XIAO Yidong
1 Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China 2 University of Chinese Academy of Sciences, Beijing 100049, ChinaQI Shengwen
1 Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Innovation Academy of Earth Science, Chinese Academy of Sciences, Beijing 100029, ChinaGUO Songfeng
1 Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Innovation Academy of Earth Science, Chinese Academy of Sciences, Beijing 100029, ChinaZHANG Shishu
4 POWERCHINA Chengdu Engineering Corporation Limited, Chengdu 610072, ChinaWANG Zan
1 Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Innovation Academy of Earth Science, Chinese Academy of Sciences, Beijing 100029, ChinaGONG Fengqiang
5 Engineering Research Center of Safety and Protection of Explosion & Impact of Ministry of Education, Southeast University, Nanjing 211189, ChinaRockburst Intensity Prediction based on Kernel Extreme Learning Machine (KELM)
XIAO Yidong, QI Shengwen, GUO Songfeng, ZHANG Shishu, WANG Zan, GONG Fengqiang.2025.[J]. Acta Geologica Sinica(),99(1):284-295
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