Trad, Daniel OsvaldoBayati, Farzaneh2022-01-172022-01-172022-01Bayati, F. (2022). 3D data interpolation and denoising by an adaptive weighting rank-reduction method using singular spectrum analysis (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/114293A difficult challenge in seismic processing and imaging is to address insufficient and irregular sampling. Most processing algorithms require well-sampled data, which involves small sampling intervals with a regular distribution. This motivates us to find new techniques that are more efficient in interpolating seismic data. The primary objective of this thesis is to study Singular Spectrum Analysis (SSA) as a tool for the reconstruction and denoising of seismic data. An overview of the methods of seismic interpolation and the potential use of SSA in time series is described. SSA as a rank-reduction method for 2-D and 3-D seismic data interpolation is studied. The rank-reduction step of SSA is improved by proposing an adaptive rank-reduction method. To improve the algorithm in denoising an adaptive weighting rank-reduction algorithm is proposed. SSA is compared with the Minimum Weighted Norm Interpolation (MWNI) algorithm. Results obtained in this work demonstrate that SSA is a promising method for simultaneous denoising and reconstructing seismic data.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.InterpolationDenoisingSVDSSAGeophysicsmaster thesis10.11575/PRISM/39508