SuperCam-LIBS光谱定量分析火星锰元素

doi:10.3969/j.issn.1007-5461.2024.03.007

量子电子学报 ›› 2024, Vol. 41 ›› Issue (3): 473-484.doi: 10.3969/j.issn.1007-5461.2024.03.007

• "LIBS 关键技术与应用"专辑 • 上一篇下一篇

SuperCam-LIBS光谱定量分析火星锰元素

苏鸣宇1,2,3, 辛艳青1,2,3, 刘长卿1,2,3, 凌宗成1,2,3*

( 1 山东大学空间科学与物理学院, 山东威海 264209; 2 山东大学空间科学研究院, 山东威海 264209; 3 山东省光学天文与日地空间环境重点实验室, 山东威海 264209 )

收稿日期:2023-12-18 修回日期:2024-03-11 出版日期:2024-05-28 发布日期:2024-05-28
通讯作者: E-mail: zcling@sdu.edu.cn E-mail:E-mail: zcling@sdu.edu.cn
作者简介:苏鸣宇 ( 1996 - ), 山东东营人, 研究生, 主要从事行星光谱学方面的研究。E-mail: sumy@mail.sdu.edu.cn
基金资助:
国家自然科学基金 (12303067, U1931211), 国家重点研发计划 (2022YFF0711403), 山东省自然科学基金 (ZR2023QD106)

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

SU Mingyu 1,2,3, XIN Yanqing 1,2,3, LIU Changqing 1,2,3, LING Zongcheng 1,2,3*

( 1 School of Space Science and Physics, Shandong University, Weihai 264209, China; 2 Institute of Space Sciences, Shandong University, Weihai 264209, China; 3 Shandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, Weihai 264209, China )

Received:2023-12-18 Revised:2024-03-11 Published:2024-05-28 Online:2024-05-28

摘要/Abstract

摘要： 毅力号火星车携带的SuperCam载荷可以探测火星表面锰元素等成分信息。本研究依据SuperCam团队发布的地质标样激光诱导击穿光谱（LIBS）数据集，提出了一种基于集成学习的火星锰元素定量方法。本研究首先对 LIBS光谱进行光谱降噪、去基线等预处理，随后进行光谱反卷积和分峰拟合，最终建立锰元素的定量方法，实现了锰元素的含量预测。实验评估了传统多变量定量方法（LASSO、弹性网络）和集成学习方法对锰元素定量精度的差异，发现后者的均方根误差相对于前两种传统方法分别平均下降了49% 和30%，定量结果更接近样品真实值，表明基于集成学习的定量方法更适用于火星锰元素的定量反演。

关键词: 光谱学, 光谱定量方法, 激光诱导击穿光谱, 集成学习, 火星, 锰元素

Abstract: 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.

Key words: spectroscopy, spectral quantification methods, laser-induced breakdown spectroscopy, ensemble learning, Mars, Mn element

中图分类号:

苏鸣宇, 辛艳青, 刘长卿, 凌宗成, . SuperCam-LIBS光谱定量分析火星锰元素[J]. 量子电子学报, 2024, 41(3): 473-484.

SU Mingyu , XIN Yanqing , LIU Changqing , LING Zongcheng , . Quantitative analysis of Mn on Mars from SuperCam⁃LIBS spectral datasets[J]. Chinese Journal of Quantum Electronics, 2024, 41(3): 473-484.

参考文献

[1]Wiens R C, Maurice S, Robinson S H, et al.The SuperCam instrument suite on the NASA Mars 2020 rover: Body unit and combined system tests[J].Space Science Reviews, 2021, 217(1):1-87
[2]Maurice S, Wiens R C, Bernardi P, et al.The SuperCam instrument suite on the Mars 2020 rover: science objectives and mast-unit description[J].Space Science Reviews, 2021, 217(1):1-108
[3]Wiens R C, Maurice S, Rull Perez F.The SuperCam remote sensing instrument suite for the Mars 2020 rover mission: A preview[J].Spectroscopy, 2017, 32(7):68-76
[4]凌宗成, 刘长卿, 柏红春, 等.基于激光诱导击穿光谱技术的火星表面物质成分探测研究进展[J].矿物岩石地球化学通报, 2022, 41(1):92-112
[5]Anderson R B, Forni O, Cousin A, et al.Post-landing major element quantification using SuperCam laser induced breakdown spectroscopy[J].Spectrochimica Acta Part B: Atomic Spectroscopy, 2022, 188(1):10-43
[6]Lanza N L, Fischer W W, Wiens R C, et al.High manganese concentrations in rocks at Gale crater,Mars[J].Geophysical Research Letters, 2014, 41(16):5755-5763
[7]Lanza N L, Wiens R C, Arvidson R E, et al.Oxidation of manganese in an ancient aquifer,Kimberley formation,Gale crater,Mars[J].Geophysical Research Letters, 2016, 43(14):7398-7407
[8]Noda N, Imamura S, Sekine Y, et al.Highly oxidizing aqueous environments on early Mars inferred from scavenging pattern of trace metals on manganese oxides[J].Journal of Geophysical Research: Planets, 2019, 124(5):1282-1295
[9]Arvidson R E, Squyres S W, Morris R V, et al.High concentrations of manganese and sulfur in deposits on Murray Ridge,Endeavour Crater,Mars[J].American Mineralogist, 2016, 101(6):1389-1405
[10]Mitra K, Moreland E L, Ledingham G J, et al.Formation of manganese oxides on early Mars due to active halogen cycling[J].Nature Geoscience, 2023, 16(2):133-139
[11]Anderson R, Bridges J C, Williams A, et al.ChemCam results from the Shaler outcrop in Gale crater,Mars[J].Icarus, 2015, 249(4):2-21
[12]Clegg S M, Wiens R C, Anderson R, et al.Recalibration of the Mars Science Laboratory ChemCam instrument with an expanded geochemical database[J].Spectrochimica Acta Part B: Atomic Spectroscopy, 2017, 129(3):64-85
[13]Ollila A M, Newsom H E, Clark III B, et al.Trace element geochemistry (Li,Ba,Sr,and Rb) using Curiosity's ChemCam: early results for Gale crater from Bradbury landing site to Rocknest[J].Journal of Geophysical Research: Planets, 2014, 119(1):255-285
[14]Payre V, Fabre C, Cousin A, et al.Alkali trace elements in Gale crater,Mars,with ChemCam: Calibration update and geological implications[J].Journal of Geophysical Research: Planets, 2017, 122(3):650-679
[15]Gasda P J, Anderson R B, Cousin A, et al.Quantification of manganese for ChemCam Mars and laboratory spectra using a multivariate model[J].Spectrochimica Acta Part B: Atomic Spectroscopy, 2021, 181(1):10-43
[16]Pathak A K, Kumar R, Singh V K, et al.Assessment of LIBS for spectrochemical analysis: a review[J].Applied Spectroscopy Reviews, 2012, 47(1):14-40
[17]El Haddad J, Canioni L, Bousquet B.Good practices in LIBS analysis: Review and advices[J].Spectrochimica Acta Part B: Atomic Spectroscopy, 2014, 101(1):171-182
[18]Hahn D W, Omenetto N.Laser-induced breakdown spectroscopy (LIBS),part I: review of basic diagnostics and plasma–particle interactions: still-challenging issues within the analytical plasma community[J].Applied spectroscopy, 2010, 64(12):335-A
[19]Windom B C, Hahn D W.Laser ablation—laser induced breakdown spectroscopy (LA-LIBS): A means for overcoming matrix effects leading to improved analyte response[J].Journal of Analytical Atomic Spectrometry, 2009, 24(12):1665-1675
[20] Yao S, Zhao J, Xu J, et al.Optimizing the binder percentage to reduce matrix effects for the LIBS analysis of carbon in coal[J].Journal of Analytical Atomic Spectrometry, 2017, 32(4):766-772
[21]Li X, Yang J, Chang F, et al.LIBS quantitative analysis for vanadium slags based on selective ensemble learning[J].Journal of Analytical Atomic Spectrometry, 2019, 34(6):1135-1144
[22]Chu Y W, Chen F, Sheng Z, et al.Blood cancer diagnosis using ensemble learning based on a random subspace method in laser-induced breakdown spectroscopy[J].Biomedical optics express, 2020, 11(8):4191-4202
[23] Gou B, Xu Y, Feng X.An ensemble learning-based data-driven method for online state-of-health estimation of lithium-ion batteries. [J].IEEE Transactions on Transportation Electrification, 2020, 7(2):422-436
[24]Yang J, Li X, Lu H, et al.An LIBS quantitative analysis method for alloy steel at high temperature based on transfer learning[J].Journal of Analytical Atomic Spectrometry, 2018, 33(7):1184-1195
[25]Song W, Hou Z, Afgan M S, et al.Validated ensemble variable selection of laser-induced breakdown spectroscopy data for coal property analysis[J].Journal of Analytical Atomic Spectrometry, 2021, 36(1):111-119
[26]Rao A P, Jenkins P R, Auxier II J D, et al.Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device[J].Journal of Analytical Atomic Spectrometry, 2021, 36(2):399-406
[27]Rao Y, Sun T, Sun C, et al.A combination of spectrum selection and machine learning regression for minor element determination in gravel stones with LIBS[J].Spectrochimica Acta Part B: Atomic Spectroscopy, 2022, 198(1):17-52
[28]Yue Z, Sun C, Chen F, et al.Machine learning-based LIBS spectrum analysis of human blood plasma allows ovarian cancer diagnosis[J].Biomedical optics express, 2021, 12(5):2559-2574
[29]Shabbir S, Xu W, Zhang Y, et al.Machine learning and transfer learning for correction of the chemical and physical matrix effects in the determination of alkali and alkaline earth metals with LIBS in rocks[J].Spectrochimica Acta Part B: Atomic Spectroscopy, 2022, 194(5):13-45
[30]Chen S, Zhang H, Zeng L, et al.Secondary Structural Ensemble Learning Cluster for Estimating the State of Health of Lithium-Ion Batteries[J].ACS omega, 2022, 7(20):17406-17415

SuperCam-LIBS光谱定量分析火星锰元素

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

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