Quantifying Uncertainty in Final Seismic Depth Image Using Structural Uncertainty Analysis - Case Study Offshore Nigeria

Letki, L.P.; Ben-Hadj-Ali, Hafedh; Desegaulx, P.

doi:10.3997/2214-4609.20131775

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…Map de-migration is the reverse procedure, converting a depth horizon picked from a preSDM image, to the corresponding (perhaps multivalued) time horizon positions. Such methods of assessing structural uncertainty have been described by Cognot et al 1995, Thore andHass, 1996;Thore and Juliard, 1999;Thore et al, 2002, andLetki et al 2013. Similar methods for anisotropic parameter uncertainty estimation are discussed Bachrach (2010).…”

Section: Estimating Image Uncertaintymentioning

confidence: 99%

3. Estimating subsurface parameter fields for seismic migration: Velocity model building

Jones¹

2014

Encyclopedia of Exploration Geophysics

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Section: Estimating Image Uncertaintymentioning

confidence: 99%

3. Estimating subsurface parameter fields for seismic migration: Velocity model building

Jones¹

2014

Encyclopedia of Exploration Geophysics

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“…We aim to classify the realisations based on an estimation of the residual error exhibited in their respective migrated common image gathers. We analyse each solution model by assessing the fractional cumulative error (Letki et al, 2013). Figure 3 displays the error curves for the total population based on the residual recovered within the Tertiary overburden.…”

Section: Uncertainty Analysismentioning

confidence: 99%

A Workflow to Quantify Velocity Model Uncertainty

Bell¹,

Lorenzo²,

Martin³

et al. 2016

78th EAGE Conference and Exhibition 2016

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A typical depth migration velocity model building project delivers final velocity model attributes and their associated seismic products. The only quantitative measures of the reliability of these data are provided through comparison with any available auxiliary data or from analysis of volumetric residual move-out. The non-linearity inherent within the tomography used to derive the earth model gives rise to multiple realizations of a solution model, which similarly honour the constraining data and yield the same convergence criterion. Therefore in isolation such data provides little useful evidence of the reliability of any individual model. In this study we propose to use multiple realizations of the solution space in order to derive estimates of the uncertainty of an individual model. The method performs multiple random perturbations of the target model followed by tomographic inversion. A solution model population is then selected based on analysis of the volumetric residual move-out; they will comprise models exhibiting a similar level of residual move-out as the target model. This subset of realizations is then used to directly derive a model parameter variance attribute. It also provides for subsequent error envelope analysis studies at key target intervals.

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Assessing uncertainties in velocity models and images with a fast nonlinear uncertainty quantification method

Ely

Malcolm²,

Poliannikov

2018

GEOPHYSICS

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Seismic imaging is conventionally performed using noisy data and a presumably inexact velocity model. Uncertainties in the input parameters propagate directly into the final image and therefore into any quantity of interest, or qualitative interpretation, obtained from the image. We considered the problem of uncertainty quantification in velocity building and seismic imaging using Bayesian inference. Using a reduced velocity model, a fast field expansion method for simulating recorded wavefields, and the adaptive Metropolis-Hastings algorithm, we efficiently quantify velocity model uncertainty by generating multiple models consistent with low-frequency full-waveform data. A second application of Bayesian inversion to any seismic reflections present in the recorded data reconstructs the corresponding structures’ position along with its associated uncertainty. Our analysis complements rather than replaces traditional imaging because it allows us to assess the reliability of visible image features and to take that into account in subsequent interpretations.

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Quantifying Uncertainty in Final Seismic Depth Image Using Structural Uncertainty Analysis - Case Study Offshore Nigeria

Cited by 3 publications

References 1 publication

3. Estimating subsurface parameter fields for seismic migration: Velocity model building

3. Estimating subsurface parameter fields for seismic migration: Velocity model building

A Workflow to Quantify Velocity Model Uncertainty

Assessing uncertainties in velocity models and images with a fast nonlinear uncertainty quantification method

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