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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

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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。

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.

Newport宣传-MKS新实验室计划
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中图分类号:O433.4

DOI:10.3969/j.issn.1007-5461. 2018.03.002

所属栏目:光谱

基金项目: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 Sciences (中国科学院科研装备研制项目, YZ201315)

收稿日期:2017-02-21

修改稿日期:2017-03-23

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贾军伟: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中国科学技术大学, 安徽 合肥 230026
付洪波: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中国科学技术大学, 安徽 合肥 230026
王华东: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中国科学技术大学, 安徽 合肥 230026
倪志波: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
董凤忠: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中国科学技术大学, 安徽 合肥 230026

联系人作者:贾军伟(jjw2014@mail.ustc.edu.cn)

备注: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的研究。

【1】Li Yichao, Li Chunshan, Liu Delun. X ray fluorescence cutting logging technology[J]. Mud Logging Engineering(录井工程), 2008, 19(1): 1-8, 13 (in Chinese).

【2】Weltje G J, Tjallingii R. Calibration of XRF core scanners for quantitative geochemical logging of sediment cores: Theory and application[J]. Earth and Planetary Science Letters, 2008, 274(3): 423-438.

【3】Ye Liu, Liu Xiaoming, Hu Zhaochu, et al. Evaluation of accuracy and long-term stability of determination of 37 trace elements in geological samples by ICP-MS[J]. Acta Petrologica Sinica, 2007, 23(5): 1203-1210.

【4】Cremers D A, Knight A K. Laser-Induced Breakdown Spectroscopy[M]. United Kingdom: Cambridge University Press, 2006: 1-20.

【5】Cousin A, Forni O, Sautter V, et al. Classification of non-homogeneous basalts using independent component analysis technique for MSL/ChemCam data[C]. Lunar and Planetary Science Conference, 2012.

【6】Cousin A, Meslin P Y, Wiens R C, et al. Compositions of coarse and fine particles in martian soils at gale: A window into the production of soils[J]. Icarus, 2015, 249(4): 22-42.

【7】Senesi, Giorgio S. Laser-induced breakdown spectroscopy (LIBS) applied to terrestrial and extraterrestrial analogue geomaterials with emphasis to minerals and rocks[J]. Earth-Science Reviews, 2014, 139: 231-267.

【8】Chen Xinglong, Dong Fengzhong, Wang Qi, et al. Quantitative analysis of slag by calibration-free laser-induced breakdown spectroscopy[J]. Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(12): 3289-3293 (in Chinese).

【9】He Wengan, Dong Fengzhong, Chen Xinglong, et al. Application of non-linear calibration method in analysis of slag composition[J]. Chinese Journal of Quantum Electronics(量子电子学报), 2014, 31(2): 213-221 (in Chinese).

【10】Meng Deshuo, Zhao Nanjing, Liu Wenqing, et al. Quantitative measurement and analysis of potassium in soil using laser-induced breakdown spectroscopy[J]. Chinese Journal of Lasers(中国激光), 2014, 41(5): 262-267 (in Chinese).

【11】Wang Qi, Dong Fengzhong, Chen Xinglong, et al. Quantitative analysis of Mn, Cr in steel based on laser-induced breakdown spectroscopy[J]. Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(9): 2546-2551 (in Chinese).

【12】Wang Qing, Tan Juan, Wu Jian, et al. Application progress of laser induced breakdown spectroscopy in the field of environment[J]. Environmental Monitoring in China(中国环境监测), 2015, 31(3): 000123 (in Chinese).

【13】NIST. Database of atomic spectral data[OL]. http://physicsnistgov/PhysRefData/ASD, 2008.

【14】Ballabio D. A MATLAB toolbox for principal component analysis and unsupervised exploration of data structure[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 149: 1-9.

【15】Gottfried J L, Harmon R S, De Lucia F C, et al. Multivariate analysis of laser-induced breakdown spectroscopy chemical signatures for geomaterial classification[J]. Spectrochimica Acta Part B: Atomic Spectroscopy, 2009, 64: 1009-1019.

【16】Meza-Márquez O G, Gallardo-Velázquez T, Osorio-Revilla G. Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef[J]. Meat Science, 2010, 8(2): 511-519.

【17】Melssen W, Wehrens R, Buydens L. Supervised Kohonen networks for classification problems[J]. Chemometrics and Intelligent Laboratory Systems, 2006, 83(2): 99-113.

【18】Ballabio D, Vasighi M, Filzmoser P. Effects of supervised self organising maps parameters on classification performance[J]. Analytica Chimica Acta, 2013, 765: 45-53.

引用该论文

JIA Junwei,FU Hongbo,WANG Huadong,NI Zhibo,DONG Fengzhong. Lithology identification methods based on laser-induced breakdown spectroscopy technology[J]. Chinese Journal of Quantum Electronics, 2018, 35(3): 264-270

贾军伟,付洪波,王华东,倪志波,董凤忠. 基于激光诱导击穿光谱技术的岩性识别方法研究[J]. 量子电子学报, 2018, 35(3): 264-270

被引情况

【1】余洋,赵南京,孟德硕,马明俊,兰智高. 基于半球形约束结合偏最小二乘法的土壤重金属LIBS检测研究. 量子电子学报, 2019, 36(1): 87-92

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