Study on damage evolution characteristics of the deep roadway surrounding rock with fault based on microseismic monitoring and fractal theory

Located in Zhenfeng County in southwest Guizhou Province, China, the Lannigou Gold Deposit is a fault-controlled mine with a relatively fragmented rock mass. As mineral extraction continues, mining depth and intensity increase. For deeper and larger rock masses in complex stress environments, engineering practice remains somewhat blind, inefficient, and uncertain [1]. The complex, high-stress conditions of deep deposits have drawn increased attention to the stability of surrounding rock in deep roadways. Liu et al. [2] explored stress-reduction methods through field investigations of joint distributions and rock damage patterns in the surrounding rocks of large-scale gold deposits. Researchers have also focused on hazardous roadway sections, such as gob-side entries and hard-roof goafs, summarizing relevant stability evaluation indexes [3], [4]. Fu et al. [5] derived an energy criterion for rock destabilization in deep goafs by introducing a local energy release rate index. Qi et al. [6] developed a comprehensive evaluation method that incorporates stope stability, self-stabilizing time, maximum safety span, support measures, and related factors. Although numerous studies have addressed deep roadway stability, effective and timely evaluation methods applicable on a broad scale are still lacking. Real-time monitoring is essential for accurate stability assessment of surrounding rock.

Given the complexity of roadway monitoring conditions, some researchers have employed microseismic monitoring for further analysis. This technology provides rich information on rock fissures. By analyzing fissure patterns, fracture networks, and microseismic source parameters, the processes leading to rock engineering failures can be better understood [7], [8], [9], [10]. Although microseismic parameters can reflect rock mass integrity and damage [11], [12], a quantitative correlation between microseismic indices and rock mass damage remains insufficiently addressed [13], [14], [15]. On the other hand, numerical simulation results of roadway damage have been validated using microseismic monitoring, which further aids in assessing the risk and stability of surrounding rock [16], [17], [18]. Ma et al. [19] established a method for simulating rock cracks by inputting microseismic data and aligning the simulation with measured microseismicity. Xie et al. [20], [21] found that microfracture events exhibit a fractal structure. During rockburst development, the spatial fractal dimension of microseismic events decreases, reflecting an increased likelihood of rockbursts. A higher spatial fractal dimension indicates a lower rockburst probability, while a lower value suggests a higher risk. Several studies have linked the fractal characteristics of the spatio-temporal distribution of microseismic events to significant rock deformation and proposed early warning methods for construction safety [22], [23]. Mao et al. [24], [25], [26] refined the sliding-window method. By analyzing variations in spatial fractal dimensions relative to microseismic event energy across large areas, they identified zones of energy concentration associated with rock damage. The local minimum points in the spatial variation curve of microseismic energy fractal dimensions were used to define the box dimension calculation zones. By sliding the window equidistantly along the time axis and plotting the time-varying fractal dimension curve of microseismic energy, sudden drops from high points were selected as precursor warning signals. Many researchers have emphasized that microseismic events exhibit a fractal structure and have further applied fractal dimensions for quantitative analysis of monitoring results.

The concept of fractal dimension was first introduced by German mathematician Hausdorff in 1910. Later, French mathematician Mandelbrot.B [27] proposed fractal geometry based on the self-similarity of coastlines, leading to the rapid global development of this field as a key branch of mathematics. Xie [28] introduced fractal geometry into the field of geotechnical engineering and proposed the concept of fractal rock mechanics. With regard to research on the evolution and distribution of fractures in the damage process of surrounding rock, Hui et al. [29] and others established a statistical damage constitutive model and damage evolution equations for rock, comprehensively considering the Weibull probability distribution characteristics of rock strength and the fractal distribution size and normal distribution orientation of joint cracks. Yang et al. [30], conversely, based on the principle of box dimension calculation, proposed a method to calculate the fractal dimension of blasting cracks under an explosion load and established the correspondence between the fractal dimension and the blast damage degree. However, relatively few studies have integrated fracture distribution fractal characteristics with microseismic monitoring during surrounding rock failure.

Based on the research background, this study investigated the surrounding rock of the roadway to identify sections at higher risk. Numerical simulation software was then used to analyze the roadway damage process. The fractal dimension was introduced to quantify the fracture distribution and its evolution during damage, and to derive the fractal characteristics of fractures in the destruction of surrounding rock in the deep section of the Lannigou gold deposit. In addition, the distribution of microseismic events and the variation in microseismic parameters prior to roadway failure were analyzed using real-time monitoring data. By combining the evolution of damage-related fractures in the surrounding rock with the spatiotemporal distribution of microseismic events, the characteristics of the surrounding rock mass before and after roadway deformation and damage were examined to provide guidance for safe mine production.