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Research and Application of Multi-Objective Computational Methods in Microseismic Source Localization
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Guoqing CHEN1, Cong PANG2, 3, Yingying SONG4, *, Haiyang PENG5, Zhongya LI2, 3
Journal of Geodesy and Geodynamics | 2025, 45(7) : 688 - 694
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Journal of Geodesy and Geodynamics | 2025, 45(7): 688-694
Research and Application of Multi-Objective Computational Methods in Microseismic Source Localization
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Guoqing CHEN1, Cong PANG2, 3, Yingying SONG4, *, Haiyang PENG5, Zhongya LI2, 3
Affiliations
  • 1 Mathematical Modeling Research Center, Chengdu Jincheng College, 1 Xiyuan Road, Chengdu 611731, China
  • 2 Institute of Seismology, CEA, 40 Hongshance Road, Wuhan 430071, China
  • 3 National Observation and Research Station for Wuhan Gravitation and Solid Earth Tides, 40 Hongshance Road, Wuhan 430071, China
  • 4 Department of Computer Science and Engineering, Guangzhou Institute of Science and Technology, 638 Third-Xingtai Road, Guangzhou 510540, China
  • 5 School of Mathematics and Statistics, Minnan Normal University, 36 Xianqianzhi Street, Zhangzhou 363000, China

    • CHEN Guoqing, lecturer, majors in mathematical modeling and data mining, E-mail:

About Author:

CHEN Guoqing, lecturer, majors in mathematical modeling and data mining, E-mail:

Published: 2025-07-15 doi: 10.14075/j.jgg.2024.08.387
Outline
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The second and third generation of non-dominated sorting genetic algorithm(NSGA-Ⅱ, NSGA-Ⅲ), multi-objective grasshopper optimization algorithm(MOGOA), multi-objective gray wolf optimizer(MOGWO), multi-objective ant lion optimizer(MOALO), and multi-objective evolutionary algorithm based on decomposition(MOEA/D) are introduced to construct multi-objective localization optimizations functions using two different classical microseismic inversion mathematical models. Based on the comparative testing of microseismic event data in deep-mining mine and manual simulation data, the effectiveness and reliability of six multi-objective localization algorithms are analyzed. The results show that the performance of six multi-objective localization models varies. The mean error of 100-round microseismic source localization of MOALO based on the polyhedral array simulation is 1.242 5 m, while the mean error of microseismic source localization of NSGA-Ⅱ based on deep-mining mine is 162.569 1 m. The robustness of MOGOA, based on Shizhuyuan mine microseismic event, is superior to that of MOALO. Considering multiple performance indices, including localization accuracy and model reliability, it is believed that MOGOA has strong application prospects for engineering physical exploration and microseismic monitoring.

microseismic source localization  /  multi-objective intelligent computation  /  MOEA/D  /  NSGA  /  MOALO  /  MOGOA  /  MOGWO
陈国庆, 庞聪, 宋莹莹, 彭海洋, 李忠亚. 微震震源定位中的多目标计算方法研究与应用. 大地测量与地球动力学, 2025 , 45 (7) : 688 -694 . DOI: 10.14075/j.jgg.2024.08.387
Guoqing CHEN, Cong PANG, Yingying SONG, Haiyang PENG, Zhongya LI. Research and Application of Multi-Objective Computational Methods in Microseismic Source Localization[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (7) : 688 -694 . DOI: 10.14075/j.jgg.2024.08.387
Funding
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  • Soft Science Project of Science and Technology Department of Sichuan Province(2023JDR0271)
  • Collaborative Project of Philosophy and Social Sciences of Guangdong Province(GD24XGL031)
  • Scientific Research Fund of Institute of Seismology, CEA and National Institute of Natural Hazards, MEM(IS202236328)
  • Open Fund of National Observation and Research Station for Wuhan Gravitation and Solid Earth Tides(WHYWZ202406)
  • The Spark Program of Earthquake Technology of CEA(XH24025YC)
Appendix
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Year 2025 volume 45 Issue 7
39
16
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Article Info
doi: 10.14075/j.jgg.2024.08.387
  • Receive Date：2024-08-13
  • Online Date：2026-10-29
  • Published：2025-07-15
Article Data
Affiliations
History
  • Received：2024-08-13
Funding
Soft Science Project of Science and Technology Department of Sichuan Province(2023JDR0271)
Collaborative Project of Philosophy and Social Sciences of Guangdong Province(GD24XGL031)
Scientific Research Fund of Institute of Seismology, CEA and National Institute of Natural Hazards, MEM(IS202236328)
Open Fund of National Observation and Research Station for Wuhan Gravitation and Solid Earth Tides(WHYWZ202406)
The Spark Program of Earthquake Technology of CEA(XH24025YC)
Affiliations
    1 Mathematical Modeling Research Center, Chengdu Jincheng College, 1 Xiyuan Road, Chengdu 611731, China
    2 Institute of Seismology, CEA, 40 Hongshance Road, Wuhan 430071, China
    3 National Observation and Research Station for Wuhan Gravitation and Solid Earth Tides, 40 Hongshance Road, Wuhan 430071, China
    4 Department of Computer Science and Engineering, Guangzhou Institute of Science and Technology, 638 Third-Xingtai Road, Guangzhou 510540, China
    5 School of Mathematics and Statistics, Minnan Normal University, 36 Xianqianzhi Street, Zhangzhou 363000, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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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