Enhanced demultiple by 3D SRME using dual-sensor measurements

Borselen, Roald van; Hegge, Rob; Martin, T.; Barnes, Simon; Aaron, Peter

doi:10.1190/1.3626500

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…In SRME, surface-related multiples are predicted through a convolution of the upgoing pressure wavefield with the downgoing vertical component of the particle velocity wavefield (Van Borselen et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

Surface-related multiple elimination for variable-depth seismic streamer acquisition

Borselen

2013

SEG Technical Program Expanded Abstracts 2013

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Section: Methodsmentioning

confidence: 99%

Surface-related multiple elimination for variable-depth seismic streamer acquisition

Borselen

2013

SEG Technical Program Expanded Abstracts 2013

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“…This wavefield-separation method is relatively straightforward, based on fundamental geophysical principles, and it permits great flexibility in how the resulting data can be used in subsequent processing steps. For example, the up-and downgoing wavefields can be combined to predict surface-related multiples, thereby eliminating some of the assumptions commonly made when conventional data are used in this procedure and yielding a superior result (Frijlink et al, 2011;van Borselen et al, 2011). A further example Figure 1.…”

Section: Introductionmentioning

confidence: 96%

Wavefield-separation methods for dual-sensor towed-streamer data

et al. 2013

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A dual-sensor towed streamer records the pressure and vertical component of particle motion associated with the incident wavefield that may be used to separate the wavefield into its upand downgoing parts. This procedure requires information about the water properties (wave-propagation velocity and density) and is robust in the presence of errors in the estimation of these quantities of the magnitude likely to be encountered. In practice, the particle motion data recorded by current towed marine streamers encounter very strong mechanical noise such that, for the lowest frequencies, the wavefield separation must be approximated by deconvolving the ghost function from the pressure data. This procedure requires information about the streamer depth and is robust to small depth errors over the frequency range for which it is required for dual-sensor streamer processing, but it is much more sensitive if applied over the bandwidth necessary to deghost pressure data acquired at a conventional streamer depth. The signal-to-noise ratio can be further enhanced by recombining the up-and downgoing pressure fields at the sea surface, which has the effect of applying a ghostlike filter to noise that is recorded by only one of the two sensors. In practical marine acquisition scenarios, spatial sampling is often insufficient to yield an accurate result, especially in the crossline direction. If each streamer is processed independently assuming that the wavefield propagation is purely inline, significant errors can be introduced. For arrivals with high emergent angles, errors may also be introduced even if the wavefield propagation actually is purely inline due to incorrect treatment of spatially aliased energy. However, these effects are almost entirely confined to very shallow events. They can be mitigated by using independently derived information about the crossline propagation angle and, for data comprising predominantly forward scattered energy, appropriate application of linear moveout.

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“…; Borselen et al . ) and receiver‐side deghosting (Posthumus ; Robertsson and Kragh ; Tenghamn et al . ; Caprioli et al .…”

Section: Introductionmentioning

confidence: 99%

“…The idea of combining measurements of pressure and the vertical pressure gradient to remove sea surface reflections was used in the context of surface-related multiple elimination (Fokkema and den Berg 1993;Söllner et al 2007;Borselen et al 2011) and receiver-side deghosting (Posthumus 1993;Robertsson and Kragh 2002;Tenghamn et al 2007;Caprioli et al 2012). Several approaches have been developed in the past such as pressure gradient approximation (Robertsson and Kragh 2002;Amundsen et al 2005), over/under twin streamer (Sønneland and Berg 1985;Posthumus 1993) or dual-sensor streamer (Tenghamn, Vaage and Borresen 2007;Orji, Söllner and Gelius 2010).…”

Section: Introductionmentioning

confidence: 99%

Prediction of signatures of airgun arrays using dual near‐field hydrophones

Kryvohuz

Campman

2019

Geophysical Prospecting

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Successful estimation of airgun‐array signatures from near‐field measurements depends on the accuracy of poorly controlled model parameters such as the effective sea surface reflection coefficient and source depth. We propose a method for prediction of robust source signatures, which are insensitive to fluctuations of the latter parameters. The method uses vertical pairs of near‐field hydrophones to measure near‐field pressure and its vertical gradient, combination of which eliminates sea surface reflections from the near‐field data. This excludes the uncertainty related to the fluctuating sea state and source depth from the process of inversion of the near‐field data for source signature. The method explicitly separates the recorded near‐field pressure into its up and down going components, which allows one to measure the effective frequency‐ and angle‐dependent sea surface reflection coefficient right at the source, as well as to estimate the actual source depth. Tests on synthetic and field data demonstrate robust performance of the method.

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Enhanced demultiple by 3D SRME using dual-sensor measurements

Cited by 7 publications

References 6 publications

Surface-related multiple elimination for variable-depth seismic streamer acquisition

Surface-related multiple elimination for variable-depth seismic streamer acquisition

Wavefield-separation methods for dual-sensor towed-streamer data

Prediction of signatures of airgun arrays using dual near‐field hydrophones

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