Identification of Mineralized and Barren Magmatic Rocks for the Pophryry−Skarn Deposits from the Qimantagh, East Kunlun: Based on Machine Learning and Whole−Rock Compositions
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摘要:
东昆仑祁漫塔格成矿带是中国西北地区重要的铜钼铁铅锌多金属成矿带，发育卡尔却卡、野马泉、维宝、乌兰乌珠儿等许多与花岗岩类有关的斑岩−矽卡岩矿床。随着新一轮找矿突破战略行动的开展，进一步加强对祁漫塔格成矿带花岗岩成矿潜力的研究，已成为推动该地区金属矿产储量增长的重要突破口。为此，笔者在系统收集祁漫塔格成矿带典型斑岩−矽卡岩多金属矿床成矿岩体和贫矿岩体（即非成矿岩体）的全岩主量和微量元素数据基础上，选取28种常见的全岩地球化学特征，借助机器学习算法——随机森林，开展机器学习模型训练，建立能够识别该地区斑岩−矽卡岩多金属矿床成矿岩体和非成矿岩体的新方法。根据模型评价指标，笔者训练得到的随机森林分类模型准确率为0.90，证明该方法能够有效识别成矿岩体和非成矿岩体。该研究为祁漫塔格成矿带斑岩−矽卡岩多金属矿床的找矿勘查提供了新思路，将极大地提高找矿效率、降低找矿经济和人力成本，从而更好的服务新一轮找矿突破战略行动。相关机器学习代码已上传至GitHub，地址为
https://github.com/ShihuaZhong/2023-Qimantagh-RF-whole-rock-classifier 。Abstract:The Qimantagh Orogenic Belt in the East Kunlun is an important Cu−Mo−Fe−Pb−Zn polymetallic mineralization belt in the northwest of China, and many porphyry-skarn deposits that are genetically related to granitoids are founded, such as Kaerqueka, Yemaquan, Weibao, and Wulanwuzhuer. With the development of a new round of strategic action to find mineral breakthroughs, further strengthening the study of granite mineralization potential in the Qimantagh Orogenic Belt has become an important breakthrough to promote the growth of metal mineral reserves in the region. In this paper, based on the systematic collection of whole−rock major and trace element data of mineralized and barren magmatic rocks of typical porphyry−skarn polymetallic deposits in the Qimantagh Orogenic Belt, 28 common whole-rock geochemical features are selected, and the machine learning algorithm (Random Forest) is used for the training of the machine learning model to establish a machine learning model capable of identifying the mineralized and barren magmatic rocks of porphyry−skarn polymetallic deposits in the region. A new method is developed to identify the mineralized and barren magmatic rocks in the porphyry−skarn polymetallic deposits in this area. According to the model evaluation metric, the accuracy of the Random Forest classification model trained in this paper is 0.90, which proves that the method can effectively recognize mineralized and barren magmatic rocks. This study provides a new idea for the prospecting and exploration of porphyry−skarn polymetallic deposits in the Qimantagh Orogenic Belt, which will greatly improve the efficiency of prospecting, reduce the economic and labor costs of prospecting, and thus better serve the new round of strategic action of prospecting and breakthrough. The machine learning code has been uploaded to GitHub at
https://github.com/ShihuaZhong/2023-Qimantagh-RF-whole-rock-classifier . -
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图 1 祁漫塔格成矿带地质图（据Zhong et al.，2021b修改）
Figure 1.
表 1 文中使用的成矿岩体和非成矿岩体数据来源表
Table 1. Data sources of mineralized and barren magmatic rocks used in this study
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表 2 文中汇编的成矿岩体和非成矿岩体的全岩地球化学特征表
Table 2. Whole–rock geochemical characterization of mineralized and barren magmatic rocks compiled in this study
元素特征 成矿岩体 非成矿岩体 含量 平均值 含量 平均值 SiO2 49.6～78.1 70.2 47.5～78.0 68.1 Al2O3 10.5～18.3 13.3 2.6～18.4 13.6 Fe2O3 0.5～12.6 3.2 0.9～13.4 4.1 MgO 0.1～1.1 0.6 0.1～1.1 0.6 CaO 0.3～10.0 2.5 0.2～13.4 2.7 Na2O 0.7～4.9 3.0 0.7～6.2 3.0 K2O 0.7～7.7 4.0 0.3～7.8 3.9 Ba 36.0～2420.0 502.5 13.0～2086.0 588.0 Rb 21.0～580.0 193.7 6.5～566.0 166.3 Nb 3.1～59.0 13.9 0.5～89.7 16.6 La 6.1～148.0 34.2 5.1～170.0 39.8 Ce 20.9～196.0 66.2 11.1～362.0 80.0 Pr 1.6～33.9 7.5 1.5～41.8 9.7 Nd 6.0～108.0 26.4 5.9～148.0 35.5 Sm 1.5～14.8 4.9 1.4～22.6 7.2 Eu 0～2.3 0.8 0.1～6.8 1.2 Gd 1.2～14.3 4.4 1.3～20.6 6.6 Tb 0.2～3.2 0.7 0.2～3.5 1.1 Dy 1.1～23.0 4.0 0.7～24.5 6.1 Ho 0.2～5.1 0.8 0.2～5.2 1.2 Er 0.7～15.2 2.4 0.5～15.0 3.4 Tm 0.1～2.6 0.4 0.1～2.3 0.5 Yb 0.7～17.6 2.6 0.5～17.0 3.3 Lu 0.1～2.9 0.4 0.1～2.4 0.5 Sr 12.4～743.0 206.5 1.1～927.0 226.7 Y 6.8～164.8 24.2 3.7～157.0 32.4 Sr/Y 0.1～64.9 11.1 0.1～75.8 11.0 La/Yb 0.9～46.5 15.6 0.9～75.2 15.3 注：主量元素含量为%；微量元素含量为10−6。
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表 3 随机森林模型分类结果表
Table 3. Classification results of Random Forest model
模型 岩体类型 总体准确率 准确率 AUC 随机森林 成矿岩体 0.90 0.84 0.93 非成矿岩体 0.94
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