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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    Figures & Tables(13)
    SuperCam device operational schematic diagram[6]
    Number statistical distribution of samples with different Mn composition in SuperCam ground sample database
    Standard LIBS spectra of Mn in the range of 200-800 nm
    LIBS spectra of SuperCam laboratory samples (a) and the relationship between 403 nmemission peak intensity and Mn content (b)
    Peak fitting to LIBS spectra of Mn (402.8-404 nm)
    Quantitative model of Mn based on univariate methods
    Flowchart of Stacking model
    Comparison of the predicted Mn content of each model with the true value. The 1:1 reference line means that the predicted value is the same as the true value, and the closer the prediction result is to the reference line,the more accurate the prediction is for the model
    • Table 1. Compositional information of SuperCam samples

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      Table 1. Compositional information of SuperCam samples

      No.Rock typeNumber of samplesComposition of MnO/%
      1Mn ore734.62~76.19
      2Manganese nodule516.52~33.09
      3Basalt560.01~4.92
      4Phyllosilicate110.08~0.59
      5Olivine40.09~0.42
      6Augite20.22~0.24
      7Plagioclase250.05~0.18
      8Fe-oxide60.03~1.95
      9Others1710.01~1
    • Table 2. Fitting parameters and R2 for three univariate models

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      Table 2. Fitting parameters and R2 for three univariate models

      Fitting typeFitting parametersR2
      y=axa=0.004910.82
      y=ax+bx2a=0.01129, b=-0.0001150.89
      y=axba=0.03309, b=0.519840.91
    • Table 3. Model parameters

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      Table 3. Model parameters

      Model nameParameters
      GBDTN_estimators=100
      RFN_estimators=100
      LGBMN_estimators=100
      SVRC=1000
      Gamma=0.03
      LASSOAlpha=0.000843
      ElasticNetAlpha=0.000968
      L1=1
    • Table 4. Validation of the models at different elemental content ranges

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      Table 4. Validation of the models at different elemental content ranges

      Training set minimum (MnO/%)Training set maximum (MnO/%)MethodErms/%R2
      070Stacking2.720.94
      LASSO5.340.86
      ElasticNet3.870.92
      01Stacking0.0250.96
      LASSO0.0630.94
      ElasticNet0.1030.91
      1070Stacking5.960.9
      LASSO10.780.82
      ElasticNet7.740.89
    • Table 5. Comparison of true and predicted values of some samples under each model

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      Table 5. Comparison of true and predicted values of some samples under each model

      Sample NameRock TypeMethodMnO Composition/%
      JMN1Mn noduleStackingTrue33.09
      Predicted34.7
      LASSOTrue33.09
      Predicted61
      ElasticNetTrue33.09
      Predicted35.1
      PMIFS0301OlivineStackingTrue0.42
      Predicted0.41
      LASSOTrue0.42
      Predicted0.37
      ElasticNetTrue0.42
      Predicted0.28
      JSC1380BasaltStackingTrue0.1
      Predicted0.105
      LASSOTrue0.1
      Predicted0.07
      ElasticNetTrue0.1
      Predicted0.15
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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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