基于LIBS技术岩性识别方法研究

量子电子学报

基于LIBS技术岩性识别方法研究

贾军伟1,2，付洪波1,2，王华东1,2，董凤忠1,2

１中国科学院合肥物质科学研究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

出版日期:2018-03-28 发布日期:2019-06-11
通讯作者: 董凤忠（1966-），山东潍坊人，博士，研究员，博士生导师，主要从事环境光学、光纤传感技术、LIBS技术用于公共安全，工业过程控制，节能减排减灾等方面的研究。 E-mail:fzdong@aiofm.ac.cn
作者简介:贾军伟（1987-），河南周口人，研究生，主要从事激光诱导击穿光谱方面研究。E-mail: jjw2014@mail.ustc.edu.cn
基金资助:
Supported by National Natural Science Foundation of China（国家自然科学基金， 61505223）， National Key Technology Research and Development Program of Ministry of Science and Technology of China（国家科技支撑计划项目， 2014BAC17B03）， Instrument Developing Project of Chinese Academy of Science(中国科学院科研装备研制项目， YZ201315)

Lithology identification methods based on LIBS technology

JIA Junwei1,2, FU Hongbo1,2, WANG Huadong1,2, DONG Fengzhong1,2*

1 Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China； 2 University of Science and Technology of China, Hefei 230026, China

Published:2018-03-28 Online:2019-06-11

摘要/Abstract

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。

关键词: 激光诱导击穿光谱, 岩性, 化学计量学

Abstract: 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.

Key words: laser-induced breakdown spectroscopy, lithology, chemometric

贾军伟1,2，付洪波1,2，王华东1,2，董凤忠1,2. 基于LIBS技术岩性识别方法研究[J]. 量子电子学报.

JIA Junwei1,2, FU Hongbo1,2, WANG Huadong1,2, DONG Fengzhong1,2*. Lithology identification methods based on LIBS technology[J]. Chinese Journal of Quantum Electronics.

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