Geodetic evidence on segmentation of Cascadia subd

Authors: Roya Mousavian Masoud Mashhadi Hossainali

Publish Date: 2014/03/02

Volume: 58, Issue: 3, Pages: 376-387

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Abstract

Episodic tremor and slip ETS events with different recurrence intervals have been observed in abundance all along the Cascadia subduction zone margin Analysis of seismic records as well as Global Positioning System GPS time series of the Pacific Northwest Geodetic Array PANGA has suggested three distinct coherent zones for the occurrence of these events In this paper multivariate harmonic estimation has been deployed for further analysis of the segmentation in this area Raw time series of 43 permanent GPS stations have been used for this purpose The GPS stations have been geographically divided into three distinct groups including those in the northern middle and southern parts of the study area After the reduction of time series for the linear trend as well as annual and semiannual effects the data series of each group has been analyzed using the multivariate harmonic estimation technique Subsequently different combinations of GPS stations including the stations located in the southern northern and middle zones have been analyzed Furthermore the northern and middle southern and middle as well as the northern and southern zone pair combinations have also been analyzed The statistical measure devised for identifying the significant frequencies suggests common periods that are consistent with the recurrence intervals of the ETS events already reported for each of the above three geographic zones Moreover the method can provide geodetic evidence in addition to geophysical ones on the segmentation of ETSs provided that the adopted time series are of a sufficient length The geodetic evidence obtained in this research is consistent with the recurrence intervals as well as the boundaries obtained by the analysis of seismic records Contrary to univariate harmonic estimation multivariate approach using spatiotemporal correlation of the GPS time series is capable to detect those ETSs whose impacts on the time series are weak

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