An adaptive Kalman-filtering approach for the calibration of finite difference models of mass movements

Landslides are natural geomorphological phenomenas that can cause hazardous situations to men and infrastructure especially in densely populated areas. For the investigation of such events often numerical slope models are used, that describe the mechanical and geological properties of bedrock. However, the adaptation of the model to the monitored data is commonly done by 'trial and error' methods. In order to improve the adaptation process, adaptive Kalman-filtering techniques shall be used in terms of a realistic model calibration. Nevertheless, this method is very computationally intensive applied on full slope models with a larger grid size. Since the deformation process is normally restricted to limited parts of the investigation area (e.g. areas close to the surface), the Kalman-filter algorithm may be applied to selected parts of the grid with predicted major displacements. The first part of the paper is focussed on the monitoring design of the landslide 'Steinlehnen' (Tyrol, Austria). For this mass movement, a numerical model is currently under development and shall be calibrated by adaptive Kalman-filtering. In the second part, some investigation results for the adaptive Kalmanfiltering approach are presented and discussed regarding a still simulated numerical test slope.