advances in seismic fault interpretation automation

7/30/2019 Advances in Seismic Fault Interpretation Automation

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PSAdvances in Seismic Fault Interpretation Automation*

By

Randolph Pepper1

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

1Schlumberger Stavanger Technology Center, Stavanger, Norway ([email protected];[email protected])

Abstract

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 trackingin 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. Explicitmethods 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 edgevolume, 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 interpreters task to the analysis of faultsystems. 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 visibleon 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 trackingfrequently 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 ourdominate 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, encompassingmore of the wave train could potentially capture the offset on neighboring events. Or a more


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sophisticated approach could use simultaneous tracking of multiple horizons, reducing thelikelihood 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 basedtracking 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 totake 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 thestructural extent of the horizon are clearly visible. This technique must be applied to each surfaceand 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 additionalinformation from surfaces and surface properties. Workflow or process managers and object

calculators are technologies not yet fully exploited by geoscientists.


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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 derivedparallel 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 becorrected 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 deducelikely 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 seismicattribute 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 avoxel-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


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discontinuity volume. The BP Center for Visualization at the University of Colorado continues tofurther 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 Number6,018,498) 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 backgroundcanvas 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 manualfault 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 justentering 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 theseismic 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 aretransferred 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


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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. Lets 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-wavecomponent for fracture identification, but thats 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 themselvesthrough 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.


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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, viasignal 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 foralgorithmic 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 encountersdiscontinuities 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 ofsmoothing 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 geoscientists 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 seismicinterpretation 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 dataconditioning before automatic interpretation produces more complete areal coverage andimproved picking stability. Further, they describe their method to reduce noise without

degradation to the fault expression contained in the original data. Randen et.al. (2000)

demonstrated a collection of seismic attributes that can be derived from local structural

orientation estimates to further advance automated interpretation. Figure 7 shows theeffectiveness of smoothing along the local structural estimate (7c) versus smoothing that does

not honor structure (7b).


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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 apriori 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.


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They demonstrate the superior results obtainable by using restrictive azimuthal volumes duringprocessing 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 forextraction or isolation of seismic objects.

Identification of faults by combining multi-attribute analysis with neural network classification is

another maturing area. Meldahl et.al. (2001) remark that the trend is shifting from horizon-based

towards volume-based interpretation. We are replacing surface and fault drawing withseismicobject 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

et.al., (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 andtracking several horizons at the same time provide additional constraints and enable betterperformance 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 et.al. (2003) to isolate andcapture 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 seismicdata. 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, et.al. 2003)


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Borgos, et.al., (2003) take the analysis further by including a fault displacement estimation byextrapolation 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 et.al., (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

Tnnies 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 usconverge on a more accurate structural framework. Tingdahl et al. (2002) offer one example of

mapping faults and horizons concurrently, extending the work of Statoils seismic object

detection technology (Meldahl et al., 2001).

S.I. Pedersen et.al. ( 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 agentsover 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 bothan 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).


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Another method offered by Goff et.al. (2003, US Patent Application 20030112704) extracts afault 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 isexciting 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 thefirst 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 afew 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 interpretedhorizon, 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 stepof 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 basemaprepresentation 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 3Dcanvas 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 becomesthe 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 thediscontinuity 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 interpretationobjective. 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 inplace, 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-volumeoperations 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 powerfulfilters. Other possibilities are iterative operations, such as running Ant-tracking on the results of

Ant-tracking.


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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 forseismic 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 asimple 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 interpretationto 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) butthis concept can also be extended to all fault objects regardless of the method used to extract


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them. Automatic and manual fault interpretation can be managed on a fault system level byfiltering 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 andpresented 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 et.al. ( 2002), Borgos, et.al. (2003), and Skov et.al. (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 theseismic 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 propertygrid 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 surfacemodeling 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.


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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, butstructurally 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 et.al. (2005), acknowledge

fault correlation from seismic as having immediate impact on structural geologic analysis bestpractices, 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 wemust 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 hasmade the bold assumption that we have a priori knowledge of where these faults are.


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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 positionof 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 faultsDetermine the areas of event continuity

Compute the orientation in continuous areas

Compute fault throw along fault planesCombine orientation displacement with fault throw displacement to get 3D

displacement

Quality control to correct erroneous estimates will be necessary, but could potentially be reducedto manual intervention in a sub-set of the data set, focusing the interpreters 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 objectssuch 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


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the voxel presentation either as surfaces or closed volumes. The next generation workstationsoffering 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 cursorcrayon 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).

Conclusions

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 ourability 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 andsubsequent feature identification are constantly arriving at the patent office and presented atinternational conferences. Lets hope the wait is not long for these marvelous tools to reside on

our workstation desktops.

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