Locating of infill drill holes in order to reduce uncertainty in mineral reserve estimation using weighting and multivariate ranking techniques

Pahlavani, Parham; Aghlan, Mohammad Hossein

doi:10.22034/ijme.2023.559269.1931

Locating of infill drill holes in order to reduce uncertainty in mineral reserve estimation using weighting and multivariate ranking techniques

Document Type : research - paper

Authors

¹ GIS department, School of Surviving and Geospatial Engineering, College of Engineering, University of Tehran

² School of Mining Engineering, University of Tehran, Tehran, Iran

10.22034/ijme.2023.559269.1931

Abstract

The topic of placement of infill drill holes and providing an optimal pattern for drilling drill hole has long been of interest in the mining process and has become an inseparable part of it. The root of this importance can be found in the financial issues related to the mining process as well as the exploration and extraction sector and issues related to classification. In order to reach an acceptable level of uncertainty, the estimation error method was used in this research to classify the blocks. Also, in order to place supplementary drill holes and weight potential drill holes, CRITIC and TOPSIS methods were used. The CRITIC method was used to assign weight and the TOPSIS method was used to select 5 of the best drill holes. At first, the drill holes were weighted by CRITIC method and with expert opinion, and then they were graded by TOPSIS method. The criteria used for the TOPSIS method include the grade of iron, weight percent grade of remaining iron (geometallurgical component), variance, error of estimation and classification. Finally, after placing the 5 wells in question, the estimation and classification process was carried out again, based on the number of definitively classified blocks, it was increased from 380 to 571, and the estimated average grade of iron also increased and 19% improvement in the classification was obtained. The improvement achieved in the number of blocks with definite classification adds to the certainty and reliability of the model.

Keywords