals are classified as corresponding to P, S, or unidentified waves using the polarization attributes of the waveforms contained within. Third, the P-, S-, and unidentified-waves arrival times are picked using the Akaike information criterion picker on the corresponding intervals. Fourth, unidentified waves are classified as P or S based on the arrivals moveouts. The application of the workflow on synthetic and real microseismic datasets shows that it yields accurate arrival picks for both high and low signal-to-noise ratio waveforms.
    Citation
    Valero Cano, E., Akram, J., & Peter, D. B. (2021). Automatic seismic phase picking based on unsupervised machine learning classification and content information analysis. GEOPHYSICS, 1–57. doi:10.1190/geo2020-0308.1
    Publisher
    Society of Exploration Geophysicists
    Journal
    GEOPHYSICS
    DOI
    10.1190/geo2020-0308.1
    Additional Links
    https://library.seg.org/doi/10.1190/geo2020-0308.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/geo2020-0308.1
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Extreme Computing Research Center; Earth Science and Engineering Program

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