Microseismic monitoring is a common monitoring tool in the mining production process; for supervising a huge amount of microseismic data, effective analysis tools are necessary. In this study, the monitoring results of microseismic events at the Maoping lead-zinc mine in Yiliang County, Yunnan Province, and the spatiotemporal distribution characteristics of microseismic events are analyzed. We analyze the temporal characteristics of microseismic events using fractal theory, combining the change in fractal dimension with the rock burst incubation process. We also construct an observation area model for event anomalies based on the spatial distribution characteristics of microseismic events. The results show that the growth of the fractal dimension is consistent with the trend of the incubation process before rock burst, and the larger the fractal dimension, the higher the rock burst risk. The observation model, based on the density of microseismic events, can effectively refine the rock burst discrimination range and facilitate subsequent observations. An effective and feasible method of microseismic analysis is provided.

Understanding the characteristics and evolution of crack propagation in rock masses is crucial for evaluating their stability. By applying clustering theory to analyze recorded microseismic events, we differentiate the development positions of individual cracks amidst multiple crack formations. Three distinct crack cluster distribution patterns are identified, allowing for the evaluation of regional stability through microseismic event density and ellipsoidal model parameters. The process of crack propagation involves independent development at nucleation positions, mutual influence between adjacent locations, and subsequent crack growth and propagation. Additionally, we examine crack evolution prior to roof collapse and establish a connectivity model between surface and goaf roof cracks. When microseismic events are identified as developing along a plane, it indicates a higher risk of damage in that area. Through the analysis of crack propagation location and angle, our study provides a theoretical foundation for predicting crack direction. Notably, our model’s findings align with onsite observations, demonstrating its practical effectiveness. The results of this research offer valuable insights for collapse prediction and early warning systems for mine roofs, contributing to advancements in mining safety and operations.

Microseismic (MS) monitoring is an effective method for tracking the development of rock fractures. However, the utilization of existing data is severely limited by current visualization techniques. In this study, the evolution characteristics of MS parameters during the rock fracture process were investigated using MS localization techniques and spatial clustering methods. Machine learning methods were applied to cluster acoustic emission events, enabling the automatic identification of hazardous regions and the construction of crack propagation trajectories. The applicability of clustering methods was assessed using MS monitoring data collected during an in situ rock fracture process. The research results show that the spatial clustering method can effectively delineate hazardous areas, and the changes in these regions reflect the progressive development of rock mass fractures. Regions with a higher proportion of high‐energy MS events pose greater risks. Additionally, the propagation trajectory of MS events can effectively characterize the concentration and development direction of fractures. This study provides valuable insights into the characterization of crack evolution and seepage channels.

To further understand the changing pattern of the bottom structure’s ground pressure activity under the influence of faults, predicting the potential occurrence of geological hazards and rock blast problems to reduce the loss of resources is important. A new microseismic monitoring system was established based on the original Pulang copper mine microseismic monitoring system. This paper analyzed the change law of the microseismic events on the fault in the first mining area of the Pulang copper mine, calculated the cumulative apparent volume and energy index changes of the microseismic events near the fault base, and quantitatively analyzed the microseismic event anomalies under the influence of the fault. The results show that rupture of the rock makes the cumulative apparent volume and energy index change significantly, while the timeliness of both makes the cumulative apparent volume predictive of the occurrence of rock fracture and rockburst, and the number of microseismic events corresponds to the intensity of the fault activation, which plays a predictive and guiding role in the subsequent study of fault activation, stability monitoring, and safe production in the mine area of Pulang copper mine.