  Seismic attributes calculated by conventional methods are susceptible to
noise. Conventional filtering reduces the noise in the cost of losing the
spectral bandwidth. The challenge of having a high-resolution and robust signal
processing tool motivated us to propose a sparse time-frequency decomposition
while is stabilized for random noise. The procedure initiates by using
Sparsity-based adaptive S-transform to regularize abrupt variations in
frequency content of the nonstationary signals. Then, considering the fact that
a higher amplitude of a frequency component results in a higher signal to noise
ratio, an adaptive filter is applied to the time-frequency spectrum which is
sparcified previously. The proposed zero adaptive filter enhances the high
amplitude frequency components while suppresses the lower ones. The performance
of the proposed method is compared to the sparse S-transform and the robust
window Hilbert transform in estimation of instantaneous attributes by applying
on synthetic and real data sets. Seismic attributes estimated by the proposed
method is superior to the conventional ones in terms of its robustness and high
resolution image. The proposed approach has a vast application in
interpretation and identification of geological structures.
