Geoscience Reference
In-Depth Information
richment mineralization units are less reliable compared to
the more populated units.
The correlogram models developed showed the following:
• The prevalent anisotropy directions are NE and NW as
expected, but not in the horizontal plane. The main axes
of continuity are dipping 20-50° towards the center of the
deposit, depending on the mineralization unit and domain.
This is not a simple, layered deposit that it is sometimes
envisioned when dealing with porphyry type deposits.
• Structural Domain 3 consistently presents a much higher
nugget effect than the other domains. The grade distri-
bution is more erratic and discontinuous. More dilu-
tion can be expected at the time of mining, relative to
other domains, which indeed has been the operation's
experience.
• Correlograms from structural Domains 2 and 4 show
evidence of a deeper enrichment process, consistent
with field observations. A NW trending zone of deeper
enrichment results in better mineralization as observed
in the pit. Correlograms from structural Domain 1 tend
to plunge towards the W-SW, while correlograms from
Domains 3 and 4 tend to plunge towards the S-SE.
• Structural Domains 1 and 4 show a stronger NE anisot-
ropy, with less emphasis on the NW or SW dipping struc-
tures. Structural Domain 2 shows also significant (long-
range) NE anisotropy overprinting the expected NW short
range anisotropy. The longer-range N-NE anisotropies
observed correspond to the general orientation of the two
main intrusive bodies that are thought to be the mineral-
ization source.
1. Fourteen estimation domains (GUs) were defined for
TCu. These include the GUs defined for the upper portion
of the deposit.
2. Two unexpected features at the time were the use of struc-
tural domains and the lesser role that lithology plays as
mineralization control in the supergene enrichment zone.
3. The correlogram models obtained for the different data-
sets and conditioned to different geologic attributes and
the GUs show a pattern of anisotropies consistent with
geologic knowledge and observations in the pit.
4. There are important details in terms of correlogram mod-
els that result from the addition of the structural domains.
The most important one is that in Domain 3 the relative
nugget effect is significantly higher than for the other do-
mains. This is a result of a local mixture of phyllic (QSA)
and SCC alterations, with a corresponding increase in
grade variability.
5. The anisotropies detected confirm that the shorter-range,
higher-grade mineralization trends mostly NW, but with
significant N-NE long-range anisotropies. Also, for units
to the south and west of the deposit, the dips and plunges
of the ellipsoids of continuity generally will dip to the
SW and plunge towards the NE; for units to the north and
North East of the deposit, the dip may still be SW, but the
plunge is more commonly to the SE.
4.4
Boundaries and Trends
The geological interpretations and modeling of estimation
domains produce boundaries that often carry significant un-
certainty. The treatment and definition of boundaries have
implications on resource estimation such as dilution, lost
ore or a mixture of geological populations. The treatment
of boundaries at the time of grade estimation is of practi-
cal importance. The terms
hard
and
soft
boundaries are used
to describe whether the change in grade distribution across
the contact is abrupt or not, respectively. Conventional grade
estimation usually treats the boundaries between geological
units as hard boundaries, whereby no mixing occurs across
the boundary. Soft boundaries allow grades from neighbor-
ing domains to be used. Sometimes, soft and hard boundaries
can be predicted or expected from geological knowledge, but
should always be confirmed with statistical contact analysis
(Ortiz and Emery
2006
; Larrondo and Deutsch 2005).
Contact analysis helps determine whether the grade esti-
mation for any given unit should incorporate characteristics
of a neighboring unit. It is a practical tool to describe grade
trends and behavior near contacts and define the data to be
used in the estimation of each unit.
The behavior of grades across contacts can be analyzed
by finding pairs of data in the two estimation domains of
interest at pre-defined distances. There are different methods
4.3.4
Final Estimation Domains
Several simplifications were made to the original proposed
estimation domains since additional constraints need to be
considered to obtain the final estimation domains. First,
both enrichment mineralization units in structural Domain 2
(18 and 19) were joined into a single estimation domain,
partly because of the similarity of the grade distribution, and
partly because of lack of data. Estimation Domains 7 and 11
were merged into a single domain (HE and LE, with SCC
alteration, for Domains 1 + 4), again because of statistical
similarity and lack of data. All primary mineralization was
combined into a single domain because of lack of data; low
TCu grades, and also because production of Cu from pri-
mary mineralization will not happen until much later in the
mine life.
The final estimation domains are shown in Table
4.3
. De-
scriptive statistics, clustering analysis, contact analysis, and
variography are used to confirm the statistical characteristics
of TCu within each domain. The results of the domain defi-
nition study can be summarized as follows:
