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Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method

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Abstract

Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions.

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Acknowledgements

We also thank the fundings from the National Natural Science Foundation (U1931211,41573056). Major Research Project of Shandong province(GG201809130208).

Funding

This work is supported by the Pre-research project on Civil Aerospace Technologies No. D020102 funded by China National Space Administration (CNSA).

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Authors and Affiliations

  1. School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, China

    Li Zhang

  2. Shandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, Institute of Space Sciences, Shandong University, Weihai, 264209, China

    Zhongchen Wu & Zongcheng Ling

Authors
  1. Li Zhang
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  2. Zhongchen Wu
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  3. Zongcheng Ling
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Correspondence to Li Zhang.

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Zhang, L., Wu, Z. & Ling, Z. Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method. Earth Sci Inform 13, 1485–1497 (2020). https://doi.org/10.1007/s12145-020-00497-y

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