Modeling of the seismotectonic provinces of Iran using the self-organizing map algorithm.

Abstract: Various studies have been conducted thus far to classify the seismicity regions of Iran into zones that share similar seismic characteristics. In these studies, seismotectonic models have been presented that implement various information layers based mostly on human expert judgment. The purpose of the present research is to develop a seismotectonic model using all of the available information about the layers along with an automated smart clustering algorithm. In this paper, based on a review of past research studies, a combined model is presented, which involves the combination of the four most-referenced seismotectonic models. To build upon past studies, a smart algorithm called the self organizing map (SOM) was selected for clustering the seismotectonic data of provinces. Input layers, such as earthquake coordinates, seismicity, faults distribution, topography, gravity, and magnetism, were used to execute the SOM smart algorithm. Based upon previous studies, the number of clusters was selected in the range of 5–25. Three of the SOM algorithm outputs, which were more compatible with the combined model, were selected for use in this study. According to the clustering validation methods and expert judgments, the highest compatibility belongs to the models that were produced either by all of the information or by the combination of seismicity, faults, and topography layers. Implementation of the clustering validation methods indicated that the optimum number of seismotectonic zones is between 9 and 13. This study emphasizes that the application of smart methods along with various geological, structural, subsurface, and aerial information layers will produce informative and reliable results. [Copyright &y& Elsevier]