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Curvature components derived from satellite gravity gradients provide new global views of Earth’s structure. The satellite gravity gradients are based on the GOCE satellite mission and we illustrate by curvature images how the Earth is seen differently compared to seismic imaging. Tectonic domains with similar seismic characteristic can exhibit distinct differences in satellite gravity gradients maps, which points to differences in the lithospheric build-up. This is particularly apparent for the cratonic regions of the Earth. The comparisons demonstrate that the combination of seismological, and satellite gravity gradient imaging has significant potential to enhance our knowledge of Earth’s structure. In remote frontiers like the Antarctic continent, where even basic knowledge of lithospheric scale features remains incomplete, the curvature images help unveil the heterogeneity in lithospheric structure, e.g. between the composite East Antarctic Craton and the West Antarctic Rift System.

The orbit and instrumental measurement of the Gravity Field and Steady State Ocean Circulation Explorer (GOCE) satellite mission offer the highest ever resolution capabilities for mapping Earth's gravity field from space. However, past analysis predicted that GOCE would not detect changes in ice sheet mass. Here we demonstrate that GOCE gravity gradiometry observations can be combined with Gravity Recovery and Climate Experiment (GRACE) gravity data to estimate mass changes in the Amundsen Sea Sector. This refined resolution allows land ice changes within the Pine Island Glacier (PIG), Thwaites Glacier, and Getz Ice Shelf drainage systems to be measured at respectively ?67?±?7, ?63?±?12, and ?55?±?9 Gt/yr over the GOCE observing period of November 2009 to June 2012. This is the most accurate pure satellite gravimetry measurement to date of current mass loss from PIG, known as the “weak underbelly” of West Antarctica because of its retrograde bed slope and high potential for raising future sea level.

With satellite radar altimetry, the oceanic geoid can be determined with high precision and resolution. Double differentiation of these data along satellite altimeter ground tracks yields along-track gravity gradients that can be used to compute vertical gravity gradients at ground track crossovers. One way to counteract the noise amplification due to the differentiation is to smooth the data using smoothing splines. Although the effect of satellite altimeter data noise has been investigated to some extent, the associated systematic errors have not been assessed so far. Here we show that some of the systematic errors cannot be neglected. In particular, we found that the negligence of the dynamic ocean topography (DOT) may introduce errors that are greater than the measurement noise induced errors. If the gravity gradients are to be used for GOCE validation, then also in this case the DOT may not be neglected as the signal at GOCE altitude of 260 km may be above the GOCE requirements. In addition, we show ...

The satellite gravity mission GOCE measured the second-order derivatives of the Earth’s gravitational potential with high accuracy. The GOCE data enrich our gravity field knowledge especially at spatial resolutions from 750 km down to 80 km. In this paper we carry out regional gravity field analysis using radial localising basis functions that permit the combination of different data types tailored to their accuracy and spectral signal content. We formulate observation equations for each individual GOCE gravity gradient as they are distinctive reflections of the gravity field and contain directional information. To optimally use the original GOCE measurements, we derive the mathematical expressions in the gradiometer reference frame. The expressions and their implementation are validated for a test area in Scandinavia by comparison with the global gravity field model GOCO03s, which yields small differences of less than ± 1 mE. The relative weighting of the observations is determined by variance component estimation. Moreover manually fixing the weights leads to smaller residuals with respect to GOCO03s, which is probably caused by systematic errors in the gradients. We demonstrate the capabilities of our method through a combination of the gradient data with terrestrial free-air anomalies. At spatial resolutions down to 40 km the terrestrial data get much larger relative weights than the GOCE data, which indicates the proper performance of the combination method.

Since 2002, the satellite mission GRACE observes the Earth’s static gravity field and its natural changes. The estimation of degree 1 and 2 coefficients is difficult, especially the accuracy of the flattening coefficient C2, 0 is weak. In temporal GRACE gravity field models, C2, 0 is therefore often replaced by a value based on Satellite Laser Ranging measurements. In this study, we combine both techniques to get a consistent normal equation matrix, which allows us to study correlations between spherical harmonic coefficients. Unlike common practice, we use 8 SLR satellites and set-up normal equations up to degree 20. The combination is done using different weighting factors to investigate the influence of both techniques on the combined normal equation matrix. Our results show that especially the coefficient C2, 0 benefits from SLR data, but also (near-) sectorial coefficients and coefficients which correspond to resonance frequencies of SLR satellites. Moreover, we find that high correlations in the SLR normal equation between zonal coefficients are reduced by the combination.

To meet increased demands in mapping, surveying, geodesy, and large infrastructure projects, an accurate national geoid model for Saudi Arabia is required to transform ellipsoid heights to orthometric heights. The lack of data and the nature of the topography make the computation of a geoid model in Saudi Arabia a difficult task. Two regional geoids were developed for the Kingdom of Saudi Arabia (KSA): (1) the KSA geoid developed by the General Directorate of Survey (GDS); and (2) a geoid developed by the Ministry of Municipality and Rural Affairs (MOMRA). The KSA model was developed using a remove and restore technique and over 5,000 observations of Global Positioning System (GPS) ellipsoidal heights on leveling benchmarks (GPS/BM). The MOMRA geoid was estimated from 861GPS/Levelling with a kriging approach. A GPS/Levelling test campaign (T campaign) was carried out in 2010 by re-observing about 391 BM stations common to the two geoid models. The two geoid models are first compared against each other and EGM2008 at the 391 BM stations from the T campaign. The absolute differences between models may reach several meters, whereas the standard deviation of the differences ranges between 0.5 and 1.5 m. Analysis revealed that KSA and MOMRA geoids seem not to coincide. Both are biased (means of 0.46 m) and highly scattered (standard deviation is ±0.72 m). Next, geoid models developed using GOCE satellite data are used to validate the T campaign data.

In recent years, global gravity models, both based only on satellite data and from combination with terrestrial data, are increasingly available and particularly useful to construct regional models before more local interpretations on the exploration scale are carried out. Often it is challenging to distinguish clearly between near-surface and regional or even subcrustal signals in the gravity field. Applying simple techniques like wavelength-filtering might lead to an incorrect estimate of the regional and residual field, which may significantly alter estimates of the thickness of sedimentary basins or the size of mineral deposits. An alternative is to use satellite gravity gradients to establish the regional components before studying local geology. Sampietro (2011) presented a global Mohorovicic discontinuity (Moho) depth map, which sparked a discussion about the validity of such results. Especially the question remains about whether crustal thickness estimates based on Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite data have a higher accuracy than models based on global gravity models or terrestrial data. For example, the density contrast between crust and mantle remains a main factor of uncertainty. Here we join this discussion by describing how satellite gravity gradients can be used in addition to terrestrial data sets or global models like EGM2008, and how the combined use of data sets at multiple levels above the Earth’s surface can help confine the uncertainty in both regional and local modeling studies

The expected high resolution and precision of a global gravity field model derived from satellite gradiometric observations is unprecedented compared to nowadays satellite-only models. However, a dedicated gravity field mission will most certainly fly in a non-polar (sun-synchronous) orbit, such that small polar regions will not be covered with observations. The resulting inhomogeneous global data coverage, together with the downward continuation problem, leads to unstable global solutions and regularization is necessary. Regularization gives rise to a bias in the solution, mainly in the polar areas although in other regions as well.