Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Abstract

Geomagnetic anomaly interpretation through inversion procedures often yields useful results in determining the key details of ore masses. However, the problem is complicated due to the known ambiguous phenomena of the inversion process. Thus, details such as location, depth and shape can only be estimated using an efficient algorithm. To this end, we presented here a novel global optimization algorithm called Hunger Games Search (HGS) for the inversion of geomagnetic anomalies caused by ore masses. HGS is a well‐organized metaheuristic inspired by the hunger‐driven instincts and social behavioral decisions of animals. This is the first work in the literature to introduce this optimizer for the inversion of geophysical anomalies. We revealed the model parameter dependencies and the optimum control parameter values of the algorithm by performing some modal analyses and parameter tuning studies, respectively. The capability of HGS was demonstrated on synthetically produced geomagnetic responses and on three real anomalies obtained from some exploration fields in India and the USA. Uncertainty appraisal studies showed the solidity of the outputs of the algorithm. Moreover, some comparative studies with the fine‐tuned standard Particle Swarm Optimization, a commonly used tool for the inversion of geophysical anomalies, indicated that HGS algorithm provides better results in terms of convergence characteristics and solution stabilities in the presented inverse problem. We therefore recommend the use of this metaheuristic in model parameter estimation studies when dealing with this kind of geophysical potential field problems.

An integrated geophysical approach for gold from Wayand‐Nilambur granulite terrain, Kerala (India)

“…The potential field data plays a significant role in mineral exploration, because of their capability of revealing subsurface structures, that is, fracture, shear zones and fault zones. Specifically, the magnetic method has a wide range of applications in visualizing economic sources, their spatial distribution, including the depth, physical and geometric properties and recognition of related geologic structures (Abdelrahman et al, 2003; Adewumi & Salako, 2018; Ai et al, 2023; Balkaya & Kaftan, 2021; Biswas & Acharya, 2016; Biswas & Rao, 2021; Ekwok & Eldosouky, 2023; Essa & Diab, 2022; Gokula & Sastry, 2022; Lehmann et al, 2015; Mandal et al, 2020; Mehanee, 2022; Mehanee et al, 2021; Ramesh et al, 2020; Sharma, 1987). Magnetic studies can map the changes in the amount of magnetic minerals as well as associated rock types.…”

Fault Structure Reconstruction From Magnetic Anomalies Using an Improved Backtracking Search Optimization Algorithm

“…Numerous studies have been conducted on developing various optimization algorithms, especially those based on natural phenomena, and their application to solve optimization problems in various fields of science and engineering (Nama et al., 2017). These algorithms have also been used to solve ill‐posed magnetic inverse problems, including Ant Colony Optimization (Liu et al., 2015; Srivastava et al., 2014), Barnacles Mating Optimization (Ai et al., 2022), Bat Optimization Algorithm (Essa & Diab, 2022), Differential Evolution (DE) (Balkaya et al., 2017; Du et al., 2021), Differential Search (Balkaya & Kaftan, 2021; Özyalın, 2023), Genetic Algorithm (Kaftan, 2017; Montesinos et al., 2016; Sohouli et al., 2022), Genetic‐Price Algorithm (GPA) (Di Maio et al., 2020), Gray Wolf Optimization (Agarwal et al., 2018), Hunger Games Search Algorithm (Ai et al., 2023), Manta Ray Foraging Optimization Algorithm (MRFO) (Ben, Ekwok, et al, 2022; Ben et al., 2021), Particle Swarm Optimization (PSO) (Ekinci et al., 2020; Ekwok et al., 2023; Liu et al., 2018; Srivastava & Agarwal, 2010), Social Spider Optimization (Ben, Akpan, et al., 2022), Whale Optimization Algorithm (WOA) (Divakar et al., 2018; Gobashy et al., 2020) and Simulated Annealing (SA) (Biswas et al., 2022; Biswas & Rao, 2021; Shinu & Dubey, 2023). The choice of the most appropriate algorithm for a given optimization problem may depend on several factors, such as the complexity of the problem, the size of the search space, the required precision, and the available computational resources (Dragoi & Dafinescu, 2021).…”

Gravity Data Enhancement Using the Exponential Transform of the Tilt Angle of the Horizontal Gradient

“…Geophysical methods are known as a powerful tool in mapping geological structures and minerals [1][2][3][4][5][6]. The gravity method is characterized by low cost and broad coverage compared to other geophysical surveys [7,8].…”