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This paper presents a new adaptive algorithm for topographic correction of gravity measurements based on a triangulated polyhedral representation of topographic surfaces. The adaptive grid resampling of topography is driven by the actual gravity effect on the station, which depends on the topographic mass and distance. This is a major improvement for accuracy and computational speed compared to some old algorithms that account only for distance. Today, global availability of gravity data allows the regional investigation (∼100 km anomaly wavelengths) of the lithosphere. The variety of different data sets (from terrestrial and aero surveys to satellite data), the size of possible investigation areas and the availability of high-resolution, digital terrain models ask for new approaches of topographic correction. Specifically, an algorithm for topographic correction should be useable on large-scale investigation areas, that is, correctly model the long wavelength signal of topography, work in spherical domain, be able to handle high-resolution topography data available and accurately represent topography. The new algorithm does consider sphericity of the Earth, calculates gravity gradients, deals with large data sets and uses an adaptive approach for resampling topography to save computation time significantly. The resampling uses an efficient quadtree representation of the topography grid. High resolution of the topography grid is only considered if it has a significant influence on the gravity effect at the station. This leads to an accurate representation of distant terrain and a massive speed up of computation time. The new algorithm is tested in an area of South America and Central Asia. The results are almost identical to calculations with constant grid resolutions, but need only 2 per cent of calculation time. This method enables to correct all gravity data sources (terrestrial, aero, satellite data) with the necessary topographic resolution with huge saving in computation time without presumptions by the user. [ABSTRACT FROM AUTHOR]

Gravity gradients of the GOCE (Gravity field and steady state Ocean Circulation Explorer) mission are available and provide free available data with global homogeneous coverage. The spatial resolution is about 80 km which makes it possible to construct regional models of large or remote areas. We study the benefit of using this data for compiling a 3D regional density model of the lithosphere with emphasis on the crustal structure for the Arabian Peninsula. We keep the data at satellite level to avoid noise amplification. The initial density model was set up with only a few seismic constraints and modified by forward modeling. Forward models benefits from the use of the full gravity gradient tensor and the invariants. The misfits between the measured and calculated field are more easily recognized in the gravity gradient tensor than in the gravity field alone. Different shape and location of anomalies are visible in the different components. Hence, modeling is better controlled as geometries which fit one component do not necessarily fit all other components. Regional models are important for a general understanding of any area and as a background for local interpretations. Here, we explain how to construct density models from satellite gravity gradients and show the advantage of such an approach by comparison to earlier studies based on either seismic or gravity data. [ABSTRACT FROM AUTHOR]

Offshore frontier sedimentary basins are characterised by a lack of constraining geological and geophysical data. This lack of data is generally the result of deep water (>500 m), difficult geology (volcanics and salt), remoteness and harsh met–ocean conditions. These characteristics present significant challenges to marine surveying, which means that frontier basins tend to be underexplored. With continuing interest in exploration for energy resources in frontier regions, many frontier basins around the world have been the focus of increasingly-sophisticated geophysical studies that integrate a range of methodologies, including those based on potential-field (gravity and magnetic) data. Underexplored frontier basins around Australia's continental margin have received increased attention during the last decade, largely as a result of government-funded programs of precompetitive data acquisition and analysis. The components of this work that have relied heavily on potential-field data include: first-pass depth-to-basement estimation using spectral techniques applied to magnetic data; enhancement of gravity and magnetic images to aid the identification of basin depocentres and to facilitate onshore–offshore geological interpretation of basement structure; multi-scale edge-detection applied to gravity and magnetic data to aid the interpretation of basement structure; 3D forward and stochastic inverse modelling of gravity data to guide seismic interpretation of sediment thickness and basement structure; and using supercomputers for high-resolution, regional-scale 3D inverse modelling of magnetic and gravity data to constrain the physical properties of the crust. Despite the additional insight offered by this work, efforts to understand frontier basins are not without challenges, one of the most fundamental of which is to ensure that non-specialists are not misinterpreting data (e.g. wrongly interpreting artefacts arising from specific processing). The other main challenge in Australian frontier basins arises from a lack of constraints on crustal structure. This leads to significant ambiguity when using gravity data to infer sediment thickness or to understand the nature of basement. This ambiguity could be vastly reduced through the acquisition of seismic refraction data that focuses on imaging crustal structure. Further opportunities exist in using alternative methods for automated depth-to-basement estimation, incorporating process-oriented rather than static potential-field modelling, and in applying 3D forward and inverse gravity and magnetic modelling to other Australian frontier basins. [ABSTRACT FROM AUTHOR]

The Central Andean subduction system is one of the most active geological structures on Earth. Although there have been a few previous studies, the structure and dynamics of the system are still not well understood. In the present study, we determine a combined regional gravity model of the Andean convergent subduction region for constraining lithospheric models. After a thorough validation and cleaning of the terrestrial gravity and height databases, the method of Least Squares Collocation was applied to consistently combine terrestrial and satellite gravity data, putting much emphasis on the stochastic modelling of the individual data components. As a result, we computed the first high-resolution regional gravity model of the study region that includes GOCE satellite gravity information. The inclusion of GOCE is an essential distinction from the independent global gravity model EGM2008. Validation against EGM2008 reveals that our regional solution is very consistent in regions where terrestrial gravity data are available, but shows systematic differences in areas with terrestrial data gaps. Artefacts in the EGM2008 of up to 150 mGal could be identified. The new combined regional model benefits from the very homogeneous error characteristics and accuracy of GOCE gravity data in the long-to-medium wavelengths down to 80-100 km. Reliable density modelling became possible also in the region of Central Andes, which lacks terrestrial gravity data. Finally, density models were adapted to fit the new regional gravity field solution. The results clearly demonstrate the capabilities of GOCE to better constrain lithospheric models. [ABSTRACT FROM AUTHOR]