Iron ore resource modeling and estimation using geostatistics

Modeling and estimation of ore grade are very essential in geostatistical ore resource 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 stu...

Full description

Saved in:
Bibliographic Details
Main Authors: Bargawa, Waterman Sulistyana, Tobing, Recky Fernando
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Bauxite
Bayer process
Boreholes
Copper ores
Exploratory drilling
Geostatistics
Iron compounds
Iron ores
Minerals
Model accuracy
Modelling
Root-mean-square errors
Statistical analysis
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Modeling and estimation of ore grade are very essential in geostatistical ore resource 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 resource estimation, the 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.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0006928