Sample Composite Mechanism to Reduce the Erratic Grade Distribution In Resource Estimation of Au-Ag Epithermal, Ciurug Vein, Block 3 South, Level 500 And 600, Pongkor Mine

• Main Author: , E. P. Zen
• Format: Article NonPeerReviewed
• Published: [Yogyakarta] : Departemnts of Geological Engineering Faculty of E 2009
• Online Access: https://repository.ugm.ac.id/95196/; http://repository.ugm.ac.id/digitasi/index.php?module=cari_hasil_full&idbuku=3014
• Institution: Universitas Gadjah Mada

<p>An outstanding characteristic exists in many Au - Ag deposit especially in vein system deposits is the erratic grade distribution. During bulk volume and grade estimation, the erratic effect has to be reduced as minimum in order to gain the higher spatial continuity especially for grade parameter. It is assumed that the erratic effect could be reduced by applying more robust geometry support. In the study, the geometry support is represented by interval of sample composite. The data set used is channel sampling obtained in the development stage in Ciurug vein, block 3 south, level 500 and 600. In the evaluation of Au - Ag grade spatial distribution, composite interval of 1 m, 2.5 m, and 5 m would be applied as block unit dimension of cube shape in deposit modeling and reserve estimation. Reserve estimation with different block size would be conducted with inverse distance square (IDS), nearest neighbor point (NNP), and kriging method. The results show the sample composite mechanism has limit of size to reduce the erratic effect. The size limit could be analyzed by several parameters of descriptive statistics: standard deviation, standard error, and confidence level. The combinations in' Au - Ag reserve estimation between block size and the third methods reveal the different trends. Based on cross validation results, IDS and NNP yield underestimated and overestimated calculation respectively, while kriging method generates moderate calculation. Furthermore, the estimation with block size of 2.5 m and kriging method is considered as the best estimation based on descriptive statistic, correlation and regression, and covarians analysis on cross validation.</p>