Three term AVO waveform inversion

Downton, Jon; Lines, Laurence R.

doi:10.1190/1.1851205

Cited by 32 publications

(8 citation statements)

References 12 publications

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“…The Bayesian framework offers a more flexible approach to including prior rock-physics information. See Downton and Lines (2004). For a general discussion of 2T truncation when prior rock-physics information is considered, see Ball et al (2014).…”

Section: Discussionmentioning

confidence: 99%

Rock-physics relationships between inverted elastic reflectivities

2016

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Quantitative relationships between true elastic properties are not always applicable to inverted elastic properties. We use the important example of two-term AVO reflectivity inversion to demonstrate this. AVO literature includes many examples of two-term AVO equations, and we provide a general relationship to convert between inverted reflectivities obtained when using these various equations. Conversion between inverted reflectivities should be based on this equation, rather than standard elastic-property relationships. This is of immediate importance when applying the method of chi-angle projections to estimate elastic reflectivities.

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Section: Discussionmentioning

confidence: 99%

Rock-physics relationships between inverted elastic reflectivities

2016

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“…Iteratively using the result as a model-space regularization can lead to high-resolution, artifact-free seismic images. This idea has been used in many fields of signal and image processing ͑Sacchi and Ulrych, 1995; Charbonnier et al, 1997;Youzwishen, 2001;Sacchi et al, 2003;Trad et al, 2003;Downtown and Lines, 2004͒. In this paper, we utilize a model-dependent sparse regularization and a model-independent smoothing regularization to estimate amplitude-variationwith-ray-parameter ͑AVP͒ common-image gathers ͑CIGs͒. Modeldependent sparse regularization is introduced via a nonquadratic norm ͑Cauchy norm͒.…”

Section: Introductionmentioning

confidence: 99%

High-resolution wave-equation amplitude-variation-with-ray-parameter (AVP) imaging with sparseness constraints

Wang

Sacchi

2007

GEOPHYSICS

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We propose a new scheme for high-resolution amplitudevariation-with-ray-parameter ͑AVP͒ imaging that uses nonquadratic regularization. We pose migration as an inverse problem and propose a cost function that uses a priori information about common-image gathers ͑CIGs͒. In particular, we introduce two regularization constraints: smoothness along the offset-ray-parameter axis and sparseness in depth. The two-step regularization yields high-resolution CIGs with robust estimates of AVP. We use an iterative reweighted leastsquares conjugate gradient algorithm to minimize the cost function of the problem. We test the algorithm with synthetic data ͑a wedge model and the Marmousi data set͒ and a real data set ͑Erskine area, Alberta͒. Tests show our method helps to enhance the vertical resolution of CIGs and improves amplitude accuracy along the ray-parameter direction.

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“…In deterministic approaches, also known as Gradient-based methods, an iterative calculus-based searching algorithm is used for moving towards an optimal point as feasible solution that minimizes the convex objective function with the search directions defined by its gradient at the current point. Examples of Gradient-based methods for pre-stack seismic inversion include the work of Roberts et al (2002), Mahmoudian and Margrave (2004), Downton and Lines (2004), Veire and Landrø (2006), Zhi, Chen and Li (2013) and Lu et al (2015).…”

Section: Introductionmentioning

confidence: 99%

“…Examples of Gradient‐based methods for pre‐stack seismic inversion include the work of Roberts et al . (), Mahmoudian and Margrave (), Downton and Lines (), Veire and Landrø (), Zhi, Chen and Li () and Lu et al . ().…”

Section: Introductionmentioning

confidence: 99%

Sparse Bayesian linearized amplitude‐versus‐angle inversion

Mollajan

Memarian

Quintal

2019

Geophysical Prospecting

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The technique of seismic amplitude‐versus‐angle inversion has been widely used to estimate lithology and fluid properties in seismic exploration. The amplitude‐versus‐angle inversion problem is intrinsically ill‐posed and generally stabilized by the use of L2‐norm regularization methods but with drawback of smoothing important boundaries between adjacent layers. In this study, we propose a sparse Bayesian linearized solution for amplitude‐versus‐angle inversion problem to preserve the sharp geological interfaces. In this regard, a priori constraint term with two regularization functions is presented: the sparse constraint regularization and the low‐frequency model information. In addition, to obtain high‐resolution reflectivity estimation, the model parameters decorrelation technique combined with dipole decomposition method is employed. We validate the applicability of the presented method by both synthetic and real seismic data from the Gulf of Mexico. The accuracy improvement of the presented method is also confirmed by comparing the results with the commonly used Bayesian linearized amplitude‐versus‐angle inversion.

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Three term AVO waveform inversion

Cited by 32 publications

References 12 publications

Rock-physics relationships between inverted elastic reflectivities

Rock-physics relationships between inverted elastic reflectivities

High-resolution wave-equation amplitude-variation-with-ray-parameter (AVP) imaging with sparseness constraints

Sparse Bayesian linearized amplitude‐versus‐angle inversion

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