黄土滑坡稳定性评价的集合卡尔曼滤波同化方法

目的] 为提升区域滑坡稳定性评价模型的预测精度,解决传统滑坡稳定性分析基于静态的物理模型过度简化滑坡发生机理与力学机制,导致过度预测的缺点,以及模型参数通常具有的时空变异性、不确定性的问题。方法] 基于集合卡尔曼滤波的数据同化方法,以甘肃省兰州市北环路周边区域为例,构建了基于TRIGRS模型和SBAS-InSAR观测数据的区域滑坡数据同化方案,对模型中的安全系数(F_s)进行同化,更新模型参数内摩擦角,进而修正滑坡稳定性,并利用均方根偏差(RMSD)检验同化值的精度。结果] 同化后研究区域滑坡安全系数明显高于模型预测的结果,不稳定区域的面积比例由12 %降低至7 %,与实际观测更为接近;试验使内摩擦角参数逐渐向观测值方向改正,实现了模型参数的动态更新;均方根偏差从0.33减小到0.04左右。结论] 基于集合卡尔曼滤波的数据同化方法有效修正了模型稳定性预测结果,可以更准确体现当前区域滑坡实际情况,具有更高的预测精度。

Ensemble Kalman Filter Assimilation Method for Stability Evaluation of Loess Landslides

Objective] The prediction accuracy of a regional landslide stability evaluation model was improved to solve the shortcomings of over-prediction caused by over-simplification of the landslide occurrence mechanism and the mechanical mechanism based on the static physical model of the traditional landslide stability analysis, and to determine the typical spatial-temporal variability and uncertainty of model parameters.Methods] The data assimilation method of ensemble Kalman filtering was used to construct a regional landslide data assimilation scheme based on the TRIGRS model and SBAS-InSAR observation data in the area around the North Ring Road of Lanzhou City, Gansu Province. The coefficients of safety (F_s) in the model were assimilated, and the model parameters for the internal friction angle were updated. Then landslide stability was corrected and root-mean-square deviation (RMSD) was used to test the accuracy of the assimilated values.Results] After assimilation, the landslide safety coefficient of the study area was significantly greater than the coefficient value predicted by the model, and the percentage of unstable area was reduced from 12 % to 7 %, which was closer to the actual observed value. The test gradually corrected the internal friction angle parameter towards the observed value, and realized the dynamic updating of the model parameters. The root-mean-square deviation decreased from 0.33 to about 0.04.Conclusion] The data assimilation method based on the ensemble Kalman filter effectively corrected the model stability prediction results so that the actual situation of landslides in the current region was more accurately reflected with greater prediction accuracy.