3d deghosting for full-azimuth and ultra-long offset ... · 3d deghosting for full-azimuth and...
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3D deghosting for full-azimuth and ultra-long offset marine data Qiaofeng Wu*, Chang-Chun Lee, Wei Zhao, Ping Wang, Yunfeng Li, CGG
Summary
Removing ghost energy in marine streamer data is
important for both seismic processing and interpretation,
especially for 3D surface-related multiple elimination
(SRME) and improving bandwidth and resolution. Full
azimuth data have abundant azimuths, which require more
robust 3D deghosting techniques. The coarse and irregular
crossline sampling in full azimuth (FAZ) seismic data
creates challenges for 3D deghosting. A fully data-driven
3D deghosting technique using a progressive sparse Tau-P
inversion has proven to be able to overcome the sparse
sampling in the crossline direction and to be effective in
attenuating the receiver ghost in a 3D mode. We
demonstrate the benefits of 3D deghosting using a
staggered FAZ and ultra-long offset data set from Keathley
Canyon, Gulf of Mexico. Using the data-driven 3D
deghosting method, we observed less residual ghost energy
in shot gathers from a side-gun when compared with the 2D
pre-migration bootstrap deghosting method. The 3D
deghosting method subsequently improved the images of
steeply-dipping top of salt (TOS) and assisted with multiple
removal.
Introduction
Marine streamer data record both primary energy and ghost
energy. The ghost’s destructive interference with the
primary energy generates notches in the amplitude
spectrum and limits the usable frequency range. Removing
the ghost energy can fill the ghost notches and provide
broader spectrum bandwidth and an improved signal-to-
noise ratio (S/N), which are all beneficial for both seismic
imaging and interpretation. Several pre-migration (Wang
and Peng, 2012; Wang et al., 2013; Poole, 2013) and post-
migration (Soubaras, 2010) deghosting methods have been
proposed to remove the ghost energy and improve imaging
results.
To address the imaging challenges in the deepwater Gulf of
Mexico (GOM), Mandroux et al. (2013) developed a
staggered acquisition configuration with variable-depth
streamers. The configuration produces FAZ coverage up to
9 km and ultra-long offsets up to 18 km. The FAZ and
ultra-long offsets in this new acquisition design have shown
benefits in bandwidth extension, multiple suppression (Yu
et al., 2013), velocity model building (Mothi et al., 2013;
Wu et al., 2013), and improved subsalt illumination (Wu
and Li, 2014). At the same time, the staggered geometry
with FAZ coverage and ultra-long offsets gives large
variations in take-off angles, which creates challenges for
deghosting.
Wang et al. (2013) proposed a pre-migration deghosting
method using a bootstrap approach in the Tau-P domain, a
pseudo-3D method that determines slowness in the x-
direction through a 2D sparse Tau-P inversion and
determines slowness in the y-direction using a bootstrap
least squares inversion. This method is effective for most
2D and 3D data in NAZ or WAZ acquisition geometry.
However, in the staggered acquisition design, for the data
from the side-guns (i.e., large azimuth and take-off angle of
ghosts), the wavefield is strongly 3D and thus generates
challenges for deghosting using this bootstrap method.
Recently, Wang et al. (personal communication, 2014)
proposed a 3D deghosting method for pressure-only data.
This method is fully data-driven and uses a progressive
sparse Tau-P inversion to perform 3D joint deghosting and
crossline interpolation in one step. We applied this 3D
deghosting to FAZ data and observed the benefits of
removing the ghost where the bootstrap method suffers.
Better ghost removal subsequently improves seismic
images and provides better multiple suppression from 3D
SRME.
Study area
The study area is located in Keathley Canyon, in the central
GOM, which is to the interior of the Sigsbee Escarpment
and features complex salt structures. The data are acquired
using multiple vessels in a staggered configuration (Figure
1a) with variable-depth streamers towed from 10 m to 50
m. The rose diagram in Figure 1b shows that this
acquisition configuration provides full azimuthal coverage
up to 9 km and ultra-long offsets up to 18 km.
3D deghosting for side-gun data
Large azimuths and long offsets provide challenges for
deghosting. Figure 2a shows a shot gather example from a
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~6000m
Red circles = 10km, 18 kma b
Figure 1: (a) Staggered acquisition layout. (b) Rose diagram
considering reciprocity. The inner red circle is the 10-km
offset, and the outer red circle denotes the 18-km offset.
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Figure 2: (a) Shot gather from red source and cable. Zoomed-in water bottom with (b) input, (c) bootstrap receiver deghosting, and (d) 3D
receiver deghosting. The same bootstrap source deghosting is applied on (c) and (d) before receiver deghosting. (e) Spectrum comparison with
input (red), bootstrap deghosting (green), and 3D deghosting (blue).
side-gun (source 3 and leading receiver marked in red)
before deghosting. This side-gun shot gather has a 3D
geometry and thus poses difficulties for the bootstrap
deghosting method that is based on a 2D Tau-P transform.
Figure 2b shows a zoomed-in section of the water bottom at
the apex of the shot gather from gun 3. The primary and
ghost energy are separated. Figure 2c shows the deghosting
result using the bootstrap method. Although this method
attenuated most of the ghost energy, obvious ghost
residuals remain. The 3D deghosting method, however, is
able to better attenuate the ghost energy and has no obvious
ghost residuals (Figure 2d).
Figure 2e shows the spectrum comparison for the shot
gathers before deghosting, after bootstrap deghosting, and
after 3D deghosting. The 3D deghosting method fills in the
ghost notches much better than the bootstrap deghosting
method.
Benefit of 3D deghosting for migrated images
To show the benefit of 3D deghosting, we ran Kirchhoff
sediment flood migrations using the side-gun data before
deghosting, after bootstrap deghosting, and after 3D
deghosting. Figures 3a and 3d show a depth slice and a
cross-section using the data before deghosting. The primary
and ghost energy both present in the migrated image. The
TOS is not clearly defined in the depth slice due to the
interference of primary and ghost energy. Figures 3b and 3e
show the same depth slice and cross-section after bootstrap
deghosting. In the cross-section, most of the ghost energy
at flat TOS areas is attenuated, and the depth slice shows a
better defined TOS event. However, obvious residual ghost
energy remains, especially at the dipping TOS region. The
migrated images with the data after 3D deghosting do not
have the residual ghost energy, and the TOS event is more
coherent in both the depth slice and cross-section (Figures
3c and 3f).
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3D deghosting for staggered ultra-long offsets and full azimuths
We also compared Kirchhoff gathers in the same locations
for input data before deghosting (Figure 3g), with bootstrap
deghosting, and with 3D deghosting. Although bootstrap
deghosting works well for near to middle offsets, there
were residual ghosts in far offsets (Figure 3h). The 3D
deghosting method removed the ghosts nicely from the far
offsets, and both the sediment layer and TOS events were
more coherent (Figure 3i).
Benefit of 3D deghost for multiple suppression
To demonstrate the deghosting effect on multiple
suppression, we used both bootstrap and 3D deghosted data
as input for 3D SRME. Ideally, SRME prediction requires
ghost-free data, and the 3D deghosting method provides
better deghosting in both the basin area and the dipping
TOS area (Figures 4a and 4c). Residual ghost energy from
the bootstrap method degraded the quality of the multiple
model prediction and thus resulted in residual multiples in
the SRME output (Figure 4b). The 3D deghosting method
resulted in better attenuation of multiples (Figure 4d). This
demonstrates that improved deghosting from the 3D
technique can more accurately predict multiple models and
subsequently produce better multiple suppression results.
Conclusions
Removing ghost energy provided a broader spectrum and
improved S/N in marine streamer data. Using FAZ and
ultra-long offset, variable-depth streamer data from the
GOM, we compared the deghosting results using bootstrap
a b c
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Figure 3: Kirchhoff migration stack depth slice at 2200 m with input data (a) before deghosting, (b) after bootstrap deghosting, and (c) after 3D
deghosting. Kirchhoff migration stack cross-section with input data (d) before deghosting, (e) after bootstrap deghosting, and after 3D
deghosting(f); Kirchhoff migration gathers with input data (g) before deghosting, (h) after bootstrap deghosting, and (i) after 3D deghosting.
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3D deghosting for staggered ultra-long offsets and full azimuths
and 3D deghosting methods for side-gun data. We showed
that the 3D deghosting method removed the ghost energy
more effectively than the bootstrap deghosting method. The
improved 3D deghosting result provided more accurate
TOS definition from the Kirchhoff sediment flood
migration and more coherent energy from mid to far offsets
in the migrated gathers. We also showed that with better
deghosting, SRME output has fewer residual multiples.
Acknowledgments
We thank Tony Huang for fruitful discussions and CGG for
permission to show this work. We also thank Xiao Huang
and John Aven for their work on the field data example.
a b
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Figure 4: Surface-related multiple elimination (SRME) (a) input and (b) output with bootstrap deghosted data. SRME (c) input and (d) output with 3D deghosted data.
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http://dx.doi.org/10.1190/segam2014-1297.1 EDITED REFERENCES Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2014 SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES
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