The Impact Of Field Survey Characteristics On Surface-Related Multiple Attenuation

Abstract

Field survey characteristics can have an important impact on the quality of multiples predicted by surface-related multiple elimination (SRME) algorithms. This paper examines the effects of three particular characteristics: inline spatial sampling, source stability, and cable feathering. Inadequate spatial sampling causes aliasing artifacts. These can be reduced by f-k filtering at the expense of limiting the bandwidth in the predicted multiples. Source signature variations create artifacts in predicted multiples due to spatial discontinuities. Variations from a well-behaved air-gun array produced artifacts having an rms amplitude about 26 dB below the rms amplitude of multiples predicted with no variations. Cable feathering has a large impact on the timing errors in multiples predicted by 2-D SRME when it is applied in areas having cross dip. All of these problems can be reduced by a combination of better survey design, use of advanced data acquisition technologies, and additional data processing steps. Introduction In this paper we assume that surface-related multiple elimination (or SRME) is the algorithm of choice for removing multiples from marine seismic data. SRME is commonly practiced as a two-step process. First, surface multiples are predicted by a multidimensional convolutionlike iterative process in which the prediction operators consist of the recorded seismic traces themselves.1 Second, the predicted multiples are adaptively subtracted from the original traces. Under ideal conditions SRME can produce nearly perfect multiple attenuation in a single inversion-like step. Under realistic conditions, however, such an inversion usually produces poor results because of errors that occur in the multiple prediction. Adaptive subtraction and the two-step process attempt to compensate for those errors. Residual multiples occur when the errors in the predicted multiples are too large or too variable to be handled adequately by adaptive subtraction. Since even relatively weak residual multiples can cause serious interpretation difficulties in seismic data sets, geophysicists strive to minimize them. Although the SRME multiple prediction algorithm requires no information about the subsurface geology, it does place strict requirements on the data acquisition process.2 For example, SRME requires full knowledge of the acquisition wavelet and complete, regular spatial sampling of the surface wavefield. Most seismic surveys fail to meet these requirements, resulting in the aforementioned errors in predicted surface multiples. There are three ways of reducing these errors: more appropriate data acquisition practices and survey design, enhancements to the multiple prediction and adaptive subtraction processing algorithms, and application of pre-processing steps to tailor the data for multiple prediction. To select the best method for reducing a particular type of error requires an understanding of exactly how that error is related to various field survey characteristics. Suppose, for example, that a geophysicist is designing a 3-D seismic survey in an area where the direction of the prevailing currents is perpendicular to the predominant dip direction. Is better error reduction achieved by cross-dip shooting, thereby minimizing the effects of cable feathering, or by dip shooting, thereby minimizing subsurface-related 3-D effects in the recorded wavefield? This question can be answered only after a careful analysis of the errors expected in the predicted multiples.

20. Signal Processing

“…An example of this phenomenon is shown for 2D SRME by Dragoset et al ͑2006;their Figures 4b and 5b͒. The actual shape of events in the MCG is complex in general, but a useful indication of the maximum dip can be obtained by considering the asymptotic behavior of events as the DRP moves to infinity.…”

“…Violations of the 2D assumptions lead to errors in the predicted multiples ͑van Dedem and Dragoset and Jeričević, 1998;Nekut, 1998;Ross et al, 1999;Dragoset et al, 2006͒. The timing component of these errors is predictable, given a model of the geology, and is also measurable in the data, e.g., using crosscorrelations.…”

“…Some of the implications of these practical issues are described in more detail by Dragoset et al ͑2006͒. For example, the tidal state and/or water velocity vary throughout the acquisition, such that the recorded data are inconsistent with a single-earth model.…”

A perspective on 3D surface-related multiple elimination

“…Violations of the 2D assumptions lead to errors in the predicted multiples ͑van Dedem and Dragoset and Jeričević, 1998;Nekut, 1998;Ross et al, 1999;Dragoset et al, 2006͒. The timing component of these errors is predictable, given a model of the geology, and is also measurable in the data, e.g., using crosscorrelations.…”

“…Therefore, the sampling requirement for the MCG is equivalent to that for unaliased shot and receiver gathers. An example of this phenomenon is shown for 2D SRME by Dragoset et al ͑2006;their Figures 4b and 5b͒. That is, a filter sufficient to remove aliasing from input shot records ͑Figure 4b͒ prevents aliasing artifacts from appearing in predicted multiples ͑Figure 5b͒.…”