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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]

We have developed an algorithm to obtain the model parameters for two co-axial structures from selfpotential data. The method uses the first numerical horizontal derivatives calculated from the observed self-potential anomaly employing filters of sequential window lengths (s-values) so as to gauge the model constraints for the shallow and deep structures. In addition, this algorithm uses a standard inversion method for solving a non-linear equation based on the lowest root-mean-square (RMS) error of the estimated model parameters. The body constraints are the depth, polarization angle and electric dipole moment of each structure. Our approach models the self-potential dataset as an aggregation of spheres, horizontal cylinders, and vertical cylinders. These simple bodies are used to approximate, without a priori expectations, the furthermost plausible position and/or area of intersection. In other words, the bodies are used to estimate the true values of the source parameters for the two-co-axial bodies at different s-values. Minimizing the RMS error has the advantage of optimizing all model factors. The proposed technique is tested using a numerical model with and without noise and on self-potential field data acquired at a site in Germany. In all cases, the assessed body parameters are reasonable approximations of the known values. [ABSTRACT FROM AUTHOR]