| 基于短时傅里叶变换和卷积神经网络的地震事件分类 |
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| 引用本文： | 张帆,杨晓忠,吴立飞,韩晓明,王树波.基于短时傅里叶变换和卷积神经网络的地震事件分类[J].地震学报,2021,43(4):463-473. |
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| 作者姓名： | 张帆 杨晓忠 吴立飞 韩晓明 王树波 |
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| 作者单位： | 1.中国北京 102206 华北电力大学控制与计算机工程学院 |
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| 基金项目： | 地震科技星火计划项目（XH20014）和中国地震局震情跟踪定向任务（2020020103）联合资助 |
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| 摘 要： | 本文选用内蒙古区域地震台网记录到的417个爆破事件和519个天然地震事件的观测资料，对其进行截取和滤波等预处理后，经过短时傅里叶变换转换为时频域的对数振幅谱，使用含有3个卷积层的卷积神经网络作为分类器，实现地震事件自动分类。5折交叉验证结果显示，本文所使用算法的平均准确率达到97.33%，测试集的准确率达到98.03%，本文采用的模型应用了较完整的原始信息，因此获得了较高的准确率和较好的稳定性。
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| 关 键 词： | 地震 爆破 分类 短时傅里叶变换 卷积神经网络 |
| 收稿时间： | 2020-07-28 |
Classification of seismic events based on short-time Fourier transform and convolutional neural network |
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| Zhang Fan,Yang Xiaozhong,Wu Lifei,Han Xiaoming,Wang Shubo.Classification of seismic events based on short-time Fourier transform and convolutional neural network[J].Acta Seismologica Sinica,2021,43(4):463-473. |
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| Authors: | Zhang Fan Yang Xiaozhong Wu Lifei Han Xiaoming Wang Shubo |
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| Affiliation: | 1.College of Control and Computer Engineering，North China Electric Power University，Beijing 102206，China2.College of Mathematics and Physics，North China Electric Power University，Beijing 102206，China3.Earthquake Agency of Inner Mongolia Autonomous Region，Hohhot 010051，China |
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| Abstract: | With the increase of seismic observation data, the application of automatic processing technology in earthquake event classification, a basic work of seismic monitoring, is becoming more and more important. In this paper, 417 explosion events and 519 natural earthquake events are selected from the rich natural and non-natural seismic observation data of the Inner Mongolia Regional Seismological Network as the original data for the study. After preprocessing, such as interception and filtering, the original data is transformed into log amplitude spectrum in time-frequency domain by short-time Fourier transform, and convolution neural network with three convolution layers is used as classifier to distinguish earthquakes from explosion events. Five folds cross validation results show that the average accuracy of the algorithm used in this paper is 97.33%, and the accuracy of the test set is 98.03%. Our model has applied more original information in the classification of natural earthquake and explosion events, therefore can get a higher accuracy and better stability. |
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