Print Email Facebook Twitter Fourier reconstruction with sparse inversions Title Fourier reconstruction with sparse inversions Author Zwartjes, P.M. Contributor Gisolf, A. (promotor) Faculty Applied Sciences Date 2005-12-06 Abstract In seismic exploration an image of the subsurface is generated from seismic data through various data processing algorithms. When the data is not acquired on an equidistantly spaced grid, artifacts may result in the final image. Fourier reconstruction is an interpolation technique that can reduce these artifacts by generating uniformly sampled data from such non-uniformly sampled data. The method works by estimating via least-squares inversion the Fourier coefficients that describe the non-uniformly sampled data. These coefficients are then used to reconstruct the data on the regular grid. In this thesis this data reconstruction method has been significantly improved through the observation that seismic data typically can be described by relatively few Fourier coefficients, which is incorporated as a model sparseness constraint in the inversion. This so-called sparse inversion improves the data reconstruction in large gaps and, with some modifications, also allows reconstruction of spatially aliased seismic data. Additionally, it enables the reconstruction of marine streamer seismic data that is sparsely sampled in four spatial dimensions, provided the sampling in the azimuthal coordinate is doubled with respect to current acquisition practice. Subject interpolationfourierinversiona priorialiasingmultidimensionalnon-quadraticseismicregularization To reference this document use: http://resolver.tudelft.nl/uuid:9dfd4050-740f-4728-b1b9-a9364945b435 ISBN 90-855-9113-9 Part of collection Institutional Repository Document type doctoral thesis Rights (c) 2005 P.M. Zwartjes Files PDF as_zwartjes_20051206.pdf 11.47 MB Close viewer /islandora/object/uuid:9dfd4050-740f-4728-b1b9-a9364945b435/datastream/OBJ/view