activities, microseismic monitoring is often used to predict and analyze the activation performance due to mining activities, and in this regard, the 14,310 working face of the Dongtan Mine through the NF6 fault was taken as the research object by [PERSON] [24]. The team studied the mechanism of rock explosion induced by the regional surrounding rock activity using relevant mathematical models and explored the microseismic law of the coal mining working face over the fault. Microseismic monitoring technology was used by [PERSON] and his team to monitor the activation process and coal production status of the small faults affected by the excavation activities in real time and track their development direction, and the results showed that excavation activities have a large impact on fault activation [25]. Through similar and numerical simulations, a new parameter (ERW index) was defined for the analysis of such data; based on microseismic analysis, a new rockburst warning model was developed by [PERSON] [26]. To improve the accuracy of the rockburst identification, a combined AE and statistical approach was proposed by [PERSON]'s team to identify the interaction of microcracks within coal and rock, and the method provided a new means of understanding the formation and distribution of cracks in coal rock bodies [27; 28; 29]. With the gradual development of microseismic technology, how to accurately and quickly identify problems became the main problem of microseismic monitoring, and the combination with the framework of machine learning has solved this problem solved effectively. The artificial intelligence recognition method was applied by [PERSON]'s team to the signal recognition of microseismic monitoring systems in mining areas, and the recognition results were consistent with the actual situation with an accuracy rate of up to 95% or more. In this way, the confusion caused by manual classification of microseismic events and blasts according to the characteristics of waveform signals was solved, the required source parameters were easily obtained, and the accuracy and timeliness of microseismic event and blast identification were ensured [30]. A new method based on deep learning was proposed by [PERSON]'s team for processing large amounts of DAS data in real/semi-real time. The proposed neural network was trained on synthetic microseismic data contaminated with real environmental noise from field data and validated in engineering practice. This reduced the manual effort of combing the data, thus maintaining the data integrity and leading to more accurate and reproducible results [31]. Because of the hard rock and complex structure at a fault, monitoring is intermittent, and it is difficult to detect ground stress in non-coal mines, so the results of fault-slip rockburst monitoring have been mainly focused on seismic monitoring. In this regard, a mechanical model of the viscous-slip instability of a fault-slip rockburst was established by [PERSON] [32]. Based on revealing the damage characteristics of this instability, the change law of mining-induced stress and the microseismic signal before the occurrence of a fault-slip rockburst was proposed, and a multiparameter integrated early warning method including mining-induced stress and energy was established. The 3D numerical model was used by [PERSON] [33] and applied to study the fault behavior of the Copper Cliff Mine in Canada, revealing the importance of distinguishing between seismic and earthquake fault sliding for optimizing underground mine support systems.