seismic attributes in your facies -...

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September, 2001 CSEG Recorder 41

Introduction

Imagine having a “seismic search engine”. Built into your seis-mic data viewer, it would rapidly locate features in your seismicdata, like search engines help you locate information on the world-wide web.

It could work like this. You are looking at a large 3-D seismicsurvey for the first time. This survey covers a block adjacent to oneon which your company has a well, Richoil #1, that produces fromturbidites. You ask, Where are the turbidites? In a matter of min-utes, the seismic search engine automatically identifies various tur-bidites in your data. Encouraged, you then ask, Which of theseresembles the turbidites encountered in Richoil #1? Within secondsthe turbidities are ranked by similarity to this reference.Investigating further, you automatically identify faults, channels,deep marine shales, and more.

This fanciful tale describes future reality; some such tool willbecome available within the next decade. Seismic pattern recogni-tion has been developing quietly but steadily for twenty years, andthe first practical applications are now appearing. But no matterhow new and sophisticated the algorithm, seismic pattern recogni-tion rests on an old and simple foundation: seismic attributes.

Seismic attributes

Seismic attributes describe seismic data. They quantify specificdata characteristics, and so represent subsets of the total informa-tion. In effect, attribute computations decompose seismic data intoconstituent attributes. This decomposition is informal in that thereare no rules governing how attributes are computed or even whatthey can be. Indeed, any quantity calculated from seismic data canbe considered an attribute. Consequently, attributes are of manytypes: prestack, inversion, velocity, horizon, multi-component, 4-D,and, the most common kind and subject of this review, attributesderived from conventional stacked data (Table 1).

Hundreds of seismic attributes have been invented, computedby a wide variety of methods, including complex trace analysis,interval statistics, correlation measures, Fourier analysis, time-fre-quency analysis, wavelet transforms, principal components, andvarious empirical methods. Regardless of the method, attributes areused like filters to reveal trends or patterns, or combined to predicta seismic facies or a property such as porosity. While qualitativeinterpretation of individual attributes has dominated attributeanalysis to date, the future belongs to quantitative multi-attributeanalysis for geologic prediction.

Seismic attribute analysis is in transition. Though marked, thistransition is but a step in a long evolution (Figure 1).

History

From the first practical seismic reflection experiments in 1921until the early 1960s, seismic reflection data interpretation waslargely a matter of mapping event times and converting these todepth to determine subsurface geologic structure. Paper recordsand analog magnetic tape recording lacked sufficient resolution togo much beyond this. Structural interpretation ruled and strati-graphic interpretation languished.

Continued on Page 42

SEISMIC ATTRIBUTES IN YOUR FACIESArthur E. Barnes

Landmark Graphics Corp., Englewood, Colorado, U.S.A.

Figure 1. Timeline outlining the development of seismic attributes from1950 to the present. Key attributes are shown italicized, and representativepapers are shown in diagonals.

Method Representative Attributes

complex trace

time-frequency

correlation/covariance

interval

horizon

miscellaneous

amplitude, phase, frequency, polarity, response phase,response fequency, dip, azimuth, spacing, parallelism

dip, azimuth, average frequency, attenuation, spectraldecomposition

discontinuity, dip, azimuth, amplitude gradient

average amplitude, average frequency, variance, maxi-mum, number of peaks, % above threshold, energy half-time, arc length, spectral components, waveform

dip, azimuth, curvature

zero-crossing frequency, dominant frequencies, rmsamplitude, principal compaonents, signal complexity

Table 1. Methods for computing poststack seismic attributes, with repre-sentative attributes. Many attributes, such as dip and azimuth can be com-puted many ways.

42 CSEG Recorder September, 2001

A few intrepid visionaries recognized that seismic reflectioncharacter contained valuable clues to stratigraphy. Foremostamongst them was Ben Rummerfield. In 1954 he published hisfamous paper on mapping “reflection quality” to reveal subtlestratigraphic changes (Rummerfield, 1954). Lindseth (1982, p. 9.2)considers this a forerunner of the bright spot concept, but it is asmuch a forerunner of seismic attribute analysis in general.Rummerfield was remarkably prescient, for he foresaw that withimprovements in seismology one could deduce fluid content,porosity, and facies changes from reflection character. Other vision-aries, such as Eduardo Merlini, Carl Savit, and Otto Koefoed,explored the tantalizing possibilities of recording seismic energyand true amplitudes. But these exceptions only prove the rule;overall geophysicists paid little attention to seismic amplitude orcharacter.

This changed dramatically in 1963 with the introduction of dig-ital recording of exploration seismic data in the field (Dobrin, 1976,p. 68). Its acceptance was so rapid that by 1968 fully half of all newseismic recording was digital, and by 1975 nearly all was digital(Sheriff and Geldart, 1989, p. 21, 26, 170). Digital recording so great-ly improved the dynamic range of seismic data that it became fea-sible to routinely investigate amplitude variations. This led straightto the discovery of the first direct hydrocarbon indicators, brightspots.

Much of the early research on bright spots was published in theSoviet Union in the late 1960s, but this was little known in the Westand consequently had negligible influence. Instead, the ideas weredeveloped independently in secrecy amongst oil companies andseismic contractors exploring in the Gulf of Mexico in the late 1960sand early 1970s. By 1971 the technique was widespread through-out the industry and by 1972 it was out in the open (Dobrin 1976, p.339; Sheriff and Geldart, 1989, p. 21). Even then, little about brightspots was ever published because the technology remained confi-dential and jealously guarded.

The stunning success of bright spot prospecting quickly estab-lished it as a key tool of exploration geophysics. Its chief contribu-tion, however, lay in convincing geophysicists to look at variationsin reflection character and stratigraphy as well as reflection timesand geologic structure. In this way, bright spots laid the cornerstonefor attribute analysis.

Thus the first seismic attribute was reflection amplitude.1 Innumerous guises, it remains the most important attribute today.

With expectations inflated by the easy success of bright spots,researchers sought other direct hydrocarbon indicators. Theirsearch led immediately to frequency. They were encouraged by theidea that anomalous attenuation in a seismic signal that passedthrough a gas reservoir can be detected as a shift to lower frequen-cies. This effect is the celebrated “low frequency shadow.” Thefondest hope was that shadows could permit attenuation to bequantified, from which the rock property Q could be inferred(Dobrin, 1976, p. 289).

Oil company researchers in the late 1950s and 1960s soughtthese frequency changes and a corresponding means to displaythem in color. A.H. Balch was the first to publish results (Balch,1971). He developed color “sonograms” using a simple filter-bankto quantify the time-variant average frequency of stacked seismicdata. His interest lay in detecting frequency changes rather than ininterpreting their origin, but to keep oil-finders hopeful he suggest-ed that his technique might detect attenuation due to gas-filledreefs. Balch’s paper is chiefly remembered as the first published inGeophysics to display seismic data in color.

Balch’s work was closely followed by Nigel Anstey’s innovativestudy of seismic attributes, published in two internal reports forSeiscom Delta and presented at the 1973 SEG annual meeting(Anstey, 1972, 1973a, 1973b). His chief attribute was an amplitudemeasure he called reflection strength, which he developed for brightspot analysis (Figure 2). He deliberately chose a descriptive yettechnically vague name to emphasize meaning over mathematics.

Reflection strength cast seismic amplitude in a form free of the dis-torting influences of reflection polarity and wavelet phase, permit-ting fairer comparisons. Anstey also invented apparent polarityand differential frequency, and showed interval velocity, frequency,cross-dip2, and stack-coherence attributes. His color technique wascostly but greatly improved upon Balch’s. His method for display-ing seismic attributes has been employed ever since: simultaneous-ly plot the attribute in color and the original seismic data in blackvariable area. He considers this his most valuable contribution toattribute analysis as it allowed the stratigraphic information of theattribute to be directly related to the structural information of theseismic data.

Anstey’s reports remain surprisingly fresh and insightful.Unfortunately they also remain inaccessible, as only a handful ofcopies were made due to the great expense of the early color plots.It was his colleagues, Turhan Taner, Robert Sheriff, and FultonKoehler, who, inheriting his work upon his departure from Seiscom


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SEISMIC ATTRIBUTES IN YOUR FACIESContinued from Page 41

Figure 2. Isometric display of reflection strength (figure from Anstey,1972). This display compares well with modern displays and was farsuperior to its contemporary competition. Unfortunately, a limited pub-lication muted the influence of Anstey’s work.


Delta 1975, popularized his ideas. In place of his various empiricalmethods, they introduced a single mathematical framework forattribute computation, complex seismic trace analysis.

Complex seismic trace analysis debuted at the 1976 SEG annualmeeting and was subsequently published in the two seminal papersthat launched seismic attributes into prominence, Taner and Sheriff(1977), and Taner et al. (1979). The timing was especially propitious.Against the background of the gathering boom in exploration driv-en by the energy crisis of the 1970s, complex seismic trace analysisappeared alongside seismic stratigraphy, one of the great advancesin reflection seismology. The first practical color plotters followedsoon after, and suddenly color attribute plots became affordable.This combination of money, science, and color proved irresistible:complex trace attributes were enthusiastically received and quicklybecame established as aids to seismic interpretation.

Taner and Sheriff introduced five attributes: instantaneous ampli-tude, instantaneous phase, instantaneous polarity, instantaneous fre-quency, and weighted average frequency. Instantaneous amplitudeis patterned after Anstey’s reflection strength and so adopted itsname. Instantaneous polarity likewise followed Anstey’s design. Forthese two attributes, mathematics follows meaning.

In contrast, instantaneous phase and frequency were new attrib-utes that fell out of the mathematics of the complex trace. Theirgeologic meanings had to be inferred empirically. These two attrib-utes have proven very useful, but they established the unhappyprecedent of subordinating meaning to mathematics.

It was no accident that complex seismic trace analysis firstappeared with seismic stratigraphy. Peter Vail and his colleagues atExxon, who developed seismic stratigraphy, learned of the newattributes and were enthralled by the possibilities they offered.They expected that additional attributes would soon quantify theirseismic facies parameters. And so it was that these two methodswere published together in the famous AAPG Memoir 26 in 1977.Seismic stratigraphy greatly boosted seismic attributes, providingthem a scientific framework for combining attributes to predictgeology, as well as endowing them with a gloss of scientificrespectability.

New attributes proliferated in the 1980s: zero-crossing frequen-cy, perigram, cosine of the phase, dominant frequencies, averageamplitude, homogeneity - and many others. Most of the newattributes lacked clear geologic significance. This was not neces-sarily a problem. To the extent that attributes reveal meaningfulpatterns in the seismic data, they have value. But determiningwhether patterns are truly meaningful is often problematic.

This prompted efforts to make more sense of seismic attributes.Several studies related complex trace seismic attributes to Fourierspectral averages, which yielded clues to wavelet properties andled to “response attributes” (e.g., Robertson and Nogami, 1984;Bodine, 1986). Work also began on seismic pattern recognition, or“multi-attribute analysis” (e.g., de Figueiredo 1982; Sonneland,1983; Conticini 1984; Justice et al. 1985; see Figure 3). While the driv-

ing force was to automatically determine seismic facies, there alsoarose the curious idea that attributes might somehow make sense incombination even if they didn’t make any sense individually.

These efforts failed to provide the geologic insights that seismicinterpreters so keenly sought, nor could they prevent the inevitabledisillusionment bred of expectations set too high. Doubts grew andenthusiasm waned; by the mid-1980s, seismic attributes had losttheir gloss of scientific respectability. Excerpts from the literature

record this fall from grace. Roy Lindseth (1982, p. 9.15) observed, “...except for amplitude, they have never become very popular, nor arethey used extensively in interpretation. The reason for this seemsto lie in the fact that most of them cannot be tied directly to geolo-gy ...” Regarding complex trace attributes, Hatton et al. (1986, p.25) opined, “... this concept is a little difficult to grasp intuitively ...While these functions do provide alternative and sometimes valu-able clues in the interpretation of seismic data, cf. Taner et al. (1979),it is probably fair to say that their usage has not been as widespreadas it might have been due to their somewhat esoteric nature.”Yilmaz (1987, p. 484) cautiously wrote, “The instantaneous fre-quency may have a high degree of variation, which may be relatedto stratigraphy. However, it also may be difficult to interpret all thisvariation.” Robertson and Fisher (1988) added, “The mix of mean-ingful and meaningless values is probably the major factor that hasfrustrated interpreters looking for physical significance in the actu-al numbers on attribute sections.”

If the experts didn’t know what to make of seismic attributes, isit any wonder that the rest of us were confused?

Even as attributes fell into neglect, work continued on new tech-niques that would restore them to favor. Chief amongst these was3-D discontinuity.

A number of two-dimensional continuity and dip attributesappeared in the 1980s (e.g., Conticini, 1984; Scheuer andOldenburg, 1988; Vossler, 1989). These met with an indifferent


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Figure 3. Basic flow chart of seismic pattern recognition (multi-attributeanalysis). A set of attributes are fed into a black-box algorithm, which couldcontain a neural network. The black-box classifies the input data at eachdata point. If additional information is given, the classification is super-vised; otherwise it is unsupervised. The output is typically a prediction ofseismic facies or a physical property such as porosity.


reception. But when in the mid-1990s they were three-dimensional-ized and continuity was recast as discontinuity, they took the explo-ration world by storm (e.g., Bahorich and Farmer, 1995). Theexcitement was reminiscent of that of bright spots, for, like ampli-tude, discontinuity had clear meaning and enabled interpreters tosee something they couldn’t easily see before. This successbreathed new life into attribute analysis. Other multi-dimensionalattributes soon followed, such as parallelism and divergence (e.g.,Oliveros and Radovich, 1997; Randen et al., 1998; Randen et al.,2000; Marfurt and Kirlin, 2000; Figures 4 and 5).

The late 1980s and early 1990s also saw the introduction of hori-zon attributes (Dalley et al., 1989), interval attributes (Sonneland etal., 1989; Bahorich and Bridges, 1992), and attributes extractedalong a horizon from a volume (Figure 6). Presented as maps andoffering superior resolution and computational efficiency, thesewere quickly and widely adopted and have become the most

important format for presenting attributes. Interval attributes areusually computed as a statistic in an interval about an interpretedhorizon. Seismic waveform mapping is a notable exception, as itbased on unsupervised classification. This popular new attributetracks facies changes (Addy, 1997; Figure 6d).

Multi-attribute analysis progressed slowly but surely throughthe late 1980s and 1990s. Attribute cross-plotting was added tovisually relate two or three attributes (e.g., White, 1991). Clusteringalgorithms were employed to classify sets of attributes as maps orvolumes. Since the mid-1990s, neural networks have largely sup-planted clustering (e.g., Russell et al., 1997; Addy, 1997; De Grootand Bril, 1999; Walls et al., 1999). The newer supervised classifica-tion algorithms automatically integrate seismic and nonseismicinformation in their solutions, increasing their prediction power.

Throughout this time, attributes continued to multiply in chaot-ic profusion. Brave workers endeavored to bring order to the chaosby classifying attributes according to function (e.g., Brown, 1996;Chen and Sidney, 1997). But could it be that these noble efforts aremost valuable precisely because many attributes are not? The geo-logic meanings of some attributes are so obscure we can only guessat them.3 Other attributes duplicate each other; amplitude attrib-utes are especially redundant (Figure 7). We do not need all theseismic attributes.

Do we need any?

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SEISMIC ATTRIBUTES IN YOUR FACIESContinued from 4

Figure 4. Reflection parallelism, a seismic stratigraphic attribute. It isquantified as the local degree of variation of reflection dip from the average.Parallel reflections indicate a lower-energy depositional environment, sug-gestive of shales; nonparallel reflections indicate a higher-energy deposi-tional environment, suggestive of sands.

Figure 5. Reflection divergence, a seismic stratigraphic attribute. It isquantified as the degree to which successive reflections diverge lookingdowndip. Yellow indicates divergent reflections and blue indicates conver-gent. (a) Vertical view; (b) 3-D opacity view. The analysis window cap-tured only small-scale divergence, such as that in the channel fill, therebyrevealing the extent of the channel.


(a)

(b)


Future

You may not need seismic attributes today, but you will needthem in the future.

The future will see more multidimensional attributes with geo-logic significance and a greater reliance on multi-attribute analysis.These trends are leading to automatic pattern recognition tech-niques for seismic facies analysis, able to rapidly characterize largevolumes of data, or retrieve subtle details hidden in the data. Inshort, the future will see seismic search engines.

So how would our seismic search engine work? The idea is sim-plicity itself (the devil is truly in the details). A template stores thecharacteristics that describe the reference turbidite of Richoil #1 asimaged in our seismic data. These characteristics are defined byspecific attribute values. These attributes are hidden behind thecharacteristics: our template describes our reference turbidite asmoderately nonparallel and quantifies it as 63%, but we don’t carehow parallelism is computed as long as it is satisfactory. The tem-plate is stored in a template database. Collectively, the templatesdescribe many geologic features, including a number of turbidites.The search engine retrieves our turbidite template from the data-base and scans the data for patterns that resemble it.

Parts of this are already available (e.g., De Groot and Bril, 1999).To progress further, we need better attributes to describe reflectionpatterns with stratigraphic significance, we need better attributesfor describing boundaries (faults, sequences, etc.), we need to inte-grate these with well logs, production reports, and other informa-tion, and we need to build databases of observed seismic patternsfor use with advanced pattern recognition algorithms.

Automated seismic data characterization - based on seismicattributes - will rewrite the rules of seismic data interpretation.Geophysical prophets foresaw the wondrous possibilities. In 1983Lars Sonneland could write (Sonneland, 1983), “Finally, automatedinterpretation techniques might release the interpreter from tediousparts of the interpretation and thereby contribute to faster turn-around.” Going back even farther to 1973, Nigel Anstey boldlywrote (Anstey, 1973a), “We are saying, then, that we are entering anew age of seismic prospecting - one that yields a new insight intothe geology, one that makes the seismic method far more quantita-tive, and one which requires a whole new arsenal of seismic inter-pretation skills”.

You will have seismic attributes in your facies - and you willlike it.

Acknowledgements

I thank Nigel Anstey for graciously presenting me photocopiesof his impossible-to-find classic studies, Seiscom ‘72 & Seiscom ‘73,and for his insightful recollections of the early history of seismicattributes. I thank Grant Geophysical for permission to reproduce


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Figure 7. Cross-plots of common amplitude attributes. The simple linearand parabolic relationships illustrate that these attributes all contain thesame information.

1 Reflection time was really the first seismic attribute.2 This is arguably the first 3-D attribute.3 If you can’t tell what an attribute means from its name, then you prob-

ably don’t need it.

Figure 6. Attribute maps computed for the same horizon. (a) RMS ampli-tude computed in a 40 ms window about the horizon; (b) instantaneousdip extracted along the horizon; (c) dip computed in the direction of thearrow and extracted along the horizon, so that the seismic data looks liketerrain illuminated by light from the top; (d) map of waveform producedby fuzzy clustering with 10 classes, computed in a 40 ms window aboutthe horizon. Attribute maps conveniently present a wide variety of infor-mation.


the reflection strength figure from Anstey’s 1972 report. I alsothank Landmark Graphics Corporation for permission to presentthe other displays of seismic attributes.

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Texas: 67th Ann. Internat. Mtg., Soc. Expl. Geophys., ExpandedAbstracts, 737-740.

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Anstey, N., 1973a, Seiscom ‘73. (Seiscom Limited internal report)

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Bahorich, M.S., and Bridges, S.R., 1992, Seismic sequence attribute map(SSAM): 62nd Ann. Internat. Mtg., Soc. Expl. Geophys., ExpandedAbstract, 227-230.

Bahorich, M., and Farmer, S., 1995, 3-D seismic discontinuity for faults andstratigraphic features: The coherence cube: 65th Ann. Internat. Mtg., Soc.Expl. Geophys., Expanded Abstracts, 93-96.

Balch, A.H., 1971, Color sonograms — a new dimension in seismic data interpre-tation: Geophysics, 36, 1074-1098.

Bodine, J.H., 1986, Waveform analysis with seismic attributes: Oil and Gas J., 84,no. 23, 59-63.

Brown, A.R., 1996, Seismic attributes and their classification: The Leading Edge,15, 1090.

Chen, Q., and Sidney, S., 1997, Seismic attribute technology for reservoir fore-casting and monitoring: The Leading Edge, 16, no. 5, 445, 447-448, 450.

Conticini, F., 1984, Seismic facies quantitative analysis: New tool in stratigraphicinterpretation: 54th Ann. Internat. Mtg., Soc. Expl. Geophys., ExpandedAbstracts, 680-682.

Dalley, R.M., Gevers, E.C.A., Stampfli, G.M., Davies, D.J., Gastaldi, C.N.,Ruijtenberg, P.A., and Vermeer, G.J.O., 1989, Dip and azimuth displays for3D seismic interpretation: First Break, 7, 86-95.

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de Figueiredo, R. J. P., 1982, Pattern recognition approach to exploration, indeFigueiredo, R. J. P., Ed., Concepts and techniques in oil and gas explo-ration: Soc. Expl. Geophys., 267-286.

Dobrin, M.B., 1976, Introduction to geophysical prospecting, 3rd Ed.: McGraw-Hill, Inc.

Hatton, L., Worthington, M.H., and Makin, J., 1986, Seismic data processing:Theory and practice: Blackwell Scientific Publications.

Justice, J.H., Hawkins, D.J., Wong, G., 1985, Multidimensional attribute analy-sis and pattern recognitions for seismic interpretation: Pattern Recognition,18, 391-407.

Lindseth, R.O., 1982, Digital processing of geophysical data: A review: Soc. Expl.Geophys.

Marfurt, K.J., and Kirlin, R.L., 2000, 3-D broad-band estimates of reflector dipand amplitude: Geophysics, 65, 304-320.

Oliveros, R.B., and Radovich, B.J., 1997, Image-processing display techniquesapplied to seismic instantaneous attributes on the Gorgon gas field, North WestShelf, Australia: 67th Ann. Internat. Mtg., Soc. Expl. Geophys., ExpandedAbstracts, 2064-2067.

Randen, T., Monsen, E., Signer, C., Abrahamsen, A., Hansen, J.O., Sæter, T.,Schlaf, J., and Sonneland, L., 2000, Three-dimensional texture attributes forseismic data analysis: 70th Ann. Internat. Mtg., Soc. Expl. Geophys.,Expanded Abstracts, 668-671.

Randen, T., Reymond, B., Sjulstad, H.I., and Sonneland, L., 1998, New Seismicattributes for automated stratigraphic facies boundary detection: 68th Ann.Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 628-631.

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ARTHUR E. BARNES

Art Barnes earned a B.Sc. in physicsin 1974 from Denison University inOhio, an M.Sc. in geophysics in 1980from the University of Arizona inTucson, Arizona, and a Ph.D. in geo-physics in 1990 from Cornell Universityin Ithaca, New York. Belatedly realizinghis mistake in 1995, he took a course inC++ object oriented programming atOhio University to become employable.

Art entered the oil industry in the halcyon days of 1980 whenone could get a job by spelling geophysix. He worked on a marineseismic crew and in seismic data processing. In 1986, encouragedby market fundamentals to seek new opportunities, he re-enteredacademics. He worked in the COCORP deep seismic explorationproject at Cornell, becoming an expert in seismic noise.Speculating that the paucity of reflections was the fault of the sed-imentary cover, he joined Lithoprobe at Ecole Polytechnique deMontréal to work on data from the Canadian shield. During thistime, he pursued research in attribute analysis and seismic signalprocessing, publishing his results in leading journals and mislead-ing journals.

In 1995, Art returned to the oil industry as a software engineer.He joined Landmark Graphics in Denver in 1997. He maintains aproduct for interpretive seismic data processing to which he isadding new attribute functionality. Don’t blame him for limita-tions in other software products. He is a member of the SEG,EAGE, and - last but not least - the CSEG.

Robertson, J.D., and Fisher, D.A., 1988, Complex seismic trace attributes: TheLeading Edge, 7, no. 6, 22-26.

Rummerfield, B.F., 1954, Reflection quality a fourth dimension: Geophysics, 19,684-694.

Scheuer, T.E., and Oldenburg, D.W., 1988, Local phase velocity from complexseismic data: Geophysics, 53, 1503-1511.

Sheriff, R.E., and Geldart, L.P., 1989. Exploration seismology volume 1: History,theory, and data acquisition. Cambridge University Press.

Sonneland, L., 1983, Computer aided interpretation of seismic data: 53rd Ann.Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 546-549.

Sonneland, L. Barkved, O., Olsen, M., and Snyder, G., 1989, Application ofseismic wavefield attributes in reservoir characterization: 59th Ann. Internat.Mtg., Soc. Expl. Geophys., Expanded Abstracts, 813-817.

Taner, M.T., Koehler, F., and Sheriff, R.E., 1979, Complex seismic trace analysis:Geophysics, 44, 1041-1063.

Taner, M.T., and Sheriff, R.E., 1977, Application of amplitude, frequency, andother attributes to stratigraphic and hydrocarbon exploration, in Payton, C.E.,Ed., Seismic stratigraphy - Applications to hydrocarbon exploration:Am. Assn. Petr. Geol. Memoir 26, 301-327.

Vossler, D.A., 1989, Automatic delineation of lateral facies changes in clastic envi-ronments: 59th Ann. Internat. Mtg., Soc. Expl. Geophys., ExpandedAbstracts, 803-804.

Walls, J.D., Taner, M.T., Guidish, T., Taylor, G., Dumas, D., and Derzhi, N.,1999: North Sea reservoir characterization using rock physics, seismic attrib-utes, and neural networks; a case history: 69th Ann. Internat. Mtg., Soc. Expl.Geophys., Expanded Abstracts, 1572-1575.

White, R.E., 1991, Properties of instantaneous seismic attributes: The LeadingEdge, 10, no. 7, 26-32.

Yilmaz, Ö., 1987, Seismic data processing: Soc. Expl. Geophys. R

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