首页 | 官方网站   微博 | 高级检索  
     

一维到三维密度分布函数及其可视化在大数据分析中的应用——以苦橄质玄武岩等为例
引用本文:葛粲,张旗,李修钰,孙贺,顾海欧,李伟伟,袁峰.一维到三维密度分布函数及其可视化在大数据分析中的应用——以苦橄质玄武岩等为例[J].地质通报,2019,38(12):2043-2052.
作者姓名:葛粲  张旗  李修钰  孙贺  顾海欧  李伟伟  袁峰
作者单位:合肥工业大学资源与环境工程学院, 安徽 合肥 230009;合肥工业大学矿集区立体探测实验室, 安徽 合肥 230009;合肥工业大学安徽省矿产资源与矿山环境工程技术研究中心, 安徽 合肥 230009,中国科学院地质与地球物理研究所, 北京 100029,安徽省地质调查院, 安徽 合肥 230001,合肥工业大学资源与环境工程学院, 安徽 合肥 230009;合肥工业大学矿集区立体探测实验室, 安徽 合肥 230009;合肥工业大学安徽省矿产资源与矿山环境工程技术研究中心, 安徽 合肥 230009,合肥工业大学资源与环境工程学院, 安徽 合肥 230009;合肥工业大学矿集区立体探测实验室, 安徽 合肥 230009;合肥工业大学安徽省矿产资源与矿山环境工程技术研究中心, 安徽 合肥 230009,合肥工业大学资源与环境工程学院, 安徽 合肥 230009;合肥工业大学矿集区立体探测实验室, 安徽 合肥 230009;合肥工业大学安徽省矿产资源与矿山环境工程技术研究中心, 安徽 合肥 230009,合肥工业大学资源与环境工程学院, 安徽 合肥 230009;合肥工业大学矿集区立体探测实验室, 安徽 合肥 230009;合肥工业大学安徽省矿产资源与矿山环境工程技术研究中心, 安徽 合肥 230009
基金项目:国家青年科学基金项目《地震和重力数据联合约束下的苏鲁皖地区壳幔结构反演研究》(批准号:41504042)、《大别山双河超高压变质大理岩及其包裹榴辉岩的Li同位素地球化学研究》(批准号:41603005)和中国地质调查局项目《资源环境重大问题综合区划与开发保护策略研究》(编号:DD20190463)
摘    要:提出不同维度的密度分布函数的计算方法和可视化方案,以解决不同数量级和不同测量误差的岩石样本数据分析对比困难的问题。通过SiO_2、全碱和MgO指标的三维密度分布函数和t-分布随机邻域嵌入可视化方法对GEOROC和PETDB数据库进行发掘,发现大洋岩(oceanite)和富辉橄玄岩(ankaramite)与苦橄质玄武岩(basalt, picritic)成分相近,而铁质苦橄岩(picrite,ferro)与侵入的橄榄辉长岩和苦橄岩(picrite)成分相似。利用二维密度分布函数和可视化技术,对比分析了不同岩石在TAS图解和硅镁图上的数据分布状态和数据集中核心区域。发现总体分布上,更富镁的苦橄岩的SiO_2含量高于苦橄质玄武岩,超基性的苦橄岩(picrate)核心区域主要分布在TAS图解的B区,这与以SiO_2=45%划分基性岩和超基性岩界线的观点矛盾。

关 键 词:密度分布函数  可视化  大数据  苦橄岩  苦橄质玄武岩
收稿时间:2019/4/17 0:00:00
修稿时间:2019/7/16 0:00:00

One-dimensional to three-dimensional density distribution functions and their applications in visualized big data analysis: Exemplified by picritic basalt and some other rocks
GE Can,ZHANG Qi,LI Xiuyu,SUN He,GU Hai'ou,LI Weiwei and YUAN Feng.One-dimensional to three-dimensional density distribution functions and their applications in visualized big data analysis: Exemplified by picritic basalt and some other rocks[J].Geologcal Bulletin OF China,2019,38(12):2043-2052.
Authors:GE Can  ZHANG Qi  LI Xiuyu  SUN He  GU Hai'ou  LI Weiwei and YUAN Feng
Affiliation:School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, Anhui, China;Laboratory of Three-Dimension Exploration for Mineral District, Hefei University of Technology, Hefei 230009, Anhui, China;Anhui Provincial Engineering Research Center for Mineral Resources and Mine Environments, Hefei University of Technology, Hefei 230009, Anhui, China,Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China,Geological Survey of Anhui Province, Hefei 230001, Anhui, China,School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, Anhui, China;Laboratory of Three-Dimension Exploration for Mineral District, Hefei University of Technology, Hefei 230009, Anhui, China;Anhui Provincial Engineering Research Center for Mineral Resources and Mine Environments, Hefei University of Technology, Hefei 230009, Anhui, China,School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, Anhui, China;Laboratory of Three-Dimension Exploration for Mineral District, Hefei University of Technology, Hefei 230009, Anhui, China;Anhui Provincial Engineering Research Center for Mineral Resources and Mine Environments, Hefei University of Technology, Hefei 230009, Anhui, China,School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, Anhui, China;Laboratory of Three-Dimension Exploration for Mineral District, Hefei University of Technology, Hefei 230009, Anhui, China;Anhui Provincial Engineering Research Center for Mineral Resources and Mine Environments, Hefei University of Technology, Hefei 230009, Anhui, China and School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, Anhui, China;Laboratory of Three-Dimension Exploration for Mineral District, Hefei University of Technology, Hefei 230009, Anhui, China;Anhui Provincial Engineering Research Center for Mineral Resources and Mine Environments, Hefei University of Technology, Hefei 230009, Anhui, China
Abstract:In this paper, the calculation methods and visualization schemes of density distribution functions of different dimensions are proposed to solve the problem of difficulties in analysis and comparison of rock sample data with different orders of magnitude and different measurement errors. Data mining based on the GEOROC and PETDB databases by using the three-dimensional density distribution function of SiO2, total alkali and MgO index as well as the t-distribution random neighborhood embedding visualization method revealed that picritic basalt is similar to oceanite and ankaramite, while picrate is similar to intrusive olivine gabbro and ferropicrate. Comparisons between two-dimensional density distribution function and cumulative density contour visualization were used to analyze the data distribution of different rocks on TAS and Si-Mg maps and the core area of data concentration. It is found that the SiO2 content of magnesium-rich picrite is higher than that of picrite basalt in general distribution. The core area of picrite is mainly located in the B area of TAS diagram, which is contrary to the traditional view that SiO2=45% is used as the boundary between basic and ultramafic rocks.
Keywords:density distribution function  visualization  big data  picrite  picritic basalt
本文献已被 CNKI 等数据库收录!
点击此处可从《地质通报》浏览原始摘要信息
点击此处可从《地质通报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号