Page 239 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS
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Knowledge-Driven Modeling of Mineral Prospectivity                   241

           TABLE 7-XI

           Principal components of fuzzified evidential scores (fS c ) of classes of proximity  to geological
           features in Aroroy district (Philippines) (see Table 7-X and Fig. 7-21B).

                                                     4
                       NNW 1    NW 2      NE 3     NA     % of variance Cum. % of variance
              PC1      0.528    0.592     0.177    0.582    42.70        42.70
              PC2      -0.242   -0.207    0.937    0.145    24.47        61.17
              PC3      0.743    -0.145    0.253   -0.603    17.76        84.93
              PC4      -0.333   0.765     0.164   -0.526    15.07       100.00
                                     2
           1 NNW-trending faults/fractures.  NW-trending faults/fractures.  NE-trending faults/fractures.
                                                             3
           4 Nabongsoran Andesite porphyry.
              For the case study area, PC1 (explaining about 43% of the total variance of fS c values
           in the input maps) indicates a strong spatial association between the NNW- and NW-
           trending faults/fractures and the Nabongsoran Andesite porphyry, which is weakly
           associated with  the NE-trending  faults/fractures (Table 7-XI). PC2 (explaining  about
           24% of the total variance of fS c values in the input maps) reflects mainly the NE-trending
           faults/fractures, whilst PC3 (explaining about 18% of the total variance of fS c values in
           the input  maps) mainly reflects either the NNW-trending faults/fractures or the
           Nabongsoran Andesite porphyry. PC4 (explaining about 15% of the total variance of fS c
           values in the input maps) mainly reflects either the NW-trending faults/fractures or the
           Nabongsoran Andesite  porphyry. Thus,  of the four PCs, PC1 is a  multivariate
           association that is the  most  plausible integrated spatial evidence of heat source and
           structural controls on hydrothermal mineralisation in the case study area. A map of the
           PC1 scores can thus be considered a geologically-constrained mineral prospectivity
           model (Carranza, 2002).
              The geologically-constrained wildcat model  of hydrothermal  mineral deposit
           prospectivity represented by the map of PC1 scores (Fig. 7-22A), obtained from the PC
           analysis of fuzzified evidential scores (Table 7-XI), shows intersecting linear patterns
           (reflecting proximity to faults/fractures), which intersect with circular patterns (reflecting
           proximity to mapped units  of Nabongsoran  Andesite  porphyry) in the southwestern
           quadrant of the case study area. The map patterns are different from those of the earlier
           models of epithermal Au prospectivity in the case study area because the geochemical
           evidence has not been integrated into the present model. The predictive performance of
           the map of PC1 scores can be aptly compared only with the map of integrated Bel (Fig.
           7-19A) because both of these maps have predictions for all locations in the case study
           area. If 20% of the case study area is considered prospective, then the map of PC1 scores
           delineates correctly six (or about  46%)  of  the cross-validation  deposits  (Fig. 7-22B).
           This means that, based on 20% predicted prospective zones, the map of PC1 scores is
           inferior to the map of integrated  Bel (Fig. 7-19B). If  50% of the case study area is
           considered  prospective, then  the map of PC1 scores delineates correctly 12 (or about
           92%) of the cross-validation deposits (Fig. 7-22B). This  means that,  based on  50%
           predicted prospective zones, the  map of PC1 scores performs equally as the  map of
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