The U-WEDGE Transformation Method for Multivariate Geostatistical Simulation

Mueller, Ute; Ferreira, Josiel

doi:10.1007/s11004-012-9384-7

Cited by 30 publications

(15 citation statements)

References 15 publications

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“…The spatial orthogonality between the components would be assessed using the following measures (Tercan, 1999;Mueller and Ferreira, 2012): Tercan (1999) reported that "the first one compares the absolute sum of the off-diagonal elements of the ICs variogram matrix h IC Γ ( ) with the sum of its diagonal elements for each lag distance, while the second one makes a comparison with the sum of squares of off-diagonal elements of the geochemical variogram matrix h X Γ ( )". As well, for the second measures Tercan (1999) indicated, "the second measure is a spatial extension of the diagonalization efficiency suggested by Xie et al (1995)".…”

Section: Decomposition and Simulation Procedures Of Correlated Variablesmentioning

confidence: 99%

“…These include uniformly weighted exhaustive diagonalization (Mueller and Ferreira, 2012;Tichavsky and Yeredor, 2009), simultaneous diagonalization (Tercan, 1999;Xie et al, 1995) and Minimum Spatial Cross-correlation Tercan, 2014a, 2014b). Desbarats and Dimitrakopoulos (2000) and Vargas-Guzman and Dimitrakopoulos (2002) used minimum and maximum autocorrelation factors (MAF) for joint simulation of key geochemical attributes such as copper (Cu), iron (Fe) and potassium (K).…”

Section: Introductionmentioning

confidence: 99%

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Geostatistical independent simulation of spatially correlated soil variables

Boluwade

Madramootoo

2015

Computers & Geosciences

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Section: Decomposition and Simulation Procedures Of Correlated Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geostatistical independent simulation of spatially correlated soil variables

Boluwade

Madramootoo

2015

Computers & Geosciences

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“…In soil science, single variable simulation methods may be ineffective for generating spatial concentration estimates owing to their inability both to incorporate correlations between variables such as multiple heavy metal concentrations [ 25 ] and to correctly assess the multivariate grade risk [ 26 ]. The method of minimum/maximum autocorrelation factors (MAF) assumes that the semivariogram function of each attribute can be modelled by a bi-structural linear model based on coregionalization, and transforms measured original data into non-orthogonal factors with weak spatial correlations by diagonalizing a pair of symmetrical coregionalization matrices [ 27 ]. Tichavsky and Yeredor presented a more general approach to approximate joint diagonalization (AJD), called Uniformly Weighted Exhaustive Diagonalization with Gauss iterations (U-WEDGE) [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Avian Conservation Areas as a Proxy for Contaminated Soil Remediation

Lin

Anthony

et al. 2015

IJERPH

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Remediation prioritization frequently falls short of systematically evaluating the underlying ecological value of different sites. This study presents a novel approach to delineating sites that are both contaminated by any of eight heavy metals and have high habitat value to high-priority species. The conservation priority of each planning site herein was based on the projected distributions of eight protected bird species, simulated using 900 outputs of species distribution models (SDMs) and the subsequent application of a systematic conservation tool. The distributions of heavy metal concentrations were generated using a geostatistical joint-simulation approach. The uncertainties in the heavy metal distributions were quantified in terms of variability among 1000 realization sets. Finally, a novel remediation decision-making approach was presented for delineating contaminated sites in need of remediation based on the spatial uncertainties of multiple realizations and the priorities of conservation areas. The results thus obtained demonstrate that up to 42% of areas of high conservation priority are also contaminated by one or more of the heavy metal contaminants of interest. Moreover, as the proportion of the land for proposed remediated increased, the projected area of the pollution-free habitat also increased. Overall uncertainty, in terms of the false positive contamination rate, also increased. These results indicate that the proposed decision-making approach successfully accounted for the intrinsic trade-offs among a high number of pollution-free habitats, low false positive rates and robustness of expected decision outcomes.

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“…The main drawbacks of the step-wise transformation is the need for a large number of drill-hole data when dealing with several geological attributes; at the same time, being a global and location-independent transformation it suffers from an inherent deconstruction of local spatial connectivity of ranges of grade values of interest. Mueller and Ferreira (2012) present the U-WEDGE transformation approach for multivariate simulation.…”

Section: Introductionmentioning

confidence: 99%

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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The U-WEDGE Transformation Method for Multivariate Geostatistical Simulation

Cited by 30 publications

References 15 publications

Geostatistical independent simulation of spatially correlated soil variables

Geostatistical independent simulation of spatially correlated soil variables

Avian Conservation Areas as a Proxy for Contaminated Soil Remediation

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

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