周聪, 曾祥芝, 袁静, 李兴泉, 王庆良, 陈文胜. 利用深度自编码算法的地震脉冲信号检测方法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(7): 980-987, 995. DOI: 10.13203/j.whugis20180348
引用本文: 周聪, 曾祥芝, 袁静, 李兴泉, 王庆良, 陈文胜. 利用深度自编码算法的地震脉冲信号检测方法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(7): 980-987, 995. DOI: 10.13203/j.whugis20180348
ZHOU Cong, ZENG Xiangzhi, YUAN Jing, LI Xingquan, WANG Qingliang, CHEN Wensheng. Earthquake Pulselike Records Detection Based on Deep Autoencoder[J]. Geomatics and Information Science of Wuhan University, 2020, 45(7): 980-987, 995. DOI: 10.13203/j.whugis20180348
Citation: ZHOU Cong, ZENG Xiangzhi, YUAN Jing, LI Xingquan, WANG Qingliang, CHEN Wensheng. Earthquake Pulselike Records Detection Based on Deep Autoencoder[J]. Geomatics and Information Science of Wuhan University, 2020, 45(7): 980-987, 995. DOI: 10.13203/j.whugis20180348

利用深度自编码算法的地震脉冲信号检测方法

Earthquake Pulselike Records Detection Based on Deep Autoencoder

  • 摘要: 四川省汶川地震发生前,一些测震和形变台站记录到了低频脉冲信号。为了进一步分析和判别这类脉冲信号的真实性,提出了基于深度自编码算法的地震脉冲信号检测方法。以汶川地震为例,首先收集四川省49个台站震前9天的波形数据作为样本集,采用连续小波变换获得波形数据的时频谱,然后利用深度自编码神经网络对其进行训练,并应用于地震脉冲异常信号的自动识别。测试结果表明,所构建的深度自编码网络模型具备良好的稳定性,对新数据的识别准确度在93%以上。最后初步统计了汶川地震前1个月四川省出现的疑似脉冲异常的空间分布,从断层慢滑移运动的角度给出了一种可能解释。

     

    Abstract: Both seismograph and deformation stations recorded low frequency pulselike signals before the Wenchuan earthquake. In order to further analyze and judge the authenticity of such pulse signals, one earthquake pulselike records detection method based on deep learning is proposed. Take the Wenchuan earthquake as an example, firstly, the continuous waveform data of 9 days before the Wenchuan earthquake in 49 stations in Sichuan province are collected and are taken as training data set. The two dimensional time ‐ spectrum of the waveform data are calculated by continuous wavelet transform method. Then, based on the deep autoencoder neural network and time‐spectrum of the waveform data, the network model of pulselike records recognition is obtained and used for automatic identification of earthquake pulselike signals. Experimental results show that our deep autoencoder network model has good stability and the recognition percentage for new data is over 93%. Finally, the spatial distribution of pulselike signals in Sich‐uan Province one month before the Wenchuan earthquake is preliminarily calculated, and a possible explanation is given from the perspective of slow slip motion of the faults.

     

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