Early-warning evaluation and warning of rock burst using acoustic emission characteristics of coal sample failure

As the most common dynamic disaster of coal rock in deep coal mining, rock burst seriously affects the safe and efficient mining of coal resources. Accurate and timely to predict the rock burst is the best way to control and prevent it. In order to study the precursory information characteristics of coal rock impact to establish an efficient and accurate dynamic prediction model. The triaxial compression test of coal bursting liability performed to get the acoustic emission characteristic parameters of coal failure process, meanwhile the monitoring values of microseismic parameters of typical impact mine face were selected and compared with the AE parameters obtained in the laboratory, then based on the similarity of them, the relationship between laboratory-scale and engineering-scale impact acoustic information representation had been built. It shows the “stepwise” increase of accumulated AE energy during coal sample compression and the “lack of earthquake” phenomenon before the occurrence of large energy seismic events have a good correlation, both of them could be used to revealed the energy breeding process of coal and rock mass. The same energy index has the same evolutionary trend for the occurrence of large energy events at different scales, and it can be used as an early warning index of rock burst risk after quantification. In addition, the static factors and stress dynamic factors affecting the occurrence of rock burst in working face are selected, and the dynamic weights are given respectively by combining the microseismic parameters obtained, which modified by using the Bayesian probability, then a static cooperative comprehensive evaluation and early warning model of rock burst is constructed. Furthermore, the model is used to evaluate and warn the danger of a mining face in Henan Province, and the results are better than those of the traditional evaluation model, which shows the engineering applicability and accuracy of the model.