使用LIBS结合机器学习和深度学习算法对小样本矿石进行分类.
Jingrong Li1, Xiaoliang Liu1,2, Min Zhang1
1Jiangxi Province Key Laboratory of Nuclear Physics and Technology, East China University of Technology, Nanchang, 330013, China. 201960177@ecut.edu.cn.
The Analyst
|February 17, 2026
概括
本研究介绍了机器学习和深度学习模型,用于使用激光诱导分解光谱 (LIBS) 来分类矿. 使用深度学习的主要组件分析 (PCA) 在小矿石样本中实现了100%的准确性.
科学领域:
- 地质化学 地质化学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 精确的矿石的分类对于资源管理和安全至关重要.
- 分析小矿石样本的传统方法可能耗时且不那么精确.
- 激光诱导分解光谱 (LIBS) 提供了快速的元素分析,但面临着小样本分类的挑战.
研究的目的:
- 开发和评估机器学习 (ML) 和深度学习 (DL) 分类模型,使用LIBS数据对小型矿物样本进行分类.
- 为了比较不同特征提取方法 (LASSO,PCA) 与各种ML/DL算法的有效性.
- 建立一个可靠的技术途径,以快速和高精度识别矿石.
主要方法:
- 从12种类型的矿样本收集LIBS光谱数据.
- 使用标准正常变量 (SNV) 预处理的光谱数据.
- 使用随机森林 (RF),前神经网络 (FNN),卷积神经网络 (CNN) 和长短期记忆 (LSTM) 算法构建的分类模型.
- 雇佣的最小绝对收缩和选择操作员 (LASSO) 和主要组件分析 (PCA) 用于特征提取.
主要成果:
- 随机森林 (RF) 模型显示,与小型训练数据集相比,存在显著的过度匹配.
- 深度学习模型与LASSO特征选择相结合,比RF提高了性能,但仍然存在错误分类.
- 主要组件分析 (PCA),使用前五个组件,有效地保留了光谱歧视性信息.
- 所有使用PCA功能的深度学习模型都在培训和测试集上实现了100%的分类准确性.
结论:
- PCA有效地提取全球光谱信息,这对于用LIBS对小型矿标本进行分类至关重要.
- 深度学习算法与PCA相结合,显著提高了分类性能和概括能力.
- 这种方法提供了一种可靠的方法,用于快速准确地识别小矿石样本.
相关概念视频
01:30Classification of Leukocytes
6.3K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
6.3K
01:26Classification of Systems-I
610
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
610
01:29Aggregates Classification
1.1K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K