This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analysed for 22 elements. Gold (Au) was first predicted at all 376 locations using linear regression (\(R^{2}=0.798\)) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold’s paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The 100 classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization.

中文翻译：

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一种多元地统计学方法，用于描述未来沉积金勘探的潜在兴趣区域。

本文介绍了一种多元地统计学方法，用于划定未来沉积金勘探的潜在兴趣区域，并将其应用于葡萄牙一个废弃的沉积金矿产区。主要挑战是仅存在于老金矿场内的十几个金测量值的存在，这妨碍了传统插值技术（例如cokriging）的应用。但是，该分析可以利用376个河流沉积物样本，并对其进行了22种元素的分析。首先使用线性回归在所有376个位置预测金（Au）（\（R ^ {2} = 0.798 \））和四种金属（铁，砷，锡和钨），已知它们主要与当地金的共生有关。使用顺序指标模拟和回归估计的软指标编码生成了金含量空间分布的一百个实现，以补充金测量的硬指标编码。然后，对每个模拟图进行局部聚类分析，以识别低值或高值的重要集合。处理了100个分类图，以得出每个模拟节点的最可能分类以及相关的出现概率。检查热点和冷点的分布，可以发现在旧沉积成矿作用下游的埃尔热斯河沿岸有明显的金富集。