|
|
|
Multi-criteria analysis in GIS environment for natural resource development - a case study on gold exploration
Integration of
pedogeochemical and hydrogeochemical data
The continuous pedogeochemical
and hydrogeochemical data were regressed with the binary response variable
"deposit proximity". The hydrogeochemical data included water quality
parameters: sulphate, chloride, alkalinity, silica, Ca, Mg, Na, K, As and Sb.
The pedogeochemical data included concentrations of As, Sb, Hg and Bi. The
factor "deposit proximity" was coded using a corridor width of 0.5 km around the
known mineral occurrences. Observations falling within this buffer zone were
coded to 1, whereas others were coded to 0.
With hydrogeochemical data,
the predicted gold occurrences at the ith location was calculated as
Y = bo
+ åbjXij.
In practice this was carried out by a step-wise method
reducing the number of variables and coefficients requiring interpretation.
These regression coefficients represent a multi-element geochemical signature
for predicting gold mineralization. These predicted values were interpolated for
all the pixels. Similar attempt was made for predicting target areas for gold
exploration with the pedogeochemical data.
As both the predicted
probability maps were not mutually exclusive, given the mineral occurrence map,
Bayesian logic which uses conditional probability could not be used. So these
maps were integrated with the use of fuzzy logic. As it was found that both the
hydrogeohemical-parameters and pedogeochemistry were controlled by the gold
mineralization, fuzzy-AND was used in the integration of the above two maps.
Binary Map Analysis
Integration of binary patterns was carried out
with the use of conditional probabilities. The method was more convenient to use
than multiple regression for two following reasons,
- It avoids the requirement to subdivide the region into cells, each cell
associated with an attribute. In order to capture the geometrical information,
large number of small sampling cells must be created and this is undesirable,
because of the resulting large attribute file and degree of spatial
auto-correlation (Wackernagel, 1995).
- Binary map method is better able to cope with the problem of missing data,
as we had. Bayes rule assumes that the patterns are conditionally independent.
Chi-square test was carried out to check for the mutual exclusiveness of
different maps, given the deposit-proximity map.
- Preparation of binary favourable geochemical signature map (FavGeochem)
Preparation of a binary favorable geochemical signature map (FavGeochem) was
attempted with the use of conditional probabilities. Several maps of different
contour-intervals were prepared. In order to determine the optimum cut-offs of
contour interval, for classifying patterns into binary maps, the weights W+ and
W- were calculated for a succession of cut-offs and under normal conditions, the
maximum value of W+ - W- giving the cut-offs at which the predictive power of
the resulting pattern is maximized.
- Integration of the lineaments and preparation of proximity map (FavLin) As
most of the lineaments were assumed to have a control on the disposition of the
auriferous lodes, and they were not conditionally independent, these maps were
integrated with Fuzzy-OR. Preparation of a binary proximity map for lineament
(FavLin) was attempted with the use of conditional probabilities. Several buffer
zone of different widths were prepared and an optimum cut-off was selected on
the technique as described earlier.
- Preparation of binary Favourable lithological map (FavLith)
Similar attempt was made to identify the conditional
probability for the rock-type in targeting the gold occurrences. The weights for
modelling posterior probabilities of the deposit occurring in 0.9 Km2 area (1
pixel) were as follows
| Map-Pattern
| W+
| W-
|
| FavLith
| 0.457
| -0.773
|
| FavGeochem
| 1.004
| -0.103
|
| FavLin
| 0.668
| -0.467 |
|
|
|