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Automatic seismic phase picking based on unsupervised machine-learning classification and content information analysis
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AI
DOI：10.1190/GEO2020-0308.1.png)
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摘要
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Accurate identification and picking of P-and S-wave arrivals is important in earthquake and exploration seismology. Often, existing algorithms are lacking in automation, multiphase classification and picking, as well as performance accuracy. We have developed a new fully automated four-step workflow for efficient classification and picking of P-and S wave arrival times on microseismic data sets. First, time intervals with possible arrivals on waveform recordings are identified using the fuzzy c-means clustering algorithm. Second, these 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 data sets indicates that it yields accurate arrival picks for high and low signal-to-noise ratio waveforms.
Keyword:
P-PHASE
LOCATION
期刊
IF:
3.2
论文数:
8.4K
被引数:
3.3W
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引用论文
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