Data-Driven Meets Model-Driven - 3D-CFP Operator Updating

Rijzen, M.J. van de; Verschuur, D.J.; Gisolf, A.

doi:10.3997/2214-4609.201405884

Cited by 1 publication

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“…This can be done either in a model-driven way, once the subsurface model is known, or in a data-driven way, using the sparse input data. In the latter case, a trace infill procedure will be incorporated in the estimation of full 3D operators from a sparse data set (Gisolf, van de Rijzen and Verschuur 2002;van de Rijzen et al 2003van de Rijzen et al , 2004.…”

Section: Introductionmentioning

confidence: 99%

3D sparse‐data Kirchhoff redatuming

Tegtmeier¹,

Gisolf²,

Verschuur³

2004

Geophysical Prospecting

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A B S T R A C TWave-equation redatuming can be a very efficient method of overcoming the overburden imprint on the target area. Owing to the growing amount of 3D data, it is increasingly important to develop a feasible method for the redatuming of 3D prestack data.Common 3D acquisition designs produce relatively sparse data sets, which cannot be redatumed successfully by applying conventional wave-equation redatuming. We propose a redatuming approach that can be used to perform wave-equation redatuming of sparse 3D data. In this new approach, additional information about the medium velocity below the new datum is included, i.e. redatumed root-mean-square (RMS) velocities, which can be extracted from the input data set by conventional velocity analysis, are used. Inclusion of this additional information has the following implications: (i) it becomes possible to simplify the 4D redatuming integral into a 2D integral such that the number of traces needed to calculate one output time sample and the computational effort are both reduced; (ii) the information about the subsurface enables an infill of traces which are needed for the integral calculation but which are missing in the sparse input data set.Two tests applying this new approach to fully sampled 2D data show satisfactory results, implying that this method can certainly be used for the redatuming of sparse 3D data sets.

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Section: Introductionmentioning

confidence: 99%