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Data-Driven Modeling of Mineral Prospectivity                        299

           locations in training set B than to the discrimination between the random-selected proxy
           deposit-type locations and non-deposit locations in training set A. This implies that the
           coherent proxy deposit-type locations  have stronger spatial association  with  NNW-
           trending faults/fractures compared to randomly-selected proxy deposit-type locations.
              In both of the two discriminant models, the contributions of the ‘ANOM’ predictor
           variables (i.e., classes of high integrated PC2 and PC3 scores obtained from  the
           catchment basin analysis) are more-or-less the same but are subordinate to the
           contributions of the ‘FI’ and ‘NNW’ predictor variables. In both of the two discriminant
           models, the contributions of the ‘NW’ predictor variables (i.e., classes of proximity to
           NW-trending  faults/fractures) are the most inferior. These results suggest that the
           presence of  multi-element  geochemical anomalies is a  more important predictor  of
           epithermal Au prospectivity in the case  study area than proximity to NW-trending
           faults/fractures.
              Both  of the  discriminant models  based  on training sets  A and B indicate that (a)
           proximity to intersections  of NNW- and  NW-trending  faults/fractures is a more
           important control on epithermal Au mineralisation in the case study area than proximity
           to either NNW- or NW-trending  faults/fractures and (b) proximity to NNW-trending
           faults/fractures is a more important control on epithermal Au mineralisation in the case
           study area than  proximity to  NW-trending  faults/fractures. These  results contrast
           somewhat with the implications of the results of the analyses of spatial associations in
           Chapter 6 and the data-driven estimates of EBFs earlier in this chapter. Nevertheless, the
           multivariate spatial associations  depicted by the  results shown in  Table 8-V are
           consistent with the knowledge that the presence and/or proximity to dilational jogs or
           zones of extensions at/near either discontinuities or intersections of faults/fractures are
           more important controls  on hydrothermal mineralisation than  faults/fractures alone
           (Sibson, 1987, 1996, 2000, 2001). These results underscore the advantage of multivariate
           techniques compared to bivariate techniques in terms  of simultaneous analysis and
           synergistic interpretation of empirical spatial associations between deposit-type locations
           and indicative geological features.
              If  the  magnitudes of the standardised function coefficients are compared and
           contrasted  with each set of  spatial evidence rather than among the classes of spatial
           evidence, then the two  discriminant  models (Table  8-V) indicate that  epithermal Au
           deposits in the case study area mostly occur within (a) about 200 m of NNW-trending
           faults/fractures, (b) about 750 m of NW-trending faults/fractures and (c) about 1 km of
           intersections  of NNW- and  NW-trending faults/fractures. These results are consistent
           with the empirical spatial associations between epithermal Au deposits and indicative
           geological features as quantified via the distance correlation method  rather than as
           quantified via the distance distribution method (see Chapter 6, Table 6-IX). In addition,
           the two discriminant models (Table 8-V) indicate that integrated PC2 and PC3 scores
           (obtained via catchment basins analysis;  Chapter  5) greater than 0.25 are associated
           spatially with  most of the known epithermal Au deposits and therefore represent
           significant anomalies. The  overall results of the application  of LDA are therefore
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