A new model for predicting surface mining subsidence: the improved lognormal function model

Mining-induced problems in the coal field seriously threaten the normal operation of the mines and cause significant property losses and environmental disruption. Thus, high precision subsidence prediction is important on the processing of mining subsidence problems. In this paper, we analyzed the formation mechanism of skewed subsidence. The rock beam on the side of the gob and coal pillar presented different supporting reaction force, and the difference resulted in the asymmetric distribution of subsidence velocity, which further led to the formation of the surface skewed subsidence basin. The relationship between the wave curve and vibration curve was determined, and the skewed subsidence process of the surface point in the mining affected area was analyzed. The total duration of the initial and accelerated subsidence phases is smaller than that of the decelerated and end subsidence phases. Then, from the skewed subsidence characteristics, the skewed subsidence prediction model based on the lognormal function was built. An application example was selected to validate the feasibility and effectiveness of the proposed model. Results showed that the model has good prediction ability.