Reconstruction and forecasting of slow-moving landslide displacement using a Kalman Filter approach

Abstract

This work presents an approach for reconstructing displacement evolution and unknown soil properties of slow-moving landslides, using a special form of so-called Kalman filter or observer. The approach relies on a mechanical model for the prediction step, with online correction based on available measurements. The observer proposed here relies on a simplified 1D viscoplastic sliding model. Landslide (slide block) motion is controlled by a balance between gravity and sliding resistance expressed in terms of friction, basal pore fluid pressure, cohesion, and viscosity. In order to improve the observer performance upon abrupt changes in parameters, a resetting method is proposed. A novel tuning algorithm, based on a combination of synthetic and actual test cases, is introduced to overcome the sensitivity to observer coefficients. Known parameter values (landslide geometrical parameters and known material properties) as well as water-table height time series are provided as inputs. The observer then reconstructs landslide displacement and the evolution of unknown parameters over time. The case of Super-Sauze landslide (French Alps), with data taken from the literature, is used to illustrate the potential of the approach. Finally, the observer is extended to forecast displacement evolution over different temporal horizons assuming that future water-table height variations are known.