Experimental study of rockburst early warning method based on acoustic emission cluster analysis and neural network identification

Three types of AE signals are obtained by carrying out the tunnel rock burst simulation experiment and clustering rock explosion signal. The acoustic emission signals which have the characteristics of the precursory anomaly, large energy and small quantity, are preferred as precursor characteristic signals of rockburst. Before the rockburst, the precursor characteristic signal of rockburst appeared densely; it is shown obvious precursory anomaly. The window functionis used to carry out the quantitative analysis of temporal density of precursor signal and put forward the quantitative index-time intensity f(t) which can measure timing evolution characteristics of signal. Before the rockburst, the precursory signal appeared densely, f(t) is greater than or equal to 3. This situation firstly appears can be as the precursor information of rockburst; therefore the early warning threshold a can be set to 3. Based on optimization of rockburst precursor signal and early warning threshold extraction, with the help of BP neural network, intelligently identify rockburst precursor signal. The time window function is used to calculate f(t) value of precursor signal. When f(t) value of precursor signal start to achieve rockburst warning threshold (f(t) >= a), it starts to warn rockburst. Therefore, a real-time warning method of rockburst based on AE data of tunnel rockburst simulation experiment is established so as to provides a new idea for the construction of early warning method of rockburst.