A Novel Surface Subsidence Prediction Model Based on Stochastic Medium Theory for Inclined Coal Seam Mining

Abstract

Most coal resources are deposited in the form of inclined coal seams, and surface subsidence basin morphology induced by the mining of inclined coal seams is frequently skewed. The probability integral method with symmetrical distribution characteristics is widely used at present in surface subsidence prediction in coal mining in China. However, this method performs poorly when the inclined coal seam mining subsidence is predicted, and prediction accuracy decreases considerably with an increase in coal seam inclination. To solve this problem, this study first establishes three coordinate systems: a working surface rectangular coordinate system, a working face body-following coordinate system, and a surface rectangular coordinate system. Then, a random medium theory is applied to realize the superposition integral operation of the subsidence influence of unit mining in the working faces body-following coordinate system. Subsequently, the subsidence effect of a certain point on the surface is converted into the surface rectangular coordinate system. Finally, the inclined coal seam mining subsidence prediction model is constructed under the surface rectangular coordinate system. Results show that the surface subsidence caused by the mining of the inclined coal seam units conforms to the Weibull polar distribution law, and the effectiveness of the prediction model is verified through examples. The relative mean squared error of the prediction is less than 10%. The results of the study can provide theoretical and technical support for the subsidence prediction of similar mining areas.