Localização hipocentral: um estudo comparativo entre metaheurísticas

Abstract

The determination of hypocentral location uses techniques to obtain the spatial coordinates of the hypocenter from temporal data observed at seismographic stations. This approach has applications ranging from geological characterization of the Earth’s crust to supporting incident prediction and hydrocarbon exploration. This work proposes the application of the OLC optimization algorithm and its hybridized version (OLCH), which incorporates genetic operators such as crossover, mutation, tournament, and elitism, as an alternative to traditional metaheuristics used in hypocentral inversion. Inspired by the behavior of grey wolf packs, the OLC, proposed by Seyedali Mirjalili in 2014, has been widely employed in optimization, system design, and machine learning. Its main mechanisms — hunting, encircling, and capturing — allow efficient exploration of the search space, finding solutions close to the global optimum in complex problems. The results were compared with algorithms widely used in hypocentral inversion, such as GN, as well as the hybridized version with MS, named GM, and other methods like AG and OEP. The comparison considered criteria such as objective function cost, computational time, and convergence. In tests with synthetic seismic data, OLC and OLCH significantly outperformed OEP in the objective function cost during the stabilization period, while OLCH surpassed GM in overall cost. With real data, OLCH stood out compared to OEP, both in the average objective function cost of the solutions and during the stabilization period. OLC also outperformed OEP, though only during the stabilization period. These results highlight the feasibility and advantages of OLC and OLCH in the context of hypocentral location using synthetic and real data, establishing them as innovative and alternative methodologies for hypocentral location.