AVO multitrace group sparse inversion

Xi, Yijun; Yin, Xingyao; Zong, Zhaoyun

doi:10.1190/segam2018-2995959.1

Cited by 1 publication

(3 citation statements)

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“…In order to extend the applicability of sparse spike deconvolution to elastic inversion, it is advisable to constraint the solutions from traces associated with the same spatial position and different reflection angles, so that the reflectivities from different angle stacks are representative of the same geological interfaces. Xi et al (2018) solves the problem by using a multivariate version of a modified Cauchy distribution. But, unlike the solution presented in this paper, the modified Cauchy distribution in Xi et al (2018) has no parameter to control the sparsity (amount of zero coefficients in the reflectivity series) of the solution and does not develop the full posterior distribution over the reflectivities.…”

Section: R a F Tmentioning

confidence: 99%

“…Xi et al (2018) solves the problem by using a multivariate version of a modified Cauchy distribution. But, unlike the solution presented in this paper, the modified Cauchy distribution in Xi et al (2018) has no parameter to control the sparsity (amount of zero coefficients in the reflectivity series) of the solution and does not develop the full posterior distribution over the reflectivities.…”

Section: R a F Tmentioning

confidence: 99%

“…and the solution will be less sparse; for smaller signal to noise ratio, more events will be ignored by the algorithm, which will be able, then to yield a sparser solution. This possibility to manually parametrize the sparsity of the solution is an advantage of the present method in comparison to Xi et al (2018),…”

Section: R a F Tmentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous Sparse Deconvolution

Talarico¹,

Sobreira²,

Gomes³

et al. 2022

Braz J Geophys

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Increasing seismic resolution has been long pursued by the geophysical community, serving, among other applications, for more detailed interpretation of seismic clinoforms, better seismic inversion and quantitative interpretation. Sparse deconvolution methods play central role in this pursuit. However, although sparse methods have performed well for single seismic stack or acoustic inversion tasks, their application for multi-stack seismic volumes and consequent use in elastic inversion is still a challenging and ongoing research topic. The challenge is to obtain reflectivity volumes with high correlation between them. In this study, we present a new method to perform simultaneous sparse deconvolution (SSD) in a group of seismic volumes associated with different reflection angles. The proposed algorithm enforces co-localization of the spikes on the estimated reflectivity traces and additionally allows user control of the sparsity via hyperparameters. The method is validated in both synthetic and real datasets proving its co-localization capability and resulting in higher correlations between the reflectivity volumes, when compared to independent sparse deconvolution (ISD) of the seismic stacks. The resulting reflectivity volumes are, henceforth, better suited for downstream tasks such as high resolution amplitude versus angle (AVA) analysis, or input for high resolution elastic inversions.

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Section: R a F Tmentioning

confidence: 99%

Section: R a F Tmentioning

confidence: 99%