Advances of seismic interpretation by southraze

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                    Advances in Seismic Fault Interpretation Automation*
                                Randolph Pepper and Gaston Bejarano1

                               Search and Discovery Article #40169 (2005)
                                            Posted September 7, 2005

*Modified by the authors of their poster presentation at AAPG Annual Convention, June 19-22, 2005
    Schlumberger Stavanger Technology Center, Stavanger, Norway (;

Since the first seismic trace was computer-rendered, automatic interpretation has been the
promised panacea of the geo-science community. Twenty years later, we still struggle for a
reasonable automatic interpretation methodology in structurally challenging areas.

While automated horizon tracking has become quite elegant, correlating across significant fault
displacements remains an obstacle. Algorithms require human intervention to guide the tracking
in newly encountered fault blocks. Constraining the horizon tracking to honor pre-existing faults
helps, and knowing the fault displacement further enhances this process.

Advances in edge-detection algorithms have allowed direct illumination of faulting and
seismically detectable fractures. These techniques improve manual interpretation, but only
represent an entry point for automatic extraction of faults.

For some geologic plays, re-sampling of the enhanced edge attribute into a geologic model
property is a simple and effective method of un-biased automated fault interpretation. Explicit
methods to extract fault surfaces can utilize an automatically picked horizon indirectly through
analysis of “non-picks” and gradient trends, followed by spatial correlation for vertical
connectivity. Alternatively, using the familiar techniques of seeded auto-picking, on an edge
volume, shows great promise. Flexible editing is essential with these methods.

Finally, we examine the recent work on fault system interpretation, which provides a
semiautomation of fault interpretation, elevating the interpreter’s task to the analysis of fault
systems. Incorporating new multi-horizon classification or displacement attributes allow
inference about surface connectivity with fault throw. The final assembly of these advanced
methods as “bread and butter” interpretation mechanics, while not completely in place, is visible
on the horizon!

                                       Historical Overview
The automatic tracking of seismic horizons has been widely available in commercial software
since the early 1990s providing our first insight into the problem of interpretation automation for
geologic faults. What is immediately obvious with a horizon auto-tracker is that the tracking
frequently breaks down at fault boundaries. Depending on the tracker, and the parameter settings,
we observe gaps in the resulting interpreted surface (non-picked areas) and possibly large time
jumps where the auto-tracker picks an erroneous event. Consider the example where the horizon
we are tracking encounters a fault that has a displacement equal (in time) to some multiple of our
dominate seismic frequency (Figure 1). In this case, our algorithm cannot distinguish an
unfortunate alignment of seismic character across the fault without additional information to
“recognize” that we have encountered a faulted surface. Using a larger window, encompassing
more of the wave train could potentially capture the offset on neighboring events. Or a more

sophisticated approach could use simultaneous tracking of multiple horizons, reducing the
likelihood for misalignment.

Figure 1. An example of horizon auto-tracking across a fault with a displacement equal to a multiple of our
seismic frequency. The auto-tracker found the top picked event as a continuous horizon, even with restrictive
tracking parameters. The lower horizon shows a correct interpretation.

Most automatic horizon tracking applications include cross-correlation or waveform based
tracking algorithms to capture the seismic character over a user controlled window length. These
methods also compute a “quality factor” attribute associated with the horizon pick position,
which give us a further indication on areas of faulting. The combination of interpretation gaps,
large gradient trends, and connected regions of low quality factor can produce an excellent visual
isolation of the fault geometry, relative to the background horizon structure.

While the fault expression was made visible from the horizon auto-tracking method alone, as
shown in Figure 2, the means to extract this fault information directly and automatically was not
available. A clever approach to isolate the fault information from an auto-picked horizon is to
take the inverse of the surface, i.e. show only areas where the interpretation does not exist.
Figure 3 shows an example of the inverse operation on a surface. The fault boundaries for the
structural extent of the horizon are clearly visible. This technique must be applied to each surface
and then linked from between one surface to the next, if a complete fault surface is required. Not
really an automatic process, but it does allow an un-bias extraction of faults from a statistically
consistent auto-tracker. Surface operations can be a powerful tool set for deriving additional
information from surfaces and surface properties. Workflow or process managers and object
calculators are technologies not yet fully exploited by geoscientists.

Figure 2. Example of auto-picked seismic horizon. Notice the clear visibility of the faults on the picked
horizon (gaps in the interpretation, or sharp gradients in the time values).

An early effort for semi-automatic fault interpretation came from Landmark Graphics
Corporation when they introduced FZAP! technology in 1997 (Hutchinson, Simpson et al., US
Patent Number 5,537,320). This technique allowed users to begin their fault interpretation task
by simply “seeding” one or more fault segments (sticks) on a vertical seismic section, and the
automatic operation would perform a cross-correlation on a series of slanted traces derived
parallel to the seeded fault segment. The method could be used for both tracking, where no
previous fault interpretation existed, or snapping, where an existing fault interpretation would be
corrected based on the slant trace cross-correlation algorithm. Each fault surface extracted would
need an initial seed point.

A “seedless” approach to fault segment extraction was presented by van Bemmel and Pepper
(1999, US Patent Number 5,999,885), where the gaps and sharp gradients from a horizon
interpretation are subjected to a connected body analysis followed by feature testing to deduce
likely fault candidates. Through the analysis of multiple horizons, the entire fault framework
could be extracted.

Seismic signal process advanced rapidly during the 1990s, allowing us to approach the problem
of fault interpretation automation in a similar vein as we attack horizon interpretation. Bahorich
and Farmer (1995) present The Coherency Cube (US Patent Number 5,563,949), a seismic
attribute for imaging discontinuities. They note that fault surfaces are distinctly separated from
neighbouring data, both visually and numerically, enabling auto-picking with the existing
horizon auto-tracking software. Lees (1999) directly demonstrates this methodology using a
voxel-picking algorithm on a seismic cube processed with a semblance attribute. Crawford and
Medwedeff (1999, US Patent Number 5,987388) demonstrate extracting faults from the 3D
seismic cube by performing linear feature detection on lateral slices through the seismic
discontinuity volume. The BP Center for Visualization at the University of Colorado continues to
further develop this work, and it is commercially available through Paradigm. These methods all
help us recognize that the fault expression in the seismic, after discontinuity processing, is most
visible in the time-slice or horizon-slice orientations. Neff et al. (2000, US Patent Number
6,018,498) introduce a method that uniquely combine many of these elements by estimating a
probability factor that a fault exists at a specific spatial location using parallel estimation planes
within the seismic volume, and then following this procedure with an orientation and extraction
method based on linear feature detection on time slices.

Figure 3. “Inverse” Surface operations can be used to isolate the fault geometry from surfaces.

These new edge attributes teach us that a vertical seismic section may not be the best background
canvas for fault interpretation. By visualizing seismic discontinuity volumes as time slices, the
major seismic interpretation systems are well suited for fault interpretation, as seen in Figure 4.
Seismic attribute processing highlight the spatial extent of each fault, allowing accurate manual
fault picking on these time-slice images. By connecting the line interpretation on just a few time-
slices, a high quality fault surface can be constructed.

The small additional step of executing seeded fault auto-picking on these edge volumes is just
entering the mainstream in terms of a commercial software offering. The reason for this
technology delay may be in our historical approach of using the seismic interpretation
workstation to emulate our “paper” interpretation from yesteryear. We characteristically use the
seismic workstations to pick “fault sticks” on vertical seismic sections and then link the
intersection of these fault sticks with the interpreted seismic horizon to develop fault traces, in
basically the same technique used historically for a paper-based interpretation. Fault contacts are
transferred from their position in the vertical seismic section to their spatial position on a
basemap for contouring of the seismic horizon. In this sense, the faults are disposable since we
are really just interested in fault planes intrusion into the horizon map (surface inverse). Our
seismic interpretation workstations simply emulate our manual interpretation process; see Figure

5. We manually draw our fault sticks on the seismic section, establish the fault contact points,
and then see them posted on the basemap.

Figure 4. The visibility of the fault geometry after seismic discontinuity processing. (A variance algorithm is
used in this example). Fault interpretation is easily performed on a few time-slices to create a triangulated
fault surface.

The current generation of geological modelling packages treat fault surfaces as legitimate objects
in a 3D structural framework, and further the cause of introducing more un-biased and automatic
methods for the identification and extraction of fault surfaces. Technologies for 3D rendering,
fast computation, and maturing signal processing workflows may finally move us away from our
“paper” interpretation mindset. Let’s now examine some key contributors towards the
advancement of fault interpretation automation.

                                      Enabling Technologies
Many emerging technologies contribute to our understanding of subsurface faulting and
fracturing. We recognize that much progress has been made in the use of the shear-wave
component for fracture identification, but that’s a different story. For now, we shall focus on
reviewing a collection of enabling technologies, which highlight the advances toward the
interpretation automation of seismically resolvable faults and fractures. Our working definition
of “seismically resolvable faults are fractures” means those features that express themselves
through a spatially coherent measure derived from a typical 3D compressional-wave seismic
survey. This measure could mean either a measure of discontinuity or another seismic attribute
that allows cognitive identification and isolation of the fault feature.

Figure 5. This shows our traditional interpretation workflow where faults are really just used to extract the
fault contacts (red circles on left image) that are using in conjunction with the horizon interpretation. Once
the fault polygon is constructed on the horizon, the fault sticks are usually discarded.

We hope that by this point you can accept that discontinuity processing of seismic data, via
signal processing of the entire cube, or as a by-product of horizon auto-tracking, enable us to
visually isolate fault features in the seismic data, particularly in a horizontal format (either
surface slices or time slices). This acceptance opens the door that interpretation automation may
be possible, but issues remain. Can we improve the quality of our images sufficiently for
algorithmic extraction of the fault features? Our images contain a significant amount of noise, or
acquisition/processing artifacts that reduce their quality for automated threshold type picking or
extraction. A simple example can demonstrate this point; consider the seismic horizon in Figure
6a, for a horizon has been auto-tracked. The tracking algorithm constantly encounters
discontinuities in the data that lead to cycle-skips, or holes in the interpreted result unrelated to
fault breaks in the data. For structural tracking, we could consider a signal processing step of
smoothing our input seismic data first, thus allowing the auto-tracker a must more consistent
signal to follow, Figure 6b. This example also demonstrates using other seismic attributes as
possible input volumes for surface tracking, i.e. an apparent polarity section.

Fast volumetric signal processing is becoming a basic element of the geoscientist’s toolkit, as
evident in the barrage of technical papers and patents related to advanced signal processing on
post-stack seismic volumes. A good example of incorporating signal processing and seismic
interpretation are a pair of papers by Fehmers and Hocker (2002, 2003) on fast structural
interpretation with structure-oriented filtering. They making a convincing argument that data
conditioning before automatic interpretation produces more complete areal coverage and
improved picking stability. Further, they describe their method to reduce noise without
degradation to the fault expression contained in the original data. Randen (2000)
demonstrated a collection of seismic attributes that can be derived from local structural
orientation estimates to further advance automated interpretation. Figure 7 shows the
effectiveness of smoothing along the local structural estimate (7c) versus smoothing that does
not honor structure (7b).

Figure 6. Example of horizon auto-tracking (a) without a pre-processing step of structural smoothing (top
right), and (b) auto-tracking with structural smoothing (bottom right).

Figure 7. Example of the effect of smoothing the seismic data to reduce noise. Original input data (a),
threedimensional gaussian smoothing operator (b), and three-dimensional Gaussian smoothing operator
honoring local structure (c).

Marfurt et al. (1999) further develop seismic discontinuity processing in the presence of local
structure using a smoothed local estimate. Chen et al. (2003) offer an alternative method for
imaging discontinuities using dip-steering. Both are examples of processes, which benefit from a
priori knowledge of the local structure. Sudhakar et al. (2000) familiarize us with the advantage
of incorporating azimuthal variation into our methodology for detecting faults and fractures.
They demonstrate the superior results obtainable by using restrictive azimuthal volumes during
processing and attribute generation. Most commercial seismic attribute packages today offer
some version of a seismic dip and seismic azimuth attributes or attributes that derive local
structure during calculation.

Many new signal-processing methods are being developed and entering commercial packages,
exploiting properties of local curvature (Roberts, 2001), local frequency variability (Partyka et
al., 1999), and seismic textures (Randen and Iske, 2005) for example. With this vast array of
seismic attribute volumes, classification and neural network analysis are natural solutions for
extraction or isolation of seismic objects.

Identification of faults by combining multi-attribute analysis with neural network classification is
another maturing area. Meldahl (2001) remark that the trend is shifting from horizon-based
towards volume-based interpretation. We are replacing surface and fault drawing with
seismicobject detection methods, combining fit-for-purpose attribute processing with pattern
recognition technologies. Others continue to exploit the horizon-based methods, but adopt a
more global approach by simultaneously operating on a collection of derived surfaces. Alberts, (2000) demonstrate a neural net method for multi horizon tracking across discontinuities.
This method is attractive because it allows multiple input volumes (i.e. seismic attributes) to be
directly incorporated in the training and the estimation. As the authors point out, classifying and
tracking several horizons at the same time provide additional constraints and enable better
performance of the neural network during learning. They recognize that this method has a
problem with lateral changes in the character of the horizons, but suggest that dynamic retraining
may offer a solution.

A more sophisticated collection of attributes were used by Borgos (2003) to isolate and
capture the significant characteristic of the seismic events at extrema positions only. Using a
trace decomposition, a reflector can be represented with one-point support. The output is a spare
cube with class values only at the minimum or maximum positions of the original input seismic
data. Notice the consistent vertical sequence of classes across the fault boundaries in Figure 8.

Figure 8. Extrema Classification (lower right) of Seismic Volume (from Borgos, 2003)

Borgos,, (2003) take the analysis further by including a fault displacement estimation by
extrapolation of the classification results onto existing fault surfaces, and calculating the
displacement as a distance along the fault surface to extrema class pairs from either side of the
fault. The fault surface now contains an additional spatially variable property of displacement.
Skov, (2004) demonstrate the use of the fault displacement property as a component of
fault system analysis. Admasu and Toennies (2004) produce a fault displacement model by
performing discreet matching of prominent regions across fault planes. Aurnhammer and
Tönnies introduce a genetic algorithm for non-rigid matching across faults.

These examples suggest another important element in our quest. The integrated interpretation of
faults and horizons, through iterative interpretation or simultaneous interpretation will help us
converge on a more accurate structural framework. Tingdahl et al. (2002) offer one example of
mapping faults and horizons concurrently, extending the work of Statoil’s seismic object
detection technology (Meldahl et al., 2001).

S.I. Pedersen ( 2002, 2003) introduced a method known as ant-tracking, based on artificial
swarm intelligence. This is an exciting method where many thousands of computational “agents”
are deployed in a volume to extract a small patch of the discontinuity. The redundancy of agents
over the same area reinforces and extends the extracted feature while increasing the confidence
in estimate. Figure 9 shows the result of running ant-tracking on an edge volume to create both
an enhanced edge volume and to automatically extract fault patches.

Figure 9. Ant-tracking algorithm on a Variance cube and the resulting “enhanced” edge volume and
automatically derived fault patches (subset of the patches actually extracted).

Another method offered by Goff (2003, US Patent Application 20030112704) extracts a
fault network skeleton by utilizing a minimum path value and further subdividing a network into
individual fault patches wherein the individual patches are the smallest, non-intersecting,
nonbifurcating patches that lie on only one geologic fault. This introduction of a patch concept is
exciting because it also introduces the idea of patch properties. We now have an additional
means of segmenting our fault information.

                                     Interpretation Automation
Interpretation automation differs conceptually from automated interpretation. The goal of the
first is to provide a tool to improve the quality and turn-around time for interpretation, whereas
the latter implies a promise of providing an interpretation without human intervention. While a
few corporate executives may like the idea of “click here to find oil”, the geoscientist needs a
flexible software toolset which can automate where appropriate, supplemented with manual input
when necessary, and most importantly offer a means of extracting the desired information easily.

This desired fault information can be classified in two different forms, implicit or explicit. An
explicit representation means surfaces are created and can then be used for framework and
geologic model construction. The simplest case here would be a traditional map of an interpreted
horizon, showing the intersection with the fault surfaces and bounded gaps in the horizon
surface, as previously shown in Figure 3. True 3D geologic modeling requires the additional step
of fault surface intersection interpretation to the bound layers.

Looking at the explicit method in more detail, we can summarize an approach to leverage the
enabling technologies previously discussed. We would like to move away from a basemap
representation of our prospect to a true 3D model representation. One limitation in the past has
been the difficulty to performing traditional interpretation, i.e. horizon and fault drawing, in a 3D
canvas with the same ease they are currently performed in a 2D canvas. When emulating paper
interpretation, a 2D view with polyline drawing functional is appropriate. If the interpretation
paradigm changes from manual drawing to surface or volume extraction, the 3D canvas becomes
the premier choice. An efficient presentation style for joint horizon/fault interpretation would be
to show vertical plane through the seismic amplitude cube and a timeslice view of the
discontinuity cube; see Figure 10.

For automatic extraction techniques, the seismic data must be pre-conditioned either during the
extraction process or as a preliminary processing step. In addition, there may be multiple
versions of the seismic data or derived attributes required depending on the interpretation
objective. For example, regional structure and major fault interpretation can be performed on
structurally smoothed data with great benefit, but at the expense of small fault displacement
expression and a loss of subtle amplitude variations. Yet, once this regional framework is in
place, we can return to our original data, pre-condition the data to emphasis the small features
and interpret them in their best light.

For fault extraction, the construction of a discontinuity volume allows the direct detection of
seismic faults. We again have the option to further condition the discontinuity data to emphasis
large-scale features and/or the subtle detail. Digital processing libraries that offer directional
filtering, connectivity filtering, volume segmentation, morphology operations, and multi-volume
operations can all be utilized to further visually isolate our features of interest. Post-processing of
the discontinuity volume can further isolate the interesting features. Processes such as
skeletonizing, pruning, thinning, and erosion (Gonzalez and Woods, 1992) can be powerful
filters. Other possibilities are iterative operations, such as running Ant-tracking on the results of
Figure 10. Ant Cube viewed as time slice to guide fault interpretation, while the vertical section is the
structural smoothed seismic interpretation of horizons. Horizon can be auto-picked initially from smoothed
seismic for regional extend, then snapped to original seismic for amplitude extraction. The faults can be auto-
picked from the edge volume, manually interpreted (red) on the time slices, or automatically extracted from
the data as surfaces (Figure 9).

While the commercial market has a wonderful inventory of signal processing methods for
seismic volumes, the tools for surface extraction from seismic volumes has been lacking. Seeded
autotracking for faults is not yet mainstream, but we can anticipate they will soon be widely
available. In addition, more sophisticated approaches for global extraction of fault surfaces; e.g.,
AntTracking and neural net classification methods, are also entering the marketplace and will
continue to mature. Parallel to these developments, hardware with enough processing power to
compute multi-trace attributes for larger seismic volumes and the corresponding disk space to
persist those results have become more affordable to users in general. If this trend continues,
then a carefully designed software platform that can host these workflows and can provide a
simple interface to control the different steps, will surely contribute to make these newer
techniques more attractive. See Figure 11 for fault interpretation workflow.

These advances open the door for the geoscientist to work with the derived fault information in
more meaningful ways. One of the greatest advantages of the migration from paper interpretation
to the workstation was the opportunity to easily access the amplitude information from the
seismic. This advantage can now be extended to faults. As previously mentioned, extracted fault
patches can be filtered based on their properties (size, quality, orientation, average throw…) but
this concept can also be extended to all fault objects regardless of the method used to extract
them. Automatic and manual fault interpretation can be managed on a fault system level by
filtering on one or more of the derived properties associated with the collection. New properties
can be added to estimate fault connectivity, strike length, etc., which will be useful in support of
well-based fracture network density analysis. Schlumberger Stavanger Research developed and
presented interpretation workflows based on system level interpretation of faults by utilizing
these collection of properties associated with extracted fault patches as visual filters, S.I.
Pedersen ( 2002), Borgos, (2003), and Skov (2004). Simple histogram and
orientation filtering allow the interpreter to reduce an automatically derived collection of fault
patches into meaningful fault systems (Figure 12).

Figure 11. Fault interpretation workflows include pre-conditioning, edge detection, edge enhancement, pos-
tconditioning, followed by fault extraction via automatic methods and structural filtering, seeded auto-
tracking, or manual interpretation.

The second form of extracting the fault information is an implicit representation, where the
seismic is re-sampled into the geologic model as the container for the fault knowledge. A simple
example here would be to take the fault expression from discontinuity processing (or further
enhancement processing of faults), then re-sample this voxel information into the 3D property
grid model (Figure 13). Incorporating implicit fault definitions with seismically constrained layer
property population will yield high-resolution geologic models. Obviously, a voxel
representation of a fault could be converted to an explicit surface representation through surface
modeling options, i.e., gridding. Implicit methods can be made more sophisticated through
advanced signal processing and custom workflows. It is not a great leap to appreciate that the
seismic displacement field itself would be a valuable seismic attribute.

Figure 12. Histogram and Stereonet filtering of fault patch collections allow fault system level interpretation.
Patches have the advantage of containing properties (average azimuth, average dip, size, confidence…),
which could be extended for manually interpreted faults as well.

The 3D displacement field means that at any x,y,z location, we could determine the geologically
equivalent position at all other locations in the prospect area. A novel means of constructing an
implicit geologic model would be to stochastically populate a model at log resolution, but
structurally guide the statistics along coherent orientation and across fault breaks from the
displacement estimate. The displacement field would also be a welcome addition to volume
restoration studies in support of structural geology interpretation. Dee (2005), acknowledge
fault correlation from seismic as having immediate impact on structural geologic analysis best
practices, but their perspective is from primarily manual interpretation methods, and does not
include the orientation estimate available from seismic and the automation processes.

An automated means of producing this displacement field would require the combination of two
separate elements. We could determine the displacement of a continuous seismic event by
computing the local orientation of the horizon. With the dip and azimuth computation at a point,
we could predict where the event will on the neighboring traces. But this only will work for
continuous events. When we encounter a fault, the orientation estimate will not give us the fault
throw, and in fact we will not get a reliable orientation estimate in the vicinity of a fault. Here we
must introduce the second element of our automation approach, which is to compute the fault
throw via some method of correlation of seismic events across the fault boundary. This step has
made the bold assumption that we have a priori knowledge of where these faults are.

Figure 13. Voxel information extracted from structural smoothing, frequency filtering, discontinuity
processing (Variance), followed by fault enhancement (Ant-tracking). The fault enhanced seismic volume is
re-sampled to a 3D property grid. Within the geologic modeling process, properties, such as permeability, can
be assigned to the fault expression based on a threshold value.

Much of this paper has been devoted to documenting the efforts to date in isolating the position
of faults and a means of measuring the displacement across faults. See Figure 15. The various
tools seem to be available to construct a workflow for creating the displacement field:
        Determine the location of faults
        Determine the areas of event continuity
        Compute the orientation in continuous areas
        Compute fault throw along fault planes
        Combine orientation displacement with fault throw displacement to get 3D

Quality control to correct erroneous estimates will be necessary, but could potentially be reduced
to manual intervention in a sub-set of the data set, focusing the interpreter’s time and energy on
the difficult regions and let automation help us where appropriate.

Besides the attribute workflows, advances in 3D visualization and 3D interaction capability are
going to commoditize volume or geobody extraction functionality which will include some
combination of fault extraction, horizon extraction, layer extraction, and confined volume objects
such as salt, carbonate build-ups, channels, fracture zones, etc. These voxel bodies can be
directly realized into our 3D geologic models to freely share across the seismic to simulation
activity. For those that wish to continue with explicit representations, these can be derived from
the voxel presentation either as surfaces or closed volumes. The next generation workstations
offering fault interpretation automation will combine interactive signal processing, classification
and automatic extraction of features, powerful 3D editing capabilities, and advanced tools for
property filtering at a system level. But not to worry, we are confident that the familiar cursor
crayon will still be available for emergencies.

Figure 14. A displacement attribute can be constructed by utilizing the variation in local structure in the
continuous areas in combination with fault throw estimates. Trace-to-trace coherence can be used as a guide
for where automation will be likely to breakdown. (Spatially distributed offsets image from Skov et al., 2004).

We hope that this paper has yielded some insight into the state of the art for geoscience
interpretation automation in general, and also highlight the advances that are going to impact our
ability to quickly and accurately interpret fault systems. Our limitation is not the computer
hardware or visualization technology at the moment, but a lack of logical integration of the
necessary interactive tools to intelligently extract the structural field from the seismic volume.
While the technical pieces are all available, the commercial software offerings still lag behind.
Many advances have been made and the research continues for both explicit and implicit
methods of representing faulted structures. New algorithms for discontinuity estimation and
subsequent feature identification are constantly arriving at the patent office and presented at
international conferences. Let’s hope the wait is not long for these marvelous tools to reside on
our workstation desktops.

Abbott, W., 1999, U.S. Patent Number 5,982,707 Method and Apparatus for Determining Geologic Relationships
    fFor Intersecting Faults.
Admasu, F., and Toennies, K., 2004, Automatic method for correlating horizons across faults in 3D seismic data:
    IEEE Conference on Computer Vision and Pattern Recognition, Washington DC, June 2004.

Alberts, P., Warner, M., and Lister, D., 2000, Artificial neural networks for simultaneous multi horizon tracking
     across discontinuities: 70th Annual Meeting SEG, Houston, 2000.
Aurnhammer, M., and Tönnies, K., Image processing algorithm for matching horizons across faults in seismic data:
     Computer Vision Group, Otto-von-Guericke University (http://isgwww.cs.uni-
Borgos, H., Skov, T., Randen, T., and Sønneland, L., 2003, Automated geometry extraction from 3D seismic data, in
     Expanded Abstracts, SEG Annual Meeting.
Cheng, Y.C., Fairchild, L.H., Farre, J.A., and May, S.R., 2003, U.S. Patent Number 6,516,274, Method for Imaging
     Discontinuities in Seismic Data Using Dip-Steering.
Dee,S., Freeman, B., Yielding, G., Roberts, A., and Bretan, P., 2005, Best practice in structural geological analysis:
     First Break, Vol. 23, April 2005.
Bahorich, M., and Farmer, S., 1995, 3-D seismic discontinuity for faults and stratigraphic features: The coherency
     cube: The Leading Edge, Vol. 24.10, October 1995.
Bahorich, M., and Farmer, S., U.S. Patent Number 5,563,949, Method of Seismic Signal Processing and
     Exploration, 1996.
Crawford, M., and Medwedeff, D., 1999, U.S. Patent Number 5,987,388, Automated Extraction Of Fault Surfaces
     From 3-D Seismic Prospecting Data.
Goff, D.F., Vincent, L., Deal, K.L., Kowalik, W.S., Bombarde, S., Lee, S., Volz, W.R., and Jones, R.C., 2003, U.S.
     Patent Application Number 20030112704, Process for Interpreting Faults from a Fault-Enhanced 3-
     Dimensional Seismic Attribute Volume.
Hocker, C., and Fehmers, G., 2002, Fast structural interpretation with structure-oriented Filtering: The Leading
     Edge, Vol. 21.3, March 2002.
Gonzalez, R., and Woods, R., 1992, Digital Image Processing: Addison-Wesley Publishing Company.
Hocker, C., and Fehmers, G., 2003, Fast structural interpretation with Structure-oriented Filtering: Geophysics, Vol.
     68, No. 4, July-August 2003.
Hutchinson, Suzi, 1997, FAZP! 1.0 offers automated fault picking (
Lees, J.A., “Constructing faults from seed picks by Voxel Tracking: The Leading Edge, Vol. 18.3, March 1999.
Meldahl, P., Heggland, R., Bril, B., and de Groot, P., 2001, Identifying faults and gas chimneys using multiattributes
     and neural networks: The Leading Edge, Vol. 20.5, May 2001.
Neff, D.B., Grismore, J.R, and Lucas, A.W., 2000, U.S. Patent Number 6,018,498, Automated Seismic Fault
     Detection and Picking.
Partyka, G., Gridley, J., and Lopez, J., 1999, Interpretational applications of spectral decomposition in reservoir
     characterization: Leading Edge, Vol. 18.3, March 1999.
Pedersen, S.I., Randen, T., Sonneland, L., and Steen, O., 2002, Automatic fault extraction using artificial ants: SEG
     International Conference.
Pedersen, S.I., Skov, T., Hetlelid, A., Fayemendy, P., Randen, T., and Sønneland, L., 2003, New paradigm of fault
     interpretation: Expanded Abstracts, SEG Annual Meeting.
Randen, T., and Iske, A., 2005, Mathematical Methods and Modelling in Hydrocarbon Exploration and Production:
     Springer Publishing.
Randen, T., Monsen, E., Signer, C., Abrahamsen, A., Hansen, J.O., Saeter, T., Schlaf, J., and Sonneland, L., 2000,
     Three-dimensional texture attributes for seismic data analysis: SEG International Meeting.
Roberts, A., 2001, Curvature attributes and their application to 3D interpreted horizons: First Break, Vol. 19.2,
     February 2001.
Simpson, A.L., Howard, R.E., 1996, U.S. Patent Number 5,537,320 , Method and Apparatus for Identifying Fault
     Curves in Seismic Data.
Skov, T., Øygaren, M., Borgos, H., Nickel, M., and Sønneland, L., 2004, Analysis from 3D fault displacement
     extracted from seismic data, in Extended Abstracts, EAGE, Paris, June 2004.
Sudhakar, V., Chopra, S., Larsen, G., Leong, H., 2000, New methodology for detection of faults and fractures: SEG
     International Meeting.
Tingdahl, K.M., Bril, B., and de Groot, P., 2002, Simultaneous mapping of faults and horizons with the help of
     object probability cubes and dip-steering: SEG International Meeting.
Van Bemmel, P., and Pepper, R., 1999, U.S. Patent Number 5,999,885, Method and Apparatus for Automatically
     Identifying Fault Cuts in Seismic Data Using a Horizon Time Structure.


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