A New Theoretical Method to Predict Strata Movement and Surface Subsidence due to Inclined Coal Seam Mining

The mining-induced strata movement and surface subsidence are closely related to the dip angle of coal seam. However, most surface subsidence prediction methods are empirical, and only suitable for nearly flat coal seam mining. In this paper, a new theoretical method is proposed to predict the strata movement boundary and surface subsidence caused by inclined coal seam mining, which considers the influence of key strata, rock quality and coal seam dip angle. The strata movement caused by inclined coal seam mining is generalized and described by three models: analogous hyperbola model (AHM), analogous hyperbola-funnel model (AHFM), and analogous funnel model (AFM). Considering the rock quality of roof and floor strata, the rock mass rating system is adopted to calculate the surface maximum subsidence and its location. Additionally, the distinct element method was used to assess the performance of the theoretical models. The numerical simulation results match well with theoretical predictions of strata movement boundary and surface subsidence. It is discovered that the appearance of surface subsidence troughs is obviously asymmetric. As the dip angle increases, the surface maximum subsidence decreases and its location is laterally displaced. When the dip angle is greater than 50 degrees, the double subsidence troughs can be visualized clearly. Furthermore, the theoretical predictions of surface subsidence are verified by field measurements of two cases. As a result, the theoretical predictions of surface subsidence are greatly improved by comparing with the empirical method.

Automated summary

This study proposes a new theoretical method for predicting strata movement and surface subsidence caused by inclined coal seam mining. By considering the influence of key strata, rock quality, and coal seam dip angle, three models are introduced to describe strata movement. The theoretical predictions of surface subsidence are validated through numerical simulations and field measurements, showing significant improvement over empirical methods.