The traditional compressed sensing method for improving resolution is realized in the frequency domain. This method is affected by noise, which limits the signal-to-noise ratio and resolution, resulting in poor inversion. To solve this problem, we improved the objective function that extends the frequency domain to the Gaussian frequency domain having denoising and smoothing characteristics. Moreover, the reconstruction of the sparse reflection coefficient is implemented by the mixed L1_L2 norm algorithm, which converts the L0 norm problem into an L1 norm problem. Additionally, a fast threshold iterative algorithm is introduced to speed up convergence and the conjugate gradient algorithm is used to achieve debiasing for eliminating the threshold constraint and amplitude error. The model test indicates that the proposed method is superior to the conventional OMP and BPDN methods. It not only has better denoising and smoothing effects but also improves the recognition accuracy of thin interbeds. The actual data application also shows that the new method can effectively expand the seismic frequency band and improve seismic data resolution, so the method is conducive to the identification of thin interbeds for beach-bar sand reservoirs.

用于提高分辨率的传统压缩感测方法是在频域中实现的。该方法受噪声影响，从而限制了信噪比和分辨率，从而导致反演效果较差。为了解决这个问题，我们改进了将频域扩展到具有降噪和平滑特性的高斯频域的目标函数。此外，稀疏反射系数的重建是通过混合L1_L2范数算法实现的，该算法将L0范数问题转换为L1范数问题。另外，引入了快速阈值迭代算法来加快收敛速度​​，并使用共轭梯度算法来实现去偏置以消除阈值约束和幅度误差。模型测试表明，该方法优于传统的OMP和BPDN方法。它不仅具有更好的去噪和平滑效果，而且还提高了薄夹层的识别精度。实际数据表明，该方法能有效扩展地震频段，提高地震数据分辨率，有利于滩涂砂岩薄层夹层的识别。