## 1 Introduction  
Coal bursts and gas outbursts are typical dynamic disasters in underground coal mines and have raised global concerns [1]. With the increasing depth of mining and the complex conditions at these depths, stress at the working face becomes severely deteriorated, resulting in dynamic disasters occurring more frequently [2, 3]. These dynamic disasters are most observed in advanced roadways. For example, there were 1355 coal burst incidents between 1983 and 2023 in China, of which about 93.1% occurred in advanced roadways [4, 5]. These dynamic disasters are typically caused by the combination of dynamic and static loads within the advanced mining influence zone of the working face [6]. This zone is affected by both the static load from the advanced abutment pressure and dynamic disturbances from the working face. Accurately determining the advanced mining influence range of the working face is crucial for ensuring safety, including bolting control, pressure-relief measures, and limiting the number of personnel, especially in areas at risk of coal bursts and gas outbursts [7, 8].  
At present, most studies have focused on the static load from the abutment pressure at the working face, using methods such as theoretical analysis, numerical calculation, lab testing, and field monitoring [9; 10; 11; 12; 13]. In theoretical analysis and numerical simulation, underground field conditions are often simplified, making them differ significantly from the actual complex conditions and mechanical properties found in the field. Stress gauges have been widely used to monitor stress in coal and rock masses. Distributed optical fibers have also been gradually introduced for stress monitoring in underground coal mines [14; 15]. However, these field monitoring methods are often costly in terms of both time and money. They also sometimes fall short of expectations due to issues with equipment reliability and stability in the harsh underground environment. To monitor the dynamic disturbance at the working face, micro-seismic monitoring systems are widely used in underground coal mines prone to dynamic disasters [16]. These systems monitor and analyze mining-induced seismic activity, focusing on the spatial, temporal, and intensity characteristics of the mining-induced seismicity, as well as seismic wave attenuation, focal mechanisms, and seismic tomography [17; 18; 19; 20]. However, these methods only monitor and analyze dynamic disturbances and do not integrate with the monitoring of the advanced static load. As a result, they fail to provide a comprehensive view of the overall mining influence at the working face.  
Mining-induced seismicity in the advanced area of the working face is caused by the superposition of the advanced static load and dynamic disturbance [21; 22]. Therefore, by analyzing the distribution characteristics of the mining-induced seismicity, the advanced mining influence range of the working face can be determined using specific methods. Based on the understanding of the mining-induced seismicity mechanism, a novel statistical method of mining-induced seismicity was proposed. This method includes a threshold and a procedure for determining the advanced mining influence range of the working face. A case study was conducted to validate the effectiveness of the novel method, and potential issues with its application are discussed in this paper. The aim of the study is to quantitatively determine the advanced mining influence range by statistical analysis of mining-induced seismicity. The results offer valuable insights for improving safety and preventing dynamic disasters in underground coal mines.