The spatial analysis and interpretation of lithological and geochemical sampling information are central in mineral prospecting and initial geological-mining exploration to delineate exploration targets and locate economic mineralization. This work compares two geostatistical approaches for the spatial prediction of lithological classes through a case study in mineral prospection, considering lithological and geochemical information at a set of surface samples. Both approaches calculate the probabilities of occurrence of the lithological classes at unsampled locations and select the most probable class as the predicted lithology. A split-sample technique is used to assess their performance, with the predictions being made at a testing data subset on the basis of the information of a training subset. The first approach relies on a cokriging of the lithological class indicators and yields an accuracy score (percentage of matches between true and predicted lithological classes) of 90.5%, while the second approach, consisting of a plurigaussian modeling of the classes, increases this score to 92.6%. Unlike the former approach, it also provides consistent outcomes of both the lithological classes and the geochemical covariates, which is valuable for mineral prospectivity mapping.

岩性和地球化学采样信息的空间分析和解释在矿产勘探和初始地质采矿勘探中至关重要，以勾勒勘探目标并确定经济矿产的位置。这项工作通过在矿物勘探中的案例研究，考虑了一组地表样品的岩性和地球化学信息，比较了两种地质统计学方法对岩性类别的空间预测。两种方法均会计算未采样位置岩性分类的发生概率，并选择最可能的岩性作为预测岩性。分割样本技术用于评估其性能，并根据训练子集的信息在测试数据子集上进行预测。第一种方法依赖于岩性分类指标的协同克立格法，得出的准确度得分（真实岩性分类和预测岩性分类的匹配百分比）为90.5％，而第二种方法则包括分类的普里高斯模型，将该分数提高至92.6％。与以前的方法不同，它还提供了岩性分类和地球化学协变量的一致结果，这对于矿物前景图很有用。