Parameter Estimations from Gravity and Magnetic Anomalies Due to Deep-Seated Faults: Differential Evolution versus Particle Swarm Optimization

doi:10.3906/yer-1905-3

Cited by 16 publications

(15 citation statements)

References 74 publications

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“…The length of the profile is 40 km, which was digitized at 1 km intervals from Radhakrishna Murthy et al (2001). Table 9 shows a brief comparison for the evaluation of this anomaly considering the results with the imBSA application in the present study and other solutions from previous studies using other metaheuristics such as DE and PSO (Ekinci et al, 2019), WOA (Gobashy et al, 2020), GPA (Di Maio et al, 2020) and SA (Biswas & Rao, 2021). From the table, it is seen that the common parameters attempted to be estimated are z1 and z2 for the metaheuristics considered.…”

Section: Perth Basin Magnetic Anomaly Australiamentioning

confidence: 99%

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

Balkaya,

Ekinci,

et al. 2024

Earth and Space Science

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Fault structure reconstruction is an important aspect of magnetic prospecting as it contributes to understanding tectonic history, hazard assessment, and infrastructure planning. Metaheuristics have proven useful in estimating fault structure parameters from geophysical anomalies. Recently, a modified version of the Backtracking Search Optimization Algorithm (BSA), named mBSA, has been proposed that combines mutation steps of BSA and Differential Evolution (DE) algorithms to achieve a better balance between exploration and exploitation. Here, we present imBSA as an efficient approach that builds on the improvements of mBSA. It uses an adaptive amplitude control factor in the mutation step derived from a normally distributed random number obtained via an exponential function. The performances of these approaches were tested on some widely used benchmark functions such as Rosenbrock, Rastrigin, Griewank, and Himmelblau. Synthetic magnetic fault anomalies were evaluated with and without noise, as well as three real anomalies from the Dehri (India), Perth Basin, and Lachlan Fold Belt (Australia). Prior to the optimizations, the solvability of the model parameters was examined using some error topography images. The obtained solutions in the synthetic cases were evaluated using principal component analysis. In addition, a novel visualization option for high‐dimensional optimization problems was introduced. The experiments showed that imBSA has a faster convergence rate and better optimization statistics compared to BSA and mBSA. Therefore, it is believed to be an efficient and good alternative tool to widely used metaheuristics such as Particle Swarm Optimization and DE in explaining geophysical anomalies and optimization problems in other geoscientific disciplines.

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Section: Perth Basin Magnetic Anomaly Australiamentioning

confidence: 99%

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

Balkaya,

Ekinci,

et al. 2024

Earth and Space Science

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“…They showed the efficiency of magnetic anisotropy by solving magnetic data with inverse solution (Liu et al 2018). They modeled the gravity and magnetic anomaly of fault-shaped structures using Differential Evolution versus Particle Swarm Optimization method (Ekinci et al 2019).…”

Section: International Journal Of Scientific Advances Issn: 2708-7972mentioning

confidence: 99%

Investigation of Bingöl Iron Ore Reserves Using Wavelet Cellular Neural Networks

Albora¹

2021

International Journal of Scientific Advances

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In the current study, the performance of a synthetic wavelet CNN (Wave-CNN) was tested and then compared with geophysical field data; two separate synthetic studies were conducted for this purpose. In the first synthetic application, the success of the Wave-CNN method for the separation of regional-residual dipole structures was tested. In the second synthetic application, using prismatic structures with magnetic properties, the success of the Wave-CNN method to determine boundaries was compared with classical methods. It was found that the Wave-CNN method could not be repeated as many times as required. At first, it was possible to distinguish between regional and residual anomalies and successfully determine boundaries. As the number of repetitions increased, it was possible to clarify structures with stronger magnetic properties and to filter out structures with weak magnetic properties. The Wave-CNN using a vertical component of the magnetic field method was then applied to magnetic anomaly data from three Avnik iron mines located in the Bingöl Province, East Anatolia, Turkey: these areas are called, from south-to-north, Gonactepe, Heylandere and Miskel. The Wave-CNN outputs were also compared to drilling results from the study areas. Using this method, Wave-CNNs can be used to solve geophysical problems by detecting surface layer boundaries. In this Avnik field application, the Wave-CNN results showed high anomaly values indicating probable an iron ore deposit. In addition, represantive anomaly values were selected from the Wave-CNN outputs of each subarea and forward modeling was performed by applying Differential Evolution (DE) method. Thus, it has been shown that a good relation is obtained with the geological cross-section and geophysical model structures.

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“…Among these methods, inversion methodologies are the frequently used data processing tool in anomaly interpretation. However, because of the well‐known ill‐posedness and non‐uniqueness nature of the geomagnetic data inversion problem, explanation of anomaly sources, that is, model parameter estimations, necessitate some special strategies and efficient approaches (Ekinci et al., 2019). Over the recent years, instead of derivative‐based local optimizers, derivative‐free nature‐inspired global optimizers and metaheuristics such as Particle Swarm Optimization (PSO) (Essa, Abo‐Ezz, et al., 2022; Essa & Elhussein, 2020; Fernández‐Martínez et al., 2010; Pallero et al., 2015; Roy et al., 2022; Santos, 2010), Very Fast Simulated Annealing (VFSA) (Biswas, 2016; Biswas & Acharya, 2016; Biswas & Rao, 2021), Ant Colony Optimization (Liu et al., 2014, 2015; Srivastava et al., 2014); Gray Wolf Optimizer (Agarwal et al., 2018; Chandra et al., 2017), Genetic‐Price Algorithm (Di Maio et al., 2020), Cuckoo Search Algorithm (Turan‐Karaoğlan & Göktürkler, 2021), Differential Search Algorithm (Alkan & Balkaya, 2018; A. Balkaya & Kaftan, 2021; Özyalın & Sındırgı, 2023), Bat Algorithm (Essa & Diab, 2022; Gobashy et al., 2021), Differential Evolution Algorithm (Ç. Balkaya, 2013; Du et al., 2021; Ekinci, Balkaya, & Göktürkler, 2020; Ekinci et al., 2023; Göktürkler et al., 2016; Hosseinzadeh et al., 2023; Roy et al., 2021a; Sungkono, 2020); Backtracking Search Algorithm (Ekinci, Balkaya, & Göktürkler, 2021), Manta‐Ray Foraging Optimization and Social Spider Optimization (Ben et al., 2022a, 2022b, 2022c), Barnacles Mating Optimization (BMO) (Ai et al., 2022) have gained increasing attention in geophysical inversion applications.…”

Section: Introductionmentioning

confidence: 99%

“…Among these methods, inversion methodologies are the frequently used data processing tool in anomaly interpretation. However, because of the well-known ill-posedness and non-uniqueness nature of the geomagnetic data inversion problem, explanation of anomaly sources, that is, model parameter estimations, necessitate some special strategies and efficient approaches (Ekinci et al, 2019). Over the recent years, instead of derivative-based local optimizers, derivative-free nature-inspired global optimizers and metaheuristics such as Particle Swarm Optimization (PSO) (Essa, Abo-Ezz, et al, 2022;Essa & Elhussein, 2020;Fernández-Martínez et al, 2010;Pallero et al, 2015;Roy et al, 2022;Santos, 2010), Very Fast Simulated Annealing (VFSA) (Biswas, 2016;Biswas & Acharya, 2016;Biswas & Rao, 2021), Ant Colony Optimization (Liu et al, 2014(Liu et al, , 2015Srivastava et al, 2014); Gray Wolf Optimizer (Agarwal et al, 2018;Chandra et al, 2017), Genetic-Price Algorithm (Di Maio et al, 2020), Cuckoo Search Algorithm (Turan-Karaoğlan & Göktürkler, 2021), Differential Search Algorithm (Alkan & Balkaya, 2018;A.…”

mentioning

confidence: 99%

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

Ai,

Ekinci,

Balkaya

et al. 2023

Earth and Space Science

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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.

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Parameter Estimations from Gravity and Magnetic Anomalies Due to Deep-Seated Faults: Differential Evolution versus Particle Swarm Optimization

Cited by 16 publications

References 74 publications

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

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

Investigation of Bingöl Iron Ore Reserves Using Wavelet Cellular Neural Networks

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

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