Risk quantification in grade-tonnage curves and resource categorization in a lateritic nickel deposit using geologically constrained joint conditional simulation

Lopes, J. A.; Rosas, C. F.; Fernandes, J. B.; Vanzela, G. A.

doi:10.1134/s1062739147020043

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…Overall, the simulations reproduce spatial features of the original data that have not been explicitly modeled. This observation has also (Dimitrakopoulos and Fonseca 2003;Bandarian et al 2008;Lopes et al 2011).…”

Section: Minimum/maximum Autocorrelation Factorsmentioning

confidence: 71%

“…Note that the MAF approach does not actually require modeling the coregionalization. Joint simulations of mineral deposits based on MAF are shown to be effective, relatively efficient, flexible and practical (Boucher 2003;Dimitrakopoulos and Fonseca 2003;Bandarian et al 2008;Lopes et al 2011;Rondon 2012). The efficiency of joint simulation with MAF is further enhanced when the simulation is done directly on a block-support scale (Godoy 2002).…”

Section: Introductionmentioning

confidence: 99%

“…The method minimizes the information stored by retaining only the block values, a procedure that significantly speeds up the simulation process while reducing the size of the output files for more efficient data storage and management. The application of MAF in mineral deposits has been shown in several studies (Dimitrakopoulos and Fonseca 2003;Lopes et al 2011) to work particularly well and reproduce the statistical, spatial continuity and cross continuity, as well as scatter-plots, in spite of being based on simulating independent service variables.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Block-Support Simulation of the Yandi Iron Ore Deposit, Western Australia

Boucher¹,

Dimitrakopoulos

2012

Math Geosci

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Mineral deposits frequently contain several elements of interest that are spatially correlated and require the use of joint geostatistical simulation techniques in order to generate models preserving their spatial relationships. Although jointsimulation methods have long been available, they are impractical when it comes to more than three variables and mid to large size deposits. This paper presents the application of block-support simulation of a multi-element mineral deposit using minimum/maximum autocorrelation factors to facilitate the computationally efficient joint simulation of large, multivariable deposits. The algorithm utilized, termed dbmafsim, transforms point-scale spatial attributes of a mineral deposit into uncorrelated service variables leading to the generation of simulated realizations of block-scale models of the attributes of interest of a deposit. The dbmafsim algorithm is utilized at the Yandi iron ore deposit in Western Australia to simulate five cross-correlated elements, namely Fe, SiO 2 , Al 2 O 3 , P and LOI, that are all critical in defining the quality of iron ore being produced. The block-scale simulations reproduce the direct-and cross-variograms of the elements even though only the direct variograms of the service variables have to be modeled. The application shows the efficiency, excellent performance and practical contribution of the dbmafsim algorithm in simulating large multi-element deposits.Keywords Multi-element mineral deposits · Block-support simulation · Joint simulation · Minimum/maximum autocorrelation factors · Iron ore deposits A. Boucher ( ) Advanced Resources and Risk Technology,

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Section: Minimum/maximum Autocorrelation Factorsmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multivariate Block-Support Simulation of the Yandi Iron Ore Deposit, Western Australia

Boucher¹,

Dimitrakopoulos

2012

Math Geosci

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“…principal component analysis (David 1988; Goovaerts 1993), stepwise conditional transformation (Rosenblatt 1956; Leuangthong and Deutsch 2003), projection pursuit multivariate transformation (Barnett et al 2013), independent component analysis (Tercan and Sohrabian 2013), and minimum/maximum autocorrelation factors (MAF) (Switzer and Green 1984). Over the past few years, the latter has been increasingly used for simulating coregionalised variables (Lopes et al 2011; Goodfellow et al 2012; da Silva 2013; Da Silva and Costa 2014).…”

Section: Introductionmentioning

confidence: 99%

Multivariate simulation of block-support grades at Mehdiabad deposit, Iran

Hosseini

Asghari

Emery

2017

Applied Earth Science

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The evaluation of process performance within mining operations at Mehdiabad complex deposit, Central Iran, requires modelling the spatial variability of six cross-correlated grade variables: zinc (Zn) and lead (Pb) as the main products, silver (Ag) and copper (Cu) as byproducts, and iron (Fe) and manganese (Mn) as contaminants. To this end, the variables are first transformed into spatially uncorrelated factors, using the minimum/maximum autocorrelation factors approach, then the factors are simulated directly at the target block support. The result is a set of realisations (equiprobable scenarios) of the grade distribution within the deposit, which reproduce the joint dependence relationships and natural variability at all spatial scales. This model can decrease the level of uncertainty in Mehdiabad project development and consequently in production.

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The U-WEDGE Transformation Method for Multivariate Geostatistical Simulation

Mueller

Ferreira

2012

Math Geosci

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Risk quantification in grade-tonnage curves and resource categorization in a lateritic nickel deposit using geologically constrained joint conditional simulation

Cited by 9 publications

References 5 publications

Multivariate Block-Support Simulation of the Yandi Iron Ore Deposit, Western Australia

Multivariate Block-Support Simulation of the Yandi Iron Ore Deposit, Western Australia

Multivariate simulation of block-support grades at Mehdiabad deposit, Iran

The U-WEDGE Transformation Method for Multivariate Geostatistical Simulation

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