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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]

The self potential data interpretation is very important to delineate and trace the mineralized zones in several regions. We study how to interpret self potential anomalies due to a finite two-dimensional inclined dike using the particle swarm algorithm. However, the precise estimation of the model parameters during the inverse solution are unknown. Here, we show that the particle swarm algorithm is capable of estimating the unknown parameters with acceptable accuracy. The evaluated parameters are the polarization parameter, the depth, the inclination angle, the width, and the location of the source of the target. We found in controlled in free-noise synthetic case that the particle swarm algorithm has a remarkable capability of assessing the parameters. For a noisy case, the results also are very competitive. Furthermore, it is utilized for real mineralized zones examples from Germany and India. Our results demonstrate how the particle swarm algorithm overcomes in trapping in local minimum solutions (undesired) and go faster to the global solutions (desired). Finally, the target parameters estimated are matched with accessible geologic and geophysical information. [ABSTRACT FROM AUTHOR]

Abstract: A new fast least-squares method is developed to estimate the shape factor (q-parameter) of a buried structure using normalized residual anomalies obtained from gravity data. The problem of shape factor estimation is transformed into a problem of finding a solution of a non-linear equation of the form f(q)=0 by defining the anomaly value at the origin and at different points on the profile (N-value). Procedures are also formulated to estimate the depth (z-parameter) and the amplitude coefficient (A-parameter) of the buried structure. The method is simple and rapid for estimating parameters that produced gravity anomalies. This technique is used for a class of geometrically simple anomalous bodies, including the semi-infinite vertical cylinder, the infinitely long horizontal cylinder, and the sphere. The technique is tested and verified on theoretical models with and without random errors. It is also successfully applied to real data sets from Senegal and India, and the inverted-parameters are in good agreement with the known actual values. [Copyright &y& Elsevier]

Abstract: We have developed a new least-squares minimization approach to depth determination from self-potential (SP) data. By defining the anomaly value at the origin and at any two symmetrical points around the origin on the profile, the problem of depth determination from the residual SP anomaly has been transformed into finding a solution to a nonlinear equation of the form f(z)=0. Procedures are also formulated to estimate the polarization angle, amplitude coefficient and the shape of the buried structure (shape factor). The method is simple and can be used as a rapid method to estimate parameters that produced SP anomalies. The method is tested on synthetic data with and without random errors. It is also applied to a field example from Turkey. In all cases, the model parameters obtained are in good agreement with actual ones. [Copyright &y& Elsevier]

A global particle swarm algorithm utilized to assess the inverted two-dimensional horizontal thin sheet parameters from the gravity anomaly profile based on applying the second moving average method. The using of the second moving average method has more advantageous than using the Bouguer gravity anomaly because this method has a capability in eliminating the regional field up to third-order impeded in the Bouguer anomaly. This algorithm is applied to interpret the gravity anomaly profile, i.e., estimating the depth, the width, the thickness, the density contrast and the origin location of the buried structure. The efficacy and stability of this method are investigated and exposed for utilizing free-noise and noisy synthetic examples. Real gravity data associated with oil and gas and mineral exploration from three different locations around the world is interpreted. The evaluated time to discovering this appropriate clarification is very short and the estimated parameters contrast the view that the proposed method is applicable for real exploration. It can extract the inverse parameters which have a geologic and economic significance. • A global particle swarm for interpreting gravity data to delineate the parameters of buried geologic structure • This technique relies on second moving average operator to eliminate the impact of the regional and noise in a main field. • This technique applied to synthetic and field examples for oil and mineral exploration. [ABSTRACT FROM AUTHOR]