一种地震动信号特征提取与分类方法

为了实时检测、识别和预警对地下基础设施的挖掘破坏活动，本文提出一种地震动信号特征提取与分类方法。通过提取小波包变换域和集合经验模态变换域的多域能量联合分布特征向量，构建改进的径向基神经网络分类模型，利用机器学习的方法提取稳定的信号多域融合特征，并实现准确的信号特征分类预测。由多类别挖掘信号的仿真实验结果可以看出，本文的算法和模型能有效提升地震动信号分类的准确率，对地震动干扰信号具有较强的鲁棒性。

A Feature Extraction and Classification Method of the Vibratory Signal on Ground

A method is proposed to detect,identify and warn the excavation and destruction of underground infrastructure in real time,by feature extraction and classification of the vibratory signal on ground.An improved radial basis neural network classification model is constructed by extracting the multi-domain energy joint distribution feature vector of the wavelet packet transform domain and the ensemble empirical mode transform domain,which adopts machine learning methods to extract stable multi-domain fusion features of the target signal and achieve accurate feature classification prediction.It can be concluded from the simulation experiment results of multi-category ground vibratory signals that the algorithm and model proposed in this paper can effectively improve the classification accuracy of the ground vibratory signals and have strong robustness to ground vibratory interference signals.