# Fisher Distribution

A **Fisher Distribution** is commonly used for modeling the distribution of 3-dimensional orientation vectors [Fisher (1953)], such as the distribution of joint orientations (pole vectors) on a sphere. A Fisher Distribution describes the angular distribution of orientations about a mean orientation vector, and is symmetric about the mean. The probability density function can be expressed as:

Eqn.1

where is the angular deviation from the mean vector, in degrees, and **K** is the "Fisher constant" or dispersion factor.

In **SWedge**, a Fisher distribution can be used to define probabilistic joint orientation or tension crack orientation.

## Fisher K

The **Fisher K** value describes the tightness or dispersion of an orientation cluster. A larger K value (e.g. 50) implies a tighter cluster, and a smaller K value (e.g. 20) implies a more dispersed cluster, as shown in the following figure.

*Effect of Fisher K on a randomly generated joint set of 500 samples*

The Fisher K value can be estimated from Eqn. 2, for data sets with greater than approximately 30 vectors (poles) [Fisher (1953)].

Eqn.2

where **N** is the number of poles, and **R** is the magnitude of the resultant vector (i.e. the magnitude of the vector sum of all pole vectors in the set).

## Standard Deviation

By analogy with a Normal distribution, it can be shown that the standard deviation of a Fisher distribution can be estimated from Eqn.3 [Butler (1992)].

Eqn.3

where is the "angular standard deviation" or "angular dispersion" of the Fisher Distribution, and **K** is the Fisher constant.