Linearized amplitude variation with offset (AVO) inversion with supercritical angles

Downton, Jon; Ursenbach, Charles P.

doi:10.1190/1.2227617

Cited by 69 publications

(23 citation statements)

References 16 publications

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“…The results also indicate that using the average angle, instead of the incidence angle, reduces the modeling error substantially, as suggested by Downton and Ursenbach (2006). But, as will be discussed later, the average angle is typically not known when performing linearized AVO inversion because the elastic parameters that are needed to compute the transmission angle are not known prior to inversion.…”

Section: Forward-modeling Errormentioning

confidence: 67%

“…Note that equation 1 is a better approximation if the average angle at the interface is used, as opposed to the incidence angle (Downton and Ursenbach, 2006). Buland and Omre (2003) adapt the following forward approximation proposed by Stolt and Weglein (1985), which expands the Aki and Richards approximation, in which reflection coefficients are now also time dependent:…”

Section: The Aki and Richards Forward Modelmentioning

confidence: 99%

“…The average standard deviation of this sample at different incidence angles is calculated for the Buland and Omre forward (equation 8) and the Aki and Richards forward (equation 1 plus 7), and it is shown in Figure 3. For the Aki and Richards forward, the average angle ϕ avg between the incidence angle and the transmitted angle is used instead of the incidence angle, as described by Downton and Ursenbach (2006). For the Buland and Omre forward, results are shown using the incidence angle and the average angle.…”

Section: Forward-modeling Errormentioning

confidence: 99%

“…If the variations in the elastic properties are assumed to be very smooth, implying small contrasts, the approximation may be valid for incidence angles θ > 30°. Furthermore, by using the average angle between the incidence and transmitted angle, the modeling error can be decreased in general (Downton and Ursenbach, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Estimation and accounting for the modeling error in probabilistic linearized amplitude variation with offset inversion

Madsen

Hansen

2018

GEOPHYSICS

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A linearized form of Zoeppritz equations combined with the convolution model is widely used in inversion of amplitude variation with offset (AVO) seismic data. This is shown to introduce a "modeling error," compared with using the full Zoeppritz equations, whose magnitude depends on the degree of subsurface heterogeneity. Then, we evaluate a methodology for quantifying this modeling error through a probability distribution. First, a sample of the unknown probability density describing the modeling error is generated. Then, we determine how this sample can be described by a correlated Gaussian probability distribution. Finally, we develop how such modeling errors affect the linearized AVO inversion results. If not accounted for (which is most often the case), the modeling errors can introduce significant artifacts in the inversion results, if the signal-to-noise ratio is less than 2, as is the case for most AVO data obtained today. However, if accounted for, such artifacts can be avoided. The methodology can easily be adapted and applied to most linear AVO inversion methods, by allowing the use of the inferred modeling error as a correlated Gaussian noise model.

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Section: Forward-modeling Errormentioning

confidence: 67%

Section: The Aki and Richards Forward Modelmentioning

confidence: 99%

Section: Forward-modeling Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation and accounting for the modeling error in probabilistic linearized amplitude variation with offset inversion

Madsen

Hansen

2018

GEOPHYSICS

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“…The PRCs of these postcritical reflections are complex (see, for instance, Downton and Ursenbach [2006] for a recent discussion) and therefore introduce a phase shift to the reflected wave. In contrast, PRCs for incidence angles smaller than the critical angle have zero phase shift.…”

Section: Introductionmentioning

confidence: 99%

An efficient coupled acoustic-elastic local solver applied to phase inversion

Willemsen

Malcolm

2017

GEOPHYSICS

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In characterizing reservoirs, we are often interested in retrieving detailed elastic parameters for only a very limited part of the subsurface. One way of doing so involves studying the seismic reflection response within the region of interest. To that end, we introduce a local solver that uses an acoustic solver to propagate the wavefield to a subdomain on which we use a local elastic solver. This avoids the use of an expensive full-domain elastic solver while still incorporating elastic physics in the region where it is most important. We then study whether this modeled phase is sufficiently accurate for recovering important subsurface reservoir properties in an inversion procedure.

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AVA Inversion of PP Reflection in a VTI Medium using Proximal Splitting Algorithm

Zidan

Cheng

2022

Preprint

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Amplitude variation with offset (AVO) inversion, particularly for more than two model parameters, is a highly ill-posed problem and, hence, regularization is indispensable. Here, we propose a regularized inverse problem to mitigate the ill-posedness of the amplitude inversion. The regularization is added to measure the difference in information between the a priori probability density function and the predicted probability density of the inverted parameters. Information theory provides a collection of contrast functions which quantify the divergence from one probability distribution to another, such as the relative entropy. The a priori density is approximated by a Gaussian mixture model, obtained from well logs and rock physics model. The mixture model is a density estimator, providing the statistical properties of the model parameters of interest. The likelihood of the data and the divergence are combined in an augmented Lagrangian scheme, the alternating direction method of multipliers (ADMM), to obtain a unique solution that best generate the recorded seismic data and satisfy the geological constraints conveyed by the a priori probability density function. The proposed inversion scheme is then applied to the anisotropy AVO inversion, for estimating the elastic and seismic anisotropy parameters of shale formations.

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Linearized amplitude variation with offset (AVO) inversion with supercritical angles

Cited by 69 publications

References 16 publications

Estimation and accounting for the modeling error in probabilistic linearized amplitude variation with offset inversion

Estimation and accounting for the modeling error in probabilistic linearized amplitude variation with offset inversion

An efficient coupled acoustic-elastic local solver applied to phase inversion

AVA Inversion of PP Reflection in a VTI Medium using Proximal Splitting Algorithm

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