Global optimization of self-potential anomalies via HGS algorithm

In this paper, a novel global optimizer named Hunger Games Search (HGS) is adapted and presented for estimating the characteristics and model parameters of idealized geometrical causative structures from self-potential (SP) anomalies. After the modal analysis of assumed objective functions and the essential parameter tuning process, the HGS algorithm has been tested on some synthetic SP anomalies and four heterologous real data cases obtained from India, Türkiye, Canada, and Indonesia. In the synthetic and real data cases, multiple intercalating structures have been considered. The second moving average method has also been tested for removing the regional background. Additionally, the post-inversion uncertainty appraisal analyses have been implemented to better understand the reliability of the model parameter estimations. The solutions obtained through HGS have been systematically and thoroughly compared with the solutions of the well-established and popular Particle Swarm Optimization (PSO) in terms of convergence rate, robustness, stability, and accuracy. Applications have shown that SP anomalies can be inverted more effectively via HGS since the algorithm can explore the search space more extensively without being trapped into numerous local minima and approximate the global minimum more precisely. Thus, it is recommended to use the HGS algorithm in ore and mineral exploration studies as well as in studies aimed at delineating subsurface structures from geophysical data sets.