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Highly sensitive mid-infrared methane remote sensor using a deep neural network filter
2024-03-25
发表期刊OPTICS EXPRESS (IF:3.2[JCR-2023],3.4[5-Year])
ISSN1094-4087
EISSN1094-4087
卷号32期号:7页码:11849-11862
发表状态已发表
DOI10.1364/OE.520245
摘要

A novel mid-infrared methane remote sensor integrated on a movable platform based on a 3.291-µm interband cascade laser (ICL) and wavelength modulation spectroscopy (WMS) is proposed. A transmitting-receiving coaxial, visualized optical layout is employed to minimize laser energy loss. Using a hollow retro-reflector remotely deployed as a cooperative target, the atmospheric average methane concentration over a 100-meter optical range is measured with high sensitivity. A deep neural network (DNN) filter is used for second harmonic (2f) signal denoising to compensate for the performance shortcomings of conventional filtering. Allan deviation analysis indicated that after applying the DNN filter, the limit of detection (LOD) of methane was 86.62 ppb with an average time of 1 s, decreasing to 12.03 ppb with an average time of 229 s, which is a significant promotion compared to similar work reported. The high sensitivity and stability of the proposed sensor are shown through a 24-hour continuous monitoring experiment of atmospheric methane conducted outdoors, providing a new solution for high-sensitivity remote sensing of atmospheric methane. © 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词Atmospheric chemistry Deep neural networks Energy dissipation Infrared devices Molecular spectroscopy Optical remote sensing Quantum cascade lasers Atmospheric methanes High sensitivity Interband cascade laser Laser modulation Midinfrared Movable platforms Neural network filters Optical layouts Remote sensors Wavelength modulation spectroscopy
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收录类别EI ; SCI
语种英语
资助项目Civil space technology pre-research of the 14-th five-years plan[D040107] ; Key basic research projects of the Basic Strengthening Program[2021-173ZD-025]
WOS研究方向Optics
WOS类目Optics
WOS记录号WOS:001206706900001
出版者Optica Publishing Group (formerly OSA)
EI入藏号20241415838415
EI主题词Methane
EI分类号443.1 Atmospheric Properties ; 461.4 Ergonomics and Human Factors Engineering ; 525.4 Energy Losses (industrial and residential) ; 741.3 Optical Devices and Systems ; 744.1 Lasers, General ; 801.1 Chemistry, General ; 804.1 Organic Compounds
原始文献类型Journal article (JA)
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/359874
专题信息科学与技术学院
信息科学与技术学院_特聘教授组_王建宇组
信息科学与技术学院_硕士生
通讯作者Li, Chunlai
作者单位
1.Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai; 200083, China;
2.University of Chinese Academy of Sciences, Beijing; 100049, China;
3.School of Information Science and Technology, ShanghaiTech University, Shanghai; 201210, China;
4.Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Zhejiang, Hangzhou; 310024, China
推荐引用方式
GB/T 7714
Wang, Senyuan,Yang, Shicheng,Zhu, Shouzheng,et al. Highly sensitive mid-infrared methane remote sensor using a deep neural network filter[J]. OPTICS EXPRESS,2024,32(7):11849-11862.
APA Wang, Senyuan.,Yang, Shicheng.,Zhu, Shouzheng.,Liu, Shijie.,He, Xin.,...&Wang, Jianyu.(2024).Highly sensitive mid-infrared methane remote sensor using a deep neural network filter.OPTICS EXPRESS,32(7),11849-11862.
MLA Wang, Senyuan,et al."Highly sensitive mid-infrared methane remote sensor using a deep neural network filter".OPTICS EXPRESS 32.7(2024):11849-11862.
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