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Identification of mine water inrush using laser-induced fluorescence spectroscopy combined with one-dimensional convolutional neural network

机译：激光诱导荧光光谱结合一维卷积神经网络识别矿井突水

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摘要

The application of laser-induced fluorescence (LIF) combined with machine learning methods can make up for the shortcomings of traditional hydrochemical methods in the accurate and rapid identification of mine water inrush in coal mines. However, almost all of these methods require preprocessing such as principal component analysis (PCA) or drawing the spectral map as an essential step. Here, we provide our solution for the classification of mine water inrush, in which a one-dimensional convolutional neural network (1D CNN) is trained to automatically identify mine water inrush according to the LIF spectroscopy without the need for preprocessing. First, the architecture and parameters of the model were optimized and the 1D CNN model containing two convolutional blocks was determined to be the best model for the identification of mine water inrush. Then, we evaluated the performance of the 1D CNN model using the LIF spectral dataset of mine water inrush containing 540 training samples and 135 test samples, and we found that all 675 samples could be accurately identified. Finally, superior classification performance was demonstrated by comparing with a traditional machine learning algorithm (genetic algorithm-support vector machine) and a deep learning algorithm (two-dimensional convolutional neural network). The results show that LIF spectroscopy combined with 1D CNN can be used for the fast and accurate identification of mine water inrush without the need for complex pretreatments.
机译：激光诱导荧光（LIF）结合机器学习方法的应用可以弥补传统水化学方法在准确，快速识别煤矿矿井突水中的不足。但是，几乎所有这些方法都需要进行预处理，例如主成分分析（PCA）或绘制光谱图，这是必不可少的步骤。在这里，我们提供了用于矿井突水分类的解决方案，其中训练了一维卷积神经网络（1D CNN）以根据LIF光谱自动识别矿井突水，而无需进行预处理。首先，优化模型的结构和参数，确定包含两个卷积块的一维CNN模型是识别矿井突水的最佳模型。然后，我们使用包含540个训练样本和135个测试样本的矿井突水的LIF光谱数据集评估了一维CNN模型的性能，发现可以准确识别所有675个样本。最后，通过与传统的机器学习算法（遗传算法-支持向量机）和深度学习算法（二维卷积神经网络）进行比较，证明了优异的分类性能。结果表明，LIF光谱结合一维CNN可以快速，准确地识别矿井的突水，而无需进行复杂的预处理。

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