Page 193 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Knowledge-Driven Modeling of Mineral Prospectivity 195

Fig. 7-3. Knowledge-based binary representation of spatial evidence of mineral prospectivity.
Knowledge of spatial association between mineral deposits of the type sought and spatial data of
indicative geological features is applied to assign binary evidential scores (upper part of the
figure). If values or classes of values of spatial data have optimum positive spatial association with
mineral deposits of the type sought, they are given a maximum evidential score of mineral
prospectivity; otherwise, they are given a minimum evidential score of mineral prospectivity.
These scores are discontinuous, meaning there are no intermediate evidential scores of mineral
prospectivity. Binary representation of spatial evidence is inconsistent with real situations of
spatial associations between mineral deposits and indicative geological features. For visual
comparison, the graph in the upper part of the figure is overlaid on schematic cross-sections of
ground conditions (lower part of the figure), but the y-axis of the graph does represent vertical
scale of the cross-sections. See text for further explanation.

deposits, the evidential scores should not be uniformly equal to the minimum evidential
score. The same line of reasoning can be accorded to the binary representation of
evidence for presence of surficial geochemical anomalies, which may be significant
albeit allochthonous (i.e., located not directly over the mineralised source) (Fig. 7-3).
Note also that the graph of binary evidential scores versus data of spatial evidence is
inconsistent with the shapes of the D curves (Figs. 6-9 to 6-12) in the analyses of spatial
associations between epithermal Au deposit occurrences and individual sets of spatial
evidential data in the case study area. Nevertheless, binary representation of evidence of
mineral prospectivity is suitable in cases when the level of knowledge applied is lacking