Prediction of landslide block movement based on Kalman filtering data assimilation method

Compared with the study of single point motion of landslides, studying landslide block movement based on data from multiple monitoring points is of great significance for improving the accurate identification of landslide deformation. Based on the study of landslide block, this paper regarded the landslide block as a rigid body in particle swarm optimization algorithm. The monitoring data were organized to achieve the optimal state of landslide block, and the 6-degree of freedom pose of the landslide block was calculated after the regularization. Based on the characteristics of data from multiple monitoring points of landslide blocks, a prediction equation for the motion state of landslide blocks was established. By using Kalman filtering data assimilation method, the parameters of prediction equation for landslide block motion state were adjusted to achieve the optimal prediction. This paper took the Baishuihe landslide in the Three Gorges reservoir area as the research object. Based on the block segmentation of the landslide, the monitoring data of the Baishuihe landslide block were organized, 6-degree of freedom pose of block B was calculated, and the Kalman filtering data assimilation method was used to predict the landslide block movement. The research results showed that the proposed prediction method of the landslide movement state has good prediction accuracy and meets the expected goal. This paper provides a new research method and thinking angle to study the motion state of landslide block.