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

doi:10.3799/dqkx.2025.253

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

doi: 10.3799/dqkx.2025.253

夏登科¹,
房立华^2, ,,
蒋策³,
范莉苹¹,
李君^1,4,
吕帅⁵,
李帅¹,
索朗占堆⁶

1. 中国地震局地球物理研究所, 北京 100081;
2. 中国地震局地震预测研究所, 北京 100036;
3. 广东省地震局, 广东广州 510070;
4. 中国地震局第二监测中心, 陕西西安 710054;
5. 云南省地震局, 云南昆明 650224;
6. 西藏自治区地震局, 西藏拉萨 850000

基金项目:

国家自然科学基金(No.42374081)

详细信息

作者简介:
夏登科(2001-),男,博士研究生,主要从事人工智能地震检测算法研究.ORCID:0009-0007-6318-941X.E-mail:dengkexia@gmail.com

通讯作者:
房立华(1981-),男,博士,研究员,主要从事人工智能地震学研究.ORCID:0000-0003-2156-4406.E-mail:fanglihua@ief.ac.cn

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- 收稿日期: 2025-09-01

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

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. Tibet Autonomous Region Earthquake Agency, Lhasa 850000, China

摘要

摘要: 2025年1月7日西藏定日发生M_W7.1地震，造成严重人员伤亡。本文利用定日地震周边12个固定台站与震后布设的6个流动台站数据，应用AIRES（ A rtificial I ntelligence R eal-time E arthquake processing S ystem）智能实时地震处理系统对余震序列进行处理，评估AIRES在稀疏台网下的应用效果。AIRES基于深度学习算法，自动从实时波形中完成地震检测、震相到时拾取、事件关联及震源参数反演。与人工目录对比表明，AIRES检测余震11，242次，是人工目录的2.53倍，完备震级降至M_L1.5；两个目录的平均震中差异为4.69km、平均震源深度差异为5.71km、平均震级差为-0.02。定日地震的余震分布在南北向长度约80km，东西向宽度约30km的区域内，并具有明显的分段和拐折特征。研究表明，在台网稀疏场景下，AIRES仍能保持稳健的检测能力与定位精度，可为密集地震序列实时监测和地震应急提供技术支撑。
- 定日地震 /
- AIRES /
- 稀疏台网 /
- 余震序列 /
- 地震检测
Abstract: On January 7, 2025, an M_W7.1 earthquake struck Dingri, Tibet, 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 ( A rtificial I ntelligence R eal-time E arthquake processing S ystem). 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. Compared with the manual catalog, AIRES detected 11,242 aftershocks—2.53 times that of the manual catalog—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

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参考文献(38)

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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

doi: 10.3799/dqkx.2025.253

作者简介: 夏登科(2001-),男,博士研究生,主要从事人工智能地震检测算法研究.ORCID:0009-0007-6318-941X.E-mail:dengkexia@gmail.com

通讯作者: 房立华(1981-),男,博士,研究员,主要从事人工智能地震学研究.ORCID:0000-0003-2156-4406.E-mail:fanglihua@ief.ac.cn

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出版历程

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

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出版历程

目录

作者简介:
夏登科(2001-),男,博士研究生,主要从事人工智能地震检测算法研究.ORCID:0009-0007-6318-941X.E-mail:dengkexia@gmail.com

通讯作者:
房立华(1981-),男,博士,研究员,主要从事人工智能地震学研究.ORCID:0000-0003-2156-4406.E-mail:fanglihua@ief.ac.cn