Indonesia Conference Directory

Modeling and estimation of ore grade are very essential in geostatistical ore resources estimation. Resource modeling is generally carried out on gold, copper, nickel and bauxite ores. This study applies the geostatistical method for modeling and estimation of iron ore grade. The objective of the study is to apply estimation techniques (OK, ordinary kriging; IDW, inverse distance weighting, and NNP, nearest neighbor polygon) and evaluate the accuracy of these techniques in iron ore resources. This study uses detailed exploration, which are 68 drill holes with 170 iron ore grade composite data. In the iron ore resources estimation, block modeling method is applied. The results showed RMSE (root mean square error) values of various estimation techniques. Based on statistical analysis, visualization of comparisons between borehole data and models, and probability plots, the accuracy of each iron ore resource estimation technique in the study area can be determined. All estimation techniques have the same accuracy on low CV (coefficient of variance) values. The relative kriging standard deviation values determine the classification of measured iron ore resources.