Lithological classification of cement quarry using discriminant algorithms

Abstract

As such in any industrial raw material site characterization study, making a lithological evaluation for cement raw materials includes a description of physical characteristics as well as grain size and chemical composition. For providing the cement components in accordance with the specifications required, making the classification of the cement raw material pit is needed. To make this identification in a spatial system at a quarry stage, the supervised pattern recognition analysis has been performed. By using four discriminant analysis algorithms, lithological classifications at three levels, which are with limestone, marly-limestone (calcareous marl) and marl, have been made based on the main chemical components such as calcium oxide (CaO), alumina (Al₂O₃), silica (SiO₂), and iron (Fe₂O₃). The results show that discriminant algorithms can be used as strong classifiers in cement quarry identification. It has also recorded that the conditional and mixed classifiers perform better than the conventional discriminant algorithms.

Keywords

cement / discriminant analysis / lithology classification / quarry identification

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