Constructing Discrimination Diagrams for Granite Mineralization Potential by Using Machine Learning and Zircon Trace Elements: Example from the Qimantagh, East Kunlun

GUO Guanghui; ZHONG Shihua; LI Sanzhong; FENG Chengyou; DAI Liming; SUO Yanhui; LIU Jiaqing; NIU Jinghui; HUANG Yu; XUE Zimeng

doi:10.12401/j.nwg.2023158

2023 Vol. 56, No. 6

Article Contents

Article navigation > Northwestern Geology > 2023 > 56(6): 57-70

Next Article Previous Article

GUO Guanghui, ZHONG Shihua, LI Sanzhong, FENG Chengyou, DAI Liming, SUO Yanhui, LIU Jiaqing, NIU Jinghui, HUANG Yu, XUE Zimeng. 2023. Constructing Discrimination Diagrams for Granite Mineralization Potential by Using Machine Learning and Zircon Trace Elements: Example from the Qimantagh, East Kunlun. Northwestern Geology, 56(6): 57-70. doi: 10.12401/j.nwg.2023158

Citation:

GUO Guanghui, ZHONG Shihua, LI Sanzhong, FENG Chengyou, DAI Liming, SUO Yanhui, LIU Jiaqing, NIU Jinghui, HUANG Yu, XUE Zimeng. 2023. Constructing Discrimination Diagrams for Granite Mineralization Potential by Using Machine Learning and Zircon Trace Elements: Example from the Qimantagh, East Kunlun. Northwestern Geology, 56(6): 57-70. doi: 10.12401/j.nwg.2023158

Constructing Discrimination Diagrams for Granite Mineralization Potential by Using Machine Learning and Zircon Trace Elements: Example from the Qimantagh, East Kunlun

1.
Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ministry of Education, Qingdao 266100, Shandong, China
2.
Key Laboratory of Submarine Geosciences and Prospecting Techniques, Ministry of Education, Qingdao 266100, Shandong, China
3.
College of Marine Geosciences, Ocean University of China, Qingdao 266100, Shandong, China
4.
Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266100, Shandong, China
5.
Institute of Exploration Techniques, CAGS, Langfang 065000, Hebei, China

More Information

Corresponding author: ZHONG Shihua, zhongshihua@ouc.edu.cn

Received Date: 24 July 2023

Revised Date: 01 September 2023

Accepted Date: 02 September 2023

Publish Date: 20 December 2023

Abstract

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.
- zircon trace elements /
- granite /
- binary discriminant diagrams /
- machine learning /
- East Kunlun

FullText(HTML)

References

[1]	程学旗, 靳小龙, 王元卓, 等. 大数据系统和分析技术综述[J]. 软件学报, 2014, 25: 1889-1908 doi: 10.13328/j.cnki.jos.004674 CrossRef Google Scholar CHENG Xueqi, JIN Xiaolong, WANG yuanzhuo, et al. Survey on Big Data System and Analytic Technology[J]. Journal of Software, 2014, 25: 1889-1908. doi: 10.13328/j.cnki.jos.004674 CrossRef Google Scholar
[2]	丰成友, 李东生, 吴正寿, 等. 东昆仑祁漫塔格成矿带矿床类型、时空分布及多金属成矿作用[J]. 西北地质, 2010, 43: 10-17 Google Scholar FENG Chengyou, LI Dongsheng, WU Zhengshou, et al. Major Types, Time-Space Distribution and Metallogeneses of Polymetallic Deposits in the Qimantage Metallogenic Belt, Eastern Kunlun Area[J]. Northwestern Geology, 2010, 43: 10-17. Google Scholar
[3]	丰成友, 王松, 李国臣, 等. 青海祁漫塔格中晚三叠世花岗岩: 年代学、地球化学及成矿意义[J]. 岩石学报, 2012, 28: 665-678 Google Scholar FENG Chengyou, WANG Song, LI Guochen, et al. Middle to Late Triassic granitoids in the Qimantage area, Qinghai Province, China: Chronology, geochemistry and metallogenic significances[J]. Acta Petrologica Sinica, 2012, 28: 665-678. Google Scholar
[4]	丰成友, 王雪萍, 舒晓峰, 等. 青海祁漫塔格虎头崖铅锌多金属矿区年代学研究及地质意义[J]. 吉林大学学报(地球科学版), 2011, 41: 1806-1817 Google Scholar FENG Chengyou, WANG Xueping, SHU Xiaofeng, et al. Isotopic Chronology of the Hutouya Skarn Lead-Zinc Polymetallic Ore District in Qimantage Area of Qinghai Province and Its Geological Significance[J]. Journal of Jilin University( Earth Science Edition), 2011, 41: 1806-1817. Google Scholar
[5]	高利娥, 曾令森, 严立龙, 等. 喜马拉雅淡色花岗岩——关键金属Sn-Cs-Tl的富集机制[J]. 岩石学报, 2021, 37: 2923-2943 doi: 10.18654/1000-0569/2021.10.01 CrossRef Google Scholar GAO Lie, ZENG Lingsen, YAN Lilong, et al. Enrichment mechanisms of Sn-Cs-Tl in the Himalaya leucogranite[J]. Acta Petrologica Sinica, 2021, 37: 2923-2943. doi: 10.18654/1000-0569/2021.10.01 CrossRef Google Scholar
[6]	高永宝. 东昆仑祁漫塔格地区中酸性侵入岩浆活动与成矿作用[D]. 西安: 长安大学, 2013 Google Scholar GAO Yongbao. The Intermediate-acid Intrusive Magmatism and Mineralization in Qimantag, East Kunlun Moutains[D]. Xi’an: Chang’an University, 2013 Google Scholar
[7]	高永宝, 李文渊, 李侃, 等. 东昆仑祁漫塔格白干湖-戛勒赛矿带成岩成矿时代及钨锡成矿作用[J]. 西北地质, 2012, 45: 229-241 doi: 10.3969/j.issn.1009-6248.2012.04.021 CrossRef Google Scholar GAO Yongbao, LI Wenyuan, LI Kan, et al. Genesis and Chronology of Baiganhu-Jialesai W-Sn Mineralization Belt, Qimantage, East Kunlun Mountain, NW China[J]. Northwestern Geology, 2012, 45: 229-241. doi: 10.3969/j.issn.1009-6248.2012.04.021 CrossRef Google Scholar
[8]	顾亚祥, 丁世飞. 支持向量机研究进展[J]. 计算机科学, 2011, 38: 14-17 doi: 10.3969/j.issn.1002-137X.2011.02.004 CrossRef Google Scholar GU Yaxiang, DING Shifei. Advances of Support Vector Machines(SVM)[J]. Computer Science, 2011, 38: 14-17. doi: 10.3969/j.issn.1002-137X.2011.02.004 CrossRef Google Scholar
[9]	韩亚楠, 刘建伟, 罗雄麟. 连续学习研究进展[J]. 计算机研究与发展, 2022, 59: 1213-1239 doi: 10.7544/issn1000-1239.20201058 CrossRef Google Scholar Han Yanan, Liu Jianwei, Luo Xionglin. Research Progress of Continual Learning[J]. Journal of Computer Research and Development, 2022, 59: 1213-1239. doi: 10.7544/issn1000-1239.20201058 CrossRef Google Scholar
[10]	刘鹏, 张德会, 吴鸣谦, 等. 浅谈花岗岩浆热液的形成及成矿作用[J]. 地质论评, 2020, 66: 699-719 doi: 10.16509/j.georeview.2020.03.012 CrossRef Google Scholar LIU Peng;ZHANG Dehui;WU Mingqian, et al. Discussion on magma-hydrothermal formation and mineralization of granites[J]. Geological Review, 2020, 66: 699-719. doi: 10.16509/j.georeview.2020.03.012 CrossRef Google Scholar
[11]	毛景文, 周振华, 丰成友, 等. 初论中国三叠纪大规模成矿作用及其动力学背景[J]. 中国地质, 2012, 39: 1437-1471 doi: 10.3969/j.issn.1000-3657.2012.06.001 CrossRef Google Scholar MAO Jingwen, ZHOU Zhenhua, FENG Chengyou, et al. A preliminary study of the Triassic large-scale mineralization in China and its geodynamic setting[J]. Geology in China, 2012, 39: 1437-1471. doi: 10.3969/j.issn.1000-3657.2012.06.001 CrossRef Google Scholar
[12]	牛警徽, 田福泉, 邱敦方, 等. 山东旧店金矿床花岗岩类锆石 U-Pb 年龄及对招平断裂带南段岩浆活动规律的约束[J]. 地质通报, 2023, 42: 813-827 Google Scholar NIU Jinghui, TIAN Fuquan, QIU Dunfang, et al. Zircon U-Pb age of granitoids in the Jiudian gold deposit, Shandong Province and its constraints on the magmatic activity patterns in the southern section of the Zhaoping fault[J]. Geological Bulletin of China, 2023, 42: 813-827. Google Scholar
[13]	申萍, 潘鸿迪. 中国还原性斑岩矿床研究进展及判别标志[J]. 岩石学报, 2020, 36: 967-994 doi: 10.18654/1000-0569/2020.04.01 CrossRef Google Scholar SHEN Ping, PAN Hongdi. Advances and its diagnostic criteria in the study of the reduced porphyry ore deposits in China[J]. Acta Petrologica Sinica, 2020, 36: 967-994. doi: 10.18654/1000-0569/2020.04.01 CrossRef Google Scholar
[14]	田承盛, 丰成友, 李军红, 等. 青海它温查汉铁多金属矿床⁴⁰Ar-³⁹Ar年代学研究及意义[J]. 矿床地质, 2013, 32: 169-176 Google Scholar LI Chengsheng, FENG Chengyou, LI Junhong, et al. ⁴⁰Ar-³⁹Ar geochronology of Tawenchahan Fe-polymetallic deposit in Qimantag Mountain of Qinghai Province and its geological implications[J]. 矿床地质, 2013, 32: 169-176. Google Scholar
[15]	王孝磊. 花岗岩研究的若干新进展与主要科学问题[J]. 岩石学报, 2017, 33: 1445-1458 Google Scholar WANG Xiaolei. Some new research progresses and main scientific problems of granitic rocks[J]. Acta Petrologica Sinica, 2017, 33: 1445-1458. Google Scholar
[16]	王瑀, 邱昆峰, 侯照亮, 等. 石英Ti/Ge-P: 基于机器学习的矿床类型判别新图解[J]. 岩石学报, 2022, 38: 281-290 doi: 10.18654/1000-0569/2022.01.18 CrossRef Google Scholar WANG Yu, QIU Kuneng, HOU Zhaoliang, et al. Quartz Ti/Ge-P discrimination diagram: A machine learning based approach for deposit classification[J]. Acta Petrologica Sinica, 2022, 38: 281-290. doi: 10.18654/1000-0569/2022.01.18 CrossRef Google Scholar
[17]	吴福元, 李献华, 杨进辉, 等. 花岗岩成因研究的若干问题[J]. 岩石学报, 2007, 1217-1238 Google Scholar WU Fuyuan, LI Xianhua, YANG Jinhui, et al. Discussions on the petrogenesis of granites[J]. Acta Petrologica Sinica, 2007, 1217-1238. Google Scholar
[18]	姚磊, 吕志成, 于晓飞, 等. 青海祁漫塔格地区虎头崖矿床Ⅵ矿带花岗岩的成岩时代、地球化学特征和成因[J]. 吉林大学学报(地球科学版), 2015, 45: 743-758 doi: 10.13278/j.cnki.jjuese.201503109 CrossRef Google Scholar YAO Lei, LV Zhicheng, YU Xiaofei, et al. Petrogenesis, Geochemistry and Zircon U-Pb Age of the Granite from No. Ⅵ Section of Hutouya Deposit, Qimantag Area, Qinghai Province, and Its Geological Significance[J]. Journal of Jilin University( Earth Science Edition), 2015, 45: 743-758. doi: 10.13278/j.cnki.jjuese.201503109 CrossRef Google Scholar
[19]	于淼, 丰成友, 刘洪川, 等. 青海尕林格矽卡岩型铁矿金云母40Ar-39Ar年代学及成矿地质意义[J]. 地质学报, 2015, 89: 510-521 Google Scholar YU Miao, FENG Chengyou, LIU Hongchuan, et al. ⁴⁰Ar-³⁹Ar Geochronology of the Galinge Large Skarn Iron Deposit in Qinghai Province and Geological Significance[J]. Acta Geologica Sinica, 2015, 89: 510-521. Google Scholar
[20]	张晓飞, 李智明, 陈博, 等. 东昆仑祁漫塔格地区滩间山群矽卡岩化成矿作用. [J]. 西北地质, 2012, 45: 40-47 doi: 10.3969/j.issn.1009-6248.2012.01.006 CrossRef Google Scholar ZHANG Xiaofei, LI Zhiming, CHEN Bo, et al. The Contribution of the Tanjianshan Group Stratum to Skarnization in Qimantage Region, Qinghai Province[J]. Northwestern Geology, 2012, 45: 40-47. doi: 10.3969/j.issn.1009-6248.2012.01.006 CrossRef Google Scholar
[21]	钟世华. 新疆维宝铜铅锌矿床成因研究[D]. 北京: 中国地质科学院, 2018 Google Scholar ZHONG Shihua. Genesis of the Weibao Cu-Pb-Zn deposit in Xinjiang, China[D]. Beijing: Chinese Academy of Geological Sciences, 2018 Google Scholar
[22]	钟世华, 丰成友, 李大新, 等. 新疆维宝矽卡岩铜铅锌矿床维西矿段矿物学特征[J]. 地质学报, 2017a, 91: 1066-1082 Google Scholar ZHONG Shihua, FENG Chengyou, LI Daxin, et al. Mineralogical Characteristics of the Weixi Ore Block in the Weibao Skarn-type Copper-Lead-Zinc Deposit, Xinjiang[J]. Acta Geologica Sinica, 2017a, 91: 1066-1082. Google Scholar
[23]	钟世华, 丰成友, 李大新, 等. 新疆维宝多金属矿区辉绿岩脉SIMS年代学和地球化学[J]. 地质学报, 2017b, 91: 762-775 Google Scholar ZHONG Shihua, FENG Chengyou, LI Daxin, et al. SIMS Chronology and Geochemistry of Diabase Dykes from the Weibao Polymetallic Orefield, Xinjiang[J]. Acta Geologica Sinica, 2017b, 91: 762-775. Google Scholar
[24]	钟世华, 丰成友, 任雅琼, 等. 新疆维宝矽卡岩铜铅锌矿床维西矿段成矿流体性质和来源[J]. 矿床地质, 2017c, 36: 483-500 doi: 10.16111/j.0258-7106.2017.02.014 CrossRef Google Scholar ZHONG Shihua, FENG Chengyou, REN Yaqiong, et al. Characteristics and sources of ore-forming fluid from Weixi ore block of Weibao skarn Cu-Pb-Zn deposit, Xinjiang[J]. Mineral Deposits, 2017c, 36: 483-500. doi: 10.16111/j.0258-7106.2017.02.014 CrossRef Google Scholar
[25]	BALLARD J R, PALIN M J, CAMPBELL I H. Relative oxidation states of magmas inferred from Ce(IV)/Ce(III) in zircon: application to porphyry copper deposits of northern Chile[J]. Contributions to Mineralogy and Petrology, 2002, 144: 347-364. doi: 10.1007/s00410-002-0402-5 CrossRef Google Scholar
[26]	BERGEN K J, JOHNSON P A, DE HOOP M V, et al. Machine learning for data-driven discovery in solid Earth geoscience[J]. Science, 2019, 363: eaau0323. doi: 10.1126/science.aau0323 CrossRef Google Scholar
[27]	CAO M, QIN K, LI G, et al. Baogutu: An example of reduced porphyry Cu deposit in western Junggar[J]. Ore Geology Reviews, 2014, 56: 159-180. doi: 10.1016/j.oregeorev.2013.08.014 CrossRef Google Scholar
[28]	CHAPPELL B, WHITE A. Two contrasting granite types[J]. Pacific geology, 1974, 8: 173-174. Google Scholar
[29]	Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16: 321−357. Google Scholar
[30]	CHELLE-MICHOU C, CHIARADIA M, OVTCHAROVA M, et al. Zircon petrochronology reveals the temporal link between porphyry systems and the magmatic evolution of their hidden plutonic roots (the Eocene Coroccohuayco deposit, Peru)[J]. Lithos, 2014, 198-199. Google Scholar
[31]	CHIARADIA M. How Much Water in Basaltic Melts Parental to Porphyry Copper Deposits?[J]. Frontiers in Earth Science, 2020, 8: 138. doi: 10.3389/feart.2020.00138 CrossRef Google Scholar
[32]	CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20: 273-297. Google Scholar
[33]	DAI J, WANG C, ZHU D, et al. Multi-stage volcanic activities and geodynamic evolution of the Lhasa terrane during the Cretaceous: Insights from the Xigaze forearc basin[J]. Lithos, 2015, 218: 127-140. Google Scholar
[34]	DEMPSTER T J, JOLIVET M, TUBRETT M N, et al. Magmatic zoning in apatite: a monitor of porosity and permeability change in granites[J]. Contributions to Mineralogy and Petrology, 2003, 145: 568-577. doi: 10.1007/s00410-003-0471-0 CrossRef Google Scholar
[35]	DILLES J H, KENT A J R, WOODEN J L, et al. Zircon compositional evidence for sulfur-degassing from ore-forming arc magmas. [J]. Economic geology and the bulletin of the Society of Economic Geologists, 2015, 110: 241-251. doi: 10.2113/econgeo.110.1.241 CrossRef Google Scholar
[36]	DU J, AUDéTAT A. Early sulfide saturation is not detrimental to porphyry Cu-Au formation[J]. Geology, 2020, 48: 519-524. Google Scholar
[37]	GAO P, ZHENG Y-F, ZHAO Z-F. Distinction between S-type and peraluminous I-type granites: Zircon versus whole-rock geochemistry[J]. Lithos, 2016, 258: 77-91. Google Scholar
[38]	GAO P, ZHENG Y-F, ZHAO Z-F. Triassic granites in South China: A geochemical perspective on their characteristics, petrogenesis, and tectonic significance[J]. Earth-Science Reviews, 2017, 173: 266-294. doi: 10.1016/j.earscirev.2017.07.016 CrossRef Google Scholar
[39]	GRIMES C B, WOODEN J L, CHEADLE M J, et al. “Fingerprinting” tectono-magmatic provenance using trace elements in igneous zircon[J]. Contributions to Mineralogy and Petrology, 2015, 170: 1-26. doi: 10.1007/s00410-015-1154-3 CrossRef Google Scholar
[40]	HANCHAR J M, WESTRENEN W V. Rare Earth Element Behavior in Zircon-Melt Systems[J]. Elements, 2007, 3: 37-42. doi: 10.2113/gselements.3.1.37 CrossRef Google Scholar
[41]	HEINONEN A P, R M O T, M NTT RI I, et al. Zircon as a proxy for the magmatic evolution of Proterozoic ferroan granites; the Wiborg rapakivi granite batholith, SE Finland[J]. Journal of Petrology, 2017, 58: 2493-2517. doi: 10.1093/petrology/egy014 CrossRef Google Scholar
[42]	Hu Q, Yu K, Liu Y, et al. The 131–134 Ma A-type granites from northern Zhejiang Province, South China: Implications for partial melting of the Neoproterozoic lower crust[J]. Lithos, 2017, 294: 39−52. Google Scholar
[43]	HUANG C, ZHAO Z, LI G, et al. Leucogranites in Lhozag, southern Tibet: Implications for the tectonic evolution of the eastern Himalaya[J]. Lithos, 2017a, 294: 246-262. Google Scholar
[44]	HUANG X, DENG J, WANG W, et al. Impact of climate and elevation on snow cover using integrated remote sensing snow products in Tibetan Plateau[J]. Remote Sensing of Environment, 2017b, 190: 274-288. doi: 10.1016/j.rse.2016.12.028 CrossRef Google Scholar
[45]	KAY S M, JICHA B R, CITRON G L, et al. The calc-alkaline Hidden Bay and Kagalaska plutons and the construction of the central Aleutian oceanic arc crust[J]. Journal of Petrology, 2019, 60: 393-439. doi: 10.1093/petrology/egy119 CrossRef Google Scholar
[46]	LEE C-T A, TANG M. How to make porphyry copper deposits[J]. Earth and Planetary Science Letters, 2020, 529: 115868. doi: 10.1016/j.jpgl.2019.115868 CrossRef Google Scholar
[47]	LIU Y, LI W, JIA Q, et al. The Dynamic Sulfide Saturation Process and a Possible Slab Break-off Model for the Giant Xiarihamu Magmatic Nickel Ore Deposit in the East Kunlun Orogenic Belt, Northern Qinghai-Tibet Plateau, China[J]. Economic Geology, 2018, 113: 1383-1417. doi: 10.5382/econgeo.2018.4596 CrossRef Google Scholar
[48]	LOADER M A, WILKINSON J J, ARMSTRONG R N. The effect of titanite crystallisation on Eu and Ce anomalies in zircon and its implications for the assessment of porphyry Cu deposit fertility[J]. Earth and Planetary Science Letters, 2017, 472: 107-119 . doi: 10.1016/j.jpgl.2017.05.010 CrossRef Google Scholar
[49]	MASSIMO C, LUCA C. Supergiant porphyry copper deposits are failed large eruptions[J]. Communications Earth & Environment, 2022, 3: 107. Google Scholar
[50]	NATHWANI C L, WILKINSON J J, FRY G, et al. Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation[J]. Mineralium Deposita, 2022, 57: 1143-1166. doi: 10.1007/s00126-021-01086-9 CrossRef Google Scholar
[51]	PETRELLI M, CARICCHI L, PERUGINI D. Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas[J]. Journal of Geophysical Research: Solid Earth, 2020, 125: e2020JB020130. Google Scholar
[52]	PETRELLI M, PERUGINI D. Solving petrological problems through machine learning: the study case of tectonic discrimination using geochemical and isotopic data[J]. Contributions to Mineralogy and Petrology, 2016, 171: 1-15. doi: 10.1007/s00410-015-1217-5 CrossRef Google Scholar
[53]	PIZARRO H, CAMPOS E, BOUZARI F, et al. Porphyry indicator zircons (PIZs): Application to exploration of porphyry copper deposits[J]. Ore Geology Reviews, 2020, 126: 103771. doi: 10.1016/j.oregeorev.2020.103771 CrossRef Google Scholar
[54]	RICHARDS J P, SENGOR A M C L. Did Paleo-Tethyan anoxia kill arc magma fertility for porphyry copper formation?[J]. Geology, 2017, 45: 7. Google Scholar
[55]	SHUMLYANSKYY L, BELOUSOVA E, PETRENKO O. Geochemistry of zircons from basic rocks of the Korosten anorthosite-mangerite-charnockite-granite complex, north-western region of the Ukrainian Shield[J]. Mineralogy and Petrology, 2017, 111: 459-466. doi: 10.1007/s00710-017-0514-2 CrossRef Google Scholar
[56]	TANG G-J, CHUNG S-L, HAWKESWORTH C J, et al. Short episodes of crust generation during protracted accretionary processes: Evidence from Central Asian Orogenic Belt, NW China[J]. Earth and Planetary Science Letters, 2017, 464: 142-154. doi: 10.1016/j.jpgl.2017.02.022 CrossRef Google Scholar
[57]	VEZINET A, PEARSON D G, THOMASSOT E, et al. Hydrothermally-altered mafic crust as source for early Earth TTG: Pb/Hf/O isotope and trace element evidence in zircon from TTG of the Eoarchean Saglek Block, N. Labrador[J]. Earth and Planetary Science Letters, 2018, 503: 95-107. doi: 10.1016/j.jpgl.2018.09.015 CrossRef Google Scholar
[58]	WADE C, PAYNE J, BAROVICH K, et al. Zircon trace element geochemistry as an indicator of magma fertility in iron oxide copper-gold provinces[J]. Economic Geology, 2022, 117: 703-718. doi: 10.5382/econgeo.4886 CrossRef Google Scholar
[59]	WANG S, LI X, SCHERTL H-P, et al. Petrogenesis of early cretaceous andesite dykes in the Sulu orogenic belt, eastern China[J]. Mineralogy and Petrology, 2019, 113: 77-97. doi: 10.1007/s00710-018-0636-1 CrossRef Google Scholar
[60]	XIA R, WANG C, QING M, et al. Molybdenite Re–Os, zircon U–Pb dating and Hf isotopic analysis of the Shuangqing Fe–Pb–Zn–Cu skarn deposit, East Kunlun Mountains, Qinghai Province, China[J]. Ore Geology Reviews, 2015, 66: 114-131. doi: 10.1016/j.oregeorev.2014.10.024 CrossRef Google Scholar
[61]	XIE F, TANG J, LANG X, et al. The different sources and petrogenesis of Jurassic intrusive rocks in the southern Lhasa subterrane, Tibet: Evidence from the trace element compositions of zircon, apatite, and titanite[J]. Lithos, 2018, 314: 447-462. Google Scholar
[62]	YU M, DICK J M, FENG C, et al. The tectonic evolution of the East Kunlun Orogen, northern Tibetan Plateau: A critical review with an integrated geodynamic model[J]. Journal of Asian Earth Sciences, 2020, 191: 104168. doi: 10.1016/j.jseaes.2019.104168 CrossRef Google Scholar
[63]	ZHANG Q, WANG Q, LI G, et al. Crucial control on magmatic-hydrothermal Sn deposit in the Tengchong block, SW China: Evidence from magma differentiation and zircon geochemistry[J]. Geoscience Frontiers, 2022, 13: 101401. doi: 10.1016/j.gsf.2022.101401 CrossRef Google Scholar
[64]	ZHAO S-Q, TAN J, WEI J-H, et al. Late Triassic Batang Group arc volcanic rocks in the northeastern margin of Qiangtang terrane, northern Tibet: partial melting of juvenile crust and implications for Paleo-Tethys ocean subduction[J]. International Journal of Earth Sciences, 2015, 104: 369-387. doi: 10.1007/s00531-014-1080-z CrossRef Google Scholar
[65]	ZHONG S, FENG C, SELTMANN R, et al. Sources of fluids and metals and evolution models of skarn deposits in the Qimantagh metallogenic belt: A case study from the Weibao deposit, East Kunlun Mountains, northern Tibetan Plateau[J]. Ore Geology Reviews, 2018a, 93: 19-37. doi: 10.1016/j.oregeorev.2017.12.013 CrossRef Google Scholar
[66]	ZHONG S, FENG C, SELTMANN R, et al. Geochemical contrasts between Late Triassic ore-bearing and barren intrusions in the Weibao Cu–Pb–Zn deposit, East Kunlun Mountains, NW China: constraints from accessory minerals (zircon and apatite)[J]. Mineralium Deposita, 2018b, 53: 855-870. doi: 10.1007/s00126-017-0787-8 CrossRef Google Scholar
[67]	ZHONG S, LI S, FENG C, et al. Geochronology and geochemistry of mineralized and barren intrusive rocks in the Yemaquan polymetallic skarn deposit, northern Qinghai-Tibet Plateau: A zircon perspective[J]. Ore Geology Reviews, 2021b, 139: 104560. doi: 10.1016/j.oregeorev.2021.104560 CrossRef Google Scholar
[68]	ZHONG S, LI S, FENG C, et al. Porphyry copper and skarn fertility of the northern Qinghai-Tibet Plateau collisional granitoids[J]. Earth-Science Reviews, 2021c, 214: 103524. doi: 10.1016/j.earscirev.2021.103524 CrossRef Google Scholar
[69]	ZHONG S, LI S, LIU Y, et al. I-type and S-type granites in the Earth’s earliest continental crust[J]. Communications Earth & Environment, 2023, 4: 61. Google Scholar
[70]	ZHONG S, SELTMANN R, QU H, et al. Characterization of the zircon Ce anomaly for estimation of oxidation state of magmas: a revised Ce/Ce* method[J]. Mineralogy and Petrology, 2019, 113: 755-763. doi: 10.1007/s00710-019-00682-y CrossRef Google Scholar
[71]	ZHONG S H, FENG C, SELTMANN R, et al. Middle Devonian volcanic rocks in the Weibao Cu–Pb–Zn deposit, East Kunlun Mountains, NW China: Zircon chronology and tectonic implications[J]. Ore Geology Reviews, 2017, 84: 309-327. doi: 10.1016/j.oregeorev.2017.01.020 CrossRef Google Scholar
[72]	H. ZS, Y. L, Z. L S, et al. A machine learning method for distinguishing detrital zircon provenance[J]. Contributions to Mineralogy and Petrology, 2023, 178: 35. doi: 10.1007/s00410-023-02017-9 CrossRef Google Scholar
[73]	ZHOU G, WU Y, WANG H, et al. Petrogenesis of the Huashanguan A-type granite complex and its implications for the early evolution of the Yangtze Block[J]. Precambrian Research, 2017, 292: 57-74. doi: 10.1016/j.precamres.2017.02.005 CrossRef Google Scholar
[74]	ZHU J-J, HU R, BI X-W, et al. Porphyry Cu fertility of eastern Paleo-Tethyan arc magmas: Evidence from zircon and apatite compositions[J]. Lithos, 2022, 424: 106775. Google Scholar
[75]	ZOU S, CHEN X, BRZOZOWSKI M J, et al. Application of machine learning to characterizing magma fertility in porphyry Cu deposits[J]. Journal of Geophysical Research: Solid Earth, 2022, 127: e2022JB024584. doi: 10.1029/2022JB024584 CrossRef Google Scholar