A Nonlinear Programming Technique for the Interpretation of Self-potential Anomalies

Using Frank and Wolfeʹs algorithm, a new interesting nonlinear programming technique has been developed in an attempt to estimate the geometric shape factor of a buried polarized body from a residual self-potential anomaly. Furthermore, the depth, the polarization angle and the electrical dipole moment have also been derived. This algorithm is noted to be robust and its application to SP data converges rapidly towards the optimal solution. The developed technique is tested through studying synthetic data with and without random noise. As a result, the near agreement between the model geometric shape factor and the evaluated one is well recognized. The validity of this proposed technique is tested on a field example from the Ergani Copper district, Turkey. The superiority of the nonlinear programming technique over other recently published methods is shown.