The emergence of high resolution satellite measurements of the gravitational field (GOCE mission) offers promising perspectives for the study of the Earth's interior. These new data call for the development of innovant analysis and interpretation methods. Here we combine a forward prism computation with a Bayesian resolution approach to invert for these gravity gradient data configuration. We apply and test our new method on satellite data configuration, i.e. 225 km height with a global and homogeneous geographic distribution. We first quantify the resolution of our method according to both data and parameteriza-tion characteristics. It appears that for reasonable density contrast values (0.1 g.cm −3) crustal structures have to be wider than ∼28 km to be detectable in the GOCE signal. Deeper bodies are distinguishable for greater size (35 km size at 50 km depth, ∼80 km at 300 km depth). We invert the six tensor components, among which five are independent. By carefully testing each of them and their different combinations, we enlighten a trade off between the recovery of data and the sensitivity to inversion parameters. We particularly discussed this characteristic in terms of geometry of the synthetic model tested 2 Plasman et al. (structures orientation, 3-D geometry, etc.). In terms of RMS value, each component is always better explained if inverted solely, but the result is strongly affected by the inversion parameterization (smoothing, variances, etc.). On the contrary, the simultaneous inversion of several components displays a significant improvement for the global ten-sor recovery, more dependent on data than on density variance or on smoothness control. Comparing gravity and gradient inversions, we highlight the superiority of the GG data to better reproduce the structures especially in terms of vertical location. We succesfully test our method on a realistic case of a complex subduction case for both gradient and gravity data. While the imaging of small crustal structures requires terrestrial gravity dataset, the longest wavelength of the slab is well recovered with both data sets. The precision and homogeneous coverage of GOCE data however, counterbalance the heterogeneous and often quite nonexistance coverage of terrestrial gravity data. This is particularly true in large areas which requires a coherent assemblage of heterogeneous data sets, or in high relief, vegetally covered and offshore zones.

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从空间到岩石圈：GOCE 重力梯度的反演。供应地球内部研究

引力场高分辨率卫星测量（GOCE 任务）的出现为地球内部的研究提供了有希望的前景。这些新数据要求开发创新的分析和解释方法。在这里，我们将前向棱镜计算与贝叶斯分辨率方法相结合，以对这些重力梯度数据配置进行反演。我们在卫星数据配置上应用并测试我们的新方法，即具有全球和同质地理分布的 225 公里高度。我们首先根据数据和参数化特征量化我们方法的分辨率。似乎对于合理的密度对比度值 (0.1 g.cm -3)，地壳结构必须比 28 公里宽才能在 GOCE 信号中检测到。更深的天体可区分为更大的尺寸（50 公里深度为 35 公里，300 公里深度为 80 公里）。我们反转六个张量分量，其中五个是独立的。通过仔细测试它们中的每一个及其不同的组合，我们启发了数据恢复和反演参数敏感性之间的权衡。我们在合成模型的几何形状方面特别讨论了这一特性 2 Plasman 等人。（结构方向、3-D 几何等）。就 RMS 值而言，如果单独反演，每个分量总是得到更好的解释，但结果受到反演参数化（平滑、方差等）的强烈影响。相反，多个分量的同时反演显示出全局十索恢复的显着改善，更依赖于数据而不是密度方差或平滑控制。比较重力和梯度反演，我们强调了 GG 数据的优越性，可以更好地再现结构，尤其是在垂直位置方面。我们成功地在梯度和重力数据的复杂俯冲案例的现实案例中测试了我们的方法。虽然小型地壳结构的成像需要陆地重力数据集，但两个数据集都可以很好地恢复板块的最长波长。然而，GOCE 数据的精确和均匀覆盖抵消了地面重力数据的异质性和通常不存在的覆盖范围。在需要异构数据集的连贯组合的大面积区域，或在高地、植被覆盖和近海区域中尤其如此。