Chinese Journal of Quantum Electronics, Volume. 41, Issue 3, 473(2024)

Quantitative analysis of Mn on Mars from SuperCam⁃LIBS spectral datasets

SU Mingyu1,2,3、*, XIN Yanqing1,2,3, LIU Changqing1,2,3, and LING Zongcheng1,2,3
Author Affiliations
  • 1School of Space Science and Physics, Shandong University, Weihai 264209, China
  • 2Institute of Space Sciences, Shandong University, Weihai 264209, China
  • 3Shandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, Weihai 264209, China
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    The SuperCam carried by the NASA's Perseverance rover can detect the surface material composition of Mars such as Mn. In order to determine the content of Mn on Mars, a quantitative method for Mn based on ensemble learning is proposed using the laser-induced breakdown spectroscopy (LIBS) dataset of geologic standards. A series of pre-processing such as spectral denosing and de-baselining are carried out firstly, then spectral deconvolution is performed to realize peak-fitting, and finally a quantitative method for Mn content prediction is established. The quantitative accuracy for Mn of the different quantitative methods were experimentally compared. The results show that, compared with the two traditional methods (LASSO and ElasticNet), the root-mean-square error of the proposed method based on ensemble learning is reduced by 49% and 30% on average, respectively, and the quantitative results of the new method are closer to the real values of the samples. This study shows that the ensemble learning based quantitative method is more suitable for Mars Mn quantification.

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    Mingyu SU, Yanqing XIN, Changqing LIU, Zongcheng LING. Quantitative analysis of Mn on Mars from SuperCam⁃LIBS spectral datasets[J]. Chinese Journal of Quantum Electronics, 2024, 41(3): 473

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    Paper Information

    Category: Special Issue on Key Technologies and Applications of LIBS

    Received: Dec. 18, 2023

    Accepted: --

    Published Online: Jul. 17, 2024

    The Author Email: SU Mingyu (sumy@mail.sdu.edu.cn)

    DOI:10.3969/j.issn.1007-5461.2024.03.009

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