New Approaches to Assess Seismic Monitoring Quality in Underground Mines: Data Completeness and Source Location Accuracy

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.