Page 145 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

144 Chapter 5

appropriate for analysis of stream sediment geochemical data in regions where there are
some occurrences of mineral deposits of interest. In regions where there are no known
occurrences of mineral deposits of interest, it is prudent to calculate productivity (Moon,
1999) or ‘stream-order-corrected’ residuals (Carranza, 2004a). Note, nonetheless, that
correcting for downstream dilution by using either equation (5.8) or (5.9) neglects
contributions from overbank materials, assumes lack of interaction between sediment
and water and that erosion is uniform in each catchment basin. Certainly, the
downstream dilution-correction model based on the idealised relation proposed by
Hawkes (1976), which is adopted in the case study, does not apply universally.
However, the idealised formula proposed Hawkes (1976) shows reasonable agreement
between theory and prediction of known porphyry copper deposits in his study area. In
addition, considering that sizes of catchment basins differ and that sizes of anomalous
sources (if present) could differ from one catchment basin to another, dilution-correction
is warranted despite limitations of the model.
Prior to stage (4), if dilution-corrected residuals are derived from ‘homogeneous’
subsets of stream sediment geochemical data, then application of robust statistics for
exploratory data analysis is preferred for standardisation of dilution-corrected residuals
per data subset instead of the conventional application of classical statistics for
standardisation. In stage (4), the results of the case study further demonstrate usefulness
of fractal analysis (Cheng et al., 1994) of discrete geochemical surfaces (i.e., catchment
basins polygons) of uni-element residuals and derivative scores representing multi-
element data. In the past, recognition of anomalies from dilution-corrected residuals was
made by visual inspection of spatial distributions of percentile-based classes of such
variables (Bonham-Carter and Goodfellow, 1986; Bonham-Carter et al., 1987; Carranza
and Hale, 1997). Now, recognition of anomalies from dilution-corrected residuals can be
made objectively by application of the concentration-area fractal method. In stage (5),
the area of influence of individual sample catchment basins is further useful in screening
of anomalies, as demonstrated in the case study using fault/fracture density estimated as
the ratio of number of pixels representing faults/fractures in a sample catchment basin to
number of pixels in that sample catchment basin. In another GIS-based case study,
Seoane and De Barros Silva (1999) prioritised sediment sample catchment basins that
are anomalous for gold by using catchment basin drainage sinuosity, which is estimated
as the ratio of total length of streams within a sample catchment basin to the total
distance between the start and end points of the main stream and its tributaries in that
sample catchment basin. Finally, it is clear that GIS supplements catchment basin
analysis of stream sediment anomalies with tools for data manipulation, integration and
visualisation in discriminating and mapping of significant geochemical anomalies.