Geoscience Reference
In-Depth Information
3
Geological Controls and Block
Modeling
Abstract
Mineral deposition is governed by complex processes. The structure of mineral deposits
is partly deterministic and partly stochastic. Large scale deterministic geological control
must be accounted for explicitly. Block models are commonly used to discretize a deposit
because they provide a spatial representation of geologic variables and a useful format to
store other important attributes, including the estimated grades.
3.1
Geological and Mineralization Controls
preferentially through the fractured permeable rock. Other
examples include the geochemical stability of certain miner-
als and the fracture density in low-grade bulk precious met-
als deposits. These specific geologic variables should be the
focus of geologic investigation and modeling for improved
resource estimation.
The information shown in Fig.
3.1
and other site-specific
information is the basis for analyzing the relationships be-
tween the geologic variables and the grade distribution.
Not all mapped geologic information will be a significant
mineralization control, and thus it may not aid in estimat-
ing grades. The geologic description shown in Fig.
3.1
is too
detailed for practical use in defining mineralization controls.
An important challenge is to identify the important geologic
variables that need to be interpreted, modeled, and carried
into the block model. These may include variables needed
to build metallurgical and geotechnical models, such as con-
centrations of certain types of clays, rock hardness indices,
metallurgical recoveries, and fracture densities.
The level of geologic detail that can be considered in a
block model is limited. It depends on the size of the deposit
and the amount of drill hole information available. There is
a compromise between the level of detail achieved and the
robustness of the statistical analysis within each geologic
population defined. A resource model with no geologic sup-
port is inadequate because geologic factors highly constrain
the distribution of grades. But too much detail is undesirable,
since it creates estimation domains with too few data for reli-
able statistical inference.
Although there are no hard rules that can be used to deter-
mine the amount of data required, the general guideline is to
The geology used to support resource estimation is under-
stood from the analysis of the recorded information gathered
through detailed exploration work, including drill holes.
Surface mapping, underground mapping and sampling, and
geochemical and geophysical investigations may also con-
tribute, particularly in the early stages of project develop-
ment (Peters
1978
). This chapter assumes that mapped drill
hole information is the basis for geologic modeling, while
acknowledging that all geological interpretations are the re-
sult of a pool of quantitative and qualitative information.
Figure
3.1
shows an example of a geologic log of the
Spence copper Project in Northern Chile. The log sheet
shows the from—to interval; mapped lithology (in charac-
ter and graphical codes); mineralization type; structures; de-
scription and percentages of the minerals found; alteration;
gangue minerals; and presence and type of veinlets. The spe-
cific information collected will vary from one deposit to the
next.
The ultimate goal of mineral resource estimation is a nu-
merical model that will accurately predict the tonnages and
grades that will be extracted from a mining operation. The
geologic variables that controlled the mineral deposition are
modeled to help with this. Certain geologic variables are of
greater interest to resource estimation, that is, the ones that
have a stronger or more direct relationship with the miner-
alization. One example is the fracturing and permeability of
certain rocks in strata-bound or sedimentary-type deposits
(for example uranium, gold, or copper in sandstones and/or
breccias); the fluids carrying the ore minerals would move
