Imaging the sea surface using a dual-sensor towed streamer

Orji, Okwudili C.; Söllner, Walter; Gelius, Leiv‐J.

doi:10.1190/1.3496439

Cited by 18 publications

(10 citation statements)

References 34 publications

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“…Similarly, if more information about the sea surface shape is available, the flat sea surface assumption could also be relaxed in principle. Such information might be derived from pressure gradient approximations (Robertsson and Kragh, 2002;Amundsen et al, 2005) or by imaging the sea surface from high-frequency data obtained using a dual-sensor streamer (Orji et al, 2010(Orji et al, , 2012.…”

Section: Noise Considerationsmentioning

confidence: 99%

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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Section: Noise Considerationsmentioning

confidence: 99%

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

et al. 2013

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“…Sea surface images can be obtained from data acquired by dual sensor streamers. This is achieved by separating the wavefield into the upgoing and downgoing components then extrapolating them upwards to the sea surface where an adequate imaging condition is applied (Orji et al, 2010 and2012). The method was validated by computing wavefields scattered by realistic rough sea conditions using the Kirchhoff-Helmholtz integral.…”

Section: Theorymentioning

confidence: 99%

“…First using the field data acquired by PGS using dual-sensor streamer, we performed wavefield separation. Then we extrapolated the separated wavefields upwards to the sea surface where an imaging condition was applied in order to recover the sea surface variations (Orji et al, 2010 and2012). Fig 8a shows the imaged sea surface which appears smoother in comparison to the PM sea surface.…”

Section: Marine Seismic Casementioning

confidence: 99%

Sea Surface Reflection Coefficient Estimation

Orji

Söllner

Gelius³

2013

SEG Technical Program Expanded Abstracts 2013

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Sea surface reflection coefficient estimates are obtained from imaged sea surfaces by applying an imaging technique that is based on decomposed wavefields acquired by dual-sensor towed streamers. The accuracy of this technique in the case of imaging has been demonstrated employing controlled data scattered by realistic timevarying rough sea surfaces (e.g., Pierson-Moskowiz sea surface). The scattered data was computed based on the Kirchhoff-Helmholtz integral. Here, the feasibility of recovering sea surface reflection coefficient estimates from deterministic and realistic sea surfaces is demonstrated. First, using existing studies, the sea surface reflectivity is benchmarked. Subsequently, sea surface imaging was employed to demonstrate the feasibility of recovering the sea surface reflectivity from marine seismic data.

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“…Sea waves are measured by a variety of methods with their associated wave energy spectra in the frequency domain now typically computed via remote sensing from satellites. In theory, if the heights and frequencies of all the contributing waves were known, we would be able to predict all the heights and frequencies of the real waves (Orji et al, 2010). In practice, this is rarely possible.…”

Section: Introductionmentioning

confidence: 99%