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.

地震属性分析已成为解释目标的有用工具。因此，它们的高分辨率图像尤其令人关注。通过常规方法对这些属性的计算容易受到噪声的影响，并且旨在减少噪声的常规滤波会导致频谱带宽的损失。具有高分辨率和鲁棒性的信号处理工具的挑战促使我们提出了一种稀疏的时频分解方法，该方法对于随机噪声是稳定的。该过程通过使用基于稀疏性的自适应S变换来启动，以规范化非平稳信号的频率内容中的突变。然后将自适应滤波器应用于先前稀疏的时间频谱。提出的零自适应滤波器增强了高振幅频率分量，同时抑制了较低的频率分量。通过研究合成和真实数据集，将所提方法的性能与稀疏S变换和鲁棒窗Hilbert变换在瞬时属性估计中的性能进行了比较。在鲁棒性和高分辨率成像方面，通过提出的方法估计的地震属性优于常规属性。所提出的方法在地质结构的解释和分类中有详细的应用。在鲁棒性和高分辨率成像方面，通过提出的方法估计的地震属性优于常规属性。所提出的方法在地质结构的解释和分类中有详细的应用。在鲁棒性和高分辨率成像方面，通过提出的方法估计的地震属性优于常规属性。所提出的方法在地质结构的解释和分类中有详细的应用。