Hypocentral Parameter Inversion using Hybrid Grey Wolf Optimizer

Hypocentral inversion is a crucial branch of seismology, estimating earthquake spatial coordinates from seismographic data. This paper presents a comparative study on hypocentral parameter inversion using various metaheuristic approaches. The optimization techniques investigated include Gauss-Newton with Multi-Start (GM), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO) and Hybrid Grey Wolf Optimizer (HGWO). The study assesses the effectiveness of these methods in determining hypocentral parameters and compares their performance, particularly evaluating whether HGWO outperforms the others. Results indicate that HGWO achieves a lower total cost of the objective function for average solutions. GWO performs well in mean objective function costs across iterations and computational efficiency, while PSO shows superior convergence speed. These findings highlight the strengths and weaknesses of each method, aiding in selecting inversion control parameters and coefficients. This work contributes to advancing optimal strategies for hypocentral inversion, offering valuable insights for researchers and practitioners in seismology.