Spatio-Temporal Modeling of Seismic Provinces of Iran Using DBSCAN Algorithm

One of the most important issues in the field of engineering seismology is identification and classification of seismic provinces. Due to the importance of this issue in Iran, various studies have been conducted using different methods such as expert judgment, computational methods, data-driven methods, and smart methods. The purpose of the present research is to develop a spatio-temporal seismic model for Iran using robust and objective clustering tools. In the present study, one of the most powerful clustering methods, DBSCAN, is selected based on its ability to analyze huge amounts of data. The DBSCAN algorithm, which acts based on the density of seismic events, is capable of detecting arbitrarily shaped clusters. The seismic datasets used in this study, which were obtained from the seismic catalog of Iran from 1900 to 2015, have been divided into three window periods including 2- , 5- , and 10-year intervals. Afterward, different seismicity patterns for each period are obtained by applying DBSCAN algorithm. Then, those exhibited high agreements in terms of shapes and locations of clusters with the other models are determined. Ultimately, by considering these models and using expert judgments, a unified spatio-temporal model is presented. The results reveal meaningful information in different parts of Iran especially in Zagros, Alborz, and Azerbaijan zones and are generally in good agreement with previous studies. Moreover, the results emphasize that a seismic model, which is obtained based on considering seismogenic zones in various time periods along with the application of density-based clustering tools, will produce reliable results.