%0 Journal Article %1 demeersmann_etal:2006 %A De Meersman, K. %A van der Baan, M. %A Kendall, J. M. %D 2006 %J Bulletin of the Seismological Society of America %K geophysics seismology %N 6 %P 2415--2430 %R 10.1785/0120050235 %T Signal extraction and automated polarization analysis of multicomponent array data %U http://dx.doi.org/10.1785/0120050235 %V 96 %X We present a method to extract seismic signals from three-component array data and estimate their polarization properties at each station. The technique is based on a singular value decomposition (SVD) of the complex three-component analytic signal and applies to linearly as well as elliptically polarized seismic phases. To increase accuracy we simultaneously analyze data from different stations and apply a noise weighting based on prearrival data. For polarization analysis, an automated routine is also included. The automated routine selects the data window with the best signal-to-noise ratio from which to obtain a polarization. A linearity measure and a confidence interval accompany the polarization estimate at each station in the array. We test our technique for automated polarization analysis on synthetic P-wave data and compare results with those from other methods. A microseismic dataset from the North Sea provides a unique opportunity to statistically compare previous and independently obtained P-wave polarizations with those provided by the automated technique presented here. We conclude that, for P-wave polarization analysis, our method is robust and significantly more accurate than conventional, mainly manual methods. This is especially so on data with polarized and correlating background noise. It is also faster and provides meaningful quality estimates. 10.1785/0120050235

@article{demeersmann_etal:2006, abstract = {We present a method to extract seismic signals from three-component array data and estimate their polarization properties at each station. The technique is based on a singular value decomposition (SVD) of the complex three-component analytic signal and applies to linearly as well as elliptically polarized seismic phases. To increase accuracy we simultaneously analyze data from different stations and apply a noise weighting based on prearrival data. For polarization analysis, an automated routine is also included. The automated routine selects the data window with the best signal-to-noise ratio from which to obtain a polarization. A linearity measure and a confidence interval accompany the polarization estimate at each station in the array. We test our technique for automated polarization analysis on synthetic P-wave data and compare results with those from other methods. A microseismic dataset from the North Sea provides a unique opportunity to statistically compare previous and independently obtained P-wave polarizations with those provided by the automated technique presented here. We conclude that, for P-wave polarization analysis, our method is robust and significantly more accurate than conventional, mainly manual methods. This is especially so on data with polarized and correlating background noise. It is also faster and provides meaningful quality estimates. 10.1785/0120050235}, added-at = {2012-09-01T13:08:21.000+0200}, author = {De Meersman, K. and van der Baan, M. and Kendall, J. M.}, biburl = {https://www.bibsonomy.org/bibtex/2872b9aaad0b98c57c839fe99e47abfd6/nilsma}, day = 1, doi = {10.1785/0120050235}, interhash = {e289d168ab72615984c9b5812f5ba05d}, intrahash = {872b9aaad0b98c57c839fe99e47abfd6}, journal = {Bulletin of the Seismological Society of America}, keywords = {geophysics seismology}, month = dec, number = 6, pages = {2415--2430}, timestamp = {2021-02-09T13:21:14.000+0100}, title = {Signal extraction and automated polarization analysis of multicomponent array data}, url = {http://dx.doi.org/10.1785/0120050235}, volume = 96, year = 2006 }