Quantifying the criteria for classification of mineral resources and reserves through the estimation of block model uncertainty using geostatistical methods: a case study of Khoshoumi Uranium deposit in Yazd, Iran. Issue 4 (3rd July 2020)

• Record Type: Journal Article
• Authors: Taghvaeenezhad, Mojtaba; Shayestehfar, Mohammadreza; Moarefvand, Parviz; Rezaei, Ali
• Is Part Of: Geosystem engineering. Volume 23:Issue 4(2020)
• Page Start: 216
• Page End: 225
• Publication Date: 2020-07-03
• Subjects: Variography -- block model -- kriging -- uncertainty -- JORC Code; Mining engineering -- Periodicals; Petroleum engineering -- Periodicals; Gas engineering -- Periodicals; Geology, Economic -- Periodicals; 620
• DOI: 10.1080/12269328.2020.1748524
• Languages: English
• ISSNs: 1226-9328

Abstract

ABSTRACT: Investments and progress of mineral projects depend on the quantity (tonnage) and quality (grade) of mineral resources and reserves. This study examines the impact of various criteria used in the classification of mineral deposits or parameters defining these criteria. The data used in this study include the uranium assay analysis from 127 exploratory boreholes, which were then subjected to a three-directional variography after statistical studies to identify regional anisotropy. A grade block model was built using the optimal parameters of variograms and with the help of kriging estimator. Then, by using different methods of estimating the block model uncertainty including kriging estimation variance, block error estimation, kriging efficiency and slope of regression, classification of mineral reserves was carried out in accordance with the JORC standard code. Based on different cut-off grades, the tonnage and average grade were calculated and plotted. An innovative quantitative method based on the distribution function of the mentioned parameters and the fractal pattern of separation of populations was used for the classification of mineral reserves. The existence of the least difference between the use of standard and fractal patterns in the slope of regression method indicated less error and was a proof of more reliable results.