摘要
我国煤炭产量位居全球前列,随着浅部能源的消耗殆尽,深部地层勘探开发工作势在必行,然而深部地层附带的技术难题也愈发显著,尤其在煤矿深部开采过程中,岩性的识别困扰着各大煤矿企业.以山西某煤矿煤田测井数据为例,采用主成分分析方法,构建两个累计方差贡献率达88.62%的主要成分A1、A2,对比常规岩性识别交汇图与主成分交汇图,证实该方法的有效性.
Chinese coal production ranks among the top in the world.With the exhaustion of shallow energy sources,the exploration and development work with deep strata is imperative.However,the technical problems associated with deep strata have become more and more significant,especially in the process of deep coal mining.The identification of coal mines troubles major coal mines.This paper,taking logging data from a coal mine in Shanxi as an example,constructed two main components A1 and A2 with a cumulative variance contribution rate of 88.62%with the use of principal component analysis and confirmed the effectiveness of this method by comparing the conventional lithology identification intersection graph and the principal component intersection graph.
作者
郎君
刘亚武
葛军
LANG Jun;LIU Ya-wu;GE Jun(Department of Mining Engineering,Lüliang University,Lishi Shanxi 033001,China)
出处
《吕梁学院学报》
2022年第2期23-26,共4页
Journal of Lyuiang University
基金
吕梁市引进高层次科技人才重点研发项目资助(Rc2020211)。
关键词
主成分分析
岩性识别
数据序列
principal component analysis
lithology identification
data sequence
