Classification of seismic events based on short-time Fourier transform and convolutional neural network
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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。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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图 1 地震和爆破事件的空间分布图
Figure 1. Spatial distribution of natural earthquakes and explosions
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图 2 天然地震和爆破的震级频次分布
Figure 2. Magnitude-frequency distribution of natural earthquakes and explosions
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图 4 天然地震与爆破的波形（a）及时频域对数振幅谱（b）对比
Figure 4. Comparison of waveform （a） and time-frequency log amplitude spectrum （b） between earthquake and explosion
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图 5 天然地震与爆破的功率谱对比
Figure 5. Power spectrum comparison between natural earthquakes and explosions
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图 7 学习率测试
（a） 损失函数曲线； （b） 准确率曲线； （c） 准确率随学习率变化
Figure 7. Learning rate test
（a） Loss curves ；（b） Accuracy curves ；（c） Accuracy rate changes with learning rate
表 1 模型的超参数设置
Table 1 Super parameter setting of the model
序号 类型 参数 输出 1 输入层 50×100×1 2 卷积层 16个 3×3卷积核 50×100×16 3 批量正则化层 50×100×16 4 RELU激活层 50×100×16 5 最大池化层 2×2 25×50×16 6 卷积层 32个3×3×16卷积核 25×50×32 7 批量正则化层 25×50×32 8 RELU激活层 25×50×32 9 最大池化层 2×2 12×25×32 10 卷积层 64个3×3×32卷积核 12×25×64 11 批量正则化层 12×25×64 12 RELU激活层 12×25×64 13 最大池化层 2×2 6×12×64 14 Dropout层 比例50% 6×12×64 15 全连接层 单元数128 128 16 批量正则化层 128 17 Relu激活层 128 18 全连接层 单元数2 2 19 Softmax激活层 2 20 输出层 
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毕明霞,黄汉明,边银菊,李锐,陈银燕,赵静. 2011. 天然地震与人工爆破波形信号HHT特征提取和SVM识别研究[J]. 地球物理学进展,26(4):1157–1164. doi: 10.3969/j.issn.1004-2903.2011.04.004 Bi M X,Huang H M,Bian Y J,Li R,Chen Y Y,Zhao J. 2011. A study on seismic signal HHT features extraction and SVM recognition of earthquake and explosion[J]. Progress in Geophysics,26(4):1157–1164 (in Chinese).
边银菊. 2002. 遗传BP网络在地震和爆破识别中的应用[J]. 地震学报,24(5):516–524. doi: 10.3321/j.issn:0253-3782.2002.05.009 Bian Y J. 2002. Application of genetic BP network to discriminating earthquakes and explosions[J]. Acta Seismologica Sinica,24(5):516–524 (in Chinese).
蔡杏辉,张燕明,陈惠芳,巫立华. 2020. 基于小波特征和神经网络的天然地震与人工爆破自动识别[J]. 大地测量与地球动力学,40(6):634–639. Cai X H,Zhang Y M,Chen H F,Wu L H. 2020. Automatic identification of earthquake and explosion based on wavelet transform and neural network[J]. Journal of Geodesy and Geodynamics,40(6):634–639 (in Chinese).
陈润航,黄汉明,柴慧敏. 2018. 地震和爆破事件源波形信号的卷积神经网络分类研究[J]. 地球物理学进展,33(4):1331–1338. doi: 10.6038/pg2018BB0326 Chen R H,Huang H M,Chai H M. 2018. Study on the discrimination of seismic waveform signals between earthquake and explosion events by convolutional neural network[J]. Progress in Geophysics,33(4):1331–1338 (in Chinese).
范晓易,曲均浩,曲保安,刘方斌,山长仑,周少辉. 2019. 支持向量分类机LIBSVM方法识别天然地震、爆破与塌陷[J]. 大地测量与地球动力学,39(9):916–918. Fan X Y,Qu J H,Qu B A,Liu F B,Shan C L,Zhou S H. 2019. Support vector machine LIBSVM method for identifying natural earthquakes,blasting and collapse[J]. Journal of Geodesy and Geodynamics,39(9):916–918 (in Chinese).
郝国成,谈帆,程卓,王巍,冯思权,张伟民. 2019. 强鲁棒性和高锐化聚集度的BGabor–NSPWVD时频分析算法[J]. 自动化学报,45(3):566–576. Hao G C,Tan F,Cheng Z,Wang W,Feng S Q,Zhang W M. 2019. Time–frequency analysis of Bgabor–Nspwvd algorithm with strong robustness and high sharpening concentration[J]. Acta Automatica Sinica,45(3):566–576 (in Chinese).
黄汉明,边银菊,卢世军,蒋正锋,李锐. 2010. 天然地震与人工爆破的波形小波特征研究[J]. 地震学报,32(3):270–276. doi: 10.3969/j.issn.0253-3782.2010.03.002 Huang H M,Bian Y J,Lu S J,Jiang Z F,Li R. 2010. A wavelet feature research on seismic waveforms of earthquakes and explosions[J]. Acta Seismologica Sinica,32(3):270–276 (in Chinese).
刘建伟,赵会丹,罗雄麟,许鋆. 2020. 深度学习批归一化及其相关算法研究进展[J]. 自动化学报,46(6):1090–1120. Liu J W,Zhao H D,Luo X L,Xu J. 2020. Research progress on batch normalization of deep learning and its related algorithms[J]. Acta Automatica Sinica,46(6):1090–1120 (in Chinese).
刘莎,杨建思,田宝峰,郑钰,姜旭东,徐志强. 2012. 首都圈地区爆破、矿塌和天然地震的识别研究[J]. 地震学报,34(2):202–214. doi: 10.3969/j.issn.0253-3782.2012.02.007 Liu S,Yang J S,Tian B F,Zheng Y,Jiang X D,Xu Z Q. 2012. Discrimination between explosions,mine collapses and earthquakes in capital region of China[J]. Acta Seismologica Sinica,34(2):202–214 (in Chinese).
王凤瑛,张丽丽. 2006. 功率谱估计及其MATLAB仿真[J]. 微计算机信息,22(31):287–289. doi: 10.3969/j.issn.1008-0570.2006.31.102 Wang F Y,Zhang L L. 2006. Power spectrum density estimation and the simulation in MATLAB[J]. Control &Automation,22(31):287–289 (in Chinese).
王婷婷,边银菊,张博. 2013. 地震和爆破的综合识别方法研究[J]. 地球物理学进展,28(5):2433–2443. doi: 10.6038/pg20130522 Wang T T,Bian Y J,Zhang B. 2013. The comprehensive identification methods between earthquakes and explosions[J]. Progress in Geophysics,28(5):2433–2443 (in Chinese).
隗永刚,杨千里,王婷婷,蒋长胜,边银菊. 2019. 基于深度学习残差网络模型的地震和爆破识别[J]. 地震学报,41(5):646–657. doi: 10.11939/jass.20190030 Wei Y G,Yang Q L,Wang T T,Jiang C S,Bian Y J. 2019. Earthquake and explosion identification based on Deep Learning residual network model[J]. Acta Seismologica Sinica,41(5):646–657 (in Chinese).
杨选辉,沈萍,刘希强,郑治真. 2005. 地震与核爆识别的小波包分量比方法[J]. 地球物理学报,48(1):148–156. doi: 10.3321/j.issn:0001-5733.2005.01.020 Yang X H,Shen P,Liu X Q,Zheng Z Z. 2015. Application of method of spectral component ratio of wavelet–packets to discrimination between earthquakes and nuclear explosions[J]. Chinese Journal of Geophysics,48(1):148–156 (in Chinese).
余凯,贾磊,陈雨强,徐伟. 2013. 深度学习的昨天、今天和明天[J]. 计算机研究与发展,50(9):1799–1804. doi: 10.7544/issn1000-1239.2013.20131180 Yu K,Jia L,Chen Y Q,Xu W. 2013. Deep learning:Yesterday,today,and tomorrow[J]. Journal of Computer Research and Development,50(9):1799–1804.
曾融生,陈运泰,吴忠良. 2000. 探测地球内部的“雷达”:地震波(续)[J]. 城市防震减灾,3(6):12–14. Zeng R S,Chen Y T,Wu Z L. 2000. Radar for detecting the earth interior:Seismic wave[J]. Ruban Earthquake Disaster Prevention and Reduction,3(6):12–14 (in Chinese).
张博,边银菊,王婷婷. 2014. 用逐步代价最小决策法识别地震与爆破[J]. 地震学报,36(2):233–243. doi: 10.3969/j.issn.0253-3782.2014.02.008 Zhang B,Bian Y J,Wang T T. 2014. Discrimination of earthquakes and explosions by SAMC decision method[J]. Acta Seismologica Sinica,36(2):233–243 (in Chinese).
张帆. 2006. 地震波时频谱分析及其在爆破识别中的应用[D]. 合肥: 中国科学技术大学: 57. Zhang F. 2006. Seismic Signal Time–Frequency Analysis and Its Application to Blast Distinguishing[D]. Hefei: University of Science and Technology of China: 57 （in Chinese）.
张帆,韩晓明,郝美仙,张晖. 2016. 内蒙古阿拉善地区爆破和地震自动识别研究[J]. 华南地震,36(3):98–103. Zhang F,Han X M,Hao M X,Zhang H. 2016. Auto identification of explosion and seismic events in Alashan area[J]. South China Journal of Seismology,36(3):98–103 (in Chinese).
张号逵,李映,姜晔楠. 2018. 深度学习在高光谱图像分类领域的研究现状与展望[J]. 自动化学报,44(6):961–977. Zhang H K,Li Y,Jiang Y N. 2018. Deep learning for hyperspectral imagery classification:The state of the art and prospects[J]. Acta Automatica Sinica,44(6):961–977 (in Chinese).
赵永,刘卫红,高艳玲. 1995. 北京地区地震、爆破和矿震的记录图识别[J]. 地震地磁观测与研究,16(4):48–54. Zhao Y,Liu W H,Gao Y L. 1995. Distinguishing earthquake,explosion and mine earthquake in Beijing area[J]. Seismological and Geomangnetic Observation and Research,16(4):48–54 (in Chinese).
Beyreuther M,Barsch R,Krischer L,Megies T,Behr Y,Wassermann J. 2010. ObsPy:A python toolbox for seismology[J]. Seismol Res Lett,81(3):530–533. doi: 10.1785/gssrl.81.3.530
Kingma D P, Ba J L. 2015. Adam: A method for stochastic optimization[C]//3rd International Conference on Learning Representations, ICLR 2015: Conference Track Proceedings. San Diego: IEEE Press: 1–15.
Krizhevsky A,Sutskever I,Hinton G E. 2017. ImageNet classification with deep convolutional neural networks[J]. Commun ACM,60(6):84–90. doi: 10.1145/3065386
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