Singular value decomposition (SVD) is a coherency-based technique that provides both signal enhancement and noise suppression. It has been implemented in a variety of seismic applications — mostly on a global scale. In this paper, we use SVD to improve the signal-to-noise ratio of unstacked and stacked seismic sections, but apply it locally to cope with coherent events that vary with both time and offset. The local SVD technique is compared with [Formula: see text] deconvolution and median filtering on a set of synthetic and real-data sections. Local SVD is better than [Formula: see text] deconvolution and median filtering in removing background noise, but it performs less well in enhancing weak events or events with conflicting dips. Combining [Formula: see text] deconvolution or median filtering with local SVD overcomes the main weaknesses associated with each individual method and leads to the best results.
Quantification of brain tumor metabolites in magnetic resonance spectroscopy using singular value decomposition
