Abstract Determining indicator element association for mineralization can not only improve mineral exploration efficiency but also reduce the cost of unnecessary element analysis during geochemical exploration. This study provides a case study of Zhuxi tungsten-copper deposits and presents a workflow using recursive feature elimination and random forest methods to select the indicator element association for copper and tungsten mineralization in regional geochemical mapping. First, a training dataset containing positive and negative samples was built based on the known mineral deposits and mineral deposit model. Second, a 100-time simulation of recursive feature elimination with cross-validation based on random forest (RFECV-RF) was run to get a robust result of indicator elements by the ranking of variable importance. Third, the random forest (RF) method was used to integrate six indicator elements for mapping geochemical anomaly. The Youden index and prediction-area (P-A) plot were used to determine the threshold value for geochemical anomaly identification. The results demonstrated the hybrid workflow was useful to determine key indicator element for geochemical anomaly identification associated with copper and tungsten mineralization. Bi, Mo, Cu, Cd, W, and As were selected as the key indicator elements for geochemical exploration of Cu–W mineralization. Bi, Mo, W and Cu elements correspond to skarn and altered granite mineralization at depth while Cd and As elements correspond to the hydrothermal-vein mineralization at shallow levels. The result of receiver operating characteristic (ROC) curve showed that geochemical anomaly identified using the hybrid method proposed in this study had the best performance in producing comprehensive geochemical signatures. The six indicator elements also exhibited an excellent performance of identifying geochemical anomaly associated to Cu–W mineralization. This study provides a cost-benefit solution to reduce the cost of unnecessary elements concentration detection by determining a small number of key indicator elements using machine learning methods in the regional geochemical mapping for discovering mineral deposits.

中文翻译：

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使用递归特征消除和随机森林方法的江西景德镇地区指示元素选择和地球化学异常绘图

摘要 确定成矿指示元素组合不仅可以提高矿产勘查效率，还可以降低地球化学勘查过程中不必要的元素分析成本。本研究提供了竹溪钨铜矿床的案例研究，并提出了使用递归特征消除和随机森林方法在区域地球化学绘图中选择铜和钨矿化指示元素组合的工作流程。首先，基于已知矿床和矿床模型，构建包含正负样本的训练数据集。其次，运行基于随机森林（RFECV-RF）的具有交叉验证的递归特征消除的 100 次模拟，以通过变量重要性的排名获得指标元素的稳健结果。第三，随机森林（RF）方法被用来整合六个指标元素来绘制地球化学异常。使用约登指数和预测面积（PA）图来确定地球化学异常识别的阈值。结果表明，混合工作流程可用于确定与铜和钨矿化相关的地球化学异常识别的关键指示元素。选择 Bi、Mo、Cu、Cd、W 和 As 作为 Cu-W 矿化地球化学勘探的关键指示元素。Bi、Mo、W 和 Cu 元素对应于深部矽卡岩和蚀变花岗岩矿化，而 Cd 和 As 元素对应于浅层热液脉矿化。接收器操作特征（ROC）曲线的结果表明，使用本研究提出的混合方法识别的地球化学异常在产生综合地球化学特征方面具有最佳性能。六种指示元素在识别与铜钨矿化相关的地球化学异常方面也表现出优异的性能。本研究提供了一种成本效益解决方案，通过在区域地球化学绘图中使用机器学习方法确定少量关键指标元素来发现矿藏，从而降低不必要元素浓度检测的成本。六种指示元素在识别与铜钨矿化相关的地球化学异常方面也表现出优异的性能。本研究提供了一种成本效益解决方案，通过在区域地球化学绘图中使用机器学习方法确定少量关键指标元素来发现矿藏，从而降低不必要元素浓度检测的成本。六种指示元素在识别与铜钨矿化相关的地球化学异常方面也表现出优异的性能。本研究提供了一种成本效益解决方案，通过在区域地球化学绘图中使用机器学习方法确定少量关键指标元素来发现矿藏，从而降低不必要元素浓度检测的成本。