• ISSN 2097-1893
  • CN 10-1855/P


杨旭 李永华 盖增喜

引用本文: 杨旭,李永华,盖增喜. 机器学习在地震学中的应用进展. 地球与行星物理论评,2021,52(1):76-88
Yang X, Li Y H, Ge Z X. Machine learning and its application in seismology. Reviews of Geophysics and Planetary Physics, 2021, 52(1):76-88


doi: 10.19975/j.dqyxx.2020-006
基金项目: 中国地震局地球物理研究所基本业务专项资助项目(DQJB19A0111);国家自然科学基金资助项目(U1839210, 41874108)

    杨旭(1991-),女,博士研究生,主要从事地震到时拾取与地震定位方面的研究. E-mail: yangxu161@cea-igp.ac.cn

  • 中图分类号: P315.0

Machine learning and its application in seismology

Funds: Supported by the Basal Research Fund of Institute of Geophysics, China Earthquake Administration (DQJB19A0111) and the National Science Foundation of China (No.U1839210, 41874108)
  • 摘要: 理解并预测多尺度、高维度和非线性的地震学现象是一个极具挑战性的科学任务. 与日俱增的海量观测数据降低了信息收集和信息解读之间的耦合程度,增加了信息解读的抽象性和不确定性. 然而,伴随大数据一同来临的还有人工智能计算机技术——机器学习. 机器学习突出的隐式关系提取和复杂任务处理能力推动着研究学者们不断将机器学习的应用推向更广阔的领域. 本文介绍了地震学中常用的机器学习算法及其应用范围,讨论了人工智能与地震数据相结合的发展方向.


  • 图  1  机器学习在地震学领域中的科技文章年度数量统计图(Scopus数据库)

    Figure  1.  Yearly number of scientific papers on topic of machine learning in seismology (Scopus Database)

    图  2  常用的机器学习算法类型

    Figure  2.  Types of commonly used machine learning algorithms

  • [1] Abdel-Hamid O, Mohamed A R, Jiang H, et al. Convolutional neural networks for speech recognition[J]. IEEE/ACM Transactions on Audio Speech and Language Processing, 2014, 22(10): 1533-1545. doi: 10.1109/TASLP.2014.2339736
    [2] Afonso J C, Salajegheh F, Szwillus W, et al. A global reference model of the lithosphere and upper mantle from joint inversion and analysis of multiple data sets[J]. Geophysical Journal International, 2019, 217(3): 1602-1628. doi: 10.1093/gji/ggz094
    [3] Ait Laasri E H, Akhouayri E S, Agliz D, et al. Seismic signal classification using multi-layer perceptron neural network[J]. International Journal of Computer Applications, 2013, 79(15): 35-43. doi: 10.5120/13821-1950
    [4] Akhtar N, Mian A. Nonparametric coupled Bayesian dictionary andclassifier learning for hyperspectral classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(9): 4038-4050. doi: 10.1109/TNNLS.2017.2742528
    [5] Alavi A H, Gandomi A H. Prediction of principal groundmotion parameters using a hybrid method coupling artificial neural networks and simulated annealing[J]. Computers and Structures, 2011, 89(23): 2176-2194.
    [6] Araya-Polo M, Jennings J, Adler A, et al. Deep-learning tomography[J]. The Leading Edge, 2018, 37(1): 58-66. doi: 10.1190/tle37010058.1
    [7] Asencio-Cortés G, Martínez-Álvarez F, Troncoso A, et al. Medium-large earthquake magnitude prediction in Tokyo with artificial neural networks[J]. Neural Computing and Applications, 2017, 28: 1043-1055.
    [8] Asim K M, Martinezalvarez F, Basit A W, et al. Earthquake magnitude prediction in Hindukush region using machine learning techniques[J]. Natural Hazards, 2017, 85(1): 471-486. doi: 10.1007/s11069-016-2579-3
    [9] Beckouche S, Ma J. Simultaneous dictionary learning and denoising for seismic data[J]. Geophysics, 2014, 79: A27-A31. doi: 10.1190/geo2013-0382.1
    [10] Bergen K J, Johnson P A, de Hoop M V, et al. Machine learning for data-driven discovery in solid Earth geoscience[J]. Science, 2019, 363(6433): eaau0323. doi: 10.1126/science.aau0323
    [11] Bishop C M. Pattern Recognition and Machine Learning[M]. New York: Springer, 2006: 205-213.
    [12] Bobin J, Moudden Y, Starck J L, et al. SZ and CMB reconstruction using generalized morphological component analysis[J]. Statistical Methodology, 2008, 5: 307-317. doi: 10.1016/j.stamet.2007.10.003
    [13] Bobin J, Starck J-L, Sureau F, et al. Sparse component separation for accurate cosmic microwave background estimation[J]. Astronomy and Astrophysics, 2013, 550, A73. doi: 10.1051/0004-6361/201219781
    [14] Böhning D. Multinomial logistic regression algorithm[J]. Annals of the Institute of Statistical Mathematics, 1992, 44(1): 197-200. doi: 10.1007/BF00048682
    [15] Böse M, Wenzel F, Erdik M. PreSEIS: a neural network-based approach to earthquake early warning for finite faults[J]. Bulletin of the Seismological Society of America, 2008, 98(1): 366-382. doi: 10.1785/0120070002
    [16] Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324
    [17] Chen Y K. Automatic microseismic event picking via unsupervised machine learning[J]. Geophysical Journal International, 2020, 222(3): 1750-1764. doi: 10.1093/gji/ggaa186
    [18] Chen Y K, Ma J, Fomel S. Double-sparsity dictionary for seismic noise attenuation[J]. Geophysics, 2016, 81(2): V103-V116. doi: 10.1190/geo2014-0525.1
    [19] Cheng M Y, Wu Y W, Syu R F. Seismic assessment of bridge diagnostic in Taiwan using the evolutionary support vector machine inference model ESIM[J]. Applied Artificial Intelligence, 2014, 28(5): 449-469. doi: 10.1080/08839514.2014.905818
    [20] Contreras-Reyes E, Muñoz-Linford P, Cortés-Rivas V, et al. Structure of the collision zone between the Nazca Ridge and the Peruvian convergent margin: Geodynamic and seismotectonic implications[J]. Tectonics, 2019, 38(9): 3416-3435. doi: 10.1029/2019TC005637
    [21] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20: 273-297.
    [22] Cox D R. The Regression Analysis of Binary Sequences[J]. Journal of the Royal Statistical Society,Series B (Methodological), 1958, 21(1): 215-232.
    [23] Cracknell M J, Reading A M. The upside of uncertainty: Identification of lithology contact zones from airborne geophysics and satellite data using random forests and support vector machines[J]. Geophysics, 2013, 78: WB113-WB126. doi: 10.1190/geo2012-0411.1
    [24] Cristianini N, Shaw-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods[M]. New York: Cambridge University Press, 2000.
    [25] De Matos M, Osorio P, Johann P. Unsupervised seismic facies analysis using wavelet transform and self-organizing maps[J]. Geophysics, 2007, 72: 9-21.
    [26] Dokht R M, Kao H, Visser R, et al. Seismic event and phase detection using time-frequency representation and convolutional neural networks[J]. Seismological Research Letters, 2019, 90(2A): 481-490. doi: 10.1785/0220180308
    [27] Dowla F U, Taylor S R, Anderson R W. Seismic discrimination with artificial neural networks: preliminary results with regional spectral data[J]. Bulletin of the Seismological Society of America, 1990, 80(5): 1346-1373.
    [28] Draelos T J, Peterson M G, Knox H A, et al. Dynamic tuning of seismic signal detector trigger levels for local networks[J]. Bulletin of the Seismological Society of America, 2018, 108: 1346-1354. doi: 10.1785/0120170200
    [29] Duda R O, Hart P E, Stork D G. Pattern Classification[M]. Hoboken, New Jersey, USA:Wiley Interscience, 2000.
    [30] Dysart P S, Pulli J J. Regional seismic event classification at the NORESS array: Seismological measurements and the use of trained neural networks[J]. Bulletin of the Seismological Society of America, 1990, 80(6B): 1910-1933.
    [31] Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Transactions on Image Processing, 2006, 15: 3736-3745. doi: 10.1109/TIP.2006.881969
    [32] Engan K, Aase S O, Hakon Husoy J. Method of optimal directions for frame design[C]// 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP99 (Cat. No.99CH36258), Phoenix, AZ, USA, 1999, 5: 2443-2446.
    [33] Esposito A M, D'Auria L, Giudicepietro F, et al. Neural analysis of seismic data: Applications to the monitoring of Mt. Vesuvius[J]. Annals of Geophysics, 2013, 56(4), S0446.
    [34] Esposito A M, Giudicepietro F, D’Auria L, et al. Unsupervised neural analysis of very-long-period events at Stromboli volcano using the self-organizing maps[J]. Bulletin of the Seismological Society of America, 2008, 98(5): 2449-2459. doi: 10.1785/0120070110
    [35] Esposito A M, Giudicepietro F, Scarpetta S, et al. Automatic discrimination among landslide, explosion-quake, and microtremor seismic signals at Stromboli volcano using neural networks[J]. Bulletin of the Seismological Society of America, 2006, 96(4A): 1230-1240. doi: 10.1785/0120050097
    [36] Essenreiter R, Karrenbach M, Treitel S. Identification and classification of multiple reflections with self-organizing maps[J]. Geophysical Prospecting, 2001, 49(3): 341-352. doi: 10.1046/j.1365-2478.2001.00261.x
    [37] Fang L H, Wu Z L, Song K. SeismOlympics[J]. Seismological Research Letters, 2017, 88(6):1429-1430. doi: 10.1785/0220170134
    [38] Fedorenko Y V, Husebye E S, Ruud B O. Explosion site recognition; neural net discriminator using single three-component stations[J]. Physics of the Earth and Planetary Interiors, 1999, 113(1): 131-142.
    [39] Fernández-Delgado M, Cernadas E, Barro S, et al. Do we need hundreds of classifiers to solve real world classification problems[J]. Journal of Machine Learning Research, 2014, 15(1): 3133-3181.
    [40] Galvis I S, Villa Y, Duarte C, et al. Seismic attribute selection and clustering to detect and classify surface waves in multicomponent seismic data by using k-means algorithm[J]. The Leading Edge, 2017, 36: 239-248. doi: 10.1190/tle36030239.1
    [41] García S R, Romo M P, Mayoral J M. Estimation of peak ground accelerations for Mexican subduction zone earthquakes using neural networks[J]. Geofísica Internacional, 2006, 46(1): 51-63.
    [42] Geng Y, Su L, Jia Y, et al. Seismic events prediction using deep temporal convolution networks[J]. Journal of Electrical and Computer Engineering, 2019, 2019: 1-14.
    [43] Gentili S, Michelini A. Automatic picking of P- and S-phases using a neural tree[J]. Journal of Seismology, 2006, 10(1): 39-63. doi: 10.1007/s10950-006-2296-6
    [44] Giacco F, Esposito A M, Scarpetta S, et al. Support vector machines and MLP for automatic classification of seismic signals at stromboli volcano[C] // Neural Nets WIRN09 - Proceedings of the 19th Italian Workshop on Neural Nets. Vietri sul Mare, Salerno, Italy: IOS Press, 2009: 116-123.
    [45] Goodfellow I, Bengio Y, Courville A, et al. Deep Learning[M]. Cambridge, Massachusetts: MIT Press, 2016.
    [46] Gutierrez L H, Vasquez L F, Jimenez C A. Fast determination of earthquake depth using seismic records of a single station, implementing machine learning techniques[J]. Revista Ingenieria E Investigacion, 2018, 38(2): 97-103.
    [47] Haykin S. Neural Networks: A Comprehensive Foundation (2nd Edition)[M]. Upper Saddle River: Prentice Hall, 1998.
    [48] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, 2016: 770-778.
    [49] Hibert C, Provost F, Malet J-P, et al. Automatic identification of rockfalls and volcano-tectonic earthquakes at the Piton de la Fournaise volcano using a Random Forest algorithm[J]. Journal of Volcanology and Geothermal Research, 2017, 340: 130-142. doi: 10.1016/j.jvolgeores.2017.04.015
    [50] Hinton G, Salakhutdinov R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. doi: 10.1126/science.1127647
    [51] Ho T K. The random subspace method for constructing decision forests[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 832–844. doi: 10.1109/34.709601
    [52] Hosmer D W, Lemeshow S. Applied Logistic Regression[M]. Hoboken, New Jersey, USA: John Wiley and Sons, Inc., 1989.
    [53] Jain A K, Murty M N, Flynn P J. Data clustering: A review[J]. ACM Computing Surveys, 1999, 31(3): 264−323. doi: 10.1145/331499.331504
    [54] Jollife I. Principal Component Analysis[M]. New York: Springer, 1986.
    [55] Kalyani P. Approaches to partition medical data using clustering algorithms[J]. International Journal of Computer Applications, 2013, 49(23): 7-10.
    [56] Kaur K, Wadhwa M, Park E K. Detection and identification of seismic P-waves using artificial neural networks[C]// The 2013 International Joint Conference on Neural Networks (IJCNN). Dallas, TX, USA, 2013: 1-6.
    [57] Kingma D P, Ba J. Adam: A method for stochastic optimization[C]// Proceedings of the 3rd International Conference on Learning Representations. San Diego, CA, USA. 2015.
    [58] Kleinbaum D G, Klein M. Logistic Regression: A Self Learning Text[M]. New York: Springer, 2010.
    [59] Klose C. Self-organizing maps for geoscientific data analysis: geological interpretation of multidimensional geophysical data[J]. Computers and Geosciences, 2006, 10(3): 265-277. doi: 10.1007/s10596-006-9022-x
    [60] Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection[C] // The 14th International Joint Conference on Artificial Intelligence. Montreal: Morgan Kaufmann Publishers Inc, 1995, 14: 1137-1143.
    [61] Köhler A, Ohrnberger M, Scherbaum F. Unsupervised feature selection and general pattern discovery using Self-Organizing Maps for gaining insights into the nature of seismic wavefields[J]. Computers and Geosciences, 2009, 35(9): 1757-1767. doi: 10.1016/j.cageo.2009.02.004
    [62] Köhler A, Ohrnberger M, Scherbaum F. Unsupervised pattern recognition in continuous seismic wavefield records using Self-Organizing Maps[J]. Geophysical Journal International, 2010, 182(3): 1619-1630, doi: 10.1111/j.1365-246X.2010.04709.x.
    [63] Kohonen T. Self-organized formation of topologically correct feature maps[J]. Biological Cybernetics, 1982, 43(1): 59-69. doi: 10.1007/BF00337288
    [64] Kohonen T, Somervuo P. How to make large self-organizing maps for nonvectorial data[J]. Neural Networks, 2002, 15(114): 945-952.
    [65] Kong Q K, Allen R M, Schreier L, et al. MyShake: A smartphone seismic network for earthquake early warning and beyond[J]. Science Advances, 2016, 2: e1501055. doi: 10.1126/sciadv.1501055
    [66] Kong Q K, Trugman D T, Ross Z E, et al. Machine learning in seismology: turning data into insights[J]. Seismological Research Letters, 2019, 90(1): 3-14. doi: 10.1785/0220180259
    [67] Kriegerowski M, Petersen G M, Vasyura-Bathke H, et al. A deep convolutional neural network for localization of clustered earthquakes based on multistation full waveforms[J]. Seismological Research Letters, 2019, 90(2A): 510-516. doi: 10.1785/0220180320
    [68] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]// Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, 2012: 1097-1105.
    [69] Kros J F, Lin M, Brown M L. Effects of the neural network s-Sigmoid function on KDD in the presence of imprecise data[J]. Computers and Operations Research, 2006, 33(11): 3136-3149. doi: 10.1016/j.cor.2005.01.024
    [70] LeCessie S, Van Houwelingen J C. Ridge estimators in logistic regression[J]. Applied Statistics, 1992, 41(1): 191-201. doi: 10.2307/2347628
    [71] Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[C]// Proceedings of the IEEE, 1998, 86(11): 2278-2324.
    [72] Li Z, Meier M A, Hauksson E, et al. Machine learning seismic wave discrimination: Application to earthquake early warning[J]. Geophysical Research Letters, 2018, 45: 4773-4779. doi: 10.1029/2018GL077870
    [73] Li Z H, Tian K, Wang F S, et al. Home damage estimation after disasters using crowdsourcing ideas and convolutional neural networks[C]// 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016). Shenzhen, 2016: 857-860.
    [74] 刘芳, 蒋一然, 宁杰远, 等. 结合台阵策略的震相拾取深度学习方法[J]. 科学通报, 2020, 65(11):1016-1026. doi: 10.1360/TB-2019-0608

    Liu F, Jiang Y R, Ning J Y, et al. An array-assisted deep learning approach to seismic phase-picking[J]. Chinese Science Bulletin, 2020: 65(11):1016-1026 (in Chinese). doi: 10.1360/TB-2019-0608
    [75] Lomax A, Michelini A, Jozinovic D. An investigation of rapid earthquake characterization using single‐station waveforms and a convolutional neural network[J]. Seismological Research Letters, 2019, 90(2A): 517-529. doi: 10.1785/0220180311
    [76] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]// IEEE Conference on Computer Vision and Pattern Regression. Boston, MA, USA: IEEE Computer Society, 2015: 3431-3440.
    [77] Maceda L, Llovido J, Satuito A. Categorization of earthquake-related tweets using machine learning approaches [C]// 2018 International Symposium on Computer, Consumer and Control (IS3C). Taichung, Taiwan, 2018:229-232.
    [78] Malfante M, Mura M D, Metaxian J, et al. Machine learning for volcano-seismic signals: Challenges and perspectives[J]. IEEE Signal Processing Magazine, 2018, 35(2): 20-30. doi: 10.1109/MSP.2017.2779166
    [79] Martínez-Alvarez J J, Garrigós J, Toledo J, et al. A scalable CNN architecture and its application to short exposure stellar images processing on a HPRC[J]. Neurocomputing, 2015, 151: 91-100. doi: 10.1016/j.neucom.2014.09.071
    [80] Masotti M, Falsaperla S, Langer H, et al. Application of support vector machine to the classification of volcanic tremor at Etna, Italy[J]. Geophysical Research Letters, 2006, 33: L20304. doi: 10.1029/2006GL027441
    [81] Maurer W, Dowla F, Jarpe S. Seismic event interpretation using self-organizing neural networks[J]. The International Society for Optical Engineering (SPIE), 1992, 1709: 950-958.
    [82] McLachlan G J, Krishnan T. The EM Algorithm and Extensions, Second Edition[M]. Hoboken, New Jersey, USA: John Wiley and Sons, Inc., 2007: 77-103.
    [83] Mojarab M, Memarian H, Zare M, et al. Modeling of the seismotectonic provinces of Iran using the self-organizing map algorithm[J]. Computers and Geosciences, 2014, 67: 150-162. doi: 10.1016/j.cageo.2013.12.007
    [84] Mousavi S M, Beroza G C. A machine-learning approach for earthquake magnitude estimation[J]. Geophysical Research Letters, 2020, 47: e2019GL085976.
    [85] Mousavi S M, Horton S P, Langston C A, et al. Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression[J]. Geophysical Journal International, 2016, 207(1): 29-46. doi: 10.1093/gji/ggw258
    [86] Mousavi S M, Zhu W Q, Ellsworth W, et al. Unsupervised clustering of seismic signals using deep convolutional autoencoders[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(11): 1693-1697. doi: 10.1109/LGRS.2019.2909218
    [87] Moya A, Irikura K. Inversion of a velocity model using artificial neural networks[J]. Computers and Geosciences, 2010, 36(12): 1474-1483. doi: 10.1016/j.cageo.2009.08.010
    [88] Murat M E, Rudman A J. Automated first arrival picking: a neural network approach[J]. Geophysical Prospecting, 1992, 40(6): 587-604. doi: 10.1111/j.1365-2478.1992.tb00543.x
    [89] Murphy K P. Machine Learning: A Probabilistic Perspective[M]. Cambridge, Massachusetts: MIT Press, 2012.
    [90] Musil M, Plešinger A. Discrimination between local microearthquakes and quarry blasts by multi-layer perceptrons and Kohonen maps[J]. Bulletin of the Seismological Society of America, 1996, 86(4): 1077-1090.
    [91] Obara K, Kasahara K, Hori S, et al. A densely distributed high-sensitivity seismograph network in Japan: hi-net by National Research Institute for Earth Science and Disaster Prevention[J]. Review of Scientific Instruments, 2005, 76(2): 021301. doi: 10.1063/1.1854197
    [92] Ochoa L H, Niño L F, Vargas C A. Fast magnitude determination using a single seismological station record implementing machine learning techniques[J]. Geodesy and Geodynamics, 2018, 9: 34-41. doi: 10.1016/j.geog.2017.03.010
    [93] Okada Y, Kasahara K, Hori S, et al. Recent progress of seismic observation networks in Japan—Hi-net, F-net, K-NET and KiK-net—[J]. Earth Planet Space, 2004, 56, xv–xxviii. doi: 10.1186/BF03353076
    [94] Paitz P, Gokhberg A, Fichtner A. A neural network for noise correlation classification[J]. Geophysical Journal International, 2018, 212(2): 1468-1474. doi: 10.1093/gji/ggx495
    [95] Patyra M J, Kwon T M. Processing of incomplete fuzzy data using artificial neural networks [C]// Proceedings of the Second IEEE International Conference on Fuzzy Systems. San Francisco, CA, USA, 1993, 1: 429-434.
    [96] Perol T, Gharbi M, Denolle M. Convolutional neural network for earthquake detection and location[J]. Science Advances, 2018, 4(2), e1700578. doi: 10.1126/sciadv.1700578
    [97] Plešinger A, Rǔžek B, Boušková A. Statistical interpretation of WEBNET seismograms by artificial neural nets[J]. Studia Geophysica et Geodaetica, 2000, 44(2): 251-271. doi: 10.1023/A:1022119011057
    [98] Press S J, Wilson S. Choosing between logistic regression and discriminant analysis[J]. Journal of the American Statistical Association, 1978, 73: 699-705. doi: 10.1080/01621459.1978.10480080
    [99] Provost F, Hibert C, Malet J P. Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier[J]. Geophysical Research Letters, 2017, 44: 113-120. doi: 10.1002/2016GL070709
    [100] Poulton M M. Neural networks as an intelligence amplification tool: A review of applications[J]. Geophysics, 2002, 67: 979-993. doi: 10.1190/1.1484539
    [101] Rabin N, Bregman Y, Lindenbaum O, et al. Earthquake-explosion discrimination using diffusion maps[J]. Geophysical Journal International, 2016, 207(3): 1484-1492. doi: 10.1093/gji/ggw348
    [102] Reddy R, Nair R R. The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan[J]. Journal of Earth System Science, 2013, 122: 1423-1434. doi: 10.1007/s12040-013-0346-3
    [103] Reichstein M, Camps-Valls G, Stevens B, et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature, 2019, 566(7743): 195-204. doi: 10.1038/s41586-019-0912-1
    [104] Reynen A, Audet P. Supervised machine learning on a network scale: Application to seismic event classification and detection[J]. Geophysical Journal International, 2017, 210(3): 1394-1409. doi: 10.1093/gji/ggx238
    [105] Roden R, Smith T, Sacrey D. Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps[J]. Interpretation, 2015, 3(4): SAE59-SAE83. doi: 10.1190/INT-2015-0037.1
    [106] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer, 2015: 234-241.
    [107] Ross Z E, Meier M, Hauksson E. P-wave arrival picking and first-motion polarity determination with deep learning[J]. Journal of Geophysical Research, 2018, 123(6): 5120-5129.
    [108] Rouet-Leduc B, Hulbert C, Lubbers N, et al. Machine Learning Predicts Laboratory Earthquakes[J]. Geophysical Research Letters, 2017, 44: 9276-9282. doi: 10.1002/2017GL074677
    [109] Ruano A E, Madureira G, Barros O, et al. Seismic detection using support vector machines[J]. Neurocomputing, 2014, 135: 273-283. doi: 10.1016/j.neucom.2013.12.020
    [110] Rubin M J, Camp T, Herwijnen A V, et al. Automatically detecting avalanche events in passive seismic data[C] // 2012 11th International Conference on Machine Learning and Applications. Boca Raton, FL: IEEE, 2012: 13-20.
    [111] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagation errors[J]. Nature, 1986, 323:533-536. doi: 10.1038/323533a0
    [112] Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation[J]. Readings in Cognitive Science, 1988, 323(6088): 399-421.
    [113] Sadeghi M, Babaie-Zadeh M, Jutten C. Dictionary Llearning for sparse representation: A novel approach[J]. IEEE Signal Processing Letters, 2013, 20(12): 1195-1198. doi: 10.1109/LSP.2013.2285218
    [114] Safavian S R, Landgrebe D. A survey of decision tree classifier methodology[J]. IEEE Transactions on Systems, Man and Cybernetics, 1991, 21(3): 660-674. doi: 10.1109/21.97458
    [115] Sermanet P, Kavukcuoglu K, Chintala S, et al. Pedestrian detection with unsupervised multi-stage feature learning[C]// 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR: IEEE, 2013: 3626-3633.
    [116] Shahnas M H, Yuen D A, Pysklywec R N. Inverse Problems in Geodynamics Using Machine Learning Algorithms[J]. Journal of Geophysical Research: Solid Earth, 2018, 123: 296-310, doi: 10.1002/2017JB014846.
    [117] Sharma M L, Arora M K. Prediction of seismicity cycles in the Himalayas using artificial neural networks[J]. Acta Geophysica Polonica, 2005, 53(3): 299-309.
    [118] Sick B, Guggenmos M, Joswig M. Chances and limits of single-station seismic event clustering by unsupervised pattern recognition[J]. Geophysical Journal International, 2015, 201(3): 1801-1813. doi: 10.1093/gji/ggv126
    [119] Spampinato S, Langer H, Messina A, et al. Short-term detection of volcanic unrest at Mt. Etna by means of a multi-station warning system[J]. Scientific Reports, 2019, 9: 6506. doi: 10.1038/s41598-019-42930-3
    [120] Tang L, Zhang M, Wen L. Support vector machine classification of seismic events in the Tianshan orogenic belt[J]. Journal of Geophysical Research: Solid Earth, 2020, 125: e2019JB018132.
    [121] Tarvainen M. Recognizing explosion sites with a self-organizing network for unsupervised learning[J]. Physics of the Earth and Planetary Interiors, 1999, 113(1-4): 143-154. doi: 10.1016/S0031-9201(99)00019-9
    [122] Titos M, Bueno A, García L, et al. A deep neural networks approach to automatic recognition systems for volcano-seismic events[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(5): 1533-1544. doi: 10.1109/JSTARS.2018.2803198
    [123] Trugman D T, Shearer P M. Strong correlation between stress drop and peak ground acceleration for recent M1-4 earthquakes in the San Francisco bay area[J]. Bulletin of the Seismological Society of America, 2018, 108(2): 929-945. doi: 10.1785/0120170245
    [124] Ursino A, Langer H, Scarfì L, et al. Discrimination of quarry blasts from tectonic microearthquakes in the Hyblean plateau (southeastern Sicily)[J]. Annals of Geophysics, 2001, 44(4): 703-722.
    [125] Van der Baan M, Jutten C. Neural networks in geophysical applications[J]. Geophysics, 2000, 65 (4): 1032-1047 doi: 10.1190/1.1444797
    [126] Vapnik V. The Nature of Statistical Learning Theory[M]. New York: Springer, 1995.
    [127] Vapnik V. Statistical Learning Theory[M]. New York: John Wiley, 1998.
    [128] Wang X J, Ma J W. Adaptive dictionary learning for blind seismic data denoising[J]. IEEE Geoence and Remote Sensing Letters, 2019, 99: 1-5.
    [129] Wang J, Teng T L. Artificial neural network-based seismic detector[J]. Bulletin of the Seismological Society of America, 1995, 85(1): 308-319.
    [130] Werbos P J. Backpropagation through time: what it does and how to do it[C]. Proceedings of the IEEE, 1990, 78(10): 1550-1560.
    [131] Werbos P J. The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting[M]. New York, USA: John Wiley, 1994.
    [132] Wu Y, Lin Y Z, Zhou Z, et al. DeepDetect: A cascaded region-based densely connected network for seismic event detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(1): 62-75.
    [133] 奚先, 黄江清. 复杂散射波场的深度学习反演成像法[J]. 地球物理学进展, 2018, 33(6): 2483-2489.

    Xi X, Huang J Q. Deep learning inversion imaging method for scattered wavefield[J]. Progress in Geophysics, 2018, 33(6): 2483-2489 (in Chinese).
    [134] 奚先, 黄江清. 基于卷积神经网络的地震偏移剖面中散射体的定位和成像[J]. 地球物理学报, 2020, 63(2): 687-714.

    Xi X, Huang J Q. Location and imaging of scatterers in seismic migration profiles based on convolution neural network[J]. Chinese Journal of Geophysics, 2020, 63(2): 687-714 (in Chinese).
    [135] Xia K Y, Hilterman F, Hu H. Unsupervised machine learning algorithm for detecting and outlining surface waves on seismic shot gathers[J]. Journal of Applied Geophysics, 2018, 157(2018): 73-86.
    [136] Xu D, Tian Y. A comprehensive survey of clustering algorithms[J]. Annals of Data Science, 2015, 2: 165-193. doi: 10.1007/s40745-015-0040-1
    [137] Xu C, Xu X W, Dai F C, et al. Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China[J]. Computers and Geosciences, 2012, 46: 317-329. doi: 10.1016/j.cageo.2012.01.002
    [138] Yilmaz I. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine[J]. Environmental Earth Sciences, 2010, 61(4): 821-836. doi: 10.1007/s12665-009-0394-9
    [139] 于子叶, 储日升, 盛敏汉. 深度神经网络拾取地震P波和S波到时[J]. 地球物理学报, 2018, 61(12): 4873-4886.

    Yu Z Y, Chu R S, Sheng M H. Pick onset time of P and S phase by deep neural network[J]. Chinese Journal of Geophysics, 2018, 61(12): 4873-4886(in Chinese).
    [140] 张正一, 范建柯, 白永良, 等. 中国海—西太平洋地区典型剖面的重-磁-震联合反演研究[J]. 地球物理学报, 2018, 61(7): 2871-2891.

    Zhang Z Y, Fan J K, Bai Y L, et al. Joint inversion of gravity-magnetic-seismic data of a typical profile in the China Sea-Western Pacific area[J]. Chinese Journal of Geophysics, 2018, 61(7): 2871-2891.(in Chinese).
    [141] Zhang G Y, Wang Z Z, Chen Y K. Deep learning for seismic lithology prediction[J]. Geophysical Journal International, 2018, 215(2): 1368-1387.
    [142] 赵明, 陈石, 房立华, 等. 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, 2019, 62(8): 3034-3042.

    Zhao M, Chen S, Fang L H, et al. Earthquake phase arrival auto-picking based on U-shaped convolutional neural network[J]. Chinese Journal of Geophysics, 2019, 62(8): 3034-3042(in Chinese).
    [143] Zhao R, Ouyang W L, Li H S, et al. Saliency detection by multi-context deep learning[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, 2015: 1265-1274.
    [144] Zhao Y, Takano K. An artificial neural network-based seismic detector[J]. Bulletin of the Seismological Society of America, 1999, 77: 670-680.
    [145] Zhou Y J, Yue H, Kong Q K, et al. Hybrid event detection and phase-picking algorithm using convolutional and recurrent neural networks[J]. Seismological Research Letters, 2019, 90: 1079-1087. doi: 10.1785/0220180319
    [146] Zhu W Q, Beroza G C. PhaseNet: A deep-neural-network-based seismic arrival-time picking method[J]. Geophysical Journal International, 2019, 216(1): 261-273.
    [147] Zhu L C, Liu E T, McClellan J H. Seismic data denoising through multiscale and sparsity-promoting dictionary learning[J]. Geophysics, 2015, 80(6): WD45-WD57. doi: 10.1190/geo2015-0047.1
    [148] Zhu L C, Liu E T, McClellan J H. Joint seismic data denoising and interpolation with double-sparsity dictionary learning[J]. Journal of Geophysics and Engineering, 2017, 14(4): 802-810. doi: 10.1088/1742-2140/aa6491
    [149] Zhu W, Mousavi S M, Beroza G C. Seismic signal denoising and decomposition using deep neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 9476-9488. doi: 10.1109/TGRS.2019.2926772
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  • 收稿日期:  2020-07-03
  • 录用日期:  2020-08-14
  • 网络出版日期:  2021-09-13
  • 刊出日期:  2021-01-01