Automatic determination of background velocities by multiple migration fitting

Chavent, Guy; Jacewitz, Chester A.

doi:10.1190/1.1889969

Cited by 2 publications

(4 citation statements)

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“…The calculation described here is essentially identical to that given by Chavent and Jacewitz (1990) for calculation of the gradient of a function of a migrated field with respect to velocity.…”

Section: Computation Of the Bilinear Operator Bmentioning

confidence: 97%

“…Computation of B[q, 41 is a straightforward application of the adjoint state method, as introduced in this context by Tarantola (1984) , Lailly (1983), and others. The calculation described here is essentially identical to that given by Chavent and Jacewitz (1990) for calculation of the gradient of a function of a migrated field with respect to velocity.…”

Section: Appendix a Computation Of The Bilinear Operator Bmentioning

confidence: 98%

“…This calculation is an extension of the adjoint state technique, and is derived in Appendix A. It is very similar to the computation of gradients for functions of migrated images proposed by Chavent and Jacewitz (1990). T h e steps are:…”

Section: Computation Of the Velocity Gradient Grad F[v]mentioning

confidence: 99%

See 2 more Smart Citations

Inversion of reflection seismograms by differential semblance analysis: algorithm structure and synthetic examples¹

Symes

Kern

1994

Geophysical Prospecting

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Seismograms predicted from acoustic or elastic earth models depend very nonlinearly on the long wavelength components of velocity. This sensitive dependence demands the use of special variational principles in waveform-based inversion algorithms. The differential semblance variational principle is well-suited to velocity inversion by gradient methods, since its objective function is smooth and convex over a large range of velocity models. An extension of the adjoint state technique yields an accurate estimate of the differential semblance gradient. Non-linear conjugate gradient iteration is quite successful in locating the global differential semblance minimum, which is near the ordinary least-squares global minimum when coherent data noise is small. Several examples, based on the 2D primaries-only acoustic model, illustrate features of the method and its performance.

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Section: Computation Of the Bilinear Operator Bmentioning

confidence: 97%

Section: Appendix a Computation Of The Bilinear Operator Bmentioning

confidence: 98%

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Inversion of reflection seismograms by differential semblance analysis: algorithm structure and synthetic examples¹

Symes

Kern

1994

Geophysical Prospecting

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“…Alternatively, one may employ non-linear inversion theory to include the variable background case (Tarantola 1984;Pratt and Worthington 1990), but this method, unlike the D T method, requires multiple iterations even on idea1 input data. Another class of variable background tomography algorithms is based on ray computations, but with the input data being traveltimes and not the wavefield itself (Chavent and Jacewitz 1990;Van Trier and Symes 1990;Schuster and Quintus-Bosz 1993).…”

Section: Introductionmentioning

confidence: 99%

Generalized acoustic diffraction tomogra phy¹

Gelius¹

1995

Geophysical Prospecting

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A generalized diffraction tomography algorithm is developed, which in principle can handle irregularly spaced data, curved acquisition lines and non‐uniform background models. By direct comparison with medical diffraction tomography, it is shown that the generalized method involves the same two processing steps: data filtering and back‐propagation. The filter handles the irregular sampling of the model space and the uneven energy coverage, while the back‐propagation operator removes the wave propagation effects. Paraxial ray‐tracing techniques are employed to compute both these quantities. In medical diffraction tomography, the resolution vector (i.e. the Fourier vector of the model space) is defined by the incident and scattered plane‐wave directions. It is shown here that a similar relationship exists for a non‐uniform background, where the resolution vector at a particular image point is defined by the incident and scattered ray directions. Consequently, the impulse response of the generalized algorithm becomes space variant. Finally, a general processing procedure for transmission mode seismic data, based on this generalized algorithm, is proposed. The potential of the method is demonstrated using synthetic cross‐hole data.

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Automatic determination of background velocities by multiple migration fitting

Cited by 2 publications

References 0 publications

Inversion of reflection seismograms by differential semblance analysis: algorithm structure and synthetic examples¹

Inversion of reflection seismograms by differential semblance analysis: algorithm structure and synthetic examples¹

Generalized acoustic diffraction tomogra phy¹

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Automatic determination of background velocities by multiple migration fitting

Cited by 2 publications

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Inversion of reflection seismograms by differential semblance analysis: algorithm structure and synthetic examples1

Inversion of reflection seismograms by differential semblance analysis: algorithm structure and synthetic examples1

Generalized acoustic diffraction tomogra phy1

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Product

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Inversion of reflection seismograms by differential semblance analysis: algorithm structure and synthetic examples¹

Inversion of reflection seismograms by differential semblance analysis: algorithm structure and synthetic examples¹

Generalized acoustic diffraction tomogra phy¹