Abstract：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 spectrogram. It is found that there were errors in the characteristic bands selected by direct observation and the iPLS algorithm. Finally, under the condition of no pretreatment such as denoising and dimension reduction, the statistical data of evaluating indexes for dividing different interval numbers according to iPLS were obtained, and the data of 410.078~478.424 and 545.078~674.104 nm characteristic wave bands with 561 wavelength points selected from 11 regions were used as the input of PSO-SVC model. we compared with full spectrum bands and direct observation bands, and the classification accuracy of the training set and the test set was as high as 100%. The optimal penalty coefficient C of PSO is 1.367, and the kernel function parameter g is 0.576 2. It can be seen from the experimental results that it is feasible to select the characteristic wave bands of the fluorescence spectrum by using iPLS, and the extracted characteristic wave bands can fully reflect the effective information of the full spectrum bands, and it provides a theoretical basis for the application of laser induced fluorescence spectroscopy in the accurate on-line identification of mine water inrush source.
Key words：Mine water inrush; Laserinduced fluorescence; Interval PLS; Characteristic wave bands; Support vector classification
周孟然，卞 凯，胡 锋，来文豪，闫鹏程. 基于iPLS的矿井突水激光诱导荧光光谱特征波段筛选[J]. 光谱学与光谱分析, 2019, 39(07): 2196-2201.
ZHOU Meng-ran, BIAN Kai, HU Feng, LAI Wen-hao, YAN Peng-cheng. Selection of Characteristic Wave Bands for Laser Induced Fluorescence Spectra of Mine Water Inrush Based on IPLS. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(07): 2196-2201.
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