This paper proposes an extension of the traditional multigaussian model, where a regionalized variable measured on a continuous quantitative scale is represented as a transform of a stationary Gaussian random field. Such a model is popular in the earth and environmental sciences to address both spatial prediction and uncertainty assessment problems. The novelty of our proposal is that the transformation between the original variable and the associated Gaussian random field is not assumed to be monotonic, which offers greater versatility to the model. A step-by-step procedure is presented to infer the model parameters, based on the fitting of the marginal distribution and the indicator direct and cross-covariances of the original variable. The applicability of this procedure is illustrated with a case study related to grade control in a porphyry copper-gold deposit, where the fit of the gold grade distribution is shown to outperform the one obtained with the traditional multigaussian model based on a monotonic transformation. This translates into a better assessment of the uncertainty at unobserved locations, as proved by a split-sample validation.

本文提出了传统多高斯模型的扩展，其中在连续定量尺度上测量的区域化变量被表示为平稳高斯随机场的变换。这种模型在地球和环境科学中很流行，用于解决空间预测和不确定性评估问题。我们建议的新颖之处在于，原始变量和相关高斯随机场之间的变换不被假定为单调的，这为模型提供了更大的多功能性。基于边际分布的拟合以及原始变量的指标直接协方差和互协方差，提出了逐步过程来推断模型参数。该程序的适用性通过与斑岩铜金矿床品位控制相关的案例研究来说明，其中金品位分布的拟合结果优于基于单调变换的传统多高斯模型获得的拟合结果。正如分割样本验证所证明的那样，这可以更好地评估未观察位置的不确定性。