基于改进AlexNet网络的泥石流次声信号识别方法  

Debris Flow Infrasound Signal Recognition Approach Based on Improved AlexNet

作　　者：袁莉 刘敦龙 桑学佳 张少杰[3] 陈乔[4] YUAN Li;LIU Dun-long;SANG Xue-jia;ZHANG Shao-jie;CHEN Qiao(College of Software Engineering,Chengdu University of Information and Technology,Chengdu 610225,China;Sichuan Province Informatization Application Support Software Engineering Technology Research Center,Chengdu 610225,China;Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China;Chongqing Institute of Green Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714,China)

机构地区：[1]成都信息工程大学软件工程学院,四川成都610225 [2]四川省信息化应用支撑软件工程技术研究中心,四川成都610225 [3]中国科学院水利部成都山地灾害与环境研究所,四川成都610041 [4]中国科学院重庆绿色智能技术研究院,重庆400714

出　　处：《计算机与现代化》2024年第3期1-6,共6页Computer and Modernization

基　　金：国家自然科学基金青年项目(42001100);四川省自然科学基金资助项目(2023NSFSC0751);四川省信息化应用支撑软件工程技术研究中心开放课题(760115027)。

摘　　要：环境干扰噪声是泥石流次声现场监测的主要挑战,极大限制了泥石流次声信号识别的准确率。鉴于深度学习在声学信号识别中的优异表现,本文提出一种基于改进的AlexNet网络的泥石流次声信号识别方法,有效提升泥石流次声信号识别准确率和收敛速度。首先对原始次声数据集进行数据扩充、滤波降噪等预处理,并利用小波变换生成时频谱图像,然后将得到的时频谱图像作为输入,通过减小卷积核、引入批量归一化层和选择Adam优化算法搭建改进的AlexNet网络模型。实验结果表明,改进的AlexNet网络模型识别准确率为91.48%,实现了泥石流次声信号的智能识别,可为泥石流次声监测预警提供高效、可靠的技术支撑。Environmental interference noise is the main challenge for on-site monitoring of debris flow infrasound,which greatly limits the accuracy of debris flow infrasound signal identification.In view of the performance of deep learning in acoustic signal recognition,this paper proposes a debris flow infrasound signal recognition method based on improved AlexNet network,which effectively improves the accuracy and convergence speed of debris flow infrasound signal recognition.Firstly,the original infra⁃sound data set is preprocessed such as data expansion,filtering and noise reduction,and wavelet transform is used to generate a time-frequency spectrum image.Then the obtained time-frequency spectrum image is used as input,and an improved AlexNet network model is built by reducing the convolution kernel,introducing a batch normalization layer and selecting the Adam opti⁃mization algorithm.Experimental results show that the improved AlexNet network model has a recognition accuracy of 91.48%,achieves intelligent identification of debris flow infrasound signals and provides efficient and reliable technical support for debris flow infrasound monitoring and early warning.

关 键 词：泥石流 次声 深度学习 监测预警 信号识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]