In seismic data processing, low-frequency random noise with non-Gaussian and non-stationary characteristics heavily contaminates the reflected signals in Tarim area, which brings great difficulties in interpretation of seismic records in northwest China. To achieve more satisfied resolution, more greater fidelity, together with much higher increased signal-to-noise ratio (SNR), this paper proposes a SNR enhancement method based on the combination of variational mode decomposition (VMD) and Semi-soft Go Decomposition (Semi-Soft GoDec), named VMD-SSGoDec, which can realize the simplification of low-rank extraction in time–frequency representation (TFR) domain. Firstly, each trace of the rough seismic record is decomposed into several modes to reconstruct a component matrix by VMD. Due to the semi-low rank or approximate low-rank character of the desert low-frequency noise component matrix in TFR domain, secondly, we apply the Semi-soft GoDec, a low-rank matrix estimation to extract the low-frequency random noise components from the VMD results obtained in the first step. Repeating the above single-trace procedure to each trace rather than decomposing the entire record but use low-rank estimation once can lead to a more reduced dimension of the component matrix, and thus simplify the low-rank selection in Semi-soft GoDec. Finally, with the extracted random noise results in the second step, we can obtain the denoised record by making a difference with the original input. The proposed algorithm is tested by both synthetic record and field desert seismic data. Experimental results show outstanding advantages in low-frequency noise attenuation comparing with those of f-x deconvolution and SSWT-OptShrink. Both low-frequency random noise and surface waves are almost thoroughly attenuated by the proposed method, while the reflected signals are left nearly intact, revealing a significant enhancement in SNR.

在地震资料处理中，具有非高斯、非平稳特征的低频随机噪声严重污染了塔里木地区的反射信号，给西北地区的地震记录解释带来了很大的困难。为了获得更满意的分辨率、更高的保真度以及更高的信噪比 (SNR)，本文提出了一种基于变分模式分解 (VMD) 和半软 Go 分解相结合的 SNR 增强方法。 Semi-Soft GoDec)，命名为 VMD-SSGoDec，可以实现时频表示 (TFR) 域中低秩提取的简化。首先，粗地震记录的每个道被分解成若干模式以通过VMD重建分量矩阵。由于沙漠低频噪声分量矩阵在TFR域中具有半低秩或近似低秩的特性，其次，我们应用Semi-soft GoDec，一种低秩矩阵估计来提取低频随机噪声来自第一步中获得的 VMD 结果的组件。对每一道重复上述单迹过程而不是分解整个记录而是使用一次低秩估计可以导致分量矩阵的维数更小，从而简化半软 GoDec 中的低秩选择。最后，利用第二步提取的随机噪声结果，我们可以通过与原始输入的差异来获得去噪记录。所提出的算法通过合成记录和野外沙漠地震数据进行了测试。fx解卷积和 SSWT-OptShrink。所提出的方法几乎完全衰减了低频随机噪声和表面波，而反射信号几乎完好无损，这表明 SNR 显着增强。