1998
DOI: 10.1515/jiip.1998.6.5.453
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Linearized inversion of data of multi-offset data for vertically inhomogeneous background

Abstract: One of the most widespread problems in seismology is the necessity to adjust some a priori given medium structure. As a rule, such a priori information is vertically inhomogeneous component of wave propagation velocity. Such theoretical aspects of the problem as its uniqueness estimates of conditional stability are studied rather well. There axe also a variety of algorithms for its numerical solution. Therefore, in this paper the main attention is paid to numerical analysis of resolving ability and information… Show more

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Cited by 3 publications
(3 citation statements)
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“…Equations (5) and (6) are now used to compute the i th row of F . Let gỹ ,i denote the i th row of the Jacobian matrix of the measure operator O:…”
Section: Assembling the Jacobian Matrix Row By Rowmentioning
confidence: 99%
See 1 more Smart Citation
“…Equations (5) and (6) are now used to compute the i th row of F . Let gỹ ,i denote the i th row of the Jacobian matrix of the measure operator O:…”
Section: Assembling the Jacobian Matrix Row By Rowmentioning
confidence: 99%
“…With this method "first order" uncertainties are computed using the derivatives of the function F that associates the output parameters to the input parameters. The hierarchization of the inputs of the function F according to their influence on the outputs of F is provided by the Singular Value Decomposition (SVD) of the Jacobian matrix F (x) [21,6,7,1]; see also [35] for the history of the SVD and [16] for a detailed description. It is interesting to note that the SVD, the main tool for the deterministic approach described here, is also commonly used in statistical data analysis, see [8,9,25,36].…”
Section: Introductionmentioning
confidence: 99%
“…Silvestrov and Tcheverda (2011) used SVD to compare different parametrizations in elastic inversion of surface and cross-well data. Cheverda et al (1998) showed that SVD for surface reflection data is strongly related to trend/reflectivity decomposition of the model. They used 1D model assumptions and infinite surface acquisition and found that smooth velocity perturbations correspond to singular values with relatively large indices.…”
Section: Introductionmentioning
confidence: 99%