LANDSLIDE MONITORING AND FORECASTING IN UPPER YELLOW RIVER BY InSAR TECHNOLOGY BASED ON DATA ASSIMILATION THEORY

The landslides in the Upper Yellow River region are frequent and widely distributed, causing serious damage. Traditional landslide recognition and monitoring methods have limitation, while the InSAR technology, due to its high precision and extraction of millimeter-scale deformation, is widely used in landslide monitoring. However, the technique has a high requirement for image coherence, resulting in large dispersion degree of data and impossibility of obtaining continuous deformation data, which greatly impacts the forecasting application of the monitoring results. With the modeling method of data assimilation theory, the multiscale, multisource and multitype data can be coprocessed to eliminate errors. In this paper, the millimeter-scale deformation of surface is obtained by SBAS-InSAR technology, and the observed data is assimilated by Kalman filtering(KF) algorithm. The single point experiment shows that the simulation results after KF are significantly improved compared with those before the assimilation, which verifies the feasibility of data assimilation algorithm in improving the precision of numerical simulation. The continuous forecast data is generated by data assimilation theory, which provides a new way for InSAR technology to monitor and forecast landslide deformation.