Advanced SearchBrowse





Global and regional gravity field recovery by combining satellite, air-shipborne and terrestrial gravimetry data


Lu,  B.
1.2 Global Geomonitoring and Gravity Field, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in GFZpublic
Supplementary Material (public)
There is no public supplementary material available

Lu, B. (2019): Global and regional gravity field recovery by combining satellite, air-shipborne and terrestrial gravimetry data, PhD Thesis, Berlin : Technische Universität Berlin, 102 p.

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5000489
One basic research field of geodesy or Earth system science is to develop and apply new methodologies and algorithms for gravity field modeling, in particular based on data from the dedicated satellite gravity missions Challenging Minisatellite Payload (CHAMP), Gravity Recovery and Climate Experiment (GRACE), Gravity field and steady-state Ocean Circulation Explorer (GOCE) and Gravity Recovery and Climate Experiment-Follow-on (GRACE-FO) as well as combined with ground gravity data (e.g., air-shipborne and terrestrial measurements). In this thesis, I investigated how to use GOCE Gravitational Gradients (GGs) to build global gravity field models based on the invariant theory. Compared to traditional methods, where these GGs are affected by attitude errors, Invariants of the Gravitational Gradient Tensor (IGGT) in combination with least squares adjustment avoid the problem of inaccurate rotation matrices. Satellite based global gravity field modeling can be improved by combination with terrestrial data as such from air- and shipborne gravimetry e.g. to fill gaps in the satellite data. This is another way to overcome the GOCE polar gap problem and is the background for my study on data processing strategies of air- and shipborne gravimetry. The first purpose of this investigation was preparation of future gravimetry campaigns especially in polar regions. The second scientific objective of this study was to overcome the polar gap problem by adding existing polar gravimetry data obtained by air and/or shipborne gravimetry. In this context, I did some methodological investigations based on airborne gravity measurements from the 2012 GEOHALO mission over Italy. I also did some investigations based on shipborne gravity measurements which are mainly from the project Finalising Surveys for the Baltic Motorways of the Sea (FAMOS). Both air-shipborne gravimetry studies and experiences of my thesis turned out to be very valuable for future gravimetry campaigns in areas with sparse gravity data or even gravity data gaps, e.g. polar areas. With the successful completion of European Space Agency (ESA)’s PolarGAP campaign, ground gravimetry data (gravity anomalies) are now available since 2018 for both polar regions. Therefore, it is now possible to overcome the polar gap problem by using real gravimetry data instead of some regularization methods. Furthermore, Variance Component Estimation (VCE) was applied to combine the normal equations from the gravity anomalies, from the GOCE GGs (e.g., IGGT_R1), from GRACE (e.g., ITSG-Grace2014s) and Kaula’s rule of thumb (higher degree/order parts) to build a global gravity field model called IGGT_R1C. This is the first time to essentially solve the GOCE polar gap problem by using real polar gravity data. One technical research domain of gravity field determination is to improve the calculation efficiency due to the huge amount of gravimetry observations, especially when computing high maximum degree/order (e.g., 240 or even higher) gravity field models with the least squares method. Considering this time-consuming task, a fast parallel algorithm was developed by combining Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) technologies to calculate and invert normal equations for global and regional gravity field recovery.
GFZpublic 8.0.17-SNAPSHOT 2020-02-17 16:08:55