Zusammenfassung
An artificial neural network (ANN) algorithm has been applied to the
automatic picking of local and regional S phase. For a set of local
three-component seismic data, a variety of features for signal detection
and phase identification were analyzed in terms of sensitivity and
efficiency. Comparing the performance of each feature in discriminating
the local S phases, four features were selected as input attributes
of the ANNS-phase picker: (1) the ratio between short-term average
and long-term average, (2) the ratio between horizontal power and
total power, (3) autoregressive model coefficients, and (4) the short-axis
incidence angle of polarization ellipsoid. The four attributes were
calculated in the frequency band of 2 to 8 Hz with a 2.56-sec moving
window. This choice of frequency band and window length is appropriate
for local microearthquake monitoring. The results of preliminary
training and testing with a set of local earthquake recordings show
that the ANNS-phase picker can achieve a good performance in identification
and onset-time estimation for local S phases. In overall result,
86\% correct rate of phase identification has been achieved by the
trained ANNS-phase picker, 74\% of them are precisely picked with
less than 0.10-sec onset time error. We believe that the method presented
here is a promising approach to automatic phase identification and
onset-time estimation.
Nutzer