The seismic P phase first arrival identification is a fundamental problem in seismology. The accurate identification of the P-wave first arrival is not a trivial process, particularly when the seismograms present a very low signal-to-noise ratio (SNR) or are contaminated with artificial transients that could produce false alarms. In this paper, a new approach based on higher order statistics and the stationary wavelet transform is presented. The P onset is obtained under a statistical criterion applied in the time-frequency domain. The results have been compared to those estimated by another P phase picking algorithm and P onsets picked by expert analysts. The comparison shows that our proposed method efficiently provides a good estimate of the P onset picks that are consistent with analyst picks, particularly in cases of very low SNR.
Seismic P Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain / Galiana-Merino, Jj; Rosa-Herranz, J; Parolai, S. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 0196-2892. - 46:11(2008), pp. 3815-3826. [10.1109/TGRS.2008.2002647]
Seismic P Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain
Parolai S
2008-01-01
Abstract
The seismic P phase first arrival identification is a fundamental problem in seismology. The accurate identification of the P-wave first arrival is not a trivial process, particularly when the seismograms present a very low signal-to-noise ratio (SNR) or are contaminated with artificial transients that could produce false alarms. In this paper, a new approach based on higher order statistics and the stationary wavelet transform is presented. The P onset is obtained under a statistical criterion applied in the time-frequency domain. The results have been compared to those estimated by another P phase picking algorithm and P onsets picked by expert analysts. The comparison shows that our proposed method efficiently provides a good estimate of the P onset picks that are consistent with analyst picks, particularly in cases of very low SNR.Pubblicazioni consigliate
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