A Laser-Induced Breakdown Spectroscopy Experiment Platform for High-Degree Simulation of MarSCoDe In Situ Detection on Mars

Cui, Zhicheng; Jia, Liangchen; Li, Luning; Liu, Xiangfeng; Wang, Xu; Shu, Rong; Xu, Xuesen

doi:10.3390/rs14091954

Cited by 2 publications

(2 citation statements)

References 18 publications

(23 reference statements)

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“…Moreover, a newly established large-scale experimental platform able to realize a high-degree simulation of Martian atmospheric environment has been put into use [59]. Thanks to the great volume of this facility, not only the target samples but also the LIBS instrument (e.g., a duplicate laboratory model of MarSCoDe) can be placed in the simulated Martian environment.…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Network Chemometrics for Rock Identification Based on Laser-Induced Breakdown Spectroscopy Data in Tianwen-1 Pre-Flight Experiments

et al. 2022

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Laser-induced breakdown spectroscopy (LIBS) coupled with chemometrics is an efficient method for rock identification and classification, which has considerable potential in planetary geology. A great challenge facing the LIBS community is the difficulty to accurately discriminate rocks with close chemical compositions. A convolutional neural network (CNN) model has been designed in this study to identify twelve types of rock, among which some rocks have similar compositions. Both the training set and the testing set are constructed based on the LIBS spectra acquired by Mars Surface Composition Detector (MarSCoDe) for China’s Tianwen-1 Mars exploration mission. All the spectra were collected from dedicated rock pellet samples, which were placed in a simulated Martian atmospheric environment. The classification performance of the CNN has been compared with that of three alternative machine learning algorithms, i.e., logistic regression (LR), support vector machine (SVM), and linear discriminant analysis (LDA). Among the four methods, it is on the CNN model that the highest classification correct rate has been obtained, as assessed by precision score, recall score, and the harmonic mean of precision and recall. Furthermore, the classification accuracy is inspected more quantitatively via Brier score, and the CNN is still the best performing model. The results demonstrate that the CNN-based chemometrics are an efficient tool for rock identification with LIBS spectra collected in a simulated Martian environment. Despite the relatively small sample set, this study implies that CNN-supported LIBS classification is a promising analytical technique for Tianwen-1 Mars mission and more planetary explorations in the future.

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Section: Discussionmentioning

confidence: 99%

Convolutional Neural Network Chemometrics for Rock Identification Based on Laser-Induced Breakdown Spectroscopy Data in Tianwen-1 Pre-Flight Experiments

et al. 2022

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“…In addition to MarSDEEP, we also have a MarSCoDe laboratory model that is a duplicate of the MarSCoDe flight model on the Zhurong rover, and the technical parameters of the two instruments are almost identical. In addition to the simulation of atmospheric pressure, gas composition, and temperature, we can use the advantages of the large size of the experimental platform to place samples and instruments in the vacuum chamber [25], hence we can simulate the filed detection of MarSCoDe on Mars to the maximum extent.…”

Section: Introductionmentioning

confidence: 99%

Investigation into the Affect of Chemometrics and Spectral Data Preprocessing Approaches upon Laser-Induced Breakdown Spectroscopy Quantification Accuracy Based on MarSCoDe Laboratory Model and MarSDEEP Equipment

Liu

Wang

et al. 2023

Remote Sensing

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As part of China’s Tianwen-1 Mars mission, the Mars Surface Composition Detector (MarSCoDe) instrument on the Zhurong rover adopts laser-induced breakdown spectroscopy (LIBS) to perform chemical component detection of the materials on the Martian surface. However, it has always been a challenging issue to achieve high accuracy in LIBS quantification. This study investigated the effect of chemometrics and spectral data preprocessing approaches on LIBS quantification accuracy based on different chemometrics algorithms and diverse preprocessing methods. A total of 2340 LIBS spectra were collected from 39 kinds of geochemical samples by a laboratory duplicate model of the MarSCoDe instrument. The samples and the MarSCoDe laboratory model were placed in a simulated Martian atmosphere environment based on equipment called the Mars-Simulated Detection Environment Experiment Platform (MarSDEEP). To quantify the concentration of MgO in the samples, we employed two common LIBS chemometrics; i.e., partial least squares (PLS) and a back-propagation neural network (BPNN). Meanwhile, in addition to necessary routine preprocessing such as dark subtraction, we used five specific preprocessing approaches, namely intensity normalization, baseline removal, Mg-peak wavelength correction, Mg-peak feature engineering, and concentration range reduction. The results indicated that the performance of the BPNN was better than that of the PLS and that the preprocessing of Mg-peak wavelength correction had the most prominent effect to improve the quantification accuracy. The results of this study are expected to provide inspiration for the processing and analysis of the in situ LIBS data acquired by MarSCoDe on Mars.

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A Laser-Induced Breakdown Spectroscopy Experiment Platform for High-Degree Simulation of MarSCoDe In Situ Detection on Mars

Cited by 2 publications

References 18 publications

Convolutional Neural Network Chemometrics for Rock Identification Based on Laser-Induced Breakdown Spectroscopy Data in Tianwen-1 Pre-Flight Experiments

Convolutional Neural Network Chemometrics for Rock Identification Based on Laser-Induced Breakdown Spectroscopy Data in Tianwen-1 Pre-Flight Experiments

Investigation into the Affect of Chemometrics and Spectral Data Preprocessing Approaches upon Laser-Induced Breakdown Spectroscopy Quantification Accuracy Based on MarSCoDe Laboratory Model and MarSDEEP Equipment

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