Dr. Tom Smith presenting on Machine Learning at the 3D Seismic Symposium on March 6th in Denver
What is the "holy grail" of Machine Learning in seismic interpretation? by Dr. Tom Smith, GSH Luncheon 2018
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

CWT -> Peak Frequency, Peak Magnitude, Peak Phase, Peak Magnitude Above Average, Roughness, Range Trimmed Mean Magnitude, and Slope

Attribute Description: 

The statistical summary attributes (i.e., Bandwidth, Reconstructed Data, Mean Frequency, Peak Frequency, Peak Magnitude, Peak Magnitude Above Average, Peak Phase, Modeled, Residual, Roughness, Range Trimmed Mean Magnitude, Slope) generated by the Continuous Wavelet Transform (CWT) can also help in the interpretation of anomalies associated with reservoirs or other zones of interest (AASPI Documentation; Zhang, 2010).

Interpretation Use: 

The statistical summary attributes can be also useful for providing more information sensitive to stratigraphy or reservoir physical properties (Chopra and Marfurt, 2007). Attribute results can be analyzed in different ways, from a plan view, vertical transects, or draped over a horizon display.

Recommended color palette: 

For the statistical summary attributes a divergent color scheme is suggested. The midpoint color is white to emphasize the progression outward two different hues. In the examples below, the hues were set to light blue and yellow to better highlight geologic features. Or even, 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. We suggest using the histogram of values to guide setting color value thresholds.

  Figure 1. Color bar examples of output attributes: bandwidth (a), bandwidth extension (b), mean frequency (c), peak frequency (d), and inverse reconstructed (e).

Figure 1. Color bar examples of output attributes: bandwidth (a), bandwidth extension (b), mean frequency (c), peak frequency (d), and inverse reconstructed (e).

  Figure 2. Color bar examples of output attributes: peak magnitude (a), peak magnitude above average (b), range trimmed mean magnitude (c), roughness (d), and slope (e).

Figure 2. Color bar examples of output attributes: peak magnitude (a), peak magnitude above average (b), range trimmed mean magnitude (c), roughness (d), and slope (e).

Examples:

  Figure 3. Time slice displays of seismic amplitude (a) and output attributes: bandwidth (b), bandwidth extension (c), mean frequency (d), peak frequency (e), and inverse reconstructed (f).

Figure 3. Time slice displays of seismic amplitude (a) and output attributes: bandwidth (b), bandwidth extension (c), mean frequency (d), peak frequency (e), and inverse reconstructed (f).

  Figure 4. Time slice displays of seismic amplitude (a) and output attributes: peak magnitude (b), peak magnitude above average (c), range trimmed mean magnitude (d), roughness (e), and slope (f).

Figure 4. Time slice displays of seismic amplitude (a) and output attributes: peak magnitude (b), peak magnitude above average (c), range trimmed mean magnitude (d), roughness (e), and slope (f).

Recommended color palette: 

For the Peak Phase attribute, a cyclic color scheme is suggested. In this color palette, the hues wrap around so that the red follows purple. A specific color is assigned to different phase ranges, so then the display can be used to infer the continuity of seismic events. We suggest using the histogram of values to guide setting color value thresholds.

  Figure 5. Color bar examples of seismic amplitude (a) and output attribute: peak phase (b).

Figure 5. Color bar examples of seismic amplitude (a) and output attribute: peak phase (b).

Examples:

  Figure 6. Vertical transect views of seismic amplitude (a) and output attribute: peak phase (b).

Figure 6. Vertical transect views of seismic amplitude (a) and output attribute: peak phase (b).

Computation: The statistical summary attributes are additional outputs of the spectral decomposition based on Complex Matching Pursuit (refer to Spectral Decomp-> CWT -> Spectral Magnitude, Spectral Phase, Spectral Voice Components, and Spectral Shape (Ridge) attributes description section).  Prior to computing these summary attributes, the amplitude volume (time or depth domain) is spectrally whitened to account for changes in the source wavelet with depth and a non-flat source spectrum. Thereafter, the output volume shows a relatively flat spectrum bound by two tails (see Figure 2). Following this behavior, the statistical summary attributes are generated.

CWT - 07.png

References

  • AASPI documentation, http://mcee.ou.edu/aaspi/documentation/Spectral_Attributes-spec_cwt.pdf
  • Chopra, S. and K. J. Marfurt, 2007, Seismic attributes for prospect identification and reservoir characterization: SEG Geophysical development series, 11, 123 – 151.
  • Zhang, K., 2010, Seismic attribute analysis of unconventional reservoirs, and stratigraphic features: PhD Thesis, University of Oklahoma.