Memory guided Aquila optimization algorithm with controlled search mechanism for seismicity analysis of earthquake prone regions

Abstract

De-clustering the seismic catalog is one of the crucial processes in determining the probability of exceeding ground motions at particular locations. Removing dependent events, such as foreshocks and aftershocks generated from mainshocks, from an earthquake catalog is known as seismicity de-clustering. This paper presents a new approach to classify seismicity using a swarm intelligence technique called the memory-guided Aquila optimizer (MGAOA). Aquila optimization is a recently reported yet popular optimization algorithm inspired by the hunting process of Aquila. In MGAOA, the Aquila searches for the prey based on the personal best history stored in the memory element, which helps MGAOA to converge faster and maintain the balance between exploration and exploitation during local and global search procedures. The search-controlled parameter is further integrated with Aquila optimization to enhance the exploitation phase of the algorithm. The effectiveness of the proposed MGAO is tested on twenty-three classical test functions and ten benchmark test suites of IEEE CEC 2021. The results show that the proposed MGAOA outperforms other algorithms regarding performance metrics and statistical tests. The MGAOA is applied to solve the problem of seismicity de-clustering using the nearest neighbor distance (NND). The NND is a widely used parameter for seismicity and consists of space–time-magnitude information. The MGAOA-based de-clustering model is used to identify the seismic clusters in highly active earthquake-prone Himalayas, California, Japan, and Indonesia regions. The results obtained by the proposed model are compared with state-of-the-art de-clustering techniques and other memory-guided swarm intelligence techniques. The obtained results in terms of aftershocks and backgrounds are evaluated with the help of cumulative plot, λ-plot, space–time plot, inter-event time versus inter-event distance plot, and statistical parameters like coefficient of variance, which show that the proposed model efficiently detects the seismic clusters, and outperforms other benchmark de-clustering techniques.