Mechanisms of surface subsidence control by heterogeneously thickened hard rock strata and its predictive modeling

Predicting surface subsidence induced by underground coal mining is a critical and challenging task in mining engineering. Although existing studies have proposed various prediction methods, their accuracy and applicability still fail to meet practical requirements, and issues related to ecological environmental damage and geological hazards still occur occasionally. Traditional prediction methods are largely developed based on the assumption of uniform rock stratum thickness. However, systematic research on the coupling mechanism between heterogeneously-thickened rock strata in the formation and surface subsidence is lacking. Furthermore, the thick-hard rock strata, which play a core controlling role in surface subsidence, are often overlooked—resulting in prediction accuracy that cannot satisfy practical demands under complex geological conditions.To address this gap, this study for the first time clearly defined the concept of “ Heterogeneously thickened hard rock strata ” ( HTHRS ), selected a typical study area containing such strata, and developed a 3D surface subsidence prediction model under HTHRS conditions using mechanical methods. Combined with multi-source on-site monitoring methods, it was found that HTHRS lead to significant skewness in the morphology of surface subsidence basins—breaking the common perception that “subsidence basins formed by horizontal coal seam mining exhibit uniform and symmetrical distribution”. Through simulation methods, it was revealed that the thickness of hard rock strata is the dominant controlling factor for the morphology of surface subsidence basins, and its thickness variation rate exhibits a positive correlation with the offset of subsidence boundaries and the degree of stress concentration. Engineering practice verification shows that compared with the traditional probability integral method, the proposed model can more accurately characterize the skewed characteristics of subsidence basins and significantly improves prediction accuracy. This study fills the theoretical gap in surface subsidence prediction under HTHRS conditions and holds significant implications for surface subsidence prediction, mining production planning, and ecological protection in similar geological settings.