回归分析与抗差卡尔曼滤波协同下大型边坡短期变形的预测方法

Forecast of Short-term Deformation of Large-scale Slope Based on Regression Analysis and Robust Kalman Filter

由于边坡滑移影响因素众多,无法建立精确的动力学模型,因此利用传统抗差卡尔曼滤波模型进行边坡短期变形预测十分困难,无法满足大型边坡高精度的预警需求。建立回归分析与抗差卡尔曼滤波协同下大型边坡短期变形预测模型,利用拟合值代替含粗差数据进行滤波预测运算,解决了卡尔曼滤波缺乏对粗差的抗干扰性问题。利用金堆城露天钼矿大型边坡监测数据开展工程案例研究,结果表明2种预测模型都是有效的,但回归分析与抗差卡尔曼滤波协同下大型边坡短期变形预测模型精度和抗差性2个指标都优于传统抗差卡尔曼滤波模型。

Due to many factors affecting the landslide,it is difficult to create an accurate kinetic model, and also it is hard to predict the short-term deformation of large-scale slope by using the traditional dynamic model to meet the demand for high-precision early warning of large-scale slopes. The short-term deformation prediction model for large-scale slope was established under the cooperation of regression analysis and robust Kalman filter. Fitting value was adopted to replace the data containing gross errors for filtering and prediction operation, which solves the problem of Kalman filter lacking for anti-interference to gross errors. The engineering case based on monitoring data of large-scale slope in Jinduicheng Open-pit Molybdenum Mine showed that both of the prediction models are effective, but the precision and robustness of the short-term deformation prediction model of large-scale slope under the cooperation of regression analysis and robust Kalman filter are better than the traditional robust Kalman filter model.