基于激光诱导击穿光谱技术的岩性识别方法研究  

Lithology identification methods based on laser-induced breakdown spectroscopy technology

作　　者：贾军伟 付洪波[1,2] 王华东 倪志波[1] 董凤忠 JIA Junwe;FU Hongbo;WANG Huadong;NI Zhibo;DONG Fengzhong(Anhui Provincial Key Laboratory of Photonic Devices and Materials, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China;University of Science and Technology of China, Hefei 230026, China)

机构地区：[1]中国科学院安徽光学精密机械研究所安徽省光子器件与材料重点实验室,安徽合肥230031 [2]中国科学技术大学,安徽合肥230026

出　　处：《量子电子学报》2018年第3期264-270,共7页Chinese Journal of Quantum Electronics

基　　金：国家自然科学基金;61505223;国家科技支撑计划项目;2014BAC17B03;中国科学院科研装备研制项目;Y2201315~~

摘　　要：岩屑录井是地层岩性及含油性等的直接鉴别方式,岩性的正确描述是岩屑录井的重要内容。选择Mg、Si、Al、Fe、Ca、Na、K七种元素的激光诱导击穿光谱(LIBS)作为分析线,结合主成分分析(PCA)、软独立建模分类法(SIMCA)、有监督Kohonen神经网络(SKNs)三种化学计量学方法,对泥质灰岩、泥岩、页岩、砂岩四种岩屑岩性进行了识别。SKNs、SIMCA模型的平均正确识别率分别为93.75%、78.75%。结果表明利用LIBS技术结合PCA和非线性SKNs方法可以实现物理特性、化学组成较为相似的岩屑岩性的有效识别。Cuttings logging is a direct identification of stratigraphic lithology and oil content. The proper description of lithology plays an important role in cuttings logging. The laser-induced breakdown spectroscopy（LIBS） of seven elements including Mg, Si, Al, Fe, Ca, Na and K are selected as the analysis lines,and the four kinds of cuttings lithology of marlite, mudstone, shale and sandstone are identified combining with three chemometric methods including principal component analysis（PCA）, soft independent modeling of class analogy（SIMCA）, supervised Kohonen networks（SKNs）. The average correct recognition rates of SKNs and SIMCA models are 93.75% and 78.75%, respectively. Results show that the combination of LIBS technology, PCA and nonlinear SKNs methods can realize the effective lithology identification of cuttings having similar physical properties and chemical composition.

关 键 词：光谱学 激光诱导击穿光谱 岩性 化学计量学 

分 类 号：O433.4[机械工程—光学工程]