3-D data Interpolation and denoising by adaptive weighting rank-reduction method using singular spectrum analysis algorithm
A 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 rankreduction 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.