中文摘要：

采用集合卡尔曼滤波方法,结合老采空区残余沉降的非确定性过程,视矿区变形为一个随机动态系统,研究并建立了老采空区残余沉降的集合卡尔曼滤波预测模型,并通过实例将集合卡尔曼滤波预测值和原始实测数据序列做对比分析。结果表明,集合卡尔曼滤波能够在减弱沉降数据中含有的随机噪声干扰的同时进行有效的数值计算,模型预测效果良好,为老采空区残余沉降预测提供了一种新方法。

英文摘要：

With respect to the uncertainty process in goaf residual subsidence, the Ensemble Kalman Filter （EnKF） was introduced, the coal mine deformation was treated as a dynamic stochastic system and a new prediction model named Ensemble Kalman Filter model was proposed. Then the ensemble Kalman filter predicted value was compared with the origi- nal measured data. The numerical example shows that the ensemble Kalman filter model can effectively deal with the meas- ured data polluted by noise. It proves that the prediction effect of Ensemble Kalman Filter is good, and Ensemble Kalman Filter offers a new way to predict the goal residual subsidence.