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

Valero Cano, Eduardo; Akram, Jubran; Peter, Daniel

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

Files

Automatic_geo2020-0308.1.pdf (11.63 MB)

Type

Article

Authors

Valero Cano, Eduardo

Akram, Jubran
Peter, Daniel

KAUST Department

Earth Science and Engineering Program
Extreme Computing Research Center
Physical Science and Engineering (PSE) Division

Online Publication Date

2021-06-30

Print Publication Date

2021-07-01

Date

2021-06-30

Submitted Date

2020-05-31

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