Constructing Discrimination Diagrams for Granite Mineralization Potential by Using Machine Learning and Zircon Trace Elements: Example from the Qimantagh, East Kunlun
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摘要:
由于锆石在中酸性岩中广泛存在且成分稳定、不易受到后期热液活动的扰动，因此锆石成分可以有效记录成矿岩浆信息。其中，锆石的Ce4+/Ce3+、Ce/Ce*、Eu/Eu*和Ce/Nd值可以反映岩浆氧逸度和含水量等成矿信息，已被广泛用于花岗岩类成矿潜力评价。然而，随着研究的深入发现，这些地球化学指标并不完全具有普适性。此外，以往研究均是根据对成矿岩体的“已知认识”提出成矿潜力判别方法，但考虑到成矿过程的复杂性，许多反映岩浆成矿能力的地球化学信息可能均尚未被揭露。为此，笔者以东昆仑祁漫塔格成矿带为例，借助当前广泛应用的机器学习算法之一——支持向量机，对来自该成矿带斑岩−矽卡岩Cu−Fe−Pb−Zn多金属矿床成矿岩体和全球非成矿岩体的锆石数据开展机器学习训练，目的在于挖掘能够反映岩浆成矿能力的锆石微量元素特征，从而构建花岗岩成矿潜力判别图解。模型训练结果显示，在21个常见的锆石微量元素特征中，Gd、Dy、Yb、Y、Tm等5种元素特征对识别岩浆成矿能力最为重要。在此基础上，笔者新建立了10个二元判别图解，它们在识别成矿岩体和非成矿岩体时的准确率均接近1。研究表明，利用机器学习方法和地质大数据，可以挖掘传统研究方法难以发现的新的地球化学指标和图解，这对深入认识矿床成因、指导找矿勘查具有重要意义。
Abstract:Zircon is widespread and compositionally stable in intermediate–acid magmatic rocks and is resistant to later hydrothermal activities. Therefore, its composition can more accurately record information about mineralizing magmas. Among them, zircon features (such as Ce4+/Ce3+, Ce/Ce*, Eu/Eu*, and Ce/Nd) have been widely used in evaluating the mineralization potential of granitoids, because they have been found to reflect ore−forming information, such as magmatic oxygen fugacity and water content. However, further studies have revealed that the universality of these geochemical indicators has been questioned. In addition, the proposed methods for discriminating mineralization capacity are all based on the current “limited understanding” of mineralized rocks, and considering the complexity of the mineralization process, much geochemical information reflecting the capacity of magmatic mineralization may not have been revealed yet. Therefore, in the paper, taking the Qimantagh mineralized zone of the East Kunlun as an example, and with the help of one of the most widely used machine learning algorithms today (Support Vector Machine), the authors trained machine learning on zircon data from porphyry skarn Cu−Fe−Pb−Zn mineralized rock bodies in the region and zircon data from non−mineralized rock bodies around the world, and the aim is to excavate zircon trace element signatures that reflect magmatic mineralization capacity, so as to construct a new discriminative schema for granite mineralization potential. The results of the model training show that among 21 common zircon trace element features, five element features, Gd, Dy, Yb, Y and Tm are the most important for identifying the magmatic mineralization ability; based on this, 10 binary discriminant diagrams are established in this paper, and their accuracy rates in identifying mineralized and non−mineralized rock bodies are close to 1. The present study show that the use of machine learning methods and geological big data can be used to explore the potential of granite mineralization which is difficult to study with traditional research methods. The study demonstrates that machine learning methods and geological big data can be used to mine new geochemical indicators and diagrams that are difficult to discover by traditional research methods, which is of great significance to deeply understand the genesis of mineral deposits and guide the prospecting and exploration of minerals.
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Key words:
- zircon trace elements /
- granite /
- binary discriminant diagrams /
- machine learning /
- East Kunlun
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图 1 祁漫塔格地质图（据Zhong et al.，2021b修改）
Figure 1.
图 7 外部独立验证数据在二元判别图解上的表现（数据来源于Zhong et al.，2021b）
Figure 7.
表 1 文中使用的锆石微量元素数据来源统计表
Table 1. Data sources of zircon trace elements used in this study
序号 矿床/地名 矿床类型 数据量 参考文献 1 祁漫塔格虎头崖 矽卡岩Cu–Pb–Zn矿床 26 Zhong et al.，2021b 2 祁漫塔格卡尔却卡 矽卡岩Cu–Pb–Zn矿床 76 3 祁漫塔格野马泉 矽卡岩Fe矿床 173 4 祁漫塔格尕林格 矽卡岩Fe矿床 19 5 祁漫塔格牛苦头 矽卡岩Pb–Zn矿床 40 6 祁漫塔格哈日扎 斑岩Cu矿床 9 7 中国西藏 – 84 Zhao et al.，2015
Dai et al.，2015
Huang et al.，2017a
Huang et al.，2017b
Xie et al.，2018
Zhou et al.，20178 中国广东 – 77 Gao et al.，2016 9 中国新疆 – 10 Tang et al.，2017 10 中国湖南 – 96 Gao et al.，2017 11 芬兰东南部 – 14 Heinonen et al.，2017 12 中国浙江 – 35 Hu et al.，2017 13 中国云南 – 36 Zhang et al.，2022 14 加拿大拉布拉多 – 1 Vezinet et al.，2018 15 中国山东 – 22 Wang et al.，2019 16 美国阿拉斯加州 – 5 Kay et al.，2019 
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表 2 两种类型锆石的成分特征统计表（10–6）
Table 2. The compositional characteristics of the two types of zircon compiled in this paper (10–6)
元素 成矿岩体 非成矿岩体 最大值 最小值 平均值 最大值 最小值 平均值 Ti 41919.34 0.02 129.17 801.34 0.12 17.02 La 0.10 0.01 0.02 0.06 0.09 0.04 Ce 60.09 2.12 11.61 31.20 18.20 19.31 Pr 1.16 0.01 0.10 0.08 0.06 0.15 Nd 14.04 0.13 1.58 1.43 0.90 2.55 Sm 25.14 0.25 3.29 4.24 1.04 5.36 Eu 7.44 0.02 0.76 1.05 0.16 0.95 Gd 238.00 1.37 25.70 32.65 5.51 29.39 Tb 114.74 1.93 19.27 13.49 1.91 10.30 Dy 34.61 0.63 6.75 213.23 24.38 126.92 Ho 403.30 10.47 86.96 84.36 9.02 48.02 Er 147.61 4.40 33.97 453.77 40.07 218.29 Tm 702.98 24.00 171.49 115.45 10.70 47.57 Yb 146.39 5.53 36.47 1288.35 137.62 468.36 Lu 1317.17 60.20 344.94 235.22 14.88 84.07 Y 279.95 15.21 74.70 2616.15 280.97 1429.95 Hf 13873.25 6422.50 10034.54 16386.39 6657.03 11348.72 U 2249.59 56.35 349.53 251.61 53.03 465.07 Th 1716.13 17.93 199.67 201.05 41.09 284.17 Eu/Eu* 0.77 0.03 0.42 0.21 0.14 0.20 Ce/Ce* 6997.36 57.52 1263.89 1589.33 608.91 864.35
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