Automated determination of S-phase arrival times using autoregressive prediction: application to local and regional distances

Details Automated determination of S-phase arrival times using autoregressive prediction: application to local and regional distances Automated determination of S-phase arrival times using autoregressive prediction: application to local and regional distances

Feb 2012

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012geoji.188..687k&link_type=abstract

Geophysical Journal International, Volume 188, Issue 2, pp. 687-702.

Statistics

Computation

Time Series Analysis, Body Waves, Computational Seismology

Scientific paper

We present an algorithm for automated S-phase arrival time determination of local, regional and teleseismic events based on autoregressive (AR) prediction of the waveform. The waveforms of the horizontal components are predicted using a scalar AR model for multicomponent recordings. The AR coefficients are estimated in a short moving window using a least-squares approach minimizing the forward prediction error. Synthetic tests with single-component data show that the least-squares approach yields similar or even better results than the Yule-Walker and Burg's algorithms. We discuss the choice of the AR model and show that the corresponding prediction error of the AR model, applied to both horizontal components, is sufficient to detect instantaneous changes in amplitude, frequency, phase and polarization. The rms prediction error of both horizontal components defines the characteristic function, to which an algorithm for the estimation of the arrival time is applied. The proposed algorithm also accounts for automatic quality assessment of the estimated S-onset times. Four quality criteria are used to define the weight of the automatically estimated S-arrival time. They are based on two different estimations of the slope of the characteristic function and on two signal-to-noise ratios (SNRs).
The proposed algorithm is applied to a large data set recorded by a dense regional seismic network in the southern Aegean. The data set contains recordings of local and regional crustal as well as intermediate deep earthquakes. The reliability and the robustness of the picking algorithm is tested by comparing more than 2600 manual S readings, serving as reference picks, with the corresponding automatically derived S-onset times. We find an average deviation from the reference picks of 0.5 s ± 0.8 s. If only excellent automatic picks are considered, the average difference from the reference picks is reduced to -0.057 s ± 0.12 s. The proposed automatic quality weighting scheme yields similar weights for the individual S onsets as the ones set by the analysts. The presented algorithm works reliably and robust even when applied to a data set with heterogeneous SNRs. Furthermore, the proposed method may be suitable for the implementation in an earthquake early-warning system as additional, accurate S-wave arrival time estimates stabilize the location, especially the determination of the event depth.

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