使用激光诱导分解光谱学对岩石切片进行定量元素氧化物分析,并与贝叶斯优化和支向量机相结合
Sara Abu Alsaud1, Aaron Swanson2
1EXPEC Advanced Research Center, Saudi Aramco, Dhahran 34466, Saudi Arabia.
ACS omega
|February 2, 2026
概括
使用激光诱导分解光谱 (LIBS) 和机器学习的快速元素分析优化了能源部门的运作. 贝叶斯优化增强了支向量机器模型,用于准确的岩石元素量化,提高了效率和安全性.
科学领域:
- 地质化学 地质化学
- 机器学习 机器学习
- 频谱学是一种光谱学.
背景情况:
- 现场元素分析对于能源部门的运营效率和安全至关重要.
- 快速地质化学数据有助于地热勘探,碳储存完整性和碳化合物勘探.
研究的目的:
- 评估贝叶斯优化与支持矢量机相结合,使用激光诱导分解光谱 (LIBS) 来量化岩石切片中的主要氧化物元素.
- 为了自动优化模型性能,并最大限度地减少手动超参数调整.
主要方法:
- 用了1000多个岩石切割样本用于模型开发.
- 采用X射线光作为元素度的参考方法.
- 集成贝叶斯优化与支持向量机用于超参数调整.
主要成果:
- 实现了高模型可靠性和准确性,R平方值在0.93和0.97.9之间.
- 证明了模型在处理LIBS数据中的高维度和非线性方面的有效性.
结论:
- LIBS,机器学习和贝叶斯优化的综合方法为能源部门的应用提供了快速,可靠的元素分析.
- 这种方法支持通过精确的地球化学表征来实现适应性和成本效益的资源开发.
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