Abstract
Deep learning (DL) methods have a high potential for earthquake detection applications because of their high efficiency at processing measurement data, such as picking seismic phases. However, the performance of DL methods must be evaluated to ensure that they can replace conventional methods so that full automation can be achieved. State-of-art DL methods incorporate advanced techniques and train with large global datasets to enhance their earthquake detection capabilities. In this study, we tested a representative DL model on the 2016 Gyeongju earthquake sequence in the Korean Peninsula and compared the results with a previously established catalog and with the results of the conventional Short Time Average/Long Time Average (STA/LTA) method. The DL model demonstrated reasonable improvements in efficiency and performance by detecting more and smaller earthquakes within a much shorter running time than the other methods. In addition, the DL algorithms generally provided precise pickings of P- and S-wave phases. The DL model showed good generalization because it appropriately detected earthquakes in the study area that were not included in the training dataset. However, our results did suggest possible errors that should be accounted for, such as inconsistent phase picking, missing large earthquakes, and detecting non-natural earthquake signals. From the result of tests, local optimization may be important for realizing fully automatic earthquake monitoring, such as retraining with a local dataset, fine-tuning, or transfer learning. In addition, incorporating post-processing techniques such as phase association and discrimination into the DL framework is necessary.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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, Canada, Aug. 1–6. https://www.iitk.ac.in/nicee/wcee/article/13_786.pdf [Accessed on 3 May 2022].
Albawi, S., Mohammed, T.A., and Al-Zawi, S., 2017, Understanding of a convolutional neural network. 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, Aug. 21–23. https://doi.org/10.1109/ICEngTechnol.2017.8308186
Allmann, B.P., Shearer, P.M., and Hauksson, E., 2008, Spectral discrimination between quarry blasts and earthquakes in southern California. Bulletin of the Seismological Society of America, 98, 2073–2079.
Beaucé, E., Frank, W.B., and Romanenko, A., 2018, Fast matched filter (FMF): an efficient seismic matched-filter search for both CPU and GPU architectures. Seismological Research Letters, 89, 165–172.
Bergen, K.J., Chen, T., and Li, Z., 2019, Preface to the focus section on machine learning in seismology. Seismological Research Letters, 90, 477–480.
Chai, C., Maceira, M., Santos-Villalobos, H.J., Venkatakrishnan, S.V., Schoenball, M., Zhu, W., Beroza, G.C., Thurber, C., and Team, E.C., 2020, Using a deep neural network and transfer learning to bridge scales for seismic phase picking. Geophysical Research Letters, 47, e2020GL088651. https://doi.org/10.1029/2020GL088651
Chamberlain, C.J., Hopp, C.J., Boese, C.M., Warren-Smith, E., Chambers, D., Chu, S.X., Michailos, K., and Townend, J., 2018, EQcorrscan: repeating and near-repeating earthquake detection and analysis in Python. Seismological Research Letters, 89, 173–181.
Chamberlain, C.J., Shelly, D.R., Townend, J., and Stern, T.A., 2014, Low-frequency earthquakes reveal punctuated slow slip on the deep extent of the Alpine fault, New Zealand. Geochemistry, Geophysics, Geosystems, 15, 2984–2999.
ESRI, 2020, Satellite Map of Korea. http://server.arcgisonline.com/arc-gis/rest/services [Accessed on 3 May 2022].
Geller, R.J., 1976, Scaling relations for earthquake source parameters and magnitudes. Bulletin of the Seismological Society of America, 66, 1501–1523. https://doi.org/10.1785/BSSA0660051501
Jozinović, D., Lomax, A., Štajduhar, I., and Michelini, A., 2022, Transfer learning: improving neural network based prediction of earthquake ground shaking for an area with insufficient training data. Geophysical Journal International, 229, 704–718.
Kim, K.H., Kim, J., Han, M., Kang, S.Y., Son, M., Kang, T.S., Rhie, J., Kim, Y., Park, Y., and Kim, H.J., 2018, Deep fault plane revealed by high-precision locations of early aftershocks following the 12 September 2016 ML 5.8 Gyeongju, Korea, Earthquake. Bulletin of the Seismological Society of America, 108, 517–523.
Kim, S., Rhie, J., and Kim, G., 2011, Forward waveform modelling procedure for 1-D crustal velocity structure and its application to the southern Korean Peninsula. Geophysical Journal International, 185, 453–468.
Kim, Y., Rhie, J., Kang, T.-S., Kim, K.-H., Kim, M., and Lee, S.-J., 2016, The 12 September 2016 Gyeongju earthquakes: 1. Observation and remaining questions. Geosciences Journal, 20, 747–752.
Kong, Q., Trugman, D.T., Ross, Z.E., Bianco, M.J., Meade, B.J., and Gerstoft, P., 2019, Machine learning in seismology: turning data into insights. Seismological Research Letters, 90, 3–14.
Krischer, L., Megies, T., Barsch, R., Beyreuther, M., Lecocq, T., Caudron, C., and Wassermann, J., 2015, ObsPy: a bridge for seismology into the scientific Python ecosystem. Computational Science & Discovery, 8, 014003.
Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012, Imagenet classification with deep convolutional neural networks. Proceedings of Neural Informatioin Processing Systems (NIPS 2012), Lake Tahoe, USA, Dec. 3–8, 25, p. 1097–1105.
Lahr, J.C., 1999, Hypoellipse: a computer program for determining local earthquake hypocentral parameters, magnitude, and first motion pattern. USGS Open File Report 99–23, Versioin 1.1, U.S. Geological Survey, Reston, USA, 119 p. https://doi.org/10.3133/ofr9923
Lapins, S., Goitom, B., Kendall, J.M., Werner, M.J., Cashman, K.V., and Hammond, J.O.S., 2021, A little data goes a long way: automating seismic phase arrival picking at Nabro Volcano with transfer learning. Journal of Geophysical Research: Solid Earth, 126, e2021JB021910. https://doi.org/10.1029/2021JB021910
Lara, F., Lara-Cueva, R., Larco, J.C., Carrera, E.V., and León, R., 2021, A deep learning approach for automatic recognition of seismo-volcanic events at the Cotopaxi volcano. Journal of Volcanology and Geothermal Research, 409, 107142.
Leonard, M., 2000, Comparison of manual and automatic onset time picking. Bulletin of the Seismological Society of America, 90, 1384–1390.
Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K., 2014, Recurrent models of visual attention. NIPS’14, Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada, Dec. 8–13, p. 2204–2212.
Mousavi, S.M., Ellsworth, W.L., Zhu, W., Chuang, L.Y., and Beroza, G.C., 2020, Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Communications, 11, 1–12.
Mousavi, S.M., Sheng, Y., Zhu, W., and Beroza, G.C., 2019, STanford EArthquake Dataset (STEAD): A global data set of seismic signals for AI. IEEE Access, 7, 179464–179476.
Münchmeyer, J., Bindi, D., Leser, U., and Tilmann, F., 2021, Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network. Geophysical Journal International, 226, 1086–1104.
Münchmeyer, J., Woollam, J., Rietbrock, A., Tilmann, F., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J., and Soto, H., 2022, Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers. Journal of Geophysical Research: Solid Earth, 127. https://doi.org/10.1029/2021JB023499
Perol, T., Gharbi, M., and Denolle, M., 2018, Convolutional neural network for earthquake detection and location. Science Advances, 4, e1700578.
Ross, Z.E., Meier, M.A., Hauksson, E., and Heaton, T.H., 2018, Generalized seismic phase detection with deep learning. Bulletin of the Seismological Society of America, 108, 2894–2901.
Ross, Z.E., Yue, Y., Meier, M.A., Hauksson, E., and Heaton, T.H., 2019, PhaseLink: a deep learning approach to seismic phase association. Journal of Geophysical Research: Solid Earth, 124, 856–869.
Sheen, D.H., Kang, T.S., and Rhie, J., 2018, A local magnitude scale for South Korea. Bulletin of the Seismological Society of America, 108, 2748–2755.
Shelly, D.R., Beroza, G.C., and Ide, S., 2007, Non-volcanic tremor and low-frequency earthquake swarms. Nature, 446, 305–307.
Son, M., Cho, C.S., Shin, J.S., Rhee, H.M., and Sheen, D.H., 2018, Spatiotemporal distribution of events during the first three months of the 2016 Gyeongju, Korea, earthquake sequence. Bulletin of the Seismological Society of America, 108, 210–217.
Tan, Y.J., Waldhauser, F., Ellsworth, W.L., Zhang, M., Zhu, W., Michele, M., Chiaraluce, L., Beroza, G.C., and Segou, M., 2021, Machine-learning-based high-resolution earthquake catalog reveals how complex fault structures were activated during the 2016–2017 central Italy sequence. The Seismic Record, 1, 11–19.
Trnkoczy, A., 2012, Understanding and parameter setting of STA/LTA trigger algorithm. In: Bormann, P. (ed.), New Manual of Seismological Observatory Practice 2 (NMSOP-2). Deutsches GeoForschungsZentrum (GFZ), Potsdam, Germany, p. 1–20. https://doi.org/10.2312/GFZ.NMSOP-2_IS_8.1
Withers, M., Aster, R., Young, C., Beiriger, J., Harris, M., Moore, S., and Trujillo, J., 1998, A comparison of select trigger algorithms for automated global seismic phase and event detection. Bulletin of the Seismological Society of America, 88, 95–106.
Woo, J.-U., Rhie, J., Kim, S., Kang, T.-S., Kim, K.-H., and Kim, Y., 2019, The 2016 Gyeongju earthquake sequence revisited: aftershock interactions within a complex fault system. Geophysical Journal International, 217, 58–74.
Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J., and Soto, H., 2022, SeisBench—a toolbox for machine learning in seismology. Seismological Research Letters, 93, 1695–1709.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E., 2016, Hierarchical attention networks for document classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Abstract), San Diego, USA, Jun. 12–17, p. 1480–1489. https://doi.org/10.18653/v1/N16-1174
Yano, K., Shiina, T., Kurata, S., Kato, A., Komaki, F., Sakai, S.I., and Hirata, N., 2021, Graph-partitioning based convolutional neural network for earthquake detection using a seismic array. Journal of Geophysical Research: Solid Earth, 126.
Yoon, C.E., O’Reilly, O., Bergen, K.J., and Beroza, G.C., 2015, Earthquake detection through computationally efficient similarity search. Science advances, 1, e1501057.
Zhang, M., Ellsworth, W.L., and Beroza, G.C., 2019, Rapid earthquake association and location. Seismological Research Letters, 90, 2276–2284.
Zhang, M. and Wen, L., 2015, An effective method for small event detection: Match and locate (M&L). Geophysical Journal International, 200, 1523–1537.
Zhou, Y., Yue, H., Kong, Q., and Zhou, S., 2019, Hybrid event detection and phase-picking algorithm using convolutional and recurrent neural networks. Seismological Research Letters, 90, 1079–1087.
Zhu, W. and Beroza, G.C., 2019, PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216, 261–273. https://doi.org/10.1093/gji/ggy423
Acknowledgments
The authors thank editor Tae-Seob Kang and two anonymous reviewers for valuable comments. This research was supported by the Basic Research Project (22–3122) of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science, ICT of Korea. We are grateful to the researchers in the Gyeongju earthquake research group for maintaining the temporary seismic network. We also thank Korea Meteorological Administration (KMA) and KIGAM for providing continuous waveform data. Source code of EQTransformer analyzed in this study is available at https://github.com/smousavi05/EQTransformer. Reference catalog used in this study (Woo et al., 2019) is available at https://github.com/Jeong-Ung/GJ.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Han, J., Kim, S., Sheen, DH. et al. Seismic event and phase detection using deep learning for the 2016 Gyeongju earthquake sequence. Geosci J 27, 285–295 (2023). https://doi.org/10.1007/s12303-023-0004-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12303-023-0004-y