Geomechanical models are indispensable for reliable design of engineering structures and processes and hazard and risk evaluation. Model predictions are however far from perfect. Errors are introduced by fluctuations in the input or by poorly known parameters in the model. To overcome these problems an inverse modelling technique to incorporate measurements into the deterministic model to improve the model results can be implemented. This allows for observations of on-going processes to be used for enhancing the quality of subsequent model predictions. In geomechanics several examples of inverse modelling exist where the improved model of the system is obtained by minimizing the discrepancy between the observed values in the system and the modelled state of the system within a time interval. This requires the implementation of the adjoint model. Even with the use of the adjoint compilers that have become available recently, this is a tremendous programming effort for the existing geomechanical model system. The Ensemble Kalman filter has been implemented to overcome this problem. The Ensemble Kalman filter analyses the state of the subsurface each time data becomes available. The Random Finite Element Method is used to simulate the heterogeneity of the subsurface. Very promising results of a conceptual example, based on the construction of a road embankment on soft clay, are presented. The Ensemble Kalman filter is not only used for a straight forward identification of the elastic Young’s modulus E of the foundation below the embankment, but also incorporates the determination of several critical parameters of the inverse modelling process.


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