A multi-objective chimp optimization algorithm for seismicity de-clustering

Ashish Sharma

doi:10.1016/j.asoc.2022.108742

A multi-objective chimp optimization algorithm for seismicity de-clustering

2022-05-01

9

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AI

A

Ashish Sharma *

S

Satyasai Jagannath Nanda

DOI：10.1016/j.asoc.2022.108742

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Earthquakes are random triggering phenomenons that generate clusters in space and time, thus creating a bias in a seismic catalog. Seismic de-clustering separates seismic catalog into mainshocks, aftershocks-foreshocks, and backgrounds, widely used in earthquake prediction models and seismic hazard assessment. The segregation of an optimal number of earthquake clusters and backgrounds is formulated as an unsupervised problem. This manuscript introduces a multi-objective chimpoptimization algorithm (MOCOA) to de-cluster seismicity of earthquake-prone regions. The chimp optimization is inspired by the natural hunting behavior of the chimp to catch the prey. The algorithm effectively balances the exploration of search space (Driving and Chasing) and exploitation around the best solution achieved so far (Attack). In MOCOA, archive controller and archive grid-based approaches are incorporated for selecting non-dominated solutions. The proposed MOCOA is tested on fifteen mathematical test problems and compared with popular algorithms like MOEA/D, MODE, MOPSO, and SPEA2. The binary version of MOCOA is designed for the de-clustering problem. In the time and space domain, respectively, two objective functions, m-Morisita Index (m-MI) and coefficient of variance (COV), are analyzed. The proposed de-clustering algorithm is applied on thirty-two-year historical seismic catalogs of the Himalayas, California, Indonesia, Japan, Iran, and Mexico. Comparative analysis between five existing benchmark de-clustering techniques is performed to check the potential of the proposed MOCOA. The simulation results generated by the proposed algorithm show that obtained de-clustered catalogs COV values lying near unity and m-MI values achieved maximum values. Validation of results using cumulative plots, lambda plots, and inter-event time versus inter-event distance plots signify the accurate discrimination of aftershocks and background events in the catalogs. (C) 2022 Elsevier B.V. All rights reserved.

Keyword:

Seismicity de-clustering

Multi-objective optimization

Chimp-optimization algorithm

Coefficient of variance

m-Morisita index

A multi-objective chimp optimization algorithm for seismicity de-clustering

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