Abstract:
In multivariate geostatistics, it is common to have different types of complexities between
variables of interest. In this context, an inequality constraint is an example of complex
bivariate relationships. Unfortunately, traditional co-kriging and co-simulation algorithms
cannot reproduce this type of bivariate complexity, leading to the overestimation of
disturbing elements. This paper proposes a new algorithm based on a hierarchical
sequential Gaussian co-simulation framework, integrated with inverse transform
sampling, to model inequality constraints between variables. First, the proposed
methodology's validity was evaluated by applying it to a real case study from an iron
deposit, with an inequality constraint between iron and aluminum oxide. Then the
simulated results were compared with a conventional hierarchical co-simulation algorithm
to investigate the effect of inverse transform sampling on the quality of the co-simulation.
The results showed that the proposed algorithm can reproduce an inequality constraint
between variables.