Chalcopyrite from 51 volcanogenic massive sulfide (VMS) and sea-floor massive sulfide (SMS) deposits from six lithostratigraphic settings was analyzed for trace elements by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to evaluate its potential as an indicator mineral for exploration. Partial least squares discriminant analysis (PLS-DA) results reveal that chalcopyrite from different lithostratigraphic settings has different compositions reflecting host-rock assemblages and fluid composition. Three random forest (RF) classifiers were developed to distinguish chalcopyrite from the six lithostratigraphic settings with a divisive approach. This method, which primarily classifies according to the major host-rock affinity and subsequently according to VMS settings, yielded an overall accuracy higher than 0.96 on test data. The model validation with literature data having the same elements required by the models yielded the highest accuracies (>0.90). In validation using published data with missing elements, the accuracy is moderate to high (0.60–1); however, the performances decrease significantly (<0.50) when the most important elements are missing. Similarly, RF regression models developed using all sets of analyzed elements to determine ccp/(ccp + sp) ratio (ccp = chalcopyrite; sp = sphalerite) in chalcopyrite within a single VMS setting reported high performances, thus showing a potential to predict the Cu/Zn ratio (Cu-rich vs. Zn-rich) of the mineralization based on chalcopyrite composition. This study demonstrates that trace element concentrations in chalcopyrite are primarily controlled by lithotectonic setting and can be used as predictors in an RF classifier to distinguish the different VMS subtypes.

中文翻译：

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火山成因块状硫化物矿床中黄铜矿的微量元素组成：变化及其对物源识别的启示

通过激光烧蚀-电感耦合等离子体质谱 (LA-ICP-MS) 对来自 6 个岩石地层环境的 51 个火山成因块状硫化物 (VMS) 和海底块状硫化物 (SMS) 矿床中的黄铜矿进行了微量元素分析，以评估其作为微量元素的潜力。勘探的指示矿物。偏最小二乘判别分析（PLS-DA）结果表明，来自不同岩石地层环境的黄铜矿具有不同的成分，反映了母岩组合和流体成分。开发了三种随机森林 (RF) 分类器，以采用划分方法将黄铜矿与六种岩石地层设置区分开来。该方法主要根据主要宿主岩石亲和力进行分类，随后根据 VMS 设置进行分类，测试数据的总体准确度高于 0.96。使用具有模型所需的相同元素的文献数据进行的模型验证获得了最高的准确度（>0.90）。在使用缺失元素的已发表数据进行验证时，准确度为中等到高（0.60-1）；然而，当缺少最重要的元素时，性能会显着下降（<0.50）。同样，使用所有分析元素集开发的 RF 回归模型在单个 VMS 设置内确定黄铜矿中的 ccp/(ccp + sp) 比率（ccp = 黄铜矿；sp = 闪锌矿），报告了高性能，从而显示了预测 Cu 的潜力。基于黄铜矿成分的矿化/Zn 比率（富铜与富锌）。这项研究表明，黄铜矿中的微量元素浓度主要受岩石构造环境控制，可用作 RF 分类器中的预测因子来区分不同的 VMS 亚型。