基于iPLS的矿井突水激光诱导荧光光谱特征波段筛选  

Selection of Characteristic Wave Bands for Laser Induced Fluorescence Spectra of Mine Water Inrush Based on IPLS

作　　者：周孟然[1] 卞凯 胡锋 来文豪 闫鹏程[1] ZHOU Meng-ran;BIAN Kai;HU Feng;LAI Wen-hao;YAN Peng-cheng(College of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学电气与信息工程学院

出　　处：《光谱学与光谱分析》2019年第7期2196-2201,共6页Spectroscopy and Spectral Analysis

基　　金：国家“十二五”科技支撑计划重点项目(2013BAK06B01);国家安全生产重大事故防治关键技术科技项目(anhui-0001-2016AQ);国家自然科学基金项目(51174258);安徽省青年科学基金项目(1808085QE157)资助

摘　　要：矿井突水一直威胁着煤矿井下施工人员的生命安全,准确且快速识别矿井突水水源类型对于矿井的安全生产起到关键性作用。激光诱导荧光(LIF)光谱技术识别矿井突水水源,有效避免了常规的水化学法需要测定多种化学参数,水源识别时间过长的缺点。提出一种间隔偏最小二乘法(iPLS)与粒子群联合支持向量分类算法(PSO-SVC)相结合的方法,iPLS算法常应用于光谱波段优选和模型的回归分析,PSO-SVC则在机器学习领域有着重要的应用,激光诱导荧光技术具有快速的时间响应、测量精度高等特点,iPLS和PSO-SVC算法运用于光谱图和光谱数据的分析,进而可以对突水水源类型识别分类。首先,用淮南矿区采集到的7种(每种水样30组)共210组荧光光谱数据进行实验,对老空水、灰岩水、灰岩水和老空水不同体积比混合水样的激光诱导荧光光谱图的差异性进行分析。比较了留出法和Kennard-Stone样本划分方法所得到的PSO-SVC模型分类准确率,采用留出法得到的训练集水样(140组)和测试集水样(70组)作为实验样本。其次,用iPLS算法将全光谱波段依次按10~25波段区间进行等分,选取划分区间的RMSECV(交叉验证均方根误差)值小于全光谱波段RMSECV值(阈值)的波段作为特征波段,结合光谱图对比分析了划分10和14个子区间的建模结果,发现通过直接观察得到的特征波段与iPLS算法筛选出的特征波段存在误差。最后,在不进行去噪、降维等预处理条件下,根据iPLS划分不同区间数的评价指标统计数据,选取划分11个区间所筛选出具有561个波长点的410.078~478.424和545.078~674.104nm特征波段范围数据作为PSO-SVC模型的输入,以iPLS结合PSO-SVC算法筛选出的特征波段与全光谱波段、直接观察得到波段建模准确率相比,训练集与测试集的分类准确率高达100%,PSO寻优到的最佳惩罚系数c为1.3670,核函数参数g为0.5762。从实验结果�Mine water inrush has been threatening the safety of underground construction personnel, so an accurate and rapid identification of mine water inrush source plays a key role in the safe production of the mine. Identification of mine water inrush source by laser induced fluorescence spectroscopy effectively avoids the shortcomings of conventional hydrochemical methods which need to determine a variety of chemical parameters and the identification time is too long. In this paper, a method of interval PLS(iPLS) and particle swarm optimization combined with support vector classification algorithm(PSO-SVC) is proposed. The iPLS algorithm is often used in spectral bands optimization and regression analysis of models, and the PSO-SVC is an important application in the field of machine learning. The laser induced fluorescence spectroscopy(LIF) technology has the characteristics of fast time response and high measurement accuracy, and the iPLS and PSO-SVC algorithms are applied to the analysis of spectral maps and spectral data, and then it can identify and classify water inrush sources. Firstly, The 210 sets of fluorescence spectrum data of 7 kinds(30 groups of each water sample) collected from Huainan mining area were used for experiment, and differences of laser-induced fluorescence spectra of mixed water samples with different volumetric ratios of old-kiln water, limestone water, limestone water and air water were analyzed. The classification accuracy of PSO-SVC model obtained by hold-out and Kennard-Stone partitioning was compared, and the training set water samples(140 groups) and test set water samples(70 groups) obtained by hold-out were used as experimental samples. Secondly, the full spectrum bands were divided into 10~25 bands by using the iPLS algorithm, and the band whose RMSECV(cross validation root mean square error) value is less than RMSECV value(threshold) of full spectrum bands was selected as the characteristic wave bands, and the results of modeling with 10 and 14 sub intervals were compared with spec

关 键 词：矿井突水 激光诱导荧光 间隔偏最小二乘法 特征波段 支持向量分类 

分 类 号：O657.3[理学—分析化学]