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Machine Learning for Automated Detection and Classification of Microseismic Signals from Avalanche Rockfalls
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Bin XIE1, Guishan GAO2, Zhiyong HUANG3, 4, Liang FENG5, *
Journal of Geodesy and Geodynamics | 2025, 45(6) : 654 - 660
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Journal of Geodesy and Geodynamics | 2025, 45(6): 654-660
Machine Learning for Automated Detection and Classification of Microseismic Signals from Avalanche Rockfalls
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Bin XIE1, Guishan GAO2, Zhiyong HUANG3, 4, Liang FENG5, *
Affiliations
  • 1 Ganzhou Earthquake Monitoring Center Station, 55 Helanshan Road, Ganzhou 341000, China
  • 2 Guangdong Dabaoshan Mining Co Ltd, Shaxi Town, Shaoguan 512128, China
  • 3 Institute of Mountain Hazards and Environment, CAS, 189 South-Qunxian Road, Chengdu 610041, China
  • 4 University of Chinese Academy of Sciences, A19 Yuquan Road, Beijing 100049, China
  • 5 College of Sources and Environmental Engineering, Jiangxi University of Science and Technology, 156 Kejia Road, Ganzhou 341000, China

    • XIE Bin, engineer, majors in earthquake monitoring and forecasting early warning, E-mail:

About Author:

XIE Bin, engineer, majors in earthquake monitoring and forecasting early warning, E-mail:

Published: 2025-06-15 doi: 10.14075/j.jgg.2024.08.369
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In this paper, based on microseismic monitoring data and 49 microseismic feature parameters of a marble mine in Perugia, central Italy, we compare and analyze the accuracy of three machine learning models, namely, neural networks, decision trees and random forests, in the automated classification of microseismic signals, and optimize the model parameters to improve the performance of the model through the grid searcher algorithm, with an emphasis on discussing the effects of the feature parameter selection and the size of the classification set on the classification results. The results show that the random forest classifier obtains the highest accuracy in microseismic signal classification, with 98.11% classification accuracy after optimization, meanwhile, by reducing some of the feature parameters with small weights, the efficiency of the classification computation is greatly improved and the accuracy decreases less, and the optimal allocation ratio of the training set to the test set is 8 ∶2.

avalanche rockfall  /  seismic monitoring  /  machine learning  /  early warning
谢斌, 高桂山, 黄志勇, 冯亮. 机器学习实现崩塌落石微震信号自动化检测和分类. 大地测量与地球动力学, 2025 , 45 (6) : 654 -660 . DOI: 10.14075/j.jgg.2024.08.369
Bin XIE, Guishan GAO, Zhiyong HUANG, Liang FENG. Machine Learning for Automated Detection and Classification of Microseismic Signals from Avalanche Rockfalls[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (6) : 654 -660 . DOI: 10.14075/j.jgg.2024.08.369
Funding
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  • Combination Project with Monitoring, Prediction and Scientific Research of Earthquake Technology, CEA(3JH-202401050)
  • National Natural Science Foundation of China(4210071309)
  • "2111" Incubation Fund of Jiangxi Earthquake Agency(FD2111-202404)
  • Natural Science Foundation of Jiangxi Province(20224BAB203040)
Appendix
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Year 2025 volume 45 Issue 6
23
8
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Article Info
doi: 10.14075/j.jgg.2024.08.369
  • Receive Date：2024-08-02
  • Online Date：2026-10-30
  • Published：2025-06-15
Article Data
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History
  • Received：2024-08-02
Funding
Combination Project with Monitoring, Prediction and Scientific Research of Earthquake Technology, CEA(3JH-202401050)
National Natural Science Foundation of China(4210071309)
"2111" Incubation Fund of Jiangxi Earthquake Agency(FD2111-202404)
Natural Science Foundation of Jiangxi Province(20224BAB203040)
Affiliations
    1 Ganzhou Earthquake Monitoring Center Station, 55 Helanshan Road, Ganzhou 341000, China
    2 Guangdong Dabaoshan Mining Co Ltd, Shaxi Town, Shaoguan 512128, China
    3 Institute of Mountain Hazards and Environment, CAS, 189 South-Qunxian Road, Chengdu 610041, China
    4 University of Chinese Academy of Sciences, A19 Yuquan Road, Beijing 100049, China
    5 College of Sources and Environmental Engineering, Jiangxi University of Science and Technology, 156 Kejia Road, Ganzhou 341000, China
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https://castjournals.cast.org.cn/joweb/ddcl/EN/10.14075/j.jgg.2024.08.369
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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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