五维地震数据的低库近似重构

IF 4.9 2区 地球科学 Q1 GEOCHEMISTRY & GEOPHYSICS
Gui Chen, Yang Liu, Mi Zhang, Yuhang Sun, Haoran Zhang
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引用次数: 0

摘要

低秩近似已成为恢复五维(5D)地震数据的一项前景广阔的技术,但追求更高的精度和更强的秩稳健性仍是一项关键任务。我们介绍了一种利用完整图张量网络(CGTN)分解和可学习变换(LT)的低秩近似方法,称为 LRA-LTCGTN 方法,可同时对五维地震数据进行去噪和重建。在 LRA-LTCGTN 框架中,LT 被用来将原始 5D 数据的频率张量投影到一个小尺度的潜在空间。随后,在该潜空间上执行 CGTN 分解。我们采用近似交替最小化算法来优化每个变量。5D 合成数据和实地数据实例都表明,与阻尼秩还原法(DRR)、并行矩阵因式分解法(PMF)和 LRA-CGTN 方法相比,LRA-LTCGTN 方法具有显著的优势和更高的效率。此外,一项敏感性分析强调,与 LRA-CGTN 方法相比,LRA-LTCGTN 方法在秩方面具有显著更强的鲁棒性,而无需对秩进行任何优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Low-Rank Approximation Reconstruction of Five-Dimensional Seismic Data

Low-Rank Approximation Reconstruction of Five-Dimensional Seismic Data

Low-Rank Approximation Reconstruction of Five-Dimensional Seismic Data

Low-rank approximation has emerged as a promising technique for recovering five-dimensional (5D) seismic data, yet the quest for higher accuracy and stronger rank robustness remains a critical pursuit. We introduce a low-rank approximation method by leveraging the complete graph tensor network (CGTN) decomposition and the learnable transform (LT), referred to as the LRA-LTCGTN method, to simultaneously denoise and reconstruct 5D seismic data. In the LRA-LTCGTN framework, the LT is employed to project the frequency tensor of the original 5D data onto a small-scale latent space. Subsequently, the CGTN decomposition is executed on this latent space. We adopt the proximal alternating minimization algorithm to optimize each variable. Both 5D synthetic data and field data examples indicate that the LRA-LTCGTN method exhibits notable advantages and superior efficiency compared to the damped rank-reduction (DRR), parallel matrix factorization (PMF), and LRA-CGTN methods. Moreover, a sensitivity analysis underscores the remarkably stronger robustness of the LRA-LTCGTN method in terms of rank without any optimization procedure with respect to rank, compared to the LRA-CGTN method.

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来源期刊
Surveys in Geophysics
Surveys in Geophysics 地学-地球化学与地球物理
CiteScore
10.00
自引率
10.90%
发文量
64
审稿时长
4.5 months
期刊介绍: Surveys in Geophysics publishes refereed review articles on the physical, chemical and biological processes occurring within the Earth, on its surface, in its atmosphere and in the near-Earth space environment, including relations with other bodies in the solar system. Observations, their interpretation, theory and modelling are covered in papers dealing with any of the Earth and space sciences.
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