5D seismic data completion and denoising using a novel class of tensor decompositions

By Gregory Ely, Shuchin Aeron, Ning Hao, Misha Elena Kilmer. 2015

68 citations recorded by the source corpus.

ABSTRACTWe have developed a novel strategy for simultaneous interpolation and denoising of prestack seismic data. Most seismic surveys fail to cover all possible source-receiver combinations, leading to missing data especially in the midpoint-offset domain. This undersampling can complicate certain data processing steps such as amplitude-variation-with-offset analysis and migration. Data interpolation can mitigate the impact of missing traces. We considered the prestack data as a 5D multidimensional array or otherwise referred to as a 5D tensor. Using synthetic data sets, we first found that prestack data can be well approximated by a low-rank tensor under a recently proposed framework for tensor singular value decomposition (tSVD). Under this low-rank assumption, we proposed a complexity-penalized algorithm for the recovery of missing traces and data denoising. In this algorithm, the complexity regularization was controlled by tuning a single regularization parameter using a statistical test. We tested t...

Subfields: Computational Mathematics, Geophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/2692006f6ba039cbeb23d88616bc96d0ffeddcbb

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In Computational Mathematics

1. Yimin Wei
2. Liqun Qi
3. Yun Miao
4. Chaoqian Li
5. Yaotang Li
6. Yisheng Song
7. J. Shao
8. Guanglu Zhou

In Geophysics

1. Yimin Wei
2. Yun Miao
3. Liqun Qi

In Nuclear and High Energy Physics

1. Yimin Wei
2. Yun Miao
3. Liqun Qi

Citations in the corpus

• Generalized tensor function via the tensor singular value decomposition based on the T-product (2019, background): “…vision [4, 16, 49, 51], signal processing [9, 33, 36, 45], low rank tensor recovery and robust tensor PCA [31, 33], data completion and denoising [11, 24, 25, 34, 36, 38, 39, 42, 47, 50, 52, 53, 54, 55], since the tensor T-product change problems to block circulant matrices which could be block…”
• T-Jordan Canonical Form and T-Drazin Inverse Based on the T-Product (2019, methodology): “such as image processing [33, 34, 49, 55, 59, 70], computer vision [20, 64], signal processing [12, 41, 44, 58], low rank tensor recovery and robust tensor PCA [39, 41], data completion and denoising [14, 28, 29, 42, 44, 47, 48, 53, 60, 65, 66, 67, 68, 69]. An approach of linearization is provided b”