Automatic P-wave arrival detection and picking with multiscale wavelet
analysis for single-component recordings
Bulletin of the Seismological Society of America, 93 (5):
1904--1912 (Oct 1, 2003)DOI: 10.1785/0120020241
Abstract
We have developed an automatic P-wave arrival detection and picking
algorithm based on the wavelet transform and Akaike information criteria
(AIC) picker. Wavelet coefficients at high resolutions show the fine
structure of the time series, and those at low resolutions characterize
its coarse features. Primary features such as the P-wave arrival
are retained over several resolution scales, whereas secondary features
such as scattered arrivals decay quickly at lower resolutions. We
apply the discrete wavelet transform to single-component seismograms
through a series of sliding time windows. In each window the AIC
autopicker is applied to the thresholded absolute wavelet coefficients
at different scales, and we compare the consistency of those picks
to determine whether a P-wave arrival has been detected in the given
time window. The arrival time is then determined using the AIC picker
on the time window chosen by the wavelet transform. We test our method
on regional earthquake data from the Dead Sea Rift region and local
earthquake data from the Parkfield, California region. We find that
81\% of picks are within 0.2-sec of the corresponding analyst pick
for the Dead Sea dataset, and 93\% of picks are within 0.1 sec of
the analyst pick for the Parkfield dataset. We attribute the lower
percentage of agreement for the Dead Sea dataset to the substantially
lower signal-to-noise ratio of those data, and the likelihood that
some percentage of the analyst picks are in error.