Migration‐based traveltime waveform inversion of 2-D simple structures: A synthetic example

Clément, François; Chavent, Guy; Gómez, Susana

doi:10.1190/1.1444974

Cited by 116 publications

(76 citation statements)

References 37 publications

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“…Similar to Plessix et al (1995), Clément et al (2001), Xu et al (2012a), and Biondi and Almomin (2014), we split the velocity into a mainly background component and a perturbed component. Let us assume that v all ¼ v þ w, where v is the background component and w is the perturbed component.…”

Section: Optimization Problemmentioning

confidence: 99%

“…Thus, to converge to an acceptable result, we need a good starting velocity model that is able to produce events within a half-cycle of the observed ones (Virieux and Operto, 2009). We pursue such models using migration-based traveltime tomography (Clément et al, 2001) or migration velocity analysis (MVA) methods (Biondi and Symes, 2004;Sava et al, 2005;Symes, 2008). This requirement comes from the separation of the velocity to those components responsible for scattering and those responsible for wave propagation.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Inversion of the Background Velocity and the Perturbation in Full Waveform Inversion

Alkhalifah

2015

Proceedings

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The gradient of standard full-waveform inversion (FWI) attempts to map the residuals in the data to perturbations in the model. Such perturbations may include smooth background updates from the transmission components and high wavenumber updates from the reflection components. However, if we fix the reflection components using imaging, the gradient of what is referred to as reflected-waveform inversion (RWI) admits mainly transmission background-type updates. The drawback of existing RWI methods is that they lack an optimal image capable of producing reflections within the convex region of the optimization. Because the influence of velocity on the data was given mainly by its background (propagator) and perturbed (reflectivity) components, we have optimized both components simultaneously using a modified objective function. Specifically, we used an objective function that combined the data generated from a source using the background velocity, and that by the perturbed velocity through Born modeling, to fit the observed data. When the initial velocity was smooth, the data modeled from the source using the background velocity will mainly be reflection free, and most of the reflections were obtained from the image (perturbed velocity). As the background velocity becomes more accurate and can produce reflections, the role of the image will slowly diminish, and the update will be dominated by the standard FWI gradient to obtain high resolution. Because the objective function was quadratic with respect to the image, the inversion for the image was fast. To update the background velocity smoothly, we have combined different components of the gradient linearly through solving a small optimization problem. Application to the Marmousi model found that this method converged starting with a linearly increasing velocity, and with data free of frequencies below 4 Hz. Application to the 2014 Chevron Gulf of Mexico imaging challenge data set demonstrated the potential of the proposed method.

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Section: Optimization Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous Inversion of the Background Velocity and the Perturbation in Full Waveform Inversion

Alkhalifah

2015

Proceedings

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“…As a result, traveltime discrepancies no longer seem to be the dominant effect to correct. We attempted to use the alternating update methods proposed by Clément et al (2001) and Xu et al (2012) to deal with reflection data, but without much success.…”

Section: Discussionmentioning

confidence: 99%

Velocity estimation via registration-guided least-squares inversion

2014

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Least-squares (LS) acoustic-waveform inversion often suffers from a very narrow basin of attraction near the global minimum. To mitigate this problem, we evaluated an iterative inversion scheme in which the notion of proximity of two traces is not the usual LS distance, but instead it involves registration as in image processing. Observed data were matched to predicted waveforms via piecewise-polynomial warpings, obtained by solving a nonconvex optimization problem in a multiscale fashion from low to high frequencies. This multiscale process required defining lowfrequency augmented signals to seed the frequency sweep at zero frequency. Custom adjoint sources were then defined from the warped waveforms. The new method, referred to as registration-guided least-squares, was successfully applied to a few scenarios of model velocity estimation in the transmission setting. We determined that the new method can converge to the correct model in situations in which conventional LS inversion suffers from cycle skipping and converges to a spurious model.

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“…This is essentially the Migration Based Traveltime Tomography (MBTT) approach, proposed by Clément et al (2001). It was shown by , however, this particular annihilator does not lead to a smooth objective functional.…”

Section: Reflection Tomographymentioning

confidence: 99%

Correlation-based seismic velocity inversion

Leeuwen

2010

GEOPHYSICS

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Migration‐based traveltime waveform inversion of 2-D simple structures: A synthetic example

Cited by 116 publications

References 37 publications

Simultaneous Inversion of the Background Velocity and the Perturbation in Full Waveform Inversion

Simultaneous Inversion of the Background Velocity and the Perturbation in Full Waveform Inversion

Velocity estimation via registration-guided least-squares inversion

Correlation-based seismic velocity inversion

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