基于FCM的煤矿突水激光诱导荧光光谱分析
|
摘要
快速识别煤矿突水水源类型对于矿井水害防治意义非凡。鉴于传统水化学方法水源识别耗时较长等诸多不足,提出了将模糊C均值聚类(FCM)算法和多维标度分析(MDS)用于激光诱导荧光光谱识别煤矿突水水源这一新思路。由于FCM算法在光谱分析和模式识别等方面都有着成功的应用,况且激光光谱具有时间响应快、灵敏度高、干扰小等优点,通过实时采集水样的荧光光谱数据,利用FCM和MDS对光谱数据分析后就可以辨别水样类型。以华东地区某矿的老空水和奥灰水以及按比例混合得到水样共7种(每种水样各20个样本)为实验材料,利用405nm激光打入被测水体,一共采集了140组荧光光谱数据,随后选择合适的波长区间进行分析。取每种水样各15组共105组光谱数据用作训练集,其余35组光谱数据用作测试集。使用MDS建立七种不同水样的模型,再利用FCM算法进行聚类分析得到七种水样的簇中心,最后使用得到的簇中心对测试集进行验证。实验结果表明,不同水样的光谱图有着较大差异,选取合适的波长区间下的光谱数据,在MDS下选择维度为2,利用FCM算法对水样进行分类,全部140组样本的准确率是100%。
Rapid identification of mine water inrush types in coal mine is of great significance for prevention and control.In view of the fact that traditional chemical method of water source identification is time-consuming and other problems,we put forward the fuzzy C mean clustering(FCM)algorithm and multidimensional scaling analysis(MDS)for laser induced fluorescence spectrum identification of mine water inrush and the new ideas.Because the FCM algorithm has been successfully used in spectral analysis and pattern recognition,and laser spectroscopy with fast response time,high sensitivity,less interference,the fluorescence spectra of the real-time data acquisition of water,the use of FCM and MDS on the spectral data analysis can identify sample types.A mine in east area of goaf water and Ordovician limestone water were mixed in proportion to get a total of 7 samples(each sample and 20 samples)as experimental materials,we used laser of 405 nm to send laser into the measured water body,collected a total of 140 groups of fluorescence spectral data,and then selected the appropriate wavelength interval analysis.105 sets of spectral data of each group were used as the training set,and the other 35 groups were used as the test set.We Used MDS to establish the model of five kinds of different water samples,and then used the FCM algorithm in cluster analysis to get the cluster center of the five kinds of water samples,finally useed the cluster center to test the test set.The experimental results show that there are dramatic difference between the spectra of different samples,we selected the appropriate wavelength range of spectral data,the dimension at 2 under MDS,and classfied the water samples by using FCM algorithm,finally the accuracy rate of all 140 samples reaches 100%.
Rapid identification of mine water inrush types in coal mine is of great significance for prevention and control.In view of the fact that traditional chemical method of water source identification is time-consuming and other problems,we put forward the fuzzy C mean clustering(FCM)algorithm and multidimensional scaling analysis(MDS)for laser induced fluorescence spectrum identification of mine water inrush and the new ideas.Because the FCM algorithm has been successfully used in spectral analysis and pattern recognition,and laser spectroscopy with fast response time,high sensitivity,less interference,the fluorescence spectra of the real-time data acquisition of water,the use of FCM and MDS on the spectral data analysis can identify sample types.A mine in east area of goaf water and Ordovician limestone water were mixed in proportion to get a total of 7 samples(each sample and 20 samples)as experimental materials,we used laser of 405 nm to send laser into the measured water body,collected a total of 140 groups of fluorescence spectral data,and then selected the appropriate wavelength interval analysis.105 sets of spectral data of each group were used as the training set,and the other 35 groups were used as the test set.We Used MDS to establish the model of five kinds of different water samples,and then used the FCM algorithm in cluster analysis to get the cluster center of the five kinds of water samples,finally useed the cluster center to test the test set.The experimental results show that there are dramatic difference between the spectra of different samples,we selected the appropriate wavelength range of spectral data,the dimension at 2 under MDS,and classfied the water samples by using FCM algorithm,finally the accuracy rate of all 140 samples reaches 100%.
引文
[1]JIN De-wu,LIU Ying-feng,LIU Zai-bin,et al(靳德武,刘英锋,刘再斌,等).Coal Science and Technology(煤炭科学技术),2013,41(1):25.
[2]LING Zhi-qiang,DENG Jun,FENG Wu-lin,et al(凌志强,邓军,冯武林,等).Safety in Coal Mines(煤矿安全),2014,45(8):196.
[3]LI Song-ying,LI Shu-wen(李松营,李书文).Journal of Henan Polytechnic University·Natural Science(河南理工大学学报·自然科学版),2013,32(5):552.
[4]LI Wen-guang,JI Dong,ZHAO Xu(李文光,冀东,赵旭).Gold(黄金),2013,(10):46.
[5]XU Xing,WANG Gong-zhong(徐星,王公忠).Coal Technology(煤炭技术),2016,35(7):144.
[6]WEN Ting-xin,ZHANG Bo,SHAO Liang-shan(温廷新,张波,邵良杉).China Safety Science Journal(中国安全科学学报),2014,24(2):100.
[7]GONG Feng-qiang,LU Jin-tao(宫凤强,鲁金涛).Journal of Mining&Safety Engineering(采矿与安全工程学报),2014,31(2):236.
[8]YANG Jian(杨建).Coal Science and Technology(煤炭科学技术),2013,41(6):100.
[9]YAN Peng-cheng,ZHOU Meng-ran,LIU Qi-meng,et al(闫鹏程,周孟然,刘启蒙,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016,36(1):243.
[10]XIE Fu-ding,LI Zhuang(谢福鼎,李壮).Bulletin of Surveying and Mapping(测绘通报),2016,(9):60.
[11]PANG Shu-jing,PENG Jian(庞淑敬,彭建).Microcomputer Information(微计算机信息),2012,(1):71.
[12]WU Gui-wu,GAO Jian-ping,LI Yu-gang,et al(吴桂武,高建平,李玉刚,等).Safety in Coal Mines(煤矿安全),2015,46(2):172.
[13]CHU Jie-wang,YAN Shi-tao(储节旺,闫士涛).Information Studies:Theory&Application(情报理论与实践),2012,35(3):5.
[2]LING Zhi-qiang,DENG Jun,FENG Wu-lin,et al(凌志强,邓军,冯武林,等).Safety in Coal Mines(煤矿安全),2014,45(8):196.
[3]LI Song-ying,LI Shu-wen(李松营,李书文).Journal of Henan Polytechnic University·Natural Science(河南理工大学学报·自然科学版),2013,32(5):552.
[4]LI Wen-guang,JI Dong,ZHAO Xu(李文光,冀东,赵旭).Gold(黄金),2013,(10):46.
[5]XU Xing,WANG Gong-zhong(徐星,王公忠).Coal Technology(煤炭技术),2016,35(7):144.
[6]WEN Ting-xin,ZHANG Bo,SHAO Liang-shan(温廷新,张波,邵良杉).China Safety Science Journal(中国安全科学学报),2014,24(2):100.
[7]GONG Feng-qiang,LU Jin-tao(宫凤强,鲁金涛).Journal of Mining&Safety Engineering(采矿与安全工程学报),2014,31(2):236.
[8]YANG Jian(杨建).Coal Science and Technology(煤炭科学技术),2013,41(6):100.
[9]YAN Peng-cheng,ZHOU Meng-ran,LIU Qi-meng,et al(闫鹏程,周孟然,刘启蒙,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016,36(1):243.
[10]XIE Fu-ding,LI Zhuang(谢福鼎,李壮).Bulletin of Surveying and Mapping(测绘通报),2016,(9):60.
[11]PANG Shu-jing,PENG Jian(庞淑敬,彭建).Microcomputer Information(微计算机信息),2012,(1):71.
[12]WU Gui-wu,GAO Jian-ping,LI Yu-gang,et al(吴桂武,高建平,李玉刚,等).Safety in Coal Mines(煤矿安全),2015,46(2):172.
[13]CHU Jie-wang,YAN Shi-tao(储节旺,闫士涛).Information Studies:Theory&Application(情报理论与实践),2012,35(3):5.