Volume 67, Issue 6 e70273
RESEARCH ARTICLE

Deep Learning-Enhanced Laser-Induced Breakdown Spectroscopy for Rapid In-Situ Analysis of Martian Surface and Atmospheric Constituents

Tianzhuang Wu

Tianzhuang Wu

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere, Nanjing University of Information Science & Technology, Nanjing, China

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Ruoyu Zhai

Ruoyu Zhai

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere, Nanjing University of Information Science & Technology, Nanjing, China

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Junzhe Huang

Junzhe Huang

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere, Nanjing University of Information Science & Technology, Nanjing, China

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Ziwei Wang

Ziwei Wang

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere, Nanjing University of Information Science & Technology, Nanjing, China

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Boyuan Han

Boyuan Han

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere, Nanjing University of Information Science & Technology, Nanjing, China

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Yuzhu Liu

Corresponding Author

Yuzhu Liu

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, China

Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere, Nanjing University of Information Science & Technology, Nanjing, China

Correspondence: Yuzhu Liu ([email protected])

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First published: 03 June 2025

ABSTRACT

The integration of laser-induced breakdown spectroscopy (LIBS) with deep learning algorithms presents great potential for autonomous planetary exploration. This study utilized data from the Zhurong rover to achieve spectral assignment for Martian sands, soil, and rocks. The Titanium alloy Ti-6Al-4V (TC4) based calibration strategy successfully achieves the spectral assignment of Martian atmospheric and contrast Martian and Earth spectra, distinguishing oxygen/carbon contributions. To overcome limited extraterrestrial data, we introduced the Principal Component Analysis-based Augmentation (PCA-Aug) for spectral data diversity expansion while preserving the intrinsic feature of the data. Two innovative architectures, the Lightweight Spectral Convolutional Neural Network (LS-CNN) and the Grid-Form Implicit Graph Network (GIGN), have demonstrated superior performance compared to conventional Convolutional Neural Networks (CNNs), with LS-CNN achieving a mean accuracy of 96.30% and GIGN achieving 92.59% across 10 independent training runs. These values represent consistent performance improvements of 18.4% (LS-CNN) and 13.6% (GIGN) over traditional CNNs. This study establishes LIBS as a dual-capability technique for simultaneous Martian material classification and atmospheric composition analysis, providing a critical methodology to enhance the scientific return of current and near-term Mars exploration missions.

Data Availability Statement

The authors have nothing to report.

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