Abstract: In this study, a large number of deepburied rockburst tunnel data are collected. The causes, processes, and characteristics of largearea rockburst that occurred in existing and underconstruction tunnels are statistically analyzed, and the rockburst trends, prediction methods, and disposal measures are summarized and analyzed. The drawn conclusions are presented as follows. (1) In the rockburst prediction stage, the rockburstclassification discriminant based on the physicomechanical properties is universal to a certain extent, and the criterion value can be applied to tunnels with similar lithology and depth. The accuracy and characteristics of the prediction results can be checked using multiple discriminants, and the fieldmonitoring measurement provides an important reference and validation for the establishment of the criterion. (2) The stressrelief methods of different rockburst grades in the prevention and control stage are water sprinkling for the slightrockburst section and stress release hole for the strongrockburst section. (3) The primary support parameters of surrounding rocks with different grades are summarized, and based on the comparison and summary of the drilling and blasting methods, the excavation methods, advancement length, and corresponding waiting time of the differentrock blasting sections are proposed. (4) It is recommended that the acquisition of rockburst prediction information and the entire monitoring of the displacement and strain fields in the rockburst process can be realized using the technologies of machine learning, large database, unmanned aerial vehicle, machine vision, signal acquisition, and multispectral and thermal imaging. A new simulation method for rockburst process using discreteelement energy iteration algorithm is proposed.

Key words: deep buried tunnel, rockburst characteristic, rockburst prediction, rockburst prevention, TBM