Evaluation of Mineral Reserves: A Simulation ApproachOxford University Press, 2004 M05 27 - 232 pages This book addresses the practice of geostatistical simulation to evaluation of mineral reserves, prediction of recovered tonnages and mineral grades and the impact of mining dilution. Such prediction is absolutely critical for mine planning and investment decisions, yet it cannot be made on maps directly interpolated from present data. Various dilution factors need to be introduced to account for · the support effect: mining unit volumes are vastly different from composite data unit volumes · the information effect: future selection of ore/waste will be based on vastly different data than that presently available. Geostatistical simulations allow a rigorous evaluation of these effects on reserves recovery. These stochastic simulations have the potential to be for the mining industry what a wind tunnel is for aircraft design. This book is written by two expert geostatisticians--Journel is the pioneer of mining geostatistics--and established academics. |
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Evaluation of Mineral Reserves: A Simulation Approach A. G. Journel,Phaedon C. Kyriakidis No preview available - 2004 |
Common terms and phrases
actual profit actual recovery actual selection algorithm anisotropy average b-data block ccdf coef coefficient of variation corresponding cumulative distribution function cutoff grade Data 50 mean data available East Ely data set Ely2 reference error estimated SMU grades estimated values evaluation Frequency future data geostatistical grayscale high-grade histogram indicator kriging indicator semivariograms information effect Journel lognormal lower quartile maximum mean ore grade median metal recovered minimum misclassification normal score Number of Data panel parameter perfect selection point support grade prediction probability interval quantile function quantity of metal random variable recoverable reserves recovery functions recovery indicators reference data set reference grades reference recovery Reference SMU grade scattergram semivariogram model simulated realizations simulated SMU grade simulated values simulation approach SMU estimates SMU size 11 SMU support SMU v(u spatial distribution statistics tonnage recovered tonnage recovery true SMU grades uncertainty upper quartile variance variogram model Volume-Variance Correction z-values
Popular passages
Page 173 - R. (1998) Conditional simulation algorithms for modelling orebody uncertainty in open pit optimization.
Page 173 - Dagbert, M. (1980). The use of simulated spatially distributed data in geology. Computers and Geosciences.
Page 8 - ... also minimize the conditional variance E{[ZV -h(z)]2} (Journel and Huijbregts, 1978) so as to minimize ore loss and dilution or misclassification at the time of mining. 5. Type 1 Estimates and their Recovery Functions Recall, that Type 1 estimates are used to predict the tons and grade of ore that will be recovered in the future at the time of mining. They are not used for selection at the time of mining.
Page 108 - Z , vr and sec var -debugging level: 0,1,2,3 -file for debugging output -file for kriged output -nx,xmn,xsiz -ny,ymn,ysiz...
Page 173 - Transactions of the Institute of Mining and Metallurgy, Sect. A: Min. Industry, A129-A132.