Hypocentral localization: A Comparative Study of Meta-heuristics
Hypocentral Inversion; Computational Geophysics; Optimization; Meta-heuristics; Hybrid Grey Wolf Optimizer; Gauss-Newton; Multi-Start; Genetic Algorithm; Particle Swarm Optimization.
The determination of hypocentral location uses techniques aimed at obtaining the spatial coordinates of the hypocenter from the temporal data observed at seismographic stations. Its applications range from the geological characterization of the Earth's crust to support for the prediction of new incidents and hydrocarbon exploration. In this context, this work proposes the application of \ac{olc}, hybridized with genetic operators of crossover, mutation, tournament, and elitism as an alternative to traditional meta-heuristics used for this problem. The \ac{olc} is an optimization algorithm inspired by the behavior of grey wolf packs, proposed by Seyedali Mirjalili in 2014, and has been applied in various areas, including optimization of complex problems, system design, and machine learning. The \ac{olc} is based on three main behaviors observed in wolf packs: hunting, searching, and communication. Through these behaviors, the algorithm is capable of efficiently exploring the search space, finding solutions close to the global optimum in optimization problems. The results of the proposed method were compared with other widely used meta-heuristics in hypocentral inversion, namely, \ac{gm}, \ac{ag}, and \ac{oep}. The criteria considered in the comparisons included Data Fitting, Cost of the Objective Function of Average Solutions, Computational Time, Average Solutions, Cost of the Objective Function in the Stabilization Period, Convergence, and Total Cost, and the tests were conducted in two different situations: on four synthetic seismic events and real seismic events. To evaluate the effectiveness of the method, a parametric statistical test was performed with \ac{af} and non-parametric tests of \ac{kw}, with post hoc test using \ac{db}. The results indicated that the \ac{olch} demonstrated superiority in the Cost of the Objective Function in the Stabilization Period on synthetic data compared to \ac{oep} and \ac{gm}, and to \ac{gm} in the Total Cost, while \ac{gm} was more efficient in computational time compared to \ac{olch}, and \ac{oep} outperformed \ac{olch} and \ac{gm} in convergence. In real data, costs of the objective function of average solutions, \ac{olch} was superior to \ac{oep}. In the cost of the objective function in the stabilization period, \ac{olch} also outperformed \ac{oep}. In total cost, \ac{olch} was superior to \ac{gm}, while \ac{ag} was better than \ac{gm}. In computational terms, \ac{gm} was superior to \ac{olch}, and in terms of convergence, \ac{oep} surpassed \ac{olch} and \ac{gm}. Thus, consolidating \ac{olch} as an innovative alternative in hypocentral location.