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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
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
Department工业控制网络与系统研究室
Source Publication地质学报
ISSN0001-5717
2020
Volume94Issue:3Pages:991-998
Indexed ByEI ; CSCD
EI Accession number20201508413245
CSCD IDCSCD:6669865
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
Abstract

岩石岩性识别在油气田探测开发、研究地球成因及演化发展、地质灾害分析预测等众多方面起着不可替代的导向作用,因此岩石的识别分类对于地质勘探分析来说至关重要。为了提高岩石的分类准确率,提出了一种基于激光诱导击穿光谱技术(LIBS)的岩石表面指纹图谱分析及分类方法。通过LIBS对岩石表面不同位置进行激发,获取原始光谱数据。对收集到的光谱数据进行去除异常点、归一化等预处理操作,根据岩石矿物成分确定五种含量差异较大元素(硅、铝、钾、钠、镁)的特征谱线并得到元素指纹图谱。然后选择支持向量机(SVM)作为分类器进行分类,分别建立利用光谱均值的分类模型和多维指纹图谱融合的分类模型,并对两种分类结果进行比较。利用光谱均值的分类模型准确率为59.4%,多维指纹图谱融合的模型分类准确率为96.5%。实验结果表明,元素指纹图谱展示了岩石表面元素分布,可以充分利用不同种类岩石本身的不均匀性结构信息,极大地提高了岩石的分类准确率。

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.

Language中文
Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26634
Collection工业控制网络与系统研究室
Corresponding Author孙兰香
Affiliation1.中国科学院沈阳自动化研究所机器人学国家重点实验室
2.东北大学信息科学与工程学院
3.中国科学院网络化控制系统重点实验室
4.中国科学院机器人与智能制造创新研究院
5.中国科学院大学
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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