Application of Geostatistics to Mineral Resource Modeling and Estimation: Case Study of Gofolo Hill Iron Ore Deposit, Western Liberia

Abstract

The viability of new mining operations depends on accurately evaluating mineral resources. Geostatistical methods are essential in providing accurate mineral resource estimation due to their ability to quantify estimation uncertainties and their ability to take spatial correlation into account. The study aims to model the orebody of the Gofolo Hill Iron Ore deposit in Western Liberia, evaluate the mineral resources using the geostatistical ordinary kriging method, and compare the results with estimates made using the inverse distance weighting method and the nearest neighbour polygon method. The study uses 39 RC drill holes with 200m x 60m grid spacing for resource modelling and estimation. The geostatistical ordinary kriging estimate of the project reported 17.169 Mt at a mean grade of 35.90%. Estimation methods are compared and correlated by descriptive statistics, grade-volume-tonnage results, and correlation coefficient. These results show that ordinary kriging, inverse distance weighting and nearest neighbour polygon have the highest accuracy in decreasing order of precision. The low standard error, low percent of global mean grade difference, and low coefficient of variation figures proved the validity of all estimating techniques.