Automatic seismic phase picking based on unsupervised machine learning classification and content information analysis
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Accepted manuscript
Type
ArticleKAUST Department
Earth Science and Engineering ProgramExtreme Computing Research Center
Physical Science and Engineering (PSE) Division
Date
2021-06-30Online Publication Date
2021-06-30Print Publication Date
2021-07-01Submitted Date
2020-05-31Permanent link to this record
http://hdl.handle.net/10754/668383
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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/geo2020-0308.1Publisher
Society of Exploration GeophysicistsJournal
GEOPHYSICSAdditional Links
https://library.seg.org/doi/10.1190/geo2020-0308.1ae974a485f413a2113503eed53cd6c53
10.1190/geo2020-0308.1