Potential faults are common sensitive geological bodies that affect the safe mining of underground mines, often leading to major accidents such as rock instability and rockburst during mining. The failure mechanism of faults has been widely studied. However, due to the spatiotemporal specificity of fault occurrence, there are few theoretical and mathematical methods suitable for effective analysis in mine safety risk management. This study aims to introduce fractal theory to characterize the spatiotemporal activity fractal characteristics of induced faults intersecting the mining site and roadway during the mining process of the Ashele copper mine in China. Using microseismic systems and fractal theory, a spatiotemporal fractal model of the fault slip process is constructed, and a fractal analysis method is proposed. The fractal dimension value is calculated based on the spatiotemporal parameters of different segments and stages. The fractal dimension is used to characterize and analyze the evolution of the fault. The physical formation process of potential faults and the relationship between fractal dimension values and multiple parameters, including spatial clustering, regional distribution characteristics, and energy-release characteristics, were analyzed based on the division of events into different time stages. Discovering fractal dimension’s temporal and spatial–temporal characteristics can provide technical references for mine disaster prevention.

The early warning of disasters such as ground pressure in deep hard rock mines has long constrained the safe and efficient development of mining activities. Based on fractal theory and fractal dimension interpretation, this study constructs a microseismic monitoring system for mining areas, extracting key elements, particularly time and energy elements. Using the box-counting method of fractal theory, the study investigates the fractal dimensions of microseismic time–energy elements, data interpretation, and disaster source early warning. Through parameter analysis, events related to local potential failure are identified and extracted, and disaster characteristics are revealed based on microseismic activity. A time–energy fractal dimension-based analysis method is developed for preliminary fractal analysis and prediction of regional damage. A time–energy-centered early warning model is constructed, narrowing the prediction range to a scale of 10 m. Based on the fractal interpretation of time–energy data, the prediction and early warning of rock mass failure in mining areas are achieved, with the reliability of nested energy warnings ranging between 91.7% and 96.2%. A comprehensive evaluation criterion for fractal dimension values is established, enabling accurate delineation of warning zones and providing scientific decision-making support for mine safety promotion.

The early warning of rock mass failure in deep hard-rock mines presents a significant challenge for mine safety management. Microseismic monitoring data offer a novel analytical approach to address this issue. This study investigates the evolutionary patterns of rock mass failure in mining areas through the analysis of spatiotemporal energy data from microseismic events. Initially, key spatiotemporal energy parameters are extracted to identify microseismic events associated with localized damage and their periodic characteristics. Subsequently, a spatiotemporal fractal dimension analysis method is established to achieve fractal interpretation of the data by integrating field cloud maps. Finally, an early warning model centered on temporal energy is constructed, which delineates warning zones through a comprehensive evaluation of fractal dimensions, thereby providing decision-making support for mine safety.

To address the severe geological hazards (e.g., high ground stress and rock burst) that threaten safety and efficiency in ultra-deep mining, this study develops a comprehensive microseismic monitoring system tailored for the Sishanling Iron Mine—a typical ultra-large-scale, ultra-deep mine with an extraction depth exceeding 1500 m. The system integrates high-sensitivity sensors, real-time data transmission, and intelligent processing algorithms. A scientifically designed sensor deployment plan achieves full-coverage of key mining areas, while a multi-level data processing framework encompassing signal acquisition, event detection, location inversion, and magnitude calculation enhances result accuracy. Applied in actual operations, the system effectively captures microseismic events with magnitudes from −2.14 to −1.96, achieving optimal planar and spatial positioning errors of 6.75 m and 9.66 m, respectively. It provides real-time early warning for hazards like rock burst, thereby mitigating risks and ensuring operational continuity. This work offers a practical reference for constructing microseismic systems in similar “double super” mines and enriches the theoretical and technical framework for safety monitoring in deep mining.

The regular ore body of the Xianglushan tungsten mine has been completely exploited. The remaining residual ore bodies face numerous hidden dangers, such as large and numerous abandoned mining areas, disorderly and small-scale mining sequences, delayed filling processes, and poor effectiveness. To achieve the zoning warning of ground pressure disasters such as roof caving, caving, and pillar collapse during the mining process of the hidden-danger ore body in the mine, a targeted warning technology system is proposed. We use microseismic monitoring systems to analyze events in the main monitoring areas and summarize specific ground pressure manifestation areas and event characteristics. Based on microseismic monitoring data that identified areas of significant ground pressure, a zoning model was constructed for risk rating and area locking. Based on this model, a safety warning technology for mining residual ore bodies with hidden dangers was established. Summarizing and analyzing, it is found that the disaster warning mode for controlling hidden dangers and residual ore body mining processes through microseismic monitoring is effective and has played a certain demonstration role, providing reference value for other similar mines.

To examine the deformation behavior and failure mechanisms of large-scale mine chutes under complex service conditions, we performed multiple C-ALS three-dimensional laser scanning surveys on the 1#, 2# and 3# chutes at an iron mine in Anhui Province. The results showed that all three chutes had severe non-uniform expansion, with maximum diameters of 12.2 m, 14.8 m and 16.9 m, respectively. The maximum annual wear rates during the detection period were 2.4 m, 6.0 m and 2.5 m, respectively. The failure of the chutes was mainly caused by local block collapse drops, influenced by the joints and fissures of the surrounding rock, and the wear showed significant discontinuous and non-uniform characteristics. Based on the detection data, a three-peak Gaussian model is established to describe the nonlinear distribution of the expansion along depth, with peak centers located at −462 m, −497 m and −520 m. By comparing linear and exponential models, it is determined that the expansion exhibits a linear evolution trend with time. Based on the maximum diameter and annual wear rate, we estimate the remaining safe service life of each chute, and provide the confidence interval for the expansion value at a 90% confidence level (such as [1.31 m, 4.83 m] at −520 m). Research shows that high-precision 3D laser scanning can effectively reveal the deformation and evolution laws inside the chute. It is recommended to immediately take reinforcement measures for chute 2 and establish a dynamic monitoring system for the common weak zone between −480 m and −520 m. Research on the evolution of surrounding rock fractures should be carried out to provide a scientific basis for chute life assessment and risk control.