Study on Characteristic Raman Shift Screening Method Based on MPA for Raman Spectrum of Mine Water Inrush Source

• Published in: Scientific programming 2022-08, Vol.2022, p.1-8
• Main Authors: Zhou, Minghao, Dai, Rongying
• Format: Article
• Language: English
• Subjects: Alcohol; Algorithms; Back propagation networks; Coal mines; Coal mining; Data analysis; Data points; Decision trees; Disasters; Feature selection; Heuristic; Lasers; Limestone; Machine learning; Mine waters; Mines; Mining industry; Model accuracy; Occupational safety; Optimization; Parameter estimation; Production methods; Raman spectra; Raman spectroscopy; Redundancy; Sandstone; Screening; Spectrum analysis; Stone; Support vector machines; Surface water; Water sampling
• ISSN: 1058-9244; 1875-919X
• DOI: 10.1155/2022/5202265

In the process of mine water inrush disaster prevention, accurate and rapid identification of water inrush source type is of great significance to coal mine safety production. However, traditional hydrochemical methods have shortcomings such as time-consuming and complex detection. Therefore, a new idea of identifying mine water inrush source by Raman spectroscopy is proposed. Goaf water, roof sandstone fissure water, Ordovician limestone water, Taiyuan limestone water, and surface water as well as their mixed water samples are selected as research objects, and Raman spectral data of different water samples are collected by the Raman spectroscopy system. To eliminate the influence of laser power fluctuation and spectrometer system noise in Raman spectrum acquisition, detrend correction (DC), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), first derivative (FD), and mean centering (MC) were used to preprocess the raw Raman spectra. Due to the large dimension and long analysis time of Raman spectrum data, the marine predator algorithm (MPA) is used to screen the characteristic Raman shifts of the Raman spectrum of water samples, and the characteristic Raman shift information that can best characterize the mine water samples is obtained. Finally, to verify the feasibility of MPA screening the characteristic Raman shifts of Raman spectrum of mine water inrush source, the selected characteristic Raman displacement information is used as input to construct BP neural network (BP), k-nearest neighbor algorithm (KNN), support vector machine (SVM), and decision tree (DT) classification models, respectively. Experiments show that SNV has the best preprocessing effect on the raw Raman spectrum, which can effectively eliminate part of the noise in the Raman spectrum data and improve the accuracy of Raman spectrum identification. Using MPA, 226 characteristic Raman shifts can be screened from 2048 Raman data points, reducing the number of Raman shifts to 11.04%, and the modeling accuracy of characteristic Raman shift information screened by MPA is higher than that of full Raman data. In addition, the average analysis speed of BP, KNN, SVM, and DT water source identification models is 7.61 times faster than that of all Raman data. The results show that MPA is adopted to screen the characteristic Raman displacement of mine water source Raman spectrum, which can effectively reduce the redundancy of Raman spectral data and greatly impr