Selection of Optimal Thresholds for Estimation and Simulation Based on Indicator Values of Highly Skewed Distributions of Ore Data
Autor: | Hemn Rahimi, Farnusch Hajizadeh, Omid Asghari |
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Rok vydání: | 2018 |
Předmět: |
Cumulative distribution function
Geostatistics 010501 environmental sciences 010502 geochemistry & geophysics 01 natural sciences Distribution (mathematics) Fractal Kriging Statistics Range (statistics) Indicator value Selection (genetic algorithm) 0105 earth and related environmental sciences General Environmental Science |
Zdroj: | Natural Resources Research. 27:437-453 |
ISSN: | 1573-8981 1520-7439 |
DOI: | 10.1007/s11053-017-9366-z |
Popis: | This study strives to outline a geostatistics model for estimation and simulation of the Qolqoleh gold ore deposit located in Saqqez, NW of Iran. Considering that this gold deposit contains high-grade values, accurate evaluation of such values is of high importance, and therefore different methods based on indicator values, such as full indicator kriging (FIK) and sequential indicator simulation (SIS), have been employed to improve the accuracy of estimation and simulation of high-grade values. FIK and SIS cover the full range of grades based on several thresholds on the indicator data. The cumulative distribution function (CDF) is typically used for selection of threshold values. Given the highly skewed distribution of gold grade and its intense fluctuations, the number of thresholds is increased using CDF, which in turn results in a whole lot of calculations. To reduce the volume of calculations, the number–size (N–S) fractal model has been used to select thresholds. From such a model, all optimal thresholds are chosen with respect to geology and the unnecessary thresholds are excluded from selection. Thus, a study of the selection of optimal thresholds for estimation and simulation of a gold ore resource by means of FIK and SIS, respectively, based on thresholds selected using the N–S fractal model is presented. Finally, it is proved that results of these geostatistical methods based on thresholds selection from the N–S model appear to be better-positioned to explain ore grade variability compared to thresholds selected from the CDF and threshold selection from the N–S model is more effective for reducing the volume of required calculations. |
Databáze: | OpenAIRE |
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