SVD (singular value decomposition) is a coherency-based technique that provides both signal retrieval and noise suppression. It has been implemented in a variety of seismic applications - mostly on a global scale only. We use SVD to improve the signal-to-noise ratio of prestack seismic gathers, but apply it locally to cope with signals that vary both with time and offset.SVD is based entirely on second order statistic (I.e., the covariance matrix) which are optimal only if the data is white and Gaussian. Independent component analysis (ICA) can overcome these restrictive assumptions and takes advantage of higher order statistics (beyond 2nd order).Local SVD/ICA techniques are compared with f-x deconvolution for improving the signal to noise ratio of prestack NMO-corrected CMP gathers. The local SVD/ICA methods are better than f-x deconvolution in removing background noise but they perform less well in enhancing the lateral coherency of weak events and/or events with conflicting dips. Combining f-x deconvolution with SVD/ICA signal enhancement overcomes the main weaknesses associated with each individual method and leads to the best results.
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