Traditional denoising methods based on fixed transforms are not suited for exploiting their complicated characteristics and attenuating noise due to their lack of adaptability. Recently, a novel method called morphological component analysis (MCA) was proposed to separate different geometrical components by amalgamating several irrelevance transforms. For studying the local singular and smooth linear components characteristics of seismic data, we propose a novel method that excels particularly in attenuating random and coherent noise while preserving effective signals. The proposed method, which combines MCA, dictionary learning (DL), and deep noise reduction consists of three steps: first, we separate the local singular and smooth linear components from the seismic signal using MCA. Second, we apply a DL method on these two components to suppress noise and obtain the denoised signal and noise. In the final step, we apply the DL method to the noise to obtain a little of the seismic signal. Afterwards, we integrate the two seismic signals to obtain the final denoised seismic signal. Numerical results indicate that the proposed method can effectively suppress the undesired noise, maximally preserve the information of geologic bodies and structures, and improve the signal-to-noise ratio (S/N) of the data. We also demonstrate the superior performance of this approach by comparing with other novel dictionaries such as discrete cosine transforms (DCTs), undecimated discrete wavelet transforms (UDWTs), or curvelet transforms. This algorithm provides new ideas for data processing to advance quality and S/N of seismic data.

传统的基于固定变换的降噪方法由于缺乏适应性，因此不适合利用其复杂的特性和衰减噪声。最近，提出了一种称为形态成分分析（MCA）的新方法，该方法通过合并几个不相关的变换来分离不同的几何成分。为了研究地震数据的局部奇异和平滑线性分量特征，我们提出了一种新颖的方法，该方法特别擅长于在保留有效信号的同时衰减随机和相干噪声。所提出的方法将MCA，字典学习（DL）和深度降噪相结合，包括三个步骤：首先，我们使用MCA从地震信号中分离出局部奇异和平滑线性分量。第二，我们对这两个分量应用DL方法来抑制噪声并获得去噪的信号和噪声。在最后一步，我们将DL方法应用于噪声以获得少量的地震信号。然后，我们对两个地震信号进行积分以获得最终去噪的地震信号。数值结果表明，该方法可以有效抑制不希望有的噪声，最大程度地保留地质体和结构信息，提高数据信噪比（S / N）。我们还通过与其他新颖的字典（例如离散余弦变换（DCT），未抽取的离散小波变换（UDWTs）或Curvelet变换）进行比较，证明了该方法的优越性能。该算法为数据处理提高地震数据的质量和信噪比提供了新思路。