Gravity Field Refinement by Radial Basis Functions from In-situ Satellite Data

In this thesis, an integrated approach is developed for the regional refinement of global gravity field solutions. The analysis concepts are tailored to the in-situ type character of the observations provided by the new satellite missions CHAMP, GRACE, and GOCE. They are able to evaluate data derived from short arcs of the satellite's orbit and, therefore, offer the opportunity to use regional satellite data for the calculation of regional gravity field solutions. The regional character of the approach will be realized at various stages of the analysis procedure. The first step is the design of specifically tailored space localizing basis functions. In order to adapt the basis functions to the signal content to be expected in the gravity field solution, they will be derived from the covariance function of the gravitational potential. To use the basis functions in gravity field modeling, they have to be located at the nodal points of a spherical grid; therefore investigations will be performed regarding a suitable choice of such a nodal point distribution. Another important aspect in the regional gravity field analysis approach is the downward continuation process. In this context, a regionally adapted regularization will be introduced which assigns different regularization matrices to geographical areas with varying signal content. Regularization parameters individually determined for each region take into account the varying frequency behavior, allowing to extract additional information out of a given data set. To conclude the analysis chain, an approach will be described that combines regional solutions with global coverage to obtain a global solution and to derive the corresponding spherical harmonic coefficients by means of the Gauss-Legendre quadrature method. The capability of the method will be demonstrated by its successful application to real data provided by CHAMP and GRACE and to a simulation scenario based on a combination of GRACE and GOCE observations.Verfeinerungen des Gravitationsfeldes mit radialen Basisfunktionen aus in-situ Satellitendaten In der vorliegenden Arbeit wird ein ganzheitliches Konzept für die regionale Verfeinerung globaler Gravitationsfeldmodelle entwickelt. Die dazu verwendeten Analyseverfahren sind dem in-situ Charakter der Beobachtungen der neuen Satellitenmissionen CHAMP, GRACE und GOCE angepasst. Sie beruhen auf kurzen Bahnbögen und ermöglichen somit die Berechnung regionaler Gravitationsfeldmodelle aus regional begrenzten Satellitendaten. Der regionale Charakter des Ansatzes wird dabei auf verschiedenen Ebenen des Analyseprozesses realisiert. Der erste Schritt ist die Entwicklung angepasster orts-lokalisierender Basisfunktionen. Diese sollen das Frequenzverhalten des zu bestimmenden Gravitationsfeldes widerspiegeln; sie werden daher aus der Kovarianzfunktion des Gravitationspotentials abgeleitet. Um die Basisfunktionen für die Schwerefeldmodellierung zu verwenden, müssen sie an den Knotenpunkten eines sphärischen Gitters angeordnet werden. Daher werden Untersuchungen durchgeführt, welche Punktverteilung für diese Aufgabe besonders geeignet ist. Einen wichtigen Aspekt bei der regionalen Gravi-tationsfeldanalyse stellt der Fortsetzungsprozess nach unten dar. In diesem Zusammenhang wird ein regional angepasstes Regularisierungsverfahren entwickelt, das verschiedene Regularisierungsmatrizen für regionale Gebiete mit unterschiedlichem Schwerefeldsignal ermöglicht. Individuell angepasste Regularisierungsparameter berücksichtigen den variierenden Signalinhalt, wodurch erreicht wird, dass zusätzliche Informationen aus einem gegebenen Datensatz extrahiert werden können. Schließlich wird ein Ansatz vorgestellt, der regionale Lösungen mit globaler Überdeckung zu einer globalen Lösung zusammenfügt und die zugehörigen sphärischen harmonischen Koeffizienten mit Hilfe der Gauss-Legendre-Quadratur berechnet. Die Leistungsfähigkeit des beschriebenen Ansatzes wird durch eine erfolgreiche Anwendung auf die Echtdatenanalyse aus Daten der Satellitenmissionen CHAMP und GRACE und auf ein Simulationsszenario aus einer Kombination simulierter GRACE- und GOCE-Beobachtungen verdeutlicht

Methods in wave propagation and scattering

Supervised by Jin A. Kong.Also issued as Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 195-213).by Henning Braunisch

Dimensional Transmutation and Dimensional Regularization in Quantum Mechanics : II. Rotational Invariance

A thorough analysis is presented of the class of central fields of force that exhibit: (i) dimensional transmutation and (ii) rotational invariance. Using dimensional regularization, the two-dimensional delta-function potential an d the D-dimensional inverse square potential are studied. In particular, the following features are analyzed: the existence of a critical coupling, the boundary condition at the origin, the relationship between the bound-state and scattering sectors, and the similarities displayed by both potentials. It is found that, for rotationally symmetric scale-invariant potentials, there is a strong-coupling regime, for which quantum-mechanical breaking of symmetry takes place, with the appearance of a unique bound state as well as of a logarithmic energy dependence of the scattering with respect to the energy.Facultad de Ciencias Exacta

A multiscale method for the double layer potential equation on a polyhedron

This paper is concerned with the numerical solution of the double layer potential equation on polyhedra. Specifically, we consider collocation schemes based on multiscale decompositions of piecewise linear finite element spaces defined on polyhedra. An essential difficulty is that the resulting linear systems are not sparse. However, for uniform grids and periodic problems one can show that the use of multiscale bases gives rise to matrices that can be well approximated by sparse matrices in such a way that the solutions to the perturbed equations exhibits still sufficient accuracy. Our objective is to explore to what extent the presence of corners and edges in the domain as well as the lack of uniform discretizations affects the performance of such schemes. Here we propose a concrete algorithm, describe its ingredients, discuss some consequences, future perspectives, and open questions, and present the results of numerical experiments for several test domains including non-convex domains

ANALYSIS OF ITERATIVE METHODS FOR THE SOLUTION OF BOUNDARY INTEGRAL EQUATIONS WITH APPLICATIONS TO THE HELMHOLTZ PROBLEM

This thesis is concerned with the numerical solution of boundary integral equations and the numerical analysis of iterative methods. In the first part, we assume the boundary to be smooth in order to work with compact operators; while in the second part we investigate the problem arising from allowing piecewise smooth boundaries. Although in principle most results of the thesis apply to general problems of reformulating boundary value problems as boundary integral equations and their subsequent numerical solutions, we consider the Helmholtz equation arising from acoustic problems as the main model problem. In Chapter 1, we present the background material of reformulation of Helmhoitz boundary value problems into boundary integral equations by either the indirect potential method or the direct method using integral formulae. The problem of ensuring unique solutions of integral equations for exterior problems is specifically discussed. In Chapter 2, we discuss the useful numerical techniques for solving second kind integral equations. In particular, we highlight the superconvergence properties of iterated projection methods and the important procedure of Nystrom interpolation. In Chapter 3, the multigrid type methods as applied to smooth boundary integral equations are studied. Using the residual correction principle, we are able to propose some robust iterative variants modifying the existing methods to seek efficient solutions. In Chapter 4, we concentrate on the conjugate gradient method and establish its fast convergence as applied to the linear systems arising from general boundary element equations. For boundary integral equalisations on smooth boundaries we have observed, as the underlying mesh sizes decrease, faster convergence of multigrid type methods and fixed step convergence of the conjugate gradient method. In the case of non-smooth integral boundaries, we first derive the singular forms of the solution of boundary integral solutions for Dirichlet problems and then discuss the numerical solution in Chapter 5. Iterative methods such as two grid methods and the conjugate gradient method are successfully implemented in Chapter 6 to solve the non-smooth integral equations. The study of two grid methods in a general setting and also much of the results on the conjugate gradient method are new. Chapters 3, 4 and 5 are partially based on publications [4], [5] and [35] respectively.Department of Mathematics and Statistics, Polytechnic South Wes

Sur le problème inverse de détection d'obstacles par des méthodes d'optimisation

Cette thèse porte sur l'étude du problème inverse de détection d'obstacle/objet par des méthodes d'optimisation. Ce problème consiste à localiser un objet inconnu oméga situé à l'intérieur d'un domaine borné connu Oméga à l'aide de mesures de bord et plus précisément de données de Cauchy sur une partie Gammaobs de thetaOmega. Nous étudions les cas scalaires et vectoriels pour ce problème en considérant les équations de Laplace et de Stokes. Dans tous les cas, nous nous appuyons sur une résultat d'identifiabilité qui assure qu'il existe un unique obstacle/objet qui correspond à la mesure de bord considérée. La stratégie utilisée dans ce travail est de réduire le problème inverse à la minimisation d'une fonctionnelle coût: la fonctionnelle de Kohn-Vogelius. Cette approche est fréquemment utilisée et permet notamment d'utiliser des méthodes d'optimisation pour des implémentations numériques. Cependant, afin de bien définir la fonctionnelle, cette méthode nécessite de connaître une mesure sur tout le bord extérieur thetaOmega. Ce dernier point nous conduit à étudier le problème de complétion de données qui consiste à retrouver les conditions de bord sur une région inaccessible, i.e. sur thetaOmega\Gammaobs, à partir des données de Cauchy sur la région accessible Gammaobs. Ce problème inverse est également étudié en minimisant une fonctionnelle de type Kohn-Vogelius. La caractère mal posé de ce problème nous amène à régulariser la fonctionnelle via une régularisation de Tikhonov. Nous obtenons plusieurs propriétés théoriques comme des propriétés de convergence, en particulier lorsque les données sont bruitées. En tenant compte de ces résultats théoriques, nous reconstruisons numériquement les données de bord en mettant en oeuvre un algorithme de gradient afin de minimiser la fonctionnelle régularisée. Nous étudions ensuite le problème de détection d'obstacle lorsque seule une mesure de bord partielle est disponible. Nous considérons alors les conditions de bord inaccessibles et l'objet inconnu comme les variables de la fonctionnelle et ainsi, en utilisant des méthodes d'optimisation de forme géométrique, en particulier le gradient de forme de la fonctionnelle de Kohn-Vogelius, nous obtenons la reconstruction numérique de l'inclusion inconnue. Enfin, nous considérons, dans le cas vectoriel bi-dimensionnel, un nouveau degré de liberté en étudiant le cas où le nombre d'objets est inconnu. Ainsi, nous utilisons l'optimisation de forme topologique afin de minimiser la fonctionnelle de Kohn-Vogelius. Nous obtenons le développement asymptotique topologique de la solution des équations de Stokes 2D et caractérisons le gradient topologique de cette fonctionnelle. Nous déterminons alors numériquement le nombre d'obstacles ainsi que leur position. De plus, nous proposons un algorithme qui combine les méthodes d'optimisation de forme topologique et géométrique afin de déterminer numériquement le nombre d'obstacles, leur position ainsi que leur forme.This PhD thesis is dedicated to the study of the inverse problem of obstacle/object detection using optimization methods. This problem consists in localizing an unknown object omega inside a known bounded domain omega by means of boundary measurements and more precisely by a given Cauchy pair on a part Gammaobs of thetaOmega. We cover the scalar and vector scenarios for this problem considering both the Laplace and the Stokes equations. For both cases, we rely on identifiability result which ensures that there is a unique obstacle/object which corresponds to the considered boundary measurements. The strategy used in this work is to reduce the inverse problem into the minimization of a cost-type functional: the Kohn-Vogelius functional. This kind of approach is widely used and permits to use optimization tools for numerical implementations. However, in order to well-define the functional, this approach needs to assume the knowledge of a measurement on the whole exterior boundary thetaOmega. This last point leads us to first study the data completion problem which consists in recovering the boundary conditions on an inaccessible region, i.e. on thetaOmega\Gammaobs, from the Cauchy data on the accessible region Gammaobs. This inverse problem is also studied through the minimization of a Kohn-Vogelius type functional. The ill-posedness of this problem enforces us to regularize the functional via a Tikhonov regularization. We obtain several theoretical properties as convergence properties, in particular when data is corrupted by noise. Based on these theoretical results, we reconstruct numerically the boundary data by implementing a gradient algorithm in order to minimize the regularized functional. Then we study the obstacle detection problem when only partial boundary measurements are available. We consider the inaccessible boundary conditions and the unknown object as the variables of the functional and then, using geometrical shape optimization tools, in particular the shape gradient of the Kohn-Vogelius functional, we perform the numerical reconstruction of the unknown inclusion. Finally, we consider, into the two dimensional vector case, a new degree of freedom by studying the case when the number of objects is unknown. Hence, we use the topological shape optimization in order to minimize the Kohn-Vogelius functional. We obtain the topological asymptotic expansion of the solution of the 2D Stokes equations and characterize the topological gradient for this functional. Then we determine numerically the number and location of the obstacles. Additionally, we propose a blending algorithm which combines the topological and geometrical shape optimization methods in order to determine numerically the number, location and shape of the objects

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters