Economic Filters for Evaluating Porphyry Copper Deposit 

Resource Assessments Using Grade-Tonnage Deposit 

Models, with Examples from the U.S. Geological Survey 

Global Mineral Resource Assessment

Scientific Investigations Report 2010–5090–H

U.S. Department of the Interior

U.S. Geological Survey

Global Mineral Resource Assessment
This page left intentionally blank.
Economic Filters for Evaluating Porphyry 

Copper Deposit Resource Assessments 

Using Grade-Tonnage Deposit Models, with 

Examples from the U.S. Geological Survey 

Global Mineral Resource Assessment

By Gilpin R. Robinson, Jr., and W. David Menzie

Scientific Investigations Report 2010–5090–H

U.S. Department of the Interior

U.S. Geological Survey

Global Mineral Resource Assessment

Michael L. Zientek, Jane M. Hammarstrom, and Kathleen M. Johnson, editors
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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pubprod

Suggested citation:

Robinson, G.R., Jr., and Menzie, W.D., 2012, Economic filters for evaluating porphyry copper deposit resource assessments using grade-tonnage deposit models, with examples from the U.S. Geological Survey global mineral resource 

assessment: U.S. Geological Survey Scientific Investigations Report 2010–5090–H, 21 p.

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iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Terminology............................................................................................................................................2

Overview of Grade and Tonnage Models and Simplified Economic Filters .........................................2

Simplified Mining Engineering Cost Models ....................................................................................4

Overview of Engineering Cost Models.....................................................................................4

USBM Simplified Mining Engineering Cost Models ..............................................................5

Mine Capacity and Lifetime ..............................................................................................6

Mine Models........................................................................................................................6

Mill Models ..........................................................................................................................7

Infrastructure Models (Tailings Pond, Dam, and Liner)................................................7

Adjustments to Cost Equations for High Cost Settings.................................................8

Cost Updating ......................................................................................................................8

Summary........................................................................................................................................8

Economic Filters for Porphyry Copper Deposits..............................................................................8

Commodity Prices and Calculation of Copper Equivalent Grades ......................................9

Updating the Engineering Cost Models ...................................................................................9

Porphyry Copper Deposit Economic Filter Example ..............................................................9

Comparison of the Economic Filters with a Recent Analysis of the 

Economic Status of Copper Deposits .......................................................................10

Application of Economic Filters on Six Assessment Tract Results .....................................................10

Economic Filters Applied to the Six Tract Areas ...........................................................................11

Application of the Economic Filter to Estimates of Undiscovered Resources 

in the Six Tracts .....................................................................................................................14

Examples of Tract-Based Economic Filter Results........................................................................17

Conclusions...................................................................................................................................................17

References Cited..........................................................................................................................................19

Figures

1. Cumlative copper resources in the general porphyry copper deposit grade-tonnage model 

as a percentage of ranked deposits accumulated .................................................................3

2. Cumulative copper resources in the general porphyry 

copper deposit grade-tonnage model showing deposit importance categories ..............4

3. Tonnage and copper grade characteristics of deposits in the general porphyry copper 

deposit grade-tonnage model ....................................................................................................5

4. Comparison of the cutoff grade calculated for porphyry copper deposits 

using updated simplified engineering cost models for open-pit and 

block caving mines as a function of deposit depth and ore tonnage................................11

5. Selected permissive tracts for porphyry copper deposits in North America ............................13

6. Sensitivity of expected number of economic deposits and amount of copper resources 

in porphyry copper deposits composing the general porphyry copper model ................14
iv

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

yard (yd) 0.9144 meter (m)

Area

square mile (mi2

) 2.590 square kilometer (km2) 

Mass

ton, short (2,000 lb) 0.9072 metric ton (mt) 

ton per day (ton/d) 0.9072 metric ton per day

SI to Inch/Pound

Multiply By To obtain

Length

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

square kilometer (km2

) 0.3861 square mile (mi2)

Mass

metric ton (mt) 1.102 ton, short (2,000 lb) 

metric ton per day 1.102 ton per day (ton/d) 

Tables

 1. Mine Dilution and Recovery Factors.................................................................................................6

 2. Equations estimating capital and operating costs of eight mine types ......................................7

 3. Equations estimating capital and operating costs of underground mine depth factors..........7

 4. Equations for estimating capital and operating costs of eleven types of mills .........................8

 5. Tailings pond features and capital costs..........................................................................................8

 6. Metal prices and metallurgical recovery rates used in equivalent 

copper grade calculation and economic resource estimates..............................................9

 7. Provided as an online file

 8. Provided as an online file

 9. Selected permissive tracts, porphyry copper deposit assessment, North America..............12

10. Economic filter results for the general porphyry copper deposit model ..................................14

11. Economic filter results for the Cu-Au porphyry copper deposit subtype .................................15

12. Economic filter results for the BCYK porphyry copper deposit subtype.............................. 15

13. Economic filter results based on tract-based scenarios ............................................................18
Abstract

An analysis of the amount and location of undiscovered 

mineral resources that are likely to be economically 

recoverable is important for assessing the long-term adequacy 

and availability of mineral supplies. This requires an economic 

evaluation of estimates of undiscovered resources generated 

by traditional resource assessments (Singer and Menzie, 

2010). In this study, simplified engineering cost models were 

used to estimate the economic fraction of resources contained 

in undiscovered porphyry copper deposits, predicted in a 

global assessment of copper resources. The cost models 

of Camm (1991) were updated with a cost index to reflect 

increases in mining and milling costs since 1989. The updated 

cost models were used to perform an economic analysis of 

undiscovered resources estimated in porphyry copper deposits 

in six tracts located in North America. The assessment 

estimated undiscovered porphyry copper deposits within 1 

kilometer of the land surface in three depth intervals.

The results of the updated engineering cost model 

analysis for open-pit porphyry copper deposits are in 

agreement with the grade-tonnage boundary defining positive 

economic returns for copper deposits developed between 

1989 and 2008. This correspondence demonstrates that the 

updated engineering cost equations are performing well and 

appear to be appropriate to evaluate the economic status of 

open-pit porphyry copper mines under current, and potentially 

future, economic conditions. Economic filters based on these 

simplified engineering cost models provide a method for 

estimating potential tonnages of undiscovered metals that may 

be economic in individual assessment areas.

One implication of the economic filter results for 

undiscovered copper resources is that global copper supply 

will continue to be dominated by production from a small 

number of giant deposits. This domination of resource supply 

by a small number of producers may increase in the future, 

because an increasing proportion of new deposit discoveries 

are likely to occur in remote areas and be concealed deep 

beneath covering rock and sediments. Extensive mineral 

Economic Filters for Evaluating Porphyry Copper Deposit 

Resource Assessments Using Grade-Tonnage Deposit 

Models, with Examples from the U.S. Geological Survey 

Global Mineral Resource Assessment

By Gilpin R. Robinson, Jr., and W. David Menzie

exploration activity will be required to meet future resource 

demand, because these deposits will be harder to find and 

more costly to mine than near-surface deposits located in 

more accessible areas. Relatively few of the new deposit 

discoveries in these high-cost settings will have sufficient 

tonnage and grade characteristics to assure positive economic 

returns on development and exploration costs.

Introduction

This paper reports on a methodology that is used with 

estimates of undiscovered resources produced as part of 

the U.S. Geological Survey (USGS) global assessment of 

undiscovered copper resources (Schulz and Briskey, 2003). 

The methodology is used to evaluate the likely contribution 

that predicted resources in undiscovered porphyry copper 

deposits will make to mineral supplies in the foreseeable 

future. The global assessment used the three-part form of 

assessment (Singer, 1993a; Singer and Menzie, 2010) to 

estimate undiscovered porphyry copper resources. In the 

three-part form of assessment, (1) assessment tracts are 

delineated according to the geologic features that permit the 

occurrence of deposits of a particular type, (2) the amount 

of metal and some ore characteristics are estimated using 

grade and tonnage models specific to the deposit type, and 

(3) the number of undiscovered deposits of that type is 

estimated at different confidence levels. The assessment 

method was modified to explicitly consider the depth 

distribution of undiscovered deposits. The selection of a 

deposit model, selection of the appropriate grade and tonnage 

model, estimation of number of undiscovered deposits, 

and specification of the depth distribution of undiscovered 

deposits constitute a resource scenario that can be subjected 

to further analysis to evaluate the portion of the estimated 

resources that may be economic to develop.

In addition, this paper presents updated engineering cost 

models that are used in the economic analysis of estimated 

resources. The engineering cost models used in this study 
2 Economic Filters for Evaluating Porphyry Copper Deposit Resource Assessments

were originally developed by Camm (1991), but have been 

updated here to account for cost inflation and for regions 

with higher costs than in the western United States due to 

inadequate infrastructure to support mining. Advantages of the 

simplified cost model approach include: (1) the methodology 

can be applied to a variety of deposit types, (2) it is easy to 

apply and modify cost escalation and cost updating factors, 

and (3) the method requires only limited design and setting 

parameters to develop a cost estimate (Camm, 1994).

After development, the economic analysis methodology 

was tested by being applied to the assessment results of six 

porphyry copper deposit tracts—three in Mexico, two in 

Canada, and one in Central America.

Terminology

The terminology used in this report follows the 

definitions used in the 1998 USGS assessment of undiscovered 

deposits of gold, silver, copper, lead, and zinc in the United 

States (U.S. Geological Survey National Mineral Resource 

Assessment Team, 2000) and in Singer and Menzie (2010). 

This terminology represents standard definitions that reflect 

general usage by the minerals industry and the resource 

assessment community.

Block caving mining—An underground mining method used 

for large massive ore bodies that have large vertical extension 

and rock that will cave and break into manageable masses.

Capital cost—Money invested in the development of an 

operation.

Cost index—An economic index that compares equivalent 

costs at different time frames.

Cutoff grade—The lowest grade of ore material that can be 

included in a resource estimate that has reasonable prospects 

for application of a feasible mining method.

Economic filter—A methodology to estimate the fraction of 

resources contained in a mineral deposit that may be recovered 

by application of a feasible mining method and provide a 

positive return on investment.

Engineering cost model—An engineering-based analysis 

of capital and operating costs associated with an industrial 

operation.

Grade-tonnage model—The frequency distributions and 

relations of tonnage and ore grades of a group of wellexplored deposits that belong to the mineral deposit type being 

modeled. The data include the average grades of metals and 

mineral commodities of economic interest and associated 

tonnage based on the total production, reserves, and resources 

at the lowest possible cutoff grade for each deposit.

Identified resources—Resources whose location, grade, 

quality, and quantity are known or can be estimated from 

specific geologic evidence. For this report, identified resources 

are those in the porphyry copper deposits included in the grade 

and tonnage models used in resource assessment.

Metal endowment—The sum of metal in an occurrence with 

specified characteristics, such as concentration, size, and depth.

Mineral deposit—A mineral concentration of sufficient 

size and grade that it might, under the most favorable of 

circumstances, be considered to have potential for economic 

development.

Mineral deposit type—A group of deposits sharing a relatively 

wide variety and large number of attributes (Cox and others, 

1986).

Monte Carlo simulation—A computer method of randomly 

sampling probability distributions of variables so that variables 

can be combined and the computational results investigated.

Net present value—A measure of the value of an investment 

after discounting the cash flow at a rate of return and 

subtracting an initial capital investment.

Open-pit mining—A surface mining method of extracting rock 

or minerals from the earth by their removal from an open pit or 

excavation.

Operating costs—The day-to-day expenses associated in 

running a business or activity.

Permissive tract—The surface projection of a volume of rock 

where the geology permits the existence of a mineral deposit 

of a specified type. The probability of deposits of the type 

being studied occurring outside the boundary is negligible.

Quantitative assessment—An assessment where the result is 

presented as numerical values.

Resource—A mineral concentration of sufficient size and 

grade and in such form and amount that economic extraction 

of a commodity from the concentration is currently or 

potentially feasible.

Sensitivity analysis—A study of how uncertainty in the output 

of a model is related to sources of uncertainty in model input 

(Pannell, 1997).

Undiscovered mineral deposit—A mineral deposit believed to 

exist within a specified depth below the surface of the ground, 

or an incompletely explored mineral occurrence or prospect 

that could have sufficient size and grade to be classified as 

a deposit. In this study, the depth limit is 1 kilometer for 

assessment of undiscovered porphyry copper deposits.

Undiscovered resources—An estimate of the resources 

contained within an individual or group of undiscovered 

deposits believed to exist within a specified area and depth.

Overview of Grade and Tonnage 

Models and Simplified Economic 

Filters

Methodologies to quantitatively estimate undiscovered 

mineral resources typically rely on tonnage and grade 

distribution models of deposits with varying economic 
Overview of Grade and Tonnage Models and Simplified Economic Filters 3

viability (Singer, 1993b). The premise underlying the use of 

these models is that the grade and tonnage characteristics of 

undiscovered deposits will be similar to those of the population 

of known deposits of a given type. The grade-tonnage models 

include many deposits that have not been mined, but could be 

mined in the future if technological advances lower mining 

costs and (or) commodity prices increase (Singer and Menzie, 

2010). A number of discovered but unmined deposits in the 

grade-tonnage model may be currently economic, as suggested 

by Drew and others (1999), because the timeline to move a 

grassroots deposit discovery through prefeasibility, permitting, 

and project development planning typically takes more than 

15 years and can take as long as a century (Doggett and 

Leveille, 2010).

The USGS three-part form of assessment (Singer, 1993a) 

relies on deposit-type-specific models of grades and tonnages 

and probabilistic estimates of numbers of undiscovered 

deposits. These distributions can be combined using Monte 

Carlo simulation to estimate undiscovered mineral resources 

(Root and others, 1992). The resource estimates based on the 

USGS three-part methodology are for undiscovered resources 

in the ground (Singer and Menzie, 2010). Land use and 

resource policy issues typically consider the importance and 

effects of resources that might be economic to develop under 

assumed conditions and settings, requiring an economic filter 

to provide an estimate of resources that may be economic to 

extract (Harris and Rieber, 1993). For the purposes of assessing 

the longer term adequacy of resources, an assessment goal is to 

report tonnages of undiscovered metal that may be economic 

to recover as well as estimates of total resources—that is, both 

resources in undiscovered deposits and those in identified 

deposits in individual assessment tracts.

A simple economic filter for evaluating mineral resource 

assessment results may be based on the premise that simulated 

deposits can be compared with the sub-population of known 

economic deposits in a grade-tonnage model to estimate what 

portion of estimated undiscovered resources in the simulation 

results may be considered economic to produce (Singer and 

others, 2000; Singer and Menzie, 2010). The traditional 

approach to estimate the proportion of contained resources that 

might be economically produced under stated conditions relies 

on simplified mining engineering cost models, such as those 

developed by the U.S. Bureau of Mines (Camm, 1991, 1994).

However, a large degree of uncertainty is involved with 

implementing a simple engineering cost model as an economic 

filter to a grade-tonnage model-based resource simulation. This 

raises questions about whether the economic analysis can be 

realistic relative to real-world conditions, geologic variability, and 

the variation of costs by location and metal values over time.

Some rudimentary insights on likely economic filter criteria 

and results are provided by the characteristics of the grade-tonnage 

models for the mineral deposits types that supply the bulk of world 

production for a given commodity. For example, porphyry copper 

deposits account for approximately 60 percent of world copper 

supply (Singer, 1995). Grade and tonnage characteristics for 422 

individual porphyry copper deposits are included in the general 

porphyry deposit model of Singer and others (2008). In this model, 

deposit tonnage varies by almost four orders of magnitude while 

deposit grade varies by one order of magnitude; endowed copper 

resources on an individual deposit scale vary by more than four 

orders of magnitude, with copper resources correlating strongly 

with tonnage. Due to economies of scale, the largest tonnage 

deposits can be economic to produce at lower ore grades (Doggett 

and Leveille, 2010; Singer, 2010; Singer and Menzie, 2010).

The copper endowment of deposits in the general 

porphyry copper model of Singer and others (2008) is highly 

skewed toward the largest tonnage deposits that represent a 

small percentage of the deposits in the database. One percent 

100

90

80

70

60

50

40

30

20

10

00 10 20 30 40 50 60 70 80 90 100

Percent copper resource in economic deposits

Percent of ranked deposits accumulated

20% deposits =

80% resource

50% deposits 

<5% resource

1% deposits =

30% resource

Figure 1. Cumlative copper 

resources in the general porphyry 

copper deposit grade-tonnage 

model (Singer and others, 2008) as 

a percentage of ranked deposits 

accumulated.
4 Economic Filters for Evaluating Porphyry Copper Deposit Resource Assessments

of the deposits in the database accounts for 30 percent of the 

global copper resource contained in porphyry copper deposits, 

5 percent of deposits account for 50 percent of the copper 

resource, 20 percent of deposits account for 80 percent of the 

copper resource, while 50 percent of the smallest deposits 

account for less than 5 percent of the copper resource (fig.1). 

As a result, the mean resource of a deposit in the gradetonnage model and resulting from a resource simulation is 

larger than the resource contained in the median deposit or 

resource simulation. The probability of a resource outcome 

equal to or greater than the mean is less than 50 percent.

Four percent of the largest tonnage deposits in the 

database, classified as giant deposits see Singer, 1995), account 

for 45 percent of the copper resource contained in porphyry 

copper deposits (fig. 2). The giant deposit group clusters 

in the large-tonnage region of the copper grade-tonnage 

diagram for the general porphyry copper deposit model (fig. 3, 

45-percent copper resource group), and giant porphyry copper 

deposits have been developed in a wide variety of settings. 

The implication for an economic filter is that these deposits 

can be considered economic under nearly all settings and 

conditions. The next 14 percent of large tonnage deposits in 

the database that account for an additional 30 percent of the 

copper resource can be considered economic to produce in 

all but high cost (remote and (or) deep) settings. The smallest 

50 percent of deposits in the database account for only 5 

percent of the copper resource, so their economic status is 

not of great importance to the economic filter for resource 

simulations based on the general porphyry copper deposit 

model. The 32 percent of deposits with moderate tonnage, 

which account for about 20 percent of the copper resource in 

the model, include deposits of considerable importance for 

discrimination by the economic filter (fig. 2). However, errors 

in economic-subeconomic classification in this deposit size 

category will likely result in errors of less than 10 percent for 

the overall economic filter results. Figure 3 shows the tonnage 

and copper grade of the deposits in the general porphyry copper 

model categorized by the resource groups shown in figure 2.

Simplified Mining Engineering Cost Models

Overview of Engineering Cost Models

The simplified mining engineering cost models developed by 

the U.S. Bureau of Mines (USBM; Camm, 1991, 1994) provide a 

practical basis to estimate the costs of mine and mill development 

and operation and allow an economic analysis useful for resource 

assessment and mineral exploration prefeasibility studies (Harris, 

1990; Singer, 2010). The models can be applied to a number of 

deposit types, mining methods, and location settings with a level 

of uncertainty common in prefeasibility engineering evaluation. In 

addition, the models include a method to update cost parameters in 

the equations to reflect changing economic conditions and inflation 

from their calibration year of 1989 (Camm, 1991).

Although the simplified engineering cost estimates have 

a large uncertainty and do not consider all costs, they provide a 

means to discriminate clearly uneconomic from clearly economic 

deposits under a variety of grade, tonnage, and deposit setting 

conditions (Singer, 2010). The tonnage of ore in individual 

deposits is used to derive estimates of the operating capacity of the 

mine, lifetime of mine, and various capital and operating mining 

costs by using the appropriate mining and processing equations. 

Due to economies of scale and the influence of operating lifetime 

on economic returns, mine capacity and mine life estimates play 

Figure 2. Cumulative copper 

resources in the general 

porphyry copper deposit gradetonnage model (Singer and 

others, 2008) as a percentage 

of ranked deposits, showing 

deposit importance categories.

100

90

80

70

60

50

40

30

20

10

0

0 10 20 30 40 50 60 70 80 90 100

Percent copper resource in economic deposits

Percent of ranked deposits accumulated

Giant deposits economic in nearly all settings

Large deposits economic in all but high cost settings

Moderate tonnage deposits are the

important discrimination area for

economic filter [~20% resource]

Economic status of small tonnage

deposits is not important
Overview of Grade and Tonnage Models and Simplified Economic Filters 5

environmental studies, taxes, corporate overhead, site reclamation, 

concentrate transportation, or smelter and refinery charges.

USBM Simplified Mining Engineering Cost Models

The simplified engineering cost models use a rule 

developed by Taylor (1978, 1986) to relate mine life to deposit 

tonnage and mine capacity. 

L = 0.2 × (Td)0.25, (1)

where 

 L is mine life in years, and 

 Td is the reserve tonnage or tonnage of ore.

Capacity is the tonnage of material to be mined or 

processed divided by mine life, L, times the number of days 

mined per year. The general form of the cost models is (Camm, 

1991, 1994):

Y = A × (C)

B, (2)

where

 Y is the cost estimate;

 C is the daily capacity of the mine or mill; and

 A and B are constants.

The parameters in the following discussion and equations 

follow the units adopted by Camm (1991) and express tonnage 

in short tons and depth in feet or yards, as noted.

dominant roles in economic cost evaluations (Singer, 2010; Singer 

and Menzie, 2010).

Metal grades and long-term commodity prices are used to 

estimate the value of each ton of ore in the deposit. The value of 

production per year is calculated as the difference between (1) 

the product of value per ton times the operating capacity per day 

times the number of operating days per year (350 days assumed 

for a full-time mining operation) and (2) the total operating costs 

per year. The present value of production is derived from the value 

of production per year and the lifetime of the mine assuming an 

acceptable rate of return sufficient to secure capital. The present 

value of the production minus the estimated total capital costs is the 

present value of the deposit.

The following section briefly examines simplified engineering 

cost models for mining and beneficiation (milling). The equation 

parameter units and equation coefficient values reported by Camm 

(1991) are maintained in this report. The equation parameters 

of Camm (1991) involving tonnage use short ton units and the 

depth parameters use either feet or yards, as noted for individual 

equations. Conversion factors are used to convert metric tons to 

short tons (1 metric ton equals 1.102 short tons) and meters to feet 

(1 meter equals 3.281 feet) for deposit feature data in SI units. 

These model equations are then used to calculate the proportion 

of resources contained in undiscovered porphyry copper deposits 

in selected tracts in North America that might be economically 

produced at stated conditions. The analysis uses updated versions 

of the Camm (1991) cost models of capital and operating 

costs required to build and operate a mine and a mill, and the 

infrastructure that supports them. These models do not include 

estimates of the costs of preproduction exploration, permitting, 

0.1

1

10

1 10 100 1,000 10,000 100,000

Copper grade (%)

Million metric tons ore

45% Copper resource

30% Copper resource

20% Copper resource

5% Copper resource

EXPLANATION

Figure 3. Tonnage 

and copper grade 

characteristics of 

deposits in the general 

porphyry copper 

deposit grade-tonnage 

model (Singer and 

others, 2008), labeled 

by the copper resource 

groupings shown in 

figure 2. Copper grade 

in percent (%).
6 Economic Filters for Evaluating Porphyry Copper Deposit Resource Assessments

Mine Capacity and Lifetime

The capacity of the mine or mill varies depending on the tonnage of material being processed and the rate at which the facility 

is operated. The daily capacity of the facility is the key variable in 

these models. Daily mine capacity can be calculated as follows:

Cm = T/(L × dpy), (3)

where

 Cm is mine capacity in tons per day,

 T is the tonnage of material to be mined or 

milled over the life of the operation,

 L is mine life in years, and

 dpy is the days of operation per year.

The tonnage of material to be mined can be calculated 

from the deposit tonnage, Td. This tonnage can be adjusted for 

dilution and recovery by the following equation:

Tm = (Td) × (rfm) × (1+dfm), (4)

where 

Tm is the mine tonnage,

Td is the tonnage of ore in the deposit,

 rfm is the mine recovery factor, and

dfm is the mine dilution factor.

Table 1 lists values for mine dilution and recovery factors 

for seven types of mining methods (Camm, 1991).

the tonnage of waste material divided by the tonnage of ore and can 

be calculated as follows using the open-pit geometry and tonnage 

to volume features reported in Camm (1991, appendix B, p. 30).

SR = (2.225 × 4.1 × d3/Td)−1, (5)

where

 2.225 is the tonnage/volume factor,

 4.1 is a constant based on open-pit geometry 

and ore recovery,

 d is the depth to the bottom of the deposit 

measured in yards, and

 Td is the tonnage of ore in short tons.

The capacity, Cm, in tons per day, of an open-pit mine 

with strip ratio, SR, and tonnage of ore, Td, may be calculated 

from the following equation (Camm, 1991):

 Cm = (SR + 1) × (Td)/[(L) × (dpy)], (6)

where

Cm is daily mine capacity,

Td is ore tonnage, and

(L) × (dpy) is the lifetime of the mine in working 

days.

If the mine works 350 days per year or 260 days per 

year, then the above mine capacity equation may be combined 

with Taylor’s rule relating mine life to deposit tonnage and 

rewritten, respectively, as follows:

Cm = [(SR + 1) × (Td)0.75]/70 (7)

Cm = [(SR + 1) × (Td)0.75]/52. (8)

Adjustment for ore recovery (rfm) and dilution (dfm) 

factors can be made by multiplying Cm by (rfm) × (1 + dfm).

Mine Models

The simplified cost models estimate capital and operating 

costs for a number of types of surface and underground mines. 

Models are available for small (1,000- to 10,000-ton-per-day) 

and large (10,000- to 50,000-ton-per-day) open-pit mines and 

for six underground mining methods—block caving, cut-andfill, room-and-pillar, shrinkage stope, sublevel longhole, and 

vertical crater retreat. For each type, equations are provided 

to estimate capital and operating costs associated with 

nine categories of expenses—labor, equipment, steel, fuel, 

lubrication (lube category), explosives, tires, construction 

material, and sales tax. For underground mines, equations 

are provided for capital and operating costs for lumber and 

electricity, as well as the nine categories estimated for open-pit 

mines. Camm (1991) also presented summary equations that 

estimate the costs associated with all categories for each mining 

method. Table2 lists the summary equations for capital and 

For some types of deposits, mining may take place at a faster 

rate than is predicted by Taylor’s rule. In such cases, Camm’s 

(1994) simplified models must be modified or new cost models 

must be developed. For example, this is the case for sedimenthosted gold (Au) (Carlin-type), hot-spring Au-silver (Ag), and 

other deposits that can be mined by open-pit heap-leach methods; 

Singer and others (1998) present a cost model for these deposit 

types. In addition, if the deposit is to be mined by open-pit 

methods, the tonnage of material to be mined must be adjusted to 

account for overburden. The stripping ratio, SR, of the deposit is 

Table 1. Mine Dilution and Recovery Factors

Mining method

Dilution 

Factor (%) 

[dfm]

Recovery 

Factor (%) 

[rfm]

Mine Ore Tonnage recovery factor (rfm)(1+dfm)

Open pit 5 90 0.95

Block caving 15 95 1.09

Cut-and-fill 5 85 0.89

Room-and-pillar 5 85 0.89

Shrinkage 10 90 0.99

Sublevel longhole 15 85 0.98

Vertical crater retreat 10 90 0.99
Overview of Grade and Tonnage Models and Simplified Economic Filters 7

operating costs for each type of mine (Camm, 1991). Summary 

cost equations have the advantage of being easier to calculate 

than equations for nine expense categories.

The underground mine models are based on adit entry. For 

deeper underground mines with shaft entry, the depth factors 

in table 3 must be added to the base capital and operating cost 

equations. For deposit depths greater than 150 m, shaft entry to 

the ore body is assumed. For larger operations, the depth factors 

reflect the costs of additional shafts as needed. The depth variable, 

D, is based on the depth to the bottom of the ore body.

Mill Models

Camm (1991) presented models for beneficiating, or 

milling, the mined ore. The mill cost models have the same 

general form as those for mine cost and, like the mine cost 

models, the daily capacity of the facility is the key variable in 

these equations. Tonnage of material that is sent to the mill is 

another important variable. Mill capacity, Cml, is defined as:

Cml=[(rfm) × (dfm) × (Td)]0.75/70 for 350 days per year operations, (9)

where

Td is the tonnage of ore,

 rfm is the mine recovery factor, and

dfm is the mine dilution factor (table 1).

The simplified cost models estimate capital and operating 

costs for 11 types of mills—autoclave carbon-in-leachelectrowinning (CIL-EW), carbon-in-leach-electrowinning 

(CIL-EW), carbon-in-pulp (CIP), countercurrent decantationMerrill Crowe (CCD-MC), float-roast-leach, one-product 

flotation, two-product flotation, three-product flotation, gravity, 

heap leach, and solvent extraction-electrowinning. For each 

type of mill, Camm (1991) presented equations that estimate the 

capital and operating costs associated with up to 10 categories 

of expenses, including labor, equipment, steel, fuel, lubrication 

(lube category), tires, construction material, electricity, 

reagents, and sales tax. He also presented summary equations 

that estimate capital and operating costs directly. Table 4 lists 

the summary equations that estimate the overall capital and 

operating costs of each milling method (Camm, 1991).

Infrastructure Models (Tailings Pond, Dam, and Liner)

Camm (1991) also presented cost models for selected 

infrastructure construction and operation, including road 

building, powerline construction, and tailings pond, dam, 

and liner.

Tailings ponds are required for most milling facilities, 

except heap leach and solvent extraction operations. To 

estimate the capital costs of tailings ponds, the total area of 

the pond and length of the retaining dam to be constructed 

are required. The mill capacity (Cml) and mine life (L) are 

used to estimate the tailings pond area. The tailings pond 

area and mine life are used to estimate the length of the 

tailings pond retaining dam and the capital costs associated 

with the tailings pond, liner, and retaining dam (summary 

equations in table 5).

Table 2. Equations estimating capital and operating costs of eight mine types 

(Camm, 1991).

[Cm = capacity of mine in short tons per day]

Mine type Capital cost Operating cost

Open pit mines

Small open pit Kc = 160,000 × Cm

0.515 Ko = 71.0 × Cm-0.414

Large open pit Kc = 2,670 × Cm

0.917 Ko = 5.14 × Cm-0.148

Underground mines

Block caving Kc = 64,800 × Cm

0.759 Ko = 48.4 × Cm-0.217

Cut-and-fill Kc = 1,250,000 × Cm

0.644 Ko = 279.9 × Cm-0.294

Room-and-pillar Kc = 97,600 × Cm

0.644 Ko = 35.5 × Cm-0.171

Shrinkage stope Kc = 179,000 × Cm

0.620 Ko = 74.9 × Cm-0.160

Sublevel longhole Kc = 115,000 × Cm

0.552 Ko = 41.9 × Cm-0.181

Vertical crater retreat Kc = 45,200 × Cm

0.747 Ko = 51.0 × Cm-0.206

Table 3. Equations estimating capital and operating costs of underground mine depth factors (Camm, 1991).

[D = depth of shaft to bottom of ore body (feet); Cm = capacity of mine in short tons per day]

Mine type Capital cost Operating cost

Underground Shaft Entry Mine Kc = 371 × Cm +180 × D × (Cm)0.404 Ko = 2343/(Cm) + 0.440 × D/(Cm) + 0.00163 × D
8 Economic Filters for Evaluating Porphyry Copper Deposit Resource Assessments

Adjustments to Cost Equations for High Cost Settings

Adjustments to the base case equations may be needed 

if the deposits being evaluated are located in a region with 

a different cost structure than that of the western United 

States, on which these models are based. In such cases, 

cost-factor differences for individual cost categories can be 

used to modify base case equations. For example, Sherman 

and others (1990) estimate that capital and operating costs 

were escalated by 2.0–2.9 and 1.3–1.6 times, respectively, 

for a mining feasibility analysis of mineral deposits located 

in four coastal areas in Alaska with limited infrastructure to 

support mining. In the high cost scenario examples considered 

here, capital and operating costs were set at 1.8 and 1.4 times 

higher, respectively, than the base case to reflect a high cost 

scenario somewhat less inflated than the 1990 Alaska example 

of Sherman and others (1990). These cost adjustment factor 

values can be modified in the filter to reflect cost estimates 

for specific areas as needed. To apply these models to high 

cost areas, one must multiply the base cost equations for 

appropriate categories by the associated cost escalation factor 

to obtain a new cost model. Cost escalation factors can be 

easily modified in the models as needed.

Cost Updating

The mine and mill cost models of Camm (1991) were 

based on average 1989 U.S. dollars. Engineering cost indices, 

such as the Marshall & Swift index, can be used to update the 

cost models to different base years. To update the costs for a 

given equation, the index for the specified date is divided by 

the base year cost index and the cost equation is multiplied by 

this factor (Camm, 1991). The cost updating index, Ku, should 

be compatible with the base year defining the commodity 

prices used in the calculations.

Summary

By estimating the net present value of these mineral 

deposits, we can identify those deposits that might be 

producible at a profit under stated conditions. We can then 

estimate the proportion of deposits, and the proportion of 

different metals in the deposits included in the mineral deposit 

model, that might be economic to produce at stated conditions. 

These variables constitute an “economic filter” that may be 

used in mineral resource assessments.

Economic Filters for Porphyry Copper Deposits

Development of the basic economic filters for deposits in 

the general porphyry copper deposit model (Singer and others, 

2008) is discussed in this section. Because an undiscovered 

porphyry copper deposit might be mined by either open-pit or 

underground (block caving) methods, depending on the depth 

at which the deposit is located, filters are developed for mining 

by both methods for deposits located at the surface and at 

depths of 125, 375, and 750 meters.

Table 4. Equations for estimating capital and operating costs of eleven types of 

mills (Camm, 1991).

[Cml (mill capacity, st/day)= [(rfm) × (dfm) × (Td)]0.75/70 for 350 days per year operations]

Mill type Capital cost Operating cost

Autoclave CIL-EW Kc = 96,500 × Cml

0.770 Ko = 78.1 × Cml -0.196

CIL-EW Kc = 50,000 × Cml

 0.745 Ko = 84.2 × Cml -0.281

CIP-EW Kc = 372,000 × Cml

 0.540 Ko = 105 × Cml -0.281

CCD-MC Kc = 414,000 × Cml

 0.584 Ko = 128 × Cml -0.300

Float-roast-leach Kc = 481,000 × Cml

 0.552 Ko = 101 × Cml -0.246

Flotation, 1 product Kc = 92,600 × Cml

 0.667 Ko = 121 × Cml -0.336

Flotation, 2 product Kc = 82,500 × Cml

 0.702 Ko = 149 × Cml -0.356

Flotation, 3 product Kc = 481,000 × Cml

 0.552 Ko = 101 × Cml -0.246

Gravity Kc = 135,300 × Cml

 0.529 Ko = 87.8 × Cml -0.364

Heap leach Kc = 296,500 × Cml

 0.512 Ko = 31.5 × Cml -0.223

Solvent extraction Kc = 14,600 × Cml

 0.596 Ko = 3.00 × Cml -0.145

Table 5. Tailings pond features and capital costs (Camm, 1991).

[ATP (tailings pond area in acres) = KT(Cml/1000), KT = 17 for mine life (L)=0-

7.5 yrs, 32 for L = 7.5-15 yrs, and 62 for L >15 years; Cml = mill capacity in 

short tons/day; DL (dam length in feet) = 4 × (43560 × ATP)0.5]

Category Capital Cost (U.S. dollars)

Tailing pond 146,000 + 1,783 × ATP

Dam 161 × DL

Liner 5 × DL+35,790 × ATP
Overview of Grade and Tonnage Models and Simplified Economic Filters 9

To develop the filters, two issues must be addressed: (1) 

selecting commodity prices for the analysis and (2) updating 

the original Camm equations to account for the change in 

prices since 1989 when the models were developed. After 

these issues are addressed, examples of the economic filter 

scenarios will be presented and discussed. Finally, we compare 

the results of our base porphyry copper deposit filters with the 

results of an economic analysis of copper mines developed 

between 1989 and 2008 (Doggett and Leville, 2010).

Commodity Prices and Calculation of Copper 

Equivalent Grades 

Many porphyry copper deposits contain potentially 

recoverable byproduct metals; these byproduct metals include 

molybdenum, gold, and silver. The economic influence of 

byproduct metals is included in the economic filter by summing 

the recoverable value of the production of copper and the 

byproduct metals into the estimate of the value of ore per ton. 

This ore value can also be expressed as a copper equivalent 

grade. Copper equivalence is used by the mining industry 

to compare economic ore grades incorporating a variety of 

byproduct metals. Copper equivalence (CuEq) is calculated as:

CuEq (percent) = Gcu × [∑iRiViGi]/(RCuVCuGCu ), (10)

where

R is the respective metallurgical metal recovery 

rate,

V is time-averaged metal price/ton, and

Gi is metal commodity grade in percent of ore for 

the suite of potentially recoverable commodities, i, relative to copper (Cu).

The 1989–2008 average prices in 2008 U.S. dollars 

reported by Doggett and Leveille (2010) are used in the 

worked examples to calculate the value of ore per short ton 

and equivalent copper grade (table 6).

Updating the Engineering Cost Models

Long and Singer (2001) and Long (2009) recognized 

that, although Camm’s engineering cost equations provided a 

robust basis for estimation of the costs of mining and milling 

deposits at an early stage of exploration, the original models, 

which were calibrated using 1989 data, needed to be updated 

to reflect the general change in price level. The cost updating 

methodology of Camm (1991, p. 4) was used to develop the 

economic filters described below. The Camm equations for 

cost parameters were updated from the 1989 calibration year 

using engineering cost index ratios, including the Marshall & 

Swift Index for mining and milling (Chemical Engineering, 

1989–2008) and engineering cost index ratios derived from 

the “World Mine Cost Data Exchange” database (Mine Cost, 

2009). Shafiee and others (2009) provided a 1980–2009 

time-series analysis of average total costs of existing mining 

projects and operations, defined relative to Mine Cost cost 

indices for mining projects, including: (1) mill construction 

and labor costs; (2) machinery, heavy equipment, and line 

haul railroads (all services); (3) explosives, accessories, 

and miscellaneous materials and supplies; (4) capital costs 

(Canada trend); and (5) Marshall & Swift Index (mining, 

milling). The Shafiee and others (2009) study was developed 

to forecast trends in mining development and operating 

costs into the future. The variable used to update the cost 

parameters in the Camm equations was calculated as 

(average index 1989–2008)/(1989 index), similar to the 

method used to calculate the 1989–2008 average metal 

commodity prices that Doggett and Leveille (2010) used 

in their analysis. The time-averaged mining cost category 

indices derived from the Mine Cost database and Marshall 

& Swift Index had average values ranging from 1.25 to 1.27 

for all of the cost group categories. An overall cost index 

(Ku

) value of 1.26 was used to update the total operating and 

capital costs in the Camm equations, in conjunction with the 

1989–2008 time averaged metal prices in table 6.

Porphyry Copper Deposit Economic Filter 

Example

 Economic filters for porphyry copper deposits that 

are mined either by open-pit or block caving methods are 

presented in table 7. The following mine development 

conditions were assumed to apply: (1) the mine has 350 

working days per year; (2) the depth to the top of the deposit, 

as specified in column D, is treated as an increase to the 

Table 6. Metal prices and metallurgical recovery rates used in equivalent copper grade 

calculation and economic resource estimates.

[Source for average metal prices is Doggett and Leveille (2010), adjusted to 2008 U.S. dollars using U.S. 

Consumer Price Index Series U. Metallurgical recovery rates from Smith (1992, Table A-1, column 8)]

Value

1989-2008

Copper Molybdenum Silver Gold

20-Year average price (2008 U.S. dollars)

Average price per short ton $3,460 $21,380 $233,921 $15,021,087 

Metallurgical recovery 0.91 0.63 0.8 0.76
10 Economic Filters for Evaluating Porphyry Copper Deposit Resource Assessments

bottom depth of the open pit or shaft to the block caving mine; 

(3) the ore is beneficiated in a two-product flotation mill; (4) 

infrastructure in the cost model consists of a tailings pond and 

dam (the tailings pond is not lined); (5) the prices of metals 

and their metallurgical recovery rates are as given in table 6; 

(6) the cost models use a cost updating index (Ku

) of 1.26, 

consistent with the base year criteria of the metal prices in 

table 6; (7) an increase in operating costs of 40 percent and 

capital costs of 80 percent is assumed for high cost mining 

settings; and (8) a 15-percent return on investment is assumed 

to attract capital. Table 7 is a worksheet that uses the updated 

cost models to estimate the net present values of 10 porphyry 

copper deposits (Singer and others, 2008) under four cover 

depth scenarios. Table 8 lists and briefly describes each 

column in the worksheet in table 7.

[Tables 7 and 8 are provided as an online electronic supplement.]

Table 7 presents the deposit characteristics, the assumed 

mining parameters, the estimated costs of mining, milling, and 

smelting the ore, and the net present values at a 15-percent 

rate of return for 10 porphyry copper deposits at four depth 

scenarios mined by either open pit (column AW) or block 

caving (column AX) methods. If either or both of the net 

present values is positive, the mining method with the larger 

present value is assumed to be used to mine the deposit, and the 

resources in the deposit can be considered to be economic. The 

final four columns of table 7 (columns BE through BH) contain 

the estimated recoverable resources of copper, molybdenum, 

gold, and silver in the deposit. A similar analysis can be 

performed for all deposits in a grade-tonnage model. The 

sum of resources that are in deposits evaluated as economic 

under each set of conditions divided by the total resources 

of the commodity in the grade-tonnage model defines the 

proportion of the undiscovered resources that are estimated to 

be economic to recover under the stated conditions.

Further, one may select some tonnages so as to cover a 

tonnage range of the deposits in the model and then vary their 

copper equivalent grade such that they have a net present 

value of zero, which is at the break-even cutoff grade of 

equivalent copper. These points can then be used to define a 

boundary between economic and uneconomic deposits. This 

limiting grade-tonnage boundary portrays the economic filter 

results for the stated depth and cost setting conditions.

Comparison of the Economic Filters with a 

Recent Analysis of the Economic Status of 

Copper Deposits

Doggett and Leveille (2010) report deposit-specific 

analysis of economic returns for 100 new copper mines 

brought into production from 1989 to 2008. Their analysis 

provides recent data on the economic characteristics of 

copper deposits that can be used for comparison with the 

results of the economic filters developed here with updated 

Camm equations. The Doggett and Leveille (2010) analysis 

is based on reported tonnages, grades, and reported costs for 

individual copper deposits, assuming a minimum return of 8 

percent to be considered economic. Although their analysis 

considered many types of copper deposits, many of the 

deposits are porphyry copper types, and most of the large 

tonnage-low grade deposits in their database that define the 

lower boundary of economic grades are porphyry copper 

deposits. Their economic analysis was in 2008 U.S. dollars 

using average commodity prices (1989–2008) for copper, 

gold, silver, zinc, molybdenum, and cobalt to calculate 

equivalent copper grade values (table 6). For porphyry copper 

deposits, gold, silver, and molybdenum are the relevant 

byproduct commodities. The break-even copper equivalent 

grade results from the simplified engineering cost economic 

filters calculated using the updated Camm cost equations 

under four depth-of-cover scenarios for porphyry copper 

deposits in the Singer and others (2008) database. The results 

are shown overlain on the grade-tonnage graph of Doggett 

and Leveille (2010) in figure 4.

Figure 4 demonstrates that the break-even gradetonnage characteristics resulting from the updated simplified 

engineering cost filters, assuming current economic conditions 

and minimal cover, are consistent with the break-even gradetonnage characteristics defining positive economic returns to 

development defined by Doggett and Leveille (2010). The 

updated economic filters show deposit depth/cost relations 

under current (2012) economic conditions consistent with 

recent industry experience and are considered appropriate to 

evaluate the economic status of porphyry copper mines under 

current economic conditions. Because metal prices and mining 

costs are similarly influenced by inflation and currency value 

trends, conclusions about relative costs and values of deposits 

of a particular type at varying depth are likely to be more 

stable over time than cost equation parameters estimated on 

any particular dataset of tonnage, grade, and prices (see Singer 

and others, 1998).

An interesting feature of the economic filters calculated by 

combining the open-pit and block caving models can be seen 

in the economic filter cutoff grades calculated for depths of 375 

and 750 meters in the large tonnage part of the filter (fig. 4). The 

change in slope of these cutoff grades reflects a transition from 

open-pit to block caving mining in the model results.

Application of Economic Filters on Six 

Assessment Tract Results

In this section, the economic filters are applied to the 

resource assessment results of six porphyry copper deposit 

resource assessment tracts in North America. Table 9 lists the 

tract identifier codes, tract names, tract area, porphyry copper 

deposit model applied in the resource assessment, and a brief 

description of the geologic setting of porphyry copper deposits 

of each tract.
Application of Economic Filters on Six Assessment Tract Results 11

The selected tracts are shown in figure 5 by tract ID and 

name as given in table 9 and identified in text using tract ID. 

Two of the tracts are from an assessment of porphyry copper 

deposits in western Canada (CA–02, CA–04; Frost and others, 

2011), three of the tracts are from an assessment of porphyry 

copper deposits in Mexico (MX–L1, MX–L2, MX–L3; 

Hammarstrom and others, 2010), and one tract is from an 

assessment of porphyry copper deposits in the Caribbean and 

Central America (CACB–T2; Gray and others, in press).

Several of the applications required the basic economic filters 

to be modified to be appropriate for porphyry copper deposit 

subtype models and for regions where existing infrastructure is 

markedly less available than that required for the basic economic 

filters based on costs in the western United States.

Economic Filters Applied to the Six Tract Areas

Undiscovered resources are estimated as:

(number of deposits) × (tonnage) × (ore grade) = (resource), (11)

where the number of deposits, tonnage, and ore grade features 

are described by probabilistic distributions (Singer and 

Menzie, 2010).

Monte Carlo simulation is used in the USGS threepart assessment method to estimate the mean undiscovered 

resource, based on the average of all simulation results. The 

economic resource is estimated as:

(resource) × (economic filter) = (economic resource), (12)

where resource is the mean undiscovered resource estimated 

by simulation; and the economic filter is the fraction of 

resources estimated to be economically recoverable based on 

the features of the grade-tonnage model used in the simulation, 

the depth distribution estimate for the undiscovered deposits, 

and adjustments for high cost settings if any are assumed for 

the assessment tract area.

The updated engineering cost equations were applied 

to the general porphyry copper deposit model and to two 

subtype models under both typical and high cost settings 

to define the economic filters that were used to calculate 

the fraction of deposits and the proportion of the resources 

within the deposit model that might be economic to recover 

at four cover depth intervals (0, 125, 375, and 750 m). The 

mine cost settings are: (1) typical cost setting with existing 

regional infrastructure to support mining and (2) a high cost 

setting typified by remote settings with a lack of infrastructure 

to support mining. For the high cost setting scenario used in 

100.0

10.0

1.0

0.1

% CuEq

Positive returns to development

Positive returns to exploration

0.01 0.1 1 10 100 1,000 10,000

Million metric tons (Mt) ore

EXPLANATION 0.01 Mt Copper

0.1 Mt Copper1 Mt Copper10 Mt Copper

Economic status of copper

deposits 1989-2008

Positive returns 

to exploration

Positive returns

to development

Developed deposits

All others

Deposits

Porphyry copper

Updated Camm (1991)

cost model

Depth

0 meters

125 meters

375 meters

750 meters

Figure 4. Comparison of the cutoff grade calculated for porphyry copper deposits using updated simplified engineering cost models 

for open-pit and block caving mines as a function of deposit depth and ore tonnage. Also shown are the grade-tonnage features 

defining the case-study economics status of recently developed copper deposits (shown as large diamond symbols) from Doggett and 

Leveille (2010). % CuEq, percent copper equivalence, as defined in equation 10.
12 Economic Filters for Evaluating Porphyry Copper Deposit Resource Assessments

the examples, operating costs are assumed to be 40 percent 

higher than the typical case and capital costs are assumed to 

be 80 percent higher than the typical case; these factors can 

be modified to reflect estimates for specific areas. Table 10 

shows the economic filter classification results for the general 

porphyry copper deposit model of Singer and others (2008), 

indicating both the fraction of undiscovered deposits and the 

fraction of ore metals in these deposits in the grade-tonnage 

model that are classified as economic under the different 

deposit depth and cost settings.

Uncertainty in estimated capital and operating costs 

influences the fraction of deposits and their contained resources 

classified as economic by the filter. The sensitivity of the 

expected number of economic deposits and amount of metal 

has been explored by applying cost escalation factors to the 

economic filter cost equations. An example using porphyry 

copper deposits under the assumption of typical mining 

conditions and a uniform depth distribution of undiscovered 

deposits from the surface to 1 km depth is shown in figure6. 

A 20 percent decrease in costs (base change, both capital and 

operating costs, equal to 0.8) results in a 41-percent increase 

in the number of deposits that are economic and a 10-percent 

increase in copper resources that are economic; a 20-percent 

increase in costs (base change equal to 1.2) results in a 29-percent 

decrease in the number of deposits that are economic and an 

11-percent decrease in copper resources that are economic. The 

base rate change of number of economic deposits exceeds or is 

similar to the base rate change in costs, whereas the base rate 

change in amount of metal that is economic is about one-half that 

of the base rate change in costs. These smaller differences likely 

reflect the importance of the resource contribution from the giant 

deposits that are economic in nearly all settings.

Due to the influence of large tonnage deposits on mean 

deposit resources, the economic metal fractions exceed the 

percent of deposits classified as economic. The sensitivity 

analysis (fig. 6) indicates that the economic filter results 

for economic resources are not highly sensitive to modest 

uncertainty in the estimated capital and operating costs.

Table 9. Selected permissive tracts, porphyry copper deposit assessment, North America.

[Tract ID is used to identify specific tracts in figures and text; km2, square kilometers. General model is that of Singer and others (2008); Cu-Au subtype, 

copper-gold porphyry copper subtype (Singer and others, 2008); BCYK subtype, Canadian calc-alkaline porphyry copper ± gold ± molybdenite model (Frost 

and others, 2011)]

Tract ID Tract name/ Location Coded ID Tract area 

(km2)

Porphyry copper 

deposit model Geologic setting for the tract

CA-02 Intermontane Island Arc 

(Canada)

003pCu2002 109,350 Cu-Au subtype Triassic to Jurassic alkaline igneous rocks of 

the Quesnel and Stikine accreted island-arc 

terranes.

CA-04 Cordilleran Continental Arc 

(Canada)

003pCu2004 684,140 BCYK subtype Jurassic to Eocene predominantly calcalkaline igneous rocks of post-accretionary 

continental magmatic arcs. 

MX-L1 Western Mexican Basin and 

Range (Mexico)

003pCu3006 74,140 General model Late Cretaceous to middle Eocene (Laramide) 

calc-alkaline magmatic arc rocks along the 

western margin of Mexico.

MX-L2 Sierra Madre Occidental (W) 

(Mexico)

003pCu3007 115,110 General model Late Cretaceous to middle Eocene (Laramide) 

calc-alkaline magmatic arc rocks in the Sierra 

Madre Occidental of northern Mexico.

MX-L3 Laramide Central Plateau 

(Mexico)

003pCu3008 58,720 General model Belt of Late Cretaceous to middle Eocene 

(Laramide) magmatic arc rocks in the eastern 

part of the Sierra Madre Occidental, eastern 

Mexican Basin and Range, and the Mesa 

Central of northern Mexico.

CACB-T2 Cocos (Central America) 003pCu4004 203,630 General model Miocene and younger volcanic arc rocks 

in southwest Mexico and Central America, 

including the modern Chiapanecan volcanic 

arc and Central American volcanic arc.
Application of Economic Filters on Six Assessment Tract Results 13

140° 120° 100°

50°

30°

10°

Laramide Central Plateau

(MX–L3)

Western Mexican Basin and Range

(MX–L1)

Intermontane Island-Arc Porphyry Cu-Au

(CA–02)

Cordilleran Continental Arc

(CA–04)

Cocos

(CACB–T2)

Sierra Madre Occidental

(MX–L2)

EXPLANATION

 PACIFIC OCEAN

Gulf of Mexico

Caribbean Sea

CANADA

UNITED STATES

MEXICO

GT

NI

CR

CO

HN

CU

SV

PA

0 500 1,000 KILOMETERS

0 250 500 MILES

Political boundary source: U.S. Department of State (2009)

Projection: North American Lambert Conformal Conic;

Central meridian 65° W; Latitude of Origin 35° N

BZ

Figure 5. Selected permissive 

tracts for porphyry copper 

deposits in North America. 

Tract identifiers, names, and 

descriptions are given in table 

9. GT, Guatemala; BZ, Belize; SV, 

El Salvador; HN, Honduras; NI, 

Nicaragua; CR, Costa Rica; PA, 

Panama; CO, Columbia; CU, Cuba.
14 Economic Filters for Evaluating Porphyry Copper Deposit Resource Assessments

Application of the Economic Filter to Estimates 

of Undiscovered Resources in the Six Tracts

Quantitative resource assessments are model-based 

estimates using information available at the time the 

assessment is conducted and the assumptions adopted by the 

assessment team. An economic filter applied to quantitative 

assessment results creates a scenario based on assumed costs, 

prices, and geologic conditions, such as depth and setting of 

the undiscovered deposits.

USGS three-part quantitative assessments provide 

estimates of in-ground undiscovered resources on a tract basis. 

In the USGS global assessment of undiscovered porphyry 

Tables 11 and 12 present results from applying economic 

filters to the porphyry copper-gold deposit subtype model (model 

20c, Cox, 1986; using grade-tonnage data from Singer and others, 

2008) and the calc-alkaline porphyry Cu-Mo-Au deposits of the 

Canadian Cordillera subtype model (Frost and others, 2011), 

abbreviated as the BCYK model in the following examples. 

Owing to the presence of giant deposits, the copper-gold subtype 

model (table 11) has similar economic filter characteristics to 

the general porphyry copper deposit model (table 10). Owing 

to the lower tonnages and grades of the BCYK subtype model, 

the economic filter results are lower (table 12). For both models, 

individual economic metal recovery rates are given for contained 

copper, molybdenum, gold, and silver resources.

Table 10. Economic filter results for the general porphyry copper deposit model (Singer and others, 2008) assuming the specified 

metal recovery rates for each commodity.

[Cu, copper; Mo, molybdenum; Au, gold; and Ag, silver. The fraction of undiscovered deposits and their contained metal resources classified as economic according 

to filter criteria are shown. Metal recovery rate is the product of the ore recovery rate times the metallurgical recovery rate for each commodity; the high cost setting 

assumes that operating costs and capital costs are 40 and 80 percent higher, respectively, than typical costs. Deposit depth is the depth to the top of ore]

Depth (meters) Cost Setting

Economic fraction of resource simulation

Deposits Cu Mo Au Ag

0 Typical 0.791 0.808 0.558 0.680 0.715

125 Typical 0.647 0.790 0.549 0.672 0.698

375 Typical 0.377 0.722 0.502 0.620 0.618

750 Typical 0.149 0.550 0.415 0.518 0.446

0 High Cost 0.464 0.732 0.513 0.639 0.622

125 High Cost 0.325 0.667 0.478 0.587 0.560

375 High Cost 0.145 0.528 0.397 0.503 0.429

750 High Cost 0.036 0.321 0.207 0.395 0.089

Metal recovery rate 0.82 0.57 0.68 0.72

Figure 6. Sensitivity of expected 

number of economic deposits and 

amount of copper resources in porphyry 

copper deposits composing the general 

porphyry copper model of Singer and 

others (2008) with respect to possible 

changes in expected base operating and 

capital costs, assuming a uniform depth 

distribution of undiscovered deposits.

0.4

0.6

0.8

1.2

1.4

1.6

1.8

1

0.6 0.8 1 1.2 1.4

Change in expected amount

Change to base expected costs

Copper

Deposits

EXPLANATION
Application of Economic Filters on Six Assessment Tract Results 15

copper deposits, an expert panel estimated the number of 

undiscovered deposits likely to occur in tracts at three or 

more different confidence levels. A probability distribution of 

deposits was calculated that is consistent with the estimates of 

the panel (Root and others, 1992). A Monte Carlo simulation 

combines the distribution of number of undiscovered deposits 

with the grade-tonnage model or submodel appropriate 

for each tract, to generate resource estimates at different 

confidence levels (Singer, 1993a). An economic filter for both 

open-pit and block caving mining methods was then applied 

to the simulated undiscovered resources estimated for each 

tract. For each tract, the assessment process provides estimates 

of undiscovered deposits at different confidence levels and 

their relative distribution by depth, a grade-tonnage model 

that is used to simulate the undiscovered resources contained 

in these deposits, and a cost setting parameter based on tract 

geology and infrastructure features, all combined to estimate 

undiscovered economic resources.

The simple tract-based economic filter scenario described 

below is based on information in the grade-tonnage model 

applied to the tract and some additional general tract setting 

information that can be estimated by the assessment panel. In 

Table 11. Economic filter results for the Cu-Au porphyry copper deposit subtype (Singer and others, 2008) assuming the specified 

metal recovery rates for each commodity.

[Cu, copper; Mo, molybdenum; Au, gold; and Ag, silver. The fraction of undiscovered deposits and their contained metal resources classified as economic according to filter criteria are shown. Metal recovery rate is the product of the ore recovery rate times the metallurgical recovery rate for each commodity; the high cost 

setting assumes that operating costs and capital costs are 40 and 80 percent higher, respectively, than typical costs. Deposit depth is the depth to the top of ore]

Depth (meters) Cost Setting

Economic fraction of resource simulation

Deposits Cu Mo Au Ag

0 Typical 0.861 0.813 0.563 0.676 0.711

125 Typical 0.704 0.798 0.553 0.658 0.683

375 Typical 0.391 0.714 0.506 0.570 0.567

750 Typical 0.157 0.554 0.399 0.443 0.316

0 High Cost 0.522 0.745 0.525 0.596 0.612

125 High Cost 0.322 0.661 0.471 0.515 0.528

375 High Cost 0.130 0.534 0.377 0.429 0.316

750 High Cost 0.044 0.333 0.299 0.231 0.065

Metal recovery rate 0.82 0.57 0.68 0.72

Table 12. Economic filter results for the BCYK porphyry copper deposit subtype assuming the specified metal recovery rates for 

each commodity.

[BCYK subtype, Canadian calc-alkaline porphyry copper±gold±molybdenite model (Frost and others, 2011). Cu, copper; Mo, molybdenum; Au, gold; and Ag, 

silver. The fraction of undiscovered deposits and their contained metal resources classified as economic according to filter criteria are shown. Metal recovery 

rate is the product of the ore recovery rate times the metallurgical recovery rate for each commodity; the high cost setting assumes that operating costs and 

capital costs are 40 and 80 percent higher, respectively, than typical costs. Deposit depth is the depth to the top of ore]

Depth (meters) Cost Setting

Economic fraction of resource simulation

Deposits Cu Mo Au Ag

0 Typical 0.647 0.739 0.513 0.556 0.689

125 Typical 0.382 0.662 0.486 0.506 0.673

375 Typical 0.118 0.375 0.309 0.246 0.384

750 Typical 0.029 0.226 0.130 0.005 0.127

0 High Cost 0.118 0.357 0.322 0.115 0.275

125 High Cost 0.029 0.226 0.130 0.005 0.127

375 High Cost 0.000 0.000 0.000 0.000 0.000

750 High Cost 0.000 0.000 0.000 0.000 0.000

Metal recovery rate 0.82 0.57 0.68 0.72
16 Economic Filters for Evaluating Porphyry Copper Deposit Resource Assessments

the examples presented here, the simple economic filter analysis 

is applied to the mean amount of undiscovered resources estimated by the Monte Carlo simulation. This approach allows the 

economic filter criteria to be defined based on the actual deposits 

in the grade-tonnage model and not on the characteristics of each 

simulated deposit resulting from the Monte Carlo simulation 

(that is, the economic filter only has to determine the potentially 

economic resource fraction once for each unique grade-tonnage 

model using the depth and cost-setting scenario criteria). However, 

it is possible to implement the economic filter analysis as part of 

the Monte Carlo simulation process to estimate the full distribution 

of estimated economic resources.

The depth distribution criteria of undiscovered deposits was 

implemented by each assessment team estimating the proportion 

of the undiscovered porphyry copper deposits in the tract likely 

to occur in three depth intervals, for example 0–250 m, 250–500 

m, and 500–1,000 m. For porphyry copper deposits, and probably many other deposit types, the likely undiscovered deposit 

distribution will be a function of recognizable tract features. 

Porphyry copper deposits are commonly associated with shallowly emplaced (1–5 km) igneous bodies of intermediate to felsic 

composition. Porphyry copper deposits have been interpreted 

to form slightly above the depth (pressure) of the critical point 

of their associated source fluids (Burnham, 1981, 1985). Under 

these depth-pressure conditions, fluid exsolving from the magma 

provides the mechanical energy to produce the extensive hydrofractured vein-breccia network that is an ore-host characteristic of 

porphyry deposits. The assignment of proportions of deposits to 

depth classes for individual tracts depends on an analysis of the 

characteristics of the existing igneous bodies as well as the uplift, 

erosion, and younger cover depositional history of the tract. Many 

deposit types may have depth distributions that could be estimated 

by assessors using mineralizing system concepts appropriate for 

the deposit type of interest, or a default assumption of uniform 

depth distribution could be assumed.

The cost settings of each tract were defined by the assessment 

team as the estimated fraction of the tract that was likely to have 

“typical” mining costs and the fraction of the tract that was likely 

to have “high” mining costs. The economic filter then consists 

of the percentages of deposits and undiscovered resources (Ru

) 

estimated to be economic for the grade-tonnage model and cost 

settings of each tract. The tract-scale economic filter is calculated 

as follows, for economic recovered copper:

Potential economic recovered Cu =

Ru,cu[∑i∑j(DDFi)(SFj)(MCuFij)], (13)

where

 Ru,cu is mean amount undiscovered Cu 

resource from the Monte Carlo simulation,

 DDFi is deposit depth distribution fraction in 

depth interval i (defined by assessors),

 SFj is cost setting fraction estimate for setting 

j (defined by assessors), and

 MCuFij is fraction of economic Cu resource in 

deposit model, as a function of depth and 

cost settings.

The fractions of the number of undiscovered deposits that 

are potentially economic in the tract is calculated as follows:

Economic deposit fraction (EDF) = 

[∑i

∑j(DDFi)(SFj)(MDFij)], (14)

where

 DDFi is deposit depth distribution fraction in 

depth interval i (defined by assessors),

 SFj is cost setting fraction estimate for setting 

j (defined by assessors), and

 MDFij is fraction of the number of deposits 

classed economic in the grade-tonnage 

model by the economic filter, as a function 

of depth and cost setting parameters.

The fraction of undiscovered resources classed as 

economic for each depth and tract setting criteria are tabulated 

in table 10 for the general porphyry copper grade-tonnage 

model. Tables 11 and 12 present the same information for the 

copper-gold porphyry copper subtype model and for the BCYK 

porphyry copper subtype model, respectively.

These economic resource fractions are used in 

conjunction with assessment team estimates of undiscovered 

deposit fractions in the depth groups and tract settings to 

estimate the economic fraction of the mean undiscovered 

resource estimates by tract. In addition to an estimate of 

mean economic resources, the probability of no economic 

resources in a tract can be estimated. The probability of 

no economic resource occurring in the tract is a binomial 

probability function involving: (1) the probability of no 

deposits occurring in the tract estimated by the assessment 

panel and as a result of Monte Carlo simulation, (2) the 

fraction of deposits in the grade-tonnage model that is 

classified by the economic filter as economic under tract 

conditions (EDF), and (3) the mean number of undiscovered 

deposits in the tract. The Monte Carlo simulation probability 

of no deposits occurring in the tract (MC0

) is based on the 

assessment panel estimates of undiscovered deposit numbers 

at different confidence levels, expressed as the probability of 

zero deposits in the Monte Carlo simulation output.

Pf = MC0 + (1–EDF)

n

, (15)

where

 Pf is the probability of no economic resource 

in the tract (probability of failure), 

 (MC0) is the Monte Carlo probability of no 

resources occurring in the tract,

(1–EDF) is the probability that a simulated 

deposit will be subeconomic under tract 

conditions, and

 n is the estimate of mean undiscovered 

deposits for the tract (which is an estimate 

of the number of sample trials for the 

tract).
Conclusions 17

Examples of Tract-Based Economic Filter 

Results

A simple economic filter scenario has been developed 

for six porphyry copper deposit tracts in North America 

included as part of the USGS Global Mineral Resource 

Assessment (Schulz and Briskey, 2003). The selected tract 

locations are shown in figure 5 and tract characteristics are 

summarized in table 9. The tract ID code in table 9 is used 

to identify the tracts discussed below. Four tracts in Mexico 

and Central America (MX–L1, MX–L2, and MX–L3, and 

CACB–T2) used the general porphyry copper deposit 

model for assessment. Tract CA–02 in Canada used the 

Cu-Au subtype model (Cox, 1986; Singer and others, 2008) 

and tract CA–04 in Canada used the BCYK subtype model 

(Frost and others, 2011).

Examples of depth distribution estimates and mining 

cost settings for each tract are given below and summarized 

in table 13. Active mining operations of a variety of 

types and scales are common in the northern Mexico 

region containing tracts MX–L1, MX–L2, and MX–L3 

(Hammarstrom and others, 2010), and the climate and 

infrastructure base is conducive for mine development. 

These tracts are classed as “typical” setting status. Tract 

MX–L2 is the most productive porphyry tract in Mexico 

and is at an optimal exhumation level for discovery of 

porphyry deposits based on known deposits and the 

distribution of permissive intrusive and volcanic rocks 

(Hammarstrom and others, 2010). The undiscovered deposit 

distribution in MX–L2 is likely to be skewed toward 

shallow depths compared with that expected by a uniform 

depth distribution (40 percent <250 m, 30 percent 250–500 

m, and 30 percent >500 m; table 13). Tract MX–L1 has 

similar characteristics to MX–L2, but is dominated by 

exposed permissive volcanic rocks and has more extensive 

younger cover. For the MX–L1 tract, the undiscovered 

deposit distribution is likely to be skewed slightly toward 

mid-level depths (20 percent <250 m, 40 percent 250–500 

m, and 40 percent >500 m; table 13). Tract MX–L3 has 

very few known porphyry copper deposits, but has a 

number of polymetallic and epithermal deposits that might 

be associated with porphyry systems at depth. Exposed 

Laramide-age intrusions are scarce, but are inferred at 

depth based on epithermal systems and aeromagnetic 

anomalies. Younger Tertiary and Quaternary volcanic rocks 

and sediments cover parts of the tract. For tract MX–L3, 

the depth distribution of undiscovered porphyry deposits 

is likely to be skewed toward greater depths and with few 

deposits in the shallow depth interval (5 percent <250 m, 25 

percent 250–500 m, and 70 percent >500 m; table 13).

Tract CACB–T2 covers part of southernmost Mexico 

and extends through Central America. This tract contains 

a few large active mines. However, most of the tract is 

covered by rain forest, and large remote areas in the tract 

have little infrastructure development (Gray and others, 

in press). Tract CACB–T2 was classified as “high cost” 

setting. The tract covers a large area, and because the 

undiscovered deposit depth distribution is uncertain, has a 

uniform deposit distribution with depth (25 percent <250 

m, 25 percent 250–500 m, and 50 percent >500 m; table 13) 

was assumed.

The BCYK subtype grade-tonnage model (Frost and 

others, 2011) is applied to the CA–04 tract in Canada 

(Mihalasky and others, 2011). The CA–04 tract is 

dominated by volcanic rocks, and the depth distribution 

of undiscovered deposits is likely to be skewed slightly 

toward greater depths than expected by a uniform depth 

distribution. Two cost filters were applied to this tract: (1) 

typical mining cost setting and (2) a 50:50 percent typical 

and high cost setting allocation to illustrate the influence of 

the cost variable for this deposit subtype.

The Cu-Au subtype model is applied to tract CA–02 

in Canada (Mihalasky and others, 2011). Tract CA–02 has 

a mix of exposed permissive volcanic and plutonic rocks; 

the depth distribution of undiscovered deposits is likely to 

be skewed slightly toward shallower depths than a uniform 

depth distribution. Much of this tract is in remote areas with 

high relief. The assessment team classified 40 percent of the 

tract as having typical mining cost and 60 percent as high 

cost mining for this example.

The results are tabulated in table 13. The individual 

commodity metal recovery rates listed in table 6 were used 

in the calculations.

Conclusions

Mineral resource estimates are dominated by the larger 

tonnage deposits in the grade-tonnage model applied in the 

assessment process. The largest tonnage deposits that make 

up 1 percent of all porphyry copper deposits (Singer and 

others, 2008) account for 30 percent of the copper resource, 

those that make up 20 percent of the deposits account for 80 

percent of the copper resource, and the smallest 50 percent 

of the deposits account for less than 5 percent of the copper 

resource. As a result, the economic filter results are not 

sensitive to economic-subeconomic discrimination criteria 

for low tonnage deposits.

The grade-tonnage break-even curve predicted using 

cost updated Camm equations for open-pit porphyry copper 

deposits is consistent with the grade-tonnage break-even 

economic curve defined by a study of copper mines of all 

types developed from 1989 to 2008 (Doggett and Leveille, 

2010). The agreements between these two different datasets 

provide evidence that the price-adjusted Camm equations 

provide a useful rudimentary economic filter to evaluate 

the economic status of porphyry copper and other mineral 

deposits under current, and potentially future, economic 

conditions.

The economic filter can be easily applied to mean 

undiscovered resource results outside of the Monte Carlo 

simulation process. In addition, it provides an estimate of 
18 Economic Filters for Evaluating Porphyry Copper Deposit Resource Assessments

Table 13. Economic filter results based on tract-based scenarios.

[Cu, copper; Mo, molybdenum; Au, gold; and Ag, silver in metric tons; m, meters; EconMetal = amount of economic recovered metal, in metric tons; R_

Ecmetal = EconMetal rounded to 2 significant figures; GIPR, gross in-place resource]

Tract Setting

Deposit 

depth

proportion

Category

Number of 

undiscovered 

deposits

Cu Mo Au Ag

Probability

of no

resource

MX–L1 Typical 4.2 16,000,000 460,000 400 5,600 4.0%

General 

model

0.2 0–250 m 2,528,241 50,478 54 782

0.4 250–500 m 4,622,175 92,434 99 1,385

0.4 >500 m 3,520,879 76,354 83 998

EconMetal 10,671,295 219,266 236 3,209

Rounded R_Ecmetal 11,000,000 220,000 240 3,165 21.5%

EconMetal/GIPR 69% 48% 60% 57%

MX–L2 Typical 13 48,000,000 1,300,000 1,200 16,000 1.0%

General 

model

0.4 0–250 m 15,169,445 285,313 322 4,467

0.3 250–500 m 10,399,894 195,919 223 2,968

0.3 >500 m 7,921,977 161,836 186 2,139

EconMetal 33,491,316 643,068 732 9,574

Rounded R_Ecmetal 33,000,000 640,000 730 9,600 1.1%

EconMetal/GIPR 69% 49% 61% 60%

MX–L3 Typical 5.5 20,000,000 560,000 510 6,500 4.0%

General 

model

0.05 0–250 m 790,075 15,363 17 227

0.25 250–500 m 3,611,074 70,330 79 1,005

0.7 >500 m 7,701,922 162,666 185 2,027

EconMetal 12,103,072 248,359 281 3,259

Rounded R_Ecmetal 12,000,000 250,000 280 3,300 27.6%

EconMetal/GIPR 60% 45% 55% 51%

CACB–T2 High Cost 14 53,000,000 1,400,000 1,300 18000 2.0%

General 

model

0.25 0–250 m 8,843,208 167,439 191 2,520

0.25 250–500 m 6,994,402 138,864 163 1,929

0.5 >500 m 8,508,797 144,573 257 801

EconMetal 24,346,407 450,876 611 5,250

Rounded R_Ecmetal 24,000,000 450,000 610 5,300 15.1%

EconMetal/GIPR 45% 32% 47% 29%

CA–04 Typical 9.6 13,000,000 530,000 640 4400 3.0%

General 

model

0.1 0–250 m 861,239 25,767 32 296

1 Typical 0.25 250–500 m 1,217,485 40,989 39 423

0 High Cost 0.65 >500 m 1,912,877 44,850 2 362

EconMetal 3,991,601 111,606 74 1,081

Rounded R_Ecmetal 4,000,000 110,000 74 1,100 44.8%

EconMetal/GIPR 31% 21% 12% 25%

CA–04 MIX 9.6 13,000,000 530,000 640 4400 3.0%

General 

model

0.1 0–250 m 577,764 16,334 16 176

0.5 Typical 0.25 250–500 m 608,742 20,494 20 211

0.5 High Cost 0.65 >500 m 956,439 22,425 1 181

EconMetal 2,142,945 59,253 37 568

Rounded R_Ecmetal 2,100,000 59,000 37 570 67.4%

EconMetal/GIPR 16% 11% 6% 13%
References Cited 19

the probability of no economic resources occurring in a 

tract based on the fraction of deposits in the grade-tonnage 

model that are classified as subeconomic under tract scenario 

conditions and the probability of no deposits occurring in 

the tract. The economic filter could be applied during the 

Monte Carlo simulation to estimate the complete distribution 

of economic resources. However, the current simulation 

program would require substantial modification to implement 

this option.

The grade-tonnage distributions of porphyry copper 

deposits provide insights into the likely future patterns of 

resource supplies needed to satisfy increasing consumption 

of mineral commodities on a global scale. Deposit tonnage, 

through its influence on total contained metal (Singer and 

Menzie, 2010), mine capacity, mine life, and economic 

status, has a profound influence on the pattern of resource 

supply. Based on the frequency and range of the grade and 

tonnages of known porphyry copper deposits, global copper 

supply will continue to be dominated byproduction from a 

small number of giant deposits (Singer, 1995). Assessment 

results of undiscovered resources and continuing mineral 

deposit discoveries suggest that mineral resource supply 

will not limit growth in the near future (Menzie and others, 

2005). However, the bulk of global resource supply will 

likely continue to come from a small number of giant 

tonnage deposits, and this limitation on resource supply may 

increase in the future, because an increasing proportion of 

new deposit discoveries are likely to occur in remote areas 

and concealed deep beneath covering rock and sediments. 

Extensive mineral exploration activity likely will be required 

to meet future resource demand, because those deposits will 

be hard to find and more costly to mine.

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[Cu, copper; Mo, molybdenum; Au, gold; and Ag, silver in metric tons; m, meters; EconMetal = amount of economic recovered metal, in metric tons; R_

Ecmetal = EconMetal rounded to 2 significant figures; GIPR, gross in-place resource]

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Produced in the Menlo Park Publishing Service Center, California

Manuscript approved for publication, August 14, 2012

Edited by Gail Sladek

Layout and design by Vivian Tuong and Jeanne S. DiLeo
Robinson and Menzie—Economic Filters for Evaluating Porphyry Copper Deposit Resource Assessments—Scientific Investigations Report 2010–5090–H