%0 Journal Article %1 saragiotis_etal:2002 %A Saragiotis, C. D. %A Hadjileontiadis, L. J. %A Panas, S. M. %D 2002 %I IEEE %J IEEE Transactions on Geoscience and Remote Sensing %K geophysics seismology %N 6 %P 1395--1404 %R 10.1109/TGRS.2002.800438 %T PAI-S/K: A robust automatic seismic P phase arrival identification scheme %U http://dx.doi.org/10.1109/TGRS.2002.800438 %V 40 %X The automatic and accurate P phase arrival identification is a fundamental problem for seismologists worldwide. Several approaches have been reported in the literature, but most of them only selectively deal with the problem and are severely affected by noise presence. In this paper, a new approach based on higher-order statistics (HOS) is introduced that overcomes the subjectivity of human intervention and eliminates the noise factor. By using skewness and kurtosis, two algorithms have been formed, namely, Phase Arrival Identification-Skewness/Kurtosis (PAI-S/K), and some advantages have been gained over the usual approaches, resulting in the automatic identification of the transition from Gaussianity to non-Gaussianity that coincides with the onset of the seismic event, despite noise presence. Experimental results on real seismic data, gathered by the Seismological Network of the Department of Geophysics of Aristotle University, demonstrate an excellent performance of the PAI-S/K scheme, regarding both accuracy and noise robustness. The simplicity of the proposed method makes it an attractive candidate for huge seismic data assessment in a real-time context.

@article{saragiotis_etal:2002, abstract = {The automatic and accurate P phase arrival identification is a fundamental problem for seismologists worldwide. Several approaches have been reported in the literature, but most of them only selectively deal with the problem and are severely affected by noise presence. In this paper, a new approach based on higher-order statistics (HOS) is introduced that overcomes the subjectivity of human intervention and eliminates the noise factor. By using skewness and kurtosis, two algorithms have been formed, namely, Phase Arrival Identification-Skewness/Kurtosis (PAI-S/K), and some advantages have been gained over the usual approaches, resulting in the automatic identification of the transition from Gaussianity to non-Gaussianity that coincides with the onset of the seismic event, despite noise presence. Experimental results on real seismic data, gathered by the Seismological Network of the Department of Geophysics of Aristotle University, demonstrate an excellent performance of the PAI-S/K scheme, regarding both accuracy and noise robustness. The simplicity of the proposed method makes it an attractive candidate for huge seismic data assessment in a real-time context.}, added-at = {2013-02-12T19:50:54.000+0100}, author = {Saragiotis, C. D. and Hadjileontiadis, L. J. and Panas, S. M.}, biburl = {https://www.bibsonomy.org/bibtex/270f628a8881790c9064dd1d5a19f9038/nilsma}, doi = {10.1109/TGRS.2002.800438}, institution = {Dept. of Electr. \& Comput. Eng., Aristotle Univ. of Thessaloniki}, interhash = {2a8de1dbccd14ff983a0499293b3a5bd}, intrahash = {70f628a8881790c9064dd1d5a19f9038}, issn = {0196-2892}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, keywords = {geophysics seismology}, month = jun, number = 6, pages = {1395--1404}, publisher = {IEEE}, timestamp = {2021-02-09T13:25:34.000+0100}, title = {PAI-S/K: A robust automatic seismic P phase arrival identification scheme}, url = {http://dx.doi.org/10.1109/TGRS.2002.800438}, volume = 40, year = 2002 }