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We describe a rapid imaging approach for the interpretation of self-potential data collected along profile by some geometrically simple model of cylinders and spheres. The approach calculates the correlation coefficient between the analytic signal (AS) of the observed self-potential measurements and the AS of the self-potential signature of the idealized model. The depth, electric dipole moment, polarization angle, and center are the inverse parameters we aim to extract from the imaging approach for the interpretative model, and they pertain to the highest value of the correlation coefficient. The approach is demonstrated on noise-free numerical experiments, and reproduced the true model parameters. The accuracy and stability of the proposed approach are examined on numerical experiments contaminated with realistic noise levels and regional fields prior to the interpretation of real data. Following that, five real field examples from geothermal systems and mineral exploration have been successfully analyzed. The results agree well with the published research. [ABSTRACT FROM AUTHOR]

A new imaging method has been developed for elucidating the observed magnetic data gauged along profile. The method is based on the calculation of the correlation factor (the R-parameter) between the analytic signal of the measured magnetic anomaly and the analytic signal of the calculated response of some geometrically simple interpretive models in the confined category of sheets, cylinders, and spheres. The characteristic parameters (amplitude coefficient, depth, location, approximative shape of the buried structure, and effective angle of magnetization) of the interpretive model correspond to the maximum R-parameter value. The scheme has been verified on a number of noise-free synthetic examples and recovered the actual model parameters. Prior to applying the developed scheme to real-field examples, the accuracy of it has been carefully investigated on synthetic examples which are contaminated with realistic noise levels, interference effects, and regional field. Finally, the method has been successfully applied to three real-field data examples from the USA, Senegal, and Egypt for mineral exploration, and it is found that the obtained results are in good concordance with those obtained from drilling and/or the published literature. [ABSTRACT FROM AUTHOR]

Geophysical methods, especially the gravity method, are very helpful in ore and mineral explorations. Here, gravity modeling and interpretation for the subsurface geologic structures generally assumes either homogenous or spatially varying densities within target source rocks and surrounding structures. Therefore, the use of simple-geometric bodies helps in the validation of the subsurface ore and mineral targets. A Bat optimization algorithm is a recently developed metaheuristic algorithm that is used in various geophysical applications to explore and explain the parameters of buried ore and mineral targets. Using the Bat optimization algorithm, we were elucidating gravity anomaly profiles for ore and mineral cases. To perform global optimization, the Bat optimization algorithm is based on the echolocation behavior of bats. The global optimum solution in the Bat optimization algorithm reached the suggested minimum value of the objective function. The Bat optimization algorithm is applied to gravity data to estimate the target parameters (e.g., amplitude coefficient, depth, origin location, and geometric shape). The stability and efficiency of the introduced optimizing algorithm have been checked on two synthetic models represented in a spherical model and an infinitely horizontal cylinder model using two different kinds of noise. Furthermore, successful applications of the proposed algorithm for discovering the ore and minerals in Canada, Cuba, and India were presented. The results match well with the available geological and borehole information and other results from the published literature. [ABSTRACT FROM AUTHOR]

• The R-parameter imaging technique for interpreting gravity data. • The method is based on the analytic signal of observed and synthetic responses. • The maximum R-parameter value is the criterion in determining the true parameters. • The accuracy of this technique is verified on numerical and real data sets. This paper describes a fast imaging technique, the so-called here the "R-parameter imaging technique", for the interpretation of gravity data measured along profile. The technique calculates the R-parameter (a statistical parameter called the correlation coefficient) between the analytic signal of the measured gravity field and that of the synthetic response of an assumed interpretive model in the restricted class of spheres and cylinders. The characteristic source parameters (depth, shape, origin point of the gravity profile, and amplitude factor) of the model correspond to the maximum R-parameter value. The method has been verified on noise free numerical examples. Following that, the accuracy of the method has been carefully assessed on both noisy numerical examples and numerical examples with interference effects. Finally, the technique has been successfully applied to five field data examples from Canada, Sweden and Cuba for mineral exploration, and it is found that the obtained results are in good agreement with those reported in the published literature. [ABSTRACT FROM AUTHOR]