2019
DOI: 10.1111/1365-2478.12899
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Low‐rank seismic denoising with optimal rank selection for hankel matrices

Abstract: A B S T R A C TBased on the fact that the Hankel matrix representing clean seismic data is low rank, low-rank approximation methods have been widely utilized for removing noise from seismic data. A common strategy for real seismic data is to perform the low-rank approximations for small local windows where the events can be approximately viewed as linear. This raises a fundamental question of selecting an optimal rank that best captures the number of events for each local window. Gavish and Donoho proposed a m… Show more

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Cited by 21 publications
(3 citation statements)
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“…We welcome the letter by Trickett (2020) and thank the author for providing the in‐depth comments on our paper (Wang et al ., 2020) and pointing out the very related work (Trickett, 2015), which we were not aware of; we apologize for the omission. Onwards, we will refer to Trickett (2020) as T20, Trickett (2015) as T15, and Wang et al ., (2020) as WZG20, respectively.…”
Section: Figurementioning
confidence: 97%
“…We welcome the letter by Trickett (2020) and thank the author for providing the in‐depth comments on our paper (Wang et al ., 2020) and pointing out the very related work (Trickett, 2015), which we were not aware of; we apologize for the omission. Onwards, we will refer to Trickett (2020) as T20, Trickett (2015) as T15, and Wang et al ., (2020) as WZG20, respectively.…”
Section: Figurementioning
confidence: 97%
“…The main requirement of the LR methods is the low rank of the frequency‐domain Hankel matrix. The rank of the Hankel matrix equals the number of distinct dips (Oropeza and Sacchi, 2011; Chen et al ., 2016; Wang et al ., 2020). However, the real seismic data are complicated, where the linear‐events assumption is not met.…”
Section: Introductionmentioning
confidence: 99%
“…A paper by Wang et al . (2020), which I will refer to as WZG20, discussed how a popular method of random noise attenuation can be made to automatically adapt to changing signal and noise conditions within the data. I delivered a similar paper at the 2015 SEG annual convention entitled `Preserving signal: Automatic rank determination for noise suppression’ (Trickett, 2015), which I will refer to as T15.…”
Section: Introductionmentioning
confidence: 99%