Instantaneous complex attributes that rely on conventional Hilbert transformation are normally susceptible to random noise and abrupt frequency variations in seismic signals. Moreover, conventional filtering methods diminish the spectral bandwidth needed to suppress noise when estimating seismic attributes. This has a significant impact on the resolution in thin-bed layers, which demand wide-band data to image properly. Therefore, in this paper, we address the noise and resolution problems in seismic attributes by applying a sparsity-aware weighting function that makes use of Geman-McClure and Laplace functions to a sparsity-based adaptive S-transform. The proposed filter not only suppresses the random noise but also increases the resolution of the Hilbert transform in the calculation of seismic attributes. Finally, to corroborate the superiority of the proposed method over some state-of-the-art approaches in synthetic and real data sets, the results are compared with the sparsity-based adaptive S-transform and the robust windowed Hilbert transform.

依赖于传统希尔伯特变换的瞬时复杂属性通常容易受到地震信号中的随机噪声和突然频率变化的影响。此外，在估计地震属性时，传统的滤波方法减少了抑制噪声所需的频谱带宽。这对需要宽带数据才能正确成像的薄层的分辨率有重大影响。因此，在本文中，我们通过将利用 Geman-McClure 和 Laplace 函数的稀疏感知加权函数应用于基于稀疏性的自适应 S 变换来解决地震属性中的噪声和分辨率问题。所提出的滤波器不仅抑制了随机噪声，而且提高了希尔伯特变换在地震属性计算中的分辨率。最后，