Coal mine rock burst risk dynamic and static coupling evaluation method based on Bayesian method

Abstract

The invention provides a coal mine rock burst risk dynamic and static coupling evaluation method based on a Bayesian method. The method comprises the following steps: S1, obtaining dynamic index parameter data; S2, obtaining static index parameter data; S3, performing normalization processing on the dynamic and static index parameter data; S4, performing abnormal index conversion on each micro-seismic index obtained by monitoring of a micro-seismic sensor; S5, performing comprehensive risk calculation on borehole stress index values obtained by a borehole stress sensor; S6, performing fusion calculation on each dynamic and static index through a Bayesian probability combination model; and S7, performing rock burst grading on calculation results of the combination model to realize intelligent grading early warning. According to the invention, data indexes of all monitoring systems are effectively fused, the dynamic and static indexes are comprehensively considered, dynamic weight calculation of all the indexes based on time sequences is achieved, the rock burst safety early warning capacity is improved, and the problems that an existing multi-index safety early warning threshold value is difficult to determine and the fusion degree of data of all the monitoring systems is low are solved.

Description

technical field [0001] The invention relates to the technical field of safety monitoring and early warning of rockburst dynamic disasters in coal mines, in particular to a dynamic-static coupling evaluation method for rockburst hazards in coal mines based on a Bayesian method. Background technique [0002] With the increase of coal mining depth, the stress of deep coal seams increases, and the possibility of disasters is also greater. Therefore, underground monitoring and early warning of common underground dynamic disasters are particularly important. In deep mining areas, rock burst, as a typical and common coal-rock dynamic disaster, has important engineering value for underground monitoring and early warning. [0003] The monitoring and early warning research work of rock burst has achieved good results. Among them, monitoring systems such as microseismic, ground sound, electromagnetic radiation, and drilling stress have been widely used, and have achieved good applicat...

AI Technical Summary

Problems solved by technology

[0010] In view of the existing rock burst safety monitoring and early warning system, the data fusion degree of each monitoring system is low, the safety warning threshold of each index is difficult to determine and random, and the dynamic index and static index lack linkage. Dynamic and static coupling evaluation method of coal mine rockburst hazard based on Bayesian method

Images