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    An algorithm for quantifying regionalized ore grades
    (South African Inst Mining Metallurgy, 2008) Tutmez, B.; Tercan, A. E.; Kaymak, U.
    We present a novel, hybrid algorithm for quantifying the ore grade variability that has central importance in ore reserve estimation. The proposed algorithm has three stages: (1) fuzzy clustering, (2) similarity measure, and (3) grade estimation. The method first considers data clustering, and then uses the clustering information for quantifying the ore grades by means of a cumulative point semimadogram function. The method provides a measure of similarity and gives an indication of the regional heterogeneity. In addition, grade estimations can be obtained at different levels of similarity using a weighting function, which is the standard regional dependence function (SRDF).
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    Öğe
    Lignite thickness estimation via adaptive fuzzy-neural network
    (Uceat-Chamber Mining Engineers Turnkey, 2007) Tuetmez, B.; Dag, A.; Tercan, A. E.; Kaymak, U.
    Thickness estimation is an important step in reserve estimation. In this study, lignite thickness is estimated using fuzzy-neural network. For this purpose, the lignite thickness data derived from Afsin-Elbistan lignite deposit were employed and the estimation has been conducted by the Adaptive Network Based Fuzzy Inference System (ANFIS). The method estimates thickness based on a data-driven model structure which is constructed from the adaptation of artificial neural networks to fuzzy modelling algorithm. Modelling process consists of data clustering, inference and learning mechanisms. The results have been compared with kriging estimations and it is seen that performance of the model is high.