# Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory
###### Abstract  
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. microseismic monitoring; spatiotemporal characteristics; fractal theory +
Footnote †: journal: Medical Imaging and Imaging and Imaging and Imaging of the Society of Photo-Optical Instrumentation  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory. **Microseismic monitoring:** spatiotemporal characteristics; fractal theory  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory. **Microseismic characteristics of Rock Burst Based on Fractal Theory. **Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory. **Microseismic characteristics of microseismic events. **  
fractal dimensions for upper and lower fracture surfaces [31]. The permeability has an exponential relationship with the fractal dimension of fracture center and fracture length [32]. It was found in laboratory research that acoustic emission count rates during failure for all loading steps exhibit fractal features [33; 34; 35]. The results of engineering research indicated that the energy distribution of microseismic events during the development of immediate rock bursts has a fractal structure [36]. By analyzing the spatial and temporal distribution characteristics of microseismic events, we can predict potential rock burst hazards. We developed a simplified two-dimensional (2D) finite element model based on microseismic and piezometer monitoring results and geological conditions of a porous rock mass [37]. The statistical analysis and numerical calculation of tunnel rock bursts also allowed for the analysis of the spatial and temporal distribution of MS events during the construction process, thereby discussing the size effect of rock burst risk [38]. Subsequently, the spatial and temporal evolution of MS events was analyzed by numerical simulations, and the spatial relationship between the potential damage zone and maximum principal stress direction was verified [39]. Studies have explored the spatiotemporal analysis of microseismic activity and its correlation with mine explosions by analyzing source parameters. These studies have shown that two clusters have different dynamic characteristics [40; 41]. From a microfracture perspective, macrofractures can be identified based on a three-dimensional (3D) fracture model, which is implemented using unsupervised machine learning algorithms and microfracture coordinates and can identify microfractures in 3D space combined with the formation of macrofractures [42].