MarSCoDe Martian Material Analysis Based on a PSO–SVR Approach

IF 2.9 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Xiong Wan*, Peipei Fang, Yian Wang, Yingjian Xin, Mingkang Duan, Hongpeng Wang, Xinru Yan, Chenhong Li, Yanhua Ma and Zhiping He*, 
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Abstract

Laser-induced breakdown spectroscopy (LIBS) has been used for deep space exploration in recent years. The advantages of LIBS include high efficiency, stand-alone detection, and the ability to analyze multiple elements simultaneously. However, due to the fluctuation of laser energy, matrix effect, and instrumental noises, the quantitative prediction of LIBS instruments for planetary exploration is not satisfactory, especially for unknown targets. Therefore, comprehensive methods with higher adaptability and prediction accuracy must be developed to meet the needs of LIBS planetary material analysis. In this paper, we proposed an approach, which is mainly based on a particle swarm optimization (PSO)–support vector regression (SVR) analysis model, for material analysis of MarSCoDe, the LIBS payload of the Chinese Zhurong Mars rover. The model adopts a PSO algorithm to optimize the parameters and hence improve the prediction accuracy of traditional SVR equations. The training of the model was completed with 3600 LIBS spectra, which involved 60 standards and were obtained in the ground simulated Martian chamber before the launch of MarSCoDe. The quantitative performance of the model was evaluated by the coefficient of determination (R2) and root-mean-square error between real contents and predicted contents. Comparison with convolutional neural network and partial least squares showed that the PSO–SVR model has the highest prediction accuracy and the best robustness. After the launch, we used the LIBS spectra of the LC-005 calibration standard on a Zhurong rover to further evaluate the prediction accuracy of the model. The main element contents of LC-005 predicted by the model are basically consistent with its real contents. Since then, the model has been used in the onboard quantitative element analysis of MarSCoDe. Finally, quantitative analysis results of eight different unknown Martian targets on different Mars days are selected and shown, which reflects the main geological composition of the landing area of the Martian Utopia plain.

Abstract Image

基于 PSO-SVR 方法的 MarSCoDe 火星材料分析
激光诱导击穿光谱(LIBS)近年来被用于深空探测。激光诱导击穿光谱法的优点是效率高、可独立检测、可同时分析多种元素。然而,由于激光能量波动、矩阵效应和仪器噪声等原因,LIBS 仪器在行星探测中的定量预测效果并不理想,尤其是对于未知目标。因此,必须开发适应性更强、预测精度更高的综合方法,以满足 LIBS 行星物质分析的需要。本文提出了一种主要基于粒子群优化(PSO)-支持向量回归(SVR)分析模型的方法,用于中国 "祝融号 "火星探测器的 LIBS 有效载荷 MarSCoDe 的材料分析。该模型采用 PSO 算法优化参数,从而提高了传统 SVR 方程的预测精度。模型的训练是在 MarSCoDe 发射前在地面模拟火星舱中获得的 3600 条 LIBS 光谱(涉及 60 个标准)完成的。模型的定量性能通过实际含量与预测含量之间的决定系数(R2)和均方根误差进行评估。与卷积神经网络和偏最小二乘法的比较表明,PSO-SVR 模型的预测精度最高,鲁棒性最好。发射后,我们在 "祝融 "号探测器上利用 LC-005 校准标准的 LIBS 光谱进一步评估了模型的预测精度。模型预测的 LC-005 主要元素含量与实际含量基本一致。此后,该模型被用于 MarSCoDe 的星载定量元素分析。最后,选取并展示了不同火星日八个不同未知火星目标的定量分析结果,反映了火星乌托邦平原着陆区的主要地质构成。
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来源期刊
ACS Earth and Space Chemistry
ACS Earth and Space Chemistry Earth and Planetary Sciences-Geochemistry and Petrology
CiteScore
5.30
自引率
11.80%
发文量
249
期刊介绍: The scope of ACS Earth and Space Chemistry includes the application of analytical, experimental and theoretical chemistry to investigate research questions relevant to the Earth and Space. The journal encompasses the highly interdisciplinary nature of research in this area, while emphasizing chemistry and chemical research tools as the unifying theme. The journal publishes broadly in the domains of high- and low-temperature geochemistry, atmospheric chemistry, marine chemistry, planetary chemistry, astrochemistry, and analytical geochemistry. ACS Earth and Space Chemistry publishes Articles, Letters, Reviews, and Features to provide flexible formats to readily communicate all aspects of research in these fields.
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