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    Automatic classification of iron ore lithologies using petrophysical and geochemical data

    Access Status
    Fulltext not available
    Authors
    Kitzig, M.
    Kepic, Anton
    Date
    2016
    Type
    Conference Paper
    
    Metadata
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    Citation
    Kitzig, M. and Kepic, A. 2016. Automatic classification of iron ore lithologies using petrophysical and geochemical data, in Near Surface Geoscience 2016 - First Conference on Geophysics for Mineral Exploration and Mining, Sep 4-8 2016. Barcelona, Spain: EAGE.
    Source Title
    Near Surface Geoscience 2016 - First Conference on Geophysics for Mineral Exploration and Mining
    DOI
    10.3997/2214-4609.201602121
    School
    Department of Exploration Geophysics
    URI
    http://hdl.handle.net/20.500.11937/9417
    Collection
    • Curtin Research Publications
    Abstract

    We use Fuzzy Inference Systems on a combination of petrophysical and geochemical data to automatically classify iron ore lithologies. Our results show that only two measurements are needed to group the data according to the major rock class and grade. Either Fe and Al, or Fe and Natural Gamma logs may be used, where the Al or gamma log are indicative of shale units. We propose a method to gather all data necessary for iron ore classification in a single down-hole logging run using Spectral Gamma-Gamma to provide a real-Time update of the iron ore resource model.

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