Seismicity Characterization of Iran: A Multivariate Statistical Approach

Abstract

This paper presents the results of two multivariate analysis techniques—principal component and cluster analysis—as they are applied to the seismicity characterization of Iran. The seismic data used in this study covers a period of 50 years, from the beginning of 1957 to the end of 2006. The values of eight seismic variables were calculated on a grid of equally spaced points at one geographic degree spacing in both latitude and longitude. The data matrix was analyzed using principal component and cluster analysis. Principal component analysis identified two significant components, introduced in this study as the Seismic Frequency Index (SFI) and the Seismic Severity Index (SSI), responsible for the data structure. The SFI and SSI explain 34.34 % and 32.33 % of the total variance of the data set, respectively, and allowed grouping of the selected variables according to their common features. The standardized data matrix was analyzed using Ward’s clustering method. The resulting seismicity pattern recognition maps of the region at three levels of similarity are presented. From these maps, differentiated seismic zones are outlined in detail and compared quantitatively. Comparison between the seismic zoning maps obtained in this analysis and the general tectonic map of the region indicates that the seismic zones are consistent with the tectonic zones of the region. This study presents the necessity and usefulness of multivariate analysis in evaluating and interpreting seismic data catalogues with the goal of obtaining more objective information about the seismicity pattern of regions.