基于一维卷积神经网络的化爆和地震次声分类

Classification of chemical explosion and earthquake infrasound based on 1-D convolutional neural network

次声事件的分类识别方法应用广泛,传统分类方法在很多方面进行了尝试,但由于次声信号具备非线性的特点,致使分类难度较大,分类精度不高,这对次声事件的分类工作提出了挑战。针对次声事件中的化学爆炸与天然地震信号分类问题,文章构建了一种改进的深度卷积神经网络分类模型用于实现两类次声信号的分类。论文采用“全面禁止核试验条约组织”官网上收集到的化学爆炸和天然地震次声信号进行数据集的构建,使用改进的深度卷积神经网络分别与BP网络和一维LeNet-5网络进行对比分析。实验结果表明,论文的测试识别率能够达到82.72%,较上述算法有优势。

The classification and recognition methods of infrasound events have been widely used,and the traditional classification methods have been tried in many aspects,but because of the nonlinear characteristics of infrasound signals,the classification is difficult and the classification accuracy is not high,which poses a challenge to the classification of infrasound events.Aiming at the problem of chemical explosion and natural earthquake signal classification in infrasound events,an improved deep convolutional neural network classification model is built to realize the classification of two kinds of infrasound signals.In this paper,the chemical explosion and natural earthquake infrasound signals collected on the official website of the Comprehensive Nuclear-Test-Ban Treaty Organization(CTBTO)are used to construct the data set,and the improved deep convolutional neural network is used for comparative analysis with BP network,one-dimensional Lenet-5 network.The experimental results show that the test paper recognition rate can reach 82.72%,which is superior to the above algorithm.