The assessment of mineral resources requires the definition of a geological domain, and the quantitative results obtained should consider geological and grade uncertainties. However, when the available data are limited, it can be difficult to estimate the shape of an ore body owing to the spatial uncertainty. This study describes a methodology for defining geological domains using multiple-point geostatistics and selecting models for realizations using a cluster analysis. Then, the selected realizations were assessed for mineral resources using two-point geostatistics. Of the various multiple-point geostatistics algorithms, the single normal equation simulation was used to generate a realistic ore body, and various cluster algorithms, such as k-means and a density-based spatial clustering of applications with noise, were applied to select a model. Based on the outcomes, a mineral resource assessment that uses a sequential Gaussian simulation was performed, and a reasonable model that reflects the original data well was selected based on the grade statistics of the original data and mineral resource assessment per cluster. This study demonstrates that it is possible to define the ore domain and assess mineral resources while accounting for the uncertainty caused by a lack of spatial information.

矿产资源评估需要定义地质领域，而获得的定量结果应考虑地质和品位的不确定性。然而，当可用数据有限时，由于空间不确定性，可能难以估计矿体的形状。这项研究描述了一种使用多点地统计学定义地质领域并使用聚类分析选择实现模型的方法。然后，使用两点地统计法对选定的矿产资源进行评估。在各种多点地统计学算法中，单个正态方程模拟用于生成真实的矿体，以及各种聚类算法，例如k-均值和基于噪声的应用程序基于密度的空间聚类被应用于选择模型。基于结果，进行了使用顺序高斯模拟的矿产资源评估，并基于原始数据的等级统计和每个集群的矿产资源评估，选择了一个能够很好地反映原始数据的合理模型。这项研究表明，在考虑到由于缺乏空间信息而引起的不确定性的同时，可以定义矿区并评估矿产资源。