Analysis of self-potential anomalies due to 2D horizontal cylindrical structures—an artificial neural network approach

The application of artificial neural network committee machine (ANNCM) for the inversion of self-potential anomalies caused by a long 2D horizontal circular cylinder is presented. ANNCM inversion extracts the parameters of the source including depth to the center of the cylinder(z), the angle between the horizontal axis and the axis of polarization(α), and the constant term(A) involving the current polarization (I) and resistivity of the earth(ρ). The inversion is demonstrated on theoretical models with and without random noise in order to study the effect of noise on the method and then extended to real field data. The ANNCM analysis of self-potential data of the Sulleymonkey anomaly in the Ergani Copper district, Turkey, has shown satisfactory results in comparison with other inversion techniques that are in vogue.