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
袁莉 1刘敦龙 1桑学佳 1张少杰 2陈乔3
作者信息
- 1. 成都信息工程大学软件工程学院,四川 成都 610225;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
- 2. 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
- 3. 中国科学院重庆绿色智能技术研究院,重庆 400714
- 折叠
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.
Abstract
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Key words
debris flow/infrasound/deep learning/monitoring and early warning/signal recognition引用本文复制引用
基金项目
国家自然科学基金青年基金(42001100)
四川省自然科学基金(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)
出版年
2024
国家科技期刊平台
NSTL
万方数据