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Simulation on Seasonal Microseismic Signals at Gravity Station Using Data-Driven Approaches
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Zhengran WANG1, Jin WEI1, *, Huiling LI2, Guangliang YANG1, Minzhang HU1
Journal of Geodesy and Geodynamics | 2025, 45(11) : 1127 - 1133
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Journal of Geodesy and Geodynamics | 2025, 45(11): 1127-1133
Simulation on Seasonal Microseismic Signals at Gravity Station Using Data-Driven Approaches
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Zhengran WANG1, Jin WEI1, *, Huiling LI2, Guangliang YANG1, Minzhang HU1
Affiliations
  • 1 Key Laboratory of Earthquake Geodesy, CEA, 40 Hongshance Road, Wuhan 430071, China
  • 2 Tai'an Earthquake Monitoring Center Station of Shandong Earthquake Agency, 2 Luohanya, Tai'an 271000, China

    • WANG Zhengran, postgraduate, major in the analysis and application of continuous gravity observation data, E-mail:

About Author:

WANG Zhengran, postgraduate, major in the analysis and application of continuous gravity observation data, E-mail:

Published: 2025-11-15 doi: 10.14075/j.jgg.2024.12.586
Outline
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The seasonal microseismic signals at gravity stations are difficult to physically simulate and estimate due to the combined effects of randomness, intermittency, and periodicity. Based on the 1 Hz gravity solid tide data of Wushi continuous gravity observation station from 2008 to 2024, we extract the microseismic signals in DF frequency band using the apparent vertical displacement analysis method. An improved variational mode decomposition(IVMD) coupled with the long short-term memory(LSTM) neural network algorithm is proposed to establish a data model for seasonal gravity microseismic signals. The results show that the IVMD-LSTM model can better reflect the annual variation differences of seasonal signals compared to the LSTM model. Moreover, the correlation coefficient between model data using data-driven methods and the snow depth data from GLDAS and ERA5 models increased to 0.5. This study can provide a new processing method for model establishment using seasonal gravity microseismic signals and their application in earthquake analysis and forecasting.

gPhone gravimeter  /  seasonal variation of apparent vertical displacement  /  long short-term memory(LSTM) neural network  /  variational mode decomposition(VMD)
王诤冉, 韦进, 李惠玲, 杨光亮, 胡敏章. 数据驱动的重力站季节性微震信号模拟研究. 大地测量与地球动力学, 2025 , 45 (11) : 1127 -1133 . DOI: 10.14075/j.jgg.2024.12.586
Zhengran WANG, Jin WEI, Huiling LI, Guangliang YANG, Minzhang HU. Simulation on Seasonal Microseismic Signals at Gravity Station Using Data-Driven Approaches[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (11) : 1127 -1133 . DOI: 10.14075/j.jgg.2024.12.586
Funding
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  • National Natural Science Foundation of China(41204058)
  • The Spark Program of Earthquake Technology of CEA(XH23026C)
  • Special Fund for Science and Technology Basic Resources Investigation(2023FY101500)
Appendix
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Year 2025 volume 45 Issue 11
28
11
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Article Info
doi: 10.14075/j.jgg.2024.12.586
  • Receive Date：2024-12-31
  • Online Date：2026-10-29
  • Published：2025-11-15
Article Data
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History
  • Received：2024-12-31
Funding
National Natural Science Foundation of China(41204058)
The Spark Program of Earthquake Technology of CEA(XH23026C)
Special Fund for Science and Technology Basic Resources Investigation(2023FY101500)
Affiliations
    1 Key Laboratory of Earthquake Geodesy, CEA, 40 Hongshance Road, Wuhan 430071, China
    2 Tai'an Earthquake Monitoring Center Station of Shandong Earthquake Agency, 2 Luohanya, Tai'an 271000, China
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https://castjournals.cast.org.cn/joweb/ddcl/EN/10.14075/j.jgg.2024.12.586
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number of
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Percentage of
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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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