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Annual Review of Earth and Planetary Sciences

Volume 51, 2023

Machine Learning in Earthquake Seismology

Abstract

Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data mining techniques can significantly improve our capability for seismic data processing. In this review we provide a comprehensive overview of ML applications in earthquake seismology, discuss progress and challenges, and offer suggestions for future work.

  • ▪  Conceptual, algorithmic, and computational advances have enabled rapid progress in the development of machine learning approaches to earthquake seismology.
  • ▪  The impact of that progress is most clearly evident in earthquake monitoring and is leading to a new generation of much more comprehensive earthquake catalogs.
  • ▪  Application of unsupervised approaches for exploratory analysis of these high-dimensional catalogs may reveal new understanding of seismicity.
  • ▪  Machine learning methods are proving to be effective across a broad range of other seismological tasks, but systematic benchmarking through open source frameworks and benchmark data sets are important to ensure continuing progress.

Keyword(s): artificial intelligence, earthquakes, machine learning, neural networks, seismology
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2023-05-31
2025-02-14
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Literature Cited

  1. Adeli H, Panakkat A. 2009.. A probabilistic neural network for earthquake magnitude prediction. . Neural Netw. 22::1018–24
     [Google Scholar]
  2. Aden-Antoniów F, Frank WB, Seydoux L. 2022.. An adaptable random forest model for the declustering of earthquake catalogs. . J. Geophys. Res. Solid Earth 127::e2021JB023254
     [Google Scholar]
  3. Albert S, Linville L. 2020.. Benchmarking current and emerging approaches to infrasound signal classification. . Seismol. Res. Lett. 91::921–29
     [Google Scholar]
  4. Barkaoui S, Lognonné P, Kawamura T, Stutzmann É, Seydoux L, et al. 2021.. Anatomy of continuous Mars SEIS and pressure data from unsupervised learning. . Bull. Seismol. Soc. Am. 111::2964–81
     [Google Scholar]
  5. Beroza GC, Segou M, Mousavi SM. 2021.. Machine learning and earthquake forecasting—next steps. . Nat. Commun. 12::4761
     [Google Scholar]
  6. Beyreuther M, Carniel R, Wassermann J. 2008.. Continuous hidden Markov models: application to automatic earthquake detection and classification at Las Canãdas caldera, Tenerife. . J. Volcanol. Geotherm. Res. 176::513–18
     [Google Scholar]
  7. Beyreuther M, Wassermann J. 2008.. Continuous earthquake detection and classification using discrete Hidden Markov Models. . Geophys. J. Int. 175::1055–66
     [Google Scholar]
  8. bin Waheed U, Haghighat E, Alkhalifah T, Song C, Hao Q. 2021.. PINNeik: Eikonal solution using physics-informed neural networks. . Comput. Geosci. 55::104833
     [Google Scholar]
  9. Bose M, Wenzel F, Erdik M. 2008.. PreSEIS: a neural network-based approach to earthquake early warning for finite faults. . Bull. Seismol. Soc. Am. 1::366–82
     [Google Scholar]
  10. Braeuer B, Bauer K. 2015.. A new interpretation of seismic tomography in the southern Dead Sea basin using neural network clustering techniques. . Geophys. Res. Lett. 42::9772–80
     [Google Scholar]
  11. Brunsvik B, Morra G, Cambiotti G, Chiaraluce L, Di Stefano R, et al. 2021.. Three-dimensional paganica fault morphology obtained from hypocenter clustering (L'Aquila 2009 seismic sequence, Central Italy). . Tectonophysics 804::228756
     [Google Scholar]
  12. Chamarczuk M, Nishitsuji Y, Malinowski M, Draganov D. 2020.. Unsupervised learning used in automatic detection and classification of ambient-noise recordings from a large-N array. . Seismol. Res. Lett. 91::370–89
     [Google Scholar]
  13. Chin T-L, Chen K-Y, Chen D-Y, Lin D-E. 2020.. Intelligent real-time earthquake detection by recurrent neural networks. . IEEE Trans. Geosci. Remote Sens. 58:(8):5440–49
     [Google Scholar]
  14. Chin T-L, Chen K-Y, Chen D-Y, Wang T-H. 2021.. An attention-based hypocenter estimator for earthquake localization. . IEEE Trans. Geosci. Remote Sens. 60::5905510
     [Google Scholar]
  15. Corradini M, McBrearty IW, Trugman DT, Satriano C, Johnson PA, Bernard P. 2022.. Investigating the influence of earthquake source complexity on back-projection images using convolutional neural networks. . Geophys. J. Int. 229::1824–39
     [Google Scholar]
  16. Dai H, MacBeth C. 1995.. Automatic picking of seismic arrivals in local earthquake data using an artificial neural network. . Geophys. J. Int. 120::758–74
     [Google Scholar]
  17. Datta A, Wu DJ, Zhu W, Cai M, Ellsworth WL. 2022.. DeepShake: shaking intensity prediction using deep spatiotemporal RNNs for earthquake early warning. . Seismol. Soc. Am. 93::1636–49
     [Google Scholar]
  18. Derras B, Bard PY, Cotton F. 2014.. Towards fully data driven ground-motion prediction models for Europe. . Bull. Earthq. Eng. 12::495–516
     [Google Scholar]
  19. Dickey J, Borghetti B, Junek W, Martin R. 2020.. Beyond correlation: a path-invariant measure for seismogram similarity. . Seismol. Res. Lett. 91::356–69
     [Google Scholar]
  20. Dowla FU, Taylor SR, Anderson RW. 1988.. Seismic discrimination with artificial neural networks: preliminary results with regional spectral data. . Bull. Seismol. Soc. Am. 80::1346–73
     [Google Scholar]
  21. Esposito AM, Giudicepietro F, Scarpetta S, D'Auria L, Marinaro M, Martini M. 2006.. Automatic discrimination among landslide, explosion-quake, and microtremor seismic signals at Stromboli volcano using neural networks. . Bull. Seismol. Soc. Am. 96::1230–40
     [Google Scholar]
  22. Feng B, Fox GC. 2020.. TSEQPREDICTOR: spatiotemporal extreme earthquakes forecasting for Southern California. . arXiv:2012.14336v1 [physics.geo-ph]
  23. Florez MA, Caporale M, Buabthong P, Ross ZE, Asimaki D, Meier M-A. 2022.. Data-driven synthesis of broadband earthquake ground motions using artificial intelligence. . Bull. Seismol. Soc. Am. 112:(4):1979–96
     [Google Scholar]
  24. Gatti F, Clouteau D. 2020.. Towards blending physics-based numerical simulations and seismic databases using generative adversarial network. . Comput. Methods Appl. Mech. Eng. 372::113421
     [Google Scholar]
  25. Geller RJ, Jackson DD, Kagan YY, Mulargia F. 1997.. Earthquakes cannot be predicted. . Science 275::1616–17
     [Google Scholar]
  26. Gong J, Fan W, Parnell-Turner R. 2022.. Microseismicity indicates atypical small-scale plate rotation at the Quebrada transform fault system, East Pacific Rise. . Geophys. Res. Lett. 49:(3):e2021GL097000
     [Google Scholar]
  27. Gong L-W, Zhang H, Chen S, Chen L-J. 2021.. Three-dimensional modeling of the Xichang crust in Sichuan, China by machine learning. . Appl. Sci. 12:(6):2955
     [Google Scholar]
  28. Hammer C, Beyreuther M, Ohrnberger M. 2012.. A seismic-event spotting system for volcano fast-response systems. . Bull. Seismol. Soc. Am. 102::948–60
     [Google Scholar]
  29. Hao M, Pascal A. 2022.. QuakeLabeler: a fast seismic data set creation and annotation toolbox for AI applications. . Seismol. Res. Lett. 93::997–1010
     [Google Scholar]
  30. Hara S, Fukahata Y, Iio Y. 2019.. P-wave first-motion polarity determination of waveform data in western Japan using deep learning. . Earth Planets Space 71::127
     [Google Scholar]
  31. Hernandez PD, Ramírez JA, Soto MA. 2022.. Deep-learning-based earthquake detection for fiber-optic distributed acoustic sensing. . J. Lightwave Technol. 40::2639–50
     [Google Scholar]
  32. Hincks T, Aspinall W, Cooke R, Gernon T. 2018.. Oklahoma's induced seismicity strongly linked to wastewater injection depth. . Science 359::1251–55
     [Google Scholar]
  33. Holtzman BK, Paté A, Paisley J, Waldhauser F, Repetto D. 2018.. Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field. . Sci. Adv. 4::eaao2929
     [Google Scholar]
  34. Hsu T-Y, Huang C-W. 2021.. Onsite early prediction of PGA using CNN with multi-scale and multi-domain P-waves as input. . Front. Earth Sci. 9::626908
     [Google Scholar]
  35. Hu J, Jin C, Zhang H, Hu L, Wang Z. 2022.. Support vector regression for developing ground-motion models for Arias intensity, cumulative absolute velocity, and significant duration for the Kanto region, Japan. . Seismol. Res. Lett. 93::1619–35
     [Google Scholar]
  36. Jenkins WF, Gerstoft P, Bianco MJ, Bromirski PD. 2022.. Unsupervised deep clustering of seismic data: monitoring the Ross Ice Shelf, Antarctica. . J. Geophys. Res. Solid Earth 126::e2021JB021716
     [Google Scholar]
  37. Jiang C, Zhang P, White MCA, Pickle R, Miller MS. 2022.. A detailed earthquake catalog for Banda arc–Australian plate collision zone using machine-learning phase picker and an automated workflow. . Seismic Rec. 2::1–10
     [Google Scholar]
  38. Jozinović D, Lomax A, Štajduhar I, Michelini A. 2020.. Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network. . Geophys. J. Int. 222:(2):1379–89
     [Google Scholar]
  39. Käufl P, Valentine AP, Trampert J. 2016.. Probabilistic point source inversion of strong-motion data in 3-D media using pattern recognition: a case study for the 2008 Mw 5.4 Chino Hills earthquake. . Geophys. Res. Lett. 43::8492–98
     [Google Scholar]
  40. Kerh T, Ting SB. 2005.. Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system. . Eng. Appl. Artif. Intel. 18::857–66
     [Google Scholar]
  41. Khosravikia F, Clayton P. 2021.. Machine learning in ground motion prediction. . Comput. Geosci. 148::104700
     [Google Scholar]
  42. Kim D, Lekić V, Ménard B, Baron D, Taghizadeh-Popp M. 2020.. Sequencing seismograms: a panoptic view of scattering in the core-mantle boundary region. . Science 368::1223–28
     [Google Scholar]
  43. Kim G, Ku B, Ko H. 2020.. Multifeature fusion-based earthquake event classification using transfer learning. . IEEE Geosci. Remote Sens. Lett. 18:(6):974–78
     [Google Scholar]
  44. Köhler A, Myklebust EB, Mæland S. 2022.. Enhancing seismic calving event identification in Svalbard through empirical matched field processing and machine learning. . Geophys. J. Int. 230::1305–17
     [Google Scholar]
  45. Köhler A, Ohrnberger M, Scherbaum F. 2009.. Unsupervised feature selection and general pattern discovery using Self-Organizing Maps for gaining insights into the nature of seismic wavefields. . Comput. Geosci. 35::1757–67
     [Google Scholar]
  46. Kong Q, Inbal A, Allen RM, Lv Q, Puder A. 2019.. Machine learning aspects of the MyShake global smartphone seismic network. . Seismol. Res. Lett. 90::546–52
     [Google Scholar]
  47. Kriegerowski M, Petersen GM, Vasyura-Bathke H, Ohrnberger M. 2019.. A deep convolutional neural network for localization of clustered earthquakes based on multistation full waveforms. . Seismol. Res. Lett. 90::510–16
     [Google Scholar]
  48. Ku B, Kim G, Ahn J-K, Lee J, Ko H. 2020.. Attention-based convolutional neural network for earthquake event classification. . IEEE Geosci. Remote Sens. Lett. 18:(12):2057–61
     [Google Scholar]
  49. Kuang W, Yuan C, Zhang J. 2021.. Real-time determination of earthquake focal mechanism via deep learning. . Nat. Commun. 12::1432
     [Google Scholar]
  50. Kuyuk HS, Ohno S. 2018.. Real-time classification of earthquake using deep learning. . Procedia Comput. Sci. 140::298–305
     [Google Scholar]
  51. Lapins S, Goitom B, Kendall J-M, Werner MJ, Cashman KV, Hammond JOS. 2021.. A little data goes a long way: automating seismic phase arrival picking at Nabro volcano with transfer learning. . J. Geophys. Res. Solid Earth 126:(7):e2021JB021910
     [Google Scholar]
  52. Lara F, Lara-Cueva R, Larco JC, Carrera EV, León R. 2021.. A deep learning approach for automatic recognition of seismo-volcanic events at the Cotopaxi volcano. . J. Volcanol. Geotherm. Res. 490::107142
     [Google Scholar]
  53. Larson J, Kramar D, Leonard K. 2020.. A geostatistical analysis of seismicity in Oklahoma using regression trees and neural networks. . Phys. Geogr. 42:(4):334–50
     [Google Scholar]
  54. Lee SC, Han SW. 2002.. Neural-network-based models for generating artificial earthquakes and response spectra. . Comput. Struct. 80::1627–38
     [Google Scholar]
  55. Li BQ, Smith JD, Ross ZE. 2021.. Basal nucleation and the prevalence of ascending swarms in Long Valley caldera. . Sci. Adv. 7:(35):eabi8368
     [Google Scholar]
  56. Liao W-Y, Lee E-J, Mu D, Chen P, Rau R-J. 2021.. ARRU phase picker: attention recurrent-residual U-net for picking seismic P- and S-phase arrivals. . Seismol. Res. Lett. 92:(4):2410–28
     [Google Scholar]
  57. Licciardi A, Bletery Q, Rouet-Leduc B, Ampuero J-P, Juhel K. 2022.. Instantaneous tracking of earthquake growth with elastogravity signals. . Nature 606::319–24
     [Google Scholar]
  58. Lin J-T, Melgar D, Thomas A, Searcy J. 2021.. Early warning for great earthquakes from characterization of crustal deformation patterns with deep learning. . J. Geophys. Res. Solid Earth 126:(10):e2021JB022703
     [Google Scholar]
  59. Linville L, Pankow K, Draelos T. 2019.. Deep learning models augment analyst decisions for event discrimination. . Geophys. Res. Lett. 46::3643–51
     [Google Scholar]
  60. Lomax A, Michelini A, Jozinović D. 2019.. An investigation of rapid earthquake characterization using single-station waveforms and a convolutional neural network. . Seismol. Res. Lett. 90::517–29
     [Google Scholar]
  61. Lomax A, Satriano C, Vassallo M. 2012.. Automatic picker developments and optimization: FilterPicker—a robust, broadband picker for real-time seismic monitoring and earthquake early warning. . Seismol. Res. Lett. 83::531–40
     [Google Scholar]
  62. Madureira G, Ruano AE, Ruano MG. 2013.. On-line operation of an intelligent seismic detector. . Soft Comput. Appl. 85::531–42
     [Google Scholar]
  63. Masotti M, Falsaperla S, Langer H, Spampinato S, Campanini R. 2006.. Application of Support Vector Machine to the classification of volcanic tremor at Etna, Italy. . Geophys. Res. Lett. 33::L20304
     [Google Scholar]
  64. McBrearty IW, Delorey AA, Johnson PA. 2022.. Pairwise association of seismic arrivals with convolutional neural networks. . Seismol. Res. Lett. 90::503–9
     [Google Scholar]
  65. McBrearty IW, Gomberg J, Delorey AA, Johnson PA. 2019.. Earthquake arrival association with backprojection and graph theory. . Bull. Seismol. Soc. Am. 109:(6):2510–31
     [Google Scholar]
  66. McKean SH, Priest JA, Dettmer J, Eaton DW. 2019.. Quantifying fracture networks inferred from microseismic point clouds by a Gaussian mixture model with physical constraints. . Geophys. Res. Lett. 46::11008–17
     [Google Scholar]
  67. McLellan M, Audet P. 2020.. Uncovering the physical controls of deep subduction zone slow slip using supervised classification of subducting plate features. . Geophys. J. Int. 223::94–110
     [Google Scholar]
  68. Michelini A, Cianetti S, Gaviano S, Giunchi C, Jozinovic D, Lauciani V. 2021.. INSTANCE—the Italian seismic dataset for machine learning. . Earth Syst. Sci. Data Discuss. 13:(12):5509–44
     [Google Scholar]
  69. Mignan A. 2014.. The debate on the prognostic value of earthquake foreshocks: a meta-analysis. . Sci. Rep. 4::4099
     [Google Scholar]
  70. Mignan A, Broccardo M. 2020.. Neural network applications in earthquake prediction (1994–2019): meta-analytic and statistical insights on their limitations. . Seismol. Res. Lett. 91:(4):2330–42
     [Google Scholar]
  71. Moseley B, Markham A, Nissen-Meyer T. 2020a.. Solving the wave equation with physics-informed deep learning. . arXiv:2006.11894 [physics.comp-ph]
  72. Moseley B, Nissen-Meyer T, Markham A. 2020b.. Deep learning for fast simulation of seismic waves in complex media. . Solid Earth 11::1527–49
     [Google Scholar]
  73. Mousavi SM, Beroza GC. 2020a.. Bayesian-deep-learning estimation of earthquake location from single-station observations. . IEEE Trans. Geosci. Remote Sens. 58:(11):8211–24
     [Google Scholar]
  74. Mousavi SM, Beroza GC. 2020b.. A machine-learning approach for earthquake magnitude estimation. . Geophys. Res. Lett. 47:(1):e2019GL085976
     [Google Scholar]
  75. Mousavi SM, Ellsworth W, Zhu W, Beroza GC. 2020.. Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. . Nat. Commun. 11::3952
     [Google Scholar]
  76. Mousavi SM, Horton SP, Langston CA, Samei B. 2016a.. Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression. . Geophys. J. Int. 207::29–46
     [Google Scholar]
  77. Mousavi SM, Langston CA, Horton SP. 2016b.. Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform. . Geophysics 81::V341–55
     [Google Scholar]
  78. Mousavi SM, Sheng Y, Zhu W, Beroza GC. 2019a.. STanford EArthquake Dataset (STEAD): a global data set of seismic signals for AI. . IEEE Access 7::179464–76
     [Google Scholar]
  79. Mousavi SM, Zhu W, Ellsworth W, Beroza G. 2019b.. Unsupervised clustering of seismic signals using deep convolutional autoencoders. . IEEE Geosci. Remote Sens. Lett. 11::1693–97
     [Google Scholar]
  80. Mousavi SM, Zhu W, Sheng Y, Beroza GC. 2019c.. CRED: a deep residual network of convolutional and recurrent units for earthquake signal detection. . Sci. Rep. 9::10267
     [Google Scholar]
  81. Münchmeyer J, Bindi D, Leser U, Tilmann F. 2021a.. Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network. . Geophys. J. Int. 226:(2):1086–104
     [Google Scholar]
  82. Münchmeyer J, Bindi D, Leser U, Tilmann F. 2021b.. The transformer earthquake alerting model: a new versatile approach to earthquake early warning. . Geophys. J. Int. 225::646–56
     [Google Scholar]
  83. Münchmeyer J, Leser U, Tilmann F. 2022a.. A probabilistic view on rupture predictability: All earthquakes evolve similarly. . Geophys. Res. Lett. 49::e2022GL098344
     [Google Scholar]
  84. Münchmeyer J, Woollam J, Rietbrock A, Tilmann F, Lange D, et al. 2022b.. Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers. . Adv. Geophys. 61::151–77
     [Google Scholar]
  85. Nakano M, Sugiyama D, Hori T, Kuwatani T, Tsuboi S. 2019.. Discrimination of seismic signals from earthquakes and tectonic tremor by applying a convolutional neural network to running spectral images. . Seismol. Res. Lett. 90::530–38
     [Google Scholar]
  86. Otake R, Kurima J, Goto H, Sawada S. 2021.. Deep learning model for spatial interpolation of real-time seismic intensity. . Seismol. Soc. Am. 91::3433–43
     [Google Scholar]
  87. Panakkat A, Adeli H. 2007.. Neural network models for earthquake magnitude prediction using multiple seismicity indicators. . Int. J. Neural Syst. 17::13–33
     [Google Scholar]
  88. Paolucci R, Gatti F, Infantino M, Smerzini C, Özcebe AG, Stupazzini M. 2018.. Broadband ground motions from 3D physics-based numerical simulations using artificial neural networks. . Bull. Seismol. Soc. Am. 108::1272–86
     [Google Scholar]
  89. Park Y, Mousavi SM, Zhu W, Ellsworth WL, Beroza GC. 2020.. Machine-learning-based analysis of the Guy-Greenbrier, Arkansas earthquakes: a tale of two sequences. . Geophys. Res. Lett. 47:(6):e2020GL087032
     [Google Scholar]
  90. Pawley S, Schultz R, Playter T, Corlett H, Shipman T, et al. 2018.. The geological susceptibility of induced earthquakes in the Duvernay play. . Geophys. Res. Lett. 45::1786–93
     [Google Scholar]
  91. Peng P, He Z, Wang L, Jiang Y. 2020.. Microseismic records classification using capsule network with limited training samples in underground mining. . Sci. Rep. 10::13925
     [Google Scholar]
  92. Perol T, Gharbi M, Denolle M. 2018.. Convolutional neural network for earthquake detection and location. . Sci. Adv. 4::e1700578
     [Google Scholar]
  93. Picozzi M, Iaccarino AG. 2021.. Forecasting the preparatory phase of induced earthquakes by recurrent neural network. . Seismol. Res. Lett. 3::17–36
     [Google Scholar]
  94. Pulli JJ, Dysart PS. 1990.. An experiment in the use of trained neural networks for regional seismic event classification. . Geophys. Res. Lett. 17::977–80
     [Google Scholar]
  95. Retailleau L, Saurel J-M, Laporte M, Lavayssière A, Ferrazzini V, et al. 2022.. Automatic detection for a comprehensive view of Mayotte seismicity. . C. R. Géosci. 354:(S2):15370
     [Google Scholar]
  96. Ristea N-C, Radoi A. 2021.. Complex neural networks for estimating epicentral distance, depth, and magnitude of seismic waves. . IEEE Geosci. Remote Sens. Lett. 19::7502305
     [Google Scholar]
  97. Rodriguez AB, Benitez C, Zuccarello L, Angelis SD, Ibanez JM. 2020.. Bayesian monitoring of seismo-volcanic dynamics. . IEEE Trans. Geosci. Remote Sens. 58::892–902
     [Google Scholar]
  98. Ross ZE, Ben-Zion Y. 2014.. Automatic picking of direct P, S seismic phases and fault zone head waves. . Seismol. Res. Lett. 199::368–81
     [Google Scholar]
  99. Ross ZE, Meier M-A, Hauksson E. 2018a.. P wave arrival picking and first-motion polarity determination with deep learning. . J. Geophys. Res. Solid Earth 123::5120–29
     [Google Scholar]
  100. Ross ZE, Meier M-A, Hauksson E, Heaton TH. 2018b.. Generalized seismic phase detection with deep learning. . Bull. Seismol. Soc. Am. 108::2894–901
     [Google Scholar]
  101. Ross ZE, Yue Y, Meier M-A, Hauksson E, Heaton TH. 2019.. PhaseLink: a deep learning approach to seismic phase association. . J. Geophys. Res. Solid Earth 124::856–69
     [Google Scholar]
  102. Rouet-Leduc B, Hulbert C, Johnson PA. 2019.. Continuous chatter of the Cascadia subduction zone revealed by machine learning. . Nat. Geosci. 12::75–79
     [Google Scholar]
  103. Rouet-Leduc B, Hulbert C, Lubbers N, Barros K, Humphreys J, Johnson PA. 2017.. Machine learning predicts laboratory earthquakes. . Geophys. Res. Lett. 44::9276–82
     [Google Scholar]
  104. Rouet-Leduc B, Hulbert C, McBrearty IW, Johnson PA. 2020.. Probing slow earthquakes with deep learning. . Geophys. Res. Lett. 47:(4):e2019GL085870
     [Google Scholar]
  105. Rumelhart DE, Hinton GE, Williams RJ. 1986.. Learning representations by back-propagating errors. . Nature 323::533–36
     [Google Scholar]
  106. Saad OM, Chen Y. 2021.. CapsPhase: capsule neural network for seismic phase classification and picking. . IEEE Trans. Geosci. Remote Sens. 60::5904311
     [Google Scholar]
  107. Seydoux L, Balestriero R, Poli P, De Hoop M, Campillo M, Baraniuk R. 2020.. Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. . Nat. Commun. 11::3972
     [Google Scholar]
  108. Shi P, Grigoli F, Lanza F, Beroza GC, Scarabello L, Wiemer S. 2022.. MALMI: an automated earthquake detection and location workflow based on machine learning and waveform migration. . Seismol. Res. Lett. 93:(5):2467–83
     [Google Scholar]
  109. Shiloh L, Eyal A, Giryes R. 2019.. Efficient processing of distributed acoustic sensing data using a deep learning approach. . J. Lightwave Technol. 37::4755–62
     [Google Scholar]
  110. Smith JD, Azizzadenesheli K, Ross ZE. 2020.. Eikonet: solving the Eikonal equation with deep neural networks. . IEEE Trans. Geosci. Remote Sens. 59:(12):10685–96
     [Google Scholar]
  111. Smith JD, Ross ZE, Azizzadenesheli K, Muir JB. 2022.. HypoSVI: hypocentre inversion with Stein variational inference and physics informed neural networks. . Geophys. J. Int. 228::698–710
     [Google Scholar]
  112. Snover D, Johnson CW, Bianco MJ, Gerstoft P. 2021.. Deep clustering to identify sources of urban seismic noise in Long Beach, California. . Seismol. Soc. Am. 92::1011–22
     [Google Scholar]
  113. Song C, Alkhalifah T, bin Waheed U. 2020.. Solving the frequency-domain acoustic VTI wave equation using physics-informed neural networks. . Geophys. J. Int. 225::846–59
     [Google Scholar]
  114. Soto H, Schurr B. 2020.. DeepPhasePick: a method for detecting and picking seismic phases from local earthquakes based on highly optimized convolutional and recurrent deep neural networks. . Geophys. J. Int. 227:(2):1268–94
     [Google Scholar]
  115. Steinberg A, Vasyura-Bathke H, Gaebler P, Ohrnberger M, Ceranna L. 2021.. Estimation of seismic moment tensors using variational inference machine learning. . J. Geophys. Res. Solid Earth 126::e2021JB022685
     [Google Scholar]
  116. Tan YJ, Waldhauser F, Ellsworth WL, Zhang M, Zhu W, et al. 2021.. Machine-learning-based high-resolution earthquake catalog reveals how complex fault structures were activated during the 2016–2017 Central Italy sequence. . Seismic Rec. 1::11–19
     [Google Scholar]
  117. Thomas AM, Inbal A, Searcy J, Shelly DR, Bürgmann R. 2021.. Identification of low-frequency earthquakes on the San Andreas fault with deep learning. . Geophys. Res. Lett. 48:(13):e2021GL093157
     [Google Scholar]
  118. Tian X, Zhang W, Zhang X, Zhang J, Zhang Q, et al. 2020.. Comparison of single-trace and multiple-trace polarity determination for surface microseismic data using deep learning. . Seismol. Res. Lett. 91::1794–803
     [Google Scholar]
  119. Tibi R, Linville L, Young C, Brogan R. 2019.. Classification of local seismic events in the Utah region: a comparison of amplitude ratio methods with a spectrogram-based machine learning approach. . Bull. Seismol. Soc. Am. 109::2532–44
     [Google Scholar]
  120. Titos M, Bueno A, Garcia L, Benitez C, Segura JC. 2020.. Classification of isolated volcano-seismic events based on inductive transfer learning. . IEEE Geosci. Remote Sens. Lett. 17::869–73
     [Google Scholar]
  121. Trugman DT, Shearer PM. 2018.. Strong correlation between stress drop and peak ground acceleration for recent M 1–4 earthquakes in the San Francisco Bay area. . Bull. Seismol. Soc. Am. 108::929–45
     [Google Scholar]
  122. Turhan Taner M, Lu L, Baysal E. 1988.. Unified method for 2-D and 3-D refraction statics with first break picking by supervised learning. . SEG Tech. Program Expand. Abstr. 1988::172–74
     [Google Scholar]
  123. Uchide T. 2020.. Focal mechanisms of small earthquakes beneath the Japanese islands based on first-motion polarities picked using deep learning. . Geophys. J. Int. 223::1658–71
     [Google Scholar]
  124. Van Den Ende M, Ampuero J-P. 2020.. Automated seismic source characterisation using deep graph neural networks. . Geophys. Res. Lett. 47:(17):e2020GL088690
     [Google Scholar]
  125. Walter JI, Ogwari P, Thiel A, Ferrer F, Woelfel I. 2021.. easyQuake: putting machine learning to work for your regional seismic network or local earthquake study. . Seismol. Res. Lett. 92::555–63
     [Google Scholar]
  126. Wang C-Y, Huang T-C, Wu Y-M. 2022.. Using LSTM neural networks for onsite earthquake early warning. . Seismol. Soc. Am. 93::814–26
     [Google Scholar]
  127. Wang J, Teng T-C. 1997.. Identification and picking of S phase using an artificial neural network. . Bull. Seismol. Soc. Am. 87::1140–49
     [Google Scholar]
  128. Wang J, Teng T-L. 1995.. Artificial neural network-based seismic detector. . Bull. Seismol. Soc. Am. 85::308–19
     [Google Scholar]
  129. Wang K, Ellsworth WL, Beroza GC, Williams G, Zhang M, et al. 2019.. Seismology with dark data: image-based processing of analog records using machine learning for the Rangely earthquake control experiment. . Seismol. Res. Lett. 90::553–62
     [Google Scholar]
  130. Wang Q, Guo Y, Yu L, Li P. 2017.. Earthquake prediction based on spatio-temporal data mining: an LSTM network approach. . IEEE Trans. Emerg. Top. Comput. 8::148–58
     [Google Scholar]
  131. Wang T, Trugman D, Lin Y. 2021.. SeismoGen: seismic waveform synthesis using GAN with application to seismic data augmentation. . J. Geophys. Res. Solid Earth 126:(4):e2020JB020077
     [Google Scholar]
  132. Wang Z, Zentner I, Zio E. 2020.. Accounting for uncertainties of magnitude- and site-related parameters on neural network-computed ground-motion prediction equations. . Bull. Seismol. Soc. Am. 110::629–46
     [Google Scholar]
  133. Wei Z, Zhao L. 2022.. P-wave velocity structure of the lower crust and uppermost mantle beneath the Sichuan–Yunnan (China) region. . Seismol. Res. Lett. 93:(4):2161–75
     [Google Scholar]
  134. Wiszniowski J. 2019.. Estimation of a ground motion model for induced events by Fahlman's Cascade Correlation Neural Network. . Seismol. Res. Lett. 131::23–31
     [Google Scholar]
  135. Withers KB, Moschetti MP, Thompson EM. 2020.. A machine learning approach to developing ground motion models from simulated ground motions. . Geophys. Res. Lett. 47:(6):e2019GL086690
     [Google Scholar]
  136. Woollam J, Münchmeyer J, Tilmann F, Rietbrock A, Lange D, et al. 2022.. SeisBench—a toolbox for machine learning in seismology. . Seismol. Res. Lett. 93::1695–709
     [Google Scholar]
  137. Wu Y, Wei J, Pan J, Chen P. 2019.. Research on microseismic source locations based on deep reinforcement learning. . IEEE Access 7::39962–73
     [Google Scholar]
  138. Xiao Z, Wang J, Liu C, Li J, Zhao L, Yao Z. 2021.. Siamese earthquake transformer: a pair-input deep-learning model for earthquake detection and phase picking on a seismic array. . J. Geophys. Res. Solid Earth 126:(5):e2020JB021444
     [Google Scholar]
  139. Yang S, Hu J, Zhang H, Liu G. 2021.. Simultaneous earthquake detection on multiple stations via a convolutional neural network. . Seismol. Res. Lett. 92:(1):246–60
     [Google Scholar]
  140. Yano K, Shiina T, Kurata S, Kato A, Komaki F, et al. 2021.. Graph-partitioning based convolutional neural network for earthquake detection using a seismic array. . J. Geophys. Res. Solid Earth 126:(5):e2020JB020269
     [Google Scholar]
  141. Yoon CE, O'Reilly O, Bergen KJ, Beroza GC. 2015.. Earthquake detection through computationally efficient similarity search. . Sci. Adv. 1:(11):e1501057
     [Google Scholar]
  142. Yu Z, Wang W. 2022.. FastLink: a machine learning and GPU-based fast phase association method and its application to Yangbi Ms 6.4 aftershock sequences. . Geophys. J. Int. 230::673–83
     [Google Scholar]
  143. Zhang H, Innanen KA, Eaton DW. 2021.. Inversion for shear-tensile focal mechanisms using an unsupervised physics-guided neural network. . Seismol. Res. Lett. 92:(4):2282–94
     [Google Scholar]
  144. Zhang X, Zhang J, Yuan C, Liu S, Chen Z, Li W. 2020.. Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method. . Sci. Rep. 10::1941
     [Google Scholar]
  145. Zhu W, Beroza GC. 2019.. PhaseNet: a deep-neural-network-based seismic arrival time picking method. . Geophys. J. Int. 216::261–73
     [Google Scholar]
  146. Zhu W, McBrearty IW, Mousavi SM, Ellsworth WL, Beroza GC. 2022a.. Earthquake phase association using a Bayesian Gaussian mixture model. . J. Geophys. Res. Solid Earth 127:(5):e2021JB023249
     [Google Scholar]
  147. Zhu W, Mousavi SM, Beroza GC. 2020.. Seismic signal augmentation to improve generalization of deep neural networks. . Adv. Geophys. 61::151–77
     [Google Scholar]
  148. Zhu W, Tai KS, Mousavi SM, Bailis P, Beroza GC. 2022b.. An end-to-end earthquake detection method for joint phase picking and association using deep learning. . J. Geophys. Res. Solid Earth 127:(3):e2021JB023283
     [Google Scholar]
/content/journals/10.1146/annurev-earth-071822-100323
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Machine Learning in Earthquake Seismology
Annual Review of Earth and Planetary Sciences 51, 105 (2023); https://doi.org/10.1146/annurev-earth-071822-100323
/content/journals/10.1146/annurev-earth-071822-100323
/content/journals/10.1146/annurev-earth-071822-100323
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