Minimum/maximum autocorrelation factors applied to grade estimation

Rem: Revista Escola de Minas

Camilla Zacché Da Silva²  João Felipe Coimbra Leite Costa¹ 

[1] ,Federal University of Rio Grande do Sul Departamento de Engenharia de Minas

关键词: minimum/maximum autocorrelations factors;    geostatistics;    kriging;    Fatores de mínimas /máximas autocorrelaçoes;    geoestatística;    krigagem;   

DOI  :  10.1590/S0370-44672014000200013

来源: SciELO

【 摘 要 】

It is frequent to face estimation problems when dealing with mineral deposits involving multiple correlated variables. The resulting model is expected to reproduce data correlation. However, is not guaranteed that the correlation observed among data will be reproduced by the model, if the variables are estimated independently, and this correlation is not explicitly taken into account. The adequate geostatistical approach to address this estimation problem is co-kriging which requires cross and direct covariance modeling of all variables, satisfying the LMC. An alternative is to decorrelate the variables and estimate each independently, using for instance, the minimum/maximum autocorrelation factors (MAF) approach, which uses a linear transformation on the correlated variables, transforming them to a new uncorrelated set. The transformed data can be estimated through kriging. Afterwards, the estimates are back-transformed to the original data space. The methodology is illustrated in a case study where three correlated variables are estimated using the MAF method combined with kriging and through co-kriging, used as a benchmark. The results show less than a 2% deviation between both methodologies.

【 授权许可】

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