In order to remove random noise in seismic data, this letter proposes a seismic data denoising method based on tensor decomposition and total variation (TDTV). Based on the self-similarity of seismic data, this method first groups similar patches into a stack, then utilizes the low-rank tensor approximation strategy to restore the structural effective information of the seismic section. Considering that the approximate seismic section obtained after CANDECOMP/PARAFAC (CP) decomposition and patch aggregation is unsmooth, this letter introduces the total variation (TV) constraint to perform anisotropic diffusion, and protects edge information while smoothing. Finally, the gradient descent method is used to solve the whole model. The TDTV method proposed in this letter can not only effectively denoise synthetic and field seismic section but also restore structural and edge information. Experimental results show that the proposed method outperforms many state-of-the-art denoising methods.

中文翻译：

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基于全变张量分解的地震数据去噪

为了去除地震数据中的随机噪声，本文提出了一种基于张量分解和总变差（TDTV）的地震数据去噪方法。该方法基于地震数据的自相似性，首先将相似的斑块组合成一个堆栈，然后利用低阶张量近似策略来恢复地震剖面的结构有效信息。考虑到CANDECOMP/PARAFAC(CP)分解和面片聚合后得到的近似地震剖面不平滑，本文引入总变差(TV)约束进行各向异性扩散，在平滑的同时保护边缘信息。最后使用梯度下降法对整个模型进行求解。本文提出的TDTV方法不仅可以有效地对合成和现场地震剖面进行去噪，还可以恢复结构和边缘信息。实验结果表明，所提出的方法优于许多最先进的去噪方法。