This paper introduces a method to generate conditional categorical simulations, given an ensemble of partially conditioned (or unconditional) categorical simulations derived from any simulation process. The proposed conditioning method relies on implicit functions (signed distance functions) for representing the categorical spatial variable of interest. Thus, the conditioning problem is reformulated in terms of signed distance functions. The proposed approach combines aspects of principal component analysis and Gibbs sampling to achieve the conditioning of the unconditional categorical realizations to the data. It is applied to synthetic and real-world datasets and compared to the traditional sequential indicator simulation. It appears that the proposed simulation technique is an effective method to generate conditional categorical simulations from a set of unconditional categorical simulations.

本文介绍了一种产生条件分类模拟的方法，它给出了从任何模拟过程中得出的部分条件分类（或无条件）分类模拟的集合。所提出的调节方法依赖于隐式函数（有符号距离函数）来表示感兴趣的分类空间变量。因此，根据有符号距离函数重新构造了条件问题。所提出的方法结合了主成分分析和Gibbs采样的方面，以实现对数据的无条件分类实现的条件化。它适用于合成数据和真实数据集，并与传统的顺序指标模拟进行了比较。