Watch videos of Presentations from SEG 2017
Using Attributes to Interpret the Environment of Deposition - A Video Course. Taught by Kurt Marfurt, Rocky Roden, and ChingWen Chen
Dr. Kurt Marfurt and Dr. Tom Smith featured in the July edition of AOGR on Machine Learning and Multi-Attribute Analysis
Rocky Roden and Ching Wen Chen in May edition of First Break - Interpretation of DHI Characteristics using Machine Learning
Seismic interpretation and machine learning by Rocky Roden and Deborah Sacrey, GeoExPro, December 2016

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Amplitude -> Curvedness, Shape Index, Shape Dome, Shape Bowl, Shape Ridge, Shape Saddle, and Shape Valley

Attribute Description:  The E_pos and E_neg attributes can be combined to generate components of curvature that describe deformation and shapes.  The Curvedness attribute is a measure of total deformation, whereas the Shape Index attribute provides a qualitative description of the local morphology (AASPI Documentation).

Interpretation Use:  The Curvedness attribute describes the deformation of rocks. The Shape Index attribute gives a qualitative description of shapes. The Shape Dome, Shape Bowl, Shape Ridge, Shape saddle, and Shape Valley are attributes extracted from the Shape Index attribute and these attributes depict the local morphology of the rocks in terms of a dome, bowl, ridge, saddle, and valley shapes (Chopra and Marfurt, 2007). Attribute results are better analyzed in plan view or draped over a horizon displays.

Recommended color palette:  For the Curvedness attribute, a grayscale gradient color scheme is suggested. The color progression could begin with white (to highlight useful geological features) and finish with black (to denote shadow areas), or vice-versa. For the Shape Index attribute, a divergent color scheme is suggested. The midpoint color is white to emphasize the progression outward two different dark hues. In the examples below, the dark hues were set to blue and red to better highlight geologic features. We suggest using the histogram of values to guide setting color value thresholds.   

Figure 1. Color bar examples of seismic amplitude (a) and output attribute: curvedness (b) and shape index (c).

Figure 1. Color bar examples of seismic amplitude (a) and output attribute: curvedness (b) and shape index (c).

Examples:

Figure 2. Time slice displays of seismic amplitude (a) and output attributes: curvedness(b) and shape index (c).

Figure 2. Time slice displays of seismic amplitude (a) and output attributes: curvedness(b) and shape index (c).

Recommended color palette:  For the Shape Dome, Shape Bowl, Shape Ridge, Shape Saddle, and Shape Valley attributes a divergent color scheme is suggested. The midpoint color is white to emphasize the progression outward two different dark hues. In the examples below, the dark hues were set to blue and red to better highlight geologic features. We suggest using the histogram of values to guide setting color value thresholds.

Figure 3. Color bar examples of output attributes: shape dome (a), shape bowl (b), shape ridge (c), shape saddle (d) and shape valley (e).

Figure 3. Color bar examples of output attributes: shape dome (a), shape bowl (b), shape ridge (c), shape saddle (d) and shape valley (e).

Examples:

Figure 4. Time slice displays of seismic amplitude (a) and output attributes: shape dome (b), shape bowl (c), shape ridge (d), shape saddle (e), and shape valley (f).

Figure 4. Time slice displays of seismic amplitude (a) and output attributes: shape dome (b), shape bowl (c), shape ridge (d), shape saddle (e), and shape valley (f).

Computation: The Curvedness, Shape Index, Shape Dome, Shape Bowl, Shape Ridge, Shape Saddle, and Shape Valley attributes are computed using this workflow:

Input data → Estimates of reflector dip → Estimates of coherent energy gradients through measurements of similarity → Curvature amplitude data

The process can be summarized as follows (AASPI documentation):

  1. Take the Inline Dip, Crossline Dip, and seismic amplitude volumes as input data (refer to Filter Dip Components -> Inline Dip, Crossline Dip, and Confidence attribute description section)
  2. Run a similarity computation by...
    1. Computing the covariance matrix from the complex trace signal
    2. Decomposing the covariance matrix into its corresponding ktheigenvalue and vtheigenvectors. Then normalize the eigenvectors to be unit length
    3. Estimating a scaled principal component, where the scale is the inner product or correlation of the eigenvector
  3. Generate estimates of inline and crossline coherent energy gradient
  4. Compute E_pos and E_neg (refer to Curvatures -> Amplitude -> E_max, E_min, E_pos, and E_neg attributes description section). Where Curvedness and Shape Index attributes are (AASPI Documentation),

Note that the Shape Index attribute range varies between -1.0 and +1.0, with a bowl-shaped indicated by -1.0, a valley shaped indicated by -0.5, a saddle shape indicated by 0.0, a ridge shape indicated by +0.5, and dome shape indicated by +1.0 (AASPI documentation).

Figure 5. Definition of 3D shapes based on their Shape Index (s), E_pos and E_neg attribute responses (after AASPI documentation).

Figure 5. Definition of 3D shapes based on their Shape Index (s), E_pos and E_neg attribute responses (after AASPI documentation).

To generate the Shape Dome, Shape Bowl, Shape Ridge, Shape Saddle, and Shape Valley attributes only requires to multiply the Curvedness attribute at every point in the volume by a filtered version of the Shape Index attribute (shown in Figure 4) that passes a shape component of interest.

Figure 6. Filters used to extract shape information from Shape Index attribute (after Chopra and Marfurt, 2007).

Figure 6. Filters used to extract shape information from Shape Index attribute (after Chopra and Marfurt, 2007).

References