为了快速而准确地判别煤矿突水水源类型，提出了一种构建多层正则极限学习机(M-RELM)模型的方法，该模型融合了非线性特征提取和分类学习。以激光诱导荧光(LIF)技术获取水样荧光光谱，作为模型的输入；以改进的自动编码器(AE)提取荧光光谱特征，形成模型隐含层的特征空间。为了减少光谱中噪声和异常对分类结果的影响，对极限学习机(ELM)算法进行了正则化优化，根据是否利用未知样本构造训练集，进行L2范数正则极限学习机(L2-RELM)或基于图的流形正则极限学习机(GM-RELM)优化，实现监督或半监督的分类学习。通过不同功能的隐含层之间进行传播，构建了多层正则化模型，完成了预训练和训练两个过程的融合。以淮南区域煤矿突水水样为实验对象，与支持向量机(SVM)和单隐含层极限学习机进行性能比较。在含有混合水的样集上，该模型的平均测试准确率可达到94%以上，训练时间为0.2 s左右。在含有未知样本的所有水样集上，相比于L2-RELM模型，采用基于图的流形正则优化的GM-RELM模型的测试准确率可提升2%左右。实验结果表明，M-RELM模型更能适应煤矿突水水源的判别要求。

In order to quickly and accurately identify the source types of coal mine water inrush, we propose a method of constructing a multilayer regularization extreme learning machine (M-RELM) model, which combines the functions of nonlinear feature extraction and classification learning. The fluorescence spectra of water samples are obtained by laser induced fluorescence (LIF) technique as the input of model. The features of fluorescence spectra are extracted by the improved auto encoder (AE) to form the feature space of the model hidden layer. In order to reduce the effect of noise and anomaly of spectra on classification results, the algorithm of extreme learning machine(ELM) is optimized regularly. According to whether the unknown samples are used to construct the training set, the model is optimized regularly by the L2 norm regularization (L2-RELM) or the graph manifold regularization (GM-RELM), which realizes the supervised or semi-supervised classification learning. By propagating between the hidden layers of different functions, M-RELM is constructed, and the integration of pre-training and training is completed. The water inrush samples in Huainan area coal mine as the experimental object, the performance compares with the support vector machine (SVM) and ELM with a single hidden layer. On the samples set containing mixed water, the average testing accuracy of the model can reach more than 94% and the training time is about 0.2 s. On all water samples containing the unknown samples, the testing accuracy of GM-RELM is increased by 2% than L2-RELM. The experimental results show that the M-RELM model is more suitable for the identification requirements of coal mine water inrush.