Geophysical Insights hosting the 2018 Oil & Gas Machine Learning Symposium in Houston on September 27, 2018
Introduction to Machine Learning for Multi–Attribute Interpretation and AASPI attributes - A 1-day, DGS Continuing Education course in Denver, CO on September 18th
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

Structural -> Gaussian and Mean Curvatures

Attribute Description:

The Gaussian and Mean curvatures are critical volumes for the derivation of other curvature attributes.

Interpretation Use:

Gaussian curvatures have been correlated to fracture systems.  Gaussian and mean curvatures often work together to identify local shapes since each attribute cannot differentiate the shapes alone. However, the two volumes are not particularly useful individually for visual interpretation.

Recommended Color Palette:

Colorbar for curvatures often includes two color themes such as blue and red or black and red to mark the anomalies. The less anomalous value along the zero value is marked by white or being transparent in the middle.

  Figure 2: Colorbar example for curvatures

Figure 2: Colorbar example for curvatures

Gaussian - 02.png

Examples:

  Figure 1: kmean(left) and kGaussian (right) curvature.

Figure 1: kmean(left) and kGaussian (right) curvature.

Gaussian - 04.png

Computation:

Roberts (2001) defines the Gaussian curvature

and mean curvature

Gaussian - 06.png

For more details on how those two volumes relate to further calculation, please refers to principal curvatures computation section

Reference