矿井中采集到的微震信号通常伴随环境噪声（如电力干扰、爆破信号等）混杂在一起，传统人工筛选方法效率低下，影响灾害预警的准确性与及时性。因此，设计了一种新的信号处理与识别框架，提出了一种结合经验小波变换（empirical wavelet transform, EWT）与卷积神经网络（convolutional neural network, CNN）的信号识别方法。首先，通过模拟信号对比EWT与经验模态分解（empirical mode decomposition, EMD）在信号分解中的效果，发现EWT在减少模态混叠方面优于EMD。然后，对微震信号、环境随机噪声（非电力因素干扰影响）、瞬态尖脉冲噪声、工频谐波干扰噪声和敲击噪声进行EWT分解，将其分解成一系列不同模态，设计了具有多通道输入和多尺度卷积核的CNN架构，使得不同信号模态能够在统一框架下实现特征提取和分类，充分利用CNN在特征提取和分类方面的优势，将数据集中混合的5类信号类别进行有效区分。最终，模型训练分类准确率达到96.42%，对微震信号的识别准确率达到了99.3%。与EMD-CNN和传统神经网络方法相比，本文方法在识别效果上表现出更高的准确性和更强的鲁棒性。多模态的EWT-CNN的信号识别方法有效提升了矿井微震信号的识别精度，为矿井地质灾害的预警和防治提供了高效、可靠的技术支持。

The microseismic signals collected in mines are often mixed with environmental noise (such as power interference, blasting signals, etc.). Traditional manual screening methods are inefficient and affect the accuracy and timeliness of disaster warnings. Therefore, a new signal processing and recognition framework was designed, and a signal recognition method combining EWT (empirical wavelet transform) and CNN (convolutional neural network) is proposed. Firstly, the signal decomposition effect of EWT and EMD (empirical modal decomposition) was compared through simulated signals, and then the EWT decomposition was carried out on the microseismic signal, environmental random noise (non-power factor interference and influence), transient tip pulse noise, power frequency harmonic interference noise, and knocking noise. It was decomposed into a series of different modes, and the CNN architecture with multi-channel input and multi-scale convolutional kernel was designed, so that different signal modes can realize feature extraction and classification under a unified framework. Make full use of CNN's advantages in feature extraction and classification, and effectively distinguish the five mixed signal categories in the data set. Finally, the model training classification accuracy reaches 96. 42%, and the recognition accuracy of microseismic signals reaches 99.3%, Compared with EMD-CNN and traditional neural network methods, the proposed method shows higher accuracy and stronger robustness in recognition. The multimodal EWT-CNN signal recognition method has effectively improved the recognition accuracy of mine microseismic signals, and provides efficient and reliable technical support for the early warning and prevention of mine geological disasters.