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In this paper, the effectiveness of recently proposed Hunger Games Search (HGS) optimization tool, which simulates behavioural choices of the predators in the animal kingdom, in estimating model parameters of idealized causative sources from Self-Potential (SP) anomalies is presented. Before the parameter estimations, the modal analyses of the model parameter pairs and parameter tuning processes were performed to characterize the optimization problem and to decide the optimal value of the control parameter of HGS algorithm, respectively. Subsequently, the proposed optimizer was applied on some synthetic SP data sets and four anomalies obtained from India, Türkiye, Canada, and Indonesia. Multiple and intercalating causative sources were considered in the synthetic and real data cases, and HGS algorithm produced satisfactory solutions. In the composite anomaly cases a second moving average procedure successfully removed the undesired background effects. Uncertainty appraisal analyses showed the validity of the model parameter estimations. The outputs of HGS optimizer were also compared with those of the well-established and popular standard particle swarm optimization regarding convergence rate, robustness, stability, and accuracy. Applications presented here clearly showed that SP anomalies can be inverted more effectively via HGS since the procedure has the ability to search the predefined model space 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 model parameter estimation studies using SP anomalies. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: A new global optimization algorithm, named Hunger Games Search (HGS), is presented for the inversion of geophysical data sets. The efficiency of the algorithm is tested on geomagnetic anomalies originated from different ore masses and synthetic data sets. The applications show that the proposed inversion algorithm produces reliable results and outperforms standard Particle Swarm Optimization, which is the most widely used global optimization algorithm in the inversion of geophysical data sets. We conclude that the HGS algorithm is a powerful inversion tool for geophysical anomalies. Key Points: This is the first study in the literature to apply Hunger Games Search algorithm to geophysical anomaliesPost‐inversion analyses showed the reliability of the solutions obtained in synthetic and field data casesCompared to the standard Particle Swarm Optimizer, proposed algorithm outperformed in the presented inverse problem [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Dykes play a key role in various geologic environments. Here, we show how we employed profile magnetic data to perceive and identify the number of dykes and their parameters by a particle swarm optimiser scheme. The results deduced from this inversion include identification and estimation of the magnetic dipole moment, depth, half-width, magnetisation angle, and origin location (dyke parameters) of dykes. Two synthetic models demonstrate this scheme: the first model tests the impact of a third-order regional anomaly and the second model considers the effect of adjacent structures. Besides, we provide two case studies related to mineral exploration, viz., the Parnaiba basin from Brazil and the Pishabo Lake example from Canada. The subsurface dyke parameters estimated from drilling information and the available literature were consistently matched with those achieved from our inversion scheme, thereby demonstrating the effectiveness of the particle swarm optimisation scheme. [ABSTRACT FROM AUTHOR]

Submarine mud diapirs (MDs) and volcanoes, often linked with gas hydrates and hydrocarbons, are prevalent features in the offshore region of SW Taiwan. Despite their significance, the gravity signatures of these MDs have not been investigated except for a few studies. Therefore, our study aims to address this gap by employing a recently proposed optimization algorithm, Hunger Games Search (HGS), to estimate model parameters of an inclined 2D anticlinal structure representative of an active MD. This approach was implemented to gravity anomaly obtained from SW coast of Taiwan. Before its application, we explored the nonlinear and high-dimensional characteristics of the cost function associated with this inverse problem by generating error landscape maps for model parameter pairs. Subsequently, to mitigate the challenges, a parameter tuning procedure was performed. Using the optimal control parameters derived from this procedure, we increased the efficacy of the HGS algorithm for the problem handled. Furthermore, we employed standard Particle Swarm Optimization (PSO), a widely utilized metaheuristic in geophysical inversion studies, for a comparison. The findings showed that the proposed novel algorithm yielded superior accuracy and reliability compared to PSO. Additionally, the inclined anticlinal structure exhibited better agreement with interpretation obtained from available seismic reflection outcomes, in contrast to approximation based on inclined dike-like structure. Besides, comparative analyses between HGS and two well-established techniques, namely correlation imaging and compact inversion algorithms further validated the effectiveness of HGS. Consequently, we assert that the novel HGS optimizer is a robust and appealing tool for inverse gravity problems. Moreover, assuming a 2D inclined anticlinal source type holds promise for extended investigations into other MDs in SW Taiwan's upper slope region. Such efforts can significantly enhance the understanding of regional mud diapirism and volcanism on a broader scale. • This study introduced a novel and powerful global optimizer, HGS algorithm. • It compared with the widely utilized PSO algorithm. • Compact inversion and correlation imaging techniques supported our results. • The mud diapiric model of Taiwan was approximated by an inclined anticlinal model. • Retrieved full parameters provided more abundant information about mud diapirs. [ABSTRACT FROM AUTHOR]

This paper describes the use of the particle swarm optimization (PSO) method for interpreting observed self-potential anomalies measured along a profile. First, the technique applies the second moving average to the observed self-potential data in order to eradicate the possible influence of the regional anomaly (up to the third-order polynomial effect) via the filter of consecutive window lengths (s-values) and to calculate the residual anomaly. Following that, the PSO method is applied to the residual response to infer the source parameters: amplitude coefficient (K), depth (z), polarization angle (𝜃) and the shape factor (q) of the underlying buried target. The technique has been applied to three different theoretical and two field examples from the USA and Turkey. Comparisons have shown that the source parameters retrieved from the technique described here are in good agreement with the available geologic and geophysical information. [ABSTRACT FROM AUTHOR]

A new efficient approach to estimate parameters that controlled the source dimensions from magnetic anomaly profile data in light of PSO algorithm (particle swarm optimization) has been presented. The PSO algorithm has been connected in interpreting the magnetic anomaly profiles data onto a new formula for isolated sources embedded in the subsurface. The model parameters deciphered here are the depth of the body, the amplitude coefficient, the angle of effective magnetization, the shape factor and the horizontal coordinates of the source. The model parameters evaluated by the present technique, generally the depth of the covered structures were observed to be in astounding concurrence with the real parameters. The root mean square (RMS) error is considered as a criterion in estimating the misfit between the observed and computed anomalies. Inversion of noise-free synthetic data, noisy synthetic data which contains different levels of random noise (5, 10, 15 and 20%) as well as multiple structures and in additional two real-field data from USA and Egypt exhibits the viability of the approach. Thus, the final results of the different parameters are matched with those given in the published literature and from geologic results. [ABSTRACT FROM AUTHOR]

A robust Particle Swarm Optimization (PSO) investigation for magnetic data by a 2D dipping dike has been presented. The interpretive model parameters are: the amplitude coefficient (K), the depth to the top of the dipping dike (z), exact origin of the dipping dike (x0), and the width of dipping dike (w). The inversion procedure is actualized to gauge the parameters of a 2D dipping dike structures where it has been confirmed first on synthetic models without and with different level of random noise. The results of the inversion demonstrate that the parameters derived from the inversion concur well with the true ones. The root mean square (RMS) is figured by the strategy which is considered as the misfit between the measured and computed anomalies. The technique has been warily and effectively applied to real data examples from China and UK with the presence of ore bodies. The present technique can be applicable for mineral exploration and ore bodies of dike-like structure embedded in the shallow and deeper subsurface. [ABSTRACT FROM AUTHOR]

This study focuses on interpreting gravity anomalies caused by fault structures. The faults types are the two-sided inclined fault, which represents mainly the normal and reverse faults, the two-sided vertical fault, the one-sided inclined fault, and the one-sided vertical fault. The study scheme is depends on the combination between the second moving average operator as a tool for separating the residual (desired) and regional (undesired) anomalies and then detecting the fault parameters applying the global particle swarm. The stability and efficiency of the proposed method has been applied to a synthetic example including the effect of regional background and to five real data sets from Iraq, USA, Egypt, and France. Available geologic and geophysical information supports our interpretation for the field examples from Iraq, USA, and Egypt. While, the forward model results from the detected parameters for the last field example (Pyrenees faults, France) was compared and matched well with measured gravity anomaly and found in a good agreement. So, this interpretation is open a sight-view for researchers around the world work in this area to use our results as a priori information for more investigations. • Global particle swarm optimization for interpreting fault-like geologic structure. • The proposed method is depends on second moving average operator to remove the regional effect. • This method is tested on synthetic and fields data for oil and mineral exploration. • This method is powerful and robust. [ABSTRACT FROM AUTHOR]

We have developed a scheme for the interpretation of gravity data by a two-sided fault-like geological structure. The scheme is based on the particle swarm optimization and determines the model parameters of the upthrown and downthrown of the fault. It uses the second horizontal derivatives, which are calculated numerically from the measured gravity data by applying some filters of consecutive graticule spacings (the so-called here window lengths or s-values). The developed scheme has been verified on various noise-free synthetic models. Following that, the accuracy of the scheme has been assessed on a number of noisy synthetic examples. Finally, the developed technique has been successfully applied to two real data examples from India and the United States of America. It is found that the model parameters obtained from the scheme developed here are in good agreement with those reported in the published literature. Unlabelled Image • New method is utilized to model two-sided faults based upon particle swarm optimization and second horizontal gradient. • The proposed method is applied to four synthetic models and two field gravity data sets. • The method can be applied in mineral exploration and studying fault hazards. [ABSTRACT FROM AUTHOR]

A new approach to the inversion of gravity data utilizing the Particle Swarm Optimization (PSO) algorithm is used to model 2D vertical faults. The PSO algorithm is stochastic in nature; its development was motivated by the communal in-flight performance of birds looking for food. The birds are represented by particles (or models). Individual particles have a location and a velocity vector. The location vectors represent the parameter value. PSO is adjusted with random particles (models) and searches for targets by updating generations. Herein, the PSO algorithm is applied to three synthetic data sets (residual only with and without noise, residual plus regional, residual plus anomaly generated by a buried cylinder structure) and two field gravity data sets acquired across known faults in Egypt. Assessment of the synthetic data demonstrates that the PSO algorithm generates superior results if a first horizontal gradient (FHG) filter is applied first. The robustness of the PSO inversion algorithm is demonstrated for both synthetic and field gravity data. • New inversion approach is used to model 2D vertical faults based upon Particle Swarm Optimization (PSO) and first horizontal gradient (FHG). • PSO algorithm is applied to three synthetic data sets and two field gravity data sets. • The robustness of the new approach is demonstrated for both synthetic and field gravity data. [ABSTRACT FROM AUTHOR]