GEOPHYSICS. VOL. 76, NO. 5 (SEPTEMBER-OCTOBER 2011); P. WB169–WB174, 8 FIGS.
10.1190/GEO2010-0392.1
Dirty salt velocity inversion: The road to a clearer subsalt image
Shuo Ji1, Tony Huang1, Kang Fu2, and Zhengxue Li1
ABSTRACT
For deep-water Gulf of Mexico, accurate salt geometry is
critical to subsalt imaging. This requires the definition of both
external and internal salt geometries. In recent years, external
salt geometry (i.e., boundaries between allochthonous salt
and background sediment) has improved a great deal due
to advances in acquisition, velocity model building, and
migration algorithms. But when it comes to defining internal
salt geometry (i.e., intrasalt inclusions or dirty salt), no efficient method has yet been developed. In common industry
practices, intrasalt inclusions (and thus their velocity
anomalies) are generally ignored during the model building
stages. However, as external salt geometries reach higher
levels of accuracy, it becomes more important to consider
the once-ignored effects of dirty salt. We have developed a
reflectivity-based approach for dirty salt velocity inversion.
This method takes true-amplitude reverse time migration
stack volumes as input, then estimates the dirty salt velocity
based on reflectivity under a 1D assumption. Results from a
2D synthetic data set and a real 3D Wide Azimuth
data set demonstrated that the reflectivity inversion scheme
significantly improves the subsalt image for certain areas.
In general, we believe that this method produces a better salt
model than the traditional clean salt velocity approach.
INTRODUCTION
For deep-water Gulf of Mexico, accurate salt geometry is
critical to subsalt imaging. This requires the definition of both external and internal salt geometries (Haugen et al, 2009). In recent
years, external salt geometry (i.e., boundaries between allochthonous salt and background sediment) has improved due to advances
in acquisition, velocity model building, and migration algorithms
(Huang and Yu, 2009, Bowling et al, 2010). But when it comes
to defining internal salt geometry (i.e., intrasalt inclusions or dirty
salt), no efficient method has yet been developed. Due to the complex salt tectonic environment in deep-water Gulf of Mexico, inclusions within salt (dirty salt), are common. Most of them are sutures
where salt and sediments are mixed together. Historically, the salt
body has been treated as homogeneous in the Gulf of Mexico. In
common industry practice, intrasalt inclusions (and thus their velocity anomalies) are generally ignored during the model building
stages. Figure 1 shows a good example from the Gulf of Mexico
Garden Banks area, where sutures and inclusions are quite obvious
in the seismic images. When looking at the subsalt area, we see
clearly a shadow zone underneath the dirty salt body located directly above the base of salt event. Subsalt events lose focus and
continuity there. Similar observations can be found from other
areas, especially when those inclusion bodies are close to the base
of salt event. We observed that the base of salt distortion, subsalt
event jittering, and subsalt dim zones correlate with dirty salt
occurrence.
Several approaches have been proposed to solve the problem of
dirty salt. Among those, full waveform inversion (FWI) (Tarantola,
1984, Zhang and Wang, 2009) and intrasalt tomography have a lot
of potential. Thus far with FWI, we are still trying to learn the limitations of the method, and we believe there is significant room for
improvement. Intrasalt traveltime based tomography will yield
good results if there are enough reflections within the salt. To
our knowledge, the only significant success with this method
was in the Brazil Santos Basin, where tomography corrected the
velocity for layered evaporites in the salt and thus yielded a superior
presalt image (Huang et al., 2009). When it comes to the Gulf of
Mexico, however, intrasalt tomography fails to produce a comparable uplift in most areas. The typical size of Gulf of Mexico inclusions is too small for tomography updates, and their sparse spatial
distribution poses a poor constraint for global inversion, resulting in
incorrectly smeared velocity updates.
Manual picking of dirty salt is another alternative. Industry practice shows that significant uplift for subsalt images can be achieved
by this method (Schoemann et al., 2010). However, this method
requires a clear boundary between inclusions and the background
Manuscript received by the Editor 30 November 2010; revised manuscript received 15 March 2011; published online 21 November 2011.
1
CGGVeritas, Houston, Texas, USA. E-mail: [email protected]; [email protected]; [email protected].
2
British Petroleum, London, UK. E-mail: [email protected];
© 2011 Society of Exploration Geophysicists. All rights reserved.
WB169
Downloaded 23 Jan 2012 to 216.52.185.72. Redistribution subject to SEG license or copyright; see Terms of Use at http://segdl.org/
Ji et al.
WB170
salt, and picking horizons for those small bodies is very time
consuming. Furthermore, accurately determining the velocity of
those inclusions requires velocity scans, making it very expensive
for tiny inclusions. In general, it is only practical for solving specific
problems.
For a better definition of internal salt geometry, we developed a
reflectivity-based inversion scheme to update dirty salt velocity.
Reflectivity was measured based on a true-amplitude reverse time
migration (RTM) (Zhang and Sun, 2009) volume. Using the intrasalt reflectivity, an initial dirty salt velocity was then estimated,
followed by iterative migrations to fine tune the inversion.
THEORY
Reflectivity, or impedance-based inversion, has been a common
practice in the oil industry (Tsemahman, 1995). Following the same
concept, using a 1D assumption, we developed a velocity inversion
scheme based on the reflectivity.
Starting from
R¼
ðρVÞinc − ðρVÞsalt
ðρVÞinc þ ðρVÞsalt
ð1Þ
where R is the reflection coefficient, ρ is density, and V is velocity,
we can rewrite the equation to get the following expression for V inc
V inc ¼ V salt
ð1 þ RÞρsalt
ð1 − RÞρinc
(2)
For all those terms on the right side of equation 2, the reflectivity
R can be measured from a true-amplitude RTM volume. The salt
velocity and density are known, and the only unknown is the density
of the salt inclusions. In Gulf of Mexico, we believe most of the
inclusions are sediment bodies inside the salt, so we can bypass
ρinc by using an empirical formula for sediment density; for example, Gardner’s equation (Gardner et al., 1974)
ρ ¼ αV βp .
(3)
In this paper, we use α ¼ 0.23 and β ¼ 0.25, with ρ in g∕cm3 ,
V in ft/s.
2D SYNTHETIC EXAMPLE
To confirm the impact of dirty salt, and to test the different
approaches we discussed earlier, a 2D synthetic data set was created
by forward modeling using a dirty salt model that had many intrasalt
inclusions added to salt body. Under the assumption that those inclusions were sediment bodies within salt, all the inclusions
had a velocity slower than salt. Their sizes were small, roughly
600–1200 m wide and 200-500 m thick. The salt density was assigned to those inclusion bodies during forward modeling. A clean
salt velocity model with the same external salt geometry was also
created. Two RTM volumes were generated with these two models.
The image comparison is in Figure 2. We clearly see the impact of
the dirty salt on both the base of salt event and subsalt events. The
base of salt became more rugose and less focused. When we look at
the subsalt, the shadow zones of the intrasalt inclusions are obvious:
The amplitude of subsalt events became much weaker and continuity of subsalt events degraded significantly. Some fault-like structures were introduced to the image due to this distortion. The
inaccuracy of the velocity model also produced more migration
swings.
To check if travel-time based tomography can solve the dirty salt
problem, RTM subsurface angle gathers were created with the clean
salt model and exact dirty salt model. The impact of dirty salt is
Figure 1. Typical subsalt image degradation due to dirty salt.
clear: Ignoring dirty salt introduces curvature variations in RTM
angle gathers (Figure 3). Unlike big sediment basins, where the relationship between curvatures in common image
gathers (CIGs) and velocity error is fairly simple,
the curvatures caused by velocity error from
those inclusion bodies are much harder to interpret. Despite the fact that the velocity is too fast
in a clean salt model for all the inclusion bodies,
we still observe base of salt and subsalt events
curving up (in CIGs) right beneath those inclusion bodies. This comes from the fact that only
near angle energy will go through those inclusion
bodies and is pushed down by a salt velocity
which is too fast. The small dimension of those
inclusion bodies (compared to input data total
offset) also explains the rapid curvature variation
Figure 2. A 2D synthetic RTM comparison. Left: Image using the clean salt model.
along the x-axis, which is a big challenge for
Right: Image using the exact (dirty) model.
Downloaded 23 Jan 2012 to 216.52.185.72. Redistribution subject to SEG license or copyright; see Terms of Use at http://segdl.org/
Dirty salt velocity inversion
WB171
tomography updates. Preliminary ray-tracing based travel-time tomography tests show that the resolution required by small sediment
inclusion bodies is very hard to achieve.
We further tested reflectivity-based dirty salt inversion on this 2D
synthetic data. The RTM image comparison between the clean
salt model and inverted dirty salt model can be found in
Figure 4. The reflectivity-based dirty salt model produces a base
of salt with much better focusing and continuity, recovers the amplitude of those subsalt events, and greatly reduces migration
swings. Similar uplift can be observed in the FWI result, where
the distortion of base of salt and subsalt events due to ignoring those
inclusion bodies is substantially reduced by the FWI model
(Figure 5). The comparison between different velocity models
can be found in Figure 6. No additional editing to salt horizons
has been carried out, and all the models share the same external
salt geometry. From top to bottom, we show a clean salt model,
a dirty salt model from reflectivity inversion, a dirty salt model from
FWI, and an exact model. It is easy to observe both reflectivitybased inversion and FWI catch those major inclusion bodies
successfully.
3D WIDE-AZIMUTH REAL DATA EXAMPLE
Figure 3. A 2D synthetic RTM angle gather comparison. The
Encouraged by the 2D synthetic result, we further tested our
yellow box in the upper panel shows the location of CIGs. Lower
reflectivity inversion scheme on real 3D wide-azimuth data, on
left: CIGs using the clean salt model. Lower right: CIGs using the
areas with many intrasalt inclusions. Figure 7 and Figure 8 show
exact (dirty) model. The incident angle range is from 0° to 60°.
the comparison between a clean salt velocity model and a reflectivitybased inversion model. From the 2D synthetic test, we expected to
see the improved focusing and continuity on both
base of salt and subsalt events. Indeed, this was
the case. The uplift from the improved internal
salt geometry can be observed in both examples.
In Figure 7, the inverted model removes the
discontinuity that existed in the clean salt velocity result, yielding a continuous base of salt
event. The better defined salt geometry, both internal and external, leads to a better subsalt image, especially in the areas circled in this
example. By removing the sag in the shallow
subsalt events, the inverted model yielded a simpler structure with stronger amplitude; for the
Figure 4. A 2D synthetic RTM comparison. Left: Image using the clean salt model.
deep subsalt, the broken events in the clean salt
Right: Image using the reflectivity-based inverted salt model.
model now connect to each other, giving us much
higher confidence in the structure down deep.
In Figure 8, the reflectivity-based dirty salt
velocity inversion helped to remove the sag in
base of salt and produced a much flatter base
of salt, which fits well with the surrounding salt
geometry. The major uplift for this area from the
inverted model is the improved subsalt continuity
and more balanced amplitude. Now we can easily
map those events across the section. We also want
to point out the image change of those inclusion
bodies. After the inversion, because the velocity is
slower, the inclusion bodies also shrink in size,
but in general the intrasalt reflections have better
focusing, indicating that the local velocity update
Figure 5. A 2D synthetic RTM comparison. Left: Image using the clean salt model.
is going toward the right direction.
Right: Image using the FWI inverted salt model.
Downloaded 23 Jan 2012 to 216.52.185.72. Redistribution subject to SEG license or copyright; see Terms of Use at http://segdl.org/
WB172
Ji et al.
DISCUSSION
Figure 6. A 2D salt model comparison. (a) Clean salt model, (b) reflection based
inversion dirty salt model, (c) FWI model, and (d) exact model. Velocity models in
Figure 7 and Figure 8 share the same color bar. The color bar represents a velocity from
1500 m/s to 4500 m/s.
Figure 7. An RTM volume comparison using real wide-azimuth data: (a) image using
the clean salt velocity, (b) image using the reflectivity inverted dirty salt velocity, (c)
clean salt model, (d) dirty salt model. The salt horizons have been adjusted during iterative migration to match the image; subsalt velocities are the same.
One of the benefits of this reflectivity-based
inversion method is its ability to catch small scale
inclusion bodies. In our 2D example, all the
inclusions are fairly small; the dimensions of
those bodies are roughly the same order as the
wavelength of a 15 Hz wave propagating inside
the salt. Tiny as they are, those inclusions have a
big impact on base of salt and subsalt imaging.
The reflectivity method catches most of those
small inclusion bodies, as long as the reflections
from those bodies show up in the stack image.
For our 2D synthetic test, the main target is to
understand/confirm the impact of those inclusion
bodies on base of salt and subsalt events, so we
keep the external salt geometry the same. In real
data, where true external salt geometry is unknown, this reflectivity-based dirty salt inversion
actually can help better define the external salt
geometry, especially for regions where small inclusion bodies are clustered together. Figure 7
and Figure 8 show a good example, where
Figure 7 shows the image along dip direction
and Figure 8 shows the image along strike direction. In this real 3D wide-azimuth data test the
external salt geometry — in this case, the base
of salt — has been updated based on the dirty
salt RTM image. Comparing the clean salt model
(Figures_7c and 8c) and dirty salt model
(Figures 7d and 8d) in both Figures, the maximum salt thickness change is around 250 meters,
which is a fairly small change considering the
major salt body is more than 3000 m thick. But
the impact of these small salt changes (due to
both dirty salt inversion and the base of salt
change) on the subsalt image is not small. Our
reflectivity-based inversion generally improves
internal salt geometry resolution, which in turn
improves external salt geometry as well.
Now let us discuss the limitation of this method. When we look at equation 2, we have uncertainties in both R and ρinc . Because this method is
reflectivity-based, noises (migration swings, artifacts, etc.) inside salt bodies will affect the measurement of R, and thus impact the accuracy of
the inversion. A clean image is the key for this
method. In general, there are two types of noise
in a migrated volume: input-related or migrationrelated. When the noise comes from the input
data, a good preprocessing flow, especially the
denoise and demultiple, is critical. This method
works best with wide-azimuth data, as the strong
stacking power of wide-azimuth data over noise
helps to produce cleaner images for those intrasalt reflectors. This flow tends to work well in
regions where external salt geometry is not extremely complex. For regions where external salt
geometry has complexities, the accuracy of this
method decreases due to uneven distribution of
Downloaded 23 Jan 2012 to 216.52.185.72. Redistribution subject to SEG license or copyright; see Terms of Use at http://segdl.org/
Dirty salt velocity inversion
WB173
Travel time based tomography has a hard time
updating velocity for small inclusion bodies with
a sparse spatial distribution. Full waveform inversion is still not a common practice in industry
due to its high computational cost. Manual picking is human labor intensive. Compared to these
approaches, our reflectivity-based dirty salt velocity inversion scheme provides another practical
solution. In both 2D synthetic data and 3D real
wide-azimuth data tests, this approach has significantly improved the subsalt image for certain
areas. We believe the output from this method
is, in general, better than the clean salt velocity
model used in the industry, and can be combined
with other methods to produce an even better result. For example, it can help manual picking determine the velocity for those inclusion bodies,
and it can be used as the new starting model
for faster FWI converging.
The reflectivity-based inversion assumes 1D
reflection, which is not always true in reality;
however, the method generally works with simple to moderately complex salt bodies. Because
3D wide-azimuth acquisition provides adequate
coverage of incident angles, it thus reduces the
sensitivity to incident angle, as it works with
RTM stack image, which is the summation of
all incident angles. Moreover, the key value of
Figure 8. An RTM volume comparison using real wide-azimuth data: (a) image using
the method is that it detects velocity contrast with
the clean salt velocity, (b) image using the reflectivity inverted dirty salt velocity, (c)
high spatial resolution, and by recovering the
clean salt model, and (d) dirty Salt Model. The salt horizons have been adjusted during
iterative migration to match the image; subsalt velocities are the same.
high frequency velocity components within salt
body, the base of salt is better defined, which is
crucial for subsalt imaging. We believe that this method, simple
illumination inside salt bodies. The uneven distribution of illuminathough it is, has captured some fundamental physics and thus protion needs to be considered in two aspects: the spatial distribution at
duces a better salt model than the traditional clean salt model
different intrasalt locations, and the angular distribution at different
approach.
incident angles. The first will apparently affect our amplitude picking, and thus the velocity estimation. The second will cause migraACKNOWLEDGMENTS
tion swings, weaken true reflectors inside the salt, and most
importantly, invalidate our 1D reflection assumption. Another unWe want to thank CGGVeritas for permission to publish this
certainty lies in ρinc . In Gardner’s equation, α and β are empirically
work. We want to thank Jerry Young and Yu Zhang for discussion
derived constants that depend on the geology; a single set parameter
and reviewing. We want to thank Scott Shonbeck and
might not fit a big area. Because we do not always have sonic and
Kristin Johnston for reviewing this work. We want to thank Timmy
density well logs to calibrate those parameters, region by region
Dy and Monica Thomas for their contributions to the paper.
estimation is recommended, followed by migration to confirm
the uplift. Despite the limitations, we believe this method moves
REFERENCES
one step closer to better salt velocity model building, and our tests,
both 2D synthetic and 3D real data, showed improvements in salt
Bowling, J., S. Ji, D. Lin, D. Chergotis, B. Nolte, and D. Yanchak, 2010,
Mad Dog TTI RTM: Better than expected, 80th Annual International
definition and subsalt imaging.
CONCLUSION
As external salt geometries reach higher levels of accuracy, the
industry is realizing the importance of internal salt geometry. The
effects of salt inclusions, or dirty salt, once believed negligible, actually have a significant impact on subsalt imaging. Ignoring those
inclusions leads to degradations in the subsalt image. For
regions where many inclusions exist inside salt bodies, the impact
of internal salt geometry could be as big as that of external salt
geometry. Proper handling of those inclusions is critical for subsalt
imaging.
Meeting, SEG, Expanded Abstracts, 29, 3313, doi: 10.1190/1.3513536.
Gardner, G. H. F., L. W. Gardner, and A. R. Gregory, 1974, Formation
velocity and density — The diagnostic basis for stratigraphic traps:
Geophysics, 39, 770–780. doi: 10.1190/1.1440465
Haugen, J. A., B. Arntsen, and J. Mispel, 2009, Modeling of “dirty salt”:
79th Annual International Meeting, SEG, Expanded Abstracts.
Huang, T., and B. Yu, 2009, Unlocking the Potential of WAZ data at the
Tongo discovery with TTI reverse time migration: 79th Annual
International Meeting, SEG, Expanded Abstracts.
Huang, Y., D. Lin, B. Bai, and C. Ricardez, 2009, Pre-salt depth imaging of
Santos Basin, Brazil: 79th Annual International Meeting, SEG, Expanded
Abstracts.
Schoemann, M., S. McLallen, D. Valasek, B. Yu, and R. Zhong, 2010, Incorporation of sediment inclusions in detailed salt modeling improves
subsalt imaging in the Gulf of Mexico: 72nd EAGE Conference,
Extended Abstracts.
Downloaded 23 Jan 2012 to 216.52.185.72. Redistribution subject to SEG license or copyright; see Terms of Use at http://segdl.org/
WB174
Ji et al.
Tarantola, A., 1984, Inversion of seismic reflection data in the acoustic
approximation: Geophysics, 49, 1259–1266. doi: 10.1190/1.1441754
Tsemahman, A. S., M. J. Jones, and F. Hron, 1995, P-Wave velocity and
density estimates from the linear inversion of VSP data: Canadian
Journal of Exploration Geophysics, 31, 11–17.
Zhang, Y., and J. Sun, 2009, Practical issues of reverse time migration:
True-amplitude gathers, noise removal and harmonic-source encoding:
First Break, 27, 53–59.
Zhang, Y., and D. Wang, 2009, Traveltime information-based wave-equation
inversion: Geophysics, 74, no. 6, WCC27–WCC36,10.1190/1.3243073.
Downloaded 23 Jan 2012 to 216.52.185.72. Redistribution subject to SEG license or copyright; see Terms of Use at http://segdl.org/