Efficient Global Optimization (EGO) is an optimization strategy based on approximating functions, namely Gaussian process models. We show the application of this technique to a model calibration problem referred to a geomechanical application. By means of the approximating function an objective relevance ranking among the problem parameters can be produced, offering valuable and reusable information on the physical problem.