Accurate source locating and a complete data catalogue of the seismic network are the prerequisites for seismic analysis methods to identify coal burst risks. Comprehensively understanding the spatial characteristics of source location errors and seismic data integrity is a key insight for optimising seismic networks and enhancing monitoring performance. Based on the monitored seismic data in a burst-prone longwall, this study develops two novel methodologies, Emulation-Testing-based Source Locating Accuracy Analysis (ETSLA) and Probability-based Magnitude of Completeness (PMC) method, to evaluate seismic monitoring performance in underground coal mines. The results indicate that ETSLA effectively quantifies vector characteristics of source location errors, revealing anisotropic error distributions in the studied longwall. The PMC method presents significant differences among geophones regarding their wave detection capacities. The detection probability of the seismic network for the events demonstrates progressive enhancement with increasing energy magnitude. In field practice, ETSLA can correct misclassified burst types by accounting for location errors. The Seismic data inferred using the PMC method can retrace missing seismic activity, and the inferred high-energy zones accurately correlate with actual burst damage locations. The study can serve as a reference to enhance the quality of seismic monitoring for precise early warning of coal burst risks.

Precise monitoring of damage evolution in coal-rock mass during mining emerges as a paramount requirement for developing accurate early warning systems for coal burst hazards. However, limited research has demonstrated the integrated damage characteristics of the coal-rock mass under static and dynamic loads during longwall mining. Therefore, in this paper, two novel seismic monitoring approaches, the Seismic Cluster Index (CI) and the Number of High Ground Motions (NHGMs), are developed to study the evolution of coal-rock mass damage during longwall mining under static and dynamic loads, respectively. Two months of monitored seismic data from a burst-prone longwall are used for analysis. The results show that CI can depict coal-rock damage conditions under static load, which identifies coalescence of fractures based on seismic source sizes and inter-event distances. Ground motion intensity has a positive correlation with seismic energy. The induced dynamic disturbance to roadways can further weaken the coal-rock mass, depending on the distance from the seismic sources. High-intensity dynamic disturbances, as indicated by elevated NHGMs and accelerated increments, strongly correlate with coal-burst damage. The proposed CI and NHGMs framework evaluate coal-rock mass damage and forecasts coal burst hazards, validated by the correlation between high CI/NHGMs values and actual burst locations.