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摘要:研究目的
磷灰石是广泛存在于各种火山岩、变质岩和沉积岩中的一种副矿物，其晶格内可以容纳Sr、Mn、REEs、U、Th、F、Cl等多种元素，而且磷灰石的化学组成对于岩浆和热液过程十分敏感并因此引起了学者的广泛关注。
研究方法本文系统分析了目前关于磷灰石在矿相学、同位素年代学、矿床地球化学、人工智能以及勘查指示中的一些常用研究方法和最新成果。
研究结果岩浆磷灰石的Sr、Y、REEs等元素和Sr−Nd同位素可以用来判别岩浆源区，Ce、Eu、Ga、Mn等对氧逸度敏感的元素可以用来指示岩浆氧化态，F、Cl等元素可以用来估算熔体初始状态下挥发分含量，其U−Pb年龄能代表其寄主岩的结晶年龄，而低温热年代学也常用于研究矿床形成后的剥蚀程度。热液磷灰石的结构和成分记录了流体的相关信息，可以用来指示流体来源、流体性质等与岩浆−热液成矿过程相关的信息。机器学习等人工智能技术可以处理海量磷灰石数据，实现基于磷灰石成分的岩石类型和矿床类型的判别。
结论磷灰石在矿床学研究和矿床勘查中具有重要作用，今后对热液磷灰石与成矿过程关系的研究工作，以及将人工智能与磷灰石结合来示踪成岩成矿过程应该是值得考虑的研究方向。
创新点:（1）系统总结了磷灰石在矿床学研究及找矿勘查中的主要研究方法和最新研究成果；（2）初步展望了磷灰石在矿床学研究与勘查指示中的发展方向。
Abstract:This paper is the result of mineral exploration engineering.
ObjectiveApatite is a mineral commonly present in volcanic, metamorphic, and sedimentary rocks. Its crystal structure can host various elements such as Sr, Mn, REEs, U, Th, F, Cl, and others. Apatite's chemical composition is dictated by magma and hydrothermal processes, which makes it a subject of interest for many researchers.
MethodsThis paper reviews common methods and the latest research achievements of apatite in mineralogy, isotope chronology, deposit geochemistry, artificial intelligence, and exploration indication.
ResultsElemental (e.g., Sr, Y, and REEs) and Sr−Nd isotopic compositions of magmatic apatite can help identify the source of its parental magma. Elements such as Ce, Eu, Ga, and Mn can indicate the oxidation state of the magma, while F and Cl can be used to estimate the volatile content of the melt. The U−Pb isotope system of apatite can record the crystallization age of its host rock. Low−temperature thermochronology is often used to study the degree of denudation after ore deposit formation. Hydrothermal apatite's structure and composition bear information about the fluid, which can indicate the fluid source, properties, and other information related to magmatic−hydrothermal mineralization processes. Artificial intelligence techniques such as machine learning can process massive amounts of apatite data to discriminate rock types and deposit types.
ConclusionsApatite is a mineral that is crucial for studying mineral deposits and exploring ore deposits. Future researches should focus on the relationship between hydrothermal apatite and the metallogenic process. Additionally, combining artificial intelligence with apatite analyses to trace the diagenetic and metallogenic process is a promising avenue for further study.
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Keywords:
- apatite /
- geochemical /
- chronology /
- indicator minerals /
- mineral exploration engineering
Highlights:(1) We summarize the main research methods and latest research achievements of apatite in ore deposit research and prospecting; (2) The development direction of apatite in the research and exploration of ore deposits is preliminarily prospected.
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图 1 不同类型磷灰石的形态特征
a−辉石正长岩中的针状和近等轴状岩浆磷灰石（Zirner et al., 2015）；b−热液磷灰石中不规则的化学分区（Yu et al., 2019）；c−具有一定磨圆程度的沉积磷灰石（Lumiste et al., 2019）；d−变质岩中形状不规则的磷灰石（Maraszewska et al., 2023）；Ap−磷灰石；Am−角闪石；Fsp−长石；Ptb−沥青铀矿；Fl−萤石；Qz−石石英；Mt−白云母；Hm−赤铁矿
Figure 1. Morphological characteristics of different types of apatite
a–Acicular and equiaxed magmatic apatite in pyroxene syenite (Zirner et al., 2015); b–Irregular chemical zoning in hydrothermal apatite (Yu et al., 2019); c–Sedimentary apatite with a certain degree of roundness (Lumiste et al., 2019); d–Irregular apatite in metamorphic rocks (Maraszewska et al., 2023); Ap−Apatite; Am−Amphibole; Fsp−Feldspar; Ptb−Pitchblende; Fl−Fluorite; Qz−Quartz; Mt−Muscovite; Hm−Hematite
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图 2 玉木沟Mo矿床热史模拟结果（a）、玉木沟Mo矿床多年代温度−时间路径（b）（Yang et al., 2020, 2022a, b; Qian et al., 2022）
Figure 2. Thermal history modeling results integrated fromYumugou Mo Deposit (a) and temperature−time path of multiple geochronometers of Yumugou Mo Deposit(b) (Yang et al., 2020, 2022a, b; Qian et al., 2022)
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图 3 不同岩石中磷灰石的稀土元素配分模式图（标准化数据来自Boyton，1984；磷灰石数据来自Mao et al., 2016）
Figure 3. Rare earth element distribution patterns of apatite in different rocks (Normalized data are from Boynton, 1984; Apatie date ate from Mao et al., 2016)
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图 5 常用磷灰石判别图解
a−LREE−Sr/Y图解（O'Sullivan et al., 2020）；b−lgCe−lg（Eu/Y）图解（周统等, 2022）；c，d−Sr−Mn、Sr−Y图解（Belousova et al., 2002）
Figure 5. Common apatite discrimination diagram
a−LREE−Sr/Y diagram (O'Sullivan et al., 2020); b−lgCe−lg(Eu/Y) diagram (Zhou et al., 2022); c, d−Sr−Mn, Sr−Y diagram (Belousova et al., 2002)
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图 4 磷灰石Sr/Th vs. La/Sm二元图解（底图根据Ding et al., 2015修改）
Figure 4. Apatite Sr/Th vs. La/Sm binary diagram (modified from Ding et al., 2015)
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图 6 磷灰石(Eu/Eu*)N−(Ce/Ce*)N氧逸度判别图（数据来自Ding et al., 2015）
Figure 6. Oxygen fugacity discrimination diagram of apatite (Eu/Eu *) N−(Ce/Ce *) N (after Ding et al., 2015)
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图 7 磷灰石的主量元素和微量元素判别图（据Cao et al., 2012；Li et al., 2022修改）
Figure 7. Discriminant diagram of major and trace elements in apatite (modified from Cao et al., 2012; Li et al., 2022)
表 1 不同类型矿床磷灰石U−Pb定年数据
Table 1 U−Pb dating data of apatite from different types of deposits
矿床类型 矿床名称 定年方法 定年结果 参考文献 卡林型金矿床 高龙 LA−ICP−MS (156.8 ± 8.3) Ma Lin et al., 2023 斑岩Mo矿床 南泥湖 LA−ICP−MS 151.5~102.3 Ma Yang et al., 2020 大黑山 LA−ICP−MS (175.5±1.3) Ma Qu et al., 2021 火山岩型U矿床 相山 LA−ICP−MS 131~127 Ma Wang et al., 2023 IOA型矿床 陶村 LA−ICP−MS (131.1±1.9) Ma Zeng et al., 2016 矽卡岩W−Cu矿床 朱溪 LA−ICP−MS (150.2±2.4) Ma 刘敏等, 2021 Sn多金属矿床 个旧 LA−ICP−MS 83.5~85.1 Ma Guo et al., 2018a 大厂 LA−ICP−MS 90.3~95.4 Ma Guo et al., 2018b 
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表 2 蚀变磷灰石与岩浆磷灰石的区别
Table 2 The difference between altered apatite and magmatic apatite
岩浆磷灰石 蚀变磷灰石 结构特征 无明显分区 核−边结构、溶解−再沉淀结构、化学分区 晶体特征 透明度高、自形、边界清晰、无流体包裹体或
少流体包裹体、无矿物包裹体透明度低、半自形−他形、边界模糊、大量流体包裹体、
有独居石等热液矿物析出阴极发光特征 阴极发光均一、黄−棕色 阴极发光有分区、绿色、黄绿色、黄褐色 成分特征 — REE含量降低、U、Th、Mn、Na、Cl等元素含量降低
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表 3 不同机器学习方法的磷灰石判别指标
Table 3 Apatite discrimination indexes using different machine learning methods
矿床类型 判别指标 XGBoost（n=8629） SDBM
（n=1551）PLS−DA
（n=4298）IOA型矿床 高Th, 低Sr, U U, Sm, Lu 高Nd, Sm, Gd, Tb, Dy，低Mn, U 斑岩矿床
矽卡岩矿床低Th, Nd
低U, Eu—
La, Ce高V, Sr, U
—造山型Au矿 高Dy, Sr Pr, Nd, Sm; — IOCG矿床 Sr, Eu — 花岗岩相关W矿床 — — 高Y, Sm, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu 花岗岩相关Cu−Pb−Zn矿床 — — 高Sr 花岗岩相关Mo矿床 — — 高Mn, Th
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