Abstract: Coal and rock dynamic hazards frequently occur in deep and complex geological environments undergoing intensive mining activities. Nevertheless, the seismic monitoring in underground mines still has deficiencies in data integrity, which significantly increases the erroneous or missed hazard pre-warnings. Therefore, this study introduces the Probability-based Magnitude of Completeness (PMC) method in an underground coal mine. PMC method assesses the seismic wave picking capabilities of geophones, explores the spatial characteristics of seismic event detection probability within the seismic network, and proposes a detection probability-based seismic compensation method. The results show that: PMC offers advantages over the classic Minimum Magnitude of Completeness approach in seismology, including independence from the Gutenberg-Richter law, simpler computation, and higher evaluation accuracy. These attributes make PMC particularly suitable for seismic monitoring in underground mines constrained by network layout, complex seismic sources, significant signal noise, and attenuation. The picking capabilities of geophones for seismic waves are influenced by local coal and rock environments, leading to substantial variations in picking probabilities for seismic events of different distances and energy levels: The shorter the distance between the seismic source and the geophone, and the higher the source energy level, the greater the probability that the geophone can identify the arrival time of the seismic wave. The detection capability of the seismic network is directly linked to the energy levels of seismic events, resulting in a highly heterogeneous spatial distribution within the mining area due to geophone layout. By applying the detection probability-based seismic compensation method, the study reconstructs the spatial distribution characteristics of seismic energy levels and frequencies ahead of the longwall face, revealing a close correspondence between areas of high-energy release and locations prone to dynamic failures, which validates the seismic compensation results. The research outcomes contribute theoretical and data foundations for enhancing the quality of seismic monitoring and improving hazard early-warning in underground mines.