Page 265 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

268 Chapter 8

deposit-type locations. For most non-deposit locations Ǔ i is less than 0.76; however, the
results show that a few non-deposit locations have multivariate spatial data signatures
similar to the coherent deposit-type and proxy deposit-type location locations and these
non-deposit locations are thus plausible prospective targets.
The two sets of logistic regression analyses indicate that, in the case study area, there
are 11 coherent locations of epithermal Au deposits having strongly similar multivariate
spatial data signatures, which are dissimilar from the multivariate spatial data signatures
of two locations of epithermal Au deposits. The first and second logistic regression
analyses indicate, respectively, that there are 86 and 85 proxy deposit-type locations with
strongly similar and thus coherent multivariate spatial data signatures, which are similar
and coherent to the multivariate spatial data signatures of the 11 coherent locations of
epithermal Au deposits. Between the results of the two logistic regression experiments,
all deposit-type locations have the same classifications whilst only three (~3% of 104)
proxy deposit-type locations have different classifications. The values of Ǔ i scores of the
coherent deposit-type and proxy deposit-type locations derived from the two logistic
regression analyses have nearly perfect correlation (Fig. 8-7C). The results shown in Fig.
8-7 indicate, therefore, the reproducibility and robustness of the proposed technique for
objective selection of coherent deposit-type (as well as proxy deposit-type) locations.
Fig. 8-8 shows the 11 coherent locations (represented as unit cells of 100×100 m) of
epithermal Au deposits in the case study area. Fig. 8-8 also shows 86 proxy locations of
epithermal Au deposits having multivariate data signatures that are coherent with the
deposit-type locations per analysis using set 1 of non-deposit locations (Fig. 8-4). The
location of epithermal Au deposit #12 and the locations immediately surrounding it are
non-coherent with the 11 coherent locations of epithermal Au deposits because they are
situated in the area without stream sediment geochemical data. The location of
epithermal Au deposit #13 and most of the locations immediately surrounding it are non-
coherent with the 11 coherent locations of epithermal Au deposits because these
locations are characterised by background integrated PC2 and PC3 scores of the multi-
element geochemical data based on catchment basin analysis (see Fig. 5-12). These
results indicate that, given the same spatial evidential data sets used in deriving the
MOFS, using deposits #12 and #13 in data-driven modeling of epithermal Au
prospectivity in the study area is likely to result in a predictive model that is poorer than
a predictive model derived by not using them. An indirect proof of this proposition is
shown in Fig. 8-9, which compares the spatial associations (quantified via the distance
distribution method; see Chapter 6) of the locations of 13 epithermal Au deposits and the
11 coherent locations of epithermal Au deposits with intersections of NNW- and NW-
trending faults/fractures in the case study area. The D curves indicate that, in the case
study area, all the deposit-type locations have weaker spatial associations with indicative
geological features compared to the coherent deposit-type locations (see also
explanations for Fig. 6-10A because Fig. 8-9A is identical to it). This goes to show that
using all deposit-type locations in data-driven modeling of mineral prospectivity is likely