Page 250 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Data-Driven Modeling of Mineral Prospectivity 253

addressing the issues of (a) objective selection of a suitable unit cell size for data-driven
modeling mineral prospectivity, (b) selection of coherent deposit-type locations for data-
driven modeling of mineral prospectivity and (c) cross-validation of data-driven mineral
prospectivity models. These three issues are vital to the application of any bivariate or
multivariate technique of GIS-based data-driven modeling of mineral prospectivity, so it
is of foremost importance that they are given prior considerations.

SELECTION OF SUITABLE UNIT CELL SIZE FOR MODELING
Because known deposit-type locations are samples of a mineralised landscape under
investigation, it is essential to consider and determine a suitable sampling density in
data-driven modeling of mineral prospectivity. By doing so makes data-driven modeling
of mineral prospectivity consistent with the general principles of the statistical theory,
sampling theory and information theory. Mineral prospectivity is one type of (spatial)
information, which is derived systematically via (statistical) analysis of multiple sets of
various geo-exploration data (i.e., explanatory/predictor variables) at known deposit-type
locations and at non-deposit locations. Prospective areas, as we recall from Chapter 2,
are not only definition-limited but also sampling-limited geo-objects.
Sampling density in data-driven modeling of mineral prospectivity is defined by a
unit cell size that is used for representing known deposit-type locations and in
discretising spatial data of explanatory/predictor variables. A unit cell is equivalent to a
grid cell in sampling. It is also equivalent to a pixel in GIS (see Fig. 2-5). The size of a
unit cell is described by its length or width, if it is a rectangle, or by its diameter or
radius, if it is a circle. The size of a unit cell defines the spatial resolution (grid or pixel
resolution) of models of spatial data (see Chapter 2). The size of a unit cell, therefore,
defines the spatial accuracy of the derived pieces of geo-information portrayed in maps
or images. Fine unit cells represent high sampling density and thus good spatial accuracy
of information, whereas coarse unit cells represent low sampling density and thus poor
spatial accuracy of information. This comparison of unit cell sizes and, thus, sampling
densities, in the context of mineral prospectivity mapping, is pertinent only to a
particular areas, because the spatial distributions of mineral deposits and their spatial
associations with geological features vary from one area to another. The size of a unit
cell also determines the scale of a cartographic map. The finer the unit cell, the larger the
map scale; the coarser the unit cell, the smaller the map scale. However, map scale does
not symbolise and, thus, should not be confused with size of a study area.
Most, if not all, studies of GIS-based data-driven modeling of mineral prospectivity
make use of equal-sized (usually square) unit cells. The choice of a suitable unit cell size
in GIS-based data-driven modeling of mineral prospectivity is based on (a) the
knowledge that every mineral deposit is unique, even if they are classified to certain
types of mineral deposits, and (b) the assumption that each unit cell must contain just
one of each of the known locations of mineral deposits of the type sought. However, the
choice of a suitable unit cell size in most studies of GIS-based data-driven modeling of