Abstract Constructing an interpretive geological model of the subsurface based on site-specific geological, geophysical and geochemical sampling information is a primary step in the prospection, exploration and modeling of ore deposits, in order to understand geological heterogeneity and to predict the rock properties in the subsurface, which is critical for all the following stages of the mining process. To improve the accuracy of such geological models, a geostatistical approach based on the conditional simulation of quantitative variables followed with their classification into geological categories is proposed. The novelty is the use of coregionalization analysis that allows decomposing the quantitative information into uncorrelated components associated with different spatial scales and filtering the undesired components from the simulation outcomes, in particular, the nugget effect that represents small-scale variability and measurement errors. The methodology is illustrated with the predictive geological mapping in an iron ore deposit hosted by banded iron formations, based on a set of physical and geochemical variables such as metal grades available from geochemical assays and rock types available from geological core logging. First, the quantitative variables are simulated conditionally to exploration drill hole data, then classified into rock types. The simulation in which the nugget effect has been removed provides more regular boundaries between rock type domains and improves the predictability of the rock type, with an 90.1% cross-validation accuracy score against a 79.9% for the simulation that reproduces the spatial variability without any filtering. Despite its small amplitude, the small-scale variability in the quantitative variables considerably deteriorates the rock type classification.

中文翻译：

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岩石物理和地球化学性质的空间过滤地质统计模拟及其在预测地质填图中的应用

摘要 基于特定地点的地质、地球物理和地球化学采样信息构建地下解释性地质模型是矿床勘探、勘探和建模的主要步骤，以了解地质非均质性并预测地下岩石的性质。地下，这对采矿过程的所有后续阶段至关重要。为了提高此类地质模型的准确性，提出了一种基于定量变量条件模拟的地质统计学方法，然后将其分类为地质类别。新颖之处在于使用共区域化分析，它允许将定量信息分解为与不同空间尺度相关的不相关成分，并从模拟结果中过滤掉不需要的成分，特别是代表小尺度变异性和测量误差的金块效应。该方法用带状铁矿床中的预测地质图来说明，该图基于一组物理和地球化学变量，例如地球化学分析中可获得的金属品位和地质岩心测井中可获得的岩石类型。首先将定量变量有条件地模拟到勘探钻孔数据中，然后将其分类为岩石类型。去除金块效应的模拟在岩石类型域之间提供了更规则的边界，并提高了岩石类型的可预测性，交叉验证准确率为 90.1%，而模拟再现空间变异性的准确率为 79.9%。过滤。尽管幅度很小，但定量变量的小尺度变化大大恶化了岩石类型分类。