AASPI Videos

• Spectral balancing aims to flatten the power spectrum of seismic data by averaging the spectral magnitude squared over all traces and a vertical analysis window. It balances the spectral components measured by the seismic amplitude volume.
• Spectral bluing numerically enhances the measured spectral components with higher and lower spectral components consistent with an earth model composed of discrete layers. This technique builds on the idea that reflectivity spectra from well logs typically follow an fβ relationship, where 0.0 < β < 0.4.

The primary goal is to improve the quality of migrated seismic data by sharpening reflector terminations, balancing bandwidth, suppressing noise, and flattening gathers. This is crucial because seismic attributes, which are used to interpret subsurface geology, are only as good as the input data. Data with smeared faults or narrow bandwidths result in blurred attribute images. Effective data conditioning helps to ensure that attributes accurately represent geological features rather than artifacts of noise or acquisition footprint.

While spectral balancing balances existing spectral components, bandwidth extension augments the measured spectral components with higher and lower spectral components, effectively broadening the range of frequencies present in the data. Bandwidth extension relies on the assumption that the ridges of the original Continuous Wavelet Transform (CWT) components accurately represent the major impedance changes in the subsurface, similar to the assumptions used in time-variant deconvolution algorithms.

An acquisition footprint is a pattern seen on time or horizon slices that mirrors the spatial arrangement of seismic sources and receivers. It arises from factors like variations in offset and azimuth distribution in common midpoint bins, velocity errors, or strong AVO anomalies. Acquisition footprints can be misinterpreted as geological features, leading to inaccurate structural and stratigraphic interpretations. Because attributes are very sensitive to subtle changes, they are also sensitive to footprint and can often exacerbate it.

The kx-ky transform decomposes seismic data into its spatial frequency components. Acquisition footprints often exhibit periodicity in the spatial domain, which manifests as distinct points or patterns in the kx-ky domain. By identifying and filtering these patterns, particularly using a pedestal filter constructed from the kx-ky transform of a footprint-characterized coherence volume, the footprint noise component can be estimated and subtracted from the original data.

SOF improves the signal-to-noise ratio by filtering data along structural dip, enhancing the continuity of reflectors and suppressing noise that cross-cuts the structure. However, SOF relies on an accurate estimate of reflector dip and azimuth. Limitations include:

• Conflicting dips: If the data contains multiples or is improperly migrated, dipping noise may be enhanced while dipping reflectors may be suppressed.
• Misalignment of fault-plane reflections: Due to anisotropy or velocity model errors, steeply dipping events may not align properly, leading to valuable information being suppressed by SOF.
• Inability to reposition mismigrated discontinuities: SOF cannot correct for incorrect positioning of subsurface discontinuities caused by inaccurate migration.

• The mean filter is a simple averaging filter that smooths data but can also blur across faults and smooth out erroneous spikes.
• The median filter is a non-linear filter that preserves edges and rejects outliers, making it suitable for maintaining sharp changes in value across faults.
• The alpha-trimmed mean filter combines properties of both, rejecting outliers while smoothing the remaining values. However, like the mean filter, it can still smooth across faults.

The choice of filter depends on the specific application and the characteristics of the noise present in the data. The median filter is generally preferred for fault interpretation due to its edge-preserving capabilities.

The Kuwahara filter concept involves using overlapping windows to determine the smoothest data.. A precomputed inline and crossline dip and coherence volumes, in addition to the seismic amplitude volume, can be used as input for the Kuwahara filter.

Here is how it works:

1. The variance and mean amplitude in a suite of five 5-point or nine 9-point windows overlapping the analysis point of interest are computed.
2. The window that exhibits the lowest variance is identified as containing the smoothest data.
3. The mean of this window is defined as the output at the analysis point1
4. If the coherence of the centered window is less than a minimum coherence smin, no smoothing takes place.
5. If the coherence in the centered window is greater than a maximum coherence smax, the smoothing occurs in the centered window.

Otherwise, the smoothing takes place in the window that exhibits the greatest coherence.

AVT is a non-linear transform that highlights geologic features such as faults, channels, carbonates, reflector unconformities, and terminations. It enhances the visibility of these features by applying the Hilbert transform to the RMS envelope of amplitudes within a defined window.

AVT is computed in three steps:

1. Compute the analytic trace U(t) = u(t) + iuH(t), where u(t) is the seismic data and uH(t) is its Hilbert transform. Calculate the envelope e(t) = |U(t)| = (u(t)^2 + uH(t)^2)^1/2.
2. Calculate the RMS envelope within a defined window: RMS(t) = [1/(2K+1) * Σ(e(j + kΔt))^2]^1/2.
3. Compute the inverse Hilbert transform of the RMS envelope: AVT = H-1[RMS(t)].

Instantaneous attributes are time- or depth-dependent measures derived from a seismic trace's complex representation (analytic trace). They provide insights into various aspects of the seismic signal, including amplitude variations, phase relationships, and frequency content, which can be related to subsurface geology and reservoir properties. Common attributes include instantaneous envelope, phase, and frequency.

• Hilbert Transform: uH(t) can be computed in the time domain as a sum over reciprocal of odd integers, or in the frequency domain by rotating the complex Fourier components by 90 degrees (i = exp(iπ/2)) and computing the inverse Fourier transform.
• Instantaneous Envelope: e(t) = (u(t)^2 + uH(t)^2)^1/2.
• Instantaneous Phase: φ(t) = ATAN2(uH(t), u(t)).
• Instantaneous Frequency: Derived from the derivative of the instantaneous phase, but computed using derivatives in the frequency domain to avoid discontinuities: f(t) = (1 / 2π) * dφ(t) / dt.

Sweetness is a composite attribute defined as s(t) = e(t) / sqrt(f(t)), where e(t) is the instantaneous envelope and f(t) is the instantaneous frequency. It is particularly useful in identifying "sweet spots" for hydrocarbon exploration, often indicating sandy facies embedded in a shale matrix in areas like the Gulf of Mexico.

The Teager-Kaiser Energy (TKE) is a measure of the instantaneous energy of a signal, derived from a mass-spring physical model. In seismic analysis, TKE can help estimate local seismic energy, potentially revealing geological features or events not easily seen in the original amplitude data. It can be computed from either the real trace or the analytic trace. A variational version of TKE (TKV) can be used to provide a version more amenable to interpretation workstation software and improve the effectiveness of subsequent seismic attribute computation such as coherence.

Relative acoustic impedance is an approximation of band-pass filtered absolute impedance obtained through trace integration. It provides information about changes in acoustic impedance but lacks the absolute scale. It relies on the assumptions of a zero-phase seismic wavelet approximated by a spike, and small reflection coefficients.

The RAI is calculated in the frequency domain: ZRAI(ω) = FOrmsby(ω) * U(ω) / (iω), where U(ω) is the Fourier transform of the input seismic amplitude, FOrmsby(ω) is a four-point Ormsby filter, and iω represents trace integration (division by iω in the frequency domain).

The RMS amplitude represents the root-mean-square amplitude of the seismic data within a running time window. It's useful for mapping energy within a specific zone, identifying high-energy anomalies, and analyzing changes in signal strength related to geological features.

The RMS amplitude is the standard deviation (σ(t)) of the data (d(t)) within a sliding window: σ(t) = [1/(2K+1) * Σ(d(j + kΔt))^2]^1/2, where K is the half-height of the RMS amplitude window. An add/drop computation scheme is used for efficiency: σ^2(t) = σ^2(t-1) + d(j + KΔt)^2 - d(j - KΔt)^2.

There are three primary methods:

• Complex-trace analysis: This approach utilizes instantaneous phase and frequency derived from the complex trace to estimate dip and azimuth. It involves calculating spatial derivatives of the seismic data and its Hilbert transform.
• Discrete scans: This method, also known as semblance scanning, searches for the most coherent planar reflector within a given analysis window by discretely sampling potential dip and azimuth values and picking the one with the highest semblance score.
• Gradient structure tensor (GST): This technique defines the direction of maximum waveform variability within a small analysis window. This direction represents the normal to the reflector plane, and the dip and azimuth are derived from the tensor's eigenvectors.

Coherence is a measure of waveform similarity between adjacent seismic traces. Low coherence values typically indicate discontinuities like faults, fractures, and stratigraphic boundaries, while high coherence values suggest continuous and undisturbed reflectors. Coherence is a valuable tool for visualizing structural and stratigraphic features that may be subtle or difficult to identify on conventional seismic data.

Several algorithms exist for coherence calculation:

• Crosscorrelation: This method directly cross-correlates adjacent seismic traces. It's computationally simple but sensitive to noise and variations in reflector dip.
• Semblance-based coherence: This approach calculates the ratio of the energy of the average trace to the average energy of all traces along a specified dip, thereby accounting for reflector dip and improving robustness.
• Eigenstructure-based coherence: This method decomposes the data covariance matrix into eigenvectors and eigenvalues. It is more robust and provides better lateral resolution than crosscorrelation, but it can be computationally intensive.
• Gradient Structure Tensor (GST) based coherence: This method relies on derivatives of the seismic amplitude and is sensitive both to changes in waveform and to lateral changes in amplitude.

A key consideration when choosing a coherence algorithm is whether it includes a "dip search" component to allow for structural dip, the lack of which can lead to "structural leakage".

Seismic curvature measures the bending of seismic reflectors. It quantifies the degree to which a reflector deviates from being planar. Unlike dip and azimuth, which provide information about reflector orientation, curvature highlights subtle flexures, folds, and fractures that may be associated with geological processes like faulting, folding, and differential compaction. It helps identify areas of strain and deformation that are not always apparent on conventional seismic data.

• Horizon-based curvature is computed by fitting a second-degree polynomial to a picked horizon using a number of grid points and then calculating the second derivatives of the polynomial. This method is limited to picked horizons but is computationally efficient.
• Volumetric curvature overcomes the limitation of horizon-based curvature by generalizing the computation to work on volumes. It uses inline and crossline dips to approximate derivatives and doesn't require knowing the vertical location of a quadratic surface. Volumetric curvature allows for longer-wavelength estimates of curvature by smoothing the computational operator rather than the volumetric dip estimates.

Different curvature attributes provide unique insights into reflector geometry:

• Mean Curvature: Average of the principal curvatures.
• Gaussian Curvature: Product of the principal curvatures.
• Maximum and Minimum Curvature: Measure the bending along the two orthogonal directions of maximum and minimum curvature.
• Most-Positive and Most-Negative Curvature: These highlight concave-down and concave-up features, respectively, and are useful for delineating faults, fractures, and folds.
• Dip and Strike Curvature: Components of curvature projected along the dip and strike directions, respectively.
• Curvedness: A measure of the overall bending of the reflector.
• Shape Index: Classifies reflector shapes into categories like domes, bowls, saddles, and ridges.

Geologic structures can exhibit curvature at different wavelengths. Short-wavelength curvature may indicate localized features like fractures, while long-wavelength curvature may reflect broader structural elements.

Multispectral curvature estimates can be generated by:

• Successively smoothing maps or dip and azimuth volumes: Smoothing attenuates the shorter-wavelength components and mixes information from grid points that lie farther and farther from the analysis point.
• Filtering the data in the kx-ky wavenumber domain: This allows specific wavelengths to be emphasized, isolating features of interest.
• Filtering the curvature operators: The interpreter is allowed to explicitly define wavelengths of interest.

These attributes, while mathematically independent, are connected through geology.

• Dip and azimuth show reflector orientation.
• Coherence reveals discontinuities, faults, and stratigraphic boundaries.
• Curvature highlights subtle bending and deformation.

Combining them helps to distinguish between structural and stratigraphic elements of the subsurface. For example, regions of low coherence coinciding with high curvature could indicate fractured zones associated with faulting, while low coherence with low curvature could indicate stratigraphic changes. Color blending of attributes (such as using dip and azimuth for hue, and coherence for intensity) allows for enhanced visualization.

The shape index (s) is a seismic attribute that describes the local 3D shape of a reflector surface. Its values range from -1.0 to +1.0, indicating different geometric forms: -1.0 for a bowl, -0.5 for a valley, 0.0 for a saddle, +0.5 for a ridge, and +1.0 for a dome. It is calculated using the most-positive and most-negative curvature values, offering a quantitative way to characterize structural features. If the curvedness is 0, the shape index is undefined, and the result is a perfect plane.

Aberrancy, or flexure, measures the bending of seismic reflectors. It's computed from the second derivatives of the dip vector in a rotated coordinate system. The apparent flexure f(ψ) at azimuth ψ involves calculating the roots of a cubic equation, where the extrema represent the aberrancy magnitudes. Total aberrancy, the vector sum of three aberrancy roots, can indicate areas of intense fracturing.

• Reflector convergence (kconv) maps angular unconformities, downlaps, toplaps, and other stratigraphic phenomena. It is derived from the curl of the unit normal vector and, for relatively flat geologies, indicates changes in reflector dip along the z-axis.
• Reflector rotation (krot) maps structural rotation about faults, laterally variable syntectonic deposition across faults, and lateral changes in the dip of channel fill. It is the component of the curl of the unit normal vector that is parallel to the smoothed normal vector, indicating lateral changes in dip along the x- and y-axes.

Spectral decomposition helps in identifying the frequencies at which constructive interference (tuning) occurs due to the presence of thin beds. By analyzing the amplitude of different frequency components, interpreters can infer the thickness and lateral extent of geological features like channels, deltas, and stratigraphic variations. Different frequencies respond differently to varying thicknesses; higher frequencies may highlight thinner features while lower frequencies reveal thicker zones.

The main difference lies in the analysis window. SWDFT uses a fixed-length window for all frequencies, making it suitable for comparing frequency content within a specific geological formation or time window. CWT, on the other hand, employs variable-length windows inversely proportional to frequency. This provides better temporal resolution, but analyzes different regions of the formation at different frequencies. SWDFT is ideal for formation-based attributes (e.g., estimating tuning thickness or mapping stratigraphic variability), while CWT is suited for detecting rapid changes in the frequency content of seismic data.

Thin-bed tuning refers to the constructive interference of seismic reflections from the top and bottom of a thin layer. This interference results in an amplitude peak at a specific frequency, known as the tuning frequency, which is related to the thickness of the layer. Spectral decomposition allows interpreters to identify this tuning frequency and, consequently, infer the thickness variations of the thin bed. Spectral decomposition techniques can detect lateral changes in thin-bed tuning well beyond the limitations of traditional one-quarter-wavelength resolution criteria.

Spectral balancing is a process aimed at generating a band-limited processed-data spectrum by compensating for the effects of the source wavelet and other factors that color the seismic data. In SWDFT, spectral balancing involves calculating an average spectrum representative of the analysis window and then applying a compensation factor to remove the effect of the source wavelet, statistically revealing the reflectivity spectrum within the window. Deconvolution algorithms, commonly used in seismic processing, are designed to accomplish similar results.

Multiple spectral components can be visualized using various techniques, including RGB blending, HLS color models, optical stacking, and statistical measures (e.g., mode of the spectrum). These techniques allow interpreters to combine information from multiple frequencies into a single image, enhancing the visibility of geological features and providing insights into reservoir characterization, fluid content, and stratigraphic variations. For example, RGB blending can highlight different geological features based on their tuning frequencies, while coherence blended with spectral mode can delineate channel edges and indicate channel thickness.

The GLCM is a tabulation of how often different combinations of voxel amplitude brightness values (gray levels) occur within an analysis window. To compute it, seismic data is first converted from floating point to a user-defined number of integer gray levels. A local analysis window is defined parallel to the local dip, and the matrix is constructed by counting the co-occurrences of gray-level pairs at a specified distance and angle. The resulting matrix is often normalized to represent probabilities. The statistics (attributes) are then derived from this matrix.

The three main categories are:

• Contrast Group: Measures amplitude differences (i-j) and is insensitive to the mean amplitude. Attributes include Contrast, Dissimilarity, and Homogeneity. They are related to the distance (i-j) from the GLCM diagonal.
• Orderliness Group: Measures how smoothly varying the voxel values are, independent of the mean amplitude. Attributes include Energy and Entropy, which are functions of the GLCM matrix values, Pij. Gao (2003) added randomness as a third attribute.
• Statistics Group: Includes measurements of mean, variance, and correlation. These provide statistical descriptions of the gray-level relationships in the analysis window.

While geometric attributes measure distinct geomorphologic components, texture attributes provide quantitative measures of subtle variations in seismic data that are useful for pattern recognition. By themselves, the attributes can seem fuzzy, but when used as input to either supervised (neural networks) or unsupervised (self-organizing maps) classification systems, GLCM-based texture attributes can be used to cluster similar seismic textures, allowing for the identification of seismic facies, geological features, and potentially reservoir characterization.

Both contrast and dissimilarity measure the lateral change in amplitude along structural dip. However, contrast is weighted by the square of the gray level differences, while dissimilarity is weighted by the absolute value of the gray level differences. Because of this, dissimilarity is less sensitive to outliers than contrast. Both can highlight faults and stratigraphic features, and produce results similar to coherence attributes.

GLCM energy measures the textural uniformity or orderliness within a window, with high values indicating nearly constant amplitudes. GLCM entropy, conversely, measures the disorder or complexity of the image. The name "energy" is misleading because it does not relate to the value of seismic amplitude itself but rather to the change in seismic amplitude. A patch of zero amplitude data can have high GLCM energy if the amplitudes are consistently zero.

Co-rendering is a multiattribute visualization technique that combines two or more seismic attributes into a single display, allowing interpreters to analyze the relationships between different data types. This can be achieved through methods like alpha-blending (transparency/opacity) or emulating color models like HLS (Hue, Lightness, Saturation). By combining attributes that highlight different aspects of the subsurface, interpreters can gain a more comprehensive understanding of complex geological structures and stratigraphic variations that might be missed when attributes are viewed individually.

• Diverging Attributes: Attributes like curvature or velocity variations that have both positive and negative values require diverging color maps (e.g., the "balance" colormap) with a distinct neutral point to clearly differentiate between the positive and negative ranges.
• Cyclic Attributes: Attributes such as phase, aberrancy, or dip azimuth, which represent cyclical data, require cyclical color maps that loop seamlessly (e.g., RomaO colormap) to avoid artificial discontinuities.
• Lithological Variations: The "sweetness" attribute, used to identify hydrocarbon-bearing sandstone units, benefits from color maps like "Lajolla," which use colors that intuitively correspond to lithological variations (e.g., yellow/tan for sandstones, brown for shales).

One limitation is the potential for misinterpretation if the underlying data quality is poor or if the chosen attributes are not relevant to the geological problem being investigated. Additionally, complex displays can become visually overwhelming if not carefully designed and interpreted. Also, many commercial software packages limit the number of colors that can be used in a visualization.

The HLS (Hue, Lightness, Saturation) color model is particularly useful for encoding three different attributes into a single image. One attribute can be mapped to Hue (the color), another to Lightness (brightness or darkness), and a third to Saturation (color intensity). This approach allows interpreters to simultaneously visualize the relationships between attributes, such as dip azimuth, dip magnitude, and coherence.

 The "hlplot," "hsplot," and "hlsplot" programs within AASPI are designed to create composite displays by mapping seismic attributes to the Hue, Lightness, and Saturation components of the HLS color model. These tools allow users to modulate one attribute by one or two others, creating complex visualizations that reveal subtle relationships in the data.

Seismic facies are mappable, three-dimensional seismic reflection units that can be distinguished from adjacent units based on their seismic characteristics (e.g., reflector continuity, amplitude, frequency, and geometry). Traditionally, seismic-facies analysis involved mapping continuous, discontinuous, and chaotic reflectors. Continuous reflectors indicate stable depositional environments, discontinuous reflectors suggest heterogeneous environments like channels, and chaotic reflectors suggest fractured, mobilized, or altered rocks. Modern techniques use a range of seismic attributes (dip, azimuth, curvature, coherence, amplitude, and frequency) as inputs to classification algorithms, creating seismic facies maps that resemble classical seismic facies interpretations. Attributes like coherence are particularly useful for mapping lateral changes in reflector continuity and chaotic areas.

Common channel-mapping workflows include:

• Picking a horizon and slicing through an appropriate attribute volume.
• Generating phantom-horizon or stratal slices.
• Painting channels directly on time slices and compiling the painted segments.

Seismic attributes such as coherence, RMS energy, energy-weighted coherent-amplitude gradients, and spectral decomposition are highly effective in channel mapping. Coherence highlights lateral changes in reflector continuity associated with channel edges, energy-weighted coherent-amplitude gradients delineate subtle channel features, and spectral decomposition can reveal channel thickness and composition.

Unconformities can be challenging to interpret because they cut across lithologic units and may appear as peaks, troughs, or zero crossings. Attributes such as coherence andcosine of instantaneous phase can aid in unconformity mapping. Low-coherence zones often indicate waveform interference and erosional unconformities, particularly angular unconformities. Displaying seismic data and attribute volumes (e.g. coherence) side-by-side with a linked cursor helps interpreters ensure consistency between attribute picks and conventional seismic interpretations.

Differential compaction occurs when sediments of different compositions (e.g., shale and sand) compact at different rates. This can cause the deformation of overlying strata and create misleading features on seismic data. Interpreters can identify differential compaction by animating through multiple attribute time slices, comparing the original seismic data with attribute volumes, and recognizing fault patterns induced by compaction over deeper features. The interpreter should be aware that structures that originally are flat may change shape over time.

The seismic attribute response to a channel depends on the channel's thickness, fill material, and the presence of differential compaction:

• Thin channels below tuning thickness: Primarily seen by amplitude-based attributes (RMS amplitude) and energy-weighted coherent-amplitude gradients. Eigenstructure coherence will likely not detect them.
• Thin channels with differential compaction: Visible with amplitude-based attributes and curvature attributes (most-positive curvature) because of the deformation of overlying sediments. Eigenstructure coherence will likely not detect them.
• Thick channels above thin-bed tuning: Coherence can highlight channel edges where the waveform changes abruptly or the channel cuts into underlying reflectors.
• Thick aggradational channels: Detected by a full suite of attributes, including coherence, amplitude, curvature, and spectral decomposition. Spectral decomposition can help approximate the channel's relative thickness.

Seismic attributes like coherence, geometric attributes, energy gradients, and spectral decomposition are helpful in identifying reef boundaries, internal porosity variations, and subtle heterogeneities within reefs. Coherence can map reef edges and faults, while spectral decomposition helps identify different peak frequencies related to reservoir thickness and porosity.

Diagenesis significantly alters carbonate rocks through processes like dissolution and karstification, which can modify reflector geometries and create or destroy porosity. Anhydrite, often associated with carbonates, can act as a seal but also causes multiples and velocity pull-ups upon dissolution, complicating seismic imaging. Diagenetic fronts can also cut across original depositional geometries, creating false positives in seismic data.

Fractures enhance porosity and permeability in carbonates, especially when matrix porosity is poor. Azimuthal AVO, multi-azimuth velocity analysis, and multicomponent acquisition techniques are used to determine the degree and orientation of fracturing. Geometric attributes, such as dip magnitude and curvature, can also be used to map fracture patterns and associated high-permeability zones, as carbonates are brittle and tend to fracture easily.

Coherence reveals features related to shoal-body geometries and shelf margins, distinguishing them from basinward-dipping foreslope deposits. Spectral decomposition highlights carbonate shoals and provides complementary information about shelf edges, which may not be as clear on coherence images. The inline and crossline components of the energy-weighted coherent-amplitude gradient provide consistent images.

Karst features are formed by the dissolution of carbonate rocks, resulting in sinkholes and collapse features. They are identified on seismic data as areas of low coherence, bowl-shaped depressions (indicated by negative curvature), and chaotic reflectivity patterns. Time or phantom-horizon slices are particularly useful in mapping the lateral extent of karst features.

Slumps are recognized as low-coherence features and can be associated with fracturing in chalk sequences. The degree of lithification and the mode of failure determine whether chalk mobilization results in grain transport, soft sediment deformation, or brittle deformation. Massively fractured chalks have enhanced permeability and reservoir capacity and can also provide routes for hydrocarbon migration.

Coherence volumes highlight areas of waveform similarity, allowing interpreters to see faults that may be subtle or obscured on conventional amplitude slices. While amplitude slices may show some faults trending perpendicularly to the seismic strike, coherence slices often reveal additional faults, including those parallel to strike and intensely fractured regions. Overlaying coherence on seismic data helps understand the seismic expression of faults and facilitates their transfer to the seismic volume for 3D visualization.

Interpreting bifurcated, en echelon faults, and fault relays can be challenging with conventional vertical seismic slices, potentially leading to misidentification of multiple faults as a single fault. Coherence slices provide a clearer image of these complex fault geometries, enabling more accurate interpretation of fault patterns and trap closures. This is achieved by highlighting discontinuities in seismic data that may not be readily apparent on amplitude data.

Coherence and curvature attributes can help predict fractures by highlighting areas of deformation and structural complexity. Low-coherence zones may indicate fractured areas, while curvature attributes reveal subtle changes in dip associated with fractures. By analyzing these attributes, interpreters can identify zones of high fracture density and determine fracture orientation, aiding in reservoir characterization and hydrocarbon exploration.

Salt and shale diapirs are found globally, including the Gulf of Mexico, offshore Brazil, West Africa, and the North Sea. On coherence volumes, salt and shale diapirs often appear as low-coherence zones (black) surrounded by radial faults. Curvature attributes can further define their structural style. Coherence helps distinguish shale from salt diapirs by identifying higher-coherence lineaments and meandering features encased in larger low-coherence areas, typical of mass-transport complexes associated with shale diapirs. These attributes also reveal the interaction between salt/shale movement and faulting, leading to hydrocarbon traps.

Geometric attributes are based on simple, localized measurements (20-40 ms of data) compared to the human interpreter's analysis of several seconds of seismic data. While interpreters apply geologic models to infer fault locations even in areas of poor data quality, geometric attributes rely solely on waveform similarity, dip azimuth, and energy changes. They may miss faults with offsets smaller than a quarter wavelength, which can be detected by curvature. Combining geometric attributes with geological knowledge improves fault interpretation accuracy.

An inaccurate velocity model can cause artificial discontinuities in horizon slices through coherence volumes, leading to misinterpretation of faults. Using time slices through coherence volumes is recommended for mapping faults, as they are less biased by velocity model errors than horizon slices, which are better suited for mapping stratigraphy. Although the deep carboniferous faults will still be imaged if salt velocity model isn't accurate it is understood that these faults will be laterally displaced.

Geometric attributes, sensitive to lateral changes in waveform similarity, dip azimuth, and energy, are less influenced by phase and frequency content of seismic source wavelets. This makes them ideal for analyzing merged surveys with varying acquisition and processing parameters. Coherence, in particular, allows the identification of faults and sedimentary environments across survey boundaries, aiding in creating a more regional view of tectonism and basin evolution.