Adaptive singular value decomposition filtering to enhance reflectors and geological structures in 3D seismic data

Abstract

We applied an adaptive seismic data filtering method, based on the singular value decomposition (SVD) to improve the identification of reflectors and geological structures in 3D stacked seismic volumes. This was done using the SVD method to perform the decomposition of the seismic data matrix in eigenimages. SVD filtering can be seen as a multichannel filtering method where each filtered seismic trace retains the coherence of neighboring seismic traces. For the filtering of a 3D volume, we use a matrix operator formed with five adjacent traces of the original volume. At each position of the filter operator, the filtered trace was obtained by taking the central trace of the first eigenimage. Thus, we reinforce the lateral coherence corresponding to the primary reflections. This filtering technique relies on the property of the SVD method in which eigenimagem associated with the highest single values retains the part of the greater spatial correlation associated to the seismic reflectors. The proposed SVD filtering method was applied to a 3D volume of synthetic seismic data. The results were compared with those obtained using the conventional FX deconvolution method. The results demonstrate the efficacy of the SVD approach both in improving spatial coherence of reflections and in noise attenuation.