| 磷灰石Eu/Y-Ce:基于大数据的源区类型判别新图解SCIEI北大核心CSCD |
| |
| 引用本文： | 周统,邱昆峰,王瑀,于皓丞,侯照亮.磷灰石Eu/Y-Ce:基于大数据的源区类型判别新图解SCIEI北大核心CSCD[J].岩石学报,2022,38(1):291-299. |
| |
| 作者姓名： | 周统 邱昆峰 王瑀 于皓丞 侯照亮 |
| |
| 作者单位： | 中国地质大学(北京)地球科学与资源学院, 北京 100083;中国地质大学(北京)地球科学与资源学院, 北京 100083;中国地质大学地质过程与矿产资源国家重点实验室, 北京 10008;维也纳大学地质系, 维也纳 1090 |
| |
| 基金项目： | 本文受国家重点研发计划（2019YFA0708603）、国家自然科学基金项目（42130801、42072087）、北京市科技新星（Z201100006820097）、成矿作用与成矿预测重点实验室开放基金（ZS2010）、111计划2.0（BP0719021）和阿里云高校数字化创新专项（2021ALA01006）联合资助 |
| |
| 摘 要： | 磷灰石广泛分布于火成岩、沉积岩和变质岩中,是一种常见的、包含丰富微量元素的副矿物。磷灰石晶格可容纳丰富的微量元素,且因其形成的物理化学条件不同会表现出差异明显的微量元素特征。利用磷灰石微量元素特征可以追踪物质来源和演化。现在常用的方法是利用磷灰石的微量元素绘制二元判别图解,经典判别图解包括Sr-Y、Sr-Mn、Y-(Eu/Eu^(*))和(Ce/Yb)_(N)-REE图解。随着微区测试技术发展,磷灰石微量元素数据日渐丰富,同时由于磷灰石化学成分的复杂性,传统图解已逐渐无法有效利用这些数据所携带的信息,进而无法准确判别其生成环境。建立能准确判别磷灰石物源的新型判别图解故而迫切。近年来,磷灰石微量元素数据的大量积累,为运用以大数据为依托,准确判别磷灰石物源奠定了数据基础。本研究将大数据技术与地球化学数据相结合,共收集整理了1925个代表性磷灰石测试点的微量元素数据,对富碱性火成岩、超镁铁质岩石、镁铁质火成岩、长英质花岗岩、中-低级变质岩、高级变质岩六种类型中磷灰石微量元素数据进行穷举端元处理,共获得7140个磷灰石物源判别图解端元组合,在轮廓系数限定下,进一步有效筛选并提取出能判别磷灰石物源类型的最优图解端元。本文构建了Eu/Y-Ce磷灰石判别新图解,相较于之前的磷灰石判别图解,其涵盖了更全面的物源类型,可以更准确地判别源区类型。
|
| 关 键 词： | 磷灰石 微量元素 物源判别 大数据分析 Eu/Y-Ce判别图解 |
| 收稿时间： | 2021/8/10 0:00:00 |
| 修稿时间： | 2021/11/24 0:00:00 |
Apatite Eu/Y-Ce discrimination diagram: A big data based approach for provenance classification |
| |
| ZHOU Tong,QIU KunFeng,WANG Yu,YU HaoCheng,HOU ZhaoLiang.Apatite Eu/Y-Ce discrimination diagram: A big data based approach for provenance classification[J].Acta Petrologica Sinica,2022,38(1):291-299. |
| |
| Authors: | ZHOU Tong QIU KunFeng WANG Yu YU HaoCheng HOU ZhaoLiang |
| |
| Institution: | School of Earth Sciences and Resources. China University of Geosciences (Beijing), Beijing 100083, China;School of Earth Sciences and Resources. China University of Geosciences (Beijing), Beijing 100083, China;State Key Laboratory of Geological Process and Mineral Resources, China University of Geosciences, Beijing 10008; Department of Geology, University of Vienna, Vienna 1090, Austria |
| |
| Abstract: | Apatite is a common and important accessory mineral in rocks. Apatite comprises rich various trace elements, which are formed in different apatite-generation environments. Constructing apatite-associated trace element discrimination diagrams is a classic and efficient technique for the investigation of apatite provenance. The typical examples include the diagrams of Sr-Y, Sr-Mn, Y-(Eu/Eu*) and (Ce/Yb)N-REE. However, the current discrimination diagrams cannot precisely distinguish apatite types due to the massive increase of apatite trace elements that are detected by the developing microscale analyses, which therefore cannot precisely predict the apatite-formation environment. Fortunately, the significant increase of apatite trace element data advances our understanding in apatite provenance by using the big data analysis technique. In this study, we applied the big data study for the collected 1925 published apatite trace element data, analyzing six apatite-associated rock types, which are alkali-rich igneous rocks, ultramafic rocks, mafic igneous rocks, felsic granitoids, low- and medium-grade metamorphic rocks and high-grade metamorphic rocks. The yield 7140 apatite-associated discrimination diagrams were further detailed evaluated by introducing the Silhouette coefficient. By the process, we proposed an Eu/Y-Ce discrimination diagram to distinguish apatite. |
| |
| Keywords: | Apatite Trace elements Provenance Big data analysis Eu/Y-Ce discrimination diagram |
| 本文献已被 维普 等数据库收录！ |
| 点击此处可从《岩石学报》浏览原始摘要信息 |
| 点击此处可从《岩石学报》下载免费的PDF全文 |
|
