拉曼光谱联合WOA特征筛选的矿井水源识别方法研究
Study on Recognition Method of Mine Water Source Based on Raman Spectrum Combined With WOA Characteristic Screening
周茗皓 1陈小刚 1崔继峰 1卞凯 2胡锋2
作者信息
- 1. 内蒙古工业大学理学院,内蒙古呼和浩特 010051
- 2. 安徽理工大学电气与信息工程学院,安徽淮南 232001
- 折叠
摘要
在矿井突水灾害防治过程中,准确、快速判别突水水源类型对煤矿安全生产意义非常重大,而传统的水化学方法存在耗时长、检测复杂等不足,为此提出采用拉曼光谱进行矿井突水水源辨识这一新思路.首先从淮南矿区采集老空水、顶板砂岩裂隙水、奥灰水、太灰水和地表水以及它们混合的水样作为实验对象,并借助拉曼光谱系统收集水样的拉曼光谱数据.随后,采用常见的光谱预处理方法对原始拉曼光谱进行降噪.接着,采用鲸鱼优化算法(WOA)对水样的拉曼光谱进行特征信息筛选,得到最能够表征矿井水样的特征拉曼信息.最后,将筛选出的特征拉曼信息作为输入,分别构建BP神经网络(BPNN)、K-近邻算法(KNN)、支持向量机(SVM)、决策树(DT)以及朴素贝叶斯(NB)分类模型,以此来验证拉曼光谱结合WOA筛选特征拉曼信息用于矿井水源识别的可行性.实验证明:利用WOA可以从2 048个拉曼数据点中筛选得到102个特征拉曼信息,将拉曼信息的点数缩减为原来的4.98％,而且WOA筛选的特征拉曼信息的建模精度高于全拉曼数据建模精度,此外,采用WOA筛选的特征拉曼信息构建BPNN、KNN、SVM、DT和NB水源辨识模型时,其分析速度都有着不同程度的提升.研究结果表明,采用WOA筛选矿井水源拉曼光谱的特征信息,可以有效地减少拉曼光谱数据的冗余,提升拉曼光谱分析的速度,这可以为矿井水源的快速检测提供借鉴.
Abstract
In the process of mine water inrush disaster prevention and control,it is very important to accurately and quickly identify the type of water inrush sources for coal mine safety production.However,the traditional hydrochemical method has the disadvantages of time-consuming and complex detection.Therefore,a new idea of identifying mine water inrush sources using Raman spectroscopy is proposed.First of all,the water samples of goaf water,roof sandstone fissure water,Ordovician limestone water,Taiyuan limestone water,surface water and their mixture were collected from the Huainan mining area as experimental objects,and the Raman spectral data of water samples were collected with the help of Raman spectroscopy system.Then,the common spectral pretreatment method is used to reduce the noise of the original Raman spectrum.Then,the whale optimization algorithm(WOA)is used to screen the characteristic information of the water sample,and the characteristic information that best represents the mine water sample is obtained.Finally,the filtered characteristic Raman information is used as input to construct BP neural network(BPNN),K-nearest neighbor algorithm(KNN),support vector machine(SVM),decision tree(DT)and naive Bayesian(NB)classification models respectively,to verify the feasibility of Raman spectrum combined with WOA screening characteristic Raman information for mine water source identification.Experiments show that 102 characteristic Raman information can be filtered from 2 048 Raman data points by WOA,reducing the number of Raman information to 4.98％,and the modeling accuracy of characteristic Raman information filtered by WOA is higher than that of full Raman data.In addition,when the characteristic Raman information filtered by WOA is used to build BPNN,KNN,SVM,DT,and NB water source identification models,the analysis speed has been improved to varying degrees.The research results show that using WOA to screen the characteristic information of the Raman spectrum of the mine water source can effectively reduce the redundancy of the Raman spectrum data and significantly improve the speed of the Raman spectrum analysis,which can provide a reference for the rapid detection of the mine water source.
关键词
拉曼光谱/矿井突水/水源识别/鲸鱼优化算法/特征筛选Key words
Raman spectrum/Mine water inrush/Water source identification/Whale optimization algorithm/Feature selection引用本文复制引用
基金项目
国家重点研发计划子课题(2018YFC0604500)
安徽省科技重大专项(201903a07020013)
安徽省能源互联网联合基金(2008085UD06)
出版年
2024
NETL
NSTL
万方数据