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High in-situ stress and rock burst have been important issues for risk assessment of deep tunnelling. The issue of prediction and control of rock burst remains to be solved, though various researches have been done. Based on the Lhasa-Linzhi tunnel project of the Sichuan-Tibet railway, the laboratory test of acoustic emission for diorite was carried out in this study. The uniaxial compression test was developed on the diorite specimens collected in the field, and the acoustic emission signals was recorded. Decision tree model was used to filter and classify key signals. Based on the feature parameters, time-frequency characteristics, rupture scale and rupture mechanism of each sort of key signal were analysed. The result shows that the decision tree model has good classification effect and key signals was divided into four groups. Signal A contains high energy, which indicates macro crack propagation and linked up. Signal B and C are continuous signal, which appear before the inflection point of load curve and are helpful for early warning. A simple early warning model was established based on signals distribution, which divides the risk into four levels and provides measures for each stage.

Experimental Study of Time-Frequency Characteristics of Acoustic Emission Key Signals during Diorite Fracture