Page 164 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Analysis of Geologic Controls on Mineral Occurrence 165

2. Discretise or classify the map of distances to geological features into a number of
classes. A classification using narrow equal intervals of percentiles or cumulative
proportions of distances is advisable. That is because the type (i.e., normal, log-
normal, etc.) of empirical density distribution of distances to a set of geological
features is usually unknown but percentiles of the distance data are robust for
classification and directly represent the cumulative proportions of distances sought in
the distance distribution analysis.
3. In the attribute table associated with classified map of distances to geological
features, perform the following table operations (see Fig. 6-8).
a. Determine the upper limit of each distance (buffer) class. In some cases, this
variable is already given in the attribute table associated with a classified map of
distances to geological features.
b. Determine the number (or frequency) of pixels (npixd) in each distance class. In
many cases, this variable is already given in the attribute table associated with a
classified map of distances to geological features.
c. Determine the cumulative number (or cumulative frequency) of distance class
pixels (npixp) in the order of increasing distances.
d. Determine the total number of pixels (npixt).
e. Determine Ê(X) by dividing values of npixp with the value of npixt. As
shown in Fig. 6-8, values of Ê(X) are stored in column propr (which stands for
cumulative proportion of ‘random’ points) of the attribute table. Note that, in
principle, a very large number of random points must be generated to properly
estimate Ê(X). Note also that any one of all the pixels in a map could probably
represent a random point (or a Poisson process), so that (i) each pixel in a map
represents a Euclidean distance E(X) to its nearest geological features and (ii)
using all pixels (i.e., npixt) leads to a reasonable estimate of the cumulative
frequency distribution of expected distances Ê(X) from a set of linear features
under examination (Bonham-Carter, 1994, pp. 163-164).
4. Perform a cross or intersect or zonal statistics operation using the classified map of
distances to a set of geological features and a map of the locations (or occurrences) of
mineral deposits of the type sought.
5. Join the geo-information of number (or frequency) of deposit pixels contained in
certain classes of distances from the cross table output to the attribute table of the
map of classified distances of a set of geological features. Note that the cross
operation functions (i) to determine the Euclidean distance [i.e., O(X)] between each
deposit pixel and then (ii) to classify the values of O(X) in the same way as the values
of E(X) were classified.
6. In the attribute table of the map of classified distances of a set of geological features,
perform further the following table operation (see Fig. 6-8).
a. Determine the cumulative number (or cumulative frequency) of deposit pixels
(npixpd) contained in every distance (buffer) class in the order of increasing
distance.
b. Determine the total number of deposit pixels (npixtd).