基于松散层厚影响的概率积分法开采沉陷预计模型

为提高厚松散层开采下概率积分法的预计地表沉陷精度,表明下沉盆地边缘收敛缓慢的问题,基于概率积分法理论提出了新的开采沉陷预计模型,该模型考虑了松散层厚度的影响,引入了松散层影响系数这一新参数。结合淮南潘北矿1212(3)工作面实例验证,研究结果表明:新预计模型比概率积分模型的预计精度高,拟合相对中误差由原来的8%减少到6%;松散层影响系数与主要影响半径呈正相关,两者之间大小关系明确。

Prediction Model of Mining Subsidence with Probability Integration Method Based on Thickness Influences of Loose Layer

In order to improve the predicted surface subsidence accuracy of the probability integration method under the mining in thick and loose layer,to show the slow convergence at the boundary of the subsidence basin,based on the theory of the probability integration method,a new prediction model was provided.The model had considered the influences of the loose layer thickness and a new parameter of the loose layer influence coefficient was introduced.In combination with a case verification of No.1212(3) coal mining face in Panbei Mine of Huainan Mining Area,the study results showed that the new prediction model would be higher predicted accuracy than the probability integration model and the fitting relative error was decreased from 8% to 6%.The influence coefficient of the loose layer was positively related to the main influence radius and the relationship between the coefficient and the radius was clear and the fitting effect was excellent.