毛世榕, 管振德, 阎春恒. 2018: 基于小波包分形和神经网络的地震与岩溶塌陷识别. 地震学报, 40(2): 195-204. DOI: 10.11939/jass.20170077
引用本文: 毛世榕, 管振德, 阎春恒. 2018: 基于小波包分形和神经网络的地震与岩溶塌陷识别. 地震学报, 40(2): 195-204. DOI: 10.11939/jass.20170077
Mao Shirong, Guan Zhende, Yan Chunheng. 2018: A technique for earthquake and karst collapse recognition based on wavelet packet fractal and neural network. Acta Seismologica Sinica, 40(2): 195-204. DOI: 10.11939/jass.20170077
Citation: Mao Shirong, Guan Zhende, Yan Chunheng. 2018: A technique for earthquake and karst collapse recognition based on wavelet packet fractal and neural network. Acta Seismologica Sinica, 40(2): 195-204. DOI: 10.11939/jass.20170077

基于小波包分形和神经网络的地震与岩溶塌陷识别

A technique for earthquake and karst collapse recognition based on wavelet packet fractal and neural network

  • 摘要: 本文以近年来广西地震台网中心记录的天然地震和岩溶塌陷为例,尝试利用基于小波包的分形和径向基函数神经网络技术对这两类事件的波形进行识别,以期有效地识别地震与岩溶塌陷。结果表明,基于小波包分形与神经网络相结合的事件识别方法对天然地震和岩溶塌陷事件的识别率高达89.5%,可作为识别天然地震与岩溶塌陷的一个有效方法。

     

    Abstract: The focal mechanism and propagation path of natural earthquakes and karst collapse are different, so the frequency characteristics of their waveforms are different, too. The wavelet packet fractal method can effectively extract the natural earthquake and karst collapse waveform characteristics, and the radial basis function (RBF for short) neural network can well identify two kinds of events, therefore by using RBF neural network based on wavelet packet this paper takes the natural earthquake and karst collapse recorded by Guangxi Earthquake Networks Center in recent years as an example to try to identify two kinds of event waveforms. The results show that the recognition rate of natural earthquake and karst collapse event is up 89.5%, suggesting it is an effective method to identify natural earthquakes and karst collapse.

     

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