Infrasound Event Classification Fusion Model Based on Multiscale SE-CNN and BiLSTM  

基于多尺度SE-CNN和Bi LSTM融合的次声事件分类模型

作　　者：Hongru Li Xihai Li Xiaofeng Tan Chao Niu Jihao Liu Tianyou Liu 李鸿儒;李夕海;谭笑枫;牛超;刘继昊;刘天佑(火箭军工程大学核工程学院,西安710025)

机构地区：[1]School of Nuclear Engineering,Rocket Force University of Engineering,Xi’an 710025,China

出　　处：《Applied Geophysics》2024年第3期579-592,620,共15页应用地球物理（英文版）

基　　金：supported by the Shaanxi Province Natural Science Basic Research Plan Project(2023-JC-YB-244).

摘　　要：The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.次声事件的有效分类对提升自然灾害种类的辨识能力有着重要意义。传统次声分类工作主要依赖于人工特征提取后利用机器学习算法分类,但提取的特征有效性难以得到保证。当前利用卷积神经网络自动提取特征进行分类成为趋势,这种方法能够通过卷积核自动提取信号空间特征,但对于次声信号,不仅包含空间信息,同时作为时间序列,时序特征也极其重要。仅使用卷积神经网络会缺失次声序列时间依赖关系,利用长短期记忆网络能够弥补时序特征,但又会造成次声信号空间特征信息的丢失。针对此问题提出了一种能够自动提取次声信号时序依赖特征与空间特征,自适应进行特征优选并实现两类特征融合的多尺度SE-CNN-BiLSTM(Squeeze-and-Excitation-Convolutional Neural Network-Bidirectional Long Short-Term Memory)次声事件分类模型。利用地震和化爆次声数据对该模型开展了分类实验,结果表明,该模型分类准确率达到了98%以上,验证了模型的有效性及优越性。

关 键 词：infrasound classification channel attention convolution neural network bidirectional long short-term memory network multiscale feature fusion 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] X43[自动化与计算机技术—控制科学与工程]