%0 Journal Article %1 wang_teng:1997 %A Wang, Jin %A Teng, Ta-liang %D 1997 %J Bulletin of the Seismological Society of America %K geophysics seismology %N 5 %P 1140--1149 %T Identification and picking of S phase using an artificial neural network %U http://www.bssaonline.org/cgi/content/abstract/87/5/1140 %V 87 %X 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.

@article{wang_teng:1997, abstract = {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.}, added-at = {2012-09-01T13:08:21.000+0200}, author = {Wang, Jin and Teng, Ta-liang}, biburl = {https://www.bibsonomy.org/bibtex/27541b7e4c50e208d1e06bacc5e0e5714/nilsma}, day = 1, interhash = {981dcbc26094e5667e943df4bd56c4fb}, intrahash = {7541b7e4c50e208d1e06bacc5e0e5714}, journal = {Bulletin of the Seismological Society of America}, keywords = {geophysics seismology}, month = oct, number = 5, pages = {1140--1149}, timestamp = {2021-02-09T13:25:34.000+0100}, title = {Identification and picking of S phase using an artificial neural network}, url = {http://www.bssaonline.org/cgi/content/abstract/87/5/1140}, volume = 87, year = 1997 }