The Nanling Range in South China is well known for its rich granite-related W–Sn deposits.To elucidate the controls of different granite-related W–Sn metallogenesis in the region,we chose five representative orerela...The Nanling Range in South China is well known for its rich granite-related W–Sn deposits.To elucidate the controls of different granite-related W–Sn metallogenesis in the region,we chose five representative orerelated granites(Yanbei,Mikengshan,Tieshanlong,Qianlishan,and Yaogangxian intrusions)in the Hunan–Jiangxi region,and studied their magmatic zircon ages and trace element geochemistry.Our new zircon data showed the differences in ages,temperatures and oxygen fugacity of the ore-forming magmas.Zircon U–Pb ages of the Yanbei and Mikengshan intrusions are characterized by 142.4±2.4 and 143.0±2.3 Ma,respectively,whereas the Tieshanlong and Qianlishan intrusions are 159.5±2.3and 153.2±3.3 Ma,respectively.The Sn-related intrusions were younger than the W-related intrusions.The Tiin-zircon thermometry showed that there was no systematic difference between the Sn-related Yanbei(680–744℃)and Mikengshan(697–763℃)intrusions and the W-related Tieshanlong(730–800℃),Qianlishan(690–755℃)and Yaogangxian(686–751℃)intrusions.However,the zircon Ce^4+/Ce^3+ratios of the Yanbei(averaged at 18.3)and Mikengshan(averaged at 18.8)intrusions are lower than those of the Tieshanlong(averaged at 36.9),Qianlishan(averaged at 38.4)and Yaogangxian(averaged at 37)intrusions,indicating that the Sn-related granitic magmas might have lower oxygen fugacities than those of the W-related.This can be explained by that,in more reduced magmas,Sn is more soluble than W and thus is more enriched in the residual melt to form Sn mineralization.The difference in source materials between the Sn-related and the W-related granites seems to have contributed to the different redox conditions of the melts.展开更多
石英的微量元素记录了石英生长的物理化学条件。通过微量元素对石英原岩进行分类的研究历史已久,经典工作是在以微量元素为坐标轴的图解上绘制各类型石英的分布范围,以区分石英类型。经典图解包括Rusk(2012)提出用于区分三种矿床类型石...石英的微量元素记录了石英生长的物理化学条件。通过微量元素对石英原岩进行分类的研究历史已久,经典工作是在以微量元素为坐标轴的图解上绘制各类型石英的分布范围,以区分石英类型。经典图解包括Rusk(2012)提出用于区分三种矿床类型石英的Al-Ti二元图解,和Schr9n et al.(1988)提出的用于判别不同岩浆岩类型石英的Ti-Al-Ge三元图解。越来越多的研究表明,上述图解不能满足对更多石英类型进行分类的需求,同时也出现与部分已知产状类型的石英微量元素判别相矛盾的情况。随着石英原位微区测试方法的成熟,高精度石英微量元素数据逐渐丰富为系统开展机器学习提供了大数据基础,为石英微量元素研究提供了新的角度和可能性。本研究运用机器学习分类方法对石英微量元素进行精确数学分析,提出Ti/Ge-P图解为石英成因研究提出新的地球化学指标。本文同时测试了六种经典机器学习分类算法,提高Ti/Ge-P图解在石英成因分类研究上的精度。此Ti/Ge-P图解适用于多种矿床研究,包括但不局限于斑岩型矿床、矽卡岩型矿床、浅成低温热液型矿床、卡林型矿床以及造山型矿床中的石英。这项工作是大数据技术与机器学习技术在地球化学研究中的积极探索。展开更多
基金supported by the National Basic Research Program of China (973 Program) (Grants No. 2014CB440906)Innovation Team Program of Chinese Academy of Sciences (Overseas Famous Scholars Program)‘‘Light of West China’’ Program of Chinese Academy of Sciences
文摘The Nanling Range in South China is well known for its rich granite-related W–Sn deposits.To elucidate the controls of different granite-related W–Sn metallogenesis in the region,we chose five representative orerelated granites(Yanbei,Mikengshan,Tieshanlong,Qianlishan,and Yaogangxian intrusions)in the Hunan–Jiangxi region,and studied their magmatic zircon ages and trace element geochemistry.Our new zircon data showed the differences in ages,temperatures and oxygen fugacity of the ore-forming magmas.Zircon U–Pb ages of the Yanbei and Mikengshan intrusions are characterized by 142.4±2.4 and 143.0±2.3 Ma,respectively,whereas the Tieshanlong and Qianlishan intrusions are 159.5±2.3and 153.2±3.3 Ma,respectively.The Sn-related intrusions were younger than the W-related intrusions.The Tiin-zircon thermometry showed that there was no systematic difference between the Sn-related Yanbei(680–744℃)and Mikengshan(697–763℃)intrusions and the W-related Tieshanlong(730–800℃),Qianlishan(690–755℃)and Yaogangxian(686–751℃)intrusions.However,the zircon Ce^4+/Ce^3+ratios of the Yanbei(averaged at 18.3)and Mikengshan(averaged at 18.8)intrusions are lower than those of the Tieshanlong(averaged at 36.9),Qianlishan(averaged at 38.4)and Yaogangxian(averaged at 37)intrusions,indicating that the Sn-related granitic magmas might have lower oxygen fugacities than those of the W-related.This can be explained by that,in more reduced magmas,Sn is more soluble than W and thus is more enriched in the residual melt to form Sn mineralization.The difference in source materials between the Sn-related and the W-related granites seems to have contributed to the different redox conditions of the melts.
文摘石英的微量元素记录了石英生长的物理化学条件。通过微量元素对石英原岩进行分类的研究历史已久,经典工作是在以微量元素为坐标轴的图解上绘制各类型石英的分布范围,以区分石英类型。经典图解包括Rusk(2012)提出用于区分三种矿床类型石英的Al-Ti二元图解,和Schr9n et al.(1988)提出的用于判别不同岩浆岩类型石英的Ti-Al-Ge三元图解。越来越多的研究表明,上述图解不能满足对更多石英类型进行分类的需求,同时也出现与部分已知产状类型的石英微量元素判别相矛盾的情况。随着石英原位微区测试方法的成熟,高精度石英微量元素数据逐渐丰富为系统开展机器学习提供了大数据基础,为石英微量元素研究提供了新的角度和可能性。本研究运用机器学习分类方法对石英微量元素进行精确数学分析,提出Ti/Ge-P图解为石英成因研究提出新的地球化学指标。本文同时测试了六种经典机器学习分类算法,提高Ti/Ge-P图解在石英成因分类研究上的精度。此Ti/Ge-P图解适用于多种矿床研究,包括但不局限于斑岩型矿床、矽卡岩型矿床、浅成低温热液型矿床、卡林型矿床以及造山型矿床中的石英。这项工作是大数据技术与机器学习技术在地球化学研究中的积极探索。
