Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

doi:10.1088/1674-1056/25/6/065201

Abstract

Laser-induced breakdown spectroscopy (LIBS) is a versatile tool for both qualitative and quantitative analysis. In this paper, LIBS combined with principal component analysis (PCA) and support vector machine (SVM) is applied to rock analysis. Fourteen emission lines including Fe, Mg, Ca, Al, Si, and Ti are selected as analysis lines. A good accuracy (91.38% for the real rock) is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA. It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program, but also solve the problem of linear inseparability by combining PCA and SVM. By this method, the ability of LIBS to classify rock is validated.

Keywords: laser-induced breakdown spectroscopy (LIBS) principal component analysis (PCA) support vector machine (SVM) lithology identification

Received: 08 January 2016
Revised: 01 March 2016
Accepted manuscript online:

PACS:	52.25.Kn	(Thermodynamics of plasmas)
	52.50.Jm	(Plasma production and heating by laser beams (laser-foil, laser-cluster, etc.))

Fund:

Project supported by the National Natural Science Foundation of China (Grant No. 11075184), the Knowledge Innovation Program of the Chinese Academy of Sciences (CAS) (Grant No. Y03RC21124), and the CAS President's International Fellowship Initiative Foundation (Grant No. 2015VMA007).

Corresponding Authors: Feng-Zhong Dong
E-mail: fzdong@aiofm.ac.cn

Cite this article:

Hong-Xing Yang(杨洪星), Hong-Bo Fu(付洪波), Hua-Dong Wang(王华东), Jun-Wei Jia(贾军伟), Markus W Sigrist, Feng-Zhong Dong(董凤忠) Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis 2016 Chin. Phys. B 25 065201

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Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

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