06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access Variance-covariance matrix estimation with LSQR in a parallel programming environment(2008) Guo, RonggangKnowledge about the gravity field allows an insight into the structure and dynamics of the earth. It provides the geoid as the most important physical reference surface in geodesy and oceanography. Since 2000, the CHAMP (CHAllenging Mini-satellite Payload) mission detects the structure of the global gravity field, followed by the launch of GRACE (Gravity Recovery And Climate Experiment) in 2002. In 2008, finally, the GOCE (Gravity field and steady-state Ocean Circulation Explorer) satellite is supposed to be set in orbit. These missions demonstrate satellite-based gravity field recovery to be at the center of geo-scientific interest. Interpretation and evaluation of satellite observations are difficult, especially the determination of the unknown gravity field parameters from a huge amount of measurements. Because of the immense demand for memory and computing time, the occurring systems of equations pose a real numerical challenge. Therefore, High-Performance Computing (HPC) is commonly adopted to overcome computational problems. Basically, parallel programming with MPI and OpenMP routines allows to speed up the solution process considerably. In this thesis, firstly global gravity field modelling by means of satellite observations is reviewed. Secondly, the LSQR method (Least-Squares using QR factorization) is introduced in detail in order to solve the resulting least-squares problems. Because the LSQR method is an iterative solver, it basically can not provide the variance-covariance information of the parameter estimate. To investigate the approximate computation of the variance-covariance matrix, two methods are introduced. The first one is based on the generalized inverse of the design matrix. The second approach applies Monte-Carlo integration techniques. Because parallel programming is very helpful to implement such iterative methods, it is necessary to introduce some basic principles and concepts about HPC.Item Open Access Surface Deformation Analysis of GPS Dense Networks based on Intrinsic Approach(2007) Moghtasad-Azar, KhosroHere we present a method of differential geometry, an intrinsic approach that allows deformation analysis of the real surface of the Earth on its own rights for a more reliable and suitable estimate of the surface deformation measures. The method takes advantage of the simplicity of the two-dimensional Riemannian manifold spaces versus the three dimensional Euclidean spaces without losing or neglecting information and effect of the third dimension in the results. Here we describe the regularized Earth's surface as a graded two-dimensional Riemann manifold, namely a curved surface, embedded in a three dimensional Euclidean space. Thus, deformation of the surface can be completely specified by the change of the first and second fundamental tensors, namely changing of metric tensor and changing of curvature tensor, of the surface, which changing of curvature tensor is responsible for detection of vertical displacements on the surface. This study describes analytical modelling, derivation, and implementation of the surface deformation measures based on the proposed method, particular attention to the formulation and implementation of the tensors of rotation and tensor of change of curvature in Earth deformation studies. The method is applied to a real data set of dense space geodetic positions and displacement vectors across the Southern California. A comparison of the patterns with the geological and geophysical evidences of the area indicated how well the patterns were able to reveal different geodynamical features of the region.