Quantitative modeling of geological heterogeneity is critical for resource management and decision-making. However, in the early stages of a mining project, the only data available for modeling the spatial variability of the variables are from a limited number of exploration drill holes. This means that the empirical cumulative distribution function of the data, which is one of the key inputs for the geostatistical simulation, is uncertain, and ignoring this uncertainty may lead to biased resource risk assessments. The parameter uncertainty can be quantified by the multivariate spatial bootstrap procedure and propagated through geostatistical simulation workflows. This methodology is demonstrated in a case study using the data from the former lead and zinc mine at Lisheen, Ireland. The joint modeling of the lead and zinc grades is carried out by using (1) all of the available data, (2) a representative subset (approximately 10% of the available data) without parameter uncertainty, and (3) the same subset with parameter uncertainty. In all cases, the turning bands simulation approach generates realizations of lead and zinc grades. In the third case, the uncertainty in the lead and zinc grade distributions is first quantified (i.e., prior uncertainty) by the correlated bootstrap realizations. This joint prior uncertainty is then updated in simulation by the conditioning data and domain limits, which results in posterior uncertainty. The results indicate that a more realistic resource risk assessment can be achieved when parameter uncertainty is considered.

地质非均质性的定量建模对于资源管理和决策至关重要。然而，在采矿项目的早期阶段，可用于对变量的空间变化进行建模的唯一数据来自有限数量的勘探钻孔。这意味着作为地统计模拟的关键输入之一的数据的经验累积分布函数是不确定的，忽略这种不确定性可能会导致资源风险评估出现偏差。参数不确定性可以通过多元空间引导程序进行量化，并通过地质统计模拟工作流程进行传播。该方法通过使用爱尔兰 Lisheen 前铅锌矿数据的案例研究进行了论证。铅和锌品位的联合建模是通过以下方式进行的：(1) 所有可用数据，(2) 没有参数不确定性的代表性子集（大约 10% 的可用数据），以及 (3) 具有参数不确定性的相同子集参数不确定性。在所有情况下，转动带模拟方法都会生成铅和锌牌号的实现。在第三种情况下，铅和锌品位分布的不确定性首先通过相关的引导实现进行量化（即先验不确定性）。然后，这种联合先验不确定性在模拟中通过调节数据和域限制进行更新，从而导致后验不确定性。结果表明，当考虑参数不确定性时，可以实现更现实的资源风险评估。