Estimating Long-Run Geological Stocks of Metals - UNEP

Estimating Long-Run Geological Stocks of Metals
UNEP International Panel on Sustainable Resource Management
Working Group on Geological Stocks of Metals*
Working Paper, April 6, 2011
Summary
Reserve base estimates of the US Geological Survey and other geological sources provide
a lower limit to the extractable global resource (EGR, the amount of a given metal in ore
that is judged to be extractable over the long term). However, reserve base estimates are
lacking for many metals of interest, though approximations are sometimes possible. For a
rough upper limit, elemental abundance ratios in the upper continental crust can be used
to estimate EGR (although these are unrealistically high estimates, because much of this
material is unsuitable for mining). These upper and lower limit end points are judged to
be too wide to be useful in arriving at central estimates. As a consequence, generating
best estimate EGR numbers across the elements of the periodic table remains a work in
progress. However, it is possible to use new approximations for EGR from the present
work together with existing Reserve Base and Reserves information to provide rough
Reserve Base and Reserve estimates for elements where those metrics have not
previously been reported. We regard these numbers as working values that can be used in
preliminary studies of long-term metal criticality until more authoritative estimates
become available.
* T.E. Graedel 1 (Chair), R. Barr 1 , D. Cordier 2 , M. Enriquez 3 , C. Hagelüken 4 , N.Q.
Hammond 5 , S. Kesler 6 , G. Mudd 7 , N. Nassar 1 , J. Peacey 8 , B.K. Reck 1 , L. Robb 9 , B.
Skinner 1 , I. Turnbull 1 , R. Ventura Santos 10 , F. Wall 11 , D. Wittmer 12
1 Yale University, New Haven, CT USA; 2 US Geological Survey, Reston, VA USA
3 Ministry of Mines and Energy, Brazil; 4 Umicore, Brussels, Belgium
5 Council for Geosciences, South Africa; 6 University of Michigan, Ann Arbor, MI USA
7 Monash University, Melbourne, Australia; 8 Queens University, Kingston, ONT Canada
9 Oxford University, Oxford, UK; 10 University of Brasilia, Brazil
11 University of Exeter, Cornwall, UK; 12 Wuppertal Institute, Wuppertal, Germany
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Glossary of Terms
Extractable Global Resource – The quantity of a given resource that is judged to be
worthy of extraction over the long term given anticipated improvements in exploration
and technology. [This metric is roughly equivalent to Reserve Base, defined below.]
Reserve Base – That part of an identified resource that meets specified minimum
physical and chemical criteria related to current mining and production practices,
including those for grade, quality, thickness, and depth. (Note that this definition is not
significantly tied to the economics of extraction.) [Reserve Base estimates as a core
activity of the US Geological Survey (USGS) ended in 1996; annual updates for some
metals were made by minerals specialists until 2009, when the entire activity was
discontinued.]
Reserves – That part of the reserve base that could be economically extracted or
produced at the time of determination. (Note that the determination of economic
feasibility is a function of ore grade, socio-environmental and technological viability,
depth of deposit, and [to some extent] location.) [Reserves estimates are published
annually by USGS, largely from data contained in company reports or otherwise
furnished by companies and governments, and similar statistics are published by other
national and private geological sources.]
Resource – A concentration of naturally occurring solid, liquid, or gaseous material in or
on Earth’s crust in such form and amount that economic extraction of a commodity from
the concentration is currently or potentially feasible. [Resource numbers are the most
expansive of the “how much is there” determinations, but are rarely estimated.]
1. Addressing the Task of Quantifying Geological Resources of Metals
The most obvious question related to mineable metal in the ground is “How much is
there?”. It is generally surprising to the non-geologist that this simple question is very
challenging to answer in any useful way. Among the complexities that must be
considered are:
• Locating and evaluating ore deposits requires regional exploration to locate
potentially favorable areas, followed by detailed sampling involving drilling and
analysis. This is time consuming and expensive, and drilling is only done on
prospects judged to have the potential to be exploited within the next decade or two.
Most such prospects turn out to be uneconomic.
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• Future exploration will focus on deeper levels of the crust where deposits are harder
to find and expensive to exploit.
• Not all possible areas around the planet have been explored (though many of the
possible remaining areas are remote geographically or have governmental or
regulatory regimes that discourage further exploration.
• The extent to which a deposit is exploited is related to the monetary return that can be
expected; this is shifting territory.
• Increased demand and rising prices for any particular resources stimulates new
exploration activity and thus definition of additional reserves.
• Some metals are found only or largely as by-products that cannot be mined
economically without mining deposits of so-called carrier metals (parent metals). The
availability of the by-products (daughter metals) is strongly dependent on the demand
for the parent metals rather than for themselves.
• Much of the useful information is proprietary.
The U.S. Geological Survey for many years surveyed companies in the United States and
governments around the world, and compiled the results of their mineral wealth
estimates. This yields abundances (reserves, reserve base, resource) that are regarded as
authoritative, and are widely cited.
The distinction among the USGS parameters (reserves, reserve base, and resources), and
the geological and economic factors that determine the boundaries, are indicated in a
diagram devised by McKelvey (1952) and reproduced in Figure 1.
To emphasize the fact that these parameters are not static, Barton (1982) devised a
version of the McKelvey diagram that is reproduced in Figure 2. It makes the case that
exploration, prices, new technology, and other factors can “push the boundaries” between
the parameters. The implication of this diagram and of the considerations discussed
above is that resource-related metrics are dynamic. It is quite useful to evaluate them at
the present point in time, especially from a comparative standpoint, but it is necessary to
realize that such evaluations need to be repeated on a regular basis, perhaps every few
years, in order to reflect the dynamic nature of the factors that drive many aspects of
long-term geological resource availability.
Considering the issues discussed above leads us to the central question for this working
paper:
“To what extent is information available on the potentially extractable geological
resources of metals?” The focus of this question is not on the near term, but on long-term
availability. Estimating contemporary reserves is useful, but merely a starting point.
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As perspective on the challenge, we recognize that the mineral resources used for
millennia have come from rich deposits located near Earth’s surface (the crust), and thus
potentially mineable. These deposits largely stem from geologic processes that occurred
over very long periods of geologic time. The most important of these processes are
hydrothermal (heated water) and magmatic (molten rock) processes, both involving fluids
that dissolve elements and allow them to be deposited in a more concentrated form at a
specific location. The driving force for these processes is plate tectonics, the movement
of lithospheric plates about Earth’s surface as a result of the planet’s internal energy
dissipation and probably also other deep-sourced mantle processes. It is an occasional
fortunate accident when a deposit is rich enough to be useful to society. Such deposits
are occasionally discovered, but many of the likely locations at or near the surface of
Earth have now been explored.
Figure 1. The McKelvey (1952) diagram of geological availability.
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Figure 2. The classification of resources and a summary of the forces that tend to enlarge
or diminish the quantity of reserves (Barton, 1982).
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Mineral deposits are, of course, of varying degrees of richness. Economic considerations
(and order of discovery) generally result in the richest deposits being mined first, the
next-richest next, and so on, although this ideal progression can be confused by
fluctuations in metal prices. As the use of poorer deposits is contemplated, it is useful to
know the abundance spectrum of the element or mineral of interest on a global basis.
There is some evidence that this spectrum forms a bimodal ore grade distribution as
shown in Figure 3. A “mineralogical barrier” is indicated on the figure; this is the ore
grade above which an element is in a form that is amenable to economic extraction.
Below that concentration, the individual metal atoms substitute for the more abundant
elements in common rocks. The quantity residing in deposits above the mineralogical
barrier (assuming Figure 3 is correct) is more or less what the USGS means by resource.
The validity of Figure 3 has not been firmly established; that would require extensive
testing of low grade deposits.
Although the USGS estimates have considerable legitimacy, there are also inherent
problems with them from a long-run perspective. The first is that the information comes
from governments around the world rather than from producers, and is not independently
verified (Gilbert, 2009). The second is that reserves, being the current focus of producers,
are thought to be much more accurate than reserve base numbers. The latter, in fact, have
seen only occasional updating, and even that is no longer taking place. The estimates of
resources are properly derived from geological research, both theoretical and exploratory,
not from current producer data, and are available in even rough estimate form for only a
few of the elements of interest.
Estimates of mineral resources other than those of USGS have occasionally appeared in
the literature. Råde (2001) has estimated the platinum resource at 66.5 Gg, and Gordon et
al. (2006) have estimated the copper resource at 1.6 Pg. These are somewhat lower than
USGS estimates, but not completely inconsistent. For many of the metals (e.g., tin,
cadmium, tantalum, etc.), no resource estimates appear to have been made.
To enable planning for the sustainability of metal supplies over the long term, estimates
of the quantities of ultimately extractable global resources (EGR) are needed. A single
number for each element is useful but difficult to defend. Rather, it could be desirable to
establish lower and upper limits to the reserve base for each metal, and perhaps to agree
upon the most likely probability distribution between these limits. In practice, the peak of
the probability distribution represents approximately what is now termed the “reserve
base”, while the upper and lower limits represent in some sense a pair of “scenarios”
roughly related to “routine approach to extraction” and “heroic approach to extraction”.
From this perspective, the ideal outcome of the considerations in this working paper
might be to generate a complete set of results something like those of Figure 4.
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Figure 3. A bimodal distribution of a technologically useful material in Earth’s crust.
(After Skinner, 1976.)
Figure 4. Hypothetical extractable global resource (EGR) probability distributions. The
vertical lines at the edges of the distributions indicate lower and upper estimates.
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The EGR probability distribution concept leads to several obvious questions:
• Can lower and upper limits be established for the EGR quantities for all metals? If
not, for which ones?
• Can probability distributions be established for the EGR quantities for all metals?
If not, for which ones?
• What is the appropriate spatial level for analysis (global, country, etc.)?
• Could the concepts inform the analyst about the EGR of daughter metals (e.g., Se,
Te, In) as well as parent metals (e.g., Cu, Zn)? If not, why not?
Geologists and mining companies know very well, but most others do not, that the
majority of metals are not mined for themselves (as are host metals such as copper or
iron) but as recovered by-products (companion metals) in parent ores. An overview of
typical host-companion relationships in ore deposits is given in Figure 5. (This diagram
might also have included Fe and Au as host metals.) As a consequence of this cooccurrence,
the availability of companion metals depends upon having the technology to
recover those metals during or following processing of the host metal ore, and also the
economic attractiveness of companion metal recovery. It is not uncommon for one or
both of these constraints to apply for desirable companion metals, and one therefore
needs to know several parameters to make a realistic estimate of anticipated availability:
typical abundance of the companion metal in the host or hosts, typical extraction
efficiencies for the companion metal, and some idea of the relative costs and benefits of
companion metal recovery.
These analyses, even if carried out extensively, do not produce a total picture of natural
metal resources. Proposals (and a few small demonstration projects) have been developed
for the mining of manganese, cobalt, and other metals from the metal-rich “seafloor
nodules” resulting from mid ocean ridge tectonic activity (e.g., Rona, 2008), and for the
“mining” of metals dissolved in seawater (the current source of a significant fraction of
recovered magnesium [e.g., Bardi, 2009]). No quantitative estimates of the retrievable
amounts of these resources are available.
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Figure 5. Typical occurrences of companion metals within host metal (“carrier metal”)
ore bodies. REE = rare earth elements; HRE = heavy rare earths; PGM = platinum group
metals. (After a diagram from Christina Meskers, Umicore.)
2. Host Metals and Their Common Alloying Elements
Focus elements: Al, Ba, Co, Cr, Cu, Fe, Mn, Nb, Ni, Pb, Si, Sn, W, Zn, Zr
The USGS reserve base estimates are by far the best published estimates of a parameter
related to EGR. Some mining companies might have better estimates for their own
properties and specific metals, but this information is largely unavailable. Data in the
public domain are not sufficient to make useful changes to these estimates. The value of
the USGS estimates is that they are based in part on confidential information that was not
made public in its original form. For most of the focus elements in the host metal group,
the reserve base estimates are thought to be reasonably reliable.
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One could imagine an EGR estimate that deals with ore grade, depth, and amount of ore.
These factors are definitely the most important in determining how much of any mineral
resource remains in the crust. However, if the three-dimensional picture formed by these
factors is meant to reflect the actual recoverability of these mineral resources, other
factors come into play. These include the amenability of the resource to mining and
processing, both of which can be largely independent of grade, depth, and amount of ore.
For instance, unstable rocks or rocks with very large flows of groundwater can
complicate and possibly even prevent mining of a relatively shallow deposit. The same is
true for factors such as associated metal (the presence of abundant As in a Cu ore, for
instance), grain size (fine-grained minerals that cannot be separated by conventional
flotation) and even societal factors such as economic (tax policy), legal (land access), and
environmental (degree of reclamation) requirements.
If an EGR estimate is supposed to represent “simply” the amount of metal present in
mineral deposits (however defined), then there is some hope that this might be done. If it
is supposed to reflect the additional parameters mentioned above, the estimate would be
much more difficult and probably highly incomplete. Two methods can be used to
estimate the “simple” resource inventory. The first would be based on geological features
and would require a comprehensive survey of known deposits of a specific type that host
the commodity of interest followed by an estimate of geological provinces that might
host undiscovered deposits (and an estimate of the number of deposits that might be
present). Compilations are available for known deposits of several types, including
volcanogenic massive sulfide (Cu, Zn, Pb, Au, Ag; Mosier et al., 2009), porphyry copper
(Cu, Au, Mo, Singer et al., 2008), orogenic gold (Au; Wilkinson and Kesler, in press),
epithermal gold (Au, Ag; Kesler and Wilkinson, 2009), Mississippi Valley-type (Pb-Zn;
Leach et al., 2005), sedimentary exhalative (Pb, Zn; Leach et al., 2005), and iron-oxide
copper gold (Cu, Au; Cox and Singer, 2007), but these differ considerably in their detail
and coverage. Regional estimates based on these data are scarce; a recent one was made
for Cu, Mo, Au and Ag in porphyry copper deposits of South America (Cunningham et
al., 2008). To expand these estimates to other areas and deposit types would be a massive
effort that would require possibly many years of work. (Global estimates of this type are
currently being undertaken by USGS.) An alternative possible approach would be based
on age-frequency distributions for known deposits using the tectonic-diffusion method of
Wilkinson and Kesler (2007), which has been used to estimate global Cu and Au
resources to depths of 1 and 3 km. Unfortunately, most other deposit types are not
amenable to this approach, largely because there is not enough information available on
their ages. These estimates are also very approximate because they do not define the
location of the resources and therefore cannot assess their likely recoverability relative to
important factors such as geologic deformation, amenability to mining or processing, and
relation to possible economic or environmental constraints. Tectonic-diffusion estimates
are definitely not the Reserve Base (RB) as defined by the USGS.
The EGR values will definitely change over time as mining exhausts deposits and new
deposits are found. No quantitative geological research effort has been made to estimate
the progress of either of these variables.
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3. Precious and Specialty Metals
In considering the methods and data upon which rough (or preliminary) estimates of
geologic stocks of precious and specialty metals could be developed, it is useful to split
the metals into two main categories, based on those that could be mined as primary
deposits or those where the metals would only be extracted as companion (by-product or
secondary) metals:
Category 1 “could be mined or are mined” as primary deposits
Au, Ag, PGMs, Co, Mo, Sb, Ta, Be, Te # , V, Sr
# Dashuigou Te deposit/mine in China
Category 2 “only extracted as by-product metals”
As, Bi, Cd, Cs, Ge, Ga, Hf, In, Sc, Se, Tl, Te
Based on existing and compiled data sets on reported ore resources (at operating mines
and undeveloped deposits), a probabilistic approach is likely to be the best way to
constrain the ‘extractable geological resource’ (EGR). The ideal approach would be to
compile all ore resource statistics, assign a geological model/deposit type (e.g., volcanic
massive sulfide, sedimentary exhalative, oxide), subdivide ore resources by ore type, and
then apply typical concentrations of category 1 or 2 metals. However, this would require
an extensive compilation, and deposit by deposit categorization is not routinely available
as of this writing. Therefore, a preliminary example was completed for indium from
global lead-zinc ore “resources”, and a second for selenium and tellurium from copper
ore “resources” (Australia only). [Australian mineral definitions are such that what is
termed “resources” by the Joint Ore Reserves Committee (2004) is about the same as
what the US Geological Survey terms the “reserve base”.) Typical concentration ranges
for the companion metals were then applied to these resources to provide a crude estimate
of EGR. This approach could be repeated for all metals in both categories, but remains a
large task.
Example 1: Indium from Pb-Zn-Ag ore resources (2009 global data)
The preliminary EGR estimate for indium (In) is shown in Table 1, based on resource
data for remaining primary lead-zinc deposits compiled in the Appendix (Table A1). The
resource data is preliminary only, especially since not all countries, especially China,
have publicly available data.
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Table 1: Preliminary extractable geologic resource (EGR) for indium, based on lead-zinc
ore resources only
Data from Table A1
Mt ore %Pb %Zn ppm Ag
Global 4,797.0 1.30 4.04 43.9
Indium (ppm) EGR (Mg)
Average Minimum Maximum Average Minimum Maximum
106 1 3000 250,000 2,400 7,200,000
EGR – allowance has been made for a typical extraction efficiency of 50%
and rounded to two significant figures (1 Mg equals 1 metric ton).
Example 2: Selenium and tellurium from copper ore resources (2009 Australian data
only)
Based on data already compiled for Australia (G. Mudd, unpublished), a preliminary
example has been prepared of Australia’s estimate of EGR for selenium (Se) and
tellurium (Te).
Table 2: Preliminary extractable geologic resource (EGR) for selenium and tellurium,
based on copper ore resources only
Mt ore %Cu
Australia 15,064 0.69
Concentration (ppm) EGR (Mg)
Average Minimum Maximum Average Minimum Maximum
Se 0.06 0.005 0.12 500 40 900
Te 0.04 0.003 0.08 300 20 600
EGR – allowance has been made for a typical extraction efficiency of 50% and rounded
to one significant figures (1 Mg equals 1 metric ton).
# Based on 2:3 ratio of Te:Se
A major issue that has not been addressed is whether it is realistic to re-process past
tailings from host ore processing to extract rare metals. Given the vast extent of ore
processed historically to extract lead, zinc, copper, and silver, for example, this could be
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a significant indium resource. However, it remains highly speculative as to the economic
conditions, technological capacity, and balance of environmental impacts that would need
to coincide to allow such resources to be developed.
4. Light, rare earth, and radioactive metals
Focus elements: Al, Li, Mg, REE, Th, Ti, and U
The approach for calculating the extractable geological resource needs to be customized
to suit each element in question. REE are present in relatively high concentration, but
substitute in concentrations of parts per million into rock forming minerals and thus
rarely form concentrations high enough to merit mining operations at the moment.
Lithium, thorium, and uranium are present at lower concentrations.
The best substantiated and absolute minimum value for the extractable geological
resource is the Reserve figure, as available from USGS Mineral Commodity Summaries,
for example. This is updated each year utilizing information based on international
standards. The reserve is always an underestimate of what is available because it requires
significant geological exploration work in order to define new or understudied deposits.
The Reserve Base is perhaps a more realistic minimum, but again it is always most likely
to be a significant underestimate, and the USGS will no longer be publishing this figure;
2009 is the last year, so this statistic will remain fixed at 2009 figures.
Figures for Resource (see Figure 1) are sometimes given, but are less well substantiated,
and may include minerals for which suitable extraction technology for the elements of
interest does not exist. The estimates may be useful for future estimates of availability,
given that these figures tend always to increase with time (even if they vary for individual
companies).
Going to the other end of the scale, it is interesting to consider the absolute maximum
numbers. The simplest estimate of this is the crustal abundance, a concentration in weight
percent or in parts per million (Taylor and McLennen, 1985; Rudnick and Gao, 2003).
However, it is not possible to mine to the total depth of the crust and it is necessary to
determine a depth limit of likely mining activity – or more importantly perhaps a depth
limit for likely exploration activity. The world’s deepest mines stretch to 4 km beneath
the surface but this is only attainable given favorable rock conditions and high priced
commodities (e.g., gold). The next deepest mines are platinum mines at 2.6 km. Mines to
1 km depth are relatively common, for base metals as well as higher priced commodities.
However, all of the world’s deep mines are extensions of shallower mining activity and
the deposits were found at or near the surface rather than at depth. The main constraint on
the amount of crust from which we can reasonably expect to extract metals is thus
probably the depth of maximum exploration rather than the depth at which it is possible
to mine. Exploration at depth is challenging. Notable successes are the Olympic Dam
deposit, Australia, which was discovered at 400 m, and the Resolution deposit, USA, a
world-class porphyry Cu-Mo system located at a depth of more than 1300 m. Copper
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deposits in Poland (The Legnica-Glogów copper basin) extend over a polygonal area of
416 km². The stratiform mineralization occurs where Permian limestone lies against New
Red Sandstone within varying combinations of sandstone, shale and dolomite. The
proportions of carbonate, shale and sandstone ore types vary from mine to mine and the
ore horizon ranges from 1.2 m to 20 m in thickness, lying at depths of between 600 m and
1,200 m from the surface. The overall average thickness in the mineable zones is 3.38 m
(http://www.mining-technology.com/projects/kghm/) and the Resolution Project, located
three miles east of Superior, Arizona, USA, was originally discovered by Magma Copper
Co. and BHP Billiton via underground and surface drilling conducted from 1994 to 1998.
Exploration conducted by Kennecott Exploration Co. (2010) from 2001 to 2003
confirmed a large body of copper mineralization at a depth of more than 1300 m. Bearing
these facts in mind, 1 km seems an appropriate value to represent the deepest we are
likely to be able to explore for minerals in the foreseeable future (50 years plus).
No account is made of areas in which mining is not likely to be allowed because of their
importance for nature conservation, or presence of large conurbations. The designation of
conservation areas in particular is a political decision, which may be changed depending
on the need for resources. Technology development, such as discrete underground
mining, may permit these areas to be mined in a way that causes absolute minimum
disturbance in the future.
The treatment then depends on the element. For the elements that are major components,
it is possible to estimate how much in minerals from which it is now technically feasible
to extract the element.
An example for Al
Al is a major component of Earth’s crust. It is present in feldspar in many rocks,
including granites and gneiss that make up a high proportion of the crust.
The main source of Al at the moment is bauxite deposits, which are generated by
weathering and restricted to near surface environments. However, Al is also produced
from nepheline in alkaline igneous complexes, e.g., Khibiny, Kola Peninsula, Russia.
This source could be extended if bauxites were exhausted past economic limits. During
the war, Al was also produced from the calcium feldspar, anorthite. This is concentrated
in ultrabasic rocks, such as anorthosites and these might be the next source for Al.
However, if Al can be produced from anorthite, the technology is also likely to be
feasible for other feldspars such as albite (solid solution mineralogical relationship with
anorthite) and the potassium feldspars. This gives a huge potential crustal extractable
geological resource for Al. Mines at 4 km are most unlikely, but even 500 m gives a large
figure. The result is an Al upper value of 20 Zg.
Mg is also a major crustal component. Seawater is an important source of Mg and it is
possible to calculate an upper value of 1.8 Zg for Mg in sea water. The supply of Mg is
stated as ‘large to virtually unlimited’ by the USGS Mineral Commodity Summary 2010.
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In contrast, the elements Li, REE, Th, and U are trace element components of the crust
and cannot be treated in the same way. They occur in low concentrations, and future
availability depends on new discoveries. In those cases, it would be preferable to have
geologically reasonable estimates of upper limits, such as Kesler and Wilkinson’s
treatment of copper deposits for the upper limit.
Rare Earth Elements
The REE form a group of 15 strictly rare earth elements, with Sc and Y often included (as
they are commonly encountered together). However, Sc behaves differently
geochemically compared to Y and the lanthanides, so is treated separately here (and in
most geological and economic geology texts). Pm has no naturally stable isotope and is
not considered further. The mid and heavy REs, at least, can be regarded as daughter
elements.
The USGS commodity survey separates Y from REE but does not differentiate among
other members of the REE series. Figures for individual elements would be useful
because elements such as Nd and Dy are in particular demand for new technologies (e.g.,
hybrid cars, wind turbines).
REE deposits occur in a wide variety of rocks. Carbonatite-related deposits are the main
variety of REE deposits exploited at the moment; other sources include alkaline rocks,
mineral sands, and ion adsorption clays on weathered granite. It would be possible to
model REE present in known carbonatites, many of which have small but high grade
deposits of REE, and in alkaline rocks, including in apatite. However, the current outlook
with rapidly increasing requirement for REE in low carbon technologies, means that
many deposits other than carbonatites and mineral sands are now under active
exploration. These include alkaline syenites and granites and a variety of hydrothermal
systems. Making a geological model to cover all of these is much more difficult, although
one approach might be an empirical one to take the REE concentration in average granite
and other common rock types and compared to the concentration in the crust overall – the
difference may relate to the amount of REE in other rocks. This needs careful
consideration of the methodology behind calculation of the overall crustal abundances
though. Processing to separate the individual rare earths is the really challenging step for
REEs
There is a factor of nearly a million between the total REE abundance in the1km of the
whole upper crust and the current Reserve figure (67 x 10 18 g versus 150 x10 12 g,. This
leaves a wide range within which more accurate estimates of EGR must fall. The ‘rule of
thumb’ method of multiplying by 0.01 (Ericson, 1973) seems unlikely to be true in the
case of REE though, probably because of the widespread substitution of REE at ppm
levels into many rock-forming minerals.
Uranium
Uranium is not included in USGS statistics but similar official figures are given by the
Nuclear Energy Agency (2010) and the International Atomic Energy Agency (2010).
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Thorium
Thorium is listed in the USGS commodity surveys. The main source is from mineral
sands but it can also be processed from other Th minerals.
5. Extraction and Processing Efficiencies
All metals in ores that are mined do not end up in the salable output of the refinery.
Losses are inevitable during extraction, crushing, separation, smelting, and refining (or
other appropriate technology). As a consequence, the following questions arise:
• What are the approximate extraction and processing efficiencies (i.e., the fraction
of the amount in mined ore that is actually separated and processed into metal) for
all of the elements?
• Should one anticipate changes in these values over time? If so, please quantify.
• Can seafloor deposits of metals (i.e., a seafloor EGR) be quantified?
• Should we anticipate deriving some portion of virgin material supply in the future
from seafloor deposits? If so, for which metals? When is this likely to happen?
What would be the extraction and processing efficiencies?
To respond to these questions, the metals of the periodic table can be grouped into four
categories:
1. Metals mined for their own value
a) Metals without companions: W, Sn, Li, Nb, Ta, Fe, V, Cr, REE, Au, Ag, Mo, Sb
(In?)
b) Host metals that have companion metals: Cu, Zn, Pb, Fe, Mn, Ni, Al, Co, Mg,
Sn, Pd, Pt
2. Companion metals that follow the metal phase of the host and must be recovered
from that flow stream: Se, Te, In, Co, Bi, Sb, Au, Ag, Pd, Pt, R, Ru, Rh, Ir
3. Companion metals that do not follow the metal phase of the host (i.e., the
companion metal ends up in the slag): In, Ge, Ga, Mo, Re
4. Environmentally critical metals, i.e., daughter metals with very little value: As, Hg,
Cd, Tl.
In addition, one could also argue for the following groups:
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5. Elements that are primarily mined for non-metal uses: P, Ti, Li, B, Si. (note that the
system boundary should be defined carefully, e.g., Zr used as a metal or in ceramics
for buildings)
6. Energy metals: Th, U
Metals in group 2 are characterized by their high price sensitivity, and ore will almost
certainly not be extracted to recover these metals if it does not make economic sense to
extract the parent metal (e.g., Se and Te are each worth about $20/Mg of refinery
products, while the Cu parent in the same products has a value of roughly $10,000
[Peacey, 2010]). From the same perspective, metals in group 3 will only be recovered if it
is economic to do so, as their recovery requires additional processing (e.g., the price of
Mo that is used in steels for pipelines will depend on the oil price).
In a number of cases, particularly Au, Cu, Mo, tailings reprocessing can serve as a new
source, but metal concentrations in tailings are not uniformly available, so no general
estimates of the magnitudes or extraction probabilities from these sources can be made.
Efficiencies
It is helpful when considering processing and recovery efficiencies to refer to Figure 6.
Figure 6. The processing chain for the ore to metal sequence.
It is appropriate to categorize yield losses during mining and ore/metal processing into
three groups:
17

1. Mining wastes (overburden)
2. Ore processing wastes (tailings)
3. Metal production wastes: refers to all steps between roasting and refining, includes gas,
liquids, and solids (slags)
Process efficiencies for metals in group 2 (companions following host) will need to be
estimated individually for each host metal. This information is not generally published,
but is available in a few cases. (Peacey [2010] gives process efficiencies of 75% for Se
from Cu and 50% for Te from Cu, for example). A challenge will be the generation of
information on mining efficiencies (type 1), e.g., from companies/industry associations,
and overall recovery efficiencies for companions such as Ga, Ge, and In that have more
than one host metal.
Changes over time will depend on the ore type and the discovery of new mines (e.g., Ni:
sulfide or laterite; Co in the Democratic Government of the Congo: which technology
will they use?)
6. Marine nodules
A poorly quantified but potentially rich resource for a number of metals is deep ocean
deposits. Hein et al. (2010) have published a review of existing information on these
metal-rich deposits, in which they point out that some twenty-five elements have
concentrations in hydrogenous iron-manganese crusts that exceed those in Earth’s crust
(Figure 7). The quantities of these materials, either globally or in particular regions or
deposit types, is not known, however.
Low mining efficiencies are likely for these deposits, but efficiencies in ore processing
and metal production will be similar to those for land-based deposits.
Estimates of the potential resources in marine deposits are given in Table 1.
18

Figure 7. Mean concentrations of selected elements in Fe-Mn crusts from the central
Pacific Ocean compared with their concentrations in seawater and the continental crust
(Hein et al., 2010).
Table 1. Potential deep-ocean sources of elements (From Hein, J. R. Unpublished)
Massive/
Polymetallic
Sulfides
Fe-Mn
Crusts
Fe-Mn
Nodules
Ag G - -
Au G - -
Bi - L -
Cd L - -
Co - G L
Cu G L G
Ga L - -
Ge L - -
In L - -
19

Li - - L
Mn - G G
Mo L L L
Nb - G -
Ni - G G
Pt - L -
REEs-Y - G L
Sb L - -
Se L - -
Te L G -
Th - G -
Ti - G L
W - L -
Zr - L -
Zn G - -
Zr - L -
G = Good potential
L = Longer-term potential
- = not applicable
7. Comparing Extractable Global Resource and Reserve Base Estimates
It is clear from the summaries above that readily accessible and reliable estimates of EGR
are challenging, and are likely not to be available for all elements. Nonetheless, at least
approximate values can be quite useful. Over the past few decades, there have been
various attempts to realize this challenge. One of the earliest was that of Erickson (1973),
who estimated global resources of most metals to a depth of 1 km. The estimate was
based on the relation of R = 10 9 *A, which was originally outlined by McKelvey (1960),
where R is reserve in Mg (metric tons) and A is crustal abundance in percent. Using
reserve data for the U.S., Erickson showed that known reserves for lead in 1973 (31.8 x
10 6 Mg) were 2.45 times larger than the amount predicted by McKelvey’s relation (13 x
10 6 Mg). Assuming that similar relations would hold for all elements, he defined
potentially recoverable reserves (metric tons) to be equal to 2.45A x 10 6 . Because most
reserve information at that time (and also now) extended to shallow depths, Erickson
indicated that this relation might be used to estimate recoverable resources (essentially
the same as EGR) to a crustal depth of 1 km. Using this relation, his estimate for copper
resources in the U.S. was 122 x 10 6 Mg. This estimate was bound to be too low in the
long run because many more copper deposits were found over the ensuing decades. By
1998, for instance, the USGS estimated U.S. copper resources to a depth of about 1 km to
be about 640 x 10 6 Mg. Using the tectonic diffusion method, Kesler and Wilkinson
(2008) estimated U.S. copper resources to a depth of 1 km to be about 7 x 10 8 Mg.
Similar estimates for hydrothermal gold resources are 8.6 x 10 3 Mg by Erickson, 45 x 10 3
Mg by the USGS in 1998 and 74 x 10 3 Mg by Kesler and Wilkinson (in press). (These
20

estimates do not include resources of gold in placer, paleoplacer, and magmatic deposits,
which are probably substantial.)
The recent geological (USGS) and tectonic-diffusion (Kesler and Wilkinson) estimates
produce similar results that are about 5 to 8 times higher than the Erickson estimate. The
tectonic diffusion estimates for the upper kilometer of the crust for the world and USGS
estimates for the upper kilometer of the USA are likely to be within at least an order of
magnitude of the actual value. In general, the tectonic-diffusion estimates are much
higher than the most recent reserve base estimates for these two elements as shown by
Table 2.
Table 2. Comparison of 2008 Reserve Base estimate from the USGS to
tectonic-diffusion estimates of copper and gold in deposits in the upper
kilometer of the crust (all amounts in metric tons).
Metal Reserve Tectonic- RB/TD
Base (RB) Diffusion (TD) (%)
Copper 9.4 x 10 8 1.1 x 10 10
8.7%
Gold 6.9 x 10 4
1.2 x 10 6 5.7%
From a global standpoint, the basic question in estimating resources is the fraction of the
element of interest in the crust that is concentrated in ore deposits, either over time (and
therefore eroded and recycled) or at any point in time. Crude estimates of this fraction
have been made by estimating the amount of metal in deposits of a specific area and
comparing it to the average content of metal in crustal rocks. Of greater interest,
however, is the fraction of metal in the entire crust that is concentrated in ore deposits.
For instance, the entire crust contains about 3.9 x 10 14 Mg of copper and 3.2 x 10 10 Mg of
gold. The tectonic-diffusion method provides an estimate of the copper and gold in
hydrothermal deposits throughout the crust (the USGS method does not estimate this).
Kesler and Wilkinson (2008) estimated that copper deposits present throughout the entire
crust contain about 0.08% of this total crustal copper. Kesler and Wilkinson (2009)
estimated that hydrothermal gold deposits contain about 0.09% of this total gold. These
values are surprisingly close, but it is not yet known whether they are representative of
other metals.
The work discussed above provides essentially the only tectonic diffusion analyses
available. Erickson’s method clearly applies only to host metals, if it can be used at all,
because companion metal abundances in host deposits are widely varying. A possible
rough alternative is the suggestion of Erickson (1973) that the EGR should “approach
0.01% of the total amount available in the crust to 1 km depth”. Table 3 compares values
derived in this way with existing Reserve Base values. The rightmost column contains the
ratios between the two sets of values. It is apparent that the extractable geologic resource
21

(EGR) values for a range of metals, compiled as sub-totals for many countries, are
significantly in excess of their national or USGS totals. For Canada and Australia, this is
largely due to the more formal way they exclude sub-economic deposits from national
resource estimates, while for others it remains unclear. This suggests that relying on
USGS data alone is insufficient to produce an order of magnitude EGR for various
metals, and that more comprehensive tabulations are likely to produce more robust data
and estimates. Further statistical analyses are clearly required to fit distributions to
deposit types and reported resources, as well as further work in classifying all compiled
ore resources. In addition, further work is required to more thoroughly assess other
sources of metals (e.g., tin resources or tailings) and their potential EGR.
Using information from Table 3, Figure 8 explores the correlation between the Reserve
Base and EGR estimates (Mg, Al, Ti, Mn, Fe, and Ba were omitted so as to explore the
relationship for the less abundant elements). It is apparent from the figure that there is
essentially no correlation between the two sets of estimates. This is, of course, not
unsurprising considering that the EGR approach did not take into account the geological
processes that influence companion elements in host formations, among many other
things.
Table 3. EGR and Reserve Base estimates for metals
Element Upper
crustal
concentration
(ppm
unless
otherwise
indicated)
+
Amount in top
1 km of crust
(Eg unless
otherwise
indicated)
Rough estimate of
EGR (0.01% of
upper crustal
amount) (Tg unless
otherwise
indicated)
USGS
Reserve Base
2009 (Tg
unless
otherwise
indicated)
EGR/RB
ratio
Li 20 8.0 800 11 73
Be 3 1.2 120
B 15 6.0 600 410 1.5
Mg 1.3% 5300 530 Pg 3.6 Pg 150
Al 8.0% 32 Zg 3.3 Eg 38 Pg 87
Sc 14 5.6 560
Ti 0.41% 1700 170 Pg 1.5 Pg 110
V 107 43 4300 38 110
Cr 83 33 3300 > 380 < 8.7
Mn 600 240 24 Pg 5.2 Pg 4.6
Fe 3.5% 14 Zg 1.4 Eg 160 Pg 8.8
Co 17 6.8 680 13 52
22

Ni 44 18 1800 150 12
Cu 25 10 1000 1000 1
Zn 71 28 2800 480 5.8
Ga 17 6.8 680
Ge 1.6 0.64 64 > 0.5 < 130
As 1.5 0.60 60 1.7 35
Se 50 20 2000 0.17 12000
Sr 350 140 14 Pg 12 1200
Y 22 8.8 880 0.61 1400
Zr 190 76 7600 77 99
Nb 12 4.8 480 3 160
Mo 1.5 0.60 60 19 Gg 3200
Ru 1.0 ppb * 400 Tg 40 Gg
Rh 0.7 ppb * 280 Tg 28 Gg
Pd 6.3 ppb * 2.5 Pg 250 Gg
Ag 0.50 0.20 20 0.57 35
Cd 0.98 0.39 39 1.2 33
In 0.50 0.20 20
Sn 5.5 2.2 220 11 20
Sb 0.2 0.08 8 4.3 1.9
Te 1.0 * 0.4 40 48 Gg 830
Ba 550 220 22 Pg 880 25
La 30 12 1200
Ce 64 26 2600
Pr 7.1 2.8 280
Nd 26 10 1000
Sm 4.5 1.8 180
Eu 0.88 0.35 35
Gd 3.8 1.5 150
Tb 0.64 0.26 26
Dy 3.5 1.4 140
Ho 0.8 0.32 32
Er 2.3 0.92 92
Tm 0.33 0.13 13
Yb 2.2 0.88 88
Lu 0.32 0.13 13
Total 168 67.4 6700 150 45
REE
Hf 5.8 2.3 230 1.1 210
Ta 1.0 0.4 40 0.18 220
W 2.0 0.8 80 6.3 13
Re 0.4 ppb 160 Tg 16 Gg 10 Gg 1.6
Os 1.8 ppb * 720 Tg 72 Gg
Ir 0.4 ppb * 160 Tg 16 Gg
Pt 37 ppb * 15 Pg 1.5
23

Total
PGM
47 ppb 19 Tg 1.9 0.08 24
Au 1.8 ppb 720 Tg 72 Gg 0.1 720
Hg 67 * 27 2700 0.24 11000
Tl 0.75 0.3 30 650 Mg 46000
Pb 17 6.8 680 170 4.0
Bi 0.13 52 Pg 5.2 0.68 7.7
Th 2.8 1.1 110 1.4 79
U 10.7 4.3 430
+ The crustal concentrations are from McLennan (2001), except that those indicated by an
asterisk are from Winter (2011)
Figure 8. Reserve Base estimates as a function of EGR estimates
Does this information prove useful in establishing a broad and reliable alternative to
Reserve Base estimates? A first comment is that we believe the EGR estimates are not
unreasonable upper limits to the EGR distributions shown in Figure 4. Second, the
Reserve Base estimates, where they exist, are not unreasonable lower limits. Third, the
EGR/RB ratio is in thr range 1-100 for most elements, and exceeds 800 only for seven
(Se, Sr, Y, Mo, Te, Hg, Tl); for all of these seven the RB estimates of USGS are based on
rather sparse knowledge.
24

It is of interest to look at the EGR and RB information for several reasonably wellstudied
companion metals, as we do on Table 4. Note that all of the EGR/RB ratio values
fall between 25 and 45. If we assume that EGR/RB = 35 for geologically similar
elements, we can fill in approximate values for RB for the elements whose RB values
have not previously been estimated. It is also probably not unrealistic to divide the
existing RB estimates for total REE and total PGM into RB estimates for the individual
elements on the basis of their relative upper crustal abundance. With less confidence, we
might apply the EGR/RB = 35 rule of thumb to the seven problematic elements with
EGR/RB > 800. The results are given in Table 5. It is of interest that the indium value is
within a factor of two of the completely independent estimate generated in Section 3 of
this report.
Table 4. EGR and Reserve Base information for selected companion metals
Element Rough estimate of
EGR (0.01% of
upper crustal
amount) (Tg)
Estimated
Reserve Base
2009 (Tg)
As 60 1.7 35
Ag 20 0.57 35
Cd 39 1.2 33
REE 6700 150 45
(total)
PGM
(total)
1.9 0.08 24
EGR/RB
Table 5. Estimated Reserve Base from EGR/RB = 35
Element Rough estimate of
EGR (0.01% of
upper crustal
amount) (Tg)
Be 120 3.4
Sc 560 16
Ga 680 19
Ge 64 1.8
Se 2000 57
Sr 14 0.4
Estimated
Reserve Base
2009 (Tg)
25

Y 880 25
Mo 60 1.7
Ru 0.04 0.076
Rh 0.028 0.053
Pd 0.25 0.48
In 20 0.57
Sb 8 0.23
Te 40 1.1
La 1200 27
Ce 2600 58
Pr 280 6.3
Nd 1000 22
Sm 180 4.0
Eu 35 0.8
Gd 150 3.4
Tb 26 0.6
Dy 140 3.1
Ho 32 0.7
Er 92 2.0
Tm 13 0.3
Yb 88 2.0
Lu 13 0.3
Os 0.072 0.14
Ir 0.016 0.03
Pt 1.5 2.9
Hg 2700 80
Tl 30 0.9
Finally, it is of interest to estimate Reserves in those cases for which no published
estimates exist. This is the situation for Be, Sc, Ga, Ge, Se, In, and Te. Except for
beryllium, which is poorly characterized geologically, all are companion metals. We note
that both Reserves and Reserve Base estimates are available for several other companion
metals, however, and for a number of “batchelor” metals (those mined for themselves,
but not commonly host minerals for companion metals). This information appears in
Table 6, and the values are plotted in Figure 9. Overall, the relationship between the
Reserve and Reserve Base values for these metals is
R = 0.54 RB (1)
with a correlation coefficient of 0.988. Applying the relationship of Equation 1 to the
seven metals without existing Reserves estimates, and dividing the existing Reserves
estimates for total REE and total PGM into Reserves estimates for the individual
elements on the basis of their relative upper crustal abundance, gives the results shown in
Table 7.
26

Figure 9. Scatterplot of existing Reserve and Reserve Base estimates.
Table 6. Reserve and Reserve Base Estimates for Metals
Element
 Reserves
 Res
Base
V 13
 38
Nb 2.7 3
Ni 70 150
Mo 0.0086 0.019
Bi 0.32 0.68
Co 7.1 13
Pb 79 170
Sn 5.6 11
Ag 0.27 0.57
Au 0.047 0.1
PGM 0.071 0.08
Sb 2.1 4.3
As 1.1 1.7
Cd 0.49 1.2
Cs 0.07 0.11
Ge 0.00045 0.0005
Hf 0.61 1.1
Li 4.1 11
Hg 0.046 0.24
Re 0.0025 0.01
Se 0.086 0.172
Sr 6.8 12
27

Ta 0.13 0.18
Te 0.022 0.048
Tl 0.00038 0.00065
W 3 6.3
Y (Y2O3) 0.54 0.61
Zr (ZrO2) 51 77
RE 88 150
Table 7. Estimated Reserves (this work)
Element Estimated Reserve Estimated Reserves
Base 2009 (Tg) 2009 (Tg)
Be 3.4 1.8
Sc 16 8.6
Ga 19 10
Ge 1.8 1.0
Se 57 31
Sr 0.4 0.2
Y 25 14
Mo 1.7 0.9
Ru 0.076 0.041
Rh 0.053 0.029
Pd 0.48 0.26
In 0.57 0.31
Sb 0.23 0.12
Te 1.1 0.59
La 27 15
Ce 58 31
Pr 6.3 3.4
Nd 22 12
Sm 4.0 2.2
Eu 0.8 0.4
Gd 3.4 1.8
Tb 0.6 0.3
Dy 3.1 1.7
Ho 0.7 0.4
Er 2.0 1.1
Tm 0.3 0.2
Yb 2.0 1.1
Lu 0.3 0.2
Os 0.14 0.08
Ir 0.03 0.02
Pt 2.9 1.6
Hg 80 43
Tl 0.9 0.5
28

It is not the claim of this working paper that the Reserve Base values of Table 5 and the
Reserves values of Table 7 represent research of normal scholarly merit, but rather that
they provide rough working values that can be used in preliminary studies of long-term
metal criticality. We encourage academic, industry, and government scientists to perform
the detailed research that will place RB values for all the elements on a much sounder
footing than presently exists.
8. Conclusions
The principal conclusion of this review is that it is not possible at present to reliably
estimate the extractable global resource (EGR) for any metal. It appears that the Reserve
Base estimates of USGS, where present, provide a lower limit to EGR. However, Reserve
Base estimates are lacking for many metals of interest, have not been newly estimated in
2009, and are not planned to be estimated in the future. Elemental abundance ratios in the
continental crust establish upper limits (although unrealistically high ones, since much of
this material is unsuitable for mining) for EGR.
A variety of approaches for dealing with these challenges are described in this working
paper. Despite some promise here and there, no tractable analytic approach to making
improved EGR estimates has been identified. The proposal to generate best estimates and
upper limits for daughter metal EGRs (the example estimate shown in Section 3 is for In)
remains to be thoroughly tested.
Contemporary estimates of Reserves (as opposed to Reserve Base) continue to be
generated, and provide useful comparative information on metal stocks that are expected
to be mineable over the next 5-10 years. Where estimates of either parameter are not
provided because of insufficient data or proprietary concerns, we have developed herein
methods for approximating Reserve and Reserve Base values.
The end points that can be established for EGR at this point in time are too wide to be
optimum in arriving at central estimates. As a result, generating best estimate EGR
numbers across the elements of the periodic table remains work in progress, at least in the
short to medium term and in the absence of extensive research and exploration.
Nonetheless, the work provides a foundation for rough estimates of Reserves and Reserve
Bases, useful for making resource estimates in the present while anticipating improved
values that could come from more detailed research over the long term.
29

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