AIRES智能实时地震处理系统在稀疏台网下的应用：以2025年定日地震为例

夏登科; 房立华; 蒋策; 范莉苹; 李君; 吕帅; 李帅; 索朗占堆

doi:10.3799/dqkx.2025.253

Volume 51 Issue 1

Jan. 2026

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2026 > 51(1): 1-13

Xia Dengke, Fang Lihua, Jiang Ce, Fan Liping, Li Jun, Lyu Shuai, Li Shuai, Suolang Zhandui, 2026. Application of Artificial Intelligence Real-Time Earthquake Processing System (AIRES) under a Sparse Seismic Network: A Case Study of 2025 Dingri Earthquake. Earth Science, 51(1): 1-13. doi: 10.3799/dqkx.2025.253

Citation:

Xia Dengke, Fang Lihua, Jiang Ce, Fan Liping, Li Jun, Lyu Shuai, Li Shuai, Suolang Zhandui, 2026. Application of Artificial Intelligence Real-Time Earthquake Processing System (AIRES) under a Sparse Seismic Network: A Case Study of 2025 Dingri Earthquake. Earth Science, 51(1): 1-13. doi: 10.3799/dqkx.2025.253

Citation:

Xia Dengke, Fang Lihua, Jiang Ce, Fan Liping, Li Jun, Lyu Shuai, Li Shuai, Suolang Zhandui, 2026. Application of Artificial Intelligence Real-Time Earthquake Processing System (AIRES) under a Sparse Seismic Network: A Case Study of 2025 Dingri Earthquake. Earth Science, 51(1): 1-13. doi: 10.3799/dqkx.2025.253

PDF( 9409 KB)

Application of Artificial Intelligence Real-Time Earthquake Processing System (AIRES) under a Sparse Seismic Network: A Case Study of 2025 Dingri Earthquake

doi: 10.3799/dqkx.2025.253

Xia Dengke^{1
,
,},
Fang Lihua^{2
,
,
,},
Jiang Ce³,
Fan Liping¹,
Li Jun^{1, 4},
Lyu Shuai⁵,
Li Shuai¹,
Suolang Zhandui⁶

1.
Institute of Geophysics, China Earthquake Administration, Beijing 100081, China
2.
Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
3.
Guangdong Earthquake Agency, Guangzhou 510070, China
4.
The Second Monitoring and Application Center, China Earthquake Administration, Xi'an 710054, China
5.
Yunnan Earthquake Agency, Kunming 650224, China
6.
Xizang Autonomous Region Earthquake Agency, Lhasa 850000, China

Received Date: 2025-09-01
Publish Date: 2026-01-25

Abstract

Abstract

On January 7, 2025, an M_W7.1 earthquake struck Dingri, Xizang, causing severe casualties. This study employs data from 12 permanent and 6 temporary seismic stations deployed around the epicentral area to process the aftershock sequence using the AIRES (Artificial Intelligence Real-time Earthquake processing System). The goal is to evaluate the performance of AIRES under a sparse seismic network configuration. AIRES, based on deep learning algorithms, automatically conducts earthquake detection, phase picking, event association, and source parameter inversion from real-time waveforms. Comparison with the manual catalog demonstrates that AIRES detected 11 242 aftershocks, which is 2.53 times the size of the manual catalog, effectively lowering the magnitude of completeness to M_L1.5. The average differences between the two catalogs are 4.69 km in epicenter, 5.71 km in focal depth, and -0.02 in local magnitude. The aftershocks are distributed in a north- south-trending zone approximately 80 km long and 30 km wide, exhibiting distinct segmentation and bending features. The study demonstrates that AIRES maintains robust detection capability and location accuracy even under sparse network conditions, providing strong technical support for real-time monitoring of dense aftershock sequences and earthquake emergency response.
- Dingri Earthquake,
- AIRES,
- sparse seismic network,
- aftershock sequence,
- earthquake detection,
- seismology

FullText(HTML)

References(50)

References

Baimasangbu, Danzengquzhu, Cidanzhuoma, et al., 2024. Analysis of the Impact of Observing Environmental Changes on the Observation Quality of Shigatse Seismic Station. Seismological and Geomagnetic Observation and Research, 45(5): 67-73 (in Chinese with English abstract).

He, J., Yang, K., Tang, W. J., et al., 2020. The First High-Resolution Meteorological Forcing Dataset for Land Process Studies over China. Scientific Data, 7: 1-11. https://doi.org/10.1038/s41597-020-0369-y

Jia, K., Zhou, S. Y., 2024. Machine Learning Applications in Seismology. Applied Sciences, 14(17): 7857. https://doi.org/10.3390/app14177857

Jiang, C., 2025. Research and Application of Intelligent Processing Algorithms of Regional Seismic Network Data (Dissertation). Institute of Geophysics, China Earthquake Administration, Beijing (in Chinese with English abstract).

Kato, A., Obara, K., 2014. Step-like Migration of Early Aftershocks Following the 2007 M_W6.7 Noto-Hanto Earthquake, Japan: Kato and Obara: Step-Like Migration of Early Aftershocks. Geophysical Research Letters, 41(11): 3864-3869. https://doi.org/10.1002/2014gl060427

Kuyuk, H. S., Allen, R. M., 2013. Optimal Seismic Network Density for Earthquake Early Warning: A Case Study from California. Seismological Research Letters, 84(6): 946-954. https://doi.org/10.1785/0220130043

Li, S., Fang, L. H., Xiao, Z. W., et al., 2023. FocMech-Flow: Automatic Determination of P-Wave First- Motion Polarity and Focal Mechanism Inversion and Application to the 2021 Yangbi Earthquake Sequence. Applied Sciences, 13(4): 1-13. https://doi.org/10.3390/app13042233

Liao, S. R., Zhang, H. C., Fan, L. P., et al., 2021. Development of a Real-Time Intelligent Seismic Processing System and Its Application in the 2021 Yunnan Yangbi M_S6.4 Earthquake. Chinese Journal of Geophysics, 64(10): 3632-3645 (in Chinese with English abstract).

Liu, M., Zhang, M., Zhu, W. Q., et al., 2020. Rapid Characterization of the July 2019 Ridgecrest, California, Earthquake Sequence from Raw Seismic Data Using Machine-Learning Phase Picker. Geophysical Research Letters, 47(4): e2019GL086189. https://doi.org/10.1029/2019gl086189

Liu, R. F., Chen, Y. T., Xue, F., et al., 2018. How to Use the New National Standard of Magnitude Correctly. Seismological and Geomagnetic Observation and Research, 39(4): 1-11 (in Chinese with English abstract).

Peng, Z. G., Zhao, P., 2009. Migration of Early Aftershocks Following the 2004 Parkfield Earthquake. Nature Geoscience, 2(12): 877-881. https://doi.org/10.1038/ngeo697

Qu, C. Y., 2008. Building to the Active Tectonic Database of China. Seismology and Geology, 30(1): 298-304 (in Chinese with English abstract).

Ross, Z. E., Meier, M. A., Hauksson, E., et al., 2018. Generalized Seismic Phase Detection with Deep Learning. Bulletin of the Seismological Society of America, 108(5A): 2894-2901. https://doi.org/10.1785/0120180080

Schweitzer, J., 2001. HYPOSAT-An Enhanced Routine to Locate Seismic Events. Pure and Applied Geophysics, 158(1): 277-289. https://doi.org/10.1007/PL00001160

Sheng, S. Z., Wang, Q. R., Li, Z. Y., et al., 2025. Investigation of the Seismogenic Structure of the 2025 Dingri M_s6.8 Earthquake in Xizang Based on the Tectonic Stress Field Perspective. Seismology and Geology, 47(1): 49-63 (in Chinese with English abstract).

Si, X., Wu, X. M., Li, Z. F., et al., 2024. An All-in-One Seismic Phase Picking, Location, and Association Network for Multi-Task Multi-Station Earthquake Monitoring. Communications Earth & Environment, 5: 1-13. https://doi.org/10.1038/s43247-023-01188-4

Tian, T. T., Wu, Z. H., 2023. Recent Prehistoric Major Earthquake Event of Dingmucuo Normal Fault in the Southern Segment of Shenzha-Dingjie Rift and Its Seismic Geological Significance. Geological Review, 69(S1): 53-55 (in Chinese).

Wang, R. J., Schmandt, B., Zhang, M., et al., 2020. Injection-Induced Earthquakes on Complex Fault Zones of the Raton Basin Illuminated by Machine-Learning Phase Picker and Dense Nodal Array. Geophysical Research Letters, 47(14): e2020GL088168. https://doi.org/10.1029/2020gl088168

Wang, Z. D., Yang, X. P., Yin, X. X., et al., 2022. Discussion on the Automatic Processing Results for the Aftershock Sequence of Menyuan, Qinghai M_S6.9 Earthquake on 8 January, 2022. China Earthquake Engineering Journal, 44(2): 408-414 (in Chinese with English abstract).

Xu, Z. Q., Yang, J. S., Li, H. B., et al., 2011. On the Tectonics of the India-Asia Collision. Acta Geologica Sinica, 85(1): 1-33 (in Chinese with English abstract). doi: 10.1111/j.1755-6724.2011.00375.x

Yang, L., Lei, H. F., You, J. X., et al., 2023. Estimation of Coda Wave Attenuation in Xiangjiaba and Xiluodu Reservoirs Region. Journal of Geodesy and Geodynamics, 43(4): 403-408 (in Chinese with English abstract).

Yang, T., Wang, S. G., Fang, L. H., et al., 2025. Analysis of Earthquake Sequence and Seismogenic Structure of the 2025 M_S6.8 Dingri Earthquake in Tibetan Plateau. Earth Science, 50(5): 1721-1732 (in Chinese with English abstract).

Yao, J. Y., Yao, D. D., Chen, F., et al., 2025. A Preliminary Catalog of Early Aftershocks Following the 7 January 2025 M_S6.8 Dingri, Xizang Earthquake. Journal of Earth Science, 36(2): 856-860. https://doi.org/10.1007/s12583-025-0210-9

Yoon, C. E., Cochran, E. S., Vanacore, E. A., et al., 2023. A Detailed View of the 2020-2023 Southwestern Puerto Rico Seismic Sequence with Deep Learning. Bulletin of the Seismological Society of America, 113(6): 2377-2415. https://doi.org/10.1785/0120220229

Zhang J. J., 2007. A Review on the Extensional Structures in the Northern Himalaya and Southern Tibet. Geological Bulletin of China, 26(6): 639-649 (in Chinese with English abstract).

Zhang, M., Liu, M., Feng, T., et al., 2022. LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow. Seismological Research Letters, 93(5): 2426-2438. https://doi.org/10.1785/0220220019

Zhang, X., Hong, S. Y., Dong, Y. F., et al., 2025. Coseismic Deformation and Fault Slip Distribution of the January 7, 2025, Dingri M_W7.1 Earthquake. Earth Science, 50(5): 1709-1720 (in Chinese with English abstract).

Zhang, X., Zhang, M., 2024. Universal Neural Networks for Real-Time Earthquake Early Warning Trained with Generalized Earthquakes. Communications Earth & Environment, 5: 1-16. https://doi.org/10.1038/s43247-024-01718-8

Zhao, M., Xiao, Z. W., Zhang, M., et al., 2023. DiTingMotion: A Deep-Learning First-Motion-Polarity Classifier and Its Application to Focal Mechanism Inversion. Frontiers in Earth Science, 11: 1-10. https://doi.org/10.3389/feart.2023.1103914

Zhou, Y. J., Ding, H. Y., Ghosh, A., et al., 2025. AI-PAL: Self-Supervised AI Phase Picking via Rule-Based Algorithm for Generalized Earthquake Detection. Journal of Geophysical Research: Solid Earth, 130(4): e2025JB031294. https://doi.org/10.1029/2025jb031294

Zhou, Y. J., Ghosh, A., Fang, L. H., et al., 2021. A High-Resolution Seismic Catalog for the 2021 M_S6.4/M_W6.1 Yangbi Earthquake Sequence, Yunnan, China: Application of AI Picker and Matched Filter. Earthquake Science, 34(5): 390-398. https://doi.org/10.29382/eqs-2021-0031

Zhou, Y. J., Yue, H., Fang, L. H., et al., 2022. An Earthquake Detection and Location Architecture for Continuous Seismograms: Phase Picking, Association, Location, and Matched Filter (PALM). Seismological Research Letters, 93(1): 413-425. https://doi.org/10.1785/0220210111

Zhu, J., Li, Z. F., Fang, L. H., 2023a. USTC-Pickers: A Unified Set of Seismic Phase Pickers Transfer Learned for China. Earthquake Science, 36(2): 95-112. https://doi.org/10.1016/j.eqs.2023.03.001

Zhu, W. Q., Hou, A. B., Yang, R., et al., 2023b. QuakeFlow: A Scalable Machine-Learning-Based Earthquake Monitoring Workflow with Cloud Computing. Geophysical Journal International, 232(1): 684-693. https://doi.org/10.1093/gji/ggac355

Zhu, W. Q., Beroza, G. C., 2019. PhaseNet: A Deep- Neural-Network-Based Seismic Arrival Time Picking Method. Geophysical Journal International, 216(1): 261-273. https://doi.org/10.1093/gji/ggy423

Zhu, W. Q., McBrearty, I. W., Mousavi, S. M., et al., 2022. Earthquake Phase Association Using a Bayesian Gaussian Mixture Model. Journal of Geophysical Research: Solid Earth, 127(5): e2021JB023249. https://doi.org/10.1029/2021jb023249

Zhu, W. Q., Mousavi, S. M., Beroza, G. C., 2019. Seismic Signal Denoising and Decomposition Using Deep Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 57(11): 9476-9488. https://doi.org/10.1109/TGRS.2019.2926772

白玛桑布, 单增曲珠, 次旦卓玛, 等, 2024. 观测环境变化对日喀则地震台观测质量的影响. 地震地磁观测与研究, 45(5): 67-73.

蒋策, 2025. 区域台网地震数据智能处理方法研究与应用(博士学位论文). 北京: 中国地震局地球物理研究所.

廖诗荣, 张红才, 范莉苹, 等, 2021. 实时智能地震处理系统研发及其在2021年云南漾濞M_S6.4地震中的应用. 地球物理学报, 64(10): 3632-3645.

刘瑞丰, 陈运泰, 薛峰, 等, 2018. 如何正确使用新的震级国家标准. 地震地磁观测与研究, 39(4): 1-11.

屈春燕, 2008. 最新1/400万中国活动构造空间数据库的建立. 地震地质, 30(1): 298-304.

盛书中, 王倩茹, 李振月, 等, 2025. 基于构造应力场研究2025年西藏定日6.8级地震的发震构造. 地震地质, 47(1): 49-63.

田婷婷, 吴中海, 2023. 西藏申扎‒定结裂谷南段丁木错正断层的最新史前大地震事件及其地震地质意义. 地质论评, 69(S1): 53-55.

王祖东, 杨晓鹏, 尹欣欣, 等, 2022.2022年1月8日青海门源M_S6.9地震余震序列自动处理结果探讨. 地震工程学报, 44(2): 408-414.

许志琴, 杨经绥, 李海兵, 等, 2011. 印度‒亚洲碰撞大地构造. 地质学报, 85(1): 1-33.

杨磊, 雷红富, 游家兴, 等, 2023. 向家坝、溪洛渡库区地震尾波Q_c值估计. 大地测量与地球动力学, 43(4): 403-408.

杨婷, 王世广, 房立华, 等, 2025.2025年1月7日西藏定日M_S6.8地震余震序列特征与发震构造. 地球科学, 50(5): 1721-1732.

张进江, 2007. 北喜马拉雅及藏南伸展构造综述. 地质通报, 26(6): 639-649.

张旭, 洪顺英, 董彦芳, 等, 2025.2025年1月7日定日M_W7.1地震同震形变与断层滑动分布. 地球科学, 50(5): 1709-1720. doi: 10.3799/dqkx.2025.072

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(12)

Get Citation

PDF

XML

Article views (881) PDF downloads(313)

Application of Artificial Intelligence Real-Time Earthquake Processing System (AIRES) under a Sparse Seismic Network: A Case Study of 2025 Dingri Earthquake

doi: 10.3799/dqkx.2025.253

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Export File

Citation

Format

Content