A novel probability integral method segmental modified model for subsidence prediction applicable to thick loose layer mining areas

In response to the problem that the actual extent of coal mining impacts on the surface in thick loose layer mines significantly exceeds the theoretical predictions, based on the literature study, the form of influence of thick loose layer on the predicted parameters of the probability integral method is summarized and analyzed; taking into account the influence of the subsidence coefficient, the sine modification formula of the major influence radius and the logistic modification formula of the subsidence coefficient are established, respectively, and based on the characteristics of the major influence radius, a new subsidence basin demarcation point is proposed and a novel probability integral method segmental parameter modified prediction model is constructed. The simulated experiment and real data experiment results prove that the constructed probability integral method segmented parameter modified model can both reduce the convergence of surface subsidence basin edge better and take into account the predicted accuracy inside the subsidence basin. The research achievements provide scientific data support for disaster warning, pollution management, ecological restoration, and coordination between coal mining and surface city construction in thick loose layer mining areas.

Automated summary

Based on literature study, this research summarizes and analyzes the influence of thick loose layer on the predicted parameters of the probability integral method. By considering the influence of the subsidence coefficient, a sine modification formula for the main influence radius and a logistic modification formula for the subsidence coefficient are established. A new subsidence basin demarcation point and a novel probability integral method segmental parameter modified prediction model are proposed. Simulated and real data experiments prove that the constructed model can reduce the convergence of surface subsidence basin edge and accurately predict subsidence inside the basin. The research findings provide scientific support for disaster warning, pollution management, ecological restoration, and coordination between coal mining and surface city construction in thick loose layer mining areas.