Rapid magnitude estimation based on BDS-3 PPP-B2b coseismic displacement and deep learning: a case study of the 2025 Tingri Mw 7.1 and Mandalay Mw 7.7 earthquakes

doi:10.11947/j.AGCS.2026.20260112

Abstract

Abstract:

Rapid and accurate source characterization is central to improving the timeliness and accuracy of earthquake early warning. Based on a universal training dataset constructed from high-rate GNSS waveforms of historical global major earthquakes, this study develops a spatiotemporal sequence deep learning model named CTSequence, which is designed to simultaneously capture the spatiotemporal evolution of earthquake rupture and determine magnitude using real-time GNSS observation streams based on PPP-B2b technology. The model is applied to the 2025 Tingri, Xizang Mw 7.1 earthquake and the Mandalay, Myanmar Mw 7.7 earthquake to validate its generalization performance. Results show that the model can provide valuable magnitude estimates within approximately 30 s after the P-wave arrival at the first station and converges rapidly to the true magnitude around 75 s after the earthquake origin time. In the test dataset, the final accuracy at 200 s reaches 99.6%. Furthermore, the model demonstrates excellent robustness, maintaining stable estimation performance even under sparse station conditions (N=8). This approach can provide stable, rapid, and uncertainty-characterized magnitude estimation support for earthquake early warning systems, and is particularly suitable for extreme environments with sparse station coverage or network communication disruptions.

Key words: high-rate GNSS, earthquake early warning, deep learning, magnitude estimation, PPP-B2b

CLC Number:

P228

He Li, Kejie Chen, Wenfeng Cui, Haishan Chai, Rongxin Fang, Chaoyong Peng, Li Sun. Rapid magnitude estimation based on BDS-3 PPP-B2b coseismic displacement and deep learning: a case study of the 2025 Tingri Mw 7.1 and Mandalay Mw 7.7 earthquakes[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(7): 1171-1182.

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References 33

[1]	Allen R M, Brown H, Hellweg M, et al. Real-time earthquake detection and hazard assessment by ElarmS across California[J]. Geophysical Research Letters, 2009, 36(5): L00B08.
[2]	孙丽. 地震预警的新发展：新型传感器网络的应用[J]. 地球与行星物理论评(中英文), 2023, 54(1): 105-107.
	Sun Li. The new development of EEW: the application of new sensor networks[J]. Reviews of Geophysics and Planetary Physics, 2023, 54(1): 105-107.
[3]	Melgar D, Bock Y, Sanchez D, et al. On robust and reliable automated baseline corrections for strong motion seismology[J]. Journal of Geophysical Research: Solid Earth, 2013, 118(3): 1177-1187.
[4]	Meier M A, Heaton T, Clinton J. Evidence for universal earthquake rupture initiation behavior[J]. Geophysical Research Letters, 2016, 43(15): 7991-7996.
[5]	Hoshiba M, Iwakiri K, Hayashimoto N, et al. Outline of the 2011 off the Pacific coast of Tohoku Earthquake (Mw 9.0): earthquake early warning and observed seismic intensity[J]. Earth, Planets and Space, 2011, 63(7): 547-551.
[6]	Marcou S, Lux A I, Akimov A, et al. Exploring end-to-end earthquake early warning performance in large earthquakes using the February 2023 Kahramanmara ş, Türkiye sequence[J]. Scientific Reports, 2026, 16: 327.
[7]	陈克杰, 柴海山, 李佳峰, 等. GNSS海啸预警：地表形变监测与电离层扰动探测[J]. 中国科学(地球科学), 2026, 56(3): 928-942.
	Chen Kejie, Chai Haishan, Li Jiafeng, et al. An overview of GNSS for tsunami early warning: From lithosphere to ionosphere[J]. Scientia Sinica (Terrae), 2026, 56(3): 928-942.
[8]	Bock Y, Melgar D, Crowell B W. Real-time strong-motion broadband displacements from collocated GPS and accelerometers[J]. Bulletin of the Seismological Society of America, 2011, 101(6): 2904-2925.
[9]	Chen Kejie, Ge Maorong, Babeyko Andrey, et al. Retrieving real-time co-seismic displacements using GPS/GLONASS: a preliminary report from the September 2015Mw 8.3 Illapel earthquake in Chile[J]. Geophysical Journal International, 2016, 206(2): 941-953.
[10]	Zumberge J F, Heflin M B, Jefferson D C, et al. Precise point positioning for the efficient and robust analysis of GPS data from large networks[J]. Journal of Geophysical Research: Solid Earth, 1997, 102(B3): 5005-5017.
[11]	张小红, 李星星, 李盼. GNSS精密单点定位技术及应用进展[J]. 测绘学报, 2017, 46(10): 1399-1407. DOI: . doi: 10.11947/j.AGCS.2017.20170327
	Zhang Xiaohong, Li Xingxing, Li Pan. Review of GNSS PPP and its application[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1399-1407. DOI: . doi: 10.11947/j.AGCS.2017.20170327
[12]	柴海山, 陈克杰, 魏国光, 等. 北斗三号与超高频GNSS同震形变监测：以2021年青海玛多Mw 7.4地震为例[J]. 武汉大学学报(信息科学版), 2022, 47(6): 946-954.
	Chai Haishan, Chen Kejie, Wei Guoguang, et al. Coseismic deformation monitoring using BDS-3 and ultra-high rate GNSS: a case study of the 2021 Maduo Mw 7.4 earthquake[J]. Geomatics and Information Science of Wuhan University, 2022, 47(6): 946-954.
[13]	Chen Kejie, Chai Haishan, Li Jiafeng, et al. An overview of GNSS for tsunami early warning: from lithosphere to ionosphere[J]. Science China Earth Sciences, 2026, 69(3): 898-912.
[14]	中国卫星导航系统管理办公室. 北斗卫星导航系统空间信号接口控制文件：精密单点定位服务信号(PPP-B2b)(1.0版)[S]. 北京: 中国卫星导航系统管理办公室, 2020.
	China Satellite Navigation Office. BeiDou navigation satellite system signal in space interface control document: precise point positioning service signal (PPP-B2b)(V1.0)[S]. Beijing: China Satellite Navigation Office, 2020.
[15]	Fang Rongxin, Lü Huanghui, Hu Zhigang, et al. GPS/BDS precise point positioning with B2b products for high-rate seismogeodesy: application to the 2021Mw 7.4 Maduo earthquake[J]. Geophysical Journal International, 2022, 231(3): 2079-2090.
[16]	Mousavi S M, Beroza G C. Deep-learning seismology[J]. Science, 2022, 377(6607): eabm4470.
[17]	LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[18]	Meier M A, Ross Z E, Ramachandran A, et al. Reliable real-time seismic signal/noise discrimination with machine learning[J]. Journal of Geophysical Research: Solid Earth, 2019, 124(1): 788-800.
[19]	Zhu Weiqiang, Beroza G C. PhaseNet: a deep-neural-network-based seismic arrival time picking method[J]. Geophysical Journal International, 2019, 216(1): 261-273.
[20]	Perol T, Gharbi M, Denolle M. Convolutional neural network for earthquake detection and location[J]. Science Advances, 2018, 4(2): e1700578.
[21]	Kuang Wenhuan, Yuan Congcong, Zhang Jie. Real-time determination of earthquake focal mechanism via deep learning[J]. Nature Communications, 2021, 12: 1432.
[22]	Mousavi S M, Beroza G C. A machine-learning approach for earthquake magnitude estimation[J]. Geophysical Research Letters, 2020, 47(1): e2019GL085976.
[23]	Taroni M, Spassiani I, Laskin N, et al. How many strong earthquakes will there be tomorrow?[J]. Frontiers in Applied Mathematics and Statistics, 2023, 9: 1152476.
[24]	Frankel A, Wirth E, Marafi N, et al. Broadband synthetic seismograms for Magnitude 9 earthquakes on the cascadia megathrust based on 3D simulations and stochastic synthetics, part 1: methodology and overall results[J]. Bulletin of the Seismological Society of America, 2018, 108(5A): 2347-2369.
[25]	Lin J T, Melgar D, Thomas A M, et al. Early warning for great earthquakes from characterization of crustal deformation patterns with deep learning[J]. Journal of Geophysical Research: Solid Earth, 2021, 126(10): e2021JB022703.
[26]	Cui Wenfeng, Chen Kejie, Wei Guoguang, et al. Simultaneous magnitude and slip distribution characterization from high-rate GNSS using deep learning: case studies of the 2021Mw 7.4 Maduo and 2023 Turkey doublet events[J]. Geophysical Journal International, 2024, 238(1): 91-108.
[27]	Quinteros-Cartaya C, Quintero-Arenas J, Padilla-Lafarga A, et al. A deep learning pipeline for large earthquake analysis using high-rate global navigation satellite system data[J]. Earth Science Informatics, 2025, 18(4): 516.
[28]	Ruhl C J, Melgar D, Geng Jianghui, et al. A global database of strong-motion displacement GNSS recordings and an example application to PGD scaling[J]. Seismological Research Letters, 2019, 90(1): 271-279.
[29]	肖懿思, 方荣新, 李瑜, 等. 利用高频GNSS观测研究2025年西藏定日Ms 6.8地震地表形变及预警震级[J]. 武汉大学学报(信息科学版), 2025, 50(11): 2176-2186.
	Xiao Yisi, Fang Rongxin, Li Yu, et al. Surface displacement and early warning magnitude of the 2025 Dingri, Xizang Ms 6.8 earthquake using high-rate GNSS observations[J]. Geomatics and Information Science of Wuhan University, 2025, 50(11): 2176-2186.
[30]	Melgar D, Crowell B W, Geng Jianghui, et al. Earthquake magnitude calculation without saturation from the scaling of peak ground displacement[J]. Geophysical Research Letters, 2015, 42(13): 5197-5205.
[31]	Laske G, Masters G, Ma Z, et al. Update on CRUST1.0: a 1-degree global model of Earth's crust[C]//Proceedings of 2013 Geophysical Research Abstracts. Munich: European Geosciences Union, 2013, 15: EGU2013-2658.
[32]	Crowell B W, Melgar D, Bock Y, et al. Earthquake magnitude scaling using seismogeodetic data[J]. Geophysical Research Letters, 2013, 40(23): 6089-6094.
[33]	Jiang Weiping, Zhao Qile, Li Min, et al. The progress of IGS analysis center at Wuhan University[J]. Journal of Geodesy and Geoinformation Science, 2023, 6(3): 46-57.

回归系数	文献[30]方法/cm	文献[28]方法/m	文献[32]方法/cm
A	－6.687	－5.919	－4.434
B	1.5	1.009	1.047
C	－0.214	－0.145	－0.138