Recognition of Rocks at Uranium Deposits by Using a Few Methods of Machine Learning

  • E. Amirgaliev
  • Z. Isabaev
  • S. Iskakov
  • Y. Kuchin
  • R. Muhamediyev
  • E. Muhamedyeva
  • K. Yakunin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 273)

Abstract

Uranium extraction in Kazakhstan is carried out using underground leaching method. Economic performance depends on the production process speed and accuracy of geophysical data interpretation. Data interpretation can be performed using learned systems, such as artificial neural network (ANN), Linear Discriminant Analysis Classifier (LDAC), Support Vector Classification (SVM), k-Nearest-Neighbor (k-NN) and etc. In the paper “adjacency cube” method for integration of results of few interpretation algorithms is proposed. Learning algorithm for the “adjacency cube” with low computational complexity was developed. The proposed method improves quality of recognition by 2-3 percent.

Keywords

Geophysical research of boreholes machine learning artificial neural network k-NN uranium deposit post-processing data learning sample “adjacency cube” method 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • E. Amirgaliev
    • 1
  • Z. Isabaev
    • 1
  • S. Iskakov
    • 1
  • Y. Kuchin
    • 1
  • R. Muhamediyev
    • 1
  • E. Muhamedyeva
    • 1
  • K. Yakunin
    • 1
  1. 1.Institute of Problems of Information and ControlMinistry of Education and Science of Republic of Kazakhstan, International Informational Technology UniversityAlmatyKazakhstan

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