Clustering analysis of the seismic catalog of Iran

Identification and classification of different seismotectonic provinces with similar characteristics in a region of interest is one of the most important subjects in seismic hazard studies. This task is usually done through subjective interpretations based on geological and seismotectonic information. Seismic data is one of the most important sources of information where visual inspection of this data is a traditional way of identification of seismotectonic provinces. Pattern recognition of historical and instrumental seismic data in a non-subjective way provides more robust results and is a more suitable tool for extracting useful knowledge from a huge amount of data. In this study, applicability and usefulness of an unsupervised fuzzy clustering algorithm in identification of hidden patterns among historical and instrumental seismic catalog of Iran is examined through a comparison between the results of such an analysis and the proposed models for seismotectonic provinces of Iran. The clustering method used in this study is based on fuzzy modification of the maximum likelihood estimation and has the capability to detect elliptical clusters with variable size. Moreover, fuzzy hyper-volume and partition density indexes are used as performance indexes for selection the best number of clusters. The comparison between the results of clustering analyses and the seismotectonic models of Iran reveals that it is possible to partition the spatially distributed epicenters of earthquake events into distinct. These partition units, or clusters, are generally in good agreement with the proposed seismotectonic provinces of Iran and show major seismotectonic features of the Iranian Plateau in addition to some hidden information. Such kind of analysis provides a mathematical basis for seismological interpretations of seismic activities. Moreover, the comparisons of the results of clustering analysis among historical data, combination of historical and instrumental data and major earthquakes with magnitude greater than 5.0 shows that the best results will be achieved by the clustering of major events (i.e. Mw>5.0).