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Deep learning with laser-induced breakdown spectroscopy (LIBS) for the classification of rocks based on elemental imaging
Chen T(陈彤)1,2,3,4; Sun LX(孙兰香)1,2,3; Yu HB(于海斌)1,2,3; Wang W(汪为)1,2,3,4; Qi LF(齐立峰)1,2,3; Zhang P(张鹏)1,2,3; Zeng P(曾鹏)1,2,3
Department工业控制网络与系统研究室
Source PublicationApplied Geochemistry
ISSN0883-2927
2022
Volume136Pages:1-10
Indexed BySCI ; EI
EI Accession number20214711201051
WOS IDWOS:000728568800005
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China (Grant No. 62173321) ; Key Research Program of Frontier Sciences, CAS (Grant No. QYZDJ-SSW-JSC037) ; Science and Technology Service Network Initiative Program, CAS (Grant No. KFJ-STSQYZD-2021-19-002) ; Youth Innovation Promotion Association, CAS
KeywordData augmentation Inception-v3 net LIBS-Based imaging Rock classification Transfer learning
Abstract

In geological research, the identification and classification of rock lithology plays an important role in many fields such as resource exploration, earth evolution and paleontology research. Laser-induced breakdown spectroscopy (LIBS), which is capable of real-time, on-situ, micro-destructive determination of the elemental composition of any substance (solid, liquid, or gas), has been developed as a technology for ‘geochemical fingerprinting’ in a variety of geochemical applications. However, for rock samples with coarse grains, the bulk analysis based on the average spectrum is insufficient. This study proposes a new method for identifying multiple types of rocks, which utilizes the rapid multi-element compositional imaging capability of LIBS, and combines with the deep learning theory. The LIBS-based images characterizing the spatial distribution of elements on rock surface were achieved firstly, and then were classified by the Inception-v3 network combined with the transfer learning method. In addition, to solve the problem of the small scale of the image dataset obtained in the laboratory, specific data augmentation methods such as cutting-recombining and filtering were proposed. Moreover, the superior of this method was verified by the three classification experiments of shale, gneiss and granite.

Language英语
WOS SubjectGeochemistry & Geophysics
WOS Research AreaGeochemistry & Geophysics
Funding ProjectNational Natural Science Foundation of China[62173321] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC037] ; Science and Technology Service Network Initiative Program, CAS[KFJ-STS-QYZD-2021-19-002] ; Youth Innovation Promotion Association, CAS
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/29922
Collection工业控制网络与系统研究室
Corresponding AuthorSun LX(孙兰香)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Chen T,Sun LX,Yu HB,et al. Deep learning with laser-induced breakdown spectroscopy (LIBS) for the classification of rocks based on elemental imaging[J]. Applied Geochemistry,2022,136:1-10.
APA Chen T.,Sun LX.,Yu HB.,Wang W.,Qi LF.,...&Zeng P.(2022).Deep learning with laser-induced breakdown spectroscopy (LIBS) for the classification of rocks based on elemental imaging.Applied Geochemistry,136,1-10.
MLA Chen T,et al."Deep learning with laser-induced breakdown spectroscopy (LIBS) for the classification of rocks based on elemental imaging".Applied Geochemistry 136(2022):1-10.
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