Page 181 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

182 Chapter 6

TABLE 6-VII

Factors related to position of an AP (arbitrary point) with respect to every ⊥L i that render
satisfactory results (table entries in bold or highest final ranks) and unsatisfactory results in the
application of the distance correlation method. Results shown are based on analysis of the spatial
association between centroids of mapped units of Nabongsoran Andesite porphyry and
intersections of NNW- and NW-trending faults/fractures in Aroroy district (Philippines).

Factor 1: mean of angles Factor 2: mean coefficients of Combination of
AP formed by an AP with ⊥L i variation* of d jy along ⊥L i factors 1 and 2
Value Rank (1) Value Rank (2) (1)×(2) Final rank
S 61º 7 0.049 7 98 9
E 31º 5 0.038 6 60 8
C 26º 3 0.077 9 54 7
NW 47º 6 0.029 4 48 6
N 29º 4 0.034 5 40 5
W 63º 8 0.015 2 32 4
SE 72º 9 0.008 1 18 3
SW 11º 1 0.076 8 16 2
NE 18º 2 0.021 3 12 1
*Ratio of standard deviation to mean.

faults/fractures (i.e., proximal- r d jx d jy > distal- r d jx d jy ), within which all the centroids
of mapped units of Nabongsoran Andesite porphyry are present. These results are similar
to the results of the distance distribution analysis (see Figs. 6-10C and 6-10D).

Synthesis and discussion of results

Table 6-IX shows that, in the applications of the distance distribution method and the
distance correlation method in the case study area, the former method results in larger
distances of optimum positive spatial associations between the point geo-objects of
interest and individual sets of geological features. This is usually not the case as shown
by previous comparisons between the two methods (Carranza, 2002; Carranza and Hale,
2002b). The differences between the results of the two methods are attributable to the
discretisation of the distance data (see step 2) in the application of the distance
distribution method, whereas there is no discretisation of the distance data in application
of the distance correlation method.
The similar results of applications of the distance distribution method and the
distance correlation method presented in this volume further demonstrates the usefulness
of the former method in characterising spatial associations of a set mineral deposit
occurrences and individual sets of linear or point geological features. An important
advantage of the distance correlation method over the distance distribution method is
that the former provides meaningful results even though it makes use of only linear (or
point) geological features that are closest to the known occurrences mineral deposits of
interest. This is an advantage because, in many cases, localities of mineral deposit