Abstract
The Moho surface, namely the density discontinuity between crust and mantle, is traditionally studied by seismic methods. However, gravity information can contribute to the Moho estimation and, more generally, to the crustal modeling. The contribution is twofold. First, gravimetry generally provides observations with much lower errors than those implied by the mass density uncertainty and other geophysical assumptions. This means that it can be used to validate existing Moho and/or crustal models by forward modeling. Second, gravity inversion is able to provide diffused (not localized) information on the mass distribution, both regionally and globally (thanks to dedicated satellite gravity missions). However, this information is weak due to its intrinsic ill-posedness. This means that it can be used to correct and spatially interpolate existing models, and to complement seismic, magnetic and geological information to create new models. In this work, the problem of estimating the Moho surface by gravity inversion assuming a two-layer model with lateral and vertical density variations is treated at a regional level. The approach consists in linearizing the forward modeling around a reference Moho at a constant depth and then inverting it through a Wiener filter. This is standard in case of two layers with homogeneous density distributions (or with lateral density variations), while it requires some additional considerations and algorithm modifications in case of vertical density variations. The basic idea is to “condensate” the masses inside the Moho undulation on the reference surface used for the linearization, thus requiring the setup of an iterative procedure. A strategy to introduce seismic information into this inversion procedure is proposed too, with the aim of improving the a priori density modeling. A closed loop test is presented for the algorithm assessment, showing the improvement with respect to a standard approach and the capability of the proposed algorithm to reconstruct the originally simulated Moho undulation by also fitting the gravity and seismic data at a level that is consistent with their observation noise.