A Binary NSGA-II Model for De-clustering Seismicity of Turkey and Chile

Seismicity de-clustering is the technique to isolate the earthquake catalog into aftershock-foreshock (clustered) and background (random) events. These isolated events are widely used in seismology for hazard assessment and to design the model for future earthquake predictions. The key challenge in seismic de-clustering is due to significant overlapping and high correlation between the space-time domain of aftershock-foreshock and background events. In this manuscript, a new model is proposed to de-cluster earthquake catalog based on a binary Non-dominated sorting genetic (B/NSGA)-II algorithm. In the fundamental version of the popular NSGA-II algorithm, one apprehension is that crossover and mutation are performed only on real-valued population. Here binary domain logical crossover and mutation operators are employed to optimally segregate the seismic events. The proposed model is tested on thirty year historical earthquake catalog of Turkey and Chile. Comparative analysis has been demonstrated with five benchmark de-clustering techniques. The simulation results demonstrate the potential of the proposed model efficiently discriminates the aftershocks and background events in the two catalogs.