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

M. Bhagwan Das, N. Sundararajan

Research output: Contribution to journalArticle

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 languageEnglish
Article number490
JournalArabian Journal of Geosciences
Volume9
Issue number7
DOIs
Publication statusPublished - Jun 1 2016

Fingerprint

self potential
artificial neural network
anomaly
polarization
electrical resistivity
copper
analysis
inversion

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)

Cite this

@article{782b67597ed34040be11a18e64f2c629,
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.",
keywords = "Artificial neural networks, Inversion, Levenberg–Marquardt algorithm and random noise, Self-potential anomaly, Trial and error method",
author = "Das, {M. Bhagwan} and N. Sundararajan",
year = "2016",
month = "6",
day = "1",
doi = "10.1007/s12517-016-2492-9",
language = "English",
volume = "9",
journal = "Arabian Journal of Geosciences",
issn = "1866-7511",
publisher = "Springer Verlag",
number = "7",

}

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.

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

UR - http://www.scopus.com/inward/record.url?scp=84973527718&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84973527718&partnerID=8YFLogxK

U2 - 10.1007/s12517-016-2492-9

DO - 10.1007/s12517-016-2492-9

M3 - Article

AN - SCOPUS:84973527718

VL - 9

JO - Arabian Journal of Geosciences

JF - Arabian Journal of Geosciences

SN - 1866-7511

IS - 7

M1 - 490

ER -