Browsing by Author "Moghtasad-Azar, Khosro"

Now showing 1 - 2 of 2

Open Access
Surface deformation analysis of dense GPS networks based on intrinsic geometry : deterministic and stochastic aspects
(2007) Moghtasad-Azar, Khosro; Grafarend, Erik W. (Prof. Dr.-Ing. habil. Dr. tech. h. c. mult. Dr.-Ing. E. h. mult)
The first step in this study is to review the properties of surface which are inherent to the surface and can be described without referring to the embedding space. In other words, it is a method of differential geometry. The methods of moving frames which allows deformation of surface could be described by its own rights as a more reliable estimate of surface deformation measures. The method takes advantage of the simplicity of the 2D surface versus the 3D Euclidean spaces without losing or neglecting information about the third dimension in the results. Based on this method, deformation can be described by using tangent vectors and the unit normal basis vector (attached to the bodies before and after deformation). However, basis vectors of the deformed configuration will need to complete information of intrinsic properties of the deformed surface. Through this method, regularized Earth's surface is considered as a graded 2D surface, namely a curved surface, embedded in a Euclidean space . Thus, deformation of the surface can be completely specified by the change of the metric and curvature tensors, namely strain tensor and tensor of change of curvature (TCC). The curvature tensor, however, is responsible for the detection of vertical displacements on the surface. The next step of this study is to concentrate the local basis vectors of the deformed surface which can be formulated in terms of the local basis vectors of undeformed surface and curvilinear components of displacement vector. This will provide a representation of the intrinsic geometry of the deformed surface with deriving information about the displacement field. The new formulation of base vectors (for the deformed body) produces meaningful numerical results for the TCC and its associated invariants (mean and Gaussian curvatures). They can propose a shape-classification of the deformed surface based upon signs of mean and Gaussian curvatures which are new tools for studying the Earth's deformation. To enhance our understanding of the capabilities of the proposed method in defining new basis vectors (for deformed body), we present two examples, one with a simulated data set and the other with a real data set. However, through a real data set we demonstrated a comparison between the proposed method with the plane strain model (2D classical method). Dealing with eigenspace components e.g., principal components and principal directions of 2D symmetric random tensors of second order is of central importance in this study. In the third step of this research, we introduce an eigenspace analysis or a principal component analysis of strain tensor and TCC. However, due to the intricate relations between elements of tensors on one side and eigenspace components on other side, we will convert these relations to simple equations, by simultaneous diagonalization. This will provide simple synthesis equations of eigenspace components (e.g., applicable in stochastic aspects). The last part of this research is devoted to stochastic aspects of deformation analysis. In the presence of errors in measuring a random displacement field (under the normal distribution assumption of displacement field), stochastic behaviors of eigenspace components of strain tensor and TCC are discussed. It is performed by a propagation of errors from the displacement vector into elements of deformation tensors (strain and TCC). However, due to the intricacy of the relations between tensor components (strain or TCC) and their eigenspace components, we proceeded via simultaneous diagonalization. This part is followed by a linearization of the nonlinear multivariate Gauss - Markov model, which links the elements of transformed tensors (obtained by simultaneous diagonalization) with the eigenspace components. Then, we set up an observation model based on a linearized model under a sampling of eigenspace synthesis. Furthermore, we establish linearized observation equations for n samples of independent random vectors from transformed tensor elements (under the normal distribution assumption), each with an individual covariance matrix. This will provide us with the second-order statistics of the eigenspace components. Then we estimate the covariance components between transformed tensor elements by Helmert estimator, based on prior variance information. To enhance conceptual understanding of stochastic aspects of deformation analysis, the method is applied to a real data set of dense GPS network of Cascadia Subduction Zone(CSZ). Comparing the results showed that, in general, after estimating the covariance matrix of observations (transformed tensors via simultaneous diagonalization), variances of eigenspace components become smaller. However, in some areas this did not occur, which can be related to an incorrect description of initial accuracies, either too optimistic or too pessimistic.
Open Access
Surface Deformation Analysis of GPS Dense Networks based on Intrinsic Approach
(2007) Moghtasad-Azar, Khosro
Here 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.