Page 270 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Data-Driven Modeling of Mineral Prospectivity 273

Harris and Sanborn-Barrie, 2006; Woldai et al., 2006), (b) the widths or intervals of C ji
classes in some of the X i evidential maps (e.g., Harris and Sanborn-Barrie, 2006) and,
perhaps, (c) the unit cell size. These and the foregoing cross-validation experiments aim
at predictive model calibration.
In the demonstration of evidential belief modeling and discriminant analysis of
mineral prospectivity in the case study (see below), the following combinations of N–n
and deposit-type classification strategies for cross-validation are applied.
ƒ In order to illustrate the utility of coherent deposit-type locations,
(a) coherent deposit-type locations comprise one training subset (n=11 out of N=13;
Fig. 8-8) and
(b) all deposit-type locations comprise another training set (N=13; Fig. 8-8).
The derived data-driven models of mineral prospectivity are then cross-validated
against a testing set consisting of all proxy deposit-type locations (N=104; Fig. 8-8).
ƒ In order to illustrate the utility of coherent proxy-deposit type locations,
(a) coherent proxy deposit-type locations are used for training (n=86 out of N=104;
Fig. 8-8) and
(b) randomly-selected proxy deposit-type locations are also used for training (n=86
out of N=104).
The derived data-driven models of mineral prospectivity are then cross-validated
against all deposit-type locations (N=13).

EVIDENTIAL BELIEF MODELING OF MINERAL PROSPECTIVITY
Brief and informal explanations of the concept of evidential belief functions (denoted
hereafter as EBFs), which are based on the theory of evidential belief (Dempster 1967,
1968; Shafer, 1976), as applied to knowledge-driven modeling of mineral prospectivity
are given in Chapter 7. Those explanations are adapted here for data-driven modeling of
mineral prospectivity.
Estimates of EBFs relate to the proposition that “this location is prospective for
mineral deposits of the type sought”. That is, estimates of EBFs represent, according to
the general definition of index of prospectivity in equation (8.2), values of wC ji for each
of the C ji classes in X i maps of spatial evidential features with respect to D known
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locations of mineral deposits of interest in a study area. For the j C ji class in the i X i
evidential map, four EBFs, each in the range [0,1], are estimated to evaluate the
proposition of mineral prospectivity. These four EBFs are belief (or Bel), disbelief (or
Dis), uncertainty (or Unc) and plausibility (or Pls). The Bel and Pls, respectively,
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represent lower and upper degrees of support to the proposition given a j C ji class of i
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X i spatial evidence. The Unc represents a measure of ‘doubt’ that the given j C ji class of
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i X i spatial evidence supports the proposition. The Dis represents a degree of opposition
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to the proposition given the j C ji class of i X i spatial evidence.
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The four EBFs are inter-related (see Fig. 7-18). The sum of Bel+Unc+Dis for the j
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C ji class in the i X i evidential map is equal to 1. Likewise, the sum of Pls+Dis for the j