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Application of Non-Natural Earthquakes Identification Method Based on Support Vector Machine in Guangdong and Surrounding Areas
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Xijiao JIANG1, Qingxi LIN1, *, Hao LIANG2, Ping TIAN1, Ce JIANG1, Ming LIANG1, Teng ZHU1
Journal of Geodesy and Geodynamics | 2025, 45(8) : 875 - 880
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Journal of Geodesy and Geodynamics | 2025, 45(8): 875-880
Application of Non-Natural Earthquakes Identification Method Based on Support Vector Machine in Guangdong and Surrounding Areas
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Xijiao JIANG1, Qingxi LIN1, *, Hao LIANG2, Ping TIAN1, Ce JIANG1, Ming LIANG1, Teng ZHU1
Affiliations
  • 1 Guangdong Earthquake Agency, 81 Mid-Xianlie Road, Guangzhou 510070, China
  • 2 China Earthquake Networks Center, 5 Nanheng Street, Sanlihe, Beijing 100045, China

    • JIANG Xijiao, engineer, majors in focal mechanism, seismic classification method and technology, E-mail:

About Author:

JIANG Xijiao, engineer, majors in focal mechanism, seismic classification method and technology, E-mail:

Published: 2025-08-15 doi: 10.14075/j.jgg.2024.08.399
Outline
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In this study, natural and non-natural seismic events in Guangdong and surrounding areas were selected as the research objects, and the typical physical characteristics of P waves and S waves in frequency domain were extracted from seismic waveform data. We used the feature combination optimal solution algorithm to input the best feature parameters into the support vector machine model for sample training, and identified the non-natural earthquakes in Guangdong and surrounding areas. The test results showed that the accuracy of earthquake classification is 99.3%. In addition, the non-natural seismic identification module based on support vector machine was developed and connected to the daily cataloging system of Guangdong seismic network, which realized the quasi-real-time automatic identification of seismic event types. The application results showed that the recognition accuracy of 130 manually cataloged seismic events was 96.9%.

support vector machine  /  non-natural earthquakes  /  Guangdong and surrounding areas  /  physical characteristics  /  machine learning
姜喜姣, 林庆西, 梁皓, 田平, 蒋策, 梁明, 朱腾. 基于支持向量机的非天然地震识别方法在广东及周边区域的应用. 大地测量与地球动力学, 2025 , 45 (8) : 875 -880 . DOI: 10.14075/j.jgg.2024.08.399
Xijiao JIANG, Qingxi LIN, Hao LIANG, Ping TIAN, Ce JIANG, Ming LIANG, Teng ZHU. Application of Non-Natural Earthquakes Identification Method Based on Support Vector Machine in Guangdong and Surrounding Areas[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (8) : 875 -880 . DOI: 10.14075/j.jgg.2024.08.399
Funding
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  • Combination Project with Monitoring, Prediction and Scientific Research of Earthquake Technology, CEA(3JH-202401005)
  • Seismological Research Foundation for Youths of Guangdong Earthquake Agency(GDDZZ202306)
  • Seismological Research Foundation for Youths of Guangdong Earthquake Agency(GDDZZ202305)
Appendix
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Year 2025 volume 45 Issue 8
26
10
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Article Info
doi: 10.14075/j.jgg.2024.08.399
  • Receive Date：2024-08-19
  • Online Date：2026-10-29
  • Published：2025-08-15
Article Data
Affiliations
History
  • Received：2024-08-19
Funding
Combination Project with Monitoring, Prediction and Scientific Research of Earthquake Technology, CEA(3JH-202401005)
Seismological Research Foundation for Youths of Guangdong Earthquake Agency(GDDZZ202306)
Seismological Research Foundation for Youths of Guangdong Earthquake Agency(GDDZZ202305)
Affiliations
    1 Guangdong Earthquake Agency, 81 Mid-Xianlie Road, Guangzhou 510070, China
    2 China Earthquake Networks Center, 5 Nanheng Street, Sanlihe, Beijing 100045, China
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https://castjournals.cast.org.cn/joweb/ddcl/EN/10.14075/j.jgg.2024.08.399
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表12种不同金属材料的力学参数
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Number of
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Number 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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