融合动态概率积分法模型和Logistic模型的地表采煤沉陷动态预测方法

    A dynamic prediction method for surface mining subsidence based on dynamic probability integral model and Logistic model

    • 摘要: 为准确描述井工煤矿开采引起的地表形变随时间的动态演变规律,首先结合概率积分法模型(PIM)和Knothe时间函数模型,构建了动态概率积分法模型(DPIM);然后,考虑预计参数采动过程的变化规律,采用Logistic模型拟合动态预计参数,构建了1种DPIM-Logistic动态融合沉陷预测模型;最后,针对预计模型函数高度非线性,引入烟花算法求解模型的参数。模拟试验表明,反演参数时,下沉拟合中误差为2.37 mm,预测结果中误差为5.74 mm。将该方法应用于淮南矿区某工作面,反演参数拟合中误差为59.67 mm,预测结果中误差为73.10 mm,有效验证预计结果的精度和可靠性。

       

      Abstract: In order to accurately describe the dynamic evolution of surface deformation over time caused by underground coal mining, a dynamic probability integral method model (DPIM) was first constructed by combining the probability integral method model (PIM) and the Knothe time function model. Then, taking into account the changing rules of the predicted parameters during mining process, the Logistic model was used to fit the dynamic predicted parameters, and a DPIM-Logistic dynamic fusion subsidence prediction model was constructed. Finally, in view of the highly nonlinear function of the predicted model, the fireworks algorithm was introduced to solve the parameters of the model. Simulation experiments show that the error in subsidence fitting is 2.37 mm and the error in prediction results is 5.74 mm in inverting parameters. This method was applied to a working face in Huainan Mining Area. The error in the inversion parameter fitting was 59.67 mm, and the error in the prediction results was 73.10 mm. It effectively verified the accuracy and reliability of the predicted results.

       

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