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通过使用元学习的激光诱导分解光谱对煤炭和石进行短拍分类

Mengran Lu¹, Haoyang Yu¹, Zhaohui Jiang²

¹School of Automation, Central South University, 410083, Changsha, China.

|August 20, 2025

概括

这项研究引入了使用激光诱导分解光谱 (LIBS) 和元学习来有效分类煤炭和. 这种方法显著提高了标记数据的精度,推进了智能采矿操作.

科学领域:

地质科学和材料科学
人工智能和机器学习

背景情况:

煤炭分类对于降低开采成本和环境可持续性至关重要.
传统的监督学习方法需要大量的标记数据,这限制了它们在煤炭分类中的应用.
激光诱导分解光谱 (LIBS) 为材料表征提供了一个快速的分析技术.

研究的目的:

使用LIBS开发一种新型的快速学习框架,用于准确的煤炭和格分类.
克服传统机器学习方法中大型标记数据集的局限性.
为采矿应用适应跨领域的分类技术.

主要方法:

激光诱导分解光谱 (LIBS) 与元学习算法的集成.
开发一个增强的超级学习框架,具有峰值意识和特征脱.
利用火星岩石样本作为源域和煤炭/团队作为跨领域学习的目标域.

主要成果:

在一次射击中达到90. 08%的准确性,在五次射击中达到95. 04%的准确性.
超越传统的机器学习方法超过9% (1次) 和4% (5次).
超过深度转移学习方法11% (1次) 和2% (5次).

结论:

拟议的少量学习框架为有限数据的煤炭和格分类提供了可靠的解决方案.
这种方法为采矿行业开发智能分类设备提供了核心技术基础.
该方法证明了有效的跨领域适应基于LIBS的材料分类.

关键词:

煤炭和激光诱导的分解光谱材料分类超级学习

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