Mineral Resource and Ore Reserve Estimation Second Edition
Transcript of Mineral Resource and Ore Reserve Estimation Second Edition
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Mineral Resource and Ore Reserve Estimation Second Edition
CHAPTER 3: GEOLOGICAL INTERPRETATION AND GEOLOGICAL MODELLING
Calculated Mineralogy and its Applications
S Halley
Contact author: Scott Halley Consulting Geochemist Mineral Mapping Pty Ltd 24 Webb Street, Rossmoyne, WA, 6148 0438 998583 [email protected]
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Calculated Mineralogy and its Applications
ABSTRACT
One of the biggest problems in generating realistic geological and alteration models is the lack of
consistency in the logging data. Mine site drill hole data bases typically contain logging data collected over a
period of many years. In most cases the logging will have been completed by tens of different geologists.
Consistent logging requires a significant level of skill and experience, but this task is generally assigned to
the most junior geologists. As a result, there is a large degree of subjectivity in the recognition of basic rock
types, and an even greater lack of consistency in correctly identifying alteration mineralogy, particularly when
it involves logging RC chips. Recent advances in analytical techniques and the application of portable
analytical devices and spectrometers makes it possible to collect systematic, quantitative geochemical and
mineralogical data that is independent of operator bias. These applications include ICP-MS/AES
geochemistry, portable XRF devices, and SWIR spectrometers. From these types of data, it is possible to
use immobile trace element geochemistry to correctly identify rock types, major element geochemistry to
identify and quantify sulphide and silicate mineralogy, pathfinder chemistry to model haloes to mineralized
structures, and SWIR mineralogy to qualitatively recognise alteration mineralogy and solid solution mineral
haloes. Having a reliable geological and mineralogical framework can help improve the reliability of resource
models, assist in the domaining of ore types and spatially map areas with possible geotechnical problems. A
more accurate and quantitative model of mineral distribution throughout the resource model may also
increase the reliability of metallurgical predictions, an important modifying factor in the conversion of
resources to reserves.
INTRODUCTION
It is commonly difficult to use geological observations made from drill core and RC chips to create geological
models because in most deposits, logging lithology and alteration is not easy. It is a task commonly
assigned to the most inexperienced geologists, and also because of staff turnover, the relevant observations
may have been made by a large number of geologists. The result is a high degree of subjectivity and
inaccuracy in the data that is collected. In this paper, the use of geochemistry and spectral logging to create
3D models of geology and mineralogy is discussed. Three case studies are presented. The first case study
shows how a geological model of an orogenic gold deposit was created from systematic logging of drill
cuttings with a portable infrared spectrometer. This shows how a variogram could be created and applied to
grade interpolation using real geological constraints rather than just from spatial distribution of assay values.
The second case study shows the classification of the silicate alteration mineralogy in an epithermal gold
deposit by using the ratios of major elements from an ICP-AES geochemical assay suite. A classification of
mineralogy in this situation highlights the three dimensional distribution of clay species that may be
deleterious in the mineral processing side of the operation or perhaps cause geotechnical issues during
mining. The third case study shows the classification of sulphide mineralogy in a porphyry copper deposit by
using ratios of Cu, Fe, S, Au and Ag. A quantitative three dimensional model of the sulphide speciation
should assist in mine planning, as the nature of the mill feed will be predictable through the life of the mine.
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CASE STUDY; SALT CREEK GOLD DEPOSIT
The Salt Creek Gold Deposit is located about 80km south east of Kalgoorlie (Integra Mining Limited, 2010). It
was discovered in 2007 by Integra Mining Limited. The pre-mining resource was 4.6MT @ 2.72g/t Au for
403,566 ounces of contained gold. The deposit is hosted in a mafic rock with pervasive, texturally destructive
chlorite alteration. There were no distinctive features within the mafic rock package that allowed recognition
of anything more than a very broad stratigraphic sequence. All of the early drilling was RC, which made rock
recognition even more difficult. The deposit has a very distinct sub-horizontal plunge, but in the early stages
of drilling, the geological controls on the plunge geometry were not obvious.
From very early on in the drill out, all drill holes were systematically logged with a portable infrared
spectrometer. The infrared logging protocol used at Salt Creek was to measure one spectrum per metre of
RC chips or diamond drill core using an Analytical Spectral Devices Terra Spec instrument. The spectra
were measured on the silicate groundmass (not on veins or sulphides) in order to consistently map the
alteration mineralogy and to measure solid solution variations in chlorite and sericite. The data were
interpreted using the CSIRO TSG (The Spectral Geologist) software (http://www.thespectralgeologist.com/).
Chlorite has a very distinctive infrared spectrum (Figure 1). It has two characteristic absorption features at
around 2250nm and 2340nm (Pontual, 2007). These correspond to absorption of light by Fe-OH bonds and
Mg-OH bonds in the mineral structure. The exact position of the absorption minima varies depending on the
solid solution composition of the chlorite. Magnesium-rich chlorites generally have shorter wavelengths, and
iron-rich chlorites have longer wavelengths (Herrmann et al., 2001).
By selecting the subset of data points where the TSG software picked only chlorite as the phyllosilicate
phase, and then mapping the wavelength of the 2250nm feature in 3D, it was recognised that there was a
very distinct stratification in the chlorite chemistry (Figure 2). A batch of 200 half-core samples was assayed
by an ICP-MS/AES method to calibrate the spectral signatures against whole rock chemistry. The chemistry
demonstrated that the mafic host rock at Salt Creek was a fractionated dolerite sill. Given the fine-grained,
texturally destructive nature of the chlorite alteration, it was impossible to recognise the fractionation units
within the sill in drill core, let alone RC chips. The chlorite chemistry determined from the infrared spectra
provided a very sensitive proxy for mapping the fractionation within the dolerite. Surprisingly, the solid
solution variations in the chlorite chemistry are controlled by the pre-existing chemistry of the host rock, not
by the nature of the hydrothermal fluid. Orogenic gold systems are driven by low salinity, neutral pH fluids, in
which Fe and Mg have a relatively low mobility. In acidic, saline hydrothermal systems, the chlorite chemistry
would have reflected hydrothermal fluid conditions rather than host rock chemistry.
From the wavelength variations in the chlorites, upper and lower boundaries of the long wavelength domain
were modelled, corresponding to the fractionated quartz dolerite. An offset on the upper margin of the
fractionated quartz dolerite unit was mapped from the spectral signatures. This corresponded to a small
displacement shear which controlled the mineralisation within the sill. The vast majority of the mineralisation
is located in a brittle vein array adjacent to the shear zone, within the quartz dolerite (Figure 3). The
geological model derived from the spectral data demonstrates a very common Kalgoorlie theme. The plunge
of the orebody is controlled by the intersection of a small displacement shear with a brittle quartz dolerite.
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In this model, 47,000 were spectra measured with a portable spectrometer. The average logging rate was
1,500m/day for RC chips, and 1,000m/day for diamond core. The total cost of the spectral logging, including
instrument hire, operator wages and interpretation was around 80c/meter. A model like this could not have
been created from visual core logging because the texturally destructive nature of the chlorite alteration
obscured the dolerite textures. The spectral logging eliminated subjective bias of the geologists.
The consequence of having a robust geological model is that the plunge geometry of the ore body is
predictable and should be along the intersection of the shear zone and the quartz dolerite. This provides an
independent validation of variograms generated entirely from grade distribution patterns.
CASE STUDY; ÇÖPLER GOLD DEPOSIT
The Çöpler Gold Mine is jointly owned by Alacer Gold (80%) and Lidya Mining (20%). It is located in Turkey,
120 km southwest of Erzincan and 550 km east of Ankara. It has Proven & Probable Reserves of 95.4Mt at
1.4g/t gold for 4.4 million ounces of contained gold, and Measured & Indicated Resources of 182.6Mt at
1.4g/t gold for 8.0 million ounces of contained gold (as at June 2012, 100% basis)
Çöpler is an epithermal gold deposit (Yigit, 2006). It is centred on a nested group of dioritic stocks that have
intruded into meta-siltstones and greywackes, and capped by a thick limestone unit. There are at least three
centres of early, low grade, porphyry Cu-Au mineralization, with phyllic (sericite-pyrite) alteration haloes,
predating the epithermal mineralisation. Intense, low temperature, intermediate-argillic (illite, illite-smectite,
smectite) alteration associated with the epithermal mineralization overprints the potassic and phyllic
alteration in the porphyry system.
All of the drill samples from Çöpler were assayed using a 4-acid digest and an ICP-AES method. This
analytical technique achieves close to a complete digest, particularly for the silicate minerals. The elements
assayed include gold, copper, a very broad suite of pathfinder elements, and all of the major elements other
than silica.
With a basic understanding of the alteration mineralogy, scatter plots of the major elements can be designed
that will allow a classification of the alteration signatures for each assay interval. In mineral systems like
Çöpler that involve feldspar-destructive alteration, the most useful plot is a molar ratio of K/Al versus Na/Al.
Consider a rock that is totally sericitised. The mineralogy of the rock might be muscovite-quartz-carbonate-
pyrite. All of the K and Al in that rock is contained within muscovite which has a composition of
KAl3Si3O10(OH)2. Therefore the ratio of K:Al in the sericitised rock is 1:3. Similarly, a totally KSpar (KAlSi3O8)
altered rock has a K:Al ratio of 1:1. In the same way, albitisation can also be tracked. Albite is NaAlSi3O8 with
Na:Al =1:1.
Figure 4 shows a K/Al versus Na/Al molar ratio plot constructed from 160,000 assay points from the Çöpler
data base. For clarity, a point density contour overlay has been applied. This figure shows an extraordinary
extent of Na-depletion. The sodium-depleted rocks have complete spectrum of K/Al ratios from 0 to 0.33.
This shows a continuum of alteration from intense phyllic alteration through to intense argillic alteration. The
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mineralogy ranges from muscovite to illite to interlayered illite-smectite to smectite and/or kaolinite. The clay
mineralogy cannot be resolved from the assays. Infrared spectra were needed to determine the clay
mineralogy. The hypogene clays are predominantly smectite, but feldspar-bearing rocks are weathered to
kaolinite within the supergene zone. It is impossible for the geologists to visually distinguish intense illite from
intense smectite alteration, or any gradation in between the two end-members, particularly when most of the
drilling is RC.
The down hole assay intervals were classified according to the mineralogy defined from the K/Al versus
Na/Al molar ratio plot in Figure 4. From this plot, the intensely Na-depleted samples were classified as strong
illite, illite-smectite, or smectite. Other points were classified as weakly or moderately sericitised depending
on how far they had shifted from the projected position of least-altered diorites or sediments towards the
projected composition of muscovite. Phengitic white mica may have K/Al ratios up to 0.45. Samples with a
K/Al ratio of greater than 0.45 must contain another potassic mineral in addition to phengitic sericite. These
points were classified as KSpar-bearing. The limestone and dolomite-bearing samples were classified from a
Ca vs Mg plot.
The mineralogy classifications are plotted on a long section through Çöpler in Figure 5. This figure highlights
the role of the limestone in acting as a cap on the hydrothermal system. A low grade porphyry copper centre
is apparent from the copper assays in Figure 6. There is also a narrow band of copper enrichment at the
base of the limestone. The alkali element contents do not change significantly in the potassic alteration zone,
because feldspars are stable here. There is a minor potassium addition where hornblende is replaced by
biotite. Gold mineralization is primarily related to the intense illite alteration (Figure 7).
A mineralogical model like this can serve a number of purposes in relation to the estimation of mineral
resource and other applications. Firstly it provides some important clues in the resource modelling to help
decision making about grade continuity and grade correlations from hole to hole. It is very useful from a
geotechnical perspective, because rock hardness will be closely correlated with the bulk mineralogy of the
rock. It provides the basis for recognising potential problems with pit wall stability, because the distribution of
clay-rich domains within planned pit walls is clearly highlighted. It is also very useful from a metallurgical
perspective. Swelling clays may cause problems on the leach pads, or in mill circuits. Knowing the 3D
distribution of the clays allows a targeted campaign of test work so that potential problems can be evaluated
before they impact on the operation.
CASE STUDY; HAQUIRA PORPHYRY COPPER DEPOSIT
The Haquira porphyry copper deposit is owned by First Quantum Mining Limited. It is located in the
Andahuaylas – Yauri porphyry copper belt in Southern Peru. It is a copper-molybdenum porphyry with minor
gold credits. Haquira currently has reported measured and indicated resources of 3.7 million tonnes of
contained copper equivalent and inferred resources of 2.4 million tonnes of contained copper equivalent.
All drill samples from Haquira were assayed with an aqua regia digest and an ICP-AES assay method. Aqua
regia dissolves sulphides, carbonates, some secondary oxides, clay minerals and some chlorite, but it will
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not dissolve most of the silicate phases. Therefore, from an aqua regia digest, it is not possible to chemically
classify rock types and alteration mineral assemblages as was done in the Çöpler case study. What can be
done with this data is a classification of the sulphide mineralogy from Cu:Fe:S:Au:Ag ratios.
Porphyry copper deposits are formed from very oxidized hydrothermal fluids (Seedorf, 2005). Initially, all of
the sulphur that is exsolved from the underlying magma chambers is in the form of SO2. As the fluid cools
between 450°C and 350°C, SO2 is converted to H2S via a disproportionation reaction;
4SO2 + 4H2O = H2S + 3H2SO4
The ore fluid initially contains copper in excess of reduced sulphur, so bornite, with a high ratio of Cu to S is
generally the first copper mineral precipitated in a porphyry deposit. As the supply of reduced sulphur
increases, chalcopyrite is precipitated, and when there is an oversupply of reduced sulphur, pyrite is formed.
Therefore porphyry copper deposits generally show a zonation pattern from an inner bornite zone, to
chalcopyrite, to an outer and higher pyrite-rich zone (Dilles and Einaudi, 1992). Modelling the distribution of
the sulphide minerals is important because the metallurgical characteristics of the ore will change
significantly from zone to zone. This has traditionally been done from visual logging, with recognition and
visual estimates of the proportions of bornite, chalcopyrite, pyrite etc. This requires a significant level of skill
and experience on the part of the loggers, and a degree of subjectivity and inconsistency is unavoidable.
Figure 8 shows a Cu:Fe:S ternary plot with the projected positions of bornite, chalcopyrite, pyrite, chalcocite
and malachite. From the ternary plot, samples with a Cu:S ratio greater than that of bornite (Cu5FeS4) were
selected, coloured green, and labelled as oxide. Samples with a Cu:S ratio greater than that of chalcopyrite
(CuFeS2) and up to the ratio of bornite were selected, coloured purple, and labelled as bornite. Samples
within this bornite group are likely to have a mixture of bornite and chalcopyrite. Similarly, three other groups
were created for pyrite-dominant, mixed pyrite-chalcopyrite, and chalcopyrite-dominant samples.
All samples with less than 0.1% Cu were assigned a grey colour and labelled as “barren”. A small group of
enargite-bearing samples were identified from a plot of Cu versus As. The enargite bearing samples have a
linear trend with a Cu:As ratio corresponding to the stoichiometry of enargite.
Figure 9 shows scatterplots of Cu vs Au and Cu vs Ag for the two colour groups defined as “bornite” and
“chalcopyrite”. Note that both of these plots show bimodal populations. Virtually all of the points classified as
bornite-bearing occur in the high-Au, high-Ag populations. The reason for this bimodal distribution is that
bornite contains significantly higher levels of Au and Ag in solid solution than chalcopyrite. Ten percent of the
points classified as “chalcopyrite” overlap with the bornite points on the Cu vs Au and Cu vs Ag plots. When
the nature of the veining at Haquira is examined, the reason for this overlap becomes apparent. There is a
later generation of quartz-molybdenite-pyrite veins that overprints earlier bornite and chalcopyrite veining.
The points with high Au/Cu and Ag/Cu classified as “chalcopyrite” bearing, are actually samples with bornite
veins, overprinted with quartz-molybdenite-pyrite, thus shifting them to lower copper:sulphur ratios.
Thus, by considering the Cu:Fe:S:Au:Ag ratios within the assay samples, every assay interval can be
classified in terms of the likely sulphide mineralogy within the samples. The metal values within each assay
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interval are a simply a reflection of the mineralogy of those samples. Recalculating the assays as weight
percent of minerals rather than weight percent of elements provides the basis for making a quantitative
mineralogical model. In the example illustrated here, this should greatly improve the reliability of
metallurgical predictions for the ore body as a whole. Bornite-rich ore makes a higher grade concentrate than
chalcopyrite-pyrite ore.
CONCLUSIONS
Over the last 40 years the standard commercially available assay techniques have changed from AAS to
INAA to ICP-AES to ICP-MS. The real cost per assay has barely changed, but we can now have 40 or more
elements routinely assayed with detection limits way lower than average crustal abundance levels. In
addition, we have an array of portable instruments that measure chemistry or directly measure mineralogy at
a rate fast enough to use as routine logging instruments. This rapid, cheap, off-the-shelf technology
quantifies core logging in a way that is vastly superior to what can be logged visually. The data is
independent of observer bias. The results can be presented in a categorized format (for example to describe
rock type signatures or alteration mineralogy characteristics), or in numeric formats to describe abundances
or solid solution compositions of minerals, or metal abundances. The data formats are ideal for 3D
modelling.
The potential applications of good quality ICP-MS/AES data and spectral logging are limited only by the
imagination of the geoscientists. Immobile trace elements can be used to geochemically fingerprint
lithological units. The major elements can be used to quantify the alteration mineralogy. Mapping clay
distributions can be used to predict potential metallurgical problems or geotechnical problems. Rock
hardness is primarily a function of mineralogy, so correlating a small number of test results against
mineralogy estimated from whole rock analyses may allow a work index to be fully predictable in a 3D model
where appropriate assay methods have been used.
Systematic geochemical and spectral logging does not replace the need for routine core logging. Rather than
wasting time on aspects of logging that can be performed far better and more consistently with other
methods, the geologists time will be freed up to concentrate on logging textures, structure and geometric
aspects that cannot be measured chemically. Over the next few years, routine ICP-MS/AES assaying and
spectral logging should become industry best practice.
ACKNOWLEDGEMENTS
Integra Mining (now part of Silver Lake Resources Limited), Alacer Gold Corporation, and First Quantum
Minerals Limited are thanked for permission to use and publish their data.
REFERENCES
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Dilles, J.H. and Einaudi, M.T., 1992. Wall-rock Alteration and Hydrothermal Flow Paths about the Ann-Mason Poprhyry Copper Deposit, Nevada; a 6km Vertical Reconstruction. Econ Geol., vol. 87, 1963-2001
Herrmann, W., Michael Blake, M., Doyle, M., Huston, D., Kamprad, J., Merry, N., Pontual, S., 2001. Short Wavelength Infrared (SWIR) Spectral Analysis of Hydrothermal Alteration Zones Associated with Base Metal Sulfide Deposits at Rosebery and Western Tharsis, Tasmania, and Highway-Reward, Queensland. Econ. Geol., vol. 96, 939-955
Integra Mining Limited, 2010. Annual Report
Pontual, S., Merry, N and Gamson, P., 2007. GMEX, Volume 1, Spectral Interpretation Field Manual. Published by
AusSpec International.
Seedorff,E., Dilles, J.H., Proffett, J.M., Einaudi, M.T., Zurcher, L., Stavast, W.J.A., Johnson, D.A., and Barton, M.D., 2005. Porphyry Deposits: Characteristics and Origin of Hypogene Features. Economic Geology, 100
th Anniversary
Volume, 251 – 298
Yigit, O., 2006. Gold in Turkey—a missing link in Tethyan metallogeny. Ore Geology Reviews, Volume 28, 147–179
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FIGURES
FIG 1. Stacked spectra of chlorite, showing the characteristic absorption features at 2250 and 2340
nanometers. Note how the wavelength at the minimum point for the 2250 feature shifts from 2250 for the
blue spectrum, up to 2260nm for the red spectrum.
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FIG 2. 3D view showing the distribution of chlorite-bearing spectra from the Salt Creek Gold deposit. This
view is looking directly down the plunge of the Salt Creek dolerite sill. The sample points are coloured by the
wavelength of the 2250nm Fe-OH absorption features from the SWIR spectra, as shown in Figure 1.
FIG 3. Cross section of Salt Creek showing the upper and lower boundaries of the fractionated quartz
dolerite unit and the modelled shear zone. The drill strings are coloured by gold assays; blue < 0.2ppm Au,
red > 2.0 ppm Au.
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FIG 4. Feldspar-Sericite K/Al versus Na/Al molar ratio plot for the Çöpler assay data, also showing the
projected compositions of some common alteration minerals. This plot forms the basis for graphically
defining the alteration mineralogy signatures for all the assay intervals.
FIG 5. Alteration mineral classifications from molar ratio plots on a long section of the Çöpler deposit.
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FIG 6. Çöpler long section with copper assays. blue = Cu < 100ppm, red = Cu > 1500ppm.
FIG 7. Çöpler long section with gold assays. blue = Au < 0.1 ppm, red = Au > 1.5ppm. The red shape is a
volume that encloses assay intervals where the alteration has been geochemically classified as “strong illite”.
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FIG 8. Ternary plot of Cu:Fe:S from the Haquira East porphyry copper deposit. The grey points are samples
with Cu assays less than 0.1%. The colour scheme shows the sulphide mineralogy predicted from this
ternary plot.
FIG 9. Copper versus gold and copper versus silver scatterplots from Haquira East, showing red points
(defined as the chalcopyrite group) and purple points (defined as the bornite group) from the ternary plot in
Figure 8. Note the bimodal distribution on both plots.
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FIG 10. The Haquira East sulfide mineralogy predicted from the ternary plot in Figure 8 and the scatterplots
in Figure 9 was plotted in 3D, and a volume was created to enclose the bornite-bearing assay intervals.
FIG 11. The modelled chalcopyrite shell from Haquira East. Assay intervals within this shell contain bornite
(Figure 10) or chalcopyrite, with only minor pyrite.