A High-Resolution Aftershock Catalog for the 2014 Ms 6.5 Ludian (China) Earthquake Using Deep Learning Methods
Abstract
:1. Introduction
2. Tectonic Background
3. Data and Methods
3.1. Seismic Data
3.2. Methods
3.3. AI Model Training
3.4. Earthquake Detection, Phase Picking, Association and Location
4. Results
4.1. Aftershocks Space Distribution, Temporal Evolution and Focal Mechanism
4.2. Seismic Rate Evolution
5. Discussion
5.1. The 2014 Ludian Earthquake’s Seismogenic Fault and Its Tectonic Implications
5.2. The Future Application of This Retraining Strategy
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Shelly, D.R. A High-Resolution Seismic Catalog for the Initial 2019 Ridgecrest Earthquake Sequence: Foreshocks, Aftershocks, and Faulting Complexity. Seismol. Res. Lett. 2020, 91, 1971–1978. [Google Scholar] [CrossRef]
- Ross, Z.E.; Idini, B.; Jia, Z.; Stephenson, O.L.; Zhong, M.Y.; Wang, X.; Zhan, Z.W.; Simons, M.; Fielding, E.J.; Yun, S.H.; et al. Hierarchical interlocked orthogonal faulting in the 2019 Ridgecrest earthquake sequence. Science 2019, 366, 346–351. [Google Scholar] [CrossRef]
- Ross, Z.E.; Trugman, D.T.; Hauksson, E.; Shearer, P.M. Searching for hidden earthquakes in Southern California. Science 2019, 364, 767–771. [Google Scholar] [CrossRef]
- Geiger, L. Probability method for the determination of earthquake epicentres from the arrival time only. Bull. St. Louis Univ. 1912, 8, 60. [Google Scholar]
- Lee, W.H.K.; Lahr, J.C. HYPO71: A Computer Program for Determining Hypocenter, Magnitude, and First Motion Pattern of Local Earthquakes; US Department of the Interior, Geological Survey, National Center for Earthquake Reasearch: Reston, VA, USA, 1972.
- Klein, F.W. Hypocenter Location Program HYPOINVERSE: Part I. Users Guide to Versions 1, 2, 3, and 4. Part II. Source Listings and Notes; US Geological Survey: Reston, VA, USA, 1978.
- Klein, F.W. User’s Guide to HYPOINVERSE-2000, a Fortran Program to Solve for Earthquake Locations and Magnitudes; Open-File Report; US Geological Survey: Reston, VA, USA, 2002.
- Lienert, B.R.; Berg, E.; Frazer, L.N. HYPOCENTER: An earthquake location method using centered, scaled, and adaptively damped least squares. Bull. Seismol. Soc. Am. 1986, 76, 771–783. [Google Scholar] [CrossRef]
- Nelson, G.D.; Vidale, J.E. Earthquake locations by 3-D finite-difference travel times. Bull. Seismol. Soc. Am. 1990, 80, 395–410. [Google Scholar] [CrossRef]
- Waldhauser, F.; Ellsworth, W. A double-difference earthquake location algorithm: Method and application to the northern Hayward fault, California. Bull. Seismol. Soc. Am. 2000, 90, 1353–1368. [Google Scholar] [CrossRef]
- Gibbons, S.J.; Ringdal, F. The detection of low magnitude seismic events using array-based waveform correlation. Geophys. J. Int. 2006, 165, 149–166. [Google Scholar] [CrossRef]
- Zhang, M.; Wen, L. An effective method for small event detection: Match and locate (M&L). Geophys. J. Int. 2015, 200, 1523–1537. [Google Scholar]
- Allen, R.V. Automatic earthquake recognition and timing from single traces. Bull. Seismol. Soc. Am. 1978, 68, 1521–1532. [Google Scholar] [CrossRef]
- Zhou, Y.J.; Yue, H.; Kong, Q.K.; Zhou, S.Y. Hybrid Event Detection and Phase-Picking Algorithm Using Convolutional and Recurrent Neural Networks. Seismol. Res. Lett. 2019, 90, 1079–1087. [Google Scholar] [CrossRef]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef]
- Perol, T.; Gharbi, M.; Denolle, M. Convolutional neural network for earthquake detection and location. Sci. Adv. 2018, 4, e1700578. [Google Scholar] [CrossRef]
- Mousavi, S.M.; Zhu, W.Q.; Sheng, Y.X.; Beroza, G.C. CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection. Sci. Rep. 2019, 9, 10267. [Google Scholar] [CrossRef]
- Mousavi, S.M.; Ellsworth, W.L.; Zhu, W.Q.; Chuang, L.Y.; Beroza, G.C. Earthquake transformer—An attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat. Commun. 2020, 11, 3952. [Google Scholar] [CrossRef] [PubMed]
- Zhu, W.Q.; Beroza, G.C. PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. Geophys. J. Int. 2018, 216, 261–273. [Google Scholar] [CrossRef]
- Zhu, W.; McBrearty, I.W.; Mousavi, S.M.; Ellsworth, W.L.; Beroza, G.C. Earthquake Phase Association Using a Bayesian Gaussian Mixture Model. J. Geophys. Res. Solid Earth 2022, 127, e2021JB023249. [Google Scholar] [CrossRef]
- Zhang, M.; Liu, M.; Feng, T.; Wang, R.; Zhu, W. LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow. Seismol. Res. Lett. 2022, 93, 2426–2438. [Google Scholar] [CrossRef]
- Zhou, Y.J.; Yue, H.; Fang, L.H.; Zhou, S.Y.; Zhao, L.; Ghosh, A. An Earthquake Detection and Location Architecture for Continuous Seismograms: Phase Picking, Association, Location, and Matched Filter (PALM). Seismol. Res. Lett. 2021, 93, 413–425. [Google Scholar] [CrossRef]
- Mousavi, S.M.; Sheng, Y.X.; Zhu, W.Q.; Beroza, G.C. STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI. IEEE Access 2019, 7, 179464–179476. [Google Scholar] [CrossRef]
- Zhao, M.; Xiao, Z.W.; Chen, S.; Fang, L.H. DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology. Earthq. Sci. 2023, 36, 84–94. [Google Scholar] [CrossRef]
- Fang, L.H.; Wu, J.P.; Wang, W.L.; Lv, Z.Y.; Wang, C.Z.; Yang, T.; Zhong, S.J. Relocation of the aftershock sequence of the M S 6.5 Ludian earthquake and its seismogenic structure. Seismol. Geol. 2014, 36, 1173–1185. [Google Scholar]
- Wen, X.Z.; Du, F.; Yi, G.X.; Long, F.; Fan, J.; Yang, F.X.; Xiong, R.W.; Liu, Q.X.; Liu, Q. Earthquake potential of the Zhaotong and Lianfeng fault zones of the eastern Sichuan-Yunnan border region. Chin. J. Geophys. 2013, 56, 3361–3372. [Google Scholar]
- He, H.L.; Ikeda, Y.; He, Y.L.; Togo, M.; Chen, J.; Chen, C.Y.; Tajikara, M.; Echigo, T.; Okada, S. Newly-generated Daliangshan fault zone—Shortcutting on the central section of Xianshuihe-Xiaojiang fault system. Sci. China Ser. D Earth Sci. 2008, 51, 1248–1258. [Google Scholar] [CrossRef]
- Xu, X.W.; Wen, X.Z.; Zheng, R.Z.; Ma, W.T.; Song, F.M.; Yu, G.H. Pattern of latest tectonic motion and its dynamics for active blocks in Sichuan-Yunnan region, China. Sci. China Ser. D Earth Sci. 2003, 46, 210–226. [Google Scholar] [CrossRef]
- Baillard, C.; Crawford, W.C.; Ballu, V.; Hibert, C.; Mangeney, A. An Automatic Kurtosis-Based P- and S-Phase Picker Designed for Local Seismic Networks. Bull. Seismol. Soc. Am. 2013, 104, 394–409. [Google Scholar] [CrossRef]
- Zhou, Y.Y.; Ghosh, A.; Fang, L.H.; Yue, H.; Zhou, S.Y.; Su, Y.J. A high-resolution seismic catalog for the 2021 MS6.4/MW6.1 Yangbi earthquake sequence, Yunnan, China: Application of AI picker and matched filter. Earthq. Sci. 2021, 34, 390–398. [Google Scholar] [CrossRef]
- Waldhauser, F.; Ellsworth, W.L. Fault structure and mechanics of the Hayward Fault, California, from double-difference earthquake locations. J. Geophys. Res. Solid Earth 2002, 107, ESE 3-1–ESE 3-15. [Google Scholar] [CrossRef]
- Dziewonski, A.M.; Chou, T.A.; Woodhouse, J.H. Determination of earthquake source parameters from waveform data for studies of global and regional seismicity. J. Geophys. Res. Solid Earth 1981, 86, 2825–2852. [Google Scholar] [CrossRef]
- Ekström, G.; Nettles, M.; Dziewoński, A.M. The global CMT project 2004–2010: Centroid-moment tensors for 13,017 earthquakes. Phys. Earth Planet. Inter. 2012, 200–201, 1–9. [Google Scholar] [CrossRef]
- Wang, W.; Wu, J.; Fang, L.; Juan, L. Double difference location of the Ludian MS6.5 earthquake sequences in Yunnan province in 2014. Chin. J. Geophys. 2014, 57, 3042–3051. (In Chinese) [Google Scholar]
- Ross, Z.E.; Meier, M.A.; Hauksson, E.; Heaton, T.H. Generalized Seismic Phase Detection with Deep Learning. Bull. Seismol. Soc. Am. 2018, 108, 2894–2901. [Google Scholar] [CrossRef]
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Li, J.; Hao, M.; Cui, Z. A High-Resolution Aftershock Catalog for the 2014 Ms 6.5 Ludian (China) Earthquake Using Deep Learning Methods. Appl. Sci. 2024, 14, 1997. https://doi.org/10.3390/app14051997
Li J, Hao M, Cui Z. A High-Resolution Aftershock Catalog for the 2014 Ms 6.5 Ludian (China) Earthquake Using Deep Learning Methods. Applied Sciences. 2024; 14(5):1997. https://doi.org/10.3390/app14051997
Chicago/Turabian StyleLi, Jun, Ming Hao, and Zijian Cui. 2024. "A High-Resolution Aftershock Catalog for the 2014 Ms 6.5 Ludian (China) Earthquake Using Deep Learning Methods" Applied Sciences 14, no. 5: 1997. https://doi.org/10.3390/app14051997
APA StyleLi, J., Hao, M., & Cui, Z. (2024). A High-Resolution Aftershock Catalog for the 2014 Ms 6.5 Ludian (China) Earthquake Using Deep Learning Methods. Applied Sciences, 14(5), 1997. https://doi.org/10.3390/app14051997