Associating earthquakes with faults using cluster analysis optimized by a fuzzy particle swarm optimization algorithm for Iranian provinces

Fault zones play an important role in seismic hazard analysis and are often selected in accordance with the judgments of experts, making them controversial and diverse in a specific region. The delineation of seismic sources has a great impact on the uncertainty in seismic hazard analysis. Applications of seismic hazard analysis demonstrate that a 3D approach, compared to a 2D one, may lead to more realistic hazard scenarios. Another problem with seismic hazard analysis is how to associate earthquakes with faults. This paper attempted to connect seismic events to faults by a fuzzy particle swarm optimization algorithm, an optimized version of the fuzzy clustering approach. In this algorithm, two objective functions were minimized: the distance of the events from the faults, and the distance to the centers of events' clusters. The study areas included two provinces in southwestern and southeast Iran, namely Fars and Kerman, respectively. Ten main faults were recognized in the Fars Province and 13 main faults in the other study area, Kerman Province. Moreover, 1100 and 665 seismic events with the Mw >= 4 and a depth of 4 km-100 km (obtained by teleseismic depth assessment) were recorded from 1900 to 2011 in these two areas, respectively. Events were clustered and associated with the faults using the algorithm. A comparison of the results of the applied algorithm and those of the known and documented earthquakes revealed an accuracy of 85.3% and 75% for Fars and Kerman Provinces, respectively, when determining the associated fault.

Automated summary

This study connected seismic events to faults using a fuzzy particle swarm optimization algorithm, achieving a high accuracy rate when determining the associated fault and providing solid support for seismic hazard analysis.