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)1,2,4; Yu HB(于海斌)1,2,4; Wang W(汪为)1,2,3,4; Qi LF(齐立峰)1,2,4; Zhang P(张鹏)1,2,4; Zeng P(曾鹏)1,2,4 | |
刊名 | Applied Geochemistry |
2022 | |
卷号 | 136页码:1-10 |
关键词 | Data augmentation Inception-v3 net LIBS-Based imaging Rock classification Transfer learning |
ISSN号 | 0883-2927 |
产权排序 | 1 |
英文摘要 | 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. |
资助项目 | National 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 |
WOS研究方向 | Geochemistry & Geophysics |
语种 | 英语 |
WOS记录号 | WOS:000728568800005 |
资助机构 | National 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 |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/29922] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Sun LX(孙兰香) |
作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.University of Chinese Academy of Sciences, Beijing 100049, China 4.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China |
推荐引用方式 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. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论