Sliding Temporal Window-based Feature Extraction with K-means Clustering for Zagros (Iran) Seismicity Analysis

In this paper, a seismicity declustering model is proposed using a sliding temporal window-based feature extraction with K-means algorithm. This approach transforms the primary features: like occurrence time, location, and magnitude of earthquake event of a catalog into the overlapping sliding window-based features (mean deviation, coefficient of variation (COV) in time and spatial domain along with the average value of magnitude). These extracted features with fewer sample sizes are used as input to the K-means algorithm for distinguishing two important classes of earthquake: aftershocks and background. This proposed method is applied to the earthquake catalog of Zagros (Iran) from the period 2006 to 2019. The simulation results revealed that three major earthquake clusters are identified in class-I which comprised of foreshock-mainshock-aftershock sequences. The events belonging to class-I have intermediate magnitude, less inter-event time (IET) & space (IED), and high COV value. The events belonging to class-II represent the characteristics of regular background seismicity (approximately 71 %) in the region. The seismicity characteristics are reported in the form of epicenter plot, space-time diagram, IET vs IED scatter plot” and other statistical values like the Silhouette index.