Reducing Spatial Aliasing in Wave-Equation Multiple Attenuation

A new nonlinear filter for wave‐equation extrapolation‐based multiple suppression is designed in the f-k domain. The realization of the new filter in the f-k domain is an extension of the conventional f-k dip filter. However, the new demultiple filter is superior to the conventional f-k dip filter in the sense that the multiple reject zones are determined automatically (based on the information of the input original data and the multiple model traces obtained by the wave‐extrapolation method) rather than by prior information on multiple moveout (dip) range. Therefore, it can easily cope with situations such as aliasing and the mixture of energy from multiples and primaries in the same quadrant. The new filter is smooth on the boundary of the reject area. Numerical examples demonstrate that the new filter is equivalent to the conventional f-k dip filter in multiple suppression for simple situations. However, when the multiples and primaries are mixed in the same quadrant and have only slight difference in dip, the new filter offers significant advantages over the conventional technique.

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On two approaches to wave equation based multiple attenuation

58th EAEG Meeting ◽

10.3997/2214-4609.201409163 ◽

1996 ◽

Author(s):

M. K. Sen ◽

P. L. Stoffa ◽

J. T. Fokkema ◽

C. Calderon

Keyword(s):

Wave Equation ◽

Multiple Attenuation

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Wave‐equation trace interpolation

Geophysics ◽

10.1190/1.1442366 ◽

1987 ◽

Vol 52 (7) ◽

pp. 973-984 ◽

Cited By ~ 118

Author(s):

Joshua Ronen

Keyword(s):

Wave Equation ◽

Conjugate Gradient ◽

Seismic Data ◽

Linear Relation ◽

Field Data ◽

Iterative Scheme ◽

Partial Migration ◽

Spatial Aliasing ◽

Zero Offset ◽

Multichannel Seismic Data

Spatial aliasing in multichannel seismic data can be overcome by solving an inversion in which the model is the section that would be recorded in a well sampled zero‐offset experiment, and the data are seismic data after normal moveout (NMO). The formulation of the (linear) relation between the data and the model is based on the wave equation and on Fourier analysis of aliasing. A processing sequence in which one treats missing data as zero data and performs partial migration before stacking is equivalent to application of the transpose of the operator that actually needs to be inverted. The inverse of that operator cannot be uniquely determined, but it can be estimated using spatial spectral balancing in a conjugate‐gradient iterative scheme. The first iteration is conventional processing (including prestack partial migration). As shown in a field data example in which severe spatial aliasing was simulated, a few more iterations are necessary to achieve significantly better results.

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