GEOPHYSICS, VOL. 68, NO. 4 (JULY-AUGUST 2003); P. 1294–1302, 9 FIGS., 1 TABLE.10.1190/1.1598122

Volume texture extraction for 3D seismic visualization and interpretation

Dengliang Gao∗

ABSTRACT

Visual inspection of poststack seismic image patternsis effective in recognizing large-scale seismic features;however, it is not effective in extracting quantitative in-formation to visualize, detect, and map seismic featuresin an automatic and objective manner. Although con-ventional seismic attributes have significantly enhancedinterpreters’ ability to quantify seismic visualization andinterpretation, very few attributes are published to char-acterize both intratrace and intertrace relationships ofamplitudes from a three-dimensional (3D) perspective.These relationships are fundamental to the characteri-zation and identification of certain geological features.Here, I present a volume texture extraction method toovercome these limitations. In a two-dimensional (2D)image domain where data samples are visualized bypixels (picture elements), a texture has been typicallycharacterized based on a planar texel (textural element)using a gray level co-occurrence matrix. I extend theconcepts to a 3D seismic domain, where reflection am-plitudes are visualized by voxels (volume picture el-ements). By evaluating a voxel co-occurrence matrix(VCM) based on a cubic texel at each of the voxellocations, the algorithm extracts a plurality of volumetextural attributes that are difficult to obtain using con-ventional seismic attribute extraction algorithms. Casestudies indicate that the VCM texture extraction methodhelps visualize and detect major structural and strati-graphic features that are fundamental to robust seismicinterpretation and successful hydrocarbon exploration.

INTRODUCTION

Since the early 1980s, three-dimensional (3D) seismic imag-ing technology has significantly contributed to subsurface geo-logic mapping and hydrocarbon exploration in the petroleumindustry. From high-quality 3D seismic data, exploration ge-ologists are able to recognize large-scale seismic features byvisual inspection of seismic reflection patterns. However, be-

Manuscript received by the Editor December 26, 2001; revised manuscript received February 3, 2003.∗Marathon Oil Corporation, Computer-Aided Interpretation, P.O. Box 3128, Houston, Texas 77253-3128. E-mail: dgao@marathonoil.com.c© 2003 Society of Exploration Geophysicists. All rights reserved.

cause of the subtlety of amplitude variations and limitation indata visibility in 3D space, it is difficult for them to extractquantitative information for automatic feature discrimination,visualization, and detection. In previous studies, various seis-mic attributes have been extracted from the amplitude in anattempt to facilitate seismic feature identification and inter-pretation. These efforts (e.g., Taner and Sheriff, 1977) havesignificantly enhanced interpreters’ ability to discriminate andvisualize geological features efficiently and objectively. How-ever, very few attributes (e.g., Taner et al., 1994; Bahorichand Farmer, 1995; M. T. Taner, 1998, personal communication;Marfurt et al., 1999) have been published to recognize certainseismic features, for example, those defined by both intratraceand intertrace relationships of amplitude from a 3D perspec-tive. To overcome these limitations and difficulties, I introducea new approach to the problem by extracting volume seismictextures using a point-relational statistical method.

An image texture is a general term that refers to a character-istic pattern defined by the magnitude and variation of neigh-boring data samples at a given location in a physical space.Although studies of image texture have been published sincethe 1970s, the early concept was primarily applied to two-dimensional (2D) image analysis (e.g., Haralick et al., 1973;Weszka et al., 1976; Reed and Hussong, 1989; Gao et al., 1998).Little has been published on its application to reflection seismicdata visualization and interpretation (e.g., Zhang and Simaan,1989; Vinther et al., 1996; Gao, 1999a, b, 2001a, b, 2002). In thispaper, I describe a methodology to characterize 3D seismictextures and investigate its potential geological implications.Such a methodology represents a new, effective approach todiscriminating and visualizing seismic features that may not beeasily recognizable using visual inspection and conventionalattribute extraction algorithms.

CONCEPTS AND METHODOLOGY

A seismic texture, as opposed to other image textures, is de-fined as a reflection amplitude pattern that is characterized bythe magnitude and variation of neighboring acoustic samplesat a given location in a seismic volume (Gao, 1999a, b, 2001a, b,2002). At each of the sample locations, a seismic textureis evaluated by analyzing an array of neighboring reflection

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amplitudes. Such an array of reflection amplitudes is here re-ferred to as a seismic texture element (texel) (Figure 1) (e.g.,Haralick et al., 1973; Reed and Hussong, 1989; Gao et al., 1998;Gao, 1999a, b, 2001a, b, 2002), which is geometrically equiv-alent to the analysis window commonly used in various seis-mic processing and attribute extraction algorithms. In previous2D image texture analysis, a texel is typically a rectangular orsquare that is formed by a finite number of neighboring pix-els (picture elements). In the 3D seismic domain, a texel is aminicube that consists of Nx × Ny× Nz voxels (volume pictureelements) in the inline, crossline, and vertical directions, re-spectively (Figures 1 and 2a). The texel size and aspect ratioare flexible and dependent upon the exploration objectives.Typically, Nx and Ny range from 3 to 9, and Nz ranges from7 to 21 to extract meaningful textural information. In certaincases, however, the texel size and aspect ratio can be quite dif-ferent to achieve special objectives. For example, a horizontalwindow (Nx = NyÀ Nz), which is equivalent to that commonlyused in horizontal image analysis, can be used to emphasize thelateral but not vertical variations in amplitude. A vertical win-dow (Ny= NzÀ Nx or Nx = NzÀ Ny), which is equivalent tothat used in line-based seismic interpretation, captures lateralvariations in the inline or crossline direction, but not both. Atrace segment (Nx = Ny¿ Nz), which is equivalent to that com-monly used in trace-based attribute analysis, is typically usedto characterize waveform but not the trace-to-trace variations.

Fundamentally different from other digital images, a 3D re-flection seismic image consists of vertical traces with alternat-ing positive and negative amplitudes (Figure 1). These ampli-tudes of opposite polarities are aligned laterally in both inline

FIG. 1. Four cubic texel (3D texture element) examples at four different locations in a seismic amplitude volume. A cubic texel,which consists of a 3D array of spatially associated voxels (volume picture element) at each of the sample locations, is fundamentalfor 3D image feature discrimination and visualization. At the shallow structural level, for example, there are differences in internaltextures between the high-amplitude laterally coherent interval (A) and the low-amplitude discontinuous interval (B). At the deepstructural level, the inclined reflection pattern (C) on the hanging wall is distinct from the reflection pattern on the footwall (D)of the listric fault. Such different reflection patterns can be identified, visualized, and mapped quantitatively using the textureextraction method (see Figures 5 and 8).

and crossline directions to form a coherent stratal pattern ofreflection amplitudes. To characterize such a unique pattern,I choose to evaluate the point-relational statistics on a 3Dtexel basis using a voxel co-occurrence matrix (VCM) (Gao,1999b) that is equivalent to the gray level co-occurrence ma-trix (GLCM) previously used in 2D image texture analysis (e.g.,Haralick et al., 1973; Reed and Hussong, 1989; Gao et al., 1998).

The VCM is a statistical representation of the amplitude pat-tern of a texel in a tabular format. More specifically, if a seismicdata set has Ng gray levels (Ng= 256 for 8-bit data), the VCMfor a texel at each of the sample locations is a square symmet-rical matrix consisting of Ng× Ng elements. By definition, theelement E(i, j, α,β) at i th row and j th column of the matrix de-notes the number of times (frequency) in the texel that a voxelwith amplitude i (<Ng) is neighbored by a voxel with amplitudej (<Ng) in the direction of α and β (Figures 2a and 2b). Here,α and β denote the azimuth and dip of a vector, respectively(Figure 2b), along which the voxel co-occurrence is evaluated.Due to the stratal pattern of the seismic images, the VCM isnormally different in different directions. For three orthog-onal directions (Figure 2b) along the x-axis (α= 0◦, β = 0◦),y-axis (α= 90◦, β = 0◦), and z-axis (β = 90◦), for example, theelements E (i, j, α, β) of the respective VCM can be mathe-matically expressed as follows (Reed and Hussong, 1989):

E(i, j, 0, 0) =∑{((m, n, o), (p,q, r ) ⊃ (x, y, z)),

(|m− p| = 1, n− q = 0, o− r = 0,

g(m, n, o) = i, g(p,q, r ) = j )}, (1)

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E(i, j, 90, 0) =∑{((m, n, o), (p,q, r ) ⊃ (x, y, z)),

(m− p = 0, |n− q| = 1, o− r = 0,

g(m, n, o) = i, g(p,q, r ) = j )}, (2)

E(i, j, α, 90) =∑{((m, n, o), (p,q, r ) ⊃ (x, y, z)),

(m− p = 0, n− q = 0, |o− r | = 1,

g(m, n, o) = i, g(p,q, r ) = j )}, (3)

where∑

denotes the total number of times that the voxel co-occurrence relationship defined in the braces exists in the texel,(x, y, z) represents the volume extent of the 3D seismic image,

FIG. 2. (a) A schematic representation of a typical seismiccubic texel (3D). The texel can also be planar (2D) andlinear (1D). The digits in the texel denote the 4-bit ampli-tude (16 intensity levels) requantized from the original 8-bit(256 intensity levels) input data. The requantization is per-formed to enhance the computational efficiency (see discus-sion in the text). (b) A schematic notation defining the di-rection in which the point-relational (voxel co-occurrence)statistics are evaluated. Typically, the point-relational statis-tics are evaluated along the inline-horizontal (α= 0◦, β = 0◦),crossline-horizontal (α= 90◦, β = 0◦), and vertical (β = 90◦)directions, respectively.

and g(m, n, o) and g(p,q, r ) stand for the values of the twovoxels at (m, n, o) and (p,q, r ) in a texel, respectively.

From the VCM, a plurality of textural attributes are derived,each of which describes a specific textural feature of the texel.Based on the comparison, I found that texture homogeneity,contrast, and randomness are among the most effective onesin characterizing seismic data. Texture homogeneity highlightsthe overall smoothness of amplitude and texture contrast em-phasizes the magnitude of differences in amplitude of neigh-boring voxels, whereas texture randomness measures the am-plitude predictability from one voxel to the next. Althoughthere is a certain degree of correlation among these three tex-tural attributes, the correlation is nonlinear, and each attributeshould contribute to minimizing the nonuniqueness in texturediscrimination. These three textural attributes are computedusing the following equations (Haralick et al., 1973; Reed and

START

Retrieve seismic amplitude data

Select texel size and geometry

Select texture orientation

Build voxel co–occurrencematrix (VCM)

Calculate texture attributes

Rescale and store attributesto attribute volumes

Visualize and interpret attribute volumes

Build and requantize texel

Next voxel location ?

N

Select first/next voxel location

Y

FIG. 3. A workflow chart for VCM seismic texture analysis. Theinput is a single amplitude volume (e.g., Figure 1). After tex-ture extraction by evaluating textural attributes at each voxellocation along different directions (Figure 2), the algorithmoutputs a plurality of texture attribute volumes for subsequentinterpretation (e.g., Figures 4–9).

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Hussong, 1989):

Homogeneity =n∑

i=1

[E(i, j, α, β)/R]2, (4)

Contrast =n−1∑m=0

m2n∑

i=1

n∑j=1

|i− j |=m

E(i, j,a, β)/R, (5)

Randomness = −n∑

i=1

E(i, j,a, β)/R log[E(i, j,a, β)/R],

(6)

VCMx =

0 20 5 5 0 0 0 0 0 0 0 0 0 0 0 0

20 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0

5 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 80 15 10 5 0 0 0 0 0 0 0 0

0 0 0 0 15 10 35 0 0 0 0 0 0 0 0 0

0 0 0 0 10 35 10 25 0 0 0 0 0 0 0 0

0 0 0 0 5 0 25 10 15 10 0 0 0 0 0 0

0 0 0 0 0 0 0 15 0 30 0 0 0 0 0 0

0 0 0 0 0 0 0 10 30 0 5 5 0 0 0 0

0 0 0 0 0 0 0 0 0 5 10 10 10 5 0 0

0 0 0 0 0 0 0 0 0 5 10 0 20 5 10 0

0 0 0 0 0 0 0 0 0 0 10 20 0 5 0 0

0 0 0 0 0 0 0 0 0 0 5 5 5 0 5 5

0 0 0 0 0 0 0 0 0 0 0 10 0 5 0 10

0 0 0 0 0 0 0 0 0 0 0 0 0 5 10 40

, (7)

VCMy =

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 16 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 96 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 48 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 80 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 56 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 48 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 32 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 48 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 32 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 24 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48

, (8)

where E(i, j ) represents the element at the i th row and thej th column of the VCM, and n is the dimension of theVCM. R is a normalization constant representing the maxi-mum possible times of the co-occurrence. Along the x (inline),y (crossline), and z (time or depth) directions, for example,R is defined by Rx = 2(Nx − 1)Ny Nz, Ry= 2Nx(Ny− 1)Nz, andRz= 2Nx Ny(Nz− 1), respectively.

To demonstrate the procedure, examine a texel (Figure 2a)consisting of 9 × 5 × 9 voxels that are requantized to 16 graylevels (4-bit precision) from the original 256 gray levels (8-bitprecision). The following matrices (VCMx, VCMy, and VCMz)are three VCMs that are derived from the texel along the x, y,and z directions, respectively.

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VCMz =

0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 00 0 0 0 10 5 0 0 0 0 0 0 0 0 0 00 0 0 0 10 0 0 0 0 0 0 0 0 0 0 00 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0

15 10 10 5 40 5 15 0 15 5 0 0 0 0 0 00 0 0 0 5 0 10 15 0 5 0 0 0 0 0 00 0 0 0 15 10 0 20 15 10 5 0 0 0 0 00 0 0 0 0 15 20 0 0 5 10 15 5 0 0 00 0 0 0 15 0 15 0 0 0 0 5 15 5 5 00 0 0 0 5 5 10 5 0 0 5 10 0 5 5 00 0 0 0 0 0 5 10 0 5 0 0 0 5 0 150 0 0 0 0 0 0 15 5 10 0 0 0 5 10 150 0 0 0 0 0 0 5 15 0 0 0 0 0 0 200 0 0 0 0 0 0 0 5 5 5 5 0 0 0 100 0 0 0 0 0 0 0 5 5 0 10 0 0 10 00 0 0 0 0 0 0 0 0 0 15 15 20 10 0 0

. (9)

In matrix (7) (VCMx), for example, “20” in row 1 and col-umn 2 indicates that there are 20 voxel couples of amplitude1 neighbored by amplitude 2 along the x direction (α= 0,β = 0) within the texel cube. These three VCMs are then nor-malized by dividing each entry with Rx = 2× 8× 5× 9= 720,Ry= 2× 9× 4× 9= 648, and Rz= 2× 9× 5× 8= 720, respec-tively (see Figure 2). Finally, the algorithm reduces the nor-malized VCM to textural attributes using equations (4), (5),and (6). Table 1 shows the texture expressions of the exampletexel and indicates that VCM textures are quite sensitive to thedirection in which the VCM is evaluated.

Therefore, by calculating the VCM texture attributes at avoxel location, local features are extracted; spatial feature vari-ations, on the other hand, are evaluated by sequentially andrepeatedly executing the same process from voxel to voxelthroughout the volume. As a result of such a running-texelprocessing (Figure 3), the original amplitude volume is trans-formed into a plurality of texture attribute volumes. Thesetexture volumes are then visualized and interpreted individ-ually, or they are selected and combined to produce a featureclass volume using a multivariate classification algorithm (e.g.,Richards, 1993; M. Taner, 1998, personal communication; Gaoet al., 1998; Gao, 1999b, 2001a). To facilitate volume textureanalysis in an interactive manner, I developed the VCM texturealgorithms and interfaced them with a 3D seismic visualizationsystem. Example results produced from these algorithms areshown and discussed in Figures 4–9.

Table 1. Textural expressions of homogeneity, contrast, andrandomness for the texel shown in Figure 2. Each textural at-tribute is evaluated along the x (intertrace in inline) direction,y (intertrace in crossline) direction, and z (intratrace in time ordepth) direction. All the textural attributes are normalized torange from 0 to 1.

Orientation

Texture x y z

Homogeneity 0.0353 0.0815 0.0188Contrast 0.0078 0.0000 0.0443Randomness 0.1597 0.1141 0.1791

FIG. 4. A salt canopy detected from a “seed” in a texture homo-geneity cube. Since texture homogeneity of salt is significantlyhigher than that in the surrounding areas, the whole salt bodycan be detected, isolated, and mapped effectively by propa-gating the seed from within the salt. However, it is generallydifficult and time-consuming to define the 3D geometry of thesalt body directly from the amplitude volume. Because ampli-tude samples within the salt body are similar to and connectedwith those in the surrounding areas, a seed-based propaga-tion may cause “bleeding” across the salt boundary and thus isnot effective for automatic salt detection. Similar problem ex-ists with mapping and isolating many other geological featuresusing amplitude data alone. Mapping and isolating these geo-logical features are fundamental for constructing an accuratesubsurface geological model and for exploring hydrocarbonsin the subsurface.

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GEOLOGICAL IMPLICATIONS

In contrast to field geologists who map surface geology basedon direct observations of outcrop structural and stratigraphicpatterns, petroleum exploration geologists map subsurface ge-ology primarily based on reflection seismic patterns, which isparticularly the case in frontier sedimentary basins where littledirect observational data are available. Therefore, successfulexploration of subsurface geology requires effective seismicpattern recognition and visualization technologies. VCM tex-ture analysis represents one such technology that allows explo-ration geologists to visualize, detect, and map major geologicalfeatures from a new perspective. Case examples (Figures 4–9)indicate that the VCM methodology significantly enhances in-terpreters’ ability to visualize and detect major structural andstratigraphic features that may otherwise not be easily recog-nizable and detectable.

The VCM seismic textures are indicative of several ma-jor seismic facies that are formed in diverse depositional set-tings. For example, in an offshore depositional setting, a dome-shaped, low-amplitude seismic feature with an amplitude highat the top is typically indicative of a salt body. Such a featurehas an abnormally high homogeneity (Figure 4). In a deep-water, low-energy depositional setting, a high-amplitude, lat-erally extensive, and coherent pattern is generally associatedwith sheetlike deposits of high impedance contrast. It has a rela-tively low homogeneity (Figure 5a), a high contrast (Figure 5b),and a high randomness (Figure 5c). A low-amplitude and later-ally extensive interval typically represents a thick sequence ofshale with low impedance contrast in the interval. It has a highhomogeneity (Figure 5a), a low contrast (Figure 5b), and a lowrandomness (Figure 5c). In a turbidite system, a linear or sin-

FIG. 5. Three different texture attributes overlaid with the amplitude on the same section demonstrating how these attributes helpdistinguish and isolate intervals of different amplitude patterns. Notice, for example, that low homogeneity (blue), high contrast(red), and low randomness (blue) correspond to the laterally extensive, high-amplitude pattern “A”; whereas high homogeneity(red), low contrast (blue), and high randomness (red) are associated with the acoustically different pattern “B” (see Figure 1 forlocation). (a) Homogeneity (color) evaluated in the trace direction and co-rendered with original amplitude (gray). (b) Contrast(color) evaluated in the trace direction and co-rendered with original amplitude (gray). (c) Randomness (color) evaluated in thetrace direction and co-rendered with original amplitude (gray).

uous feature on a map view with a concave or lenticular shapeon a sectional view (Figures 6a and 6b) is generally associatedwith a channel. It has variable textural features, depending onthe morphology, thickness, and lithology of channels, and the

FIG. 6. (a) An original amplitude section. (b) A texture ho-mogeneity section. (c) A homogeneity cube with opacity filterapplied. From homogeneity data, interpreters can effectivelyisolate the high-homogeneity feature (red) along a channel sys-tem by rendering transparent the low-homogeneity features(blue). It is very difficult to visualize and isolate the same fea-tures from the original amplitude volume due to the limitationof the amplitude in discriminating channels from other geolog-ical features.

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lateral/vertical partitioning patterns of the channel deposits.With massive deposits, for example, it may have a high ho-mogeneity and a low contrast (Figures 6 and 7); with hetero-geneous lithology and complex depositional geometry, it mayhave a low homogeneity and a high contrast (Figure 9c). A lat-erally more extensive and coherent feature that systematicallydistributes on both sides of channels or in the distal portionof a channel-fan system generally suggests levee/overbank de-posits or lobes, which have different textural features than thechannel-fill deposits (Figures 6 and 7).

Because they are sensitive to dip and azimuth of seismicreflections (e.g., Table 1; Figures 8 and 9), VCM textures areable to differentiate between seismic features having differentgeometry and orientations (Figures 8 and 9). Such sensitiv-ity helps highlight and detect deformational features such asrollover anticlines or monoclines on the hanging walls of listricnormal faults (Figure 8) or slumps in mass transport complexes.Similarly, such sensitivity also helps identify and map uncon-formities, onlaps, downlaps, and other oblique, progradationaldepositional features that have characteristic reflection geom-etry in the offshore depositional setting from the shelf margindown to the basin floor. By evaluating VCM textures in a spe-cific direction, the algorithm helps enhance faults or fractureswith a preferred orientation (Figures 9a and 9b). In addition,

FIG. 7. A comparison between average absolute amplitude(a) and homogeneity (b) in a horizon slice at the same strati-graphic level. To avoid a biased comparison, the same process-ing parameters (texel size and dimension) and a normalizedcolor mapping function are used. Notice that the channel/leveedeposits can be recognized, mapped, and detected more effec-tively from the homogeneity volume than from the amplitudevolume.

the direction sensitivity helps enhance the visibility of bothhigh-angle normal or wrench faults and low-angle detachmentor listric faults. This enhancement is achieved not only by thetexture attribute anomalies along the faults (Figure 9), but alsoby textural differences across the faults (Figure 8). Such tex-tural differences are particularly obvious across listric faultswhere the hanging walls have different dip and azimuth fromthe foot walls due to the rotational deformation that occurs inthe vicinity of listric faults (Figure 8).

Unlike the coherence algorithm that highlights external ge-ometry and boundaries of geological features such as faultsand channels, VCM texture analysis emphasizes their inter-nal textures that provide hints on the facies variations withinfault blocks or channel systems (Figure 9c). These internal fa-cies variations may not be visible to the coherence algorithm.In addition, anomalous textural features (e.g., high contrast)along a fault zone (Figure 9b) enable interpreters to map anddetect faults and their spatial connectivity more efficiently thanvisual inspection and manual picking. Thus, VCM textures helpdefine fault zone geometry, kinematics, and relationships to thedepositional facies (Figure 9), which are all important to theunderstanding of depositional and deformational history of asedimentary basin.

DISCUSSION

Three-dimensional texel-based VCM texture extraction hasmany advantages over conventional 2D texel-based GLCMtexture extraction. First, a 3D texel includes textural informa-tion from both inline and crossline directions, and allows eval-uating textural features along different directions in 3D space.Thus, the 3D texel-based processing significantly reduces in-terpretational biases and overcomes limitations of 2D textureprocessing and visual inspection. Second, a reliable extractionof the VCM seismic textures requires a sufficient number ofsamples that, in the 2D image space, can only be accommo-dated by increasing the size of the texel, thereby decreasingthe resolution of the results. Whereas in the 3D image space,the accommodation problem is solved by an additional, thirddimension of the texel cube, thereby significantly enhancingthe spatial resolution of the results.

The structural and stratigraphic implications of VCM tex-tures are attributable to the fact that different deformationaland depositional features have characteristic internal am-plitude patterns in response to the differences in acousticimpedance configuration and distribution patterns. Such in-ternal amplitude patterns can be better defined on a volumetexture basis from a 3D perspective. However, due to the com-plexity and nonuniqueness of seismic response to the subsur-face geology, there is no simple, universal correlation betweenseismic textures and geological features that can be appliedto any data sets in any geological settings. For example, lithol-ogy, thickness, and facies architecture of channel-levee systemsin submarine turbidite systems may be distinctive in differ-ent sedimentary basins or at different times as a sedimentarybasin evolves. In addition to geological complexities, variableacquisition and processing parameters, data quality, and fre-quency attenuation with depth may also affect textural signa-tures of geological features. Since prediction and classificationof the subsurface geology rely on the input textural attributes,selecting textural attributes is a critical step from seismicallyextracted textures to a geologically meaningful prediction and

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classification. Thus, a good understanding of geological im-plications of each textural attribute is fundamental to a ro-bust interpretation and meaningful classification of the sub-surface geology from seismic textures in a specific geologicalsetting.

Like any other seismic-attribute extraction algorithms thatinvolve multiple wiggle traces in the analysis window, VCM tex-tures are sensitive to the dip and azimuth of reflection events(Table 1). Based on the comparison, I found that texture con-trast is more sensitive to the direction than homogeneity andrandomness. Although this sensitivity is favorable in certainaspects of structural and stratigraphic interpretation, it mayalso be unfavorable in interpreting depositional facies if thedip and azimuth variations are the result of postdepositionaltectonic deformation. The effect can be minimized by reduc-ing the texel size along the inline and/or crossline directions, ormore effectively by searching the instantaneous dip of reflec-tion events similar to evaluating the coherence in the presenceof structural dip (Marfurt et al., 1999).

The VCM texture extraction methodology has a major limi-tation in computational efficiency for high-resolution 3D seis-mic data. For example, for an 8-bit (Ng= 256) amplitude vol-ume, the algorithm has to manipulate a 256× 256 matrix at eachsample location throughout the volume, and thus the processis computationally intensive for a large data volume that con-tains billions of voxels. In an attempt to solve this problem, thealgorithm typically requantizes all the texels to 4-bit (Ng= 16),thereby significantly improving the computational efficiency(Haralick et al., 1973; Reed and Hussong, 1989; Gao et al.,

FIG. 8. Three different texture attributes overlaid with the amplitude on the same section demonstrating how these attributeshelp enhance the listric normal fault and rollover structures. Notice the differences in textures between the hanging wall (C)and the footwall (D) (see Figure 1 for location), and the distinctive textures of the rollover monocline. In this specific example,mapping and delineating both the listric fault and the rollover monocline are important for understanding migration pathways,reservoir continuity, and trapping geometry of the hydrocarbon system. (a) Homogeneity (color) evaluated in the crossline directionand co-rendered with original amplitude (gray). (b) Contrast (color) evaluated in the crossline direction and co-rendered withoriginal amplitude (gray). (c) Randomness (color) evaluated in the crossline direction and co-rendered with original amplitude(gray).

1998). Unfortunately, the enhancement in computational effi-ciency is achieved at the expense of sacrificing the bit resolutionof the original data set. A practical solution to that problem isto run the algorithm within the interval and area of interest oron an interpreted horizon.

CONCLUSIONS

VCM seismic texture analysis, a new methodology extendedfrom classical 2D image analysis to 3D seismic interpretation,helps visualize and detect seismic features from a differentperspective than conventional seismic-attribute analysis. Sucha perspective sheds new lights on certain geological featuresthat may not be easily recognizable and detectable fromthe amplitude and other conventional seismic attributes.Case examples indicate that the VCM textural attributeshave important implications for visualizing and mappingstructural and stratigraphic features. For example, a saltbody can be efficiently isolated due to its high homogeneityand low contrast; a sand-filled channel can be discriminatedfrom levee/overbank deposits based on their distinctivehomogeneity and contrast. In addition, VCM textures helpidentify and map rollover structures or slumps producedby rotational deformation in the vicinity of listric or de-tachment faults. They also help identify and highlight faultswith a preferred orientation and a complex geometry froma 3D perspective. Thus, VCM texture analysis significantlyenhances exploration geologists’ ability to visualize, iso-late, and map critical seismic features that are fundamental

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FIG. 9. (a) Texture contrast along the x direction (east-west).(b) Texture contrast along the y direction (north-south).(c) Texture contrast along the z direction (vertical). (d) Inter-pretation. Notice that contrast evaluated along the x direc-tion (a) helps highlight the north-south trending fractures f1,whereas contrast evaluated along the y direction (b) highlightsthe primary east-west trending fault and fractures f2, and con-trast evaluated along the z direction (c) helps identify depo-sitional features such as channels. The geometric relationshipbetween the major fault (F) and the two conjugate fractures(f1 and f2) suggests left-lateral displacement along the fault.Such an interpretation is consistent with the offset of the pre-fault depositional facies across the fault. Also note that thereare at least two stages of channel development (c). The chan-nels to the east was developed prior to the fault displacementand were subsequently truncated and offset left laterally bythe fault. The channels to the west were developed after themajor fault displacement and ran across the fault. These inter-pretations are shown in (d) based on the observations from thetexture data shown in (a), (b), (c), and the regional geology ofthe study area.

to robust geological interpretation and successful hydrocarbonexploration.

ACKNOWLEDGMENTS

I started this study at Exxon Production Research Company(in 1997) and developed it at Marathon Oil Corporation (in1998). I am grateful to Marathon management for permissionto publish this work. Thanks are due to Sharon Crawford, TomEvans, and Steve Peterson for their support and suggestionsin this study. I used the application program interface (API)functions from Magic Earth Inc. and Paradigm GeophysicalInc. in the development of the VCM texture extraction andvisualization algorithms. The 3D seismic data sets used in thispublication are provided courtesy of the Bureau of EconomicGeology, Austin, Texas, and Seitel, Houston, Texas. Journalreviews by the associate editor Kurt J. Marfurt and two anony-mous reviewers helped improve the quality of the paper.

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