基于大数据的基性-超基性岩定量分类及成矿预测研究——以北山地区为例
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  • 英文篇名:Quantitative classification and metallogenic prognosis of basic-ultrabasic rocks based on big data: Taking the Beishan area as an example
  • 作者:王怀涛 ; 罗建民 ; 王金荣 ; 杜君 ; 宋秉田 ; 王玉玺 ; 王晓伟 ; 周煜祺
  • 英文作者:WANG HuaiTao;LUO JianMin;WANG JinRong;DU Jun;SONG BingTian;WANG YuXi;WANG XiaoWei;ZHOU YuQi;Key Laboratory of Mineral Resources in Western China,School of Earth Sciences,Lanzhou University;Geological Survey of Gansu Province;Gansu Provincial Bureau of Geology and Mineral Exploration and Development;
  • 关键词:基性-超基性岩 ; 定量分类模型 ; 铜镍矿床 ; 成矿预测 ; 北山地区
  • 英文关键词:Basic-ultrabasic rocks;;Quantitative classification model;;Cu-Ni deposit;;Metallogenic prognosis;;Beishan area
  • 中文刊名:YSXB
  • 英文刊名:Acta Petrologica Sinica
  • 机构:兰州大学地质科学与矿产资源学院甘肃省西部矿产资源重点实验室;甘肃省地质调查院;甘肃省地质矿产勘查开发局;
  • 出版日期:2018-11-15
  • 出版单位:岩石学报
  • 年:2018
  • 期:v.34
  • 基金:中国地质调查局地调项目(121201004000150017-32);; 国家自然科学基金项目(41872073);; 甘肃省自然科学基金项目(18JR3RA266)联合资助
  • 语种:中文;
  • 页:YSXB201811005
  • 页数:12
  • CN:11
  • ISSN:11-1922/P
  • 分类号:31-42
摘要
岩浆岩分类是研究岩浆岩成因及其成矿作用的基础,传统的岩浆岩二维图解分类方法,因仅有个别元素参与分类、岩石矿物学定名无法保证统一性等因素,其分类结果不够精确。本文基于地质大数据研究基本思想、方法,应用多元统计"聚类分析"和"判别分析"的算法,分别构建起"北山地区基性-超基性岩氧化物定量分类系列模型"和"北山地区基性-超基性岩氧化物+微量元素定量分类系列模型"。前者将北山地区406件基性-超基性岩样本,在10个氧化物变量空间分为2大类共9个小类,其中89%的含矿样本分布在占总样本量44%的3个小类中。后者将北山地区266件基性-超基性岩样本,在34个氧化物及微量元素变量空间分为2大类共8个小类,其中98%的含矿样本分布在占总样本量33%的2个小类中,且81%的含矿样本分布在仅占总样本量21%的Ⅰ222分类中,在该分类中含矿样本数占该分类样本的61%,则有理由认为剩余39%的样本所在岩体为找矿的有利岩体。上述二个模型分类结果中含矿样本对定量分类的高度选择性,充分证明所建定量分类系列模型对北山地区基性-超基性岩成矿岩体分类的高度有效性,为北山地区基性-超基性岩成矿岩体预测研究提供了依据。
        Magmatic rock classification is the basis for studying the genesis and mineralization of magmatic rocks. The traditional two-dimensional graphical classification method of magmatic rocks is not accurate enough because only individual elements participate in the classification; rock mineralogy can not guarantee the uniformity and other factors. Applying multivariate statistical "cluster analysis"and"discriminant analysis"algorithms,this paper is on the strength of the basic ideas and methods of geological big data research,striving to build a series of"basic-ultrabasic rock oxide quantitative classification series model"and "basic-ultrabasic rock oxide + trace element quantitative classification series model in Beishan area". The former divides 406 basic-ultrabasic rock samples in the Beishan area into 9 categories and 9 subcategories in 10 oxide variable spaces. It was found that 89% of the ore-containing samples were distributed in three sub-categories of 44% of the total sample size. The latter divides 266 basic-ultrabasic rock samples from Beishan area into 34 categories of oxide and trace element variables into 2 categories and 8 sub-categories. 98% of the ore samples were found to be distributed in 2 sub-categories of 33% of the total sample size. In addition,81% of the ore-containing samples are distributed in the I222 category,which accounts for only 21% of the total sample size. In this classification,the number of orecontaining samples accounts for 61% of the sample. So there is reason to believe that the remaining 39% of the sample is located in a favorable rock mass for prospecting. The high selectivity of the ore samples to the quantitative classification in the above two model classification results fully demonstrates the high validity of the quantitative classification series model for the classification of basicultrabasic rock mineralization in the Beishan area. It provides a basis for the prediction of the basic-ultrabasic rock-forming orebodies in the Beishan area.
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