The Investigation of Cu geochemical behavior respect to those of Mo, Pb and Zn in Parkam porphyry copper deposit, Kerman province, applying K-means method.

Authors

Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

      One of the important aspects in data analysis for a large quantity of available data would be the clustering approach. This approach includes important techniques such as Hierarchical approach, K-Means approach based on density methods such as Kohonen method and Genetic algorithm. Clustering method has been used by various researchers.  One of the most fame clustering algorithms is K algorithm means (K-means). Based on this algorithm the aim is to separate the data into K clusters in terms of the distance factor. In the present research, by applying the K-means method, an attempt was made to classify the drill cores in Parkam deposit, into four different assay combinations of Cu, Mo, Pb and Zn, to find the optimum K in every case. Then it continues to classify the data and analyzes the elemental behavior respect to each other.   It is also an attempt to define the appropriate K for sign the class numbers, applying the different class values of K=3 to K=10 to group the analyzed data, reaching to optimize K content. Based on the results, clustering by K=2 clusters for Cu and Mo, K=4 for Cu and Pb, and also K=3 for Cu and Zn are more appropriate respect to the rest available results.  According to the defined presented clusters, it is obvious that through the Cu assay increasing, the Mo assay increased faster and the Pb assay decreases to 0.16 and then increases again,  the same thing happened for Zn. Therefore, it could be suggested that the K-means method, applied for present data analysis, gives a very appropriate aspect to the industry for the making decisions. By applying this method, it is possible to identify the Cu fluctuations in respect to the other elements within the analysis which could reach to take the better decision by industrial co-operators. So, in addition to its help to Cu mine explorations, it could aid to predict the mineral processing approaches like the freedom degrees and also the size efficiencies.
 

Keywords

References

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