TY - JOUR
T1 - Analysis of self-potential anomalies due to 2D horizontal cylindrical structures—an artificial neural network approach
AU - Das, M. Bhagwan
AU - Sundararajan, N.
N1 - Publisher Copyright:
© 2016, Saudi Society for Geosciences.
PY - 2016/6/1
Y1 - 2016/6/1
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Inversion
KW - Levenberg–Marquardt algorithm and random noise
KW - Self-potential anomaly
KW - Trial and error method
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U2 - 10.1007/s12517-016-2492-9
DO - 10.1007/s12517-016-2492-9
M3 - Article
AN - SCOPUS:84973527718
SN - 1866-7511
VL - 9
JO - Arabian Journal of Geosciences
JF - Arabian Journal of Geosciences
IS - 7
M1 - 490
ER -