multigaussian kriging min-max autocorrelation factors
DESCRIPTION
The estimation version of Cosimulation using MAFTRANSCRIPT
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Total and soluble copper grade estimation using minimum/maximum autocorrelation
factors and multigaussian krigingAlejandro Cáceres, Rodrigo Riquelme, Xavier Emery, Jaime Díaz, Gonzalo Fuster
Geoinnova Consultores Ltda
Department of Mining Engineering, University of Chile
Advanced Mining Technology Centre, University of Chile
Codelco Chile, División MMH
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Introduction
• Joint estimation of coregionalised variables– grades of elements of interest, by-products and contaminants
– abundances of mineral species
– total and recoverable copper grades
• Multivariate estimation methods must account for the dependence relationships between variables
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Objective
• To jointly estimate total and soluble copper grades– Inequality relationship should be reproduced as well as possible
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Current approaches for modelling total and soluble copper grades
• Separate kriging and cokriging– Provide unbiased and accurate estimates
– Cokriging accounts for the spatial correlation between the variables
– Do not reproduce the inequality relationship
estimated grades must be post-processed
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Current approaches for modelling total and soluble copper grades
• Gaussian co-simulation– Transform each grade variable into Gaussian
– Calculate direct and cross variograms and fit a linear model of coregionalisation
– Co-simulate the Gaussian variables, conditionally to the data
– Back-transform the simulated variables into grades
Again, this approach does not reproduce the inequality relationship
simulated grades must be post-processed
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Current approaches for modelling total and soluble copper grades
• Co-simulation via a change of variables– Consider the total copper grade and the solubility ratio
– Consider the soluble and insoluble copper grades
variables are no longer linked by an inequality constraint
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Current approaches for modelling total and soluble copper grades
• Co-simulation via orthogonalisation
– Transform original grades into spatially uncorrelated variables (factors) that may ideally be seen as independent.
– Main orthogonalisation approaches include principal component analysis (PCA), minimum/maximum autocorrelation factors (MAF), and stepwise conditional transformation
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Current approaches for modelling total and soluble copper grades
• Example: co-simulation via MAF orthogonalisation– Transform original grades into Gaussian variables
– Transform Gaussian variables into factors, using MAF
– Perform variogram analysis of each factor
– Simulate the factors
– Back-transform simulated factors into Gaussian variables
– Back-transform Gaussian variables into grades
– Post-process realisations in order to correct for inconsistencies
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Proposed approach
• The proposed approach is similar to MAF co-simulation, except that simulation step is replaced by multigaussiankriging in order to obtain estimated values of total and soluble copper grades
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Proposed approach
• Algorithm– Transform total and soluble copper grades into Gaussian variables
– Transform Gaussian variables into uncorrelated factors, using MAF
– Perform variogram analysis of each factor
– Perform multigaussian kriging of each factor. At each target location, one obtains the conditional distribution of each factors, which can be sampled via Monte Carlo simulation
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Proposed approach
– Back-transform simulated factors into a Gaussian variables, then into total and soluble copper grades
– From the distributions of simulated grades, compute the mean values as the estimates at the target locations.
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Units Exotic
– Green oxides: chrysocolla, malachite.
– Mixed: trazes chrysocolla, malachite and copper wad.
– Black oxides: copper wad, limonitepitch and pseudomalachite
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Application
• 1289 DDH samples
(1.5 m) , with information of total and soluble copper grades, from oxides unit of Mina Ministro Hales (MMH)
• Isotopic data set
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Samples scatter plot by unit
Green oxides
Black oxides
Mixed
All
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Application
• Steps─ Gaussian transformation of copper grades
─ Orthogonalisation with minimum/maximum autocorrelation factors. A lag distance of 50 m is considered to construct factors
─ Variogram analysis of the factors. Variogram model contain nugget effect, anisotropic spherical and exponential structures
─ Multigaussian kriging (point support)
─ Back-transformation to Gaussian, then to grades
─ Calculation of expected grade values
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2*,Z
Raw Variables Cut and Cus
Gaussian Variables F1, F2: uncorrelated
Kriging F1 F2
N( )
2*,Z
Gaussian local distribution
Local data distributiion( local average)
1
Normal score transformation
Normal score back transformation
1MAF
MAF
Numerical integrationgaussian simulation
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2*,Z
Raw Variables Cut and Cus
Gaussian Variables F1, F2: uncorrelated
Kriging F1 F2
N( )
2*,Z
Gaussian local distribution
Local data distributiion( local average)
1
Normal score transformation
Normal score back transformation
1MAF
MAF
Numerical integrationgaussian simulation
![Page 18: Multigaussian Kriging Min-max Autocorrelation factors](https://reader034.vdocument.in/reader034/viewer/2022052507/558995d5d8b42abf298b4597/html5/thumbnails/18.jpg)
2*,Z
Raw Variables Cut and Cus
Gaussian Variables F1, F2: uncorrelated
Kriging F1 F2
N( )
2*,Z
Gaussian local distribution
Local data distributiion( local average)
1
Normal score transformation
Normal score back transformation
1MAF
MAF
Numerical integrationgaussian simulation
![Page 19: Multigaussian Kriging Min-max Autocorrelation factors](https://reader034.vdocument.in/reader034/viewer/2022052507/558995d5d8b42abf298b4597/html5/thumbnails/19.jpg)
2*,Z
Raw Variables Cut and Cus
Gaussian Variables F1, F2: uncorrelated
Kriging F1 F2
N( )
2*,Z
Gaussian local distribution
Local data distributiion( local average)
1
Normal score transformation
Normal score back transformation
1MAF
MAF
Numerical integrationgaussian simulation
![Page 20: Multigaussian Kriging Min-max Autocorrelation factors](https://reader034.vdocument.in/reader034/viewer/2022052507/558995d5d8b42abf298b4597/html5/thumbnails/20.jpg)
2*,Z
Raw Variables Cut and Cus
Gaussian Variables F1, F2: uncorrelated
Kriging F1 F2
N( )
2*,Z
Gaussian local distribution
Local data distributiion( local average)
1
Normal score transformation
Normal score back transformation
1MAF
MAF
Numerical integrationgaussian simulation
![Page 21: Multigaussian Kriging Min-max Autocorrelation factors](https://reader034.vdocument.in/reader034/viewer/2022052507/558995d5d8b42abf298b4597/html5/thumbnails/21.jpg)
Application
• Comparison with ordinary kriging and cokriging─ Local estimates
DataOrdinary
Kriging
Ordinary
Cokriging
Multigaussian
kriging + MAF
Variable Mean value Correlation Mean value Correlation Mean value Correlation Mean value Correlation
Total copper
grade0.381
0.939
0.386
0.85
0.348
0.904
0.383
0.966Soluble
copper grade0.173 0.167 0.149 0.170
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Application
─ Dependence between total and soluble copper grades
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Conclusions
• Proposed approach combines multigaussian kriging in order to model local uncertainty, and MAF transformation in order to model dependence relationship between grade variables.
It better reproduces the inequality constraint and linear correlation between total and soluble copper grades than traditional approaches. Applications possible in polymetalic deposit or geometallurgical modelling
It is faster than simulation
MAF transformation loses information in the case of a heterotopicsampling
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Acknowledgements
• GeoInnova
• ALGES Laboratory at University of Chile
• Codelco Chile– Ricardo Boric
– Enrique Chacón