Page 238 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

240 Chapter 7

TABLE 7-X

Fuzzified evidential scores [fS c ; equation (7.21)] of classes of proximity to individual sets of
geological features, Aroroy district (Philippines). The ranges of proximity classes are 5-percentile
intervals of distances to individual sets of geological features.

4
Proximity to NNW 1 Proximity to NW 2 Proximity to NE 3 Proximity to NA
Range (km) fS c Range (km) fS c Range (km) fS c Range (km) fS c
0.00–0.04 1.0000 0.00–0.01 1.0000 0.00–0.03 1.0000 0.00–0.36 1.0000
0.04–0.08 1.0000 0.01–0.18 1.0000 0.03–0.06 1.0000 0.36–0.74 1.0000
0.08–0.11 1.0000 0.18–0.27 1.0000 0.06–0.09 1.0000 0.74–1.10 1.0000
0.11–0.15 1.0000 0.27–0.36 1.0000 0.09–0.12 1.0000 1.10–1.45 1.0000
0.15–0.19 1.0000 0.36–0.44 1.0000 0.12–0.15 1.0000 1.45–1.79 1.0000
0.19–0.23 0.9997 0.44–0.53 0.9998 0.15–0.17 0.9997 1.79–2.19 0.9998
0.23–0.27 0.9948 0.53–0.64 0.9967 0.17–0.20 0.9955 2.19–2.60 0.9845
0.27–0.32 0.9526 0.64–0.75 0.9453 0.20–0.24 0.9526 2.60–3.05 0.9845
0.32–0.36 0.8176 0.75–0.88 0.7211 0.24–0.27 0.7685 3.05–3.51 0.5000
0.36–0.41 0.5000 0.88–1.01 0.5000 0.27–0.30 0.5000 3.51–3.96 0.5000
0.41–0.46 0.2451 1.01–1.15 0.1301 0.30–0.33 0.2315 3.96–4.39 0.0155
0.46–0.52 0.0953 1.15–1.29 0.0547 0.33–0.37 0.1091 4.39–4.80 0.0155
0.52–0.60 0.0474 1.29–1.44 0.0219 0.37–0.41 0.0630 4.80–5.21 0.0155
0.60–0.71 0.0159 1.44–1.65 0.0086 0.41–0.45 0.0266 5.21–5.68 0.0155
0.71–0.84 0.0076 1.65–1.92 0.0086 0.45–0.50 0.0148 5.68–6.22 0.0155
0.84–1.06 0.0036 1.92–2.24 0.0033 0.50–0.55 0.0082 6.22–6.80 0.0155
1.06–1.35 0.0012 2.24–2.60 0.0013 0.55–0.64 0.0045 6.80–7.40 0.0002
1.35–1.73 0.0006 2.60–3.02 0.0013 0.64–0.80 0.0018 7.40–8.01 0.0002
1.73–2.23 0.0004 3.02–3.58 0.0005 0.80–1.06 0.0007 8.01–8.62 0.0002
2.23–3.55 0.0002 3.58–5.32 0.0002 1.06–2.04 0.0002 8.62–9.96 0.0002
2
1 NNW-trending faults/fractures. NW-trending faults/fractures. NE-trending faults/fractures.
3
4 Nabongsoran Andesite porphyry.
with the conceptual model of multi-class representation of spatial evidence of mineral
prospectivity (Fig. 7-8).
The values of S c and fS c are calculated and stored in attribute tables associated with
maps of classes of proximity to individual sets of geological features. Maps or images of
fS c of individual sets of faults/fractures are then created and input to principal
components (PC) analysis (cf. Luo, 1990). The application of PC analysis here is based
on the assumption that an integrated spatial evidence of geologic controls on
hydrothermal mineralisation can be characterised and can be derived by a quantitative
function of linear combinations of proximity to individual sets of geological structures.
Interpretation of a particular PC (or eigenvector) as a favourability function representing
mineral prospectivity is based on the multivariate association between the input
geological variables, as indicated by the magnitude and signs (positive or negative) of
the eigenvector loadings. The geological meaning of the multivariate association of the
different geological features represented by each of the PCs is also interpreted in terms
of general knowledge about mineralisation.