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    Global to Local Moho Estimate Based on GOCE Geopotential Model and Local Gravity Data
    Collocation approach has been applied to get a global Moho model in spherical approximation based on a GOCE geopotential model. A simple single layer model, with known density contrast, has been considered and a linearized relationship between the spherical harmonic coefficients of the anomalous potential and those of the Moho depth has been derived. This allows the covariance propagation from gravity to Moho depth. The derived covariance functions are then used in the collocation estimate of the global Moho depth. In order to be as close as possible to the considered model, reductions for the gravity signal related to topography/bathymetry have been applied. Simulated and real data tests have been performed and the obtained global solution has been compared with Moho estimates available in literature. The obtained global Moho has been then used as a starting solution for a regional refinement assuming planar approximation. In this second step the computation has been performed in the Central Mediterranean area, based on collocation, local gravity and topography/bathymetry data.
      49  3
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    Statistical assessment of predictive modelling uncertainty: a geophysical case study
    When the results of geophysical models are compared with data, the uncertainties of the model are typically disregarded. This paper proposes a method for defining the uncertainty of a geophysical model based on a numerical procedure that estimates the empirical auto- and cross-covariances of model-estimated quantities. These empirical values are then fitted by proper covariance functions and used to compute the covariance matrix associated with the model predictions. The method is tested using a geophysical, spherical, thin-sheet finite element model of the Mediterranean region. Using a χ2 analysis, the model's estimated horizontal velocities are compared with the velocities estimated from permanent GPS stations while taking into account the model uncertainty through its covariance structure and the covariance of the GPS estimates. The results indicate that including the estimated model covariance in the testing procedure leads to lower observed χ2 values and might help a sharper identification of the best-fitting geophysical models.
      56  1