U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2010–5090–Z

Global Mineral Resource Assessment

Spatial Database for a Global Assessment of Undiscovered 

Copper Resources

Copper Tracts

sediment-hosted copper

porphyry copper

Global Mineral Resource Assessment

Michael L. Zientek, Jane M. Hammarstrom, and Kathleen M. Johnson, editors

Spatial Database for a Global Assessment 

of Undiscovered Copper Resources

By Connie L. Dicken, Pamela Dunlap, Heather L. Parks, Jane M. Hammarstrom, 

and Michael L. Zientek

Scientific Investigations Report 2010–5090–Z

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016 

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Although this information product, for the most part, is in the public domain, it also may contain 

copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be 

secured from the copyright owner.

Suggested citation:

Dicken, C.L., Dunlap, Pamela, Parks, H.L., Hammarstrom, J.M., and Zientek, M.L., 2016, Spatial database for a global 

assessment of undiscovered copper resources: U.S. Geological Survey Scientific Investigations Report 2010–5090–Z, 

29 p., and GIS data, available at http://dx.doi.org/10.3133/sir20105090Z.

ISSN 2328-0328
 iii

Contents

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

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

Spatial Data for Porphyry Copper...............................................................................................................2

Porphyry Copper Deposits and Prospects .......................................................................................4

Permissive Tracts for Undiscovered Porphyry Copper Deposits..................................................6

Spatial Data for Sediment-Hosted Stratabound Copper.........................................................................9

Sediment-Hosted Stratabound Copper Deposits, Prospects, and Mineralized Sites...............9

Permissive Tracts for Undiscovered Sediment-Hosted Stratabound Copper Deposits ...........9

Spatial Data for Basemaps...........................................................................................................................9

Using the Data..............................................................................................................................................13

Summary........................................................................................................................................................14

Acknowledgments.......................................................................................................................................14

References Cited..........................................................................................................................................14

Appendix A. Mineral Resource Assessment Methods and Procedures Used in the Global 

Mineral Resource Assessment....................................................................................................18
iv 

Figure

1. Image showing contents and organization of the data model for the global copper file 

geodatabase..................................................................................................................................1

Tables

1. Regional assessment reports for the global assessment of undiscovered porphyry copper 

resources .......................................................................................................................................2

2. Description of porphyry copper digital data files within the file geodatabase and supporting 

metadata files................................................................................................................................3

3. Description of user-defined fields in the feature class pCu_deps_pros ......................................4

4. Description of group-specific, user-defined fields in the feature class pCu_groups.................5

5. Description of user-defined fields in the feature class pCu_Precambrian_deps_pros.............6

6. Description of user-defined fields in the feature class pCu_tracts...............................................6

7. Regional assessment reports for the global assessment of undiscovered sediment-hosted 

stratabound copper resources.................................................................................................10

8. Description of sediment-hosted stratabound copper digital data files within the file geodatabase and supporting metadata files ........................................................................................10

9. Definitions of user-defined attribute fields in the feature class sedCu_deps_pros .................11

10. Definitions of user-defined attribute fields in the feature class sedCu_tracts........................12

11. Description of basemap digital data files within the file geodatabase and map 

document .....................................................................................................................................13
 v

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2)

square foot (ft2) 0.09290 square meter (m2)

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2) 

Mass

ounce, avoirdupois (oz) 28.35 gram (g) 

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

ton, short (2,000 lb) 0.9072 megagram (Mg) 

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 0.0002471 acre

square meter (m2) 10.76 square foot (ft2) 

hectare (ha) 0.003861 square mile (mi2) 

square kilometer (km2) 0.3861 square mile (mi2)

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

megagram (Mg) 1.102 ton, short (2,000 lb)

Conversion Factors

U.S. customary units to International System of Units

International System of Units to U.S. customary units

Datum

Horizontal coordinate information is referenced to the World Geodetic System Datum of 1984 

(WGS 1984).

Spatial Database for a Global Assessment of 

Undiscovered Copper Resources

By Connie L. Dicken, Pamela Dunlap, Heather L. Parks, Jane M. Hammarstrom, and Michael L. Zientek

Abstract

As part of the first-ever U.S. Geological Survey global 

assessment of undiscovered copper resources, data common 

to several regional spatial databases published by the U.S. 

Geological Survey, including one report from Finland and 

one from Greenland, were standardized, updated, and compiled into a global copper resource database. This integrated 

collection of spatial databases provides location, geologic 

and mineral resource data, and source references for deposits, 

significant prospects, and areas permissive for undiscovered 

deposits of both porphyry copper and sediment-hosted copper. 

The copper resource database allows for efficient modeling on 

a global scale in a geographic information system (GIS) and is 

provided in an Esri ArcGIS file geodatabase format.

Introduction

In the first-ever global assessment of undiscovered copper resources, the U.S. Geological Survey (USGS) estimated 

undiscovered copper (Cu) resources for the two most significant sources of global copper supply—porphyry copper 

deposits and sediment-hosted stratabound copper deposits 

(Johnson and others, 2014). The purpose of the global assessment was to (1) compile databases of known copper deposits 

and significant copper prospects for the two deposit types, (2) 

delineate geology-based permissive areas (tracts) for undiscovered copper deposits at a scale of 1:1,000,000, (3) if possible, estimate numbers of undiscovered deposits within those 

permissive tracts, and (4) provide probabilistic estimates of 

amounts of copper and co-product resources that could be contained in those undiscovered deposits. The global assessment 

was facilitated by incorporating regional reports that describe 

data sources, methods, and results for each study area and are 

accompanied by spatial data in the form of a geographic information system (GIS) constructed in Esri1

 ArcGIS. Economic 

filters were applied to quantitative assessment results for porphyry copper deposits using the economic filter developed by 

Robinson and Menzie (2012) and are included in this global 

compilation. See appendix A for description.

1

Esri is a software development and services company providing geographic 

information system software and geodatabase management applications.

This report describes global data that were compiled 

from regional reports; it includes point data for deposits and 

prospects and polygon data for areas that represent permissive 

tracts for undiscovered deposits of porphyry copper and sediment-hosted stratabound copper. The GIS for global copper 

is a single Esri file geodatabase (USGS_global_copper.gdb) 

composed of feature datasets, feature and relationship classes, 

and nonspatial tables (fig. 1). These data are packaged along 

with metadata and a map document2 file in the compressed 

archive file sir20105090z_gis.zip which is available at http://

dx.doi.org/10.3133/sir20105090Z.

2

In this report, the term “map document” refers to an ArcMap document.

Figure 1. Image showing contents and organization of the data 

model for the global copper file geodatabase.
2 Spatial Database for a Global Assessment of Undiscovered Copper Resources

Spatial Data for Porphyry Copper

Data for porphyry copper deposits, prospects, and 

mineral resource tracts were compiled from eighteen regional 

assessment reports (table 1) into a single global spatial dataset 

for use in a GIS. These data can be queried in many database 

management systems as well as a GIS to reveal the distribution, age, geologic setting, and resource potential of porphyry 

copper deposits and to model resource grade and tonnage.

Table 2 lists the vector features, nonspatial tables, and 

associated relationship classes that describe porphyry copper assessment outcomes compiled from regional reports and 

enforce referential integrity. Metadata that describe the spatial 

data are embedded within the feature classes; they are also 

provided as standalone files (table 2).

Regional report Reference

Asia

Afghanistan Ludington and others (2007)

Central Asian Orogenic Belt and eastern Tethysides—China, 

Mongolia, Russia, Pakistan, Kazakhstan, Kyrgyzstan, Tajikistan, 

Afghanistan, India, and Myanmar

Mihalasky, Ludington, Hammarstrom, and others 

(2015)

East and Southeast Asia—Philippines, Taiwan (Republic of China), 

Republic of Korea (South Korea), and Japan Hammarstrom and others (2014)

Mesozoic of East Asia—China, Vietnam, North Korea, Mongolia, 

and Russia Ludington, Mihalasky, and others (2012)

Northeast Asia—Far East Russia and northeasternmost China Mihalasky, Ludington, Alexeiev, and others 

(2015)

Southeast Asia and Melanesia Hammarstrom and others (2013)

Tethys Region of western and southern Asia Zürcher and others (2015)

Tibetan Plateau, China Ludington, Hammarstrom, and others (2012)

Western Central Asia Berger and others (2014)

Australia

Eastern Australia Bookstrom and others (2014)

Europe

Europe, exclusive of the Fennoscandian Shield Sutphin and others (2013)

Finland Rasilainen and others (2014)

Urals Hammarstrom (written commun., 2015)

North America

British Columbia and Yukon Territory, Canada Mihalasky and others (2011, revised 2013)

Central America and the Caribbean Basin Gray and others (2014)

Mexico Hammarstrom and others (2010)

United States U.S. Geological Survey National Mineral 

Resource Assessment Team (2002)

South America

Andes Mountains of South America Cunningham and others (2008)

Table 1. Regional assessment reports for the global assessment of undiscovered porphyry copper resources.
Spatial Data for Porphyry Copper 3

Feature dataset name Description

porphyry_copper Feature dataset that contains feature classes and relationship classes, 

stored in geographic coordinates using the world geodetic system 

datum of 1984 (GCS WGS 1984).

Feature class name Description

pCu_deps_pros Feature class (points) of 2,199 Phanerozoic porphyry copper and 

related deposits and prospects of the world.

pCu_groups Feature class (points) of select groups of Phanerozoic porphyry copper and related deposits and prospects of the world.

pCu_Precambrian_deps_pros Feature class (points) of Precambrian porphyry copper and related 

deposits and prospects of the world.

pCu_sites_references Nonspatial table of references for abbreviated citations used in the 

feature class pCu_deps_pros associated with Short_ref field; full 

reference for Short_ref field can be found in nonspatial table 

pCu_tracts_references.

pCu_sites_refs_relate Relationship class that manages the association between objects in 

the nonspatial table pCu_sites_references to objects in the feature 

class pCu_deps_pros.

pCu_tracts Feature class (polygons) of permissive tracts of porphyry copper.

pCu_tracts_references Nonspatial table of full references for abbreviated citations used in 

the feature class pCu_tracts.

pCu_tracts_refs_relate Relationship class that manages the association between objects 

in the nonspatial table pCu_tracts_references to objects in the 

feature class pCu_tracts.

pCu_tracts_sites_relate Relationship class that manages the association between objects 

in the pCu_tracts feature class to objects in the pCu_deps_pros

feature class.

Adobe Acrobat Portable Document Format (.pdf) files for Federal Geographic Data Committee (FGDC) metadata

pCu_deps_pros_metadata.pdf Information about the spatial and descriptive data in the file geodatabase feature class pCu_deps_pros.

pCu_groups_metadata.pdf Information about the spatial and descriptive data in the file geodatabase feature class pCu_groups.

pCu_Precambrian_deps_pros_metadata.pdf Information about the spatial and descriptive data in the file geodatabase feature class pCu_Precambrian_deps_pros.

pCu_tracts_metadata.pdf Information about the spatial and descriptive data in the file geodatabase feature class pCu_tracts.

Extensible markup language (.xml) files for metadata

pCu_deps_pros_metadata.xml Metadata for file geodatabase feature class pCu_deps_pros.

pCu_groups_metadata.xml Metadata for file geodatabase feature class pCu_groups.

pCu_Precambrian_deps_pros_metadata.xml Metadata for file geodatabase feature class pCu_Precambrian_deps_

pros.

pCu_tracts_metadata.xml Metadata for file geodatabase feature class pCu_tracts.

Table 2. Description of porphyry copper digital data files within the file geodatabase and supporting 

metadata files.
4 Spatial Database for a Global Assessment of Undiscovered Copper Resources

Porphyry Copper Deposits and Prospects

The feature class pCu_deps_pros contains vector data 

(points) for 2,199 Phanerozoic deposits and significant prospects with porphyry copper or porphyry-related mineralization. Attributes are listed and described in table 3.

The feature class pCu_groups contains vector data 

(points) for a small select set of 47 records, each of which 

represents a group of related deposits and (or) prospects; data 

were compiled from the feature class pCu_deps_pros. Location coordinates can represent a generalized location or a 

specific mineralized site; for all of the deposits in the group, 

“grade” represents a weighted average whereas “tonnage” 

represents total ore tonnage. Data field names for pCu_groups

are identical to those for pCu_deps_pros; however, definitions 

of some fields were revised to account for the group nature of 

the data (table 4).

Field name Field alias Description

GMRAP_ID GMRAP ID Unique identifier for each record. [GMRAP, Global Mineral Resource Assessment Project.]

Coded_ID Tract identifier Coded, unique identifier assigned to permissive tract within which the site is located; corresponds to Coded_ID in the feature class pCu_tracts.

Tract_name Tract Name of permissive tract in which the site is located; corresponds to Tract_name in the feature 

class pCu_tracts.

Name Site Site name.

Name_other Other site names Other names used for the site.

Name_group Group name Group name, applied when 2-kilometer rule was used to cluster deposits and (or) prospects for 

the purpose of calculating grades and tonnages.

Includes Includes Names of deposits and (or) prospects that have been combined with the primary deposit as a 

result of the 2-kilometer spatial aggregation rule used for calculating grades and tonnages, 

or sites described in the original source reference(s) as being included in the description of 

primary site.

SiteStatus Site status Type of site; “deposit” is a site for which grade and tonnage are reported; “prospect” is a site 

for which grade and tonnage are not reported. 

Country Country Country in which the site is located.

State_Prov State/Province State, province, or region in which the site is located.

Latitude Latitude Latitude, in decimal degrees; positive number represents latitude north of the equator; negative 

number represents latitude south of the equator.

Longitude Longitude Longitude, in decimal degrees; positive number represents longitude east of the Greenwich 

meridian; negative number represents longitude west of the Greenwich meridian.

Dep_type Deposit type Type of mineral deposit.

Dep_subtype Deposit subtype Subtype of porphyry copper (Cu) deposit type based on ratio of gold (Au) in grams per metric 

ton (g/t) to molybdenum (Mo) (weight percent); entries include Cu-Au, Cu-Mo, and NA (not 

applicable).

Age_Ma Age date Age of mineralization; in millions of years before present; based on reported absolute ages, or 

midpoints of ranges of absolute ages, or midpoints of geologic time scale units.

Age_method Age method Method used to determine age reported in field Age_Ma.

Age_range Age range Range of ages, listed from oldest to youngest; in absolute ages or in geologic time scale units.

Age_ref Age references Abbreviated citation, in author and year format, for reference(s) for age information; full references are listed in the file geodatabase nonspatial table pCu_sites_references.

Comm_major Major commodities Major commodities, listed in decreasing order of economic importance. Chemical symbols are 

used for commodities; multiple commodities are comma-delimited.

Comm_minor Minor commodities Minor commodities, listed in decreasing order of economic importance; queried where 

uncertain. Chemical symbols are used for commodities; multiple commodities are commadelimited.

Comm_trace Trace commodities Trace commodities, listed in decreasing order of economic importance. Chemical symbols are 

used for commodities; multiple commodities are comma-delimited.

Tonnage_Mt Tonnage ore rock Ore tonnage, for deposit, in millions of metric tons (Mt); -9,999 indicates no data.

Cu_pct Copper grade Average copper grade, for deposit, in weight percent; -9,999 indicates no data.

Table 3. Description of user-defined fields in the feature class pCu_deps_pros.
Spatial Data for Porphyry Copper 5

Field name Field alias Description

Mo_pct Molybdenum grade Average molybdenum grade, for deposit, in weight percent; -9,999 indicates no data.

Au_g_t Gold grade Average gold grade, for deposit, in grams per metric ton (g/t); -9,999 indicates no data.

Ag_g_t Silver grade Average silver grade, for deposit, in grams per metric ton (g/t); -9,999 indicates no data.

Con_Cu_t Contained copper Contained copper, for deposit, in metric tons (t), rounded to nearest significant figure; -9,999 

indicates no data.

Au_Mo Au/Mo (ratio) Ratio of Au (g/t) to Mo (weight percent), for deposit, rounded to nearest significant figure; 

-9,999 indicates no data.

Comments Comments Significant comments; may include grade and tonnage information for prospects, name of 

magmatic arc or belt, and other significant comments. 

Minerals Minerals Minerals present, as reported in literature cited; listed in alphabetical order. Most rock-forming 

minerals such as feldspar and quartz are not reported.

Assd_rock Associated rocks Associated rocks in the deposit and on regional source map.

Dev_Status Development status The status or nature of operations at the time the record was entered or this field was last modified; entries include “Occurrence,” “Prospect,” “Producer,” “Past Producer,” or “Unknown.”

Ref_list References List of abbreviated citations, in author and year format, for references used in compiling information for site, delimited by semicolons; full references are listed in the file geodatabase 

nonspatial table pCu_sites_references.

Short_ref Regional report short 

reference

Abbreviated citation for author and year of the U.S. Geological Survey (USGS) regional report 

from which data were originally compiled; full reference located in the lineage section of the 

metadata and listed in file geodatabase nonspatial table pCu_tracts_references.

Strat_age Stratigraphic age (Eon/

Era/Period) Stratigraphic age; Eon, Era, or Period (Cohen and others, 2013, revised 2015).

Setting Tectonic setting Tectonic setting in which mineralization was emplaced; entries include continental margin, 

island arc, mixed, or postconvergence.

Study_area Study area Geographic area of USGS report from which data were originally compiled.

Field name Field alias Description

Name_group Group name Group name, applied when 2-kilometer rule was used to cluster deposits and (or) prospects 

for the purpose of calculating grades and tonnages.

Includes Includes Names of deposits and (or) prospects that have been grouped as a result of the 2-kilometer 

spatial aggregation rule used for calculating grades and tonnages.

Latitude Latitude Latitude, in decimal degrees, for a general location of the group; positive number represents latitude north of the equator; negative number represents latitude south of the 

equator.

Longitude Longitude Longitude, in decimal degrees, for a general location of the group; positive number represents longitude east of the Greenwich meridian; negative number represents longitude 

west of the Greenwich meridian.

Tonnage_Mt Tonnage ore rock Total ore tonnage for all deposits in the group, in millions of metric tons (Mt); -9,999 

indicates no data.

Cu_pct Copper grade Weighted average copper grade, for all deposits in the group, in weight percent; -9,999 

indicates no data.

Mo_pct Molybdenum grade Weighted average molybdenum grade, for all deposits in the group, in weight percent; 

-9,999 indicates no data.

Au_g_t Gold grade Weighted average gold grade, for all deposits in the group, in grams per metric ton (g/t); 

-9,999 indicates no data.

Ag_g_t Silver grade Weighted average silver grade, for all deposits in the group, in grams per metric ton (g/t); 

-9,999 indicates no data.

Con_Cu_t Contained copper Total contained copper, for all deposits in the group, in metric tons (t), rounded to nearest 

significant figure; -9,999 indicates no data.

Table 4. Description of group-specific, user-defined fields in the feature class pCu_groups.
6 Spatial Database for a Global Assessment of Undiscovered Copper Resources

The feature class pCu_Precambrian_deps_pros contains 

vector data (points) for 21 Precambrian significant deposits 

and prospects for porphyry copper from Singer and others 

(2002) and is included for reference only. Precambrian deposits and prospects were not used to delineate permissive tracts 

except in the Finland regional report. Deposits and prospects 

for the Finland report are included with the pCu_deps_pros 

feature class. Attribute field names and descriptions are 

described in table 5.

Permissive Tracts for Undiscovered Porphyry 

Copper Deposits

The feature class pCu_tracts contains vector data (polygons) for 192 tracts permissive for porphyry copper mineralization. The tracts outline principal areas of the world that 

have potential for undiscovered porphyry copper resources; 

the tracts were used to estimate the probable amounts of those 

resources at depths within 1 kilometer (km) below the Earth’s 

surface. Attributes are listed and described in table 6.

 Field name Field alias Description

Name Site Site name.

Name_other Other site names Other names used for the site.

Includes Includes Names of deposits and (or) prospects that have been combined with the primary deposit as a 

result of the 2-kilometer spatial aggregation rule used for calculating grades and tonnages, 

or sites described in the original source reference(s) as being included in the description of 

primary site.

SiteStatus Site status Type of site; deposit is a site for which grade and tonnage are reported; prospect is a site for 

which grade and tonnage are not reported.

Country Country Country in which the site is located.

State_Prov State/Province State, province, or region in which the site is located.

Latitude Latitude Latitude, in decimal degrees; positive number represents latitude north of the equator; negative number represents latitude south of the equator.

Longitude Longitude Longitude, in decimal degrees; positive number represents longitude east of the Greenwich 

meridian; negative number represents longitude west of the Greenwich meridian.

Dep_type Deposit type Type of mineral deposit.

Dep_subtype Deposit subtype Subtype of porphyry copper (Cu) deposit type based on ratio of gold (Au) in grams per metric 

ton (g/t) to molybdenum (Mo) (weight percent); entries include Cu-Au, Cu-Mo, and NA.

Age_Ma Age date Age of mineralization; in millions of years before the present; based on reported absolute 

ages, or midpoints of ranges of absolute ages, or midpoints of geologic time scale units.

Tonnage Tonnage ore rock Ore tonnage, for deposit, in millions of metric tons (Mt); -9,999 indicates no data.

Cu_pct Copper grade Average copper grade, for deposit, in weight percent; -9,999 indicates no data.

Mo_pct Molybdenum grade Average molybdenum grade, for deposit, in weight percent; -9,999 indicates no data.

Au_g_t Gold grade Average gold grade, for deposit, in grams per metric ton (g/t); -9,999 indicates no data.

Ag_g_t Silver grade Average silver grade, for deposit, in grams per metric ton (g/t); -9,999 indicates no data.

Table 5. Description of user-defined fields in the feature class pCu_Precambrian_deps_pros.

Field name Field alias Description

Coded_ID Tract identifier Coded, unique identifier assigned to permissive tract.

Tract_name Tract Informal name of permissive tract.

Unregcode United Nations Region 

code

Three-digit United Nations (2013) code for the region that underlies most of the permissive 

tract.

Country Country Country(ies) in which the permissive tract is located.

Commodity Commodity Primary commodity being assessed.

Study_area Study area Geographic area of U.S. Geological Survey (USGS) report from which data were originally 

compiled.

Quant Quantitative Dichotomous variable for whether a quantitative resource assessment was performed for tract.

Table 6. Description of user-defined fields in the feature class pCu_tracts.
Spatial Data for Porphyry Copper 7

Table 6. Description of user-defined fields in the feature class pCu_tracts.—Continued

Field name Field alias Description

Region Region Name of large geographic region used for reporting copper resources in Johnson and others 

(2014).

Dep_type Deposit type Type of mineral deposit.

GT_model Grade/tonnage model Grade-tonnage model used for the undiscovered resource simulation.

Geology Geology Geologic feature assessed.

Age Age Age of the assessed geologic feature.

Age_group Age group Generalized geologic age.

Asmt_date Assessment date Year assessment was conducted.

Asmt_depth Assessment depth Maximum depth beneath the Earth’s surface used for the assessment, in kilometers.

N90 90th percentile deposit 

estimate

Estimated number of deposits associated with the 90th percentile (90 percent chance of at 

least the indicated number of deposits); -9,999 indicates no data; <Null> indicates segment 

data (partial tract); data were recorded in main tract and were not duplicated. 

N50 50th percentile deposit 

estimate

Estimated number of deposits associated with the 50th percentile (50 percent chance of at 

least the indicated number of deposits); -9,999 indicates no data; <Null> indicates segment 

data (partial tract); data were recorded in main tract and were not duplicated.

N10 10th percentile deposit 

estimate

Estimated number of deposits associated with the 10th percentile (10 percent chance of at 

least the indicated number of deposits); -9,999 indicates no data; <Null> indicates segment 

data (partial tract); data were recorded in main tract and were not duplicated.

N05 5th percentile deposit 

estimate

Estimated number of deposits associated with the 5th percentile (5 percent chance of at least 

the indicated number of deposits); -9,999 indicates no data; <Null> indicates segment data 

(partial tract); data were recorded in main tract and were not duplicated.

N01 1st percentile deposit 

estimate

Estimated number of deposits associated with the 1st percentile (1 percent chance of at least 

the indicated number of deposits); -9,999 indicates no data; <Null> indicates segment data 

(partial tract); data were recorded in main tract and were not duplicated.

N_expected Expected number of 

deposits

Expected (mean) number of deposits, where N_expected = (0.233×N90) + (0.4×N50) + 

(0.225×N10) + (0.045×N05) + (0.03×N01); -9,999 indicates no data; <Null> indicates segment data (partial tract); data were recorded in main tract and were not duplicated.

s Standard deviation Standard deviation, where s = 0.121 − (0.237×N90) − (0.093×N50) + (0.183×N10) + 

(0.073×N05) + (0.123×N01); -9,999 indicates no data; <Null> indicates segment data (partial tract); data were recorded in main tract and were not duplicated.

Cv_percent Coefficient of variance Coefficient of variance, in percent, where Cv = (s/N_expected) × 100; -9,999 indicates no 

data. <Null> indicates segment data (partial tract); data were recorded in main tract and 

were not duplicated.

Est_levels Percentile estimate 

levels

The set of percentile (probability) levels at which undiscovered deposit estimates were made.

N_known Known deposits Number of known deposits in the tract; <Null> indicates segment data (partial tract); data 

were recorded in main tract and were not duplicated.

N_total Total deposits Total number of deposits, where N_total = N_expected + N_known; <Null> indicates segment 

data (partial tract); data recorded in main tract so it would not be duplicated.

Area_km2 Tract area in square 

kilometers

Area of permissive tract, in square kilometers, calculated in a locally appropriate equal-area 

projection; <Null> indicates segment data (partial tract); data were recorded in main tract 

and were not duplicated.

DepDensity Deposit density Deposit density (total number of deposits per square kilometer), where DepDensity = N_total/

Area_km2; -9,999 indicates no data; <Null> indicates segment data (partial tract); data were 

recorded in main tract and were not duplicated.

DepDen10E5 Deposit density per 

100,000 square 

kilometers

Deposit density per 100,000 square kilometers, where DepDen10E5 = DepDensity × 100,000; 

-9,999 indicates no data; <Null> indicates segment data (partial tract); data were recorded 

in main tract and were not duplicated.

Estimators Estimators Names of people on the estimation team.

Mean_Cu_t Mean copper resourcesMean copper resources, in metric tons, based on Monte Carlo simulation.
8 Spatial Database for a Global Assessment of Undiscovered Copper Resources

Field name Field alias Description

Mean_Mo_t Mean molybdenum 

resources

Mean molybdenum resources, in metric tons, based on Monte Carlo simulation.

Mean_Au_t Mean gold resources Mean gold resources, in metric tons, based on Monte Carlo simulation.

Mean_Ag_t Mean silver resources Mean silver resources, in metric tons, based on Monte Carlo simulation.

Mean_Rock_Mt Mean ore tonnage Mean ore tonnage, in metric tons, based on Monte Carlo simulation.

Cu_95 Copper resource 

estimate at 95th 

percentile

Undiscovered copper resource estimate, 95th percentile value, in metric tons (t); -9,999 indicates no data.

Cu_90 Copper resource 

estimate at 90th 

percentile

Undiscovered copper resource estimate, 90th percentile value, in metric tons (t); -9,999 indicates no data.

Cu_50 Copper resource 

estimate at 50th 

percentile

Undiscovered copper resource estimate, median (50th percentile) value, in metric tons (t); 

-9,999 indicates no data.

Cu_10 Copper resource 

estimate at 10th 

percentile

Undiscovered copper resource estimate, 10th percentile value, in metric tons (t); -9,999 indicates no data.

Cu_05 Copper resource 

estimate at 5th 

percentile

Undiscovered copper resource estimate, 5th percentile value, in metric tons (t); -9,999 indicates no data.

Prob_mean Probability of mean 

copper

Probability of the mean value, undiscovered copper resource estimate; -9,999 indicates no 

data.

Prob_0_Cu Probability of no copper resourcesProbability of no copper, based on Monte Carlo simulation.

Ident_Cu Known copper resourcesKnown copper resources, in metric tons (t).

N_prospects Number of prospects Number of identified porphyry copper prospects in tract.

Scales Map scales used List of map scales used, by denominator, to delineate tract boundary.

Begin_age Oldest age Oldest age of permissive rocks in the tract, from geologic source maps, in millions of years 

before the present.

End_age Youngest age Youngest age of permissive rocks in the tract, from geologic source maps, in millions of years 

before the present.

Duration Age range Age range of permissive rocks in the tract, from geologic source maps.

Short_ref Regional report short 

reference

Abbreviated citation for source reference; full reference located in the lineage section of the 

metadata and listed in file geodatabase nonspatial table pCu_tracts_references.

MeanCu_km2 Mean copper resource 

per square kilometers

Mean copper resources per square kilometer (km2), in metric tons (t).

CtryFraser Country used for 

Fraser ranking

Country used for infrastructure rankings as compiled by the Fraser Institute (McMahon and 

Cervantes, 2012); <Null> indicates no economic evaluation.

Fras_typ Fraser typical Percentage of categories 1 and 2, from quality of infrastructure table (McMahon and Cervantes, 2012), used to classify cost setting; <Null> indicates no economic evaluation.

Fras_high Fraser high Percentage of categories 3, 4, and 5, from quality of infrastructure table (McMahon and Cervantes, 2012), used to classify cost setting; <Null> indicates no economic evaluation.

Typ_cost Typical cost Ranking for typical cost, in percent; <Null> indicates no economic evaluation.

High_cost High cost Ranking for high cost, in percent; <Null> indicates no economic evaluation.

Infra_set Infrastructure setting Mine cost setting based on infrastructure development; <Null> indicates no economic evaluation.

Depth_dist Depth distribution Comments on rationale for assignment of depth distributions categories; <Null> indicates no 

economic evaluation.

Table 6. Description of user-defined fields in the feature class pCu_tracts.—Continued
Spatial Data for Basemaps 9

Table 6. Description of user-defined fields in the feature class pCu_tracts.—Continued

Field name Field alias Description

D_0_250m Surface to 250 meters Proportion of undiscovered deposits occurring in the interval between the surface and 

250 meters in depth; <Null> indicates no economic evaluation.

D_250_500m 250-500 meters Proportion of undiscovered deposits occurring in the interval 250–500 meters in depth; 

<Null> indicates no economic evaluation.

D_over500m Over 500 meters Proportion of undiscovered deposits occurring in the interval 500–1,000 meters in depth; 

<Null> indicates no economic evaluation.

Ec_Cu Economic copper Estimated amount of economically recoverable copper, in metric tons (t); <Null> indicates no 

economic evaluation.

Ec_Mo Economic molybdenumEstimated amount of economically recoverable molybdenum, in metric tons (t); <Null> indicates no economic evaluation.

Ec_Au Economic gold Estimated amount of economically recoverable gold, in metric tons (t); <Null> indicates no 

economic evaluation.

Ec_Ag Economic silver Estimated amount of economically recoverable silver, in metric tons (t); <Null> indicates no 

economic evaluation.

Ec_0_prob Probability of no economic resourcesProbability of no economic resources occurring in the tract, in percent; <Null> indicates no 

economic evaluation.

Ec_NPV Economic net present 

value

Net present value of simulated economic deposits in the tract, in million U.S. dollars; <Null> 

indicates no economic evaluation.

EV_ID Expected value Expected value of simulated resources in the tract, in million U.S. dollars/100,000 km2; 

<Null> indicates no economic evaluation.

EV_class Expected value classificationQualitative classification of expected value of simulated resources in the tract; <Null> indicates no economic evaluation.

Tect_set Tectonic setting Tectonic setting.

Econ_ref Economic reference Abbreviated citation for economic source reference; full reference is listed in file geodatabase 

nonspatial table pCu_tracts_references; <Null> indicates no economic evaluation.

Spatial Data for Sediment-Hosted 

Stratabound Copper

Data were compiled from nine regional assessment 

reports (table 7) into a single feature dataset. Similar to the 

porphyry copper compilation, the two feature classes and associated relationship class contain spatial and descriptive data 

for deposits, prospects, and other mineralized sites, and for 

permissive tracts. These data can be queried in many database 

management systems as well as a GIS to reveal the distribution, geologic setting, and resource potential of sedimenthosted copper deposits and to model grade and resource 

tonnage.

The sediment-hosted stratabound copper data are briefly 

described in table 8 and include two feature classes and a 

relationship class. Metadata that describe the spatial data are 

embedded within the feature classes; they are also provided as 

standalone files (table 8).

Sediment-Hosted Stratabound Copper Deposits, 

Prospects, and Mineralized Sites

The feature class sedCu_deps_pros contains vector data 

(points) and documents locations and descriptive data for 

1,200 sites around the world. Abbreviated source references 

(Short_ref) are listed in the feature attribute table of sedCu_

deps_pros; full source citations are provided in the nonspatial 

table sedCu_references. Attributes and their descriptions are 

listed in table 9.

Permissive Tracts for Undiscovered SedimentHosted Stratabound Copper Deposits

The feature class sedCu_tracts contains vector data 

(points) for 48 sediment-hosted copper permissive tracts of the 

world, which were used to outline the principal areas in the 

world that have potential for undiscovered sediment-hosted 

copper resources. Those tracts with a quantitative estimate 

were assessed to depths of 1 to 2.5 km below the Earth’s surface. Attributes and their descriptions are listed in table 10.

Spatial Data for Basemaps

Base data have been included, as a courtesy, to assist 

users with spatial references (table 11). The world_background

feature class provided by Esri (2007) contains polygons that 

represent grid cells of 30 by 30 degrees for the world. The second dataset contains vector data (polygons) which represent 

political boundaries of the world (world_political_boundaries) 

compiled by the U.S. Department of State (2009). 
10 Spatial Database for a Global Assessment of Undiscovered Copper Resources

Regional report Reference

Africa

Neoproterozoic Roan Group, Central African Copperbelt, Katanga Basin, Democratic 

Republic of the Congo and Zambia

Zientek, Bliss, and others (2014)

Asia

Afghanistan Ludington and others (2007)

Chu-Sarysu Basin, Central Kazakhstan Box and others (2012)

Kodar-Udokan area, Russia Zientek, Chechetkin, and others (2014)

Teniz Basin, Kazakhstan Cossette and others (2014)

Europe

Permian Kupferschiefer, Southern Permian Basin, Europe Zientek, Oszczepalski, and others (2015)

North America

Greenland Stensgaard and others (2011)

United States national assessment Ludington and Cox (1996)

World

Qualitative assessment for selected areas: (1) Mesoproterozoic Belt-Purcell Basin, 

United States and Canada; (2) Cretaceous Benguela and Cuanza Basins, Angola; (3) 

Cretaceous Chuxiong Basin, China; (4) Paleo- to Mesoproterozoic Dongchuan Group 

rocks, China; (5) Cambrian Egypt-Israel-Jordan Rift, Egypt, Israel, and Jordan; (6) 

Carboniferous Maritimes Basin, Canada; (7) Cretaceous Neuquen Basin, Argentina; 

(8) Neoproterozoic Northwest Botswana Rift, Botswana and Namibia; (9) Upper Proterozoic Redstone Copperbelt, Canada; and (10) Upper Cretaceous Salta Rift System 

Argentina.

Zientek, Wintzer, and others (2015)

Table 7. Regional assessment reports for the global assessment of undiscovered sediment-hosted stratabound copper resources.

Table 8. Description of sediment-hosted stratabound copper digital data files within the file geodatabase and supporting 

metadata files.

Feature dataset name Description

sediment_hosted_copper Feature dataset that contains feature classes, stored in geographic 

coordinates using the world geodetic system datum of 1984 (GCS 

WGS 1984).

Feature class name Description

edCu_deps_pros Feature class (points) of 1,200 sediment-hosted copper deposits and 

prospects of the world.

sedCu_references Nonspatial table of references for abbreviated citations used in the 

feature classes sedCu_deps_pros and sedCu_tracts.

sedCu_tracts Feature class (polygons) of sediment-hosted copper permissive tracts 

of the world.

sedCu_tracts_sites_relate Relationship class that manages the association between objects in 

the sedCu_tracts feature class to objects in the sedCu_deps_pros

feature class.

Adobe Acrobat Portable Document Format (.pdf) files for Federal Geographic Data Committee (FGDC) metadata

sedCu_deps_pros_metadata.pdf Metadata for sedCu_deps_pros file.

sedCu_tracts_metadata.pdf Metadata for sedCu_tracts file.

Extensible markup language (.xml) files for metadata

sedCu_deps_pros_metadata.xml Metadata for file geodatabase feature class sedCu_deps_pros.

sedCu_tracts_metadata.xml Metadata for file geodatabase feature class sedCu_tracts.
Spatial Data for Basemaps 11

Field name Field alias Description

GMRAP_ID GMRAP ID Unique identifier for each record. [GMRAP, Global Mineral Resource Assessment Project.]

Coded_ID Tract identifier Coded, unique identifier assigned to permissive tract within which the site is located; corresponds to Coded_ID in the feature class sedCu_tracts.

Tract_name Tract Name of permissive tract in which the site is located; corresponds to Tract_name in the 

feature class sedCu_tracts.

Name Site Site name.

Name_other Other site names Other names used for the site.

Name_group Group name Group name, applied when 500-meter (m) rule was used to cluster deposits and (or) prospects for the purpose cacluating grades and tonnages.

Includes Includes Names of deposits that have been combined with the primary deposit as a result of the 500-

m spatial aggregation rule used for calculating grades and tonnages.

SiteStatus Site status Type of site; “deposit” is a site for which grade and tonnage are reported; “historic mine19th century or earlier” is a site that is no longer an active historic mine; “prospect” 

is a site with no grade and tonnage values provided; “prospect - mineralized material 

estimated” is a site that has estimated mineralized material; and “site” is a site that occurs 

in sedimentary rock but has no other descriptive information.

SiteStatus2 Site status 2 Additional information on the status of the site. 

Country Country Country in which the site is located.

State_Prov State/Province State, province, or region in which the site is located.

Latitude Latitude Latitude, in decimal degrees; positive number represents latitude north of the equator; negative number represents latitude south of the equator.

Longitude Longitude Longitude, in decimal degrees; positive number represents longitude east of the Greenwich 

meridian; negative number represents longitude west of the Greenwich meridian.

Dep_type Deposit type Type of mineral deposit.

Dep_subtype Deposit subtype Subtype of sediment-hosted copper. Subtype based on host lithology.

Age_host Age host Age of host rock, in geologic time scale units.

Age_Ma Age date Age, in millions of years before present. Age may be an average for geologic era, period, or 

epoch listed.

Unit Unit Geologic unit in which site is located.

Host_rocks Host rocks Simplified lithologic description of host rocks.

Comm_major Major commodities Major commodities, listed in decreasing order of economic importance. Chemical symbols 

are used for commodities; multiple commodities are comma-delimited.

Tonnage_Mt Tonnage ore rock Ore tonnage, for deposit, in millions of metric tons (Mt); -9,999 indicates no data.

Cu_pct Copper grade Average copper grade, for deposit, in weight percent; -9,999 indicates no data.

Co_pct Cobalt grade Average cobalt grade, for deposit, in weight percent; -9,999 indicates no data.

Ag_g_t Silver grade Average silver grade, for deposit, in grams per metric ton (g/t); -9,999 indicates no data.

Con_Cu_t Contained copper Contained copper, for deposit, in metric tons (t), rounded to nearest significant figure; -9,999 

indicates no data.

Comments Comments Significant comments; may include grade and tonnage information for prospects, mineral 

alteration, and other significant comments. 

Minerals Minerals Ore and gangue minerals, listed in approximate order of abundance.

Ref_list References List of abbreviated citations, in author and year format, for references used in compiling 

information for site, delimited by semicolons; full references are listed in the file geodatabase nonspatial table sedCu_references.

Short_ref Regional report short 

reference

Abbreviated citation for author and year of the U.S. Geological Survey (USGS) regional 

report from which data were originally compiled; full reference located in the lineage section of the metadata and listed in file geodatabase nonspatial table sedCu_references.

Table 9. Definitions of user-defined attribute fields in the feature class sedCu_deps_pros.
12 Spatial Database for a Global Assessment of Undiscovered Copper Resources

Field name Field alias Description

Coded_ID Tract identifier Coded, unique identifier assigned to permissive tract.

Tract_name Tract Informal name of permissive tract.

Unregcode United Nations Region 

code

Three-digit United Nations (2013) code for the region that underlies most of the permissive 

tract.

Country Country Country(ies) in which the permissive tract is located.

Commodity Commodity Primary commodity being assessed.

Region Region Name of large geographic region used for reporting copper resources in Johnson and others 

(2014).

Basin Basin Name of sedimentary basin in which the permissive tract is located.

Dep_type Deposit type Type of mineral deposit.

Dep_model Deposit model Model used to classify deposit type.

Dmodel_ref Deposit model reference Deposit type model short reference; full reference is provided in the nonspatial table sedCu_

references.

GT_model Grade/tonnage model Grade-tonnage model used for the undiscovered deposit estimate.

GATM_ref Grade/tonnage model 

short reference

Abbreviated citation for grade and tonnage model; full reference is provided in the nonspatial 

table sedCu_references.

Geology Geology Geologic feature assessed.

Age Age Age of the assessed geologic feature.

Asmt_date Assessment date Year assessment was conducted.

Asmt_depth Assessment depth Maximum depth beneath the Earth’s surface used for the assessment, in kilometers.

Est_levels Percentile estimate levels The set of percentile (probability) levels at which undiscovered deposit estimates were made; 

-9,999 indicates no data.

N90 90th percentile deposit 

estimate

Estimated number of deposits associated with the 90th percentile (90 percent chance of at 

least the indicated number of deposits); -9,999 indicates no data.

N50 50th percentile deposit 

estimate

Estimated number of deposits associated with the 50th percentile (50 percent chance of at 

least the indicated number of deposits); -9,999 indicates no data.

N10 10th percentile deposit 

estimate

Estimated number of deposits associated with the 10th percentile (10 percent chance of at 

least the indicated number of deposits); -9,999 indicates no data.

N05 5th percentile deposit 

estimate

Estimated number of deposits associated with the 5th percentile (5 percent chance of at least 

the indicated number of deposits); -9,999 indicates no data.

N01 1st percentile deposit 

estimate

Estimated number of deposits associated with the 1st percentile (1 percent chance of at least 

the indicated number of deposits); -9,999 indicates no data.

N_expected Expected number of 

deposits

Expected (mean) number of deposits, where N_expected = (0.233×N90) + (0.4×N50) + 

(0.225×N10) + (0.045×N05) + (0.03×N01); -9,999 indicates no data.

s Standard deviation Standard deviation, where s = 0.121 − (0.237×N90) − (0.093×N50) + (0.183×N10) + 

(0.073×N05) + (0.123×N01); -9,999 indicates no data.

Cv_percent Coefficient of variance Coefficient of variance, in percent, where Cv = (s/N_expected) × 100; -9,999 indicates no data.

N_known Known deposits Number of known deposits in the tract.

N_total Total deposits Total number of deposits, where N_total = N_expected + N_known.

Area_km2 Tract area in square 

kilometers

Area of permissive tract, in square kilometers.

DepDensity Deposit density Deposit density (total number of deposits per square kilometer), where DepDensity = N_total/

Area_km2; -9,999 indicates no data.

DepDen10E5 Deposit density per 

100,000 square kilometers

Deposit density per 100,000 square kilometers, where DepDen10E5 = DepDensity×100,000; 

-9,999 indicates no data.

Estimators Estimators Names of people on the estimation team.

Table 10. Definitions of user-defined attribute fields in the feature class sedCu_tracts.
Using the Data 13

Using the Data

The Esri ArcGIS map document format (.mxd) allows 

users to view and query data in a map format. Global_copper.

mxd displays site and tract data in the Robinson map projection. Custom spatial bookmarks are included and identify 

specific geographic regions for quick and easy reference. 

Data in the spatial databases can be viewed by opening their 

attribute tables from the “Table of Contents” window. Nonspatial tables containing references can be viewed when their 

attribute table is opened in the “List By Source” view of the 

“Table of Contents” window. One way the data can be queried 

is by using the tools available in the attribute tables and within 

the “Definition Query” tab in the “Layer Properties” window 

accessed by right-clicking on an individual layer and selecting 

“Properties.” Points and polygons can also be selected in the 

map view window using the “Identify” tool, which allows the 

user to view the data in the form of a list of fields and values.

When working with the pCu_deps_pros feature class, the 

outcome of having selected a deposit or prospect will display 

both a hierarchical catalog of deposits and prospects in one 

frame of the “Identify” window and a list of fields and values 

for the selected site(s) in the other frame. There are three 

hierarchical catalogs associated with each porphyry copper 

deposit and prospect. The first is pCu_site_references in which 

one can view full references by expanding the catalog and 

selecting the desired abbreviated source reference; hyperlinks 

provide access to the referenced article on the Internet where 

available. The second catalog is pCu_tracts; expanding this 

catalog for the tract in which the deposit occurs displays the 

third catalog, pCu_tract_references, and one can view the tract 

reference information in the same manner as for deposits and 

prospects. Similarly, there are three hierarchical catalogs associated with each porphyry copper tract: pCu_tract_references, 

pCu_deps_pros, and pCu_site_references.

Table 10. Definitions of user-defined attribute fields in the feature class sedCu_tracts.—Continued

Field name Field alias Description

Ident_Cu Known copper resources Known copper resources, in metric tons (t).

Cu_95 Copper resource estimate 

at 95th percentile

Undiscovered copper resource estimate, 95th percentile value, in metric tons (t); -9,999 indicates no data.

Cu_90 Copper resource estimate 

at 90th percentile

Undiscovered copper resource estimate, 90th percentile value, in metric tons (t); -9,999 indicates no data.

Cu_50 Copper resource estimate 

at 50th percentile

Undiscovered copper resource estimate, median (50th percentile) value, in metric tons (t); 

-9,999 indicates no data.

Cu_10 Copper resource estimate 

at 10th percentile

Undiscovered copper resource estimate, 10th percentile value, in metric tons (t); -9,999 indicates no data.

Cu_05 Copper resource estimate 

at 5th percentile

Undiscovered copper resource estimate, 5th percentile value, in metric tons (t); -9,999 indicates no data.

Mean_Cu_t Mean copper resources Undiscovered copper resource estimate, mean value, in metric tons (t); -9,999 indicates no 

data.

Prob_mean Probability of mean 

copper

Probability of the mean value, undiscovered copper resource estimate; -9,999 indicates no 

data.

Prob_0_Cu Probability of no copper 

resources

Probability of no undiscovered copper resources; -9,999 indicates no data.

Study_area Study area Geographic area of U.S. Geological Survey (USGS) report from which data were originally 

compiled.

Quant Quantitative Dichotomous variable for whether a quantitative resource assessment was performed for tract.

Short_ref Regional report short 

reference

Abbreviated citation for source reference; full reference located in the lineage section of the 

metadata and listed in file geodatabase nonspatial table sedCu_references.

Feature class name Description

world_background Feature class (polygons) of world map background represents grid cells of 30 by 30 degrees that cover 

the world (Esri, 2007).

world_political_boundaries Feature class (polygons) of countries of the world (U.S. Department of State, 2009).

Esri map document (.mxd)

Global_copper.mxd ArcMap document used for querying and viewing data.

Table 11. Description of basemap digital data files within the file geodatabase and map document.
14 Spatial Database for a Global Assessment of Undiscovered Copper Resources

When working with the sedCu_deps_pros and sedCu_

tracts feature classes, using the “Identify” tool will display 

one hierarchical catalog for each file; one that lists deposits 

and prospects within each tract and one that lists the name of 

the permissive tract in which the site occurs.

These two hierarchical catalogs are the result of relationship classes stored in the file geodatabase. Another way 

to determine if relationships exist is to click on the “Related 

Tables” icon within the table view of a feature class or nonspatial table; all relationship classes in which the current table 

participates will appear in a list.

Summary

This final compilation of spatial and nonspatial data 

for all twenty-seven regional reports in the Global Mineral 

Resource Assessment Project describing the two most significant sources of global copper supply resulted in a single Esri 

file geodatabase, USGS_global_copper.gdb. It contains three 

feature datasets: (1) porphyry copper, (2) sediment-hosted 

stratabound copper, and (3) basemap data. Within the two 

feature datasets for copper are feature classes and relationship 

classes pertaining to a specific deposit type. The final feature 

dataset includes elements for a political boundary feature 

class (world_political_boundaries, U.S. Department of State, 

2009) and a world map background feature class (world_background, Esri, 2007), which are used to display a single background color fill and lines of latitude and longitude in the map 

document USGS_global_copper.mxd. Additional elements 

included in the file geodatabase are three nonspatial tables of 

source references, two of which are used in the relationship 

classes.

Acknowledgments

Many U.S. Geological Survey colleagues, contractors, 

and students contributed to the development of the spatial 

data for the Global Mineral Resource Assessment Project. The 

authors are grateful to Deborah A. Briggs, C. Douglas Causey, Pamela M. Cossette, John C. Wallis, and Niki Wintzer in 

Spokane, Washington, and interns from Eastern Washington 

University, including Craig Caton, Cassandra Hennings, M. 

Christopher Jenkins, Hannah Campbell, Kassandra Lindsay, 

Michael E. Landkammer, Andrew Luders, Shyla Hatch, Shane 

Kostka, and David J. Williams. Robert Miller, Barry C. Moring, and Steve Ludington in Menlo Park, California, John D. 

Horton and Paul D. Denning in Denver, Colorado, and Boris 

Barrios, Michael DeMarr, Mukul Sonwalker, and Sewit Yenie 

in Reston, Virginia, provided GIS support for a number of 

regional reports. This report benefitted from helpful reviews 

by Pamela Cossette, Michaela Johnson, and Ruth Schulte.

References Cited

Berger, B.R., Mars, J.C., Denning, P.D., Phillips, J.D., Hammarstrom, J.M., Zientek, M.L, Dicken, C.L., and Drew, 

L.J., with contributions from Alexeiev, D., Seltmann, R., 

and Herrington, R.J., 2014, Porphyry copper assessment 

of western Central Asia: U.S. Geological Survey Scientific 

Investigations Report 2010–5090–N, 219 p., 8 plates, and 

spatial data, accessed February 7, 2015, at http://dx.doi.

org/10.3133/sir20105090N.

Bookstrom, A.A., Glen, R.A., Hammarstrom, J.M., Robinson, 

G.R., Jr., Zientek, M.L., Drenth, B.J., Jaireth, Subhash, 

Cossette, P.M., and Wallis, J.C., 2014, Porphyry copper 

assessment of eastern Australia: U.S. Geological Survey 

Scientific Investigations Report 2010–5090–L, 160 p., and 

spatial data, accessed February 20, 2015, at http://dx.doi.

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Box, S.E., Syusyura, Boris, Hayes, T.S., Taylor, C.D., Zientek, 

M.L., Hitzman, M.W., Seltmann, Reimer, Chechetkin, 

Vladimir, Dolgopolova, Alla, Cossette, P.M., and Wallis, 

J.C., 2012, Sandstone copper assessment of the ChuSarysu Basin, Central Kazakhstan: U.S. Geological Survey 

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Cohen, K.M., Fionney, S.C., Gibbard, P.L., and Fan, 

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Jr., Wallis, J.C., and Zientek, M.L., 2014, Sandstone copper 

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References Cited 15

Gray, Floyd, Hammarstrom, J.M., Ludington, Steve, Zürcher, 

Lukas, Nelson, C.E., Robinson, G.R., Jr., Miller, R.J., 

and Moring, B.C., 2014, Porphyry copper assessment of 

Central America and the Caribbean Basin: U.S. Geological 

Survey Scientific Investigations Report 2010–5090–I, 81 

p., and GIS data, accessed April 19, 2015, at http://dx.doi.

org/10.3133/sir20105090I.

Hammarstrom, J.M., Bookstrom, A.A., DeMarr, M.W., 

Dicken, C.L., Ludington, Steve, Robinson, G.R., Jr., and 

Zientek, M.L., with contributions from Cox, D.P., Manipon, 

C.J.C., Rollan, L.A., Sakimoto, Takehiro, Takagi, Tetsuichi, 

and Watanabe, Yasushi, 2014, Porphyry copper assessment 

of East and Southeast Asia—Philippines, Taiwan (Republic 

of China), Republic of Korea (South Korea), and Japan: 

U.S. Geological Survey Scientific Investigations Report 

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10, 2015, at http://dx.doi.org/10.3133/sir20105090P.

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Appendix
18 Spatial Database for a Global Assessment of Undiscovered Copper Resources

Appendix A. Mineral Resource 

Assessment Methods and Procedures 

Used in the Global Mineral Resource 

Assessment1

By Michael L. Zientek2and Jane M. Hammarstrom3

Introduction

When evaluating mineral potential, geologists distinguish 

rocks that are barren from those that could contain valuable 

concentrations of useful minerals or materials. Exploring for 

minerals is expensive; therefore, mineral potential is evaluated 

in a series of steps that minimize the cost of excluding barren 

areas while increasing knowledge about the possible existence 

of undiscovered mineral resources (Singer and Menzie, 2010). 

Initially, geologists use regional-scale datasets to discriminate 

geologic settings that are barren from those that may be mineralized. Next, targets or prospective areas that may contain 

mineralized rock are defined. If mineralization is found at a 

target area, then systematic sampling is conducted to determine if an economic volume of rock is present. If an economic 

volume can be defined, then economic and feasibility studies are conducted to determine how much of the volume can 

be developed. After a mine is developed, detailed geologic 

information guides the application of mining technology that 

will be used to separate ore from waste material in the mining 

process.

U.S. Geological Survey (USGS) mineral resource assessments generally correspond to the early regional reconnaissance step in the process of determining mineral potential 

and address two basic questions: (1) where are undiscovered 

mineral resources likely to exist? and (2) how much undiscovered mineral resource may be present? Results are presented 

as mineral potential maps and as frequency distribution of 

in-place, undiscovered metal.

We can make inferences about undiscovered mineral 

resource potential because natural accumulations of useful minerals or rocks (“mineral deposits”) can be classified 

using common characteristics and associations into groups or 

“deposit types” that reflect processes of formation. Using the 

deposit-type paradigm, we can predict the geologic settings in 

which various types of deposits may be found and as well as 

anticipate the distribution and concentration of ore materials at 

the scale of the deposit.

1

Reprinted from Zientek and Hammarstrom (2014).

2

U.S. Geological Survey, Spokane, Washington, United States.

3

U.S. Geological Survey, Reston, Virginia, United States.

The USGS strives to conduct consistent and unbiased 

assessments by applying a methodology4 to select areas having 

mineral resource potential and to probabilistically estimate the 

amount of mineral resources likely to be present. Integrated 

models and procedures reduce the likelihood of introducing 

bias in the assessment process.

USGS mineral assessment protocols are based on science practices derived from the fields of economic geology5

, 

mineral inventory estimation, and undiscovered mineral 

resource appraisal. The assessments are based on our fundamental understanding of the geologic processes that concentrate valuable mineral materials near the surface of the earth. 

The method extends the scientific and engineering principles 

that are used to establish mineral inventories. The science and 

mathematics of making forecasts and predictions are an essential part of the assessment process.

This document summarizes the technical language used 

in mineral assessments, the underlying principles, and an outline of operational procedures used for USGS mineral resource 

assessment.

Technical Language and the Assessment 

Process

Successful assessments require consistent use of technical 

language in order to reduce bias. The use of technical language 

comes at a cost, however, because economic geologists take 

common terms and restrict their meaning in order to communicate precisely with each other. Mineral assessment scientists 

may understand each other but the general user community 

may not understand the subtle distinctions between terms. 

So, it is necessary to discuss technical terms used in mineral resource assessment studies before considering mineral 

resource assessment methodology.

Mineral Resources and Reserves

Bodies of mineralized rock are classified according to (1) 

their geological, physical, and chemical properties; (2) their 

profitability; and (3) the level of certainty associated with the 

estimates of mineral potential. For estimation and assessment 

studies, the words “deposit,” “resource,” “reserve,” “discovered,” and “undiscovered” are used, but with specialized 

meanings. “Mineral inventories” are formal quantifications of 

the amounts of naturally occurring materials estimated by a 

variety of empirically or theoretically based procedures using 

the spatial distribution of grade and the particular locations 

4

A system of interrelated, internally consistent, and integrated models and 

procedures.

5

The study and analysis of geologic bodies and materials that can be used 

profitably by man, including fuels, metals, nonmetallic minerals, and water; 

the application of geologic knowledge and theory to the search for and the 

understanding of mineral deposits (AGI Glossary Online).
Appendix A. Mineral Resource Assessment Methods and Procedures Used in the Global Mineral Resource Assessment 19

of volumes of mineralized rock that are above cutoff grade6

(Sinclair and Blackwell, 2002). “Mineral inventories” include 

mineral resources and mineral reserves. “Mineral resources” 

are defined as concentrations or occurrences of material of 

economic interest in or on the Earth’s crust in such form, 

quality, and quantity that there are reasonable prospects for 

eventual economic extraction. The location, quantity, grade, 

continuity, and other geological characteristics of a mineral 

resource are known, estimated, or interpreted from specific 

geological evidence, sampling, and other knowledge (Committee for Mineral Reserves and Reporting Standards, 2006). 

The term “mineral reserve” is restricted to the economically 

mineable part of a mineral resource. On the basis of the level 

of confidence in the estimates, mineral resources are divided 

into “measured,” “indicated,” and “inferred” categories, and 

mineral reserves are subdivided into “proven” and “probable” 

groupings.

Mineral Deposit

“‘When I use a word,’ Humpty Dumpty said, in rather a 

scornful tone, ‘it means just what I choose it to mean—neither 

more nor less.’” –Lewis Carroll

“Deposit” has more than one meaning for most people, 

but “a layer or mass of accumulated matter” is close to how 

it is used by earth scientists. The following definitions are 

examples of how earth scientists may enhance the meaning of 

“mineral deposit”:

“geologic bodies which consist mainly of a single useful 

mineral or which contain, throughout or in places, valuable 

minerals which can be profitably extracted” (Lindgren, 1933).

“natural concentrations of useful minerals or rocks, which 

can be economically exploited” (Pohl, 2011).

“a mass of naturally occurring mineral materials, e.g. 

metal ores or nonmetallic minerals, usually of economic value, 

without regard to mode of origin” (Bates and Jackson, 1987).

“a mineral occurrence of sufficient tonnage and grade that 

it might, under the most favorable of circumstances, be considered to have economic potential” (Cox and others, 1986).

“an accumulation of associated mineralized bodies that 

constitute a single mineralizing event, including subsequent 

processes (e.g., oxidation and supergene enrichment) affecting 

part or all of the accumulation” (Barton and others, 1995).

Almost all agree that a deposit is an accumulation of 

potentially economic material, but some include additional 

constraints on size and genesis. For USGS mineral resource 

assessments, “mineral deposit” refers to natural accumulations of minerals or mineral materials that (1) formed by the 

same mineralizing event, (2) might have economic potential, 

(3) have a formally defined mineral inventory based upon a 

sampling density that is appropriate for the deposit type, and 

(4) are well explored. In order to be well explored, a mineral 

6

Cutoff grade is the lowest grade, or quality, of mineralized material that 

qualifies as economically mineable and available in a given deposit (Committee for Mineral Reserves and Reporting Standards, 2006).

inventory based on mapping, drilling, and sampling should 

encompass most of the potentially economic mineralized rock 

at the site. Accumulations of minerals or mineral materials 

that lack a mineral inventory or are incompletely explored are 

referred to as “prospects.”

Deposit Type

The concept of deposit type underlies the geologically 

based mineral resource assessments conducted by the USGS. 

Geologists, engineers, and miners have long recognized that 

mineral deposits can be classified into groups or types based 

on common characteristics and associations (Skinner and Barton, 1973). According to Eckstrand (1984), “a mineral deposit 

type is defined as a hypothetical composite of the geological 

characteristics common to a group of similar mineral deposits.” Mineral deposit types are defined as follows:

Characteristic ore body7 geometries

Distributions of tonnage and grade

Rock and mineral properties that determine the potential 

value of the deposit

Amount of sampling that will be required to delimit 

mineral resources

Amount of valuable material that can be mined and 

processed

Furthermore, each deposit type has a specific impact on 

the environment, whether through natural weathering 

processes or mining.

When referring to deposits that are members of a type, 

Eckstrand (1984) states, “It is implicit that such deposits, 

because of their similarities, are expected to have a common 

mode of genesis, whether or not that mode of genesis is well 

understood.” The genetic foundation of deposit types allows a 

scientific approach to assessing mineral resources. Scientific 

investigations of mineral deposits show they are extraordinary 

geologic features, formed by rare conjunctions of ordinary 

geologic processes. Even though mineral deposits are rare 

events, the principle of uniformity allows us to make predictions about their location and potential value based on geologic observations. The association of deposits to types gives 

even greater predictive capability.

Undiscovered Mineral Resources and Mineral 

Deposits

“Undiscovered” is a term that also has specific usage 

in USGS mineral resource assessments. To most people, an 

undiscovered resource would refer to a quantity of material 

that is completely unknown. In assessments, the terms “undiscovered mineral resources” refer to a variety of situations in 

which location, grade, quality, and quantity of mineralized 

7

A continuous, well-defined mass of material of sufficient ore content to 

make extraction economically feasible (AGI Glossary Online). Ore is a naturally occurring solid material from which a metal or valuable mineral can be 

profitably extracted (Oxford English Dictionary).
20 Spatial Database for a Global Assessment of Undiscovered Copper Resources

material are not constrained by specific geologic evidence. 

The presence of mineralized rock might be recognized at a 

site (location is known) but the grade, quality, and quantity 

of mineralized material is not sufficiently characterized to 

estimate mineral resources using industry-standard practices. 

In this example, the location of mineralized rock is discovered 

but the amount of mineral resource is unknown; therefore, any 

mineral resources that exist are undiscovered. In a similar situation, a well-characterized volume of mineralized rock with 

a resource estimate is surrounded by mineralized rocks for 

which the sample density is too sparse to classify the material 

as mineral resource. Undiscovered mineral resources may be 

present in the poorly characterized material. Finally, undiscovered mineral resources may be associated with a completely 

unknown, undiscovered mineral deposit, in which location, 

grade, quality, and quantity of mineralized rocks are unknown.

Assessment Methodology—Parts and 

Procedures

An assessment method consists of “parts,” each of which 

incorporates appropriate scientific theories, methods, and 

findings into the process. Rigorous reasoning integrates the 

parts into a consistent system or method that will indicate the 

possible location and potential value of undiscovered mineral 

resources in a form that can be consistently replicated and 

compared to other assessments. Parts of a method are usually 

models, but can also be subjective information provided by 

experts, or a product.

For example, for quantitative mineral resource assessments, the USGS uses the three-part form of assessment 

(Singer, 1993; Singer and Menzie, 2010). The first part 

consists of models of grades and tonnages of deposits used to 

estimate the amount of metal; the second is a mineral resource 

map in which areas are delineated according to the types of 

deposits permitted by the geology; and the third provides estimated numbers of undiscovered deposits of each type. These 

parts are essential for a quantitative assessment but do not 

completely describe an assessment method; in other words, 

they are not steps in an assessment method.

Assessing Location

The USGS mineral potential maps show geographic areas 

where undiscovered mineral resources may be present. For 

most USGS mineral resource assessments, mineral potential 

maps show “permissive tracts,” where geology permits the 

existence of deposits of one or more types. However, some 

studies create prospectivity maps, which delineate mineral 

exploration targets by combining various evidential layers in a 

geographic information system (GIS). Mineral potential maps 

can be represented cartographically as figures or plates in 

reports or as digital files that can be incorporated into a GIS.

Permissive tracts represent the surface projection of 

part of the Earth’s crust and overlying surficial materials to a 

predetermined depth where undiscovered mineral resources 

may be present. The criteria used to select the permissive 

volume of rock, or assessment unit, are provided by descriptive mineral deposit models and mineral systems models, as 

described below. The assessment geologist determines how to 

apply the criteria in the models to the specific datasets available for the assessment. Boundaries of the rock volume are 

defined such that the occurrence of deposits of the type being 

assessed outside the volume is negligible. According to Singer 

and Menzie (2010), negligible means a chance of less than 1 

in 100,000. Areas are excluded from these tracts only on the 

basis of geology, knowledge about unsuccessful exploration, 

or the presence of barren overburden exceeding some predetermined thickness (Singer and Menzie, 2010). In assessment 

reports, maps commonly show the permissive tracts along 

with mineral deposits, prospects, and occurrences of the 

deposit type being assessed.

Mineral prospectivity analysis is a predictive tool used 

for regional- to camp-scale exploration targeting (Porwal 

and Kreuzer, 2010). Mineral systems models are used to 

synthesize ideas about the processes related to mineralization. Critical processes act together to form mineral deposits; 

although processes cannot be directly observed, expressions 

of the processes can be mapped. The probabilities of occurrence of the critical mineralization processes can either be 

assigned subjectively based on expert assessment of available 

spatial and nonspatial geoscience information (knowledgedriven approach) or estimated empirically from the distribution of known mineral deposits (data-driven approach). From 

this information, resource potential maps can be generated 

in which each cell is attributed with a favorability value that 

represents the probability that the cell contains a deposit of the 

targeted type.

Descriptive Mineral Deposit Models

A mineral deposit model is systematically arranged 

information describing the essential attributes (properties) of 

a class of mineral deposits (Cox and others, 1986). Descriptive models used in USGS studies focus on observations and 

use theories of origin only to guide what to observe (Singer 

and Menzie, 2010). The function of the model is to provide 

assessment geologists with information that they can interpret 

and use to discriminate (1) possible mineralized environments 

from barren environments, and (2) types of known deposits 

from each other.

Descriptive models used in USGS assessments, such 

as those in Cox and Singer (1986), have two parts. The first 

lists characteristics of the geologic environments in which the 

deposits are found; the second gives identifying characteristics 

of deposits. The information in the first part can be interpreted 

by the assessment geologist and used to delineate tracts of land 

geologically permissive for the occurrence of undiscovered 

deposits. The second part of the descriptive model, the deposit 

description, includes information on host rocks, mineralogy, 

alteration, and geochemical and geophysical anomalies that 
Appendix A. Mineral Resource Assessment Methods and Procedures Used in the Global Mineral Resource Assessment 21

are used by the assessment geologist to recognize the deposit 

type and to discriminate one deposit type from another.

The descriptive models are lists of information. Therefore, the theory of ore formation that guided what was 

included in the list is not explicitly stated. The models also 

do not provide any suggestions on how the information can 

be used to delineate tracts or to identify deposits by type. 

Information needed to assess the potential economic value of 

the deposit type, such as typical mining, beneficiation, and 

remediation methods, are not usually included.

Mineral Systems Model

The concept of a mineral system can be used to incorporate concepts of regional ore genesis into mineral resource 

assessment and exploration targeting studies (Wyborn and others, 1994; Knox-Robinson and Wyborn, 1997; Cox and others, 

2003; Hronsky, 2004; Hitzman and others, 2005; Barnicoat, 

2006; Hronsky and Groves, 2008; Blewett and others, 2009). 

Mineral systems models use components and processes to 

organize ideas about how different mineral deposit types relate 

to regional-scale movements of energy and mass in the Earth. 

For example, hydrothermal ore deposits can be understood 

by considering the source of the ore-forming fluid, its physical and chemical character, the mechanisms for dissolving 

and transporting ore-forming components, and the causes of 

precipitation from it (Skinner and Barton, 1973). Sites where 

appropriate combinations of structural, chemical, and physical 

conditions that force ore mineral precipitation reactions are 

called ore traps (Reed, 1997). Variations of the source-transport-trap paradigm are used to define both petroleum and mineral systems models (Magoon and Dow, 1994; Wyborn and 

others, 1994; Magoon and Schmoker, 2000). All proponents of 

mineral systems models agree that deposition of ore minerals 

will not occur unless all essential components are present and 

processes occur in the correct sequence and location (Magoon 

and Dow, 1994; Kreuzer and others, 2008; McCuaig and others, 2010).

Mineral systems models serve two functions in mineral 

resource assessments. All components and processes that 

relate to ore deposit type can be systematically evaluated to 

identify areas where a mineral-forming system could be present and to create prospectivity maps that identify target areas 

for exploration. Another function of these models is to use 

the components and processes of the mineral system model 

to define the assessment unit in areas where the existence of a 

mineral-forming system is known from the presence of deposits and prospects.

Assessing Probable Amounts of Undiscovered 

Metal

Mineral resource assessments should be in a form that 

allows for comparison of potential value and benefit of mineral resource development with other socioeconomic benefits 

and consequences. Uncertainty of assessment results must also 

be indicated. Mineral potential can be expressed qualitatively, 

for example, high, medium, and low; however, this form of 

valuation cannot be related to other types of information, 

such as the value of other natural resources or the integrity 

of ecosystem function and process. Therefore, USGS mineral resource assessments express amounts of undiscovered 

mineral resources using probabilistic estimates of the amount 

of in-place metal. This form of assessment result can then be 

filtered economically to give some idea of the potential value 

of the mineral resource.

At least two strategies are used by the USGS to assess 

undiscovered mineral resources. The first is to estimate the 

number of undiscovered deposits; this approach has been 

widely used in USGS mineral resource assessments since the 

1970s. A second approach uses geostatistical methods to estimate undiscovered mineral resources associated with incompletely explored extensions of stratabound ore deposits.

Estimating Undiscovered Resources by 

Estimating Undiscovered Deposits

Mineral resource assessments completed by the USGS 

during the past three decades express geologically based 

estimates of numbers of undiscovered mineral deposits as 

probability distributions. Numbers of undiscovered deposits 

of a given type are estimated in geologically defined regions. 

Using Monte Carlo simulations, these undiscovered deposit 

estimates are combined with grade and tonnage models 

to derive a probability distribution describing amounts of 

commodities and rock that could be present in undiscovered 

deposits within a study area.

Grade and Tonnage Models

Mineral deposits of a given type have characteristic 

distributions of size and grade that can be used to constrain the 

probable size and grade of undiscovered deposits of the same 

type. Frequency distributions of tonnages and average grades 

of well-explored deposits of each type are used as models for 

grades and tonnages of undiscovered deposits of the same type 

in geologically similar settings (Singer and Menzie, 2010). 

These models are based on the average grades of each metal 

or mineral commodity of possible economic interest and the 

associated tonnage, prior to mining. Data used in the models 

should represent an estimate of the total endowment of each 

of the known deposits so that the final models can accurately 

represent the endowment of undiscovered deposits. In order to 

be consistent, the deposits used to estimate tonnages and grade 

in a model should (1) form by the same mineralizing event 

and be the same deposit type as other sites used in the same 

model (Barton and others, 1995); (2) be well explored (Singer 

and Menzie, 2010); (3) be an estimate of pre-mining, in-place 

mineral endowment (Singer and Menzie, 2010); (4) be based 

on sampling consistent with industry practices for defining 

mineral resources and reserves; (5) use similar cutoff grades; 
22 Spatial Database for a Global Assessment of Undiscovered Copper Resources

(6) use consistent rules for defining how ore bodies are spatially grouped into a deposit (Singer and Menzie, 2010); and 

(7) be developed on the basis of similar mining and processing 

methods as other sites in the model (Bliss and others, 1987).

The stipulation that the data used in a grade and tonnage model should represent total endowment affects what is 

considered a deposit or a prospect for assessment purposes. In 

the USGS three-part form of assessment (Singer and Menzie, 

2010), deposits must be (1) described in published literature 

(including grades and tonnages), (2) well explored in three 

dimensions, and (3) completely delineated (not open in any 

part). Mineral deposits that do not meet these three criteria are 

classified by Singer and Menzie (2010) as “undiscovered” for 

the sake of mineral resource assessment. For example, if there 

is any indication that an ore body is open, they count the site 

as an undiscovered deposit for assessment purposes (Singer 

and Menzie, 2010). Or, if a mineral deposit is well explored 

and completely delineated, but the mineral resource information is not published, then the deposit is considered undiscovered, but with a high probability for occurrence.

Number of Undiscovered Deposits

An estimate of some fixed, but unknown, number of 

undiscovered deposits of each type that are inferred to exist 

in the delineated tracts is another part of the three-part form 

of assessment (Singer and Menzie, 2010). Ore tonnages and 

metal grades of the undiscovered deposits are assumed to be 

distributed similarly to those of identified deposits of the same 

types. Expert panels estimate the number of undiscovered 

deposits at several confidence levels, usually the 90th, 50th, 

and 10th percentiles. An algorithm converts these estimates 

into a continuous distribution for use in the simulation of 

undiscovered mineral resources (Root and others, 1992). Two 

strategies typically are used when estimating 90th, 50th, and 

10th percentiles. In one scenario, an expert chooses a “best 

estimate” (for example, the median) and then adjusts up or 

down from that estimate in order to get the extreme percentiles 

(Clemen, 2001). In another scenario, the expert decides on 

the extremes first, assessing the 10th and 90th percentiles, and 

then selects the 50th.

Singer and Berger (2007) and Singer and Menzie (2010) 

offer guidelines for estimating numbers of undiscovered 

deposits. Estimates at the 90th and 50th percentile can be 

guided by counting and ranking prospects and mineral occurrences or by visualizing exploration targets based on data such 

as geochemical or geophysical anomalies or the presence of 

hydrothermal alteration. Probabilities can be assigned to each 

“target” and then combined to give an overall probability. 

Estimates are also guided by analogy with well-explored areas 

that are geologically similar to the study area. If a quantitative 

deposit density model is available, some estimators will use 

a predicted density from the model to guide their estimates 

(Singer and others, 2005; Singer, 2008).

Monte Carlo Simulation

Monte Carlo simulation is used to combine grade and 

tonnage distributions with the probability distribution of 

undiscovered deposits to obtain probability distributions of 

undiscovered metals in each tract (Root and others, 1992; 

Duval, 2012; Bawiec and Spanski, 2012). USGS software uses 

a number of techniques to avoid introducing bias into Monte 

Carlo simulation results. For example, dependencies between 

grades and tonnages of deposits and between grades of different metals in the same deposit are preserved. In addition, 

grades and tonnages are approximated by piecewise linear 

distributions to avoid unrealistically large values.

Simulation results are reported at select quantile levels, 

together with the mean expected amount of metal, the probability of the mean, and the probability of no deposits being 

present. The amount of metal reported at each quantile represents the least amount of metal expected.

Estimating Undiscovered Resources using 

Geostatistical Methods

Estimating undiscovered resources for some stratabound8

and stratiform9 deposit types by estimating the number of 

undiscovered deposits is problematic. Examples of stratabound deposit types include iron formations; beds of halite or 

potash-salt; layers rich in chromitites and platinum group element reefs in a layered igneous complex (Schulte and others, 

2012; Zientek, 2012); and sediment-hosted stratabound copper 

deposits (Cox and others, 2003; Hitzman and others, 2005; 

Zientek, Hayes, and Hammarstrom 2013; Zientek, Hayes, 

and Taylor, 2013; Hayes and others, 2015). The difficulty in 

making such estimates arises because valid grade and tonnage 

models cannot be constructed because most deposits are open 

at depth. In addition, deposit tonnage correlates with the extent 

of basin or layered igneous intrusion; a global tonnage model 

could have values that are geologically impossible for the size 

of a particular basin or intrusion.

Metal Surface Density

Probabilistic estimates can be made for undiscovered 

mineral resources in incompletely explored extensions of 

large, stratabound deposits if appropriate data are available to 

calculate metal surface density surfaces. The justification for 

using metal surface density in layered ore bodies follows.

In-place contained metal in an ore body is given by this 

relation:

M = T × g (1)

9

A special type of stratabound deposit in which the desired rock or ore 

constitutes, or is strictly coextensive with, one or more sedimentary, metamorphic, or igneous layers.

8

A mineral deposit confined to a single stratigraphic unit.
Appendix A. Mineral Resource Assessment Methods and Procedures Used in the Global Mineral Resource Assessment 23

where 

M is contained metal, in metric tons; 

T is the mass (tonnage) of the ore body, 

measured in metric tons; and 

g is the average grade of the ore body, 

measured in grams/metric ton. 

Tonnage is determined by this equation:

T = V × ρb (2)

where 

V is the volume of the ore body, measured 

in cubic meters; and 

ρb is the bulk density of the ore, measured 

in metric tons/cubic meters.

For tabular ore bodies, the volume can be approximated by:

V = t

t

 × S (3)

where

t

t

 is the average true thickness of the 

tabular ore body, in meters; and

S is the surface area, in squaremeters, 

measured in the plane of the tabular layer. 

Alternatively, for a dipping layer, the volume can be estimated 

by:

V = t

a

 × Sh (4)

where

t

a

 is the apparent thickness of the 

tabular ore body, in meters, measured 

perpendicular to the horizon; and

Sh is the surface area of the dipping ore 

body, in square meters, projected to the 

surface.

Combining equations, the in-place contained metal content of 

a dipping stratiform ore body is:

M = Sh × (t

a

 × ρb × g) (5)

This estimation method is a form of the area-averaging 

method of mineral resource estimation described by Noble 

(1992), which requires only an interpretation of the shape of 

the ore body and the average grades within the shape. This 

formula can be used to estimate the metal that is undiscovered 

in extensions to known mineral inventory if information is 

available for all the parameters.

Metal surface density (MSD) is calculated by dividing 

metal content (M) for the mineral resource block by its area Sh:

 (6)

Metal surface density can be estimated from samples 

collected through the mineralized intervals in stratabound or 

stratiform ore bodies. Mineralized intervals can be sampled in 

outcrop, drill hole, or underground workings. Metal surface 

density can also be estimated for a resource or reserve block if 

the tonnage, grade, and surface extent of the block are known.

Interpolation and Simulation Techniques

A single value of contained metal can be calculated from 

the kriged metal surface density surface for an assessment 

area. The spatial variation in metal surface density in the area 

is represented using geostatistical interpolation techniques 

(kriging). This approach is used because it quantifies the 

spatial autocorrelation among measured points and accounts 

for the spatial configuration of the sample points around the 

prediction location. From the metal surface density surface, 

contained metal is calculated by multiplying the value of metal 

surface density for a cell by the cell, and then summing the 

values for all cells.

Geostatistical simulation techniques can provide probabilistic estimates of the amount of undiscovered metal. Simulation techniques approximate solutions to uncertain and 

complex systems through statistical sampling. The system 

is represented by a model in which uncertainties in inputs, 

represented by probability distributions, are explicitly and 

quantitatively propagated into model outputs, also known as 

a probability distribution. For each simulation, or realization 

of the system, all of the uncertain parameters are sampled. In 

geostatistics, each simulation is the realization of a random 

function (surface) that has the same mean, variance, and semivariogram as the sample data used to generate it. The system is 

simulated many times, resulting in a large number of separate 

and independent realizations that represent a range of plausible possibilities, and in this case, the contained metal that 

can be estimated from metal surface density relations. Gaussian geostatistical simulation is an example of a simulation 

technique that is available in the geostatistical tools in ArcGIS. 

Therefore, each metal surface realization can be processed to 

estimate contained metal and the results from all realizations 

can then be tabulated to give a probability distribution of contained metal for the model.

Working with Assessment Results

Using deposit types, assessment geologists define areas 

in which undiscovered resources may be present and derive 

a frequency distribution of undiscovered, in-place metal. An 

assessment study may delineate many permissive tracts and 

probabilistic estimates of undiscovered resource. In order to 

integrate mineral assessment results with other types of information, it may be necessary to aggregate mineral assessments 

results into a single mineral resource theme and to indicate 

what proportion of the undiscovered mineral resource could 

potentially be economic.

MSD = M

Sh
24 Spatial Database for a Global Assessment of Undiscovered Copper Resources

Aggregation of Assessment Results

Permissive tracts are polygons that are represented by 

using a vector model. Two classes of attributes, spatially 

intensive and spatially extensive, are associated with the vector model (Longley and others, 2001). These two classes of 

attributes represent fundamentally different types of information that are governed by different rules for spatial analysis. 

Spatially intensive attribute values are true for each part of an 

area. For a vector spatial representation of counties, county 

name would be an example of a spatially intensive attribute 

value. No matter how small a part of the county polygon is 

considered, the county name attribute is always true. Spatially 

extensive attribute values are true only for entire areas. County 

population is an example of a spatially extensive attribute 

value. If the county is subdivided into four parts, the value 

of county population is not true for each of the subdivisions. 

Spatially extensive attribute information can be aggregated but 

not subdivided.

Permissive tracts have spatially extensive and spatially 

intensive attributes. Attributes like tract name and deposit type 

assessed are spatially intensive. However, the undiscovered 

deposit estimate is a spatially extensive attribute; the estimate applies to the entire tract. The results of Monte-Carlo 

simulation (in-place, undiscovered metal and ore [reported as 

percentiles and mean values], mean number of deposits, and 

the probability of zero deposits) are also spatially extensive 

attributes that apply only to an entire tract. Spatially intensive 

attributes can be aggregated and applied to a new tract that 

represents a union of the input tracts in which all the internal boundaries between overlapping areas are removed. The 

probability distributions of undiscovered metal from several 

mineral resource assessments can also be aggregated into a 

single result. However, the degree of association (dependencies) between geologically based assessment regions and tracts 

must be understood before aggregating assessment results. The 

mean of the aggregated distributions is the sum of the means 

of the individual distributions. However, aggregation does 

affect the spread of the functions because the variance of the 

combined distribution is affected by the dependency between 

the random variables. Quantile estimates of distributions can 

be added if the assumption of complete dependence among 

tracts can be made. Adding percentiles results in underestimating variance of the joint distribution if the distributions 

between assessment areas are independent or partially correlated (see, for example, Pike, 2008).

Schuenemeyer and others (2011) published a script that 

aggregates undiscovered deposit estimates for tracts of a given 

deposit type, assuming independence, total dependence, or 

some degree of correlation among aggregated areas, given a 

user-specified correlation matrix. The aggregated undiscovered 

deposit estimate, along with appropriate grade and tonnage 

models, are then input into Monte Carlo simulation software 

to obtain an aggregated distribution of undiscovered metal.

Economic Filters

Mineral supply, economic, environmental, and land-use 

planning studies often require an estimate of the amount of 

undiscovered mineral resources that are likely to be economically recoverable. Economic filters based on simplified 

engineering cost models provide a method for estimating 

potential tonnages of undiscovered metals that may be economic in individual assessment areas. For example, Robinson 

and Menzie (2012) use this approach to perform an economic 

analysis of undiscovered resources estimated in porphyry copper deposits in six tracts located in North America.

The economic filter developed by Robinson and Menzie 

(2012) modified and updated mining engineering cost models 

from the former U.S. Bureau of Mines (Taylor, 1978, 1986) 

that consider mine capacity, mine life, capital and operating costs to build and operate a mine and mill, metallurgical 

recovery, and 20-year average metal prices. To apply the 

economic filter for each permissive tract for porphyry copper 

deposits, a depth distribution of undiscovered deposits within 

the upper 1 kilometer (km) of the Earth’s crust is specified 

along with an estimate of the cost setting determined by the 

country or countries covered by the tract.

Simplified engineering cost models, updated with a 

cost index, were used to estimate the economic fraction of 

resources contained in undiscovered porphyry copper deposits as predicted in the USGS assessment of global copper 

resources (Robinson and Menzie, 2012).

The economic resource is estimated as:

economic resource = resource × economic filter, where

resource is the mean undiscovered resource estimated 

by simulation and the economic filter is the fraction of the 

resources estimated to be economic based on the grade and 

tonnage model used in the simulation, the depth distribution for the undiscovered deposits, and adjustments for cost 

settings. The economic filters were computed using an Excel 

workbook developed by Robinson and Menzie (2012). The 

20-year (1989–2008) average metal prices and metallurgical 

recovery rates were used in filter calculations. A number of 

parameters can be varied, as described here.

Depth Percentages

The depth distribution affects the amount of material that 

would have to be moved to develop a mine and determines 

the mining method (open pit or underground mining by block 

caving). Porphyry systems typically form within 1–4 km of 

the Earth’s surface. Some deposits are exposed at the surface 

but many are not. The amount of cover material that must be 

removed to access the ore (overburden) adds to the cost of 

developing a mine.

For each tract, a subjective estimate was made of a hypothetical depth distribution for undiscovered deposits. These 

estimates refer to the part of the upper kilometer of the Earth’s 

crust that the tops of any undiscovered porphyry copper 

deposits are expected to lie within. Three depth distribution 
Appendix A. Mineral Resource Assessment Methods and Procedures Used in the Global Mineral Resource Assessment 25

scenarios were developed. In the default depth distribution, 

25 percent of the undiscovered deposits are accessible in the 

upper 250 meters (m) of the crust, 25 percent are accessible 

between 250 and 500 m, and 50 percent lie below 500 m but 

above 1 km. For areas that have significant amounts of volcanic rocks or other cover, it was assumed that a greater percentage of the undiscovered deposits would lie at deeper depths, so 

the distribution was skewed to place 10 percent of the undiscovered deposits in the upper 250 m of the crust, 30 percent 

between 250 and 500 m, and 60 percent below 500 m but 

above 1 km. For areas that were uplifted and eroded based on 

tectonic history and relative amounts of volcanic and intrusive 

permissive rocks, it was assumed that any preserved undiscovered deposits would lie closer to the present surface than 

the default distribution. The depth distribution adopted for 

the economic filter for those areas was skewed shallow—35 

percent of the deposits would be intersected in the upper 250 

m, 25 percent of the deposits would be intersected below 250 

m and above 500 m, and 40 percent of the deposits would lie 

below 500 m and above 1 km depth.

Cost Settings

The economic filter incorporates a parameter for cost 

setting to express the quality of existing regional infrastructure (Robinson and Menzie, 2012). A “typical cost” setting 

has existing regional infrastructure to support mining (for 

example, northern Mexico and southwestern United States or 

established mining areas in Chile). A “high cost” setting refers 

to remote areas that lack existing infrastructure to support 

mining (for example, the Tibetan Plateau or Mongolia).

Country-by-country infrastructure rankings compiled 

by the Fraser Institute (McMahon and Cervantes, 2012) were 

used as an independent guide to select appropriate cost settings 

for mining in each tract. Data from the 2011–2012 quality of 

infrastructure table (McMahon and Cervantes, 2012, table 

A10) are expressed in terms of five categories of response: (1) 

encourages investment, (2) not a deterrent to investment, (3) 

mild deterrent, (4) strong deterrent, and (5) would not pursue 

investment due to this factor. The category scores were used 

to classify cost settings as typical where categories 1 and 2 

represent more than 50 percent of the response, as high cost 

where categories 3, 4, and 5 represent more than 50 percent of 

the response, and as mixed otherwise.

Many tracts cross jurisdictions, and some tract areas are 

in jurisdictions that were not evaluated by the Fraser Institute. 

In these cases we used the jurisdiction most closely associated 

with the tract as a proxy.

Operational Procedures

Previous text in this appendix describes the various 

aspects of the USGS mineral resource assessment methodology but does not actually describe how an assessment is 

conducted. Thus, the following is a list of steps or procedures 

used to conduct a mineral resource assessment using the threepart form of assessment:

Understand the assignment

• Commodities and deposit types to assess

• Anticipated end use

• Scope of work, including assessment depth

• Available resources (models, procedures, personnel, 

and budget)

• Required products

• Timeframe for completion

Gather and compile data

• Review literature

• Acquire geologic maps and databases of known mineral deposits and mineral occurrence (all datasets for 

the assessment should be at a scale appropriate for the 

study)

• Acquire geochemical, geophysical, and exploration 

data, if available

• Acquire specialized data required to assess and delineate tracts for a particular deposit type

• Organize digital library and share with project staff

Enhance geologic data

• Add attributes as needed for assessment study based on 

criteria in deposit and mineral system models

• Process data as needed to delineate tracts

Review and enhance mineral occurrence data

• Classify known mineral deposits and occurrences by 

deposit type using models

• Verify locations of deposits and prospects

• Update information using literature and technical 

reports published by exploration companies

• Review and apply spatial rules so that data will correspond to rules used to construct grade and tonnage and 

spatial density models

• Assess if deposits (sites with grade and tonnage) 

are well explored and should be classified as known 

deposits

Select appropriate grade and tonnage model

• Select published grade and tonnage models that might 

be appropriate for a quantitative assessment

• Use statistical tests to compare known deposits in 

assessment area with published models. If all the published grade and tonnage models fail statistical tests, 
26 Spatial Database for a Global Assessment of Undiscovered Copper Resources

determine if an appropriate model can be developed 

for the quantitative assessment. If a unique model is 

developed for the quantitative assessment of an area, it 

must be published with the assessment

Complete a preliminary study prior to assessment meeting

• Using descriptive and mineral system models, select 

the assessment unit for tract delineation

• Delineate permissive tracts

• Make preliminary undiscovered deposit estimates

Assemble assessment panel

The assessment team should include a mix of scientists with appropriate backgrounds for the deposit type 

being assessed. Ideally, the team should include geologists 

with expertise in (1) the deposit type being assessed, (2) 

the regional geology of the study area, and (3) the mineral 

resource assessment methodology.

Conduct a workshop to quantitatively assess the area

• Discuss ground rules, purpose, and goals of the workshop

• Summarize geology of the deposit type, geology of the 

study area, the characteristics of the grade and tonnage 

model, and the assessment method

• Present preliminary tracts and revise as needed

• If using grade and tonnage models to estimate undiscovered mineral resources, emphasize that undiscovered deposit estimates must be consistent with the 

models

• Estimate the number of undiscovered deposits. Each 

panel member initially determines estimates independently. The independent estimates are then compared 

and discussed. Regression equations are used to 

calculate mean of deposits and coefficient of variation. 

Consensus value for simulation value is determined

• Document assessment information—deposit type 

assessed; descriptive model used; grade and tonnage 

model used; the geologic feature being assessed (the 

assessment unit); geologic criteria used for tract delineation; known deposits, prospects, and occurrences; 

exploration history; sources of information; estimate of 

the number of undiscovered deposits; and rationale for 

the estimate

Estimate undiscovered mineral resources using Monte 

Carlo simulation

•	 Check Monte Carlo simulation results to make 

sure they are consistent with the cited grade and 

tonnage model

Present results of assessment to Assessment Oversight 

Committee

• Provide all data used to the committee, in digital format, for their review prior to presentation

• Revise the assessment in response to committee comments

Prepare report with assessment results

• Obtain two or more peer reviews, in addition to coauthor, supervisor, project manager, Science-Center 

Director reviews, editor input, and Bureau approval.

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Menlo Park Publishing Service Center, California

Manuscript approved for publication June 23, 2016

Edited by Sarah E. Nagorsen

Layout and design by Vivian Nguyen
Dicken and others—Spatial Database for a Global Assessment of Undiscovered Copper Resources—Scientific Investigations Report 2010–5090–Z

ISSN 2328-0328

http://dx.doi.org/10.3133/sir20105090Z