Particle Swarm Optimization (PSO) of High-Quality Magnetic Data of the Obudu Basement Complex, Nigeria

Abstract

The particle swamp optimization procedure was applied to high-quality magnetic data acquired from the Precambrian Obudu basement complex in Nigeria with the object of estimating the distinctive body parameters (depth (z), index angle (θ), amplitude coefficient (K), shape factor (Sf), and location of the origin (x0)) of magnetic models. The magnetic models were obtained from four profiles that ran perpendicular to the observed magnetic anomalies within the study area. Profile A–A’ with a length of 2600 m is characterized by inverted model parameters of K = 315.67 nT, z = 425.34 m, θ = 43°, Sf = 1.15, and x0 = 1554.86 m, while profile B–B’ with a length of 5600 m is described by K = 257.71 nT, z = 543.75 m, θ = 54°, Sf = 0.96, and x0 = 3645.42 m model parameters. Similarly, profile C–C’ with a length of 3000 m is defined by K = 189.53 nT, z = 560.87 m, θ = 48, Sf = 1.2, and x0 = 1950 m. Profile D–D’, which is well-defined by a 2500 m length, started at the crest of the observed magnetic anomaly and displays inverted model parameters of 247.23 nT, 394.16 m, 39°, 1.26, and 165.41 m. Correlatively, the estimated shape factor of the four models (Sf = 1.15, 0.96, 1.2, and 1.26) shows that the magnetic models are linked to thin sheets. Furthermore, quantitative interpretations of the models show that the PSO operation is rapid and proficient.

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).…”