| 基于小波包分形和神经网络的地震与岩溶塌陷识别 |
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| 引用本文： | 毛世榕,管振德,阎春恒.基于小波包分形和神经网络的地震与岩溶塌陷识别[J].地震学报,2018,40(2):195-204. |
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| 作者姓名： | 毛世榕 管振德 阎春恒 |
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| 作者单位： | 1.中国广西桂林 541004 广西壮族自治区地震局桂林地震台 |
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| 基金项目： | 中国地质科学院岩溶塌陷防治重点实验室开放研究基金项目（2016013）和地震科技星火计划项目（XH14041Y）共同资助 |
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| 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。
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| 关 键 词： | 天然地震 岩溶塌陷 小波包变换 分形维数 径向基函数神经网络 |
| 收稿时间： | 2017-03-10 |
A technique for earthquake and karst collapse recognition based on wavelet packet fractal and neural network |
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| Affiliation: | 1.Guilin Seismic Station，Earthquake Agency of Guangxi Zhuang Autonomous Region，Guangxi Guilin 541004，China2.Institute of Karst Geology，Chinese Academy of Geological Sciences，Guangxi Guilin 541004，China3.Earthquake Agency of Guangxi Zhuang Autonomous Region，Nanning 530022，China |
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| 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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