Random noise attenuation using a structure-oriented weighted singular value decomposition

Abstract

Singular value decomposition (SVD) is a useful method for random noise suppression in seismic data processing. A structure-oriented SVD (SOSVD) approach which incorporates structure prediction to the SVD filter is effcient in attenuating noise except distorting seismic events at faults and crossing points. A modified SOSVD approach using a weighted stack, called structure-oriented weighted SVD (SOWSVD), is proposed. In this approach, the SVD filter is used to attenuate noise for prediction traces of a primitive trace which are produced via the plane-wave prediction. A weighting function related to local similarity and distance between each prediction trace and the primitive trace is applied to the denoised prediction traces stacking. Both synthetic and field data examples suggest the SOWSVD performs better than the SOSVD in both suppressing random noise and preserving the information of the discontinuities for seismic data with crossing events and faults.