In mining, waste rock dilution occurs when uneconomic rock is mined and processed with economic mineralized ore. A survey of selected stopes from various Noranda Group mines estimated that aggregate dilution and oreloss stood at 20% and 8% respectively in 1994. Various technologies are being investigated by Noranda Group mines in an ongoing effort to minimize dilution and oreloss. The following three potential applications of geophysical logging have been identified for dilution and oreloss reduction through improved orebody delineation.
Increasing Orebody Sampling at the Delineation Stage to Reduce
Geological Dilution
Diamond core drilling, with geological core-logging, is the most
commonly used method for orebody delineation. Unfortunately, the many
task-intensive steps and expense of this method constrain the amount
of samples one can obtain. However, by allotting an optimum
percentage of the delineation drilling budget for geophysical logging
of percussion-drilled holes, increased sampling would occur. Since
percussion drilling is significantly cheaper than diamond drilling,
more delineation holes could be drilled to better define the ore
boundary geometry and, ultimately reduce geological dilution and
oreloss.
Controlling Geological Dilution at the Production Stage
In production, thousands of meters of percussion holes are drilled
annually for blasting purposes. However, since no core is recovered
in this drilling process, information about the ore-waste contact is
seldom acquired. Thus, there is the potential to geophysically log
production holes to identify the ore-waste contact for optimal blast
design with a resulting reduction in dilution and oreloss.
Interpreting Crosshole Geophysics Tomograms
By geophysically logging boreholes that were used in a crosshole
geophysics survey, relationships can be developed to correlate logged
geology and geophysical properties along the boreholes. These
relationships could then be used in the interpretation of crosshole
tomograms. Using commercially-available geophysical logging
equipment, combined with multivariate statistics and
pattern-recognition techniques, we have analyzed data acquired at
selected Noranda Group minesites to provide answers to the following
questions:
Geophysical Logging for Lithology
Based on our test data, interpretation of geophysical logs for
lithology was very much site-dependent. In simple cases a single log
or a cross-plot sufficed, while several logs and pattern recognition
techniques were necessary in others.
The lithology of the Louvicourt deposit was found to be complicated from a geophysical point of view, and pattern recognition techniques were required to interpret the data. Two models, one using neural networks and one using discriminant analysis were used. Ore boundaries could be successfully mapped, however, the models could not distinguish between Zn- and Cu sulphide. Zones of pyrite were, in some cases, mis-interpreted as Zn- sulphides, especially by the discriminant model. The neural-network model did manage to locate selected zones of altered rock. Most waste rocks were interpreted as tuff by both models.
At Heath Steele Mine, it was found that gamma-gamma, natural gamma, gamma ratio, and magnetic susceptibility data could be successfully used to isolate waste (i.e. tuff and pyrite/pyrrhotite) and ore (i.e. massive and semi-massive Pb-Zn) units. At Isle Dieu Mine, crossplots of gamma-gamma versus natural gamma clearly identified four main groupings of data, corresponding to the four main lithological units.
Geophysical Logging for Grade
Using multiple linear regression techniques, it was found that three
of the metal assays at Louvicourt could be estimated -- in order of
certainty: Fe, Cu, and Zn. Although these techniques tended to
underestimate very high Zn grades, neural-network techniques were
found to improve Zn-grade estimates (i.e. R2 increased from 0.45 to
0.70). Similarly, the Cu-equivalent grade could be estimated by
applying neural-network techniques. Data from three of test holes
showed that the correlation between the neural-network model and the
Cu-equivalent assays was quite high with R2 = 0.77.
At Heath Steele, there was a good correlation between Pb-grade, Zn-grade, and the geophysical logs within the Pb-Zn sulphides. However, in boreholes containing many kinds of sulphides, these relationships were much more difficult to define. Cu grade could not be estimated with the available data using linear regression. In fact, the Cu grade was difficult to estimate using other methods as well, since the conductivity and susceptibility logs behaved peculiarly in three of the boreholes. However, reasonable estimates of Pb grade were obtained using neural networks. At Isle Dieu, simple linear regression of various geophysical logs could be used to estimate four different metal grades (i.e. Zn, Ag, Pb, & Cu). Estimates of the Zn, Ag, and Pb grades seemed to fit the assays fairly well.
Geophysical Logging for Interpretation of Crosshole
Tomograms
Sonic logging results from the Louvicourt deposit showed that, of all
of the minerals and metals assayed at Louvicourt, seismic velocity is
most sensitive to iron content, followed by pyrite content.
Therefore, as zones of higher seismic velocities may indicate zones
of higher iron and pyrite content, this result was used in the
interpretation of seismic tomography data acquired at the Louvicourt
deposit.