地震事件自动识别的标准时频变换方法

A Normal Time-Frequency Transform Method for Automatic Recognition of Earthquake Event

提出将标准时频变换(normal time-frequency transform,NTFT)与卷积神经网络(convolutional neural networks,CNN)结合,尝试实现地震信号的自动准确识别。单纯利用神经网络方法识别地震通常需要人工方式判别收集地震信号样本,对受到噪声污染的信号进行相关预处理操作。采用NTFT+CNN模型无需预处理去噪,更具有实用性。从中国云南省盈江地区3个台站连续两周的地震记录中截取19884个地震事件和17640个噪声数据作为样本,分别利用CNN模型与NTFT+CNN模型进行3台站与单台站地震信号识别实验。在3台站实验中,CNN模型的地震信号识别准确率为93.10%,NTFT+CNN模型的地震信号识别准确率提升至97.80%,引入NTFT使得识别错误率降低了3倍,表明NTFT+CNN模型可更为有效地识别信噪比低的地震信号。与此同时,CNN模型的训练次数为29,而NTFT+CNN模型训练18次即可达到上述识别准确率,说明NTFT+CNN模型收敛快速且稳定。在单台站实验中,对比考察3种典型噪声情况下的模型表现,进一步验证了NTFT对噪声-地震信号的识别作用与模型结果的正确性。并将NTFT+CNN模型应用于识别美国南加州地震台网公开的地震-脉冲噪声数据,相对于CNN模型,NTFT+CNN模型在识别准确率、收敛速度与所需训练样本数量方面均表现出明显的优势。NTFT为基于神经网络结构的地震信号自动识别提供了新的有效途径。

Objectives:In the era of earthquake big data,it is of great significance to develop efficient automatic and accurate seismic recognition algorithms.Convolutional neural networks(CNN)has played an effective role in seismic automatic recognition in seismology.However,in the environment of noise interference,CNN identification accuracy is easily affected by noise and leads to decline.Generally,CNN methods eliminate the data with serious noise pollution through filtering and preprocessing or manual screening,so as to improve the accuracy of identification.Methods:In view of the problem of seismic event recognition,we propose a new method,normal time-frequency transform(NTFT)and CNN are combined for realizing automatic and accurate identification of seismic signals.The NTFT+CNN model has good robustness and does not require manual pre-processing and denoising of seismic data in advance.Taking 19884 seismic events and 17640 noise data randomly sampled from the Yingjiang County of Yunnan province,China,the comparison between CNN and NTFT+CNN was conducted from the respect of large samples and small samples.Results:In the experiments of the three stations,the recognition accuracy of CNN model is 93.10%,and the recognition accuracy of the NTFT+CNN model is 97.80%,which means that the introduction of NTFT reduces the recognition error rate by three times.It shows that NTFT+CNN model can recognize the seismic signal more effectively under the condition of noise interference.At the same time,the epoch in CNN model is 29 and that of in NTFT+CNN is 18,which shows that the convergence in NTFT+CNN model is quicker and more stable.In the experiment of a single station,the comparison of three typical noise conditions was investigated.The recognition accuracy of CNN model was 95.98%,91.56%and 90.36%in weak background noise,strong background noise and similar waveform noise,respectively,while the recognition accuracy of NTFT+CNN model was 99.80%,97.21%and 98.50%,respectively,which further verified the recognition effect of NTFT on noise and seismic signal,the correctness of model results.The NTFT+CNN model was applied to identify the seismic-impulse noise data shared by the Southern California Earthquake Data Center.Comparing to CNN,NTFT+CNN model has more advantages on convergence speed,recognition accuracy and the number of training samples required.Conclusions:NTFT provides a new and efficient way for the recognition of seismic signal in neural network structure.