基于多模式数据的火星类岩石中硫元素的定量分析
Yuhang Dong1, Zhengfeng Shi1, Junsheng Yao1
1School of Mechanical, Electrical & Information Engineering, Shangdong University, Weihai 264209, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
研究人员开发了一种新的方法来量化火星硫酸盐类型中的硫,使用激光诱导分解光谱 (LIBS) 和红外光谱学. 这种多模式深度学习方法显著提高了分析过去火星水化和大气条件的准确性.
科学领域:
- 星球科学和天体生物学
- 地质化学和矿物学
- 光谱学和分析化学 分析化学
背景情况:
- 朱龙探测器在火星上检测到了硫酸盐,这表明了过去的水合和大气演变.
- 激光诱导分解光谱 (LIBS) 用于火星上的元素分析,但由于弱光谱线,它难以对硫等离子元素进行定量分析.
- 准确的硫量化对于了解火星地质历史和潜在的可居住性至关重要.
研究的目的:
- 开发一种可靠的方法,用于对含硫酸盐的火星同类物质中硫的定量分析.
- 通过整合补充的光谱数据来解决传统的硫检测LIBS的局限性.
- 为了提高火星探测任务的元素分析的准确性和可靠性.
主要方法:
- 开发两个分析模型,用于高度和低度的硫.
- 使用红外光谱学根据含硫量对样品进行分类.
- 整合LIBS和红外频谱数据使用多式联网深度学习模型,结合基于XGBoost的特征选择与先前知识.
主要成果:
- 多模式深度学习模型有效地集成了基本 (LIBS) 和分子 (红外线) 光谱信息.
- 综合光谱方法捕捉了互补的样本特征,大大提高了预测的准确性和稳定性.
- 与单模方法相比,拟议的方法实现了至少92.36%的根平均平方误差 (RMSE) 和46.3%的R平方 (R2) 改进.
结论:
- 多式联机深度学习方法为火星同类物质的定量硫分析提供了卓越的解决方案.
- 这种方法克服了LIBS在硫量化方面的局限性,使得火星地质样本的分析更加精确.
- 这些发现支持未来火星探索任务的现场分析能力,有助于研究过去的火星环境.
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