Seismic noise attenuation using an online subspace tracking algorithm
Seismic noise attenuation using an online subspace tracking algorithm
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DOI：
10.1093/gji/ggx422
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发表时间：
2018-02
影响因子：
2.8
通讯作者：
Yatong Zhou;Shuhua Li;Dong Zhang;Yangkang Chen
中科院分区：
文献类型：
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作者：
Yatong Zhou;Shuhua Li;Dong Zhang;Yangkang Chen
我们提出了一种新的基于低秩的噪声衰减方法，该方法使用一种有效的算法来跟踪来自高度损坏的地震观测的子空间。子空间跟踪算法只需要基本的线性代数操作。该算法通过分析子空间的格拉斯曼流形上的增量梯度下降得到。当多维地震数据被映射到低秩空间时，子空间跟踪算法可以直接应用到输入的低秩矩阵中来估计有用信号。由于子空间跟踪算法是一种在线算法，因此比传统的基于截断奇异值分解（TSVD）的子空间跟踪算法对随机噪声具有更强的鲁棒性。与现有的去噪算法相比，所提出的去噪方法可以获得更好的性能。更具体地说，该方法比基于tsvd的奇异谱分析方法产生的残余噪声更小，并且节省了一半的计算成本。几个具有不同复杂程度的合成和现场数据示例证明了该算法在拒绝不同类型的噪声（包括随机噪声、尖噪声、混合噪声和相干噪声）方面的有效性和鲁棒性。
We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.