ABSTRACT
In most mining projects often multiple variables, which may be correlated, are required to build models. The relationship among variables has a significant impact on processing plant performance. Therefore, such relationships should be accounted for so that the final model is representative of the reality in the deposit. The projection pursuit multivariate transform is a modern approach that simplifies multivariate geostatistical modeling while assuring multigaussianity. Even though, it simplifies significantly multiple variables modeling, it remains a laborious and crucial step: calculate and fit a semivariogram model to each and every variable for geostatistical estimation and simulation methods. We propose a methodology using a covariance table to map automatically spatial continuity to replace the traditional covariance explicit defined model. A three dimensional case study of an iron ore deposit illustrates the practical applicability for the work flow proposed. The results are satisfactory, validated by the standard simulations verification.
