Uncertain or indirect “soft” data, such as geologic interpretation, driller’s logs, geophysical logs or imaging, offer potential constraints or “soft conditioning” to stochastic models of discrete categorical subsurface variables in hydrogeology such as hydrofacies. Previous bivariate geostatistical simulation algorithms have not fully addressed the impact of data uncertainty in formulation of the (co) kriging equations and the objective function in simulated annealing (or quenching). This paper introduces the geostatistical simulation code tsim-s, which accounts for categorical data uncertainty through a data “hardness” parameter. In generating geostatistical realizations with tsim-s, the uncertainty inherent to soft conditioning is factored into both 1) the data declustering and spatial correlation functions in cokriging and 2) the acceptance probability for change of category in simulated quenching. The degree or sensitivity to which soft data conditions a realization as a function of hardness can be quantified by mapping category probabilities derived from multiple realizations. In addition to point or borehole data, arrays of data (e.g., as derived from a depth-dependency function, probability map, or “prior realization”) can be used as soft conditioning. The tsim-s algorithm provides a theoretically sound and general framework for integrating datasets of variable location, resolution, and uncertainty into geostatistical simulation of categorical variables. A practical example shows how tsim-s is capable of generating a large-scale three-dimensional simulation including curvilinear features.

不确定或间接的“软”数据（例如地质解释，司钻测井，地球物理测井或成像）为水文地质学（例如水相）中离散的分类地下变量的随机模型提供了潜在的约束或“软条件”。先前的双变量地统计模拟算法尚未完全解决数据不确定性对（共）克里金方程的公式化和模拟退火（或淬火）目标函数的影响。本文介绍了地统计模拟代码尖峰，它通过数据“硬度”参数说明分类数据的不确定性。在生成地统计实现时尖峰，则软调节所固有的不确定性将被归入以下两个因素：1）共克里金法中的数据去聚散和空间相关函数，以及2）模拟淬火中类别变化的接受概率。可以通过映射从多个实现中得出的类别概率来量化软数据将实现作为硬度函数的条件的程度或敏感性。除了点或钻孔数据之外，数据阵列（例如，从深度相关函数，概率图或“先验实现”得出的数据）也可以用作软条件。的尖峰该算法为将变量位置，分辨率和不确定性的数据集集成到分类变量的地统计模拟中提供了理论上合理的通用框架。一个实际的例子说明了尖峰 能够生成包括曲线特征的大规模三维仿真。