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《地震研究》[ISSN:1000-0666/CN:53-1062/P]

卷:: 47
期数:: 2024年03期

页码:: 336-349

栏目:: 人工智能

出版日期:: 2024-05-01

文章信息/Info

Title:: Advancements of Deep Learning in Seismic Monitoring and Prediction

作者:: 贾漯昭¹; 孟令媛²; 闫睿¹; (1.河南省地震局,河南郑州 450018; 2.中国地震台网中心,北京 100045)

Author(s):: JIA Luozhao¹; MENG Lingyuan²; YAN Rui¹; (1.Henan Earthquake Agency,Zhengzhou 450018,Henan,China;2.China Earthquake Networks Center,Beijing 100045,China)

关键词:: 深度学习; 监测预报; 地震检测; 地震定位; 地震预测

Keywords:: deep learning; seismic monitoring and prediction; earthquake detection; earthquake location; seismic forecasting

分类号:: P315.72

DOI:: 10.20015/j.cnki.ISSN1000-0666.2024.0037

摘要:: 对深度学习的方法原理及主流的前馈神经网络、卷积神经网络、循环神经网络、Transformer网络、自编码器、生成对抗网络以及深度强化学习网络等进行了介绍,讨论了不同网络的适用领域。从震相拾取、震相关联、地震定位与事件检测,地震信号和地震事件的分类,地震预测预报等方面对近年来深度学习方法的应用技术进行了提炼总结,综述了深度学习方法的应用进展,讨论了当前常见深度学习方法在地震监测预报领域中的主要应用方式、优势特点及解决的主要问题。总结了现阶段深度学习方法在地震监测预报领域中存在的应用局限性以及后续发展方向。

Abstract:: This article provides an overview of deep learning methods and their application in earthquake monitoring and prediction.It introduces mainstream methods such as feedforward neural networks,convolutional neural networks,recurrent neural networks,transformer networks,autoencoders,generative adversarial networks,and deep reinforcement learning networks.The article summarizes their application in phase picking,phase correlation,event detection,earthquake location,signal and event classification,and earthquake prediction.It also discusses the progress,advantages,challenges,and future directions of deep learning in earthquake monitoring and prediction.This summary serves as a valuable reference for applying deep learning in earthquake monitoring and prediction.

参考文献/References:

陈德武,杨午阳,魏新建,等.2020.基于混合网络 U-SegNet 的地震初至自动拾取[J].石油地球物理勘探,55(6):1188-1201.
Chen D W,Yang W Y,Wei X J,et al.2020.Automatic picking of seismic first arrivals based on hybrid network U-SegNet[J].Oil Geophysical Prospecting,55(6):1188-1201.(in Chinese)
陈德武,杨午阳,魏新建,等.2021.一种基于改进的 U-Net 网络的初至自动拾取研究[J].地球物理学进展,36(4):1493-1503.
Chen D W,Yang W Y,Wei X J,et al.2021.Research on first-break automatic picking based on an improved U-Net network[J].Progress in Geophysics,36(4):1493-1503.(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.
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)
甘露,吴庆举,黄清华,等.2021.卷积神经网络快速挑选接收函数[J].地球物理学报,64(7):2394-2404.
Gan L,Wu Q J,Huang Q H,et al.2021.Quick selection of receiver function based on convolutional neural network[J].Chinese Journal of Geophysics,64(7):2394-2404.(in Chinese)
孔韩东,边银菊,刘瑞丰,等.2017.地震定位方法研究进展[J].地震地磁观测与研究,38(4):81-92.
Kong H D,Bian Y J,Liu R F,et al.2017.Review of seismic location study[J].38(4):81-92.(in Chinese)
李辉峰,邹强,金文昱.2006.基于边缘检测的初至波自动拾取方法[J].石油地球物理勘探,41(2):150-155,159.
Li H F,Zou Q,Jin W Y.2006.Method of automatic first breaks pick-up based on edge detection[J].Oil Geophysical Prospecting,41(2):150-155,159.(in Chinese)
李健,王晓明,张英海,等.2020.基于深度卷积神经网络的地震震相拾取方法研究[J].地球物理学报,63(4):1591-1606.
Li J,Wang X M,Zhang Y H,et al.2020.Research on the seismic phase picking method based on the deep convolution neural network[J].Chinese Journal of Geophysics,63(4):1591-1606.(in Chinese)
李薇薇,龚仁彬,周相广,等.2021.基于深度学习 UNet++网络的初至波拾取方法[J].地球物理学进展,36(1):187-194.
Li W W,Gong R B,Zhou X G,et al.2021.UNet++:a deep-neural-network-based seismic arrival time picking method[J].Progress in Geophysics,36(1):187-194.(in Chinese)
李志强,田有,赵鹏飞,等.2021.基于深度学习的接收函数自动挑选方法[J].地球物理学报,64(5):1632-1642.
Li Z Q,Tian Y,Zhao P F,et al.2021.Receiver functions auto-picking method on the basis of deep learning[J].Chinese Journal of Geophysics,64(5):1632-1642.(in Chinese)
刘芳,蒋一然,宁杰远,等.2020.结合台阵策略的震相拾取深度学习方法[J].科学通报,65(11):1016-1026.
Liu F,Jiang Y R,Ning J Y,et al.2020.An array-assisted deep learning approach to seismic phase-picking[J].Chin Sci Bull,65(11):1016-1026.(in Chinese)
石耀霖,李林芳,程术.2022.运用LSTM神经网络对川滇地区的地震中期预报——回溯性预测2008年汶川M_S8.0地震的探索[J].中国科学院大学学报,39(1):1-12.
Shi Y L,Li L F,Cheng S,et al.2022.Application of LSTM neural network for intermediate-term earthquake prediction:retrospective prediction of 2008 Wenchuan M_S8.0 Earthquake[J].Journal of University of Chinese Academy of Sciences,39(1):1-12.(in Chinese)
苏金波,刘敏,张云鹏,等.2021.基于深度学习构建2021年5月21日云南漾濞M_S6.4地震序列高分辨率地震目录[J].地球物理学报,64(8):2647-2656.
Su J B,Liu M,Zhang Y P,et al.2021.High resolution earthquake catalog building for the 21 May 2021 Yangbi,Yunnan,M_S6.4 earthquake sequence using deep-learning phase picker.Chinese Journal of Geophysics[J].Chinese Journal of Geophysics,64(8):2647-2656.(in Chinese)
隗永刚,杨千里,王婷婷,等.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.
Wei Y G,Yang Q L,Wang T T,et al.2019.Earthquake and explosion identification based on deep learning residual network model[J].Acta Seismologica Sinica,41(5):646-657.(in Chinese)
杨庭威,曹丹平,杜南樵,等.2022.基于深度学习的接收函数横波速度预测[J].地球物理学报,65(1):214-226.
Yang T W,Cao D P,Du N Q,et al.2022.Prediction of shear-wave velocity using receiver functions based on the deep learning method[J].Chinese Journal of Geophysics,65(1):214-226.(in Chinese)
尹欣欣,杨晓鹏,蔡润,等.2022.基于PhaseNet的地震信号自动处理方法准确性分析[J].大地测量与地球动力学,42(8):870-873.
Yi X X,Yang X P,Cai R,et al.2022.Accuracy analysis of automatic seismic signal processing method based on PhaseNet[J].Journal of Geodesy and Geodynamics,42(8):870-873.(in Chinese)
张逸伦,喻志超,胡天跃,等.2021.基于 U-Net 的井中多道联合微地震震相识别和初至拾取方法[J].地球物理学报,64(6):2073-2085.
Zhang Y L,Yu Z C,Hu T Y,et al.2021.Multi-trace joint downhole microseismic phase detection and arrival picking method based on U-Net[J].Chinese Journal of Geophysics,64(6):2073-2085.(in Chinese)
张肇诚,张炜.2016.地震预报可行性的科学与实践问题讨论[J].地震学报,38(4):564-579.
Zhang Z C,Zhang W.2016.Discussion on scientific and practical problems of feasibility of earthquake prediction[J].Acta Seismologica Sinica,38(4):564-579.(in Chinese)
赵明,陈石,房立华,等.2019.基于U形卷积神经网络的震相识别与到时拾取方法研究[J].地球物理学报,62(8):3034-3042.
Zhao M,Chen S,Fang L H,et al.2019.Earthquake phase arrival auto-picking based on U-shaped convolutional neural network[J].Chinese Journal of Geophysics,62(8):3034-3042.(in Chinese)
赵明,唐淋,陈石,等.2021.基于深度学习到时拾取自动构建长宁地震前震目录[J].地球物理学报,64(1):54-66.
Zhao M,Tang L,Chen S,et al.2021.Machine learning based automatic foreshock catalog building for the 2019 M_S6.0 Changning,Sichuan earthquake[J].Chinese Journal of Geophysics,64(1):54-66.(in Chinese)
郑晶,曹子原,姜天琪,等.2018.基于深度信念神经网络的微震波到时拾取方法[J].矿业科学学报,3(6):521-526.
Zheng J,Cao Z Y,Jiang T Q,et al.2018.Deep belief neural network based arrival picking for microseismic data[J].Journal of Mining Science and Technology,3(6):521-526.(in Chinese)
周创,居兴国,李子昂,等.2020.基于深度卷积生成对抗网络的地震初至拾取[J].石油物探,59(5):795-803.
Zhou C,Ju X G,Li Z A,et al.2020.A deep convolutional generative adversarial network for first-arrival pickup from seismic data[J].Geophysical Prospecting for Petroleum,59(5):795-803.(in Chinese)
Aden-Antoniów F,Frank W B,Seydoux L.2022.An adaptable random forest model for the declustering of earthquake catalogs[J].Journal of Geophysical Research:Solid Earth,127(2):e2021JB023254.
Alpaydin E.2020.Introduction to machine learning[M].Cambridge,MA:MIT Press.
Araya-Polo M,Jennings J,Adler A,et al.2018.Deep-learning tomography[J].The Leading Edge,37(1):58-66.
Asencio-Cortés G,Martínez-álvarez F,Morales-Esteban A,et al.2017a.Using principal component analysis to improve earthquake magnitude prediction in Japan[J].Logic Journal of the IGPL,25(6),949-966.
Asencio-Cortés G,Martínez-álvarez F,Troncoso A,et al.2017b.Medium-large earthquake magnitude prediction in Tokyo with artificial neural networks[J].Neural Computing and Applications,28(5):1043-1055.
Bergen K J,Beroza G C.2018.Detecting earthquakes over a seismic network using single-station similarity measures[J].Geophysical Journal International,213(3):1984-1998.
Bergen K J,Johnson P A,de Hoop M V,et al.2019.Machine learning for data-driven discovery in solid Earth geoscience[J].Science,363(6433):eaau0323.
Bilal M A,Ji Y,Wang Y,et al.2022.Early earthquake detection using Batch Normalization Graph Convolutional Neural Network(BNGCNN)[J].Applied Sciences,12(15):7548.
Brown T,Mann B,Ryder N,et al.2006.Language models are few-shot learners[C]//NIPS'20: Proceedings of the 34th international conference on neural information processing systems.New York:1877-1901.
Chen H,Yang H,Zhu G,et al.2022.Deep outer-rise faults in the southern mariana subduction zone indicated by a machine-learning-based high-resolution earthquake catalog[J].Geophysical Research Letters,49(12):e2022GL097779.
Chen Y,Zhang G,Bai M,et al.2019 Automatic waveform classification and arrival picking based on convolutional neural network[J].Earth and Space Science,6(7):1244-1261.
Chen Y.2020.Automatic microseismic event picking via unsupervised machine learning.Geophysical Journal International,222(3):1750-1764.
Cianetti S,Bruni R,Gaviano S,et al.2021.Comparison of deep learning techniques for the investigation of a seismic sequence:An application to the 2019,Mw 4.5 Mugello(Italy)earthquake[J].Journal of Geophysical Research:Solid Earth,126(12):e2021JB023405.
Dahmen N L,Clinton J F,Meier M A,et al.2022.MarsQuakeNet:A more complete marsquake catalog obtained by deep learning techniques[J].Journal of Geophysical Research:Planets,127(11):e2022JE007503.
DeVries P M R,Viégas F,Wattenberg M,et al.2018.Deep learning of aftershock patterns following large earthquakes[J].Nature,560(7720):632-634.
Draelos T J,Ballard S,Young C J,et al.2015.A new method for producing automated seismic Bulletins:Probabilistic event detection,association,and location[J].Bulletin of the Seismological Society of America,105(5):2453-2467.
Elman J L.1990.Finding structure in time[J].Cognitive science,14(2):179-211.
Feng T,Mohanna S,Meng L,et al.2022.Edgephase:A deep learning model for multi-station seismic phase picking[J].Geochemistry,Geophysics,Geosystems,23(11):e2022GC010453.
Field E H,Jordan T H,Page M T,et al.2017.A synoptic view of the third Uniform California Earthquake Rupture Forecast(UCERF3)[J].Seismological Research Letters,88(5):1259-1267.
Fieseler C,Mitchell C A,Pyrak-Nolte L J,et al.2022.Characterization of acoustic emissions from analogue rocks using sparse regression-DMDC[J].Journal of Geophysical Research:Solid Earth,127(7):e2022JB024144.
Goodfellow I,Bengio Y,Courville A.2016.Deep learning[M].Cambridge,MA:MIT Press.
Goodfellow I J,Le Q V,Saxe A M,et al.2009.Measuring invariances in deep networks[C]// Curran Associates Inc.Proceedings of 23rd annual conference on neural information processing systems 2009:Advances in neural information processing systems 22.Vancouver:646-654.
Goodfellow I J,Pouget-Abadie J,Mirza M,et al.2014.Generative adversarial nets[C]// NIPS'14:Proceedings of the 27th international conference on neural information processing systems——Volume 2:Advances in neural information processing systems.Cambridge,MA:MIT Press,2672-2680.
Hinton G E,Salakhutdinov R R.2006.Reducing the dimensionality of data with neural networks[J].Science,313(5786):504-507.
Hochreiter S,Schmidhuber J.1997.Long short-term memory[J].Neural Computation,9(8):1735-1780.
Hu L,Zheng X,Duan Y,et al.2019.First-arrival picking with a U-net convolutional network[J].Geophysics,84(6):U45-U57.
Ida Y,Ishida M.2022.Analysis of seismic activity using self-organizing map:implications for earthquake prediction[J].Pure and Applied Geophysics,179(1):1-9.
Janbakhsh P,Pysklywec R,Shahnas M H.2019.Earthquake magnitude,distance,first motion polarity,and noise/event determination using AE,CNN1D,RNN Networks[C]//AGU Fall Meeting Abstracts.
Jasperson H,Bolton D C,Johnson P,et al.2021.Attention network forecasts time-to-failure in laboratory shear experiments[J].Journal of Geophysical Research:Solid Earth,126(11):e2021JB022195.
Jia L Z,Chen H F,Xing K.2022.Rapid classification of local seismic events using machine learning[J].Journal of Seismology,26(5):897-912.
Jordan M I,Mitchell T M.2015.Machine learning:Trends,perspectives,and prospects[J].Science,349(6245):255-260.
Kail R,Burnaev E,Zaytsev A.2021.Recurrent convolutional neural networks help to predict location of earthquakes[J].IEEE Geoscience and Remote Sensing Letters,19:1-5.
Kim S,Yoon B,Lim J T,et al.2021.Data-driven signal-noise classification for microseismic data using machine learning[J].Energies,14(5):1499.
Klein F W.2002.User's guide to HYPOINVERSE-2000,a Fortran program to solve for earthquake locations and magnitudes[R].US Geological Survey.
Kriegerowski M,Petersen G M,Vasyura-Bathke H,et al.2019.A deep convolutional neural network for localization of clustered earthquakes based on multistation full waveforms[J].Seismological Research Letters,90(2A):510-516.
Lapins S,Goitom B,Kendall J M,et al.2021.A little data goes a long way:Automating seismic phase arrival picking at Nabro volcano with transfer learning[J].Journal of Geophysical Research:Solid Earth,126(7):e2021JB021910.
Last M,Rabinowitz N,Leonard G.2016.Predicting the maximum earthquake magnitude from seismic data in Israel and its neighboring countries[J].PloS One,11(1):e0146101.
LeCun Y,Bengio Y,Hinton G.2015.Deep learning[J].Nature,521(7553):436-444.
Li Y E,O'Malley D,Beroza G,et al.2023.Machine Learning Developments and Applications in Solid-Earth Geosciences:Fad or Future?[J].Journal of Geophysical Research:Solid Earth,128(1):e2022JB026310.
Li Z F,Meier M A,Hauksson E,et al.2018.Machine learning seismic wave discrimination:Application to earthquake early warning[J].Geophysical Research Letters,45(10):4773-4779.
Linville L,Pankow K,Draelos,et al.2019.Deep learning models augment analyst decisions for event discrimination[J].Geophysical Research Letters,46(7):3643-3651.
Lomax A,Michelini A,Jozinovi D.2019.An investigation of rapid earthquake characterization using single-station waveforms and a convolutional neural network[J].Seismological Research Letters,90(2A):517-529.
Ma S,Li Z,Wang W.2022.Machine learning of source spectra for large earthquakes[J].Geophysical Journal International,231(1):692-702.
Magaña-Zook S A,Ruppert S D.2017.Explosion monitoring with machine learning:A LSTM approach to seismic event discrimination[C]//AGU Fall Meeting Abstracts.S43A-0834.
MajstoroviAc'1 J,Giffard-Roisin S,Poli P.2021.Designing convolutional neural network pipeline for near-fault earthquake catalog extension using single-station waveforms[J].Journal of Geophysical Research:Solid Earth,126(7):e2020JB021566.
Meier M A,Ross Z E,Ramachandran A,et al.2019.Reliable real-time seismic signal/noise discrimination with machine learning[J].Journal of Geophysical Research:Solid Earth,124(1):788-800.
Mignan A,Broccardo M.2020.Neural network applications in earthquake prediction(1994-2019):Meta-analytic and statistical insights on their limitations[J].Seismological Research Letters,91(4):2330-2342.
Mirrashid M.2014.Earthquake magnitude prediction by adaptive neuro-fuzzy inference system(ANFIS)based on fuzzy C-means algorithm[J].Natural Hazards,74(3):1577-1593.
Mnih V,Kavukcuoglu K,Silver,D,et al.2015.Human-level control through deep reinforcement learning[J].Nature,518(7540):529-533.
Mosher S G,Audet P.2020.Automatic detection and location of seismic events from time-delay projection mapping and neural network classification[J].Journal of Geophysical Research:Solid Earth,125(10):e2020JB019426.
Mousavi S M,Beroza G C.2020b.A machine-learning approach for earthquake magnitude estimation[J].Geophysical Research Letters,47(1):e2019GL085976.
Mousavi S M,Beroza G C.2022a.Deep-learning seismology[J].Science,377(6607),doi:10.1126/science.abm4470.
Mousavi S M,Beroza G.2022b.A dataset of published journal papers using neural networks for seismological tasks[J/OL].(2022-03-26)[2023-02-15].https://explore.openaire.eu/search/dataset?pid=10.5281%2Fzenodo.6386952.
Mousavi S M,Ellsworth W L,Zhu W,et al.2020a.Earthquake transformer-an attentive deep-learning model for simultaneous earthquake detection and phase picking[J].Nature Communications,11(1):3952.
Mousavi S M,Sheng Y,Zhu W,et al.2019a.Stanford earthquake dataset(STEAD):A global data set of seismic signals for AI[J].IEEE Access,7:179464-179476.
Mousavi S M,Zhu W,Ellsworth W,et al.2019b.Unsupervised clustering of seismic signals using deep convolutional autoencoders[J].IEEE Geoscience and Remote Sensing Letters,16(11):1693-1697.
Mousavi S M,Zhu W,Sheng Y,et al.2019c.CRED:A deep residual network of convolutional and recurrent units for earthquake signal detection[J].Scientific Reports,9(1):1-14.
Mousavi S M,Beroza,G C.2020c.Bayesian-deep-learning estimation of earthquake location from single-station observations[J].IEEE Transactions on Geoscience and Remote Sensing,58(11):8211-8224.
Münchmeyer J,Woollam J,Rietbrock A,et al.2022.Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers[J].Journal of Geophysical Research:Solid Earth,127(1):e2021JB023499.
Nolte D D,Pyrak-Nolte L J.2022.Monitoring fracture saturation with internal seismic sources and twin neural networks[J].Journal of Geophysical Research:Solid Earth,127(2):e2021JB023005.
Ogata Y.2017.Statistics of earthquake activity:Models and methods for earthquake predictability studies[J].Annual Review of Earth and Planetary Sciences,45:497-527.
Panakkat A,Adeli H.2007.Neural network models for earthquake magnitude prediction using multiple seismicity indicators[J].International Journal of Neural Systems,17:13-33.
Panakkat A,Adeli H.2009.Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators[J].Computer-Aided Civil and Infrastructure Engineering,24(4):280-292.
Perol T,Gharbi M,Denolle M.2018.Convolutional neural network for earthquake detection and location[J].Science Advances,4(2):e1700578.
Qin B,Huang F,Huang S,et al.2022.Machine learning investigation of clinopyroxene compositions to evaluate and predict mantle metasomatism worldwide[J].Journal of Geophysical Research:Solid Earth,127(5):e2021JB023614.
Radford A,Wu J,Child R,et al.2019.Language models are unsupervised multitask learners[J/OL].(2019)[2023-02-15].https://cdn.openai.com/better-language- models/language_models_are_unsupervised_multitask_learners.pdf
Rong K,Yoon C E,Bergen K J,et al.2018.Locality-sensitive hashing for earthquake detection:A case study of scaling data-driven science[J/OL].(2018-07-24)[2023-02-15].https://arxiv.org/abs/1803.09835v1.
Rosenblatt F.1958.The perceptron:A probabilistic model for information storage and organization in the brain[J].Psychological review,65(6):386-408.
Ross Z E,Meier M-A,Hauksson E.2018.P wave arrival picking and first-motion polarity determination with deep learning[J].Journal of Geophysical Research:Solid Earth,123(6):5120-5129.
Ross Z E,Yue Y S,Meier M A,et al.2019.Phaselink:A deep learning approach to seismic phase association[J].Journal of Geophysical Research:Solid Earth,124(1):856-869.
Rouet-Leduc B,Hulbert C,Lubbers N,et al.2017.Machine learning predicts laboratory earthquakes[J].Geophysical Research Letters,44(18):9276-9282.
Saad O M,Huang G,Chen Y,et al.2021.Scalodeep:A highly generalized deep learning framework for real-time earthquake detection[J].Journal of Geophysical Research:Solid Earth,126(4):e2020JB021473.
Seydoux L,Balestriero R,Poli P,et al.2020.Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning[J].Nature communications,11(1):3972.
Shcherbakov R,Zhuang J,Zller G,et al.2019.Forecasting the magnitude of the largest expected earthquake[J].Nature Communications,10(1):1-11.
Shreedharan S,Bolton D C,Rivière J,et al.2021.Machine learning predicts the timing and shear stress evolution of lab earthquakes using active seismic monitoring of fault zone processes[J].Journal of Geophysical Research:Solid Earth,126(7):e2020JB021588.
Silver D,Huang A,Maddison C J,et al.2016.Mastering the game of Go with deep neural networks and tree search[J].Nature,529(7587):484-489.
Silver D,Schrittwieser J,Simonyan K,et al.2017.Mastering the game of go without human knowledge[J].Nature,550(7676):354-359.
Song Z,Zhang Z,Zhang G,et al.2022.Identifying the types of loading mode for rock fracture via convolutional neural networks[J].Journal of geophysical research:solid Earth,127(2):e2021JB022532.
Steinberg A,Vasyura-Bathke H,Gaebler P,et al.2021.Estimation of seismic moment tensors using variational inference machine learning[J].Journal of Geophysical Research:Solid Earth,126(10):e2021JB022685.
Waldhauser F,Ellsworth W L.2000.A double-difference earthquake location algorithm:Method and application to the northern Hayward fault,California[J].Bulletin of the Seismological Society of America,90(6):1353-1368.
Wang F,Song X,Li J.2022.Deep learning-based h-κ method(HKNET)for estimating crustal thickness and vp/vs ratio from receiver functions[J].Journal of Geophysical Research:Solid Earth,127(6):e2022JB023944.
Wang J,Xiao Z,Liu C,et al.2019.Deep learning for picking seismic arrival times[J].Journal of Geophysical Research:Solid Earth,124(7):6612-6624.
Wiszniowski J,Plesiewicz B M,Trojanowski J.2014.Application of real time recurrent neural network for detection of small natural earthquakes in Poland[J].Acta Geophysica,62:469-485.
Withers M,Aster R,Young C,et al.1998.A comparison of select trigger algorithms for automated global seismic phase and event detection[J].Bulletin of the Seismological Society of America,88(1):95-106.
Wu Y,Lin Y,Zhou Z,et al.2017.Cascaded region-based densely connected network for event detection:A seismic application[J/OL].(2017-11-29)[2023-02-15].https://arxiv.org/abs/1709.07943.
Wu Y,Lin Y,Zhou Z,et al.2018.Seismic-net:A deep densely connected neural network to detect seismic events[J/OL].(2018-01-17)[2023-02-15].https://doi.org/10.48550/arXiv.1802.02241.
Wyss M.1997.Second round of evaluations of proposed earthquake precursors[J].Pure and Applied Geophysics,149:3-16.
Xiao Z,Wang J,Liu C,et al.2021.Siamese earthquake transformer:A pair-input deep-learning model for earthquake detection and phase picking on a seismic array[J].Journal of Geophysical Research:Solid Earth,126(5):e2020JB021444.
Yeck W L,Patton J M,Johnson C E,et al.2019.GLASS3:A standalone multiscale seismic detection associator[J].Bulletin of the Seismological Society of America,109(4):1469-1478.
Yoon C E,O'Reilly O,Bergen K J,et al.2015.Earthquake detection through computationally efficient similarity search[J].Science advances,1(11):e1501057.
Yu S,Ma J.2021.Deep learning for geophysics:Current and future trends[J].Reviews of Geophysics,59(3):e2021RG000742.
Zhang M,Ellsworth W L,Beroza G C.2019.Rapid earthquake association and location[J].Seismological Research Letters,90(6):2276-2284.
Zhang X,Reichard-Flynn W,Zhang M,et al.2022.Spatiotemporal graph convolutional networks for earthquake source characterization[J].Journal of Geophysical Research:Solid Earth,127(11):e2022JB024401.
Zhang X,Zhang J,Yuan C,et al.2020.Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method[J].Scientific Reports,10(1):1941.
Zhao M,Chen S,Fang L,et al.2019.Earthquake phase arrival auto-picking based on U-shaped convolutional neural network[J].Chinese Journal of Geophysics,62(8):3034-3042.
Zheng J,Lu J,Peng S,et al.2018.An automatic microseismicor acoustic emission arrival identification scheme with deep recurrent neural networks[J].Geophysical Journal International,212(2):1389-1397.
Zhou Y,Yue H,Fang L,et al.2022.An earthquake detection and location architecture for continuous seismograms:Phase picking,association,location,and matched filter(PALM)[J].Seismological Research Letters,93(1):413-425.
Zhou Y,Yue H,Kong Q,et al.2019.Hybrid event detection and phase-picking algorithm using convolutional and recurrent neural networks[J].Seismological Research Letters,90(3):1079-1087.
Zhu S,Li S,Peng Z,et al.2021.Imitation learning of neural spatio-temporal point processes[J].IEEE Transactions on Knowledge and Data Engineering,34(11):5391-5402.
Zhu W,Beroza G C.2019.PhaseNet:A deep-neural-network-based seismic arrival-time picking method[J].Geophysical Journal International,216(1):261-273.
Zhu W,McBrearty I W,Mousavi S M,et al.2022a.Earthquake phase association using a Bayesian Gaussian mixture model[J].Journal of Geophysical Research:Solid Earth,127(5):e2021JB023249.
Zhu W,Tai K S,Mousavi S M,et al.2022b.An end-to-end earthquake detection method for joint phase picking and association using deep learning[J].Journal of Geophysical Research:Solid Earth,127(3):e2021JB023283.
Zou S,Chen X,Brzozowski M J,et al.2022.Application of machine learning to characterizing magma fertility in porphyry Cu deposits[J].Journal of Geophysical Research:Solid Earth,127(8):e2022JB024584.

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备注/Memo:: 收稿日期:2023-05-09.
基金项目:国家重点研发计划(2021YFC3000705); 中国地震局震情跟踪定向工作任务(2023010111).
第一作者简介:贾漯昭(1982-),高级工程师,主要从事数字地震学和数值分析研究.E-mail:123@eqha.gov.cn.
通信作者简介:孟令媛(1983-),研究员,博士,主要从事地震活动性和地震危险性研究.E-mail:meng.lingyuan@hotmail.com.

更新日期/Last Update: 2024-05-01

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