Enhancing Data Sets From Rudna Deep Copper Mine, SW Poland: Implications on Detailed Structural Resolution and Short-Term Hazard Assessment

Applying the software BackTrackBB (Poiata et al., 2016) for automated detection and location of seismic events to data sets from Rudna Deep Copper Mine, SW Poland, lead to an enhancement of existing routine catalogs by about a factor of 10.000 in number of events. Following our hypothesis that all types of seismic events contribute to seismic hazard in a mine, we included all events from major mine collapses (M>3), recorded blasting works and detonations, to machinery noise. These enhanced data sets enabled a detailed spatio-temporal distribution of seismicity in the mine and a short-term hazard assessment on a daily basis.

In this study, we focus on the data from two days with major mine collapses: the 2016-11-29 Mw=3.4, and the 2018-09-15 Mw=3.7 events. The spatio-temporal distribution of seismicity of both days deciphered detailed horizontal and vertical structures and revealed the increase of seismic activity after the daily blasting work. The daily histograms exhibit similar patterns, suggesting the dominant influence of explosions on the overall seismicity in the mine. Using the enhanced data sets for short-term hazard assessment, we observed gaps in the activity rates before the main shocks. They were followed by sudden increase of seismicity, a simultaneous drop in seismic b-value, and an increase in exceedance probability for the assumed largest magnitude events. This demonstrates the usefulness of enhanced data sets from surface networks for revealing precursory phenomena before destructive mine collapses and suggests a testing strategy for early warning procedures.