Seismic noise attenuation using an online subspace tracking algorithm

Yatong Zhou, Shuhua Li, Dong Zhang, Yangkang Chen*

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    44 Citations (Scopus)
    16 Downloads (Pure)

    Abstract

    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 TSVDbased 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.

    Original languageEnglish
    Pages (from-to)1072-1097
    Number of pages26
    JournalGeophysical Journal International
    Volume212
    Issue number2
    DOIs
    Publication statusPublished - 2018

    Bibliographical note

    Retracted

    Keywords

    • Image processing
    • Inverse theory
    • Time-series analysis

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