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Alternative TitleFingerprint analysis and classification of rock surface based on laser-induced breakdown spectroscopy
张蕊1,2; 孙兰香1,3,4; 陈彤1,3,4,5; 王国栋1,3,4,5; 张鹏1,3,4; 汪为1,3,4,5
Source Publication地质学报
Indexed ByEI ; CSCD
EI Accession number20201508413245
Contribution Rank1
Funding Organization中国科学院前沿科学重点研究计划( 编号QYZDJ-SSW-JSC037 ) ; 中国科学院青年创新促进会
Keyword激光诱导击穿光谱 支持向量机 特征提取 指纹图谱


Other Abstract

Rock lithology identification plays an irreplaceable guiding role in many aspects, such as exploration and development of oil and gas fields, study of the origin and evolution of the earth, analysis and prediction of geological hazards etc. Therefore, rock identification and classification are very important for geological exploration and analysis. In order to improve the classification accuracy of rocks, a method of rock surface fingerprint analysis and classification based on laserinduced breakdown spectroscopy (LIBS) was proposed. In the experiment, six rock samples were placed on a three-dimensional displacement platform, and different positions of the rock surface were excited by LIBS to obtain the original spectral data. After removing abnormal points, normalization and other pretreatment operations on the collected spectral data, the characteristic spectral lines of five elements (silicon, aluminum, potassium, sodium and magnesium) with large content differences were determined according to the rock mineral composition, and the element fingerprint was obtained. Then, the support vector machine (SVM) was selected as the classifier for classification. The classification model using the spectral mean and the classification model of multidimensional fingerprint fusion were established respectively, and the two classification results were compared. The accuracy of traditional classification model based on spectral mean is 59.4%, while that of multidimensional fingerprint fusion model can reach 96. 5 %. The experimental results show that the element fingerprint shows the element distribution on the rock surface, which can make full use of the heterogeneous structure information of different kinds of rocks, thus greatly improving the classification accuracy of rocks.

Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Corresponding Author孙兰香
Recommended Citation
GB/T 7714
张蕊,孙兰香,陈彤,等. 基于激光诱导击穿光谱技术的岩石表面指纹图谱分析及分类方法[J]. 地质学报,2020,94(3):991-998.
APA 张蕊,孙兰香,陈彤,王国栋,张鹏,&汪为.(2020).基于激光诱导击穿光谱技术的岩石表面指纹图谱分析及分类方法.地质学报,94(3),991-998.
MLA 张蕊,et al."基于激光诱导击穿光谱技术的岩石表面指纹图谱分析及分类方法".地质学报 94.3(2020):991-998.
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