An adaptive local‐slope SVD filtering approach to enhance events on seismic sections

Abstract

We present an adaptive singular value decomposition (SVD) filtering method for enhancement of the spacial coherence of the reflections and for the attenuation of the uncorrelated noise. The SVD filtering is performed on a small number of traces and a small number of samples collected around each data component. The method uses the local slope of the reflections to re-sample the data set surrounding each data component and the SVD filtering is locally applied to compute the filtered data. The filtered data component is obtained by stacking the components of the first K eigenimages along the slope. The method is applied in two steps: (i) before the SVD computation, the normal move-out (NMO) correction is applied to the seismograms, with the purpose of flattening the reflections. We use the local slopes equal to 90◦ to preserve the horizontal coherence of the primary reflections and (ii) for the second step the SVD filtering uses as input the filtered data of step-1 and the method is applied in the common-offset domain. Now the local slopes of the reflections are used in order to drive the SVD filtering. We illustrate the method using land seismic data of the Tacutu basin, located in the Northeast of Brazil. The results show that the proposed method is effective and is able to reveal reflections masked by the ground-roll.