Attenuation of high energy marine towed-streamer noise Nick Moldoveanu, WesternGeco
Summary
Marine seismic data have been traditionally contaminated
by bulge waves propagating along the streamers that were
generated by tugging and strumming from the vessel,
paravanes, tail buoys, and lead-in cables. With the progress
of streamer technology bulge-wave interference has been
significantly reduced. However, weather and flow noise
still affects marine seismic data. The level of cross-flow-
induced noise is increased when the data are acquired
during turns or along circles, like in Coil shooting, and
when marine currents are strong. In this paper, we present a
new technique to attenuate towed-streamer noise acquired
in these conditions. The method has been used
successfully to process coil and wide-azimuth data in the
Gulf of Mexico and offshore Brazil.
Introduction
Towed-streamer marine acquisition technology evolved
significantly in the last decade in terms of the in-sea
equipment, particularly streamers, streamer control devices
and towing systems, and the result of this was a reduction
of noise induced by tugging, birds (streamer control
devices) and electrical interferences. However, towed
marine data are still affected by the vertical and horizontal
cross-flow of water across the streamers. Vertical cross-
flow can be induced by wave action and results in so-called
swell noise. Horizontal cross-flow is induced by ocean
currents and as the vessel turns or when the vessel sails
along a circular path. All sources of cross-flow generate
vibrations that propagate along the streamer and are
recorded as high-amplitude low-frequency noise. Figure 1
(Curtis and Davis, 2001) shows typical signal and ambient
noise spectra for towed streamers prior to array forming.
The cross-flow noise is more than 20 dB higher in
amplitude than the seismic signal in the frequency range of
0 to 5 Hz and comparable with the signal from 5 to 10 Hz.
Figure 1: Signal (blue) and ambient noise (red) for towed streamers
prior to array forming
Coil shooting acquisition acquires data along circles that
overlaps in x- and y-directions to cover the entire survey
area (Moldoveanu, 2008). For coil shooting, the level of
the horizontal cross-flow noise vs. the low-frequency signal
can be higher than 20 dB when ocean currents affect the
streamer spread. Figure 2 shows an example of a raw shot
gather recorded during coil shooting acquisition with a
point-receiver streamer that has single hydrophones spaced
at 3.125-m intervals, and without any acquisition filter
applied. The corresponding FK spectrum and amplitude
spectrum are displayed in Figures 3 and 4. It can be seen
that the noise is 35 dB stronger than the signal at low
frequencies.
Figure 2: Raw point-receiver gather acquired during a coil shooting survey
Figure 3: FK-spectrum of the raw point-receiver record
Improving the signal-to-noise ratio in the low-frequency
range is important for imaging deep targets, velocity model
building and seismic inversion. The latest developments in
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Attenuation of high energy marine towed-streamer noise
velocity model building using full-waveform inversion
(FWI) require very low frequencies, in the range 0 to 6 Hz,
to obtain maximum resolution of the velocity field (Vigh et
al., 2010).
Increasing the signal at the very low frequencies could be
an alternative to improve the signal-to-noise ratio, but this
is not practical with the airgun source technology available
today. For this reason it is important to develop effective
noise attenuation algorithms for low-frequency marine
noise.
Figure 4: Amplitude spectrum of the raw point-receiver record
Ozbek (2000) introduced the linearly-constrained adaptive
noise attenuation (LACONA) method that has been used
successfully as a core component of noise attenuation
workflows to attenuate the swell noise on marine seismic
data recorded with finely sampled point receivers (Martin
et al., 2000). The method proposed in this paper adds a
preconditioning step to such workflows that addresses the
strong horizontal cross-flow noise recorded during vessel
turn or coil shooting acquisition.
Method description and implementation
Singular value decomposition (SVD) is well known in
linear algebra and allows us to decompose a matrix D(m,n)
with m rows and n columns, in a product of three unitary
matrices, '** VSUD . Matrixes U, V and S have
dimensions (m,n), (n,m) and (n,n), respectively. S is a
diagonal matrix whose elements are the singular values of
the matrix D.
The SVD method has been used in seismic data processing
for signal-to-noise ratio enhancement using Karhunen-
Loeve transform (Jones and Levy, 1987), footprint removal
(Al-Bannagi, 2005), and ground-roll attenuation. Two
recent papers addressing SVD for ground-roll attenuation
are Chiu and Howell (2008) and Cary and Zhang (2009).
The method presented here is based on the following
assumptions:
Data=Seismic + Noise
Noise amplitudes >> Signal amplitudes
Largest singular values of the matrix D
correspond to the largest amplitude values,
which are associated with the cross-flow
streamer noise
In our application the matrix D corresponds to a shot gather
or a sub-gather (group of traces) that has m samples and n
traces. If the singular values of matrix D are calculated and
sorted in decreasing order,nnsss .......2211
, we
can select the largest k singular values kksss ,..., 2211 , and
reconstruct a matrix '
111 ** VSUN . N represents an
estimation of the noise and has the same dimension, (m,n),
as the data matrix D. This allows subtraction of the noise
from the data, NDS1, where 1S is a representation
of the seismic signal plus residual noise. The noise is
estimated iteratively as shown in Figure 3. Noise estimation
is done in a frequency band, typically 0 to 5 Hz or 0 to 10
Hz.
The number k of largest singular values that will be kept in
the SVD decomposition and the number of iterations are
the critical parameters for this method. If these numbers
are too high, the signal could be attenuated. In this
implementation, the numbers of singular values and the
number of iterations can vary from shot to shot as a
function of the noise level. Also, the process can be
stopped if the difference of the noise estimated in two
consecutive iterations is less than a user-defined threshold.
Figure 5: Iterative estimation of the noise using SVD method
The criterion to detect the noisy traces is based on the
calculation of the RMS amplitude in a window where the
noise dominates the signal.
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Attenuation of high energy marine towed-streamer noise
Data examples
The examples included in the abstract are from a 2x4 coil
shooting survey and a wide-azimuth (WAZ) survey
acquired in the Gulf of Mexico in 2010 and 2011. Both
surveys were acquired with a point-receiver system.
Figure 6 shows the result after the SVD noise attenuation
method was applied on the raw point-receiver gather shown
in Figure 2. The FK spectrum derived after SVD noise
attenuation is displayed in Figure 7 and the noise removed
is shown in Figure 8. This example illustrates that the
strong horizontal cross-flow noise recorded during vessel
turns or during coil shooting acquisition can be efficiently
attenuated using the SVD method, without affecting the
underlying signal.
The next example is from a WAZ survey. Figure 9 shows a
point-receiver shot gather with swell noise. The data were
recorded in rough weather conditions. A 1.75-Hz low-cut
Kaiser filter was applied to this shot before SVD. The
point-receiver shot gather after SVD is displayed in Figure
10, and the noise removed from the data is shown in Figure
11. This example again demonstrates that strong swell-
induced cross-flow noise can be attenuated without
damaging the low-frequency signal.
Figure 6: Point-receiver gather after SVD noise attenuation was
applied
Figure 7: Point-receiver gather after SVD noise attenuation was
applied
Figure 8: Noise removed by the SVD method
Figure 9: Point-receiver shot gather recorded in rough weather during a WAZ survey. 1.75 Hz low cut filter was applied
Figure 10: Point-receiver data after SVD noise attenuation was
applied
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Attenuation of high energy marine towed-streamer noise
Figure 11: Noise removed by the SVD method
Discussions and conclusions
Our strategy for attenuation of the strong marine noise
generated by cross-flow of water across the streamer is to
to take advantage of recording single hydophone data with
fine receiver sampling and no acquisition filter, and to have
a multistep approach for noise attenuation in data
processing. The processing seqeuence performed onboard
of the vessel is shown in Figure 12.
Figure 12: Onboard processing for strong marine noise attenuation
The SVD noise attenuation method discriminates the signal
from noise based on amplitudes and requires careful testing
to properly select the parameters that will protect the signal.
Considering that the noise amplitudes are more than 30 dB
higher than the signal it is safe to attenuate the very high-
amplitude noise components. The rest of the marine noise is
efficiently attenuated by a standard Lacona based noise attenuation
workflow. Figure 13 shows an example of applying such a standard noise
attenuation workflow on a point-receiver shot record processed
through SVD (Figure 6).
Figure 13: Result of standard noise attenuation workflow applied
after SVD on the first data set
The noise attenuation flow presented here is done in the
shot domain, where the receiver sampling is 3.125 m.
Although the SVD process is not sensitive to aliasing, the
fine spatial sampling is required prior to further noise
attenuation and receiver motion correction.
The proposed flow using the SVD noise attenuation method
was used to process 2x4 coil shooting data acquired in the
Gulf of Mexico in 2010, a single vessel coil survey
acquired ofshore Brazil and WAZ data acquired in the Gulf
of Mexico in 2011.
Noise attenuation methods based on SVD were used in the
past to attenuate ground roll, random noise and acquisition
footprints. We demonstrated that an iterative method based
on SVD can be used to efficiently attenuate the high-energy
streamer noise recorded during turns or coil shooting
acquisition and strong swell noise.
Acknowledgements
I aknowledge WesternGeco for permission to present the
paper and my colleagues Stephen Bracken and Kristen
Doty for their contribution to the implementation and
testing of this new method.
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EDITED REFERENCES
Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2011
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.
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