Self potential data interpretation utilizing the particle swarm method for the finite 2D inclined dike: mineralized zones delineation

Abstract

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.