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Magnitude estimation of graph attention networks based on multi-station inputs
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Zhongli YU1, 2, Jingbao ZHU1, 2, Shanyou LI1, 2, Jindong SONG1, 2
Earthquake Engineering and Engineering Dynamics | 2025, 45(2) : 22 - 32
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Earthquake Engineering and Engineering Dynamics | 2025, 45(2): 22-32
Magnitude estimation of graph attention networks based on multi-station inputs
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Zhongli YU1, 2, Jingbao ZHU1, 2, Shanyou LI1, 2, Jindong SONG1, 2
Affiliations
  • 1.Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
  • 2.Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China
Published: 2025-04-24 doi: 10.13197/j.eeed.2025.0203
Outline
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Earthquake magnitude estimation is one of the important tasks in earthquake early warning. Accurate earthquake magnitude estimation is critical to quick judgment of earthquake influence areas and timely release of earthquake warning information. Existing methods usually extract the characteristic information based on the acceleration time history of a single station to estimate the magnitude, and then obtain the result by the multi-station averaging method. In this paper, an end-to-end magnitude estimation model (GAT_M) is constructed using a multi-input graph attention network algorithm. The time history of multi-station seismic acceleration within 3 s after the first P-wave is triggered is input into the GAT_M model. The multi-station seismic acceleration waveforms within 3 s after the first P-wave are used as the input of the GAT_M model. In this study, the strong earthquake data from of the K-NET strong earthquake observation network of Japan Institute of Disaster Prevention Science and Technology were used for model training and test experiments. Within 3 s after the first P-wave triggers, the mean error and standard deviation of magnitude estimation are -0.077 and 0.40 respectively, and R2 is 0.72. The effects of magnitude, time window and number of stations on the performance of GAT_M model are also analyzed. Simultaneously, within 3 s after the initial P-wave triggers, the GAT_M model demonstrates a reduced magnitude estimation error compared to the traditional Pd method. In the case of complex sample data, the GAT_M model has a greater advantage and can be better applied to magnitude estimation.

graph attention network  /  earthquake monitoring and early warning  /  magnitude  /  multi-station
Zhongli YU, Jingbao ZHU, Shanyou LI, Jindong SONG. Magnitude estimation of graph attention networks based on multi-station inputs[J]. Earthquake Engineering and Engineering Dynamics, 2025 , 45 (2) : 22 -32 . DOI: 10.13197/j.eeed.2025.0203
Year 2025 volume 45 Issue 2
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Article Info
doi: 10.13197/j.eeed.2025.0203
  • Receive Date:2024-03-04
  • Online Date:2026-03-20
  • Published:2025-04-24
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History
  • Received:2024-03-04
  • Revised:2024-04-07
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Affiliations
    1.Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
    2.Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China
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https://castjournals.cast.org.cn/joweb/dzgcygczd/EN/10.13197/j.eeed.2025.0203
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表12种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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