2019
DOI: 10.1109/lgrs.2018.2881102
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Abstract: Seismic reflectivity inversion is widely applied to improve the seismic resolution to obtain detailed underground understandings. Based on the convolution model, seismic inversion removes the wavelet effect by solving an optimization problem. Taking advantage of the sparsity property, the ℓ 1 norm is commonly adopted in the regularization terms to overcome the noise/interference vulnerability observed in the ℓ p-losses minimization. However, no one has provided a deterministic conclusion that ℓ 1 norm regulari… Show more

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Cited by 23 publications
(9 citation statements)
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References 22 publications
(29 reference statements)
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“…In this paper, the selection of the regularization parameter is mainly based on the integrity and clarity of geological bodies in the spectral amplitude slice of horizon as a quality-control standard. How to pick a reasonable p and the regularization parameter adaptively (e.g., Li et al 2019) is also worth studying in the future.…”
Section: Discussionmentioning
confidence: 99%
“…In this paper, the selection of the regularization parameter is mainly based on the integrity and clarity of geological bodies in the spectral amplitude slice of horizon as a quality-control standard. How to pick a reasonable p and the regularization parameter adaptively (e.g., Li et al 2019) is also worth studying in the future.…”
Section: Discussionmentioning
confidence: 99%
“…The sparsity of seismic data is difficult to estimate in practice and the adequacy of the 1 norm for the reflectivity inversion problem has not been established [8], [19]. Also, 1 -norm regularization results in a biased estimate of x [20], [21], [22].…”
Section: A Prior Artmentioning
confidence: 99%
“…The advantages of adopting nonconvex regularizers over 1 have been demonstrated in sparse recovery problems [22], [24], [25]. Particularly pertinent to this discussion is the data-driven q -norm regularization (0 < q < 1) for seismic reflectivity inversion proposed by [19], wherein the optimal q was chosen based on the input data.…”
Section: A Prior Artmentioning
confidence: 99%
See 1 more Smart Citation
“…The fast iterative shrinkage-thresholding algorithm (FISTA) [11] has been employed for reflectivity inversion [12] along with an amplitude recovery boost through debiasing steps of least-squares inversion [13] and "adding back the residual" [14]. The 1 -norm, although convex, is not the best sparsity constraint for reflectivity inversion, and the accurate estimation of the sparsity of seismic reflections is challenging [15], [16]. Further, 1 -norm regularization underestimates the high-amplitude components and introduces a bias in the estimate of the sparse code x [17], [18], [19].…”
Section: Introductionmentioning
confidence: 99%