Uncertainty in Mineral Prospectivity Prediction

  • Pawalai Kraipeerapun
  • Chun Che Fung
  • Warick Brown
  • Kok Wai Wong
  • Tamás Gedeon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


This paper presents an approach to the prediction of mineral prospectivity that provides an assessment of uncertainty. Two feed-forward backpropagation neural networks are used for the prediction. One network is used to predict degrees of favourability for deposit and another one is used to predict degrees of likelihood for barren, which is opposite to deposit. These two types of values are represented in the form of truth-membership and false-membership, respectively. Uncertainties of type error in the prediction of these two memberships are estimated using multidimensional interpolation. These two memberships and their uncertainties are combined to predict mineral deposit locations. The degree of uncertainty of type vagueness for each cell location is estimated and represented in the form of indeterminacy-membership value. The three memberships are then constituted into an interval neutrosophic set. Our approach improves classification performance compared to an existing technique applied only to the truth-membership value.


Type Error Geographic Information System Data Geographic Information System Layer Dynamic Combination Mineral Prospectivity Mapping 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pawalai Kraipeerapun
    • 1
  • Chun Che Fung
    • 2
  • Warick Brown
    • 3
  • Kok Wai Wong
    • 1
  • Tamás Gedeon
    • 4
  1. 1.School of Information TechnologyMurdoch UniversityAustralia
  2. 2.Centre for Enterprise Collaboration in Innovative SystemsAustralia
  3. 3.Centre for Exploration TargetingThe University of Western AustraliaAustralia
  4. 4.Department of Computer ScienceThe Australian National UniversityAustralia

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