Adaptive noise attenuation of seismic image using singular value decomposition and texture direction detection

Singular value decomposition (SVD) is an efficient tool for separation of signal and noise subspace. When it is used to process seismic image, SVD can enhance the signal-to-noise ratio (SNR) of horizontal events effectively. An adaptive SVD filter is proposed to enhance the non-horizontal events by detecting seismic image texture direction and then adjusting the input matrix of SVD. The features derived from the co-occurrence matrix are used to detect the texture direction. The parameter of the SVD filter is designed by the ratio of the stacking energy along the detected direction and the energy of the whole image adaptively. The coherent noise events are recognized by their direction difference from the signal events and attenuated by high-rank approximation firstly. Then the signal events are enhanced by low-rank approximation.