A new seismic-based strain energy methodology for coal burst forecasting in underground coal mines

被引:113
作者
Cai, Wu [1 ,2 ]
Dou, Linming [1 ]
Si, Guangyao [3 ]
Cao, Anye [1 ]
Gong, Siyuan [1 ]
Wang, Guifeng [1 ]
Yuan, Shasha [1 ]
机构
[1] China Univ Min & Technol, Sch Mines, State Key Lab Coal Resources & Safe Min, Xuzhou 221116, Jiangsu, Peoples R China
[2] Imperial Coll, Royal Sch Mines, Dept Earth Sci & Engn, London SW7 2AZ, England
[3] Univ New South Wales, Sch Minerals & Energy Resources Engn, Sydney, NSW 2052, Australia
基金
中国国家自然科学基金;
关键词
Coal burst forecasting; Microseismic (MS) monitoring; Strain energy; Stress inversion; Bursting strain energy index; ROCK BURST; VELOCITY TOMOGRAPHY; ROCKBURST; MODEL; PREDICTION; STRESS; IDENTIFICATION; DISPLACEMENT; INSTABILITY; MECHANISM;
D O I
10.1016/j.ijrmms.2019.104086
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
Coal burst has become a common safety issue in underground coal mines and its triggering mechanism is believed to be highly associated with coal state parameters including stress, strain and energy. As a powerful tool for coal burst forecasting, microseismic (MS) monitoring has the capability of directly monitoring energy release and indirectly capturing stress and strain changes. In this paper, the strain energy transfer in the process of coal burst during underground coal mining was investigated, which revealed that coal burst is caused by strain energy released from the surrounding rock, plus the additional energy input provided by the superposition of static and dynamic stresses. The seismic energy, derived from the strain energy transfer process, was defined and simulated in numerical models. Based on the modelling results, a damage mechanics model was developed to correlate stress, strain, damage and seismic energy release. In this context, a new index named as 'bursting strain energy (BSE)' was proposed to quantitatively assess coal burst propensity. This BSE index was first calibrated via numerical modelling and then successfully applied to a Chinese coal mine for coal burst forecasting. Results showed that the BSE index can effectively assess the likelihood of coal burst occurrence in the temporal domain and assess high risk regions in the spatial domain. Such practices can be conducted on a daily basis, which will contribute to the improvement of mine safety and productivity.
引用
收藏
页数:11
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