Seismicity Patterns

Earthquake prediction is an active topic in among researchers. As a complex physical phenomenon, it is very intricate to predict the magnitude and location of future event. Although epicentre of earthquakes seems to be random, the occurrence of earthquakes is more in the plate boundaries. The plate intersections are experiencing more number of earthquakes which are known as interplate events (Bolt 2005). But some of the plate boundaries have more number of earthquakes than other boundaries as an indication of high seismic activity. These intersections are experiencing most of the large earthquakes and deeper earthquakes. These regions are called as seismogenic zones of the world. Hence it is necessary to study the seismicity pattern of these

This grouping clearly reduces the size of the data to be handled. Additionally, it also enhances some of the signals present on larger spatial scales (Nicholson, 1986). Identification of homogeneous zones, also called regionalization, can be attempted with the yearly or monthly data. The criterion adopted for delineating the groups can be based on variety of measures, such as means, coefficients of variation and correlation coefficients (CC). In the present study, principal component analysis (PCA) is used for the regionalization of global seismogenic zones using annual seismic energy time series. Principal components analysis is a multivariate statistical technique used to find a few mutually orthogonal linear combinations of the original variables which capture most of the variability present in the data. It is possible to capture the large part of the variance with a small number of components. This methodology has been widely used in meteorology (Kutzbach 1967; Overland and Preisendorfer 1982; Ehrendorfer 1987). Iyengar and Basak (1994) have used PCA technique to group the regions with homogeneous variability of monsoon rainfall. In recent

The annual seismic energy time series of seismogenic zones are constructed by adding the energy releases of all the events in a particular year. The empirical mode decomposition technique is used to extract finite number of intrinsic mode functions (IMFs) from the seismic energy time series. The influence of solar and lunar cycles on earthquake occurrence of each seismogenic zone is confirmed by the estimated periodicities and percentage variances of IMFs. In the present article, PCA is used for the regionalization of global seismogenic regions. The principal components are estimated from the annual seismic energy series of 41 seismogenic zones. The significance of principal components is estimated by the methodology proposed in detail by Preisendorfer et al. (1981). The significant principal components which have important signals of seismic energy release are identified in the present study. It helps to arrange zones in relation to their connection with