基于U形卷积神经网络的震相识别与到时拾取方法研究
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摘要
精确获取震相到时是地震定位和地震走时成像等研究的重要基础.近年来,随着地震台站的不断加密,地震台网监测到的地震数量成倍增长,发展快速、准确、适用性强的震相到时自动拾取算法是地震行业的迫切需求.本文在前人工作基础上,发展了Pg、Sg震相自动识别与到时拾取的U网络算法(Unet_cea),使用汶川余震和首都圈地震台网记录的89344个不同震级、不同信噪比的样本进行训练和测试.研究表明,U网络能够较好地识别Pg、Sg震相类型和拾取到时,Pg、Sg震相的正确识别率分别为81%和79.1%,与人工标注到时的均方根误差分别为0.41s和0.54s.U网络在命中率、均方根误差等性能指标上均明显优于STA/LTA和峰度分析自动拾取方法.研究获得的最优模型可以为区域地震台网的自动处理提供辅助.
Accurate seismic phase arrival time picking is the basis for earthquake location and seismic travel time tomography.With the increase of seismic stations and the improvement of monitoring capabilities,it is an urgent need to develop fast,accurate and adaptable algorithms.Based on previous work,this paper developed a U-shaped neural network(Unet_cea)for Pg and Sg phase detection and arrival time picking,and trained model on 89344 waveform samples with different magnitudes and signal-to-noise ratio levels from Wenchuan aftershocks and the Beijing capital circle seismic network.The research shows that the U-net can recognize Pg and Sg phases with high correct rate,81% and 79.1% respectively.Compared with manual picks,the mean square root errors of Pg and Sg are 0.41 sand 0.54 s,respectively.The U-net is superior to the STA/LTA and kurtosis analysis methods in performance and has higher hit rate and lower root mean square error.The optimal model obtained from the study can assist automatic cataloging work of the regional seismic networks.
Accurate seismic phase arrival time picking is the basis for earthquake location and seismic travel time tomography.With the increase of seismic stations and the improvement of monitoring capabilities,it is an urgent need to develop fast,accurate and adaptable algorithms.Based on previous work,this paper developed a U-shaped neural network(Unet_cea)for Pg and Sg phase detection and arrival time picking,and trained model on 89344 waveform samples with different magnitudes and signal-to-noise ratio levels from Wenchuan aftershocks and the Beijing capital circle seismic network.The research shows that the U-net can recognize Pg and Sg phases with high correct rate,81% and 79.1% respectively.Compared with manual picks,the mean square root errors of Pg and Sg are 0.41 sand 0.54 s,respectively.The U-net is superior to the STA/LTA and kurtosis analysis methods in performance and has higher hit rate and lower root mean square error.The optimal model obtained from the study can assist automatic cataloging work of the regional seismic networks.
引文
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Allen R V.1978.Automatic earthquake recognition and timing from single traces.Bulletin of the Seismological Society of America,68(5):1521-1532.
Baer M,Kradolfer U.1987.An automatic phase picker for local and teleseismic events.Bulletin of the Seismological Society of America,77(4):1437-1445.
Fang L H,Wu Z L,Song K.2017.Seism Olympics.Seismological Research Letters,88(6):1429-1430.
Jiang Y R,Ning J Y.2019.Automatic detection of seismic bodywave phases and determination of their arrival times based on support vector machine.Chinese Journal of Geophysics(in Chinese),62(1):361-373,doi:10.6038/cjg2019M0442.
Küperkoch L,Meier T,Lee J,et al.2010.Automated determination of P-phase arrival times at regional and local distances using higher order statistics.Geophysical Journal International,181(2):1159-1170.
Küperkoch L,Meier T,Brüstle A,et al.2012.Automated determination of S-phase arrival times using auto regressive prediction:Application to local and regional distances.Geophysical Journal International,188(2):687-702.
Kong Q K,Trugman D T,Ross Z E,et al.2018.Machine learning in seismology:turning data into insights.Seismological Research Letters,90(1):3-14,doi:10.1785/0220180259.
Kortstrom J,Uski M,Tiira T.2016.Automatic classification of seismic events within a regional seismograph network.Computers&Geosciences,87:22-30.
Krischer L,Megies T,Barsch R,et al.2015.ObsPy:a bridge for seismology into the scientific Python ecosystem.Computational Science&Discovery,8(1):014003,doi:10.1088/1749-4699/8/1/014003.
Liu H L,Wu Q J.2017.Developments of research on earthquake detection and seismic phases picking.Progress in Geophysics(in Chinese),32(3):1000-1007,doi:10.6038/pg20170308.
Liu J S,Wan Gym Yao Z X.2013.On micro-seismic first arrival identification:A case study.Chinese Journal of Geophysics(in Chinese),56(5):1660-1666,doi:10.6038/cjg20130523.
Long J,Shelhamer E,Darrell T.2015.Fully convolutional networks for semantic segmentation.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA,USA:IEEE,3431-3440.
Maggi A,Ferrazzini V,Hibert C,et al.2017.Implementation of a multistation approach for automated event classification at Piton de la Fournaise volcano.Seismological Research Letters,88(3):878-891.
Perol T,Gharbi M,Denolle M.2018.Convolutional neural network for earthquake detection and location.Science Advances,4(2):e1700578.
Ronneberger O,Fischer P,Brox T.2015.Invited Talk:U-net Convolutional networks for biomedical image segmentation.International Conference on Medical Image Computing and Computer-Assisted Intervention,Cham:Springer,234-241.
Ross Z E,White M C,Vernon F L,et al.2016.An improved algorithm for real-time S-wave picking with application to the(augmented)ANZA network in southern California.Bulletin of the Seismological Society of America,106(5):2013-2022,doi:10.1785/0120150230.
Ross Z E,Meier M A,Hauksson E.2018.P wave arrival picking and first-motion polarity determination with deep learning.Journal of Geophysical Research:Solid Earth,123(6),5120-5129.
Saragiotis C D,Hadjileontiadis L J,Panas S M.2002.PAI-S/K:Arobust automatic seismic P phase arrival identification scheme.IEEE Transactions on Geoscience and Remote Sensing,40(6),1395-1404.
Sleeman R,Van Eck T.1998.Robust automatic P-phase picking:an on-line implementation in the analysis of broadband seismogram recordings.Physics of the Earth and Planetary Interiors,113(1-4):265-275.
Withers M,Aster R,Young C,et al.1998.A comparison of select trigger algorithms for automated global seismic phase and event detection.Bulletin of the Seismological Society of America,88(1):95-106.
Zhao M,Chen S,Yuen D.2019.Waveform classification and seismic recognition by convolution neural network.Chinese Journal of Geophysics(in Chinese),62(1):374-382,doi:10.6038/cjg2019M0151.
Zhu W,Beroza G C.2019.PhaseNet:a deep-neural-network-based seismic arrival-time picking method.Geophysical Journal International,216(1):261-273.
蒋一然,宁杰远.2019.基于支持向量机的地震体波震相自动识别及到时自动拾取.地球物理学报,62(1):361-373,doi:10.6038/cjg2019M0442.
李力,张建国,李盛乐.2018.余震自动识别技术研究进展.地震研究,41(1):1-13.
刘翰林,吴庆举.2017.地震自动识别及震相自动拾取方法研究进展.地球物理学进展,32(3):1000-1007,doi:10.6038/pg20170308.
刘劲松,王赟,姚振兴.2013.微地震信号到时自动拾取方法.地球物理学报,56(5):1660-1666,doi:10.6038/cjg20130523.
赵明,陈石,Yuen D.2019.基于深度学习卷积神经网络的地震波形自动分类与识别.地球物理学报,62(1):374-382,doi:10.6038/cjg2019M0151.
中国地震局.2017.DB/T66-2016地震编目规范.北京:地震出版社.
Akazawa T.2004.A technique for automatic detection of onset time of P-and S-phases in strong motion records.13th World Conference on Earthquake Engineering.Vancouver B C,Canada:International Association for Earthquake Engineering.
Allen R V.1978.Automatic earthquake recognition and timing from single traces.Bulletin of the Seismological Society of America,68(5):1521-1532.
Baer M,Kradolfer U.1987.An automatic phase picker for local and teleseismic events.Bulletin of the Seismological Society of America,77(4):1437-1445.
Fang L H,Wu Z L,Song K.2017.Seism Olympics.Seismological Research Letters,88(6):1429-1430.
Jiang Y R,Ning J Y.2019.Automatic detection of seismic bodywave phases and determination of their arrival times based on support vector machine.Chinese Journal of Geophysics(in Chinese),62(1):361-373,doi:10.6038/cjg2019M0442.
Küperkoch L,Meier T,Lee J,et al.2010.Automated determination of P-phase arrival times at regional and local distances using higher order statistics.Geophysical Journal International,181(2):1159-1170.
Küperkoch L,Meier T,Brüstle A,et al.2012.Automated determination of S-phase arrival times using auto regressive prediction:Application to local and regional distances.Geophysical Journal International,188(2):687-702.
Kong Q K,Trugman D T,Ross Z E,et al.2018.Machine learning in seismology:turning data into insights.Seismological Research Letters,90(1):3-14,doi:10.1785/0220180259.
Kortstrom J,Uski M,Tiira T.2016.Automatic classification of seismic events within a regional seismograph network.Computers&Geosciences,87:22-30.
Krischer L,Megies T,Barsch R,et al.2015.ObsPy:a bridge for seismology into the scientific Python ecosystem.Computational Science&Discovery,8(1):014003,doi:10.1088/1749-4699/8/1/014003.
Liu H L,Wu Q J.2017.Developments of research on earthquake detection and seismic phases picking.Progress in Geophysics(in Chinese),32(3):1000-1007,doi:10.6038/pg20170308.
Liu J S,Wan Gym Yao Z X.2013.On micro-seismic first arrival identification:A case study.Chinese Journal of Geophysics(in Chinese),56(5):1660-1666,doi:10.6038/cjg20130523.
Long J,Shelhamer E,Darrell T.2015.Fully convolutional networks for semantic segmentation.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA,USA:IEEE,3431-3440.
Maggi A,Ferrazzini V,Hibert C,et al.2017.Implementation of a multistation approach for automated event classification at Piton de la Fournaise volcano.Seismological Research Letters,88(3):878-891.
Perol T,Gharbi M,Denolle M.2018.Convolutional neural network for earthquake detection and location.Science Advances,4(2):e1700578.
Ronneberger O,Fischer P,Brox T.2015.Invited Talk:U-net Convolutional networks for biomedical image segmentation.International Conference on Medical Image Computing and Computer-Assisted Intervention,Cham:Springer,234-241.
Ross Z E,White M C,Vernon F L,et al.2016.An improved algorithm for real-time S-wave picking with application to the(augmented)ANZA network in southern California.Bulletin of the Seismological Society of America,106(5):2013-2022,doi:10.1785/0120150230.
Ross Z E,Meier M A,Hauksson E.2018.P wave arrival picking and first-motion polarity determination with deep learning.Journal of Geophysical Research:Solid Earth,123(6),5120-5129.
Saragiotis C D,Hadjileontiadis L J,Panas S M.2002.PAI-S/K:Arobust automatic seismic P phase arrival identification scheme.IEEE Transactions on Geoscience and Remote Sensing,40(6),1395-1404.
Sleeman R,Van Eck T.1998.Robust automatic P-phase picking:an on-line implementation in the analysis of broadband seismogram recordings.Physics of the Earth and Planetary Interiors,113(1-4):265-275.
Withers M,Aster R,Young C,et al.1998.A comparison of select trigger algorithms for automated global seismic phase and event detection.Bulletin of the Seismological Society of America,88(1):95-106.
Zhao M,Chen S,Yuen D.2019.Waveform classification and seismic recognition by convolution neural network.Chinese Journal of Geophysics(in Chinese),62(1):374-382,doi:10.6038/cjg2019M0151.
Zhu W,Beroza G C.2019.PhaseNet:a deep-neural-network-based seismic arrival-time picking method.Geophysical Journal International,216(1):261-273.
蒋一然,宁杰远.2019.基于支持向量机的地震体波震相自动识别及到时自动拾取.地球物理学报,62(1):361-373,doi:10.6038/cjg2019M0442.
李力,张建国,李盛乐.2018.余震自动识别技术研究进展.地震研究,41(1):1-13.
刘翰林,吴庆举.2017.地震自动识别及震相自动拾取方法研究进展.地球物理学进展,32(3):1000-1007,doi:10.6038/pg20170308.
刘劲松,王赟,姚振兴.2013.微地震信号到时自动拾取方法.地球物理学报,56(5):1660-1666,doi:10.6038/cjg20130523.
赵明,陈石,Yuen D.2019.基于深度学习卷积神经网络的地震波形自动分类与识别.地球物理学报,62(1):374-382,doi:10.6038/cjg2019M0151.
中国地震局.2017.DB/T66-2016地震编目规范.北京:地震出版社.