Surface dynamic subsidence prediction method based on mining sufficiency degree

Underground mining and its influence on surface infrastructure are a dynamic process. Currently,most com- monly used prediction results of mining influence are static results,thus,it is difficult to accurately evaluate the influ- ence degree of the protected objects. Based on the analysis of domestic and foreign dynamic prediction methods,the ex- isting dynamic prediction methods are divided into three categories according to their establishment methods and prin- ciples including time function method,velocity function method and subsidence reduction method,and the advantages and limitations of these methods are analyzed. With the analysis of the distribution characteristics of surface subsidence velocity with mining sufficiency change,through the long-time observation and theoretical analysis for the surface sub- sidence of four work faces under the condition of nearly horizontal mining,the calculation formula of maximum ground subsidence velocity in the process of mining are proposed. To avoid the inaccuracy of measured ground subsidence ve- locity due to different observation time intervals,the dynamic relationships between the starting distance,advancing distance,maximum subsidence velocity lag distance and advancing speed are respectively established. The distribution function of surface subsidence velocity including mining sufficiency and the prediction method of dynamic subsidence of main strike section are proposed. By using the established dynamic subsidence prediction method, the dynamic ground subsidence of Bulianta coal mine under the geological and mining conditions are predicted and analyzed,and also compared with the measured subsidence data. The results shows that when the advancing speed of work face chan- ges little,the average relative error of the method used is 2. 1% ,and it has good prediction accuracy. The established dynamic subsidence prediction method makes up for the deficiency of previous research on the initial mining stage and is more reliable than the prediction model for the whole process of single point movement in full mining area.