Regional local integrated rockburst monitoring and early warning for multi-seam mining

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Rockburst has become a main problem affecting the safety and efficiency of coal mine production. Early warning of it is essentially important for safety management. The dynamic and static load sources of rockburst, induced by multi-coal seam mining, are more complex, making rockburst prediction more difficult. The problem of rockburst early warnings in multi-seam mining was addressed by establishing a regional local integrated rockburst monitoring and early warning method based on the following criteria. (i) In regional space, the microseismic (MS) frequency ratio index and dispersion index were used to predict the regional dynamic load. (ii) In the local excavation and mining space, the risk degree of local static load concentration was characterised by the overrun amplitude indexes of electromagnetic radiation (EMR) intensity and pulse. (iii) According to the predicted spatial location of dynamic load and the attenuation law of regional dynamic load, the influence weight of the regional dynamic load on the excavation and mining space was determined. Based on this, the danger degrees of regional dynamic load and local static load were superposed to realise the regional local integrated warning of rockburst. This prediction method was applied for predicting rockburst in the Xinzhouyao mine, and the result indicated that the method can effectively predict the occurrence of dynamic load, the risk degree of local static load and rockburst induced by multiple types of dynamic and static loading combined together. The reliability of this prediction method was verified by records of roadway deformation and rockburst, and detection results from passive velocity tomography.
Funding Information
  • National Natural Science Foundation of China (51634001, 51774023)
  • State Key Research Development Program of China (2016YFC0801408)
  • Major Science and Technology Innovation Project of Shandong Province (2019SDZY01)

This publication has 44 references indexed in Scilit: