A review of tunnel rockburst prediction methods based on static and dynamic indicators

Rockbursts frequently occur in tunneling projects and pose a serious threat to workers and the environment. Therefore, accurate prediction of rockbursts is of great practical significance. Currently, various rockburst prediction methods exist, with static and dynamic indicators playing a key role. This paper analyzes the importance of rockburst prediction methods based on Citespace software. The results indicate that microseismic monitoring, acoustic emission, and machine learning are the most important methods. The paper focuses on four common rockburst prediction methods: empirical methods, microseismic monitoring, acoustic emission, and machine learning, from the perspective of static and dynamic indicators. The performance and application of static and dynamic indicators in the four common prediction methods in recent years are summarized, the limitations of static and dynamic indicators at this stage are discussed, and possible future development directions are proposed. This paper provides the necessary perspective and tools for better understanding the advantages and disadvantages of static and dynamic indicators in the four rockburst prediction methods.