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

M. Bhagwan Das; N. Sundararajan

doi:10.1007/s12517-016-2492-9

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

M. Bhagwan Das, N. Sundararajan^*

^*Corresponding author for this work

Earth Science

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Article number	490
Journal	Arabian Journal of Geosciences
Volume	9
Issue number	7
DOIs	https://doi.org/10.1007/s12517-016-2492-9
Publication status	Published - Jun 1 2016

Keywords

Artificial neural networks
Inversion
Levenberg–Marquardt algorithm and random noise
Self-potential anomaly
Trial and error method

ASJC Scopus subject areas

Earth and Planetary Sciences(all)
Environmental Science(all)

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10.1007/s12517-016-2492-9

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

abstract = "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.",

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AU - Das, M. Bhagwan

AU - Sundararajan, N.

PY - 2016/6/1

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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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JF - Arabian Journal of Geosciences

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

Abstract

Keywords

ASJC Scopus subject areas

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