A Quantum Grey Wolf Optimizer based declustering model for analysis of earthquake catalogs in an ergodic framework.

• A Quantum Grey Wolf Optimizer (QGWO) algorithm is proposed. • Superior performance of QGWO over original GWO is demonstrated on 24 function optimization. • A de-clustering model is developed with QGWO to analyze the activities in earthquake prone regions. • Proposed model considers ergodicity present in the catalog using Thirumalai-Mountain metric. • The earthquake catalogues of Taiwan, Himalaya, Indonesia, Japan and Switzerland are analyzed. Declustering refers to the removal of clustered events (i.e. foreshock–aftershocks triggered due to mainshocks) from the earthquake catalog, which is essential to obtain the meaningful background rate for time-independent seismic hazard assessment in a region. In this paper, the clustered events are identified and extracted from the catalog based on the concept of ergodicity using Thirumalai-Mountain (TM) metric. Ergodicity is characterized by observing a linear trend in inverse TM metric with the discretization of data into a finite number of square boxes. The number of clustered events to be removed from the boxes are achieved by an optimization algorithm. The optimization problem is dealt with introducing a Quantum Grey Wolf Optimizer (QGWO). The superior performance of QGWO is demonstrated over GWO on 24 benchmark functions. With variable dimensionality, the performance of QGWO is also illustrated on seven benchmark functions over three variants: Quantum Particle Swarm Optimizer (QPSO), standard PSO and conventional GWO. The QGWO based declustering model is applied to prone seismic activities of Taiwan, Himalaya, Japan, Indonesia and Switzerland. The superior performance of the proposed model is demonstrated by comparing it with PSO based model from Cho et al., variable ϵ -DBSCAN algorithm and three other benchmark declustering methods. [ABSTRACT FROM AUTHOR]