The Mohorovičić discontinuity (or Moho) that marks the transition from the crust to the mantle, is studied almost exclusively through indirect geophysical methods. Inverse theory, Satellite gravity, Gravity anomalies and Earth structure, South America 1 INTRODUCTION These differences suggest the existence of crustal or mantle density anomalies that were unaccounted for during gravity data processing. Discrepancies with the seismological data are greatest in the Guyana Shield, the central Solimões and Amazonas Basins, the Paraná Basin, and the Borborema province. Similarly to previous results, the model suggests a thinner crust of 30–35 km under the Andean foreland basins. The misfit to the gravity and seismological data is worse in the Andes and best in oceanic areas, central Brazil and Patagonia, and along the Atlantic coast. The final Moho model is in accordance with previous gravity-derived models and seismological data. We apply the proposed method to estimate the Moho depth for the South American continent using satellite gravity data and seismological data. Additionally, we estimate the density-contrast and the reference depth using knowledge of the Moho depth at certain points. We estimate the regularization parameter using the method of hold-out cross-validation. The inversion results are controlled by three hyperparameters: the regularization parameter, the anomalous Moho density-contrast, and the reference Moho depth.
The computational efficiency of our method is attained by harnessing the fact that all matrices involved are sparse. To achieve this, we combine the highly efficient Bott's method with smoothness regularization and a discretization of the anomalous Moho into tesseroids (spherical prisms). We present a regularized nonlinear gravity inversion method that has a low computational footprint and employs a spherical Earth approximation. What is more, the modelling must take the Earths curvature into account when the study area is of regional scale or greater. Estimating the relief of the Moho from gravity data is a computationally intensive nonlinear inverse problem.
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