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.

传统上通过地震方法研究莫霍面，即地壳和地幔之间的密度不连续性。但是，重力信息会有助于Moho估计，更一般地说，会有助于地壳建模。贡献是双重的。首先，重量分析法提供的观测结果误差远低于质量密度不确定性和其他地球物理假设所隐含的误差。这意味着可以通过正向建模将其用于验证现有的Moho和/或地壳模型。其次，重力反演能够提供区域和全球质量分布的分散（而非局部）信息（这要归功于专门的卫星重力任务）。但是，此信息由于其固有的不适性而较弱。这意味着它可用于校正和空间内插现有模型，并补充地震，磁和地质信息以创建新模型。在这项工作中，在区域级别上处理了假设重力具有两层模型的横向和垂直密度变化的情况下通过重力反演估算莫霍面的问题。该方法包括以恒定深度围绕参考Moho线性化正向建模，然后通过维纳滤波器对其进行反转。这是两层具有均匀密度分布（或横向密度变化）的标准，而在垂直密度变化的情况下，它需要一些其他考虑和算法修改。基本思想是将Moho波动内部的质量“压缩”在用于线性化的参考表面上，因此需要建立一个迭代过程。还提出了将地震信息引入该反演程序的策略，目的是改善先验密度模型。提出了用于算法评估的闭环测试，显示了相对于标准方法的改进以及所提出算法通过还以与重力和地震数据一致的水平拟合重力和地震数据来重建原始模拟的Moho波动的能力。观察噪声。