LIF和CNN的矿井突水水源类型判别

doi:10.3964/j.issn.1000-0593(2019)08-2425-06

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Abstract As deep mining goes, the water inrush threat is from the roof goaf water and the bottom pressure karst water. Coal mines water inrush water types on-line discrimination, serving as an effective monitoring method to predict mine water hazards, is an important step in Mine water disaster prevention and control work to ensure coal mine safety production. Representative ion method, as a traditional method to discriminate mine water inrush sources, must collect and seal water samples on-site, test samples in laboratory using 7 typical inorganic ion concentrations, and calculate water bursting evaluation factor. The method has disadvantages of too long detection time,easy contamination for samples, delayed warning response and misjudgment. Due to above reasons, the paper proposes a mine water inrush sources discrimination model based on Laser Induced Fluorescence (LIF) and Convolutional Neural Network (CNN). First, based on 4 types of water sources, 161 samples were collected from Xinji Second Mine of Huainan mining group during June 2016 to June 2017, including oaf water 46 items, Sandstone water 59 items, Limestone water 42 items and Ordovician limestone water 14 items. In the experiment, samples were stimulated by 405 nm laser using LIFS-405 Laser Induced Fluorescence System, and the fluorescence spectra of four kinds of 161 groups of water inrush samples were obtained. During principal component analysis, the cumulative contribution rate of the top ten components was less than 85%, making 4 types of water samples almost indistinguishable. Second, considering the random high frequency fluctuations in water fluorescence spectra, first-order lags filtering method should be used to reduce periodic high frequency fluctuations. Considering data update rate, recursive averaging method should be adopted. The paper proposes an improved recursive average first-order lag smoothing filtering method further to calculate autocorrelation processing to get enhanced two-dimensional autocorrelation characteristic fluorescence spectra. The experimental results show that calculated autocorrelation characteristic fluorescence spectra have excellent performance on interference elimination and discrimination. Finally, based on autocorrelation characteristic fluorescence spectra, mine water inrush sources discrimination model using CNN was constructed to discriminate water inrush types. The method adopts deep learning framework using autocorrelation characteristic fluorescence spectra to avoid selecting features in subjective ways. Theoretical analysis and experimental results show that the correct recognition rate of water source type can reach 98%. It is an effective way to discriminate the source of water inrush from mines and provides a new idea to discriminate the types of mine water inrush sources.

Key words：Discriminate inrush water source types; Laser-induced fluorescence; Convolution neural network

Received: 2018-05-22 Accepted: 2018-10-20

ZTFLH:

O657.3

Corresponding Authors: YUE Jian-hua E-mail: yuejh@cumt.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)08-2425-06 OR https://www.gpxygpfx.com/EN/Y2019/V39/I08/2425