Dip Averaging

In RDA the dip averages are calculated by three methods

- Vector mean
- Vector median
- Major eigenvector

Poles, or normals, to dips are used for averaging and then the averaged pole is converted back to the averaged dip. The underlying reason for this is that a pole uniquely represents the dip plane, while the dip is really a line within the plane. Following is an example of why poles are used instead of dips. The average of a 5 degrees eastward dip with a 5 degrees westward dip should be horizontal. A vector mean of the dips yields a vertical dip, while a vector mean of the poles yields the correct, horizontal dip.

The different averaging methods have their strengths and
weaknesses. Vector mean is a widely-used statistical technique for dip
averaging. Vector median, although it is not as rigorous statistically, is
generally superior to the mean and eigenvector methods for generic dips because it is less
affected by "wild" values. When used in smoothing, vector median also
seems to preserve the character better than either vector mean or the major
eigenvector. The major eigenvector method is superior for averaging
non-directional features such as fractures. For example, consider two
fractures, one dipping 85 degrees East and the other dipping 85 degrees West.
Although their dips are in the opposite direction, their dip planes differ by
only 10 degrees. The eigenvector method treats these dips as if they are
pointing roughly the same direction, while in the mean and median methods they
will nearly cancel out. On the other hand, consider a normal and
overturned bed dipping the same direction. These should not be treated as
pointing in the same direction, but __should__ cancel out. Thus, the
vector mean or vector median should be used on bedding in preference to the
major eigenvector method.