Seismic attributes via robust and high-resolution seismic complex trace analysis

Seismic attribute analysis has been a useful tool for interpretation objectives; therefore, high-resolution images of them are of particular concern. The calculation of these attributes by conventional methods is susceptible to noise, and the conventional filtering supposed to lessen the noise causes the loss of the spectral bandwidth. The challenge of having a high-resolution and robust signal processing tool motivated us to propose a sparse time–frequency decomposition which is stabilised for random noise. The procedure initiates by using sparsity-based, adaptive S-transform to regularise abrupt variations in the frequency content of the non-stationary signals. An adaptive filter is then applied to the previously sparsified time–frequency spectrum. The proposed zero adaptive filter enhances the high-amplitude frequency components while suppressing the lower ones. The performance of the proposed method is compared to the sparse S-transform and the robust window Hilbert transform in the estimation of instantaneous attributes through studying synthetic and real data sets. Seismic attributes estimated by the proposed method are superior to the conventional ones, in terms of robustness and high-resolution imaging. The proposed approach has a detailed application in the interpretation and classification of geological structures.