Search

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]

We have developed a new method for the interpretation of a magnetic anomaly profile by idealized-geometrical bodies using the variance analysis. This method is based on estimating the fourth horizontal derivative of the magnetic data profile. The advantage behind the use of the fourth horizontal derivative method is to reduce the regional background effect. The model parameters estimated are the depth and shape of the buried body using all available window lengths (s-value). Then, the variance value for the calculated depth at each shape value is estimated using all available s-values. The minimum variance is considered as the best criterion for determining the best-inverted parameters (depth and shape). The developed method has been verified on some noise free examples. Following that, the accuracy of the method was assessed by studying the impact of synthetic data with 5%, 10%, and 15% random noise, the effect of the regional background, the influence of interfering structures, and the selection for the wrong origin of the body. The presented method has been successfully applied to three real case studies from Brazil and India from mineral exploration. [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]

—We have developed a least-squares minimization approach to depth determination from magnetic data. By defining the anomaly value T(0) at the origin and the anomaly value T(N) at any other distance (N) on the profile, the problem of depth determination from magnetic data has been transformed into finding a solution to a nonlinear equation of the form f(z)=0. Formulas have been derived for a sphere, horizontal cylinder, dike, and for a geologic contact. Procedures are also formulated to estimate the effective magnetization intensity and the effective magnetization inclination. A scheme for analyzing the magnetic data has been formulated for determining the model parameters of the causative sources. The method is applied to synthetic data with and without random errors. Finally, the method is applied to two field examples from Canada and Arizona. In all cases examined, the estimated depths are found to be in good agreement with actual values. [ABSTRACT FROM AUTHOR]