Toward Accurate Velocity Models by 3D Tomographic Velocity Analysis

Guo, Jenhwa; Zhou, Hongju; Young, J.A.; Gray, Samuel H.

doi:10.3997/2214-4609-pdb.5.b024

Cited by 5 publications

(2 citation statements)

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“…where F is the Frechet derivative matrix, m is the vector of attenuation perturbations that involve Q and a is the vector containing the logarithmic amplitude ratios. With appropriate preconditioning in the data and model space (Guo et al, 2002), the vector m, and hence Q, can be solved by standard methods such as least-squares conjugate gradient method.…”

Section: Methods and Resultsmentioning

confidence: 99%

3-D tomographic Q inversion for compensating attenuation anomalies

Xin¹,

Hung²

2010

ASEG Extended Abstracts

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Section: Methods and Resultsmentioning

confidence: 99%

3-D tomographic Q inversion for compensating attenuation anomalies

Xin¹,

Hung²

2010

ASEG Extended Abstracts

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“…In tomographic MVA (e.g. Stork 1992; Kosloff et al 1996; Woodward et al 1998; Guo et al 2002), residual moveouts in common‐image gathers (CIGs) obtained by prestack depth migration are picked and converted into traveltime errors that are assigned to the corresponding specular rays. A model update is then calculated from these traveltime errors by tomographic inversion.…”

Section: Introductionmentioning

confidence: 99%

3D tomographic velocity model estimation with kinematic wavefield attributes

Duveneck

2004

Geophysical Prospecting

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A B S T R A C TA tomographic inversion method is presented that uses kinematic information in the form of zero-offset traveltimes and kinematic wavefield attributes (first and second spatial traveltime derivatives) to determine smooth, laterally inhomogeneous 3D subsurface velocity models for depth imaging. The kinematic wavefield attributes can be extracted from the seismic prestack data by means of the common reflection surface (CRS) stack. The input for the tomography is then taken from the resulting attribute volumes at a number of pick locations in the CRS stacked zero-offset volume. As a smooth model description based on B-splines is used and reflection points are treated independently of each other, only locally coherent events in the stacked volume are required and very few picks are needed. Thus, picking is considerably simplified.During the iterative inversion process, the required forward-modelled quantities are obtained by dynamic ray tracing along normal rays pertaining to the input data points. Fréchet derivatives for the tomographic matrix are calculated with ray perturbation theory. The inversion algorithm is demonstrated on a 3D synthetic data example, where the kinematic wavefield attributes have directly been obtained by forward modelling. I N T R O D U C T I O NThe construction of velocity models is an essential step in the process of seismic depth imaging, particularly in the case of laterally inhomogeneous media. A commonly used tool for velocity model building in laterally inhomogeneous media is reflection tomography (e.g. Farra and Madariaga 1988;Stork and Clayton 1991). In reflection tomography, an optimum model is determined iteratively by calculating global model updates such that the misfit between traveltimes picked along reflection events in the seismic data and the corresponding values calculated in the model is minimized. The main drawback of conventional reflection tomography is the fact that for its application, picking of a large number of reflection traveltimes, usually along continuous horizons and across several

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A Quadratic precision generalized nonlinear global optimization migration velocity inversion method

Ting

He³

et al. 2009

Appl. Geophys.

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An important research topic for prospecting seismology is to provide a fast accurate velocity model from pre-stack depth migration. Aiming at such a problem, we propose a quadratic precision generalized nonlinear global optimization migration velocity inversion. First we discard the assumption that there is a linear relationship between residual depth and residual velocity and propose a velocity model correction equation with quadratic precision which enables the velocity model from each iteration to approach the real model as quickly as possible. Second, we use a generalized nonlinear inversion to get the global optimal velocity perturbation model to all traces. This method can expedite the convergence speed and also can decrease the probability of falling into a local minimum during inversion. The synthetic data and Marmousi data examples show that our method has a higher precision and needs only a few iterations and consequently enhances the practicability and accuracy of migration velocity analysis (MVA) in complex areas.

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Toward Accurate Velocity Models by 3D Tomographic Velocity Analysis

Cited by 5 publications

References 0 publications

3-D tomographic Q inversion for compensating attenuation anomalies

3-D tomographic Q inversion for compensating attenuation anomalies

3D tomographic velocity model estimation with kinematic wavefield attributes

A Quadratic precision generalized nonlinear global optimization migration velocity inversion method

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