摘要
针对短时窗平均/长时窗平均算法从次声台站监测数据中提取的信号仍然包含噪声的问题,对支持向量机和人工神经网络的机器学习方法进行了研究。采用小波包分解的方法对信号进行重构,提取出各频带内的重构信号能量特征,对事件信号和噪声进行了识别实验,并分析了提高识别能力的方法,为工程应用提供理论参考。实验结果表明,在训练数据集不大的情况下,通过优化模型结构可以将两种方法的识别能力提高到可以接受的水平。
Aiming at the problem that the signal extracted from infrasound station monitoring data by short term averaging/long term averaging(STA/LTA)algorithm still contained noise,preliminary experimental studies were made on the machine learning method of support vector machine and artificial neural network.A method of wavelet packet decomposition was used to reconstruct the signals,and the energy characteristics were extracted from them.The methods were analyzed to improve the recognition ability.The experimental results showed that the recognition ability of the two methods can be improved to an acceptable level by optimizing the model structure such as the training data set was small.
作者
吴涢晖
邹士亚
庞新良
陈晓雷
WU Yunhui;ZOU Shiya;PANG Xinliang;CHEN Xiaolei(Research Institute of Chemical Defense,Beijing 102205,China)
出处
《应用声学》
CSCD
北大核心
2020年第2期207-215,共9页
Journal of Applied Acoustics
关键词
次声信号检测
小波包分解
神经网络
支持向量机
Infrasound signal detection
Wavelet packet decomposition
Neural network
Support vector machine
作者简介
吴涢晖(1983–),男,湖北安陆人,博士研究生,工程师,研究方向:核爆探测与辐射防护;通信作者:陈晓雷,E-mail:chenxlei2002@163.com。
