A neuro-computing based model for anomaly recognition in geochemical exploration

  • Authors:

  • Mansour Ziaii;Ali A. Pouyan;Mehdi Ziaii

  • Affiliations:

  • Faculty of Mining Engineering and Geophysics, Shahrood University of Technology, Iran;Faculty of ICT and Computer Engineering, Shahrood University of Technology, Iran;IASP, Shahrood, Iran

  • Venue:
  • ICS'06 Proceedings of the 10th WSEAS international conference on Systems
  • Year:
  • 2006

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Abstract

In this paper an abstract model for anomaly recognition in geochemical exploration has been developed based on neural networks. Traditional geochemical exploration methods are based on multivariate statistical analysis, which suffer several shortcomings including lack of geo-statistical generalized approach for separating anomalies from background. These shortcomings make the interpretation process time consuming and costly. We have proposed a novel approach for quantitatively recognition between blind anomalies and false anomalies' patterns using back propagation artificial neural networks with fuzzy C-means cluster analysis. It has been revealed that the resultant output has been significantly improved, comparing traditional methods. The major advantage of the proposed method is that it computationally enables us to distinguish zone of dispersed ore mineralization from blind mineralization without exploration drilling.