Abstract:

With the increase of mining depth and intensity of mining face，the risk of microseismic

events and disasters in underground coal mines continues to increase. Combined with the microseismic moni⁃

toring data of the tunneling roadway in Zhangcun Coal Mine，the b-value optimization analysis and informa⁃

tion recognition classification of the microseismic events in the area in front of the tunneling face are carried

out. A variational root-square algorithm based on the least square method is proposed. A variational root

square algorithm based on the least square method is proposed. The b-value and source of the fault area are

analyzed from the three angles of ′time，space and strength′，and the accuracy of the b-value is optimized.

The research improves the reliability of microseismic monitoring and early warning and better guides mine

safety production.

Key words:

microseismic monitoring, b-value optimization, information identification, tunneling

roadway