Prepared in cooperation with the Alaska Division of Geologic and Geophysical 

Surveys 

GIS-Based Identification of Areas with Mineral 

Resource Potential for Six Selected Deposit Groups, 

Bureau of Land Management Central Yukon Planning 

Area, Alaska

U.S. Department of the Interior

U.S. Geological Survey
Prepared in cooperation with the Alaska Division of Geologic and Geophysical 

Surveys

GIS-Based Identification of Areas with Mineral 

Resource Potential for Six Selected Deposit Groups, 

Bureau of Land Management Central Yukon Planning 

Area, Alaska

By James V. Jones, III, Susan M. Karl, Keith A. Labay, Nora B. Shew, Matthew Granitto, Timothy S. Hayes, 

Jeffrey L. Mauk, Jeanine M. Schmidt, Erin Todd, Bronwen Wang, Melanie B. Werdon, and Douglas B. 

Yager

Open-File Report 2015–1021

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, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

For more information on the USGS—the Federal source for science about the Earth,

its natural and living resources, natural hazards, and the environment—visit 

http://www.usgs.gov or call 1–888–ASK–USGS

For an overview of USGS information products, including maps, imagery, and publications,

visit http://www.usgs.gov/pubprod

To order this and other USGS information products, visit http://store.usgs.gov

Suggested citation:

Jones, J.V., III, Karl, S.M., Labay, K.A., Shew, N.B., Granitto, M., Hayes, T.S., Mauk, J.L., Schmidt, J.M.,

Todd, E., Wang, B., Werdon, M.B., and Yager, D.B., 2015, GIS-based identification of areas with mineral 

resource potential for six selected deposit groups, Bureau of Land Management Central Yukon Planning 

Area, Alaska: U.S. Geological Survey Open-File Report 2015–1021, 78 p., 5 appendixes, 12 pls., 

http://dx.doi.org/10.3133/ofr20151021.

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report.

ISSN 2331-1258 (online)

COVER

Granitic rock exposures of the Melozitna pluton in the Kokrines Hills, central Alaska, contain multiple rare 

earth element (REE) occurrences. Gamma ray scintillometer on the outcrop in the foreground for scale. 

USGS photo by Sue Karl. 
iii

Contents

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

Introduction.................................................................................................................................................... 2

Deposit Group Characteristics....................................................................................................................... 4

REE-Th-Y-Nb (-U-Zr) Deposits Associated with Peralkaline to Carbonatitic Igneous Rocks ..................... 5

Placer and Paleoplacer Gold (Au) Deposits............................................................................................... 6

PGE (-Co-Cr-Ni-Ti-V) Deposits Associated with Mafic and Ultramafic Igneous Rocks .............................. 7

Carbonate-Hosted Cu (-Co-Ag-Ge-Ga) Deposits....................................................................................... 7

Sandstone U (-V-Cu) Deposits................................................................................................................... 8

Sn-W-Mo (-Ta-In-Fluorspar) Deposits Associated with Specialized Granites ............................................ 9

Datasets ...................................................................................................................................................... 10

Geochemical Data Sources ..................................................................................................................... 11

Stream-Sediment Geochemistry .......................................................................................................... 11

Igneous-Rock Geochemistry ................................................................................................................ 12

Heavy Mineral Concentrate Mineralogy and Geochemistry.................................................................. 14

Alaska Resource Data File................................................................................................................... 14

Geologic Map Data for the State of Alaska........................................................................................... 15

Aerial Gamma-Ray Surveys................................................................................................................. 15

National Hydrography Dataset and Watershed Boundary Dataset....................................................... 15

GIS-Based Methodology and Results by Deposit Group ............................................................................. 16

General Methodology............................................................................................................................... 16

REE-Th-Y-Nb (-U-Zr) Deposits Associated with Peralkaline to Carbonatitic Intrusive Rocks .................. 18

Mineral Resource Potential Estimation Methodology ........................................................................... 20

Igneous-Rock Geochemistry ............................................................................................................ 20

Alaska Resource Data File................................................................................................................... 21

Stream-Sediment Geochemistry .......................................................................................................... 22

Aerial Gamma-Ray Survey Data .......................................................................................................... 23

Results and Discussion ........................................................................................................................ 23

Placer and Paleoplacer Au....................................................................................................................... 25

Mineral Resource Potential Estimation Methodology ........................................................................... 26

ARDF................................................................................................................................................ 26

Heavy Mineral Concentrate Mineralogy............................................................................................ 26

Stream-Sediment Geochemistry....................................................................................................... 26

Lithology ........................................................................................................................................... 27

Results and Discussion ........................................................................................................................ 27

PGE (-Co-Cr-Ni-Ti-V) Deposits Associated with Mafic-to-Ultramafic Intrusive Rocks .............................. 29

Deposit Group Characteristics.............................................................................................................. 29

Mineral Resource Potential Estimation Methodology ........................................................................... 29

Lithology ........................................................................................................................................... 29

ARDF................................................................................................................................................ 30

Heavy Mineral Concentrate Mineralogy............................................................................................ 30

Approach for Geochemical Datasets ................................................................................................ 30

Results and Discussion ........................................................................................................................ 31

Carbonate-Hosted Cu (Co-Ag-Ge-Ga) Deposits...................................................................................... 32

Deposit Group Characteristics.............................................................................................................. 32

Mineral Resource Potential Estimation Methodology ........................................................................... 33
iv

Lithology ........................................................................................................................................... 33

Rock Geochemistry .......................................................................................................................... 34

Heavy Mineral Concentrate Mineralogy............................................................................................ 34

Stream-Sediment Geochemistry....................................................................................................... 34

ARDF................................................................................................................................................ 35

Results and Discussion ........................................................................................................................ 35

Sandstone U (-V-Cu) Deposits................................................................................................................. 37

Deposit Group Characteristics.............................................................................................................. 37

Mineral Resource Potential Estimation Methodology ........................................................................... 38

Lithology ........................................................................................................................................... 38

Coal .................................................................................................................................................. 38

Stream-Sediment and Sedimentary-Rock Geochemistry ................................................................. 39

Igneous-Rock Geochemistry ............................................................................................................ 39

Aerial Gamma-Ray Survey Data....................................................................................................... 40

ARDF................................................................................................................................................ 40

Results and Discussion ........................................................................................................................ 40

Sn-W-Mo (-Ta-In-fluorspar) Deposits Associated with Specialized Granites ........................................... 41

Mineral Resource Potential Estimation Methodology ........................................................................... 43

Igneous-Rock Geochemistry ............................................................................................................ 44

ARDF................................................................................................................................................ 45

Stream-Sediment Geochemistry....................................................................................................... 45

Aerial Gamma-Ray Survey Data....................................................................................................... 45

Results and Discussion ........................................................................................................................ 46

Summary ..................................................................................................................................................... 47

Data Resources........................................................................................................................................... 48

References Cited......................................................................................................................................... 49

Appendixes

[Available online at http://pubs.usgs.gov/of/2015/1021/]

A. Stream-sediment geochemistry summary statistics and percentile cutoffs (Excel spreadsheet)

B. Igneous rock geochemistry peer-reviewed literature sources (text pdf)

C. Alaska Resource Data File (ARDF) mineral deposit keyword and scoring templates (Excel spreadsheet)

D. Lithology keyword search terms for an anticipated U.S. Geological Survey geologic map of Alaska

(Excel spreadsheet)

E. Scoring results for HUC analysis of selected deposit groups (folder containing Excel spreadsheets and 

geospatial data files)

Figures

1. Reference map of northern Alaska showing the Bureau of Land Management Central Yukon Planning 

Area..........................................................................................................................................................3

2. Map showing the boundaries for 12-digit hydrologic unit codes within and surrounding the Bureau of 

Land Management Central Yukon Planning Area................................................................................... 17
v

Tables

1. Mineral deposit groups and types considered in this study, and their commodities, characteristics, 

and representative localities ................................................................................................................... 62

2. Scoring template for HUC analysis of REE-Th-Y-Nb (-U-Zr) potential..................................................... 64

3. Mineral resource potential versus certainty classification matrix for REE-Th-Y-Nb (-U-Zr) deposits 

associated with peralkaline-to-carbonatitic intrusive rocks...................................................................... 65

4. Scoring template for HUC analysis of placer and paleoplacer Au potential. ............................................ 66

5. Mineral resource potential versus certainty classification matrix for placer and paleoplacer 

Au deposits............................................................................................................................................ 67

6. Platinum group element (PGE) ore deposit types.................................................................................... 68

7. Scoring template for HUC analysis of PGE (-Co-Cr-Ni-Ti-V) potential..................................................... 69

8. Mineral resource potential versus certainty classification matrix for PGE (-Co-Cr-Ni-Ti-V) deposits. ...... 71

9. Scoring template for HUC analysis of carbonate-hosted Cu (-Co-Ag-Ge-Ga) potential........................... 72

10. Mineral resource potential versus certainty classification matrix for carbonate-hosted Cu (-Co-AgGe-Ga) deposits. .................................................................................................................................... 73

11. Scoring template for HUC analysis of sandstone U (-V-Cu) potential.................................................... 74

12. Mineral resource potential versus certainty classification matrix for sandstone U (-V-Cu) deposits. ..... 75

13. Scoring template for HUC analysis of Sn-W-Mo (-Ta-In-fluorspar) potential.......................................... 76

14. Average element concentrations in the upper continental crust (from Taylor and McLennan, 1995)..... 77

15. Mineral resource potential versus certainty classification matrix of Sn-W-Mo (-Ta-In-fluorspar) 

deposits in specialized granites............................................................................................................. 78

Plates

[Available online at http://pubs.usgs.gov/of/2015/1021/]

1. Estimated mineral resource potential and certainty for REE-Th-Y-Nb (-U-Zr) deposits associated with 

peralkaline-to-carbonatitic intrusive rocks

2. Annotated mineral resource potential map for REE-Th-Y-Nb (-U-Zr) deposits associated with peralkalineto-carbonatitic intrusive rocks

3. Estimated mineral resource potential and certainty for placer and paleoplacer Au deposits

4. Annotated mineral resource potential map for placer and paleoplacer Au deposits

5. Estimated mineral resource potential and certainty for PGE (-Co-Cr-Ni-Ti-V) deposits associated with 

mafic-to-ultramafic intrusive rocks 

6. Annotated mineral resource potential map for PGE (-Co-Cr-Ni-Ti-V) deposits associated with mafic-toultramafic intrusive rocks 

7. Estimated mineral resource potential and certainty for carbonate-hosted Cu (-Co-Ag-Ge-Ga) deposits 

8. Annotated mineral resource potential map for carbonate-hosted Cu (-Co-Ag-Ge-Ga) deposits 

9. Estimated mineral resource potential and certainty for sandstone U (-V-Cu) deposits

10. Annotated mineral resource potential map for sandstone U (-V-Cu) deposits

11. Estimated mineral resource potential and certainty for Sn-W-Mo (-Ta-In-fluorspar) deposits in 

specialized granites

12. Annotated mineral resource potential map for Sn-W-Mo (-Ta-In-fluorspar) deposits in specialized 

granites
vi

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

yard (yd) 0.9144 meter (m)

Area

acre 0.4047 hectare (ha)

acre 0.004047 square kilometer (km2)

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2) 

Mass

ounce, troy (troy oz) 31.103 gram (g) 

ounce, troy (troy oz) 0.0000311 megagram (Mg) 

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

SI to Inch/Pound

Multiply By To obtain

Length

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

hectare (ha) 2.471 acre

hectare (ha) 0.003861 square mile (mi2) 

square hectometer (hm2) 2.471 acre

square kilometer (km2) 0.3861 square mile (mi2)

Mass

gram (g) 0.03215 ounce, troy (troy ounce)

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

megagram (Mg) 0.9842 ton, long (2,240 lb)

Other conversions used in this report

metric ton (t) 1 megagram (Mg)

troy ounce per short ton 34.2857 gram per metric ton (g/t)

percent (%) 10,000 parts per million (ppm) or grams per 

metric ton (g/t)

percent 0.01 × ore tonnage, 

metric tons

metric tons of metal
vii

List of Abbreviations Used

ADGGS Alaska Division of Geological and Geophysical Surveys

AMRAP Alaska Mineral Resource Appraisal Program

ANK modal Al/[Na+K]

ASI Aluminum Saturation Index

BLM Bureau of Land Management

CYPA Central Yukon Planning Area

GIS Geographic Information System

g/t grams per metric ton

ICP-MS inductively coupled plasma mass spectrometry

HFSE high field strength elements

HUC hydrologic unit code

Mt million metric tons

MALI modified alkali-lime index

MUM mafic and ultramafic rocks

NHD National Hydrologic Dataset

NURE National Uranium Resource Evaluation

NURE-HSSR National Uranium Resource Evaluation Hydrogeochemical and Stream Sediment 

Reconnaissance

PGE platinum-group elements

ppm parts per million

REE rare earth elements

ssU Sandstone uranium deposit

t metric ton

USGS U.S. Geological Survey

WBD Watershed Boundary Dataset
GIS-based Identification of Areas with Mineral 

Resource Potential for Six Selected Deposit Groups, 

Bureau of Land Management Central Yukon Planning 

Area, Alaska

By James V. Jones III1, Susan M. Karl1, Keith A. Labay1, Nora B. Shew1, Matthew Granitto1, Timothy S. 

Hayes1, Jeffrey L. Mauk1, Jeanine M. Schmidt1, Erin Todd1, Bronwen Wang1, Melanie B. Werdon2, and 

Douglas B. Yager1

Abstract

This study, covering the Bureau of Land Management (BLM) Central Yukon Planning 

Area (CYPA), Alaska, was prepared to aid BLM mineral resource management planning. 

Estimated mineral resource potential and certainty are mapped for six selected mineral deposit 

groups: (1) rare earth element (REE) deposits associated with peralkaline to carbonatitic 

intrusive igneous rocks, (2) placer and paleoplacer gold, (3) platinum group element (PGE) 

deposits associated with mafic and ultramafic intrusive igneous rocks, (4) carbonate-hosted 

copper deposits, (5) sandstone uranium deposits, and (6) tin-tungsten-molybdenum-fluorspar 

deposits associated with specialized granites. These six deposit groups include most of the 

strategic and critical elements of greatest interest in current exploration.

This study has used a data-driven, geographic information system (GIS)-based method 

for evaluating the mineral resource potential across the large region of the CYPA. This method 

systematically and simultaneously analyzes geoscience data from multiple geospatially 

referenced datasets and uses individual subwatersheds (12-digit hydrologic unit codes or HUCs) 

as the spatial unit of classification. The final map output indicates an estimated potential (high, 

medium, low) for a given mineral deposit group and indicates the certainty (high, medium, low) 

of that estimate for any given subwatershed (HUC). Accompanying tables describe the data 

layers used in each analysis, the values assigned for specific analysis parameters, and the relative 

weighting of each data layer that contributes to the estimated potential and certainty 

determinations. Core datasets used include the U.S. Geological Survey (USGS) Alaska 

Geochemical Database (AGDB2), the Alaska Division of Geologic and Geophysical Surveys 

Web-based geochemical database, data from an anticipated USGS geologic map of Alaska, and 

the USGS Alaska Resource Data File. Map plates accompanying this report illustrate the mineral 

prospectivity for the six deposit groups across the CYPA and estimates of mineral resource 

potential.

 

1U.S. Geological Survey

2Alaska Division of Geological and Geophysical Surveys
2

There are numerous areas, some of them large, rated with high potential for one or more 

of the selected deposit groups within the CYPA. The CYPA has recognizable potential for REE 

deposits associated with the Hogatza plutonic belt and parts of the Ruby batholith. Another area 

of high potential surrounds a known carbonatite occurrence near Tofty, Alaska, in the Hot 

Springs placer district. Placer gold potential is relatively high along many drainages scattered 

across the CYPA. Prospecting for and production from relatively small placer gold deposits were 

widespread in the past and have continued to the present. Areas with high potential for PGE 

deposits associated with mafic and ultramafic rocks are in the northwestern Brooks Range to the 

west of the CYPA and in the Killik River quadrangle within the CYPA. Another area of high 

PGE potential flanks the Ruby batholith to the northwest, generally within and near the Kanuti 

River drainage. High potential for carbonate-hosted copper deposits exists outside the CYPA 

within the Gates of the Arctic National Park and Wildlife Preserve along the south flank of and 

into the core of the Brooks Range. A discontinuous belt of high potential extends far to the 

northeast and includes areas within the CYPA in the Wiseman, Chandalar, and Philip Smith 

Mountains quadrangles. Other areas with high potential for copper deposits in carbonate rocks 

are in the southeastern CYPA and in the Brooks Range foreland. Sandstone uranium potential 

within the CYPA appears most closely associated with areas proximal to felsic intrusive igneous 

rocks such as the Hogatza plutonic belt and the Ruby batholith; the potential appears highest for 

a basal-type sandstone uranium deposit analogous with the Death Valley (Boulder Creek) deposit 

adjacent to the Darby pluton on the eastern Seward Peninsula. The highest potential for tintungsten-molybdenum-fluorspar deposits is associated with the Hogatza belt and Ruby batholith 

plutons, but high potential also occurs along the Tofty-Livengood belt of intrusive igneous rocks 

and in scattered areas within the Yukon-Tanana uplands.

Introduction

The Bureau of Land Management (BLM) in Alaska is developing a resource management 

plan (RMP) for their Central Yukon Planning Area (CYPA; fig. 1), which includes consideration 

of mineral exploration and development activities and their management. The U.S. Geological 

Survey (USGS) was requested to provide an analysis of the geologic potential for selected types 

of as-yet-undiscovered mineral resources to contribute to the RMP. This analysis is not a 

comprehensive review of known mines, prospects, occurrences, or mineral deposit types that 

occur within the planning area. Instead, it is an evaluation of where mineral deposits of several 

specific groups might occur in the region on the basis of geoscientific data and (or) features such 

as geology (for example, lithology, mineralogy, known prospects), geochemistry (of rock, soil, 

water, and drainage sediment samples), and (or) geophysical properties.
3

Figure 1. Reference map of northern Alaska showing the Bureau of Land Management Central Yukon 

Planning Area (outlined in black).

For this study, mineral “deposits” are localities with reported inventory or past 

production, whereas mineral “prospects” and “occurrences” describe localities where minerals of 

the mineral commodity are known but with no reported inventory. Mineral deposit “types” are 

recognized styles of mineralization with published deposit models, and mineral deposit “groups”

contain two or more mineral deposit types with similar commodities that occur in broadly similar 
4

geological settings. For example, the placer and paleoplacer Au group contains many different 

types of deposits on a global basis, but herein we focus on the alluvial placer, alluvial 

paleoplacer, and coastal placer types because they have the highest probability of occurrence 

within the CYPA.

This study considered six groups of mineral deposits that occur in Alaska and that contain 

critical elements (table 1; note that tables appear at the end of this report). Critical elements are 

those for which the United States imports more than half of its total supply and which come in 

large part from nations that cannot be considered reliable trading partners (National Research 

Council, 2008). Key characteristics of these deposit groups were identified and scored in terms 

of importance for indicating potential for the occurrence of each group. A geographic 

information system (GIS) was then used to systematically and simultaneously consider disparate 

types of geological, geochemical, mineral occurrence, and geophysical data as tools for 

prospectivity mapping for these six deposit groups within the CYPA. This study separates three 

levels of prospectivity on the basis of presence and abundance of favorable deposit-group 

attributes and three levels of certainty of the analysis. It is not a three-part probabilistic mineral 

resource potential assessment (Singer, 1993; Singer and Menzie, 2010) as conducted by the 

USGS for many areas in recent years (for example, Zientek and others, 2014).

The six specific mineral deposit groups considered in this study are (1) rare earth 

elements-thorium-yttrium-niobium(-uranium-zirconium) [REE-Th-Y-Nb (-U-Zr)] deposits 

associated with peralkaline to carbonatitic igneous rocks, (2) placer and paleoplacer gold (Au) 

deposits, (3) platinum group element(-cobalt-chromium-nickel-titanium-vanadium) [PGE (-CoCr-Ni-Ti-V)] deposits associated with mafic and ultramafic igneous rocks, (4) carbonate-hosted 

copper (-cobalt-silver-germanium-gallium) [Cu (-Co-Ag-Ge-Ga)] deposits, (5) sandstone 

uranium (-vanadium-copper) [U (-V-Cu)] deposits, and (6) tin-tungsten-molybdenum (-tantalumindium-fluorspar) [Sn-W-Mo (-Ta-In-fluorspar)] deposits associated with specialized granites. 

For each deposit group, a map indicates the relative density of favorable data (high, medium, or 

low) for all of the 12-digit hydrologic units that constitute the CYPA (see below regarding 12-

digit hydrologic units), as well as the relative certainty (high, medium, or low), derived by the 

method described below. Associated tables indicate which datasets were used in the analysis for 

each deposit group, which parameters were queried, and how much each parameter contributed 

to the estimate of mineral prospectivity. Data tables and associated geospatial data files 

(appendix E) provide the results for each deposit group and permit the user to analyze and query 

the findings in a spatial context.

Deposit Group Characteristics

The work here addresses the six deposit groups listed above and described below. These 

deposit groups do not include all of the mineral deposit types that are likely to occur in the 

CYPA. Instead, they were chosen for study because they are known to occur in Alaska and 

contain one or more critical elements as defined above (National Research Council, 2008). Table 

1 summarizes the six deposit groups together with associated commodities, examples in Alaska 

and elsewhere in the world, and key references. In the lists of elements for each deposit group, 

elements preceding the parentheses are the major products of the deposit groups, or at least their 

most abundant elements of interest. Elements and minerals listed in parentheses are coproducts

or byproducts that occur in some deposits of the group but are less common than those listed 

before. The following section provides an overview each deposit group that was the focus of this 
5

study. Other deposit types known in the CYPA or elsewhere in central Alaska were not 

considered in this work.

REE-Th-Y-Nb (-U-Zr) Deposits Associated with Peralkaline to Carbonatitic Igneous Rocks

Worldwide, economic deposits of rare earth elements (REE) are most commonly 

associated with intrusive igneous rocks that are not fully saturated with silica, and, thus, have a 

paucity of quartz or none at all (Long and others, 2010). Associated intrusive-rock compositions 

vary from peralkaline granite to syenite and also include ultra-alkaline rocks and carbonatites.

Mafic rocks within the same igneous complexes seldom contain ore. Although carbonatites are 

relatively rare worldwide, they are particularly rich in the REE (Rankin, 2005). Carbonatite and 

peralkaline intrusive complexes are derived from partial melting of mantle material, and deposits 

and associated rock types tend to occur within stable continental regions and are generally 

associated with intracontinental rift and fault systems (Verplanck and others, 2014). Protracted 

fractional crystallization of parental magmas leads to enrichment of REE and other incompatible 

elements. In carbonatites, REE mineralization can occur as either late stage mineral phases or as 

mineralization associated with late-stage orthomagmatic fluids. Deposits associated with 

carbonatite tend to be enriched in the light rare earth elements (atomic numbers from lanthanum, 

57, through europium, 63). In alkaline intrusive complexes, REE mineralization may occur as 

primary phases in magmatic layered complexes or as late-stage dikes and veins (Verplanck and 

others, 2014). These deposits tend to be enriched in the heavy REE (atomic numbers from 

gadolinium, 64, through lutetium, 71). In some places, REE-bearing ore deposits of an 

intermediate enrichment pattern are found in association with intrusive rocks with compositions 

between the peralkaline granite and carbonatite end members.

Minerals that contain REE also commonly contain, or may contain, Nb, Th, and Y; these 

three elements form their own minerals, and they are most abundant in association with syenitic 

to ultra-alkaline to carbonatitic intrusive rocks (Notholt and others, 1990). Minerals containing 

REE might also contain U and Zr, and minerals with this latter set of elements together with Ta, 

Nb, and Sn are generally associated with peralkaline granite and syenite (Pollard, 1995). In 

syenitic to carbonatitic deposits, REE-bearing ore minerals are either fluorocarbonates such as 

bastnäsite [hexagonal (Y,REE)CO3(F,OH)], synchysite [orthorhombic Ca(REE,Y)(CO3)2F], or 

parisite [rhombohedral Ca(REE)2(CO3)3F2] at places with carbonates like ancylite [orthorhombic 

(Ca,Sr,REE)2(CO3)2(OH,H2O)], or they are phosphate minerals like monazite [monoclinic 

(Th,REE)PO4] and others closely related to it (Long and others, 2010). Carbonatites commonly 

contain large concentrations of apatite, another phosphate mineral that might be economically 

mineable for fertilizer (Richardson and Birkett, 1996). However, apatite typically does not 

contain REE in concentrations that are economically recoverable. The two largest producing 

REE mines in the world—Mountain Pass, California, and Bayan Obo, China—both produce 

more bastnäsite than any other mineral, and both are associated with carbonatites (Le Bas and 

others, 1992; Castor, 2008). Bastnäsite at Mountain Pass is probably igneous in origin, but 

bastnäsite at Bayan Obo is thought to be entirely hydrothermal (Chao and others, 1997; Castor, 

2008). In less silica-undersaturated systems, such as peralkaline granite or syenite, the REE 

minerals that may be found in economic concentrations are mainly oxides like fergusonite 

[tetragonal(?) (REE,Y)(Nb,Ti)O4] and silicates like allanite [monoclinic (Ca, 

REE)2(Al,Fe)3(SiO4)3(OH)] (Sheard and others, 2012). Uranium might occur in oxides with or 

without Nb, REE, Y, Th, or Ta in these deposits; for example, aeschynite, betafite, samarskite, 

and polycrase. Zirconium might occur in igneous and (or) hydrothermal zircon (tetragonal 
6

ZrSiO4) and eudialyte [rhombohedral Na16Ca6Fe3Zr3(Si3O9)(Si9O27)2(OH,Cl)]. The zircon of this 

kind of deposit might have greater economic value than any other REE, U, or Nb ore minerals.

Alaska currently has only one known REE deposit; it is associated with the Bokan 

Mountain peralkaline granite of Jurassic age in the far southeastern part of the State on Prince of 

Wales Island (fig. 1). The Bokan Mountain deposit has a 5.2 million metric ton (Mt) resource 

that contains 0.548 percent total REE (Bentzen and others, 2013), and the potential ore is 

concentrated in narrow, tabular dikes and veins that occur in a swarm and that display both 

quartz-vein and igneous-dike mineralogy and textures.

Placer and Paleoplacer Gold (Au) Deposits 

Placer deposits are concentrations of high-density minerals formed by gravity separation 

during sedimentary processes. Heavy minerals are separated from their primary host rocks by 

weathering and erosion and then transported and concentrated in surficial deposits. Placer 

deposits exhibit a wide range of textures, form in many different environments, and host a 

variety of minerals, including valuable resources for gold, titanium, tin, platinum group 

elements, REE, and iron (Slingerland and Smith, 1986; Garnett and Bassett, 2005). Gold in 

placer deposits was initially derived from bedrock containing gold-quartz veins, disseminated 

gold, or other gold-bearing mineral deposits such as porphyry copper, Cu skarn, and polymetallic 

replacement deposits (Yeend, 1986). Primary gold-bearing deposits typically occur in igneous 

(Baker, 2002; Seedorff and others, 2005; Hart, 2007; Sinclair, 2007; Taylor, 2007) or 

metamorphic rocks (Berger and Henley, 1989; Groves and others, 1998; Goldfarb and others, 

2005), although sedimentary deposits such as paleoplacers and modified paleoplacers (Pretorius, 

1981; Minter, 2006) are also known to occur.

Alluvial placer deposits form in rivers and streams and represent the initial concentration 

of heavy minerals relative to source rocks within a drainage network. Heavy mineral deposition 

and concentration occurs where gradients flatten and (or) transport velocities decrease, such as at 

the inside of meanders, below rapids and falls, beneath boulders, and in vegetation mats (Yeend, 

1986). Placer gold deposits are typically found in alluvial gravel and conglomerate. Gold grains 

and, more rarely, nuggets are most concentrated at the base of gravel deposits where natural traps 

such as riffles, fractures, bedding planes, or other features are oriented transverse to the water 

flow. Additional concentration can take place as sediment moves downstream and as older 

alluvial deposits are reworked by younger systems. The latter takes place in geomorphically 

stable areas where erosion has proceeded for a long time and multiple generations of sediment 

and (or) sedimentary rock are recycled into potentially multiple stages of terrace and streambed 

gravels (Yeend, 1986).

Coastal placer and paleoplacer deposits, or beach placers, form in a range of coastal 

sedimentary environments that are typically dominated by eolian, wave, and tidal processes 

(Hamilton, 1995). Heavy minerals in the coastal environment are derived from sources that could 

include deeply weathered local bedrock exposures, sediment deposited at river mouths, or 

offshore sand deposits scoured from the seafloor during storm events. Along wave-dominated 

coastlines, heavy mineral enrichment occurs in the foreshore and uppermost part of the shoreface 

environment where sediment is repeatedly reworked by wind, waves, and wave-induced currents. 

Strong onshore winds winnow shoreface deposits at low tide and ultimately transport sediment 

inland from the beach environment (Roy, 1999). Longshore-transport currents, or littoral drift, 

moves sand along the coast in the direction of prevailing winds, and headlands along driftaligned coasts trap heavy minerals on the up-drift side of embayments. Most large coastal placer 
7

deposits formed along passive tectonic margins with long histories of erosion and repeated 

cycles of sea level change (Force, 1991). Coastal placer deposits formed along convergent and 

transform tectonic margins are found worldwide but are generally much smaller in size and are 

dominated by sediment derived from relatively local sources.

PGE (-Co-Cr-Ni-Ti-V) Deposits Associated with Mafic and Ultramafic Igneous Rocks

The six platinum-group elements (PGE) are ruthenium (Ru), rhodium (Rh), palladium 

(Pd), osmium (Os), iridium (Ir), and platinum (Pt). These elements all share similar physical and 

chemical properties and, therefore, tend to occur together in mineral deposits (Harris and Cabri, 

1991). The PGE are split into the iridium group (Os, Ir, Ru) and the palladium group (Rh, Pt, Pd) 

because these two subgroups tend to behave coherently during magmatic processes (Rollinson, 

1993). PGE are found in many geologic settings and are hosted by igneous and sedimentary 

rocks; PGE ore deposits form from a variety of physical and chemical processes. In deposits 

associated with mafic-ultramafic (MUM) rocks, PGE are variably to strongly associated with Ni, 

Cu, Cr, Co, Ti, and V. Thus, for this study, only PGE ore-deposit models genetically associated 

with MUM rocks were considered because of the strong association with these critical elements.

Characteristics of MUM-rock-related deposit models are summarized by Naldrett (2004), 

and the specific deposit types that factored into this assessment are listed in table 1. Mafic and 

ultramafic magmas originate in the upper mantle and contain small amounts of copper, nickel, 

and PGE and variable but minor amounts of sulfur (Eckstrand and Hulbert, 2007). As the magma 

ascends through the crust and cools and the sulfur content of the magma is sufficient, sulfide 

liquid forms as droplets. These droplets can take in nickel, copper, and PGE, and they tend to 

sink toward the base of the magma body to form sulfide concentrations and, ultimately, ore 

deposits that contain the PGE and associated metals.

Carbonate-Hosted Cu (-Co-Ag-Ge-Ga) Deposits

Carbonate-hosted Cu deposits in Alaska consist of Kennecott and Kipushi types, and they 

contain four critical elements: Ag, Co, Ge, and Ga. Kennecott-type carbonate-hosted Cu deposits 

are named for the town of Kennecott, Alaska, where deposits were mined out by about 1938 

(Price and others, 2014). The district is in southeastern mainland Alaska (fig. 1), now in the 

Wrangell-St. Elias National Park and Preserve. The deposits at Kennecott were northeaststriking, very high-grade (13 percent Cu) veins filling either normal or reverse faults that cut the 

Triassic Chitistone Limestone. They had considerable wall-rock replacement by copper sulfides 

and broad dolomitized alteration envelopes. The deposit bases were along probable bedding 

plane faults about 25 m above the contact between the Chitistone and the underlying Nikolai 

Greenstone. Silver (65 grams per metric ton, g/t) was recovered as well as Cu (MacKevett and 

others, 1997; Price and others, 2014). 

The Ruby Creek deposit in the southwestern Brooks Range (fig. 1) is an Alaskan 

example of the Kipushi type, which is named for the Kipushi deposit, Democratic Republic of 

Congo (DRC). These are massive base-metal-sulfide replacement bodies or breccia fillings in 

carbonates, now dolomitized, that are either peneconcordant or cut the beds as high-angle breccia 

bodies, or both. All deposits classified as Kipushi type by Cox and Singer (1986) have Gebearing minerals. This appears to be a distinguishing characteristic for their classification, as 

there are other types of Cu-Pb-Zn-sulfide ores in carbonate rocks that also feature mineralized 

breccias and dolomitic alteration but that lack Ge minerals, such as the carbonate-hosted sulfide 
8

deposits of Central Colorado (Beaty and others, 1990). The Ruby Creek deposit is hosted by the 

Middle Silurian to Early Devonian Cosmos Hills Sequence (Till and others, 2008). Copper-CoGe mineralization occurs as sulfide minerals that fill former open space in breccias or replace 

breccia matrix and wall-rock carbonates within a very large halo of hydrothermal dolomite 

containing minor sphalerite (Runnells, 1969; Bernstein and Cox, 1986; Hitzman, 1986). Copper 

sulfide minerals are mainly chalcopyrite, bornite, and chalcocite. Cobalt sulfides are mainly 

carrollite and cobaltiferous pyrite. Germanium sulfides are mainly renierite and germanite. 

Grades of the trace metals Co, Ge, and Ga in the Ruby Creek deposit have not been published. 

Elsewhere, Kipushi-type ores contain approximately 1,000 parts per million (ppm) Ge, but 

calculations from production figures from Kipushi, DRC, itself, suggest that recovery of Ge there 

ranged from 23 to 600 ppm and varied dramatically in time (Höll and others, 2007).

Sandstone U (-V-Cu) Deposits

Sandstone uranium deposits (ssU) are epigenetic deposits and have a worldwide 

distribution. They form in sandstone host rocks that range in age from Carboniferous to Tertiary. 

Sandstone uranium deposits are commonly divided into four types—basal, tabular, roll front, and 

tectonolithologic (table 1; Cuney and Kyser, 2009). Regardless of type, all ssU deposits are 

thought to form by redox-controlled processes that can be generalized as (1) oxidative 

mobilization of the uranium from the source rock, commonly granite or tuff, (2) transport of 

soluble uranyl (U6+) complexes through an oxidized nonmarine-sandstone aquifer, and (3) 

reduction and precipitation as U4+ minerals by encounter with reduced host rocks that are 

laterally continuous with or lying below the aquifer in which uranium is transported (Guilbert 

and Park, 1986; Cuney and Kyser, 2009). Reducing conditions in the host rock typically result 

from entrained carbonized plant matter. In this analysis, all types of ssU deposits were initially 

targeted, but basal-type deposits are the most likely to occur in Alaska and the CYPA. Other Uenriched deposits that occur within similar host rocks are sediment-hosted V and sedimenthosted Cu deposits (Turner-Peterson and Hodges, 1986).

The best described ssU deposit in Alaska is the Death Valley deposit, located near the 

east end of the Seward Peninsula (fig. 1). It was discovered in 1977 by an airborne radiometric 

survey and has an average grade of 0.27 percent U3O8 in beds averaging 3 m thick and calculated 

reserves of about 1,000,000 pounds (~453.59 t) U3O8 (Dickinson and others, 1987). The host 

rocks are early-Eocene carbonaceous arkosic sandstones of fluvial or colluvial origin deposited 

in a graben formed on granitic bedrock (Dickinson and others, 1987). An underlying Late 

Cretaceous granitic pluton forms the western side of the graben. Basalt, coal and lacustrine 

laminated sideritic mudstone and turbidites are interbedded with the sandstone. An early-Eocene 

basalt flow dammed the ancestral river valley, forming the lake in which the lacustrine sediments 

were deposited (Dickinson and others, 1987).

Primary epigenetic mineralization took place in the early Eocene, and supergene 

enrichment occurred in the Holocene. The high-standing adjacent pluton is considered the likely 

source for the uranium. Primary mineralization is thought to have developed when uranium was 

dissolved by oxidizing surface water, carried east from the pluton, recharged into the alluvial 

aquifer, and then deposited in the reducing environment of the carbonaceous Tertiary 

sedimentary rocks (Dickinson and others, 1987). The mineralized rocks are fairly widespread in 

the subsurface, occurring both above and below the Eocene basalt and lacustrine rocks, and 

extending to a depth of about 91 m. Uranium minerals in the primary ore are coffinite 

(U(SiO4)·nH2O) in the reduced zones and autunite (Ca(UO2)2(PO4)2·10-12H2O) where the 
9

primary ore has been oxidized. The secondary supergene enrichment is related to the present 

surface exposure and is possibly ongoing since the involvement of recent surficial mudflows and 

soil formation processes indicate a recent age (Dickinson and others, 1987). Uranium minerals in 

the secondary enrichment include meta-autunite (Ca(UO2)2(PO4)2·6H2O). The uranium-rich 

rocks of the Death Valley deposit are strongly carbonaceous and have anomalously low 

manganese (Mn) concentrations. The features of Death Valley are most consistent with those of 

a basal-type ssU deposit (Dickinson and others, 1987), and the low Mn concentration of the Ubearing rock is inconsistent with roll-front type deposits (Harshman, 1972).

Sn-W-Mo (-Ta-In-Fluorspar) Deposits Associated with Specialized Granites

Fluorspar (industry term for fluorite, cubic CaF2) and ore minerals containing Sn, W, Mo, 

Ta, In, and do not always occur as a group, but they are commonly found in various 

combinations closely associated with highly evolved felsic intrusive rocks that are grouped 

herein as specialized granites (for example, Reed, 1986). Specialized granites include granite 

porphyries, peraluminous granites, and high-silica (>73 percent SiO2; Rogers and Greenberg, 

1990; Blatt and others, 2006) granites. Such granites tend to be found in tectonic environments 

that involve thickening, contraction, or extension of continental crust. With some exceptions, the 

common petrogenetic characteristics of specialized granites and their associated dikes and sills

are that they (1) are late-stage magmatic phases containing enriched concentrations of 

incompatible elements as a result of fractional crystallization and (2) have sources that include 

continental crust. Mineral deposits associated with specialized granites mainly occur in dikes, 

veins, stockworks, and greisens or hydrothermally altered zones near the top of granite intrusions 

and in carbonate wall rocks as skarns. In systems associated with specialized granites, Sn ore 

minerals are mainly cassiterite and less commonly stannite; W is mainly found in scheelite and 

wolframite, and Mo is mainly found in molybdenite. Tantalum occurs mainly in tantalite 

(MgTa2O6) and less commonly in wodginite (Mn2+Sn4+Ta2O8) or microlite 

(Na,Ca)2Ta2O6(O,OH,F). Indium typically occurs as a substitution in sphalerite, cassiterite, and 

(or) stannite and is found rarely as roquesite (CuInS2).

Specialized granites include a wide variety of compositions because many factors can 

influence production and crystallization of silicic magma. The formation of specialized granites 

may include late phases associated with large magma systems or localized anatectic melts, and a 

common characteristic of these diverse granites is their relatively small volume. Most economic 

concentrations of Sn, W, Mo, Ta, and In, with or without fluorspar, are associated with 

peraluminous granites. Veins, dikes, and pegmatites that contain enriched concentrations of Sn, 

Ta, W, Mo, Cs, and Li are classified as lithium-cesium-tantalum (LCT) pegmatites (Černý and 

others, 2005), which commonly occur within and peripheral to the cupolas of peraluminous and 

highly evolved granites (Černý, 1991; Černý and others, 2005; Johan and others, 2012). Indium 

is a common impurity in Zn-, Sn-, and Cu-sulfides and in Sn oxides (cassiterite) in Sn-W veins, 

pegmatites, and skarns associated with evolved granitic rocks (Černý and others, 2005; Ishihara 

and others, 2006; Sinclair and others, 2006). Fluorine and H2O are important agents for transport 

of Sn, In, Ta, and W in fluids exsolved from peraluminous granite melts (Dobson, 1982; 

Swanson and others, 1990; Černý and others, 2005; Johan and others, 2012). Highly fractionated 

LCT granites and pegmatites are also thought to be enriched in F by anatectic crustal melts 

(Černý, 1991), which is consistent with strontium isotopic ratios for tin-bearing granites in 

Alaska (Hudson and Arth, 1983; Arth and others, 1989). 
10

Mineable deposits of tin are mainly found in skarns, greisens, simple Sn-veins and Snpolymetallic veins (table 1). Greisens associated with evolved granites consist of quartz, white 

mica, and cassiterite, with or without scheelite, molybdenite, pyrite, and (or) fluorite, that form in 

the upper roof zones of hydrothermally altered granite (endogreisen), or as alteration of wall 

rocks of the intrusions (exogreisen). Additional minerals found less commonly in greisen include 

tourmaline, topaz, garnet, axinite, arsenopyrite, sphalerite, and chalcopyrite. A characteristic 

mineral that occurs in greisens in cupolas of Sn-granites is Li-rich annite 

(KFe2+

3(AlSi3O10)(OH)2) (formerly “zinnwaldite”), where annite is the iron-rich end member of 

the biotite-phlogopite micas. Alaska has a well-known province of Sn-granites and associated Sn 

occurrences on the northwestern Seward Peninsula (fig. 1; Hudson and Arth, 1983), as well as 

widely scattered Sn occurrences in interior Alaska between the central Alaska Range and the 

central Brooks Range and to the west of the Alaska Range (fig. 1).

Concentrations of tungsten are recognized in both oxidized (magnetite-series) and 

reduced (ilmenite-series) granites. Porphyry-W deposits are rare (Seedorff and others, 2005), 

although granite porphyries with associated W are described in the Fairbanks area in Alaska (fig. 

1; Newberry and others, 1990). Tungsten is typically concentrated in veins and greisens around 

the margins of highly evolved granites (Werner and others, 1998) and is mined mainly from 

skarns and veins (table 1). Although high W contents result from extended melt fractionation, W 

contents of granites associated with scheelite skarns are not commonly elevated relative to other 

igneous rocks (Newberry and Swanson, 1986; Černý and others, 2005). It is common for W

skarns to contain Sn and some Mo and common for Sn skarns to contain W. Fluorspar is 

typically found as either a principal product with Sn and W in vein, greisen, and skarn deposits 

associated with highly evolved granites. Late-stage Sn-, Zn-, and Cu-sulfide veins and breccias 

that postdate W-Mo mineralization can contain significant concentrations of indium (Sinclair and 

others, 2006).

Magmatic-associated Mo deposits include a broad range of compositions for shallowly 

emplaced to hypabyssal intrusive rocks, but molybdenum is mined principally from two types of 

deposits—(1) Climax-type porphyry Mo systems, where it is the principal product (White and 

others, 1981; Seedorff and Einaudi, 2004), and (2) porphyry Cu (-Mo) systems, where it is a 

byproduct of mining large-tonnage, low-grade copper deposits (Seedorff and others, 2005). In 

the range of compositions between those two granite porphyry types are other deposits classified 

as low-fluorine porphyry Mo deposits (Theodore, 1986; Ludington and Plumlee, 2009). In 

Climax-type deposits, molybdenum is associated with high-K, aluminous granite and rhyolite 

porphyry with more than 73 weight percent SiO2 (White and others, 1981). 

Late intrusive or hydrothermal activity associated with specialized granites can 

remobilize Sn, W, Mo, Ta, and In. However, high volatile (especially F) contents in near-solidus 

melts make it difficult to distinguish magmatic versus aqueous fluid effects (Černý and others, 

2005, and references therein).

Datasets

To evaluate mineral potential in the CYPA for the deposit groups described above, the 

following datasets were assembled and analyzed using ArcGIS. Most of the data are publicly 

available, and direct links to the Internet resources are included in the dataset descriptions or at 

the end of this report.
11

Geochemical Data Sources

The geochemical dataset used in this study is a compilation of three geochemical 

databases with a statewide distribution of geologic material samples for Alaska. The file 

structure and data format of each database is markedly different. They were reformatted for 

consistency and optimized for geochemical mapping and statistical evaluation. 

The Alaska Geochemical Database, version 2.0 (AGDB2), of the USGS is the most 

recent compilation for new and historical geochemical analyses of rock, stream sediment 

(henceforth, simply, “sediment samples”), soil, and heavy-mineral concentrate samples in Alaska 

(Granitto and others, 2013). The AGDB2 includes analyses of 108,966 rock samples, 92,694 

sediment samples, 6,869 soil samples, 7,470 mineral samples, and 48,096 heavy-mineral 

concentrate samples. These samples were collected between 1962 and 2009 and prepared 

according to various USGS standard methods (Miesch, 1976; Arbogast, 1990, 1996; Taggart, 

2002). For this work, data for all sediment and heavy-mineral concentrate samples, as well as 

data for 48,731 igneous rock samples and 18,281 sedimentary rock samples, were used.

The National Uranium Resource Evaluation (NURE) database contains data derived from 

the NURE Hydrogeochemical and Stream Sediment Reconnaissance (NURE-HSSR) program 

that was overseen by the U.S. Atomic Energy Commission. NURE sediment samples were 

collected in Alaska between 1976 and 1979 and prepared according to various standard methods. 

For this work, data from 65,993 of these Alaska sediment samples were used.

The Alaska Division of Geological and Geophysical Surveys (ADGGS) geochemical 

database includes analyses of 12,437 rock samples, 10,919 sediment samples, 100 soil samples, 

and 1,061 heavy-mineral concentrate samples that were collected between 1960 and 2014. These 

data were provided by the ADGGS and are also available for download from their Web site

(http://www.dggs.alaska.gov/webgeochem). For this work, the data for all the ADGGS sediment 

and heavy-mineral concentrate samples, as well as the data from 5,653 igneous rock samples, 

were used.

Many samples in the AGDB2 and ADGGS databases were analyzed by more than one 

analytical method, and therefore, some samples have more than one determination for a given 

element. Some elements have as many as four determinations for a given sample analyzed by 

different techniques; Ag, As, Au, Bi, Cd, Cu, Mo, Pb, Sb, and Zn have the greatest number of 

multiple determinations. To minimize the complexity of multiple determinations for individual 

elements, a single best value was used for each element for each sample. Granitto and others 

(2013) provide a detailed description of the ranking criteria accompanying analytical methods 

and how they were used to determine best values.

Many of the samples in the geochemical datasets have also been reanalyzed. For these 

samples, the original and the reanalyzed data might coexist because the samples were issued a 

second laboratory identification number on submission for reanalysis, effectively creating a 

second data record in the datasets. In this work, for each element for which there was more than 

one analysis at a specific location, the larger value of the two analyses was selected for use in 

calculation of statistics, to display on point plots, and to use for gridded map layers.

Stream-Sediment Geochemistry

Geochemical data of sediment samples represents one of the most comprehensive, evenly 

spaced, and highest-density datasets for examining mineral resource potential across Alaska.

Bedrock is obscured in many areas by unconsolidated sediment and vegetation. Sediment data 
12

show patterns of element concentration that reflect rock types in their respective drainage basins 

and, thus, provide clues about the composition of rocks in areas of poor exposure or that lack 

geologic mapping. This dataset is herein referred to as sediment geochemistry because, although 

some samples were collected from a variety of surface water bodies, more than 80 percent of the 

sediment samples in the AGDB2 were collected from streams. 

For this analysis, sediment geochemical data from the AGDB2, NURE, and ADGGS 

datasets were combined into a single dataset for which the median, 75th, 91st, and 98th

percentiles were calculated. These percentile cutoff values were used in the mineral potential 

scoring methods described below. Appendix A shows summary statistics for each element, 

which database(s) contained data for each element, and the statistical method by which percentile 

values were determined (see below). The combined dataset produces the greatest statewide 

coverage of geochemical data, but analytical methods differed among samples. Furthermore, the 

combined datasets contain multiple detection limits for a single element, thus resulting in 

multiple data censoring limits. Censored data are those that fall outside the detection limit for a 

specific element analyzed by a particular method. Some elements have highly censored data. For 

these elements, summary statistics were strongly skewed if censored data were omitted or if 

substitute values were introduced for the censored values. For example, if the geochemical data 

for an element are 90 percent censored, then substitution of a value such as one-half of the lowest 

determination limit for the censored data will yield artificially low mean, median, and percentile 

values. Similarly, if the data are 90-percent censored, then omission of the censored data will 

leave only anomalously enriched data, and statistics calculated on that portion of the data will 

yield artificially high mean, median, and percentile values. Therefore, two methods were used to 

calculate statistical values for each element depending on the amount of censored data—

substitution method and Kaplan-Meier method.

Both methods were initially applied to data for several elements that have different 

degree of censoring. For low degree of censoring, results were nearly identical when summary 

statistics were calculated by both methods. Critical assessment of these comparisons indicated 

that for data with less than about 40-percent censoring, the differences for the median and upper 

percentiles were negligible to insignificant. As a result, for elements with 40 percent or less 

censored data, substitution was used, and all censored values were replaced with a value of half 

the minimum noncensored value, or half of the minimum lower limit of detection, whichever 

was the lesser of the two. The digits “1, 1” were added at the end of the substitutes to make those 

replaced values identifiable in the database. Summary statistics were then calculated by standard 

procedures on the combination of the analyzed and substituted values. For elements with more 

than 40 percent censored data, the Kaplan-Meier method was used for estimating summary 

statistics. This method is a nonparametric statistical approach to calculating cumulative 

probability distributions from which summary statistics for censored data are calculated (Helsel, 

2012). The procedure used here is detailed in Helsel (2012) and was run using Minitab.

Appendix A shows the level of censoring for each element and for which elements the KaplanMeier method was used. 

Igneous-Rock Geochemistry

Igneous rock geochemical indices were calculated for nearly 49,000 igneous rock 

samples statewide for use in identifying permissive rock types for REE deposits and Sn-W-Mo 

deposits. The REE deposits considered herein are associated with igneous rocks with peralkaline, 

alkaline, or carbonatitic composition; Sn-W-Mo deposits of greatest interest in this study are 
13

associated with high-silica, peraluminous intrusive igneous rocks. These distinctive igneous rock 

compositions are relatively easy to identify using geochemical data from individual rock samples 

or suites of rock samples. The distribution of igneous rock samples across the State provides 

more detail than geological maps can portray, especially considering the compositional 

complexity of many igneous systems and general lack of detailed geologic mapping statewide. 

Thus, the geochemical characteristics of individual rock samples provides a means of identifying 

favorable rock types within broader belts of igneous rock outcrop, in areas where igneous rocks 

were exposed at a scale not represented on available geologic maps, or in cases where 

geochemical trends of interest were not obvious or apparent in outcrop.

Major and trace element geochemical data for igneous rock samples were compiled from 

four primary sources—the AGDB2, the ADGGS database, peer-reviewed literature (see 

appendix B for a list of literature sources), and unpublished USGS data. For all geochemical 

ratios discussed below, available data were screened to include only high-resolution analyses, as 

follows. For an igneous rock analysis to be used, where two or more elements for an igneous 

rock sample contribute to a ratio or geochemical index (see below) both data components of the 

ratio had to be of similarly high precision. In general, only major-element data were used for 

samples where all major rock forming element oxides were measured such as by x-ray 

fluorescence (XRF) methods. For trace elements such as Y and Nb, only XRF and inductively 

coupled plasma mass spectrometry (ICP-MS) analyses were used, avoiding methods for which 

there is an increased risk of insoluble residues of accessory phases that may contain these 

elements. In addition, all major-element-oxide concentrations used in calculating geochemical 

indices were normalized to an anhydrous basis.

Use of single-element geochemical data can, in a broad sense, help identify geochemical 

anomalies. However, all commonly used igneous classification schemes using geochemical data 

require two or more elements or element oxides. Well-developed geochemical criteria exist for 

the classification of the igneous rocks, including the discrimination of alkaline and silicaundersaturated rock types using major and trace elements (for example, Irvine and Baragar, 

1971; Frost and others, 2001; Frost and Frost, 2008; Frost and Frost, 2011). Similar conventions 

were followed in classifying the igneous rocks by using major element ratios to calculate the 

aluminum saturation index, or ASI (modal Al/[(Ca-1.67×P)+Na+K]) (Shand, 1947), and modal 

Al/[Na+K] (ANK). These parameters allow the differentiation between peraluminous (ASI>1), 

metaluminous (ANK>1), and peralkaline (ANK>1) rock types.

In some cases, trace element concentration ratios (for example, Nb/Y>1; 

10,000×Ga/Al>2.6) can distinguish alkaline and subalkaline igneous rock types (Winchester and 

Floyd, 1977; Pearce and others, 1984; Whalen and others, 1987; Pearce, 1996). However, many 

useful geochemical discrimination diagrams are X-Y plots that have subfields defined by one or 

more established boundaries. For example, distinctions between the various rock types using 

major elements (that is, alkaline versus calcic; ferroan versus magnesian) can be made using 

plots of SiO2 versus the modified alkali-lime index (MALI, which is Na2O+K2O-CaO), and the 

Fe# (FeO/[FeO + MgO]) which were proposed by Frost and others (2001).

Igneous classifications based on elemental comparisons relative to a boundary or array in 

geochemical X-Y space is traditionally done by eye and, thus, is inefficient for large data sets 

containing thousands of samples, such as those for the State of Alaska (for example, ADGGS 

and AGDB2; Granitto and others, 2013). Instead, a method was devised to quantify a sample’s 

displacement from established boundaries in geochemical x-y space. Specifically, for each 

igneous rock sample, the displacement was calculated in the y-axis direction above or below the 
14

accepted boundaries. For example, relative to the MALI array (the alkali-calcic/calcic-alkalic 

boundary in Frost and others, 2001), the displacement equation for a sample is:

MALIdisplacement = MALIcalculated − MALIexpected ,

where MALIexpected is the MALI predicted by the boundary equation for the SiO2 content in a 

given sample. In this way, the MALIdisplacement and Fe#displacement index values were calculated for 

each sample for which the appropriate data are available. For the Fe# versus SiO2 plot, the 

boundary proposed by Frost and Frost (2008) was used because all data could be converted to 

total iron (FeOtotal), but not all samples were analyzed for FeO and Fe2O3 (compare to Fe-Index 

in Frost and others, 2001). In general, both Fe# and MALI displacements are more positive for 

samples representing more ferroan and alkalic compositions and more negative for magnesian 

and subalkaline compositions, respectively. 

These displacement-type (MALIdisplacement, and Fe#displacement) and simpler cutoff-type (that 

is, Nb/Y, Ga/Al) geochemical discriminant indices allow rapid recognition of geochemical 

spatial trends and anomalies potentially associated with mineral deposits considered herein. 

Different combinations of multiple indices provide robust petrologic constraints on the 

composition, sources, and probability of mineral potential of igneous rocks in the CYPA. Greater 

detail on the use of these indices is given within each relevant section below.

Heavy Mineral Concentrate Mineralogy and Geochemistry

Mineralogical data based on visual identifications are available for more than 18,000 

nonmagnetic bulk-panned concentrate or heavy-mineral concentrate (HMC) samples in the 

AGDB2 (Granitto and others, 2013). The HMC samples were derived from sediments, soils, or 

rocks, and the data were derived from data entry sheets, USGS Open-File Reports, and archival 

digital spreadsheets. The entries include several different ways to quantify heavy minerals such 

as gold, cassiterite, monazite, and scheelite (Granitto and others, 2011; Granitto and others, 

2013). Some samples have grain count data, and other samples are described by a variety of 

qualitative values (for example, “present” or “abundant” or “trace”), estimated percentages, or 

percentage ranges. Null values indicate that the mineral was not observed in the sample. The 

mineralogy data in the AGDB2 are presented as they were originally recorded and interpreted, 

and the data sources are listed in Granitto and others (2011). The AGDB2 also contains bestvalue geochemical data for more than 48,000 HMC samples across the State of Alaska. The 

specific methods for incorporating HMC mineralogy and (or) geochemistry into our assessment 

of mineral resource potential are described in more detail in the relevant sections below.

Alaska Resource Data File

The Alaska Resource Data File (ARDF; http://ardf.wr.usgs.gov/) contains more than 

7,000 reports of mines, prospects, and mineral occurrences in Alaska. The ARDF records are 

published for individual USGS 1:250,000-scale quadrangles. USGS Open-File Reports 

containing records for each quadrangle are available for download separately or as part of a 

single composite ARDF database from the Web address shown above. The ARDF records 

represent a broad spectrum of mineral deposit types. For this study, methods were developed to 

search for records that were most likely to represent each of the six deposit models described 

below. For each deposit group, a list of searchable keywords was developed for key ARDF 

record fields. The keywords were weighted for their relevance to the mineral deposit of interest 

and were also assigned to a “definite” or “maybe” column depending on the strength of their 
15

association with, or relevance to, the model of interest (appendix C). In some cases, keywords 

were assigned negative scores if they were indicative of a geological system known to be not 

associated with the mineral deposit of interest. Complete lists of keywords and associated 

weights are shown in appendix C for each deposit group. 

Using a custom Python script in ArcGIS, all ARDF records were searched and assigned a 

total score for each deposit group on the basis of the total keyword hits and sum of the associated 

scores. High-scoring ARDF records for each deposit group were initially reviewed against areas 

of known mineral potential and (or) mining activity to ensure that known occurrences were 

identified by the keyword search method. If ARDF scoring results did not adequately reflect 

deposits of known type, then scoring parameters were modified to better calibrate the method. In 

some cases, scrutiny of individual ARDF records revealed problems such as misspellings and 

imprecise location information. All records that were discovered to contain errors were corrected 

in the original database before the final analyses described below. However, individual users are 

cautioned that ARDF records might contain other errors that were not recognized but that ideally 

would be outweighed or counteracted by other datasets used herein. The specific deposit-group 

sections below provide more information on how the ARDF scores were used to help assess 

mineral potential.

Geologic Map Data for the State of Alaska

An anticipated geologic map of Alaska (Wilson, Frederic H., and others, USGS, written 

commun.) portrays the distribution of different rock types across the State. For most mineral 

deposit groups, the catalog of lithology descriptions for the geologic-map database was used to 

search for and identify rock types that were most prospective and (or) best suited for hosting the 

deposit group. The lithology query results were used to develop derivative, generalized lithology 

map layers to show the distribution of favorable rock types for each deposit group across the 

State. The specific lithologies queried for each deposit group are described in detail below and 

are summarized in appendix D.

Aerial Gamma-Ray Surveys

Aerial gamma-ray surveys for the State of Alaska were calibrated and compiled by Duval 

(2001) and Saltus and others (1999), and they reflect the radioactive signatures of bedrock and 

surficial materials. These surveys, flown as part of the National Uranium Resource Evaluation 

(NURE) Program (conducted by the U.S. Department of Energy from 1976 to 1984), cover most 

of Alaska except for parts of the Brooks Range. These surveys measured the flux of gamma rays 

emitted as a result of the radioactive decay of the naturally occurring radioactive elements 40K, 

238U, and 232Th and were used to identify areas with potential for REE, Sn-W-Mo, and uranium 

deposits, as described below.

National Hydrography Dataset and Watershed Boundary Dataset

The USGS National Hydrography Dataset and Watershed Boundary Dataset (NHD and 

WBD; http://nhd.usgs.gov/) delineate surface water networks and drainage basins throughout the 

United States using standardized criteria based on topography and hydrology. Relative drainage 

basin size, geographic location, and nested hierarchy are encoded within a string of digits known 

as a hydrologic unit code (HUC). A classical hydrologic unit is a division of a watershed with 

but a single discharging stream, so it corresponds with a physical watershed on the ground 
16

defined by topography. The great majority of the 12-digit HUCs comprising the CYPA are 

classical HUCs. The WBD currently reviews and certifies HUCs that range in length from two 

digits, for the largest drainage systems known as regions, to as many as 12 digits, for systems 

known as subwatersheds. For this study, 12-digit HUC boundaries were used as a geographic 

reference frame for evaluating mineral resource potential across the CYPA. Scores assigned for 

favorability for each deposit group were summed on a HUC by HUC basis. Numeric codes, 

names, and boundaries that are associated with each HUC provide unique identifiers that can be 

used in a GIS to geographically associate other digital data from multiple sources. In this report, 

we commonly use the term “HUC” to refer to a physical drainage subbasin and not solely the 

string of digits used to identify the subbasin. Rivers and streams from the NHD were used 

primarily for the placer model to map drainage networks downstream from areas with high 

placer potential (see below).

GIS-Based Methodology and Results by Deposit Group

General Methodology

The goal of this project was to identify and rate areas of mineral resource potential across 

the CYPA for the selected deposit groups described above. The six deposit groups were 

prioritized for reasons described above, and it is acknowledged that there are likely many other 

important deposit groups that exist or for which there is potential across the CYPA. 

Subwatersheds defined by 12-digit HUCs were chosen as the primary spatial feature for 

evaluating and mapping mineral potential (fig. 2). The use of HUCs as analysis cells provided a 

few key advantages over other types of surveyed units (for example, Public Land Survey System, 

or latitude-longitude quadrangle). First, each HUC in the CYPA has unique, numeric identifiers 

and names that can be used in a GIS to geographically associate other digital data from multiple 

sources. This feature could be useful to the BLM in addressing multiple issues involving landuse decisions. Second, the HUC dataset is standardized, available for, and continuous across 

most of the State and surrounding region. The 2,460 12-digit HUCs in the CYPA (fig. 2) have an 

average area of approximately 100 square kilometers (km2), or 27,500 acres. This average unit 

size is generally larger than any single mineral deposit or some mining districts; however, with 

2,460 HUCs within the CYPA, this unit size provides sufficient detail for comparison and 

contrast. The HUCs represent a balance between an appropriate level of detail for displaying data 

patterns at large scales and optimizing computing efficiency when employing data from large 

relational databases. However, perhaps most importantly, subwatershed boundaries (most 

commonly, drainage divides) are typically well marked on the ground; they are actual 

physiographic features with a direct link to processes (for example, erosion and weathering) and 

features (for example, streams and rivers) that expose, transport, and potentially enrich the 

sought-after ore minerals. Using subwatersheds as an analysis unit also provides a direct link to 

sampling that was focused along streams and other surface water bodies.
17

Figure 2. Map showing the boundaries (blue) for 12-digit hydrologic unit codes (HUCs) within and 

surrounding the Bureau of Land Management Central Yukon Planning Area (outlined in black).

The analysis area included HUCs within a 100-kilometer (km) buffer outward from the 

CYPA boundary (fig. 2), providing a more continuous view of geologic-scale trends in mineral 

prospectivity over the CYPA and its surroundings (fig. 2). Using a customized Python script in 

ArcGIS, each HUC was assigned a mineral prospectivity score using the criteria and treatments 

described for each deposit group below. Total scores were used to guide the classification for 
18

each HUC as having high, medium, low, or unknown potential for each deposit group. The 

relative certainty of the estimated potential for each HUC was also assigned high, medium, low, 

or unknown values by procedures described for each deposit group below. Scoring templates and 

potential versus certainty classification templates (tables 2–15) accompany the following 

sections and are unique to each mineral deposit group. Plates 1 through 12 show maps with 

results for each deposit group and annotated maps highlighting specific deposits and (or) selected 

areas with high to medium potential for each deposit group. The accompanying tables in 

appendix E present results by deposit group and include the scores for each HUC by individual 

component, the percent contribution of each scoring element to the total score, and resulting 

classification. Results for all HUCs within the analysis area (5,365 HUCs total) are shown in 

plates 1 through 12 and in appendix E. However, because this effort was ultimately focused on 

mineral potential in the CYPA proper, only the results for the 2,460 HUCs intersecting or within 

the CYPA are described and discussed in the following sections.

REE-Th-Y-Nb (-U-Zr) Deposits Associated with Peralkaline to Carbonatitic Intrusive Rocks

REE-Th-Y-Nb (-U-Zr) deposits are most commonly associated with alkaline igneous 

rocks, in particular peralkaline, syenitic, and carbonatitic intrusive rocks, and their weathering 

products (Wall, 2013; Verplanck and others, 2014). Mineralization in alkaline intrusive rocks 

can occur as primary minerals within the main intrusive phase, in late-stage orthomagmatic 

fluids, or as secondary minerals in late-stage hydrothermal deposits (Verplanck and others, 

2014). Late-stage orthomagmatic fluids form deposits in cupolas, pegmatites, veins, and dikes, 

which are included in the alkaline intrusive deposit model because of the association of these 

features with the alkaline igneous complexes. REE-Th-Y-Nb (-U-Zr) mineralization in 

hydrothermal deposits and alteration zones associated with alkaline igneous complexes (Wall, 

2013) are also included in this deposit model because of their direct association with the alkaline 

igneous complexes. There are other deposit types that contain REE- and HFSE-bearing minerals 

but in relatively minor amounts. REE-bearing minerals can also occur in placer deposits and, 

thus, might indicate a bedrock source in the drainage basin. However, for this study, these types 

of deposits were not evaluated as independent prospective REE resources. For the purposes of 

this study, the most permissive and prospective rock types for REE-Th-Y-Nb (-U-Zr) deposits 

were restricted to carbonatites, alkaline and peralkaline igneous rocks, and felsic igneous rocks 

enriched in U and Th.

Alkaline igneous rocks are enriched in Na2O, K2O, and CaO relative to SiO2 and Al2O3, 

in excess of amounts needed to form feldspars. Peralkaline igneous rocks have (Na + K)/Al 

greater than 1, and aluminum saturation index (ASI) [Al/(Ca-1.67P+Na+K)] values less than 1 

(Frost and others, 2001). For the purposes of this study, alkaline rocks include peralkaline and 

alkali granites and their volcanic equivalents (for example, rocks with alkali contents within or 

above the saturation field on the total alkali versus silica diagram of Le Bas and others [1986]). 

Alkaline igneous rocks are common in Alaska and the CYPA. Carbonatites, which are classified 

with alkaline rocks, contain more than 50 modal percent primary carbonate minerals and less 

than 20 percent silica (Le Maitre, 2002). They may also contain albite and (or) potassium 

feldspar. Carbonatites are rare, with few known occurrences in Alaska, but there are small 

occurrences in the CYPA.

Carbonatites and alkaline granites include a range of compositions, and the petrogenesis 

of these rocks is debated. They can evolve through a variety of processes, but all igneous rocks 

of this type require a mantle source that has been enriched in lithophile elements, H2O, and CO2 
19

(Bailey, 1987, 1989). A similar mantle source for alkaline rocks in general is also supported by 

the common spatial association of carbonatitic and peralkaline complexes in addition to their 

association with REE concentrations (Verplanck and others, 2014). Magma sources that are 

enriched in REE and HFSE include primary magma derived from enriched mantle and magma 

derived from partial melting of continental mantle and continental crust (Arth and others, 1989). 

Magmas derived from enriched mantle tend to be found in tectonic environments that involve 

thickening, contraction, or extension of continental crust. Processes such as protracted fractional 

crystallization of enriched mantle magmas can result in concentrations of rare earth metals and 

other incompatible elements, including Y, Nb, Zr, U, and Th, have been inferred by multiple 

workers for mineralization at Bokan Mountain in southeast Alaska (fig. 1; Thompson, 1988; 

Philpotts and others, 1998; Dostal and others, 2013). Neodymium isotopic data from peralkaline 

granite at Bokan Mountain are consistent with these incompatible elements being derived from a 

parental mantle magma (Philpotts and others, 1998).

Fractional crystallization of alkaline magmas results in the formation of late-stage phases 

rich in Na, K, SiO2, HFSE, U, Th, and REE. These elements are commonly incorporated in 

monazite, allanite, brannerite, and zircon; trace amounts are present in apatite, fluorite, hematite, 

feldspars, and micas. It is thought that fluorine, which is also concentrated in highly fractionated 

magmas, is crucial for the transport of REE and Th in late-stage melts and aqueous fluids 

because it reduces minimum melt temperatures and viscosity and increases solubility of trace 

mineral phases, extending fractionation of REE and HFSE to lower temperatures (Whalen and 

others, 1987; Černý and others, 2005). As a result, REE and Th concentrations are further

enhanced. In alkaline magmatic systems, because incompatible elements become concentrated in 

late-stage melts and aqueous fluids, REE- and HFSE-bearing minerals are often deposited in 

cupolas, greisens, alteration zones, pegmatites, veins, and dikes near the top of the intrusive 

complex. These minerals can also crystallize in hydrothermal alteration zones associated with 

pluton emplacement. 

Rare earth and HFSE are concentrated in late-stage magmatic phases because they are 

incompatible elements. The REE consist of 15 elements in the lanthanide series (La to Lu). They 

typically have small ionic radii and a 3+ valence (with notable exceptions of Ce and Eu, which 

can also be 4+ and 2+ depending on fO2). The atomic radii of REE increase systematically from 

light (LREE) to heavy (HREE), so LREE tend to be more incompatible than HREE (for 

example, Blundy and Wood, 2003). REE do not easily substitute into crystal lattices of the 

common rock-forming minerals, and, thus, they behave as incompatible elements in most 

igneous systems. As a result of their incompatibility, they are often concentrated in the last 

minerals to crystallize from magma or aqueous fluid.

The high field strength elements (HFSE), including Nb, Zr, Hf, Ti, and Ta, have similarly 

low atomic radii and high valences (4+ to 6+), so they behave similarly to REE. Lanthanide 

series REE have similar electron configurations to other group 3 elements that include transition 

metals Sc and Y, which are sometimes called rare earth metals. They have chemical and physical 

properties similar to the REE and for the purposes of this model are included with REE. 

Similarly, the actinide series elements, of which only Th and U are naturally occurring, typically 

have similar ionic radii and valences to Zr, Hf, and Ce and, thus, easily substitute for LREE and 

HFSE in accessory mineral phases. As a result, REE (that is, lanthanides plus rare earth metals), 

HFSE, and actinides (Th + U) tend to be concentrated together in igneous systems. For this 

reason, the interpretation of deposits that contain REE, Th, Y, Nb, U, and (or) Zr was focused 

into a single model. Because they contain the largest concentrations of REE and HFSE, alkaline 

intrusive-rock associations are a first-order exploration tool for REE and HFSE deposits. 
20

Alkaline magmas are also associated with magmatic gold deposits, but pluton-related gold in 

Alaska is found in unfractionated plutons that do not contain REE mineralization (Newberry and 

Solie, 1995). REE and HFSE have also been variably associated with a range of peralkaline to 

peraluminous granites (Černý and others, 2005). Peraluminous granites are characterized by high 

alumina with respect to sodium and potassium, and may contain subordinate REE. However,

increasing aluminum saturation correlates with depletion of REE, Y, Th, and U (Bea, 1996), and 

these rocks are more appropriately included in the specialized granite model below.

Carbonatitic and alkaline intrusions tend to be associated with cratons or areas of 

thickened crust affected by active deformation, including extension, transtension, and 

contraction, accompanied by some combination of processes such as metasomatism of mantle 

magma, crustal assimilation, and fractional crystallization. Carbonatitic and alkaline intrusions 

are commonly associated with continental rift settings (Verplanck and others, 2014, and 

references therein) but are not uniquely constrained to such rift settings. An example of an 

exception to this tectonic setting is the Bokan Mountain peralkaline granite, which was emplaced 

in an oceanic arc complex (Dostal and others, 2013). Alternative tectonic settings and magmatic 

and metasomatic processes that can produce carbonatitic and alkaline igneous suites have not 

been fully investigated.

There are few known REE-Th-Y-Nb (-U-Zr) deposits in Alaska at present (table 1), and 

there are no known deposits in the CYPA at this time. Most known REE-Th-Y-Nb (-U-Zr) 

prospects and occurrences in the CYPA are either placer deposits containing REE-bearing ore 

minerals (for example, Barker and others, 2009) or relatively localized features such as veins or 

greisens associated with larger igneous bodies (for example, Barker and Foley, 1986).

Mineral Resource Potential Estimation Methodology

For the REE-Th-Y-Nb (-U-Zr) analysis, HUCs were scored on the basis of the following 

criteria: (1) igneous rock geochemistry, (2) ARDF records, (3) stream-sediment chemistry for 

elements of interest (HFSE + REE + Th), and (4) aerial gamma-ray surveys. These criteria are 

briefly described below and summarized in table 2.

Igneous-Rock Geochemistry

Igneous rock geochemical data were used to identify permissive rock types for REE-ThY-Nb (-U-Zr) deposits, particularly igneous rocks with peralkaline, alkaline, or carbonatitic 

composition. These distinctive igneous rock compositions are relatively easy to identify using 

geochemical data from individual rock samples or suites of rock samples. The distribution of 

igneous rock samples across the CYPA provides more detail than geological maps can portray, 

especially considering the compositional complexity of many igneous systems and a general lack 

of detailed geologic mapping. The method of discriminating the presence of alkaline rocks 

described herein offers three advantages—(1) geochemistry provides an objective test of key 

compositional criteria for identification of alkaline rocks, (2) geochemical data are also spatially 

referenced to dikes and small satellite bodies that can be associated with REE mineralization but 

are not typically captured in mapped units, and (3) rock-sample data correspond to discrete 

locations within a HUC, which provides greater geospatial precision for rock compositions than 

is provided by granite map units that typically span several HUCs. For these reasons, igneous 

rock geochemistry was favored over using geological map units alone to better identify the 

specific compositions of interest across the area.
21

The geochemical criteria selected to identify igneous rocks that are favorable for REETh-Y-Nb (-U-Zr) deposits are (1) MALIdisplacement and Fe#displacement (described above), (2) Ga/Al, 

and (3) Nb/Y. As discussed in detail above, positive values for Fe#displacement and (or)

MALIdisplacement are typical of rocks more likely to be associated with REE-Th-Y-Nb (-U-Zr) 

deposits (that is, A-type, intraplate peralkaline to alkaline rocks). However, caution must be used 

when deciding which rocks to include in scoring. Few, if any, exploitable REE deposits are 

known to be associated with mafic rocks (for example, Linnen and others, 2014). In addition, in 

scoring MALIdisplacement it is crucial to avoid mafic rocks, especially those that might be 

cumulates because, for example, olivine or biotite will increase MALI, while decreasing SiO2; in 

this case, a high value of MALIdisplacement is not characteristic of the equilibrium liquid 

composition. Therefore, the MALIdisplacement calculation was only applied to rocks with SiO2>56 

weight percent (or >200 ppm Ni); more mafic rocks were excluded in scoring this parameter.

Similarly, use of the Ga/Al ratio to classify A-type granites (Whalen and others, 1987)

has only been calibrated for granitic (intermediate to felsic) compositions. The Ga/Al ratio is a 

proxy monitor of fluorine contents because of the higher solubility of GaF6 relative to AlF6. 

However, for fluorine content to be an effective index of a potential REE deposits, highly 

incompatible element concentrations must be high enough to combine with fluorine to form 

accessory mineral phases in which REE and HFSE are compatible. Thus, late-stage, 

differentiated magmas are preferred when using the Ga/Al ratio. For this reason, scoring of 

Ga/Al was restricted to rocks with SiO2>60 weight percent.

On the other hand, Fe#displacement and Nb/Y are important proxies for broader patterns of 

differentiation and tectonic setting for a range of igneous rocks. For example, Frost and others 

(2001) and Frost and Frost (2008) demonstrated that plutonic and volcanic alkaline rocks with 

SiO2>48 weight percent are generally ferroan (that is, Fe#displacement>0). A similarly broad range 

of compositions are appropriate for interpretation using Nb/Y ratios. The distinction of alkaline 

and subalkaline rock types associated with different tectonic settings using this ratio is well 

established (Winchester and Floyd, 1977; Pearce and others, 1984; Eby, 1990; Pearce, 1996). In 

both cases, even primitive mafic rock compositions were included for scoring. Although

Fe#displacement and Nb/Y do not directly indicate the possible occurrence of REE-enriched rocks, 

they can be used to identify the tectonic environments such as within-plate settings where REErich rocks are expected to occur. Therefore, all igneous rocks were scored for Fe# and Nb/Y 

indices.

Databases of element ratios from igneous geochemistry in Alaska generally have poor 

coverage in some areas because of limited sample collection and inconsistent igneous rock 

exposure. However, some HUCs contain multiple igneous rock sample analyses for reasons such 

as high interest, good outcrop, and (or) relative ease of access. To minimize possible effects of

inconsistent sample density on HUC scoring results, only a single point was assigned for each 

geochemical index if one sample within the HUC had a value exceeding the cutoffs listed in 

table 2. Thus, the maximum possible igneous rock score contributing to the composite HUC 

score is 4.

Alaska Resource Data File

Records in the ARDF were scored for their relevance to REE-Th-Y-Nb (-U-Zr) potential 

on the basis of keywords used in various categories of the ARDF record (see appendix C). 

Examples of keywords for the REE-Th-Y-Nb (-U-Zr) deposit group include “carbonatites,” 

“alkaline rock associated,” “hydrothermal,” “radioactive,” “replacement,” and “uraniferous.” 
22

Keywords scored for alteration include terms such as “carbonatization,” “dolomitization,” 

“albitization,” “metasomatism,” “potassic,” “Fe-carbonate,” and “pyroxene-fluorite.” Keywords 

for commodities include individual elements (La, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, 

Tm, Yb, Lu, Sc, U, Th, Y, Zr, and Nb), as well as “REE” and “HFSE.” Keywords were also 

ranked such that REE ore or gangue minerals such as xenotime, bastnaesite, and synchysite 

received 3 points, whereas less economic minerals such as monazite received 2 points. Other 

relevant minerals such as zircon and fluorite received 1 point. The cumulative keyword hits 

contributed to a net ARDF keyword score, and ARDF keyword scores higher than 2 appeared to 

capture any occurrences in a HUC with potential for a REE-Th-Y-Nb (-U-Zr) deposit. Many 

ARDF localities with scores greater than 2 are placer occurrences of REE-, HFSE-, or U-Thbearing minerals, which were considered to be important in the CYPA because bedrock is 

generally poorly exposed and placer occurrences suggest a bedrock source nearby. An ARDF 

keyword score higher than 5 corresponded to prospects and occurrences with keyword hits for 

multiple categories in an ARDF record and was considered to be most favorable for REE-Th-YNb (-U-Zr) deposit potential. By this reasoning, HUCs containing ARDF localities with scores of 

2 to 4 were assigned 1 point, and HUCs containing localities with scores of 5 or higher were 

assigned 2 points (table 2). Multiple ARDF localities in a HUC were not considered to 

significantly add to the REE deposit potential, so the maximum possible ARDF score for any 

given HUC was 2. This score cap was also chosen to limit the potential impact of ARDF records 

with long, detailed descriptions that are simply artifacts of the ARDF recording process. In some 

cases, more verbose records received higher keyword scores that did not necessarily indicate 

higher potential for a REE-Th-Y-Nb (-U-Zr) deposit. 

Stream-Sediment Geochemistry

Stream-sediment sampling and geochemical analysis for the CYPA is more 

comprehensive, more evenly spaced, and higher in density than any other database available.

Bedrock is obscured in many areas by unconsolidated sediment and vegetation, and thus, streamsediment data can provide important clues about the composition of obscured bedrock. Streamsediment geochemical data show patterns of element concentration that reflect rock types in their 

respective drainage basins and provide clues to the composition of rocks in areas of poor 

exposure and limited geologic mapping. In general, high values for REE and other HFSE in 

stream sediments in the CYPA closely follow mapped belts of igneous rocks that contain 

alkaline phases and also coincide with known occurrences and prospects that contain these 

elements. This agreement provides some measure of confidence that high geochemical values for 

target elements in stream sediments in areas of poor exposure have high potential for indicating 

the presence of permissive rock types. In addition, enriched values for multiple elements of 

interest reinforce the potential for as-yet-undiscovered concentrations of these elements in a 

given HUC. 

The most comprehensive coverage for LREE in the CYPA area is best represented by the 

elements lanthanum (La) and cerium (Ce). The element ytterbium (Yb) has the most extensive 

regional coverage of the HREE in the database in the CYPA, and the distribution of Th in stream 

sediments is quite similar to that of the HREE in general. Thorium is an important trace element 

and that is commonly associated with REE (Bea, 1996). Because it is radioactive, it is also a very 

useful exploration tool. Niobium (Nb) also has strong affinities with REE. Geochemical data for 

Ce (representing LREE), Yb (representing HREE), Nb (representing HFSE), and Th in streamsediment samples were used as proxies in scoring HUCs for REE-Th-Y-Nb (-U-Zr) potential. 
23

The 91st and 98th percentile concentration values for each element (listed in appendix A) were 

used as scoring value cutoffs. If a HUC contained samples with concentrations of Ce, Yb, Nb, 

and (or) Th above the 98th percentile value, a score of 2 was added for each element. If a HUC 

contained samples with concentrations of Ce, Yb, Nb, and (or) Th between the 91st and 98th 

percentile values, a score of 1 was added for each element. Each of the four elements considered 

here could only contribute once to a HUC’s score. Thus, the maximum for any HUC on the basis 

of stream-sediment chemistry was 8.

Aerial Gamma-Ray Survey Data

Aerial gamma-ray survey data do not cover all of Alaska, but the coverage is relatively 

complete in the CYPA. The aerial survey data are gridded in 5 km cells. Areas with high Th/K 

ratios characterize Sn, W, REE, and rare-metal deposits (Portnov, 1987), and a Th/K ratio >5 

indicates Th and HFSE concentrations in igneous rock or sediment that substantially exceed 

background levels in Alaska (Saltus and others, 1999). Scintillometer readings are commonly 

used to identify outcrop sources of Th (and REE by proxy, because of the affinity between Th 

and REE) concentrations in minerals, veins, seams, and alteration zones, and provide groundbased data to determine sources of radioactivity in areas that stand out in airborne radiometric 

surveys. Thus, radiometric surveys provide a remote-sensing proxy for identifying areas possibly 

underlain by Th-bearing (and REE-bearing) rocks. Th/K ratios above 12 represent the highest 1

percent of rocks in the CYPA. Accordingly, HUCS with Th/K ratios above 5 were given a score 

of 1, and those with Th/K ratios above 12 were given a score of 2 (table 2).

Results and Discussion

The total scores for HUCs in the CYPA were classified using natural statistical breaks 

(Jenks method) into three categories corresponding to high, medium, and low potential for REE 

mineral deposits (table 3), The respective score range for each category was 16–6, 5–3, and 2–0. 

There are 18 HUCs with a null value (indicating lack of data) for all 4 datasets in the CYPA and 

the buffer zone, and these were assigned a potential of unknown. Some measure of certainty was 

also assigned to our estimation on the basis of how many datasets contributed to the mineral 

potential score (table 2). A HUC assigned high certainty received scores from all four 

contributing datasets. There are 211 HUCs, approximately 9 percent of the 2,460 HUCs in the 

CYPA, that were ranked as having high potential to contain REE-Th-Y-Nb-(U-Zr) 

mineralization associated with alkaline igneous rocks in the area. High certainty was assigned to 

48 of the 211 high-potential HUCs on based on contributions from 4 datasets, 157 have medium 

certainty based on contributions from 2–3 datasets, and 6 have low certainty based on 

contributions from only 1 dataset (table 3; plate 1). There are 453 HUCs that have medium 

potential for REE-Th-Y-Nb-(U-Zr) mineralization, representing 18 percent of the HUCs. Of 

these, 51 have high certainty, 372 have medium certainty, and 30 have low certainty (table 3, 

plate 1). The remaining 1,780 HUCs have low potential for REE-Th-Y-Nb-(U-Zr) mineralization

on the basis of our scoring method. Of these, 381 have high certainty, 1,325 have medium 

certainty, and 741 have low certainty (table 3; plate 1).

The areas that stand out as having high to medium potential for REE-Th-Y-Nb-(U-Zr) 

mineralization in the CYPA include the Hogatza igneous belt—especially the Zane Hills and 

Indian Mountain—the Ruby batholith, the Sischu-Prindle igneous belt, parts of the central 

Brooks Range, parts of the northern Alaska Range, and parts of the Yukon Tanana Upland. 
24

These areas are all highlighted in plate 2. Many of these areas contain known or suspected REETh-Y-Nb-(U-Zr) mineral prospects and (or) occurrences. The Hogatza igneous belt contains 

numerous middle to Late Cretaceous alkaline intrusive and extrusive rocks that cross into the 

western part of the CYPA. The Hogatza belt includes syenite, nepheline syenite, lamprophyre, 

monzonite, and granite that variably contain disseminated fluorite, goethite, xenotime, zircon 

with radioactive haloes, and radioactive minerals. Many of the igneous rocks are cut by 

contemporaneous shear zones that contain quartz, xenotime, and radioactive minerals (Barker, 

1985). In the Zane Hills, monzonite, syenite, bostonite stocks, dikes, and associated polymetallic 

quartz veins contain allanite, betafite, fluorite, and tourmaline (plate 2; Miller and Ferrians, 

1968; Miller and others, 2002). These rocks also contain known U-Th-Nb-Y mineralization and 

are overlain by sedimentary rocks that also contain U-bearing minerals presumably sourced from 

these alkaline intrusive rocks (Miller and Ferrians, 1968). Stream-sediment geochemical samples 

throughout this belt have high values for LREE, HREE, Th, and U, and placer deposits in the 

Hogatza River area (plate 2) contain gold, uranothorite, and titanite; pan concentrates have high 

values for Au, REE, U, Th, and PGE (Miller and Ferrians, 1968).

The Ruby batholith, extending from the southeastern Brooks Range to the Kaiyuh 

Mountains (plate 2), contains a mix of HUCs with high and medium potential. The Ruby 

batholith contains late Early Cretaceous calc-alkaline, alkaline, and peraluminous intrusive rocks 

with localized HFSE and Sn mineralization. Multiple placer occurrences throughout and around 

the Ruby batholith exposure area contain REE- and (or) HFSE-bearing minerals such as 

monazite and uranothorite. Although these placer deposits could have a variety of sources 

including granitic rocks in the Brooks Range to the north, their proximity to exposures of the 

Ruby batholith raise the possibility of local sources. Individual plutons within the Ruby batholith 

have associated Sn-bearing veins that also have high values for HSFE including Rb, Ta, and W.

The Sithylemenkat pluton has associated tin greisen deposits as thick as 3 m and as long as 400 

m that contain a large assortment of minerals, including cassiterite, tourmaline, magnetite, 

hematite, ilmenite, garnet, wolframite, monazite, and molybdenite. The greisens also yield high 

values for Sn, Nb, Ta, W, Cs and variable amounts of REE, Th, and U (Barker and Foley, 1986). 

The Ray Mountains pluton is cut by polymetallic veins that contain hematite, Ag, Pb, Zn, Bi, La, 

Mo, Sn, U, and W, and adjacent gravel deposits contain monazite and xenotime inferred to be 

derived from the pluton. In the southwestern part of the Ruby batholith, porphyritic granite of the 

Melozitna pluton contains fluorite, monazite, tourmaline, pyrite, and molybdenite, and Th-, and 

U-bearing veins cut coarse-grained biotite quartz monzonite (Solie and others, 1993). 

In a belt southeast of the Ruby batholith, alkaline intrusive rocks of Late Cretaceous to 

Paleocene age include the granite at Mount Prindle (plate 2) and associated tin greisens, Snpolymetallic veins, Pb-, Zn-, Mn-, and U-bearing epithermal veins. The Late Cretaceous Roy 

Creek syenite contains U-Th-REE mineralization (plate 2; Burton, 1981; Armbrustmacher, 

1989). In the Sischu Mountain area (plate 2), early Tertiary volcanic rocks consistently yield 

high values for U, Th, and REE (Miller and others, 1980). The location, age, composition, and 

mineralization of these rocks suggests they may be on trend and geologically related to igneous 

rocks in the Mount Prindle area, and this area is herein referred to as the Sischu-Prindle igneous 

belt (plate 2). Carbonatite sills containing aeschynite, apatite, monazite, hematite and magnetite 

(Warner and others, 1986; Reifenstuhl and others, 1998) also outcrop to the northwest of this 

area, and associated placer deposits in the Hot Springs district (plate 4; Nokleberg and others, 

1987) contain gold, cassiterite, monazite, aeschynite, apatite, ilmenite, magnetite, euxenite, and 

xenotime. The mineralogical diversity of the placer deposits reflects the variety of sources in this 

zone, shown as the Tofty belt in plate 2.
25

One intriguing result of this study is the distribution of HUCs with medium potential and 

low certainty in parts of the CYPA. A number of these HUCs occur in the western YukonKoyukuk Basin (plate 2) and the southeastern corner of the CYPA. These areas are not 

associated with known or suspected REE-Th-Y-Nb-(U-Zr) mineralization, and the low certainty 

indicates that the assignment of medium potential is based on relatively limited data. These areas 

warrant further study and more detailed sampling to better quantify the mineral potential and 

assess the relative certainty of the mineral potential in these areas.

Placer and Paleoplacer Au

Placer deposits are concentrations of high-density minerals formed by gravity separation 

during sedimentary processes. Heavy minerals are separated from their primary host rocks by 

weathering and erosion and then transported and concentrated in surficial deposits. Placer 

deposits exhibit a wide range of textures, form in many different environments, and host a 

variety of minerals, including valuable resources for gold, titanium, tin, platinum group 

elements, REE, and iron (Slingerland and Smith, 1986; Garnett and Bassett, 2005; Van Gosen 

and others, 2014). Gold in placer deposits was initially derived from bedrock containing goldquartz veins, disseminated gold, or other types of gold-bearing mineral deposits such as porphyry 

copper, Cu skarn, and polymetallic replacement deposits (Yeend, 1986). Primary gold-bearing 

deposits typically occur in igneous (Baker, 2002; Seedorff and others, 2005; Hart, 2007; Sinclair, 

2007; Taylor, 2007) or metamorphic rocks (Berger and Henley, 1989; Groves and others, 1998; 

Goldfarb and others, 2005), although sedimentary deposits such as paleoplacers and modified 

paleoplacers (Pretorius, 1981; Minter, 2006) also occur in some places.

Alluvial placer deposits form in rivers and streams and represent the initial concentration 

of heavy minerals relative to source rocks within a drainage network. Heavy mineral deposition 

and concentration occurs where gradients flatten and (or) transport velocities decrease, such as at 

the inside of meanders, below rapids and falls, beneath boulders, and in vegetation mats (Yeend, 

1986). Placer gold deposits are typically found in alluvial gravel and conglomerate. Gold grains 

and, more rarely, nuggets are most concentrated at the base of gravel deposits where natural traps 

such as riffles, fractures, bedding planes, or other features are oriented transverse to the water 

flow. Additional concentration can take place as sediment moves downstream and as older 

alluvial deposits are reworked by younger systems. The latter takes place in geomorphically 

stable areas where erosion has proceeded for a long time and multiple generations of sediment 

and (or) sedimentary rock are recycled into potentially multiple stages of terrace and streambed 

gravels (Yeend, 1986).

Coastal placer and paleoplacer deposits, or beach placers, form in a range of coastal 

sedimentary environments that are typically dominated by eolian, wave, and tidal processes 

(Hamilton, 1995; Van Gosen and others, 2014). Heavy minerals in the coastal environment are 

derived from sources that could include deeply weathered local bedrock exposures, sediment 

deposited at river mouths, or offshore sand deposits scoured from the seafloor during storm 

events. Along wave-dominated coastlines, heavy mineral enrichment occurs in the foreshore and 

uppermost part of the shoreface environment where sediment is repeatedly reworked by wind, 

waves, and wave-induced currents. Strong onshore winds winnow shoreface deposits at low tide 

and ultimately transport sediment inland from the beach environment (Roy, 1999). Longshore 

transport currents, or littoral drift, move sand along the coast in the direction of prevailing winds,

and headlands along drift-aligned coasts trap heavy minerals on the up-drift side of embayments. 

Most large coastal placer deposits formed along passive tectonic margins with long histories of
26

erosion and repeated cycles of sea-level change (Force, 1991). Coastal placer deposits formed 

along convergent and transform tectonic margins are found worldwide but are generally much 

smaller in size and are dominated by sediment derived from relatively local sources.

The northern part of the CYPA contains approximately 200 km of coastline (fig. 1), but 

most of the area is inland, and therefore, alluvial placer deposits were the primary focus of this 

study. The CYPA contains all or part of 18 known placer gold districts; the Hot Springs district, 

a placer-gold and placer-tin district in the southeastern part of the Tanana quadrangle, is the only 

one that recovers another commodity in addition to gold (plate 4; Nokleberg and others, 1987). 

Although placer deposits have the potential to host a variety of economically important minerals 

and PGE, and although Sn and Ti placer deposits are known around the State (Nokleberg and 

others, 1987), gold is the only commodity considered herein because it is the primary commodity 

produced in placer mines throughout Alaska at this time (Athey and others, 2013).

Mineral Resource Potential Estimation Methodology

For placer and paleoplacer Au potential, HUCs were scored on the basis of the following 

criteria: (1) ARDF records, (2) HMC mineralogy, (3) stream-sediment geochemistry, (4) 

lithology, and (5) downstream potential. These criteria are described and discussed below and 

summarized in table 3. 

ARDF

An HUC containing one or more ARDF records that scored positive for a placer or 

paleoplacer occurrence, prospect, or mine (that is, score of nonzero on basis of the word-hit 

method described above) was given a score of 10 and automatically assigned high potential. The 

maximum possible score for each HUC on the basis of ARDF records was 10 (table 4).

Heavy Mineral Concentrate (HMC) Mineralogy 

An HUC containing one or more HMC samples in which visible gold was present or 

inferred (that is, value not null) was given a score of 10 and automatically assigned high

potential. An HUC containing one or more samples with an HMC report indicating the presence 

of cassiterite, powellite, scheelite, cinnabar monazite, and (or) thorite (that is, value not null) was 

given a score of 1 for each of these minerals present. The maximum possible score for each HUC 

on the basis of HMC mineralogy was 10 or the total number of other non-Au minerals present 

(table 4). 

Stream-Sediment Geochemistry

An HUC containing one or more stream-sediment samples with Au and (or) Ag values in 

the 75th percentile or higher (≥0.007 ppm for Au, ≥0.16 ppm for Ag) was assigned a score of 3 

for each element. An HUC containing one or more stream-sediment samples with Ti and (or) W 

values in the 75th percentile or higher (≥0.57 ppm for Ti, ≥2 ppm for W) was given a score of 1 

for each element. The maximum possible score for each HUC on the basis of stream-sediment

chemistry was 8 (table 4).
27

Lithology

A generalized lithology layer of mapped igneous-rock units across the State was derived 

from an anticipated geologic map of Alaska (Wilson, Frederic H., and others, USGS, written 

commun.) and used to identify areas with permissive lithologies. An HUC was given a score of 3 

if it contained plutonic igneous-rock units, a score of 2 if it contained hypabyssal igneous-rock 

units, or a score of 1 if it contained volcanic or meta-igneous-rock units. For an HUC that 

contained multiple types of igneous-rock units, the highest single unit score was assigned to the 

entire HUC. Thus, the maximum possible score for each HUC on the basis of lithology was 3 

(table 4). A generalized lithology layer of coarse-grained sedimentary rocks and surficial 

deposits was also initially used to contribute to the HUC scores. However, a general lack of 

detailed surficial mapping and challenges in delineating the most prospective coarse-grained 

sedimentary rocks made it difficult to confidently identify the most suitable surficial and 

sedimentary units for placer and paleoplacer deposits. Therefore, a weighting for sedimentary 

rocks and surficial deposits was not assigned, which might limit the ability to identify 

paleoplacer deposits by this method.

Results and Discussion

Initial total scores ranged from 0 to 32. Table 5 shows how the first four scoring 

components were used to assign values for potential to each HUC. HUCs with ARDF or 

mineralogy scores of 10 were automatically assigned high potential, as the presence of a known 

placer occurrence, prospect, or mine and (or) the presence of visible gold in one or more HMC 

samples was interpreted to be the most reliable predictors of placer or paleoplacer Au potential 

among the available datasets. For HUCs that did not meet either of these criteria, a total score of 

16 or greater was also assigned high potential (table 5). This cutoff value was chosen because 

scores of 16 or greater represented maximum possible scores for at least three of the remaining 

scoring components. HUCs with scores of 6–15 and 0–5 were assigned medium and low

potential, respectively. The cutoff value of 6 for medium potential was chosen because scores of 

6 or greater generally represented a maximum possible score from at least one of the scoring 

elements. HUCs with a score of zero that did not have stream-sediment geochemistry data were 

assigned unknown potential.

Following the preliminary scoring and classification using the four scoring components 

described above, a fifth scoring component was added to the analysis to account for the 

possibility of placer occurrences downstream from HUCs with high placer or paleoplacer Au 

potential (table 4). In ArcGIS, all river and stream segments downstream of each high-potential 

HUC were selected from NHD and National Atlas datasets. Each HUC containing one or more 

of these downstream segments was given a score of 6 if it was within 25 km of the upstream

high-potential HUC. HUCs that contain one or more downstream segments that are more than 25 

km from the upstream high-potential HUC were given a score of 3. HUCs that contain one or 

more downstream segments, were more than 25 km from the upstream high-potential HUC, and 

were downstream of an intersection with an equal- or higher-order river segment were given a 

score of 1. The downstream scoring method excluded HUCs with high potential, and so only 

total scores for HUCs with medium, low, or unknown potential after the initial scoring process 

were eligible to change during the subsequent analysis.

The criteria by which the certainty of estimated resource potential was assigned for each 

HUC are outlined in table 5. Certainty was largely assigned depending on the number of datasets 
28

that contributed to the total score (table 5). The presence of a known placer or paleoplacer 

occurrence, prospect, or mine, and (or) the presence of visible gold in one or more HMC samples 

provide a relatively high degree of certainty that placer or paleoplacer Au potential is high in a 

particular HUC. Therefore, high certainty was assigned to HUCs containing ARDF and (or)

mineralogy scores of 10 (table 5). An HUC was also assigned high certainty if four or more 

datasets contributed to the total score. An HUC with medium certainty had two or three datasets 

that contributed to the total score. An HUC with unknown potential (that is, a zero score and no 

stream-sediment geochemistry data of any kind) was automatically assigned a certainty of 

unknown.

The results of the scoring analysis for placer and paleoplacer Au are shown in plate 3, 

and accompanying data are presented in appendix E. After adding in the downstream scoring 

elements, our analysis indicates that 206 HUCs, or 8.2 percent of the 2,460 in the CYPA, have 

high estimated potential for placer or paleoplacer Au deposits. Of these, all but 3 are also 

estimated to have high certainty. 511 HUCs (20.8 percent of total) have medium estimated 

potential, with most (71.4 percent of medium) having medium certainty. Most of the HUCs in 

the CYPA (1,740, 71 percent of total) have low estimated potential for placer or paleoplacer Au, 

and all within this category have either high (36.3 percent of low potential) or medium (63.7 

percent of low potential) certainty. 480 HUCs have a total score of 0, but only 3 of these (0.1 

percent of total) are classified as having unknown potential and certainty because they contain no 

stream-sediment data.

Throughout the CYPA, areas with high placer Au potential are generally associated with 

known placer districts (plate 4) as outlined in Nokleberg and others (1987). Regions with the 

highest concentration of high-scoring HUCs are in the Wiseman and Chandalar quadrangles in 

the north-central CYPA, along the Yukon and Koyukuk Rivers (fig. 1; plate 4) in the southern 

CYPA, and in the southern Fairbanks and northern Healy quadrangles in the southeastern CYPA 

(plate 4). In these and other areas, there are HUCs adjacent to the placer districts that also have 

high potential, suggesting that ancillary drainages feeding into areas with past and (or) present 

placer mining activity might also be prospective.

There are a few areas that stand out as having high to medium potential but are not 

associated with known placer districts. An east-west-trending, discontinuous belt of mediumpotential HUCs in the Howard Pass, Killik River, and Chandler Lake quadrangles are on the 

northern side of the Brooks Range (plate 4). There is a cluster of high- and medium-potential 

HUCs in the southern Bettles and northern Tanana quadrangles; these HUCs appear to be 

associated with the outcrop trend of the Ruby batholith, a belt of felsic plutonic rocks that is also 

prospective for REE and Sn-W-Mo deposits (see plates 2 and 12 for general location and 

discussions above and below). Two other areas with high and medium potential but not 

associated with known placer districts are in the central and southern Nulato and eastern Norton 

Bay quadrangles in the southwestern part of the CYPA. In the Nulato quadrangle, the highpotential HUCs contain known placer prospects in the vicinity of a Cretaceous granitic pluton 

and multiple Au-Ag-Pb-Zn prospects and occurrences in the Kaiyuh Mountains (plate 4; Cobb, 

1972; Patton and Moll-Stalcup, 2000). In the eastern Norton Bay quadrangle, the high-potential 

HUC contains an ARDF locality describing gold reports but does not list an exact location 

(ARDF record NR011; Dashevsky, 2002). The drainage is primarily underlain by Cretaceous 

sedimentary rocks of the western Yukon-Koyukuk Basin (plate 4), and there are no mapped 

igneous units in the vicinity (Patton and others, 2009). However, there are multiple HUCs in the 

same region with medium potential for other deposit types (for example, REE deposits; see plate 
29

2) and other HUCs with no data (plate 4). Thus, the western Yukon-Koyukuk Basin in the 

southwestern part of the CYPA might be a suitable target for more detailed sampling and study.

PGE (-Co-Cr-Ni-Ti-V) Deposits Associated with Mafic-to-Ultramafic Intrusive Rocks 

Deposit Group Characteristics

The platinum-group elements (PGE) consist of six metallic elements comprising Ru, Rh, 

Pd, Os, Ir, and Pt. They share similar physical and chemical properties and therefore tend to 

occur together in mineral deposits (Harris and Cabri, 1991). The PGE are split into the iridium

subgroup (Os, Ir, Ru) and the palladium subgroup (Rh, Pt, Pd), because these two subgroups tend 

to behave coherently during magmatic processes (Rollinson, 1993). PGE are variably to strongly 

associated with Co, Cr, Cu, Ni, Ti, and V in deposits associated with mafic-ultramafic (MUM) 

rocks and with many other elements in non-MUM-related PGE deposits.

PGE are found in many geologic settings and are hosted by igneous and sedimentary 

rocks; PGE ore deposits form from a variety of physical and chemical processes. For this study, 

only PGE ore deposit models genetically associated with MUM rocks were considered. 

Characteristics of MUM-rock-related deposit models are summarized by Naldrett (2004), and the 

specific deposit types that factored into our assessment are listed in tables 1 and 6. There are 

other types of ore deposits known in Alaska that have associated PGE enrichments or deposits, 

and there are also other types of PGE deposit types that are not presently known or are suspected 

to occur in Alaska (table 6). The scoring criteria outlined below were only optimized for deposits 

associated with MUM rocks, but some of the more general PGE indicators, such as geochemical 

data, might reveal potential for a wider variety of deposit types.

Mineral Resource Potential Estimation Methodology

The datasets described below were scored for parameters that identify potential for the 

occurrence of PGE (Co, Cr, Cu, Ni, Ti, V; see table 6). Datasets were ranked according to their 

suitability for predicting potential for these elements. For this deposit group, HUCs were scored 

on the basis of the following criteria: (1) presence of MUM rocks, (2) presence of PGE mineral 

occurrences, prospects, or mines, (3) PGE- or PGE-indicator-minerals in placers and heavymineral-concentrate mineral reports, and (4) geochemical values for Pt, Pd, Os, Ir, Rh, Ru, Co, 

Cr, Cu, Ni, Ti, and (or) V in (a) heavy mineral concentrate samples, (b) stream-sediment

samples, and (c) rock samples. These criteria are described below and summarized in table 6. 

Although copper is often associated with various PGE models, it also occurs with numerous nonPGE-bearing deposit types. Consequently, copper was excluded from the PGE scoring layers to 

avoid an overwhelming number of non-PGE-related anomalies.

Lithology

A generalized lithology layer that included all mapped mafic and ultramafic igneous-rock

units statewide was used to identify areas of permissive rock types, particularly MUM rocks 

(Wilson, Frederic H., and others, USGS, written commun.). An HUC received a score of 2 points 

if it contains mafic or ultramafic igneous rocks as major components of the geologic map units, 

and a score of 1 point if it contains mafic or ultramafic igneous rocks as minor or incidental 

components (table 6). For an HUC that contains multiple types or locations of MUM igneous 
30

rocks, the highest score is assigned to the entire HUC. Thus, the maximum possible score for 

each HUC on the basis of lithology is 2 (table 7).

ARDF

ARDF records for placer occurrences, prospects, and mines were removed from the 

ARDF layer and incorporated into the Heavy Mineral Concentrate mineralogy section (see 

below). The remaining lode-related ARDF records were scored for PGE favorability on the basis 

of the word-hit method described above; keywords included MUM lithologies, associated 

elements (Pt, Pd, Ir, Os, Rh, Ru, Co, Cr, Ni, Ti, and V), and PGE-bearing minerals (for example, 

sperrylite, native platinum—for complete list, see appendix C). For lode mineral sites, each 

highly ranked ARDF record was reviewed after scoring, and occurrences that clearly had no 

relation to one of the PGE models discussed above were excluded from the list. The remaining 

list was further classified into ARDF records with (1) PGE reported as major or minor 

commodities (3 points), (2) chromite and favorable geology (2 points), and (3) permissible 

geology but no direct evidence for PGE (1 point). The maximum possible score for each HUC on 

the basis of lode ARDF records was 3 points (table 7).

Heavy Mineral Concentrate (HMC) Mineralogy

For this study, PGE or PGE-indicator minerals in placers in ARDF records were 

considered equivalent to the same elements in HMC mineralogy records. After applying the 

word-hit method (described above), the highly ranked ARDF placer list was subdivided into 

records with: (1) PGE reported as major or minor elements (3 points), (2) chromite or other 

PGE-related minerals, or questioned PGE identifications (2 points), and (3) mineralogy for 

MUM rocks in a drainage (for example, jade, serpentine-group minerals) but no direct evidence 

for PGE (1 point; see table 6). An HUC containing one or more samples with an HMC 

mineralogy report of chromite, Cu-Co sulfides, Ni-Co sulfides, Ni sulfides, or Cr-Ni silicates 

received a score of 2 points. A HUC containing one or more samples with an HMC mineralogy 

report of serpentine or Cr diopside received a score of 1 point. The maximum possible score for 

each HUC on the basis of ARDF placer records and HMC mineralogy was 3 points (table 7).

Approach for Geochemical Datasets

HMC geochemistry for drainages, stream-sediment geochemistry, and rock geochemistry 

datasets are also used to assign scores to HUCs. Where possible, the natural breaks method was 

used to assign scores of 3 points to the ≥91st percentile break for PGE. Similarly, where 

possible, the natural breaks method was used to assign scores of 2 points to the ≥98th percentile, 

and 1 point to the ≥91st to <98th percentile breaks for Co, Cr, Ni, Ti, and (or) V.

The spatial distribution of samples assigned to these percentile categories for each 

element was examined in ArcGIS to check for spatial artifacts. The geochemical datasets contain 

qualitative emission spectroscopic analyses in addition to analyses run by quantitative analytical 

methods. The spatial distribution of percentile values determined by natural breaks commonly 

contains artifacts that result from the “qualitative step levels” for elemental values in emission 

spectroscopic data. Where artifacts were present, scoring was manually assigned by examining 

the histogram of values for each element, and visually selecting a reasonable number of 

anomalous (1 point) and highly anomalous (2 points) values. Additionally, highly anomalous 

values were initially checked for possible spatial correlation with known PGE occurrences. If the 
31

correlation was high, cutoff values (in ppm) were adjusted downward to allow for a geologically 

reasonable number of anomalous samples not necessarily associated with known occurrences. 

Documentation of how points are assigned to each element in the three geochemical datasets is 

presented in table 7 and discussed by dataset type below.

Heavy Mineral Concentrate (HMC) Geochemistry—An HUC containing one or more HMC 

geochemical samples with Pt, Pd, Os, and Ir values >0 ppm was given a score of 3 points. No Rh 

or Ru values were present in the dataset. An HUC containing one or more HMC geochemical 

samples with TiO2 values greater than 5 weight percent was given a score of 1 point. For Co, Cr, 

Ni, and V, scoring was manually assigned by examining their histogram of values and visually 

selecting a reasonable number of anomalous (1 point) and highly anomalous (2 points) values for 

each element. The maximum possible score for each HUC on the basis of HMC geochemistry is 

3 points (table 7).

Stream-Sediment Geochemistry—An HUC containing one or more stream-sediment geochemical 

samples with Pt, Pd, Os values in or above the 91st percentile was given a score of 3 points. No 

Ir, Rh, or Ru values were present in the dataset. For Co, the natural breaks method was used to 

assign scores of 2 points to the 98th percentile, and 1 point to the 91st percentile values. For Cr, 

Ni, Ti, and V, scoring was manually assigned by examining their histogram of values and 

visually selecting a reasonable number of anomalous (1 point) and highly anomalous (2 points) 

values for each element. The maximum possible score for each HUC on the basis of streamsediment chemistry was 3 points (table 7).

Rock Geochemistry—For rock geochemistry, elements in the USGS and ADGGS datasets were 

scored separately. The ADGGS database primarily contains samples collected for evaluating 

mineral potential. The USGS database contains samples analyzed for background rockgeochemical values, as well as samples collected for evaluating mineral potential. As a result, the 

natural breaks for each element are significantly different in the two datasets. Past USGS 

geochemical programs collected hundreds of samples to determine low-level background values 

for igneous rocks, thus resulting in generally lower mean, median, and natural-break values. For 

the USGS dataset, an HUC containing one or more rock geochemical samples with Pt >0.004 

ppm or Pd>0.005 ppm was given a score of 3 points. For the ADGGS dataset, an HUC 

containing one or more rock geochemical samples with Pt >0 ppm or Pd >0 ppm was given a 

score of 3 points. For both the USGS and ADGGS datasets, an HUC containing one or more 

rock geochemical samples with Ir, Rh, or Ru >0 ppm is given a score of 3 points. No values for 

Os are present in either dataset. For Co, Cr, Ni, Ti, and V, scoring was manually assigned by 

examining their histogram of values and visually selecting a reasonable number of anomalous (1 

point) and highly anomalous (2 points) values for each element. The maximum possible score for 

each HUC on the basis of stream-sediment chemistry is 3 points (table 7).

Results and Discussion

Table 8 outlines how PGE (-Co-Cr-Ni-Ti-V) resource potential and certainty were 

assigned for each HUC. Total scores ranged from 0 to 11. Plate 5 shows the results of the scoring 

analysis, and accompanying data are presented in appendix E. Of the 2,460 HUCs in the CYPA, 

there are 127 HUCs (5 percent of total) with high estimated potential for PGE (plate 5, appendix 

E). Of these, 52 HUCs (40.9 percent of high potential) are also estimated to have high certainty. 

1,250 HUCs (51 percent of total) have medium estimated potential, with most (82 percent of 

medium) having low certainty. 44 percent of the HUCs in the CYPA (1,068 total) have low

estimated potential for PGE, although most within this category have either high (62.4 percent of 
32

low) or low (37.6 percent of low) certainty. There are 673 HUCs that have a total score of zero, 

but only a small number of these (7, 1 percent of total) were assigned unknown potential and 

certainty.

Future geologic investigations might be best focused on areas that lack information but 

have relatively high potential scores based on very limited available data. Areas with high 

potential occur along a discontinuous band in the western part of the northern Brooks Range in 

the Killik River and Chandler Lake quadrangles and in scattered areas throughout the southern 

CYPA (plate 6). To more fully understand areas with high and medium PGE potential, future 

work might include (1) taking the generic MUM-related deposit criteria developed in this study 

and tailoring it to individual MUM-related, and non-MUM-related PGE models (table 6); (2) 

using MUM rock geochemistry to determine the spatial distribution, types, and geologic settings 

of MUM rocks; (3) obtaining quantitative geochemical analyses for historical sediment and rock 

samples, which originally were only analyzed for a limited number of elements by qualitative 

emission spectroscopic methods; (4) conducting stream-sediment and rock-geochemical 

sampling in high- and medium-PGE-potential HUCs to identify lode source(s) of PGE, Co, Cr, 

Cu, Ni, Ti, and (or) V anomalies within the watersheds; (5) conducting rock sampling to fill gaps 

in igneous geochemical and geochronology databases; (6) analyzing available geophysical data 

to identify areas of PGE-hosting lithologies; and (7) conducting geologic mapping in high- and 

medium-PGE-potential HUCs to delineate MUM rocks or to identify other PGE-hosting 

lithologies. The PGE scoring criteria described above were only optimized for PGE deposits 

associated with MUM rocks. Thus, it is possible that scoring parameters relevant to other types 

of PGE deposits (for example, those not associated with MUM rocks or not presently known to 

occur in Alaska) might also yield insight into PGE potential.

Carbonate-Hosted Cu (Co-Ag-Ge-Ga) Deposits

Deposit Group Characteristics

Alaskan carbonate-hosted Cu deposits are of two distinct types, the Kennecott and 

Kipushi types. These deposits commonly contain Ag, and they may also contain three critical 

elements—Co, Ge, and Ga. Kennecott-type carbonate-hosted Cu deposits are named for the town 

of Kennecott, Alaska (fig. 1), where deposits were mined out by about 1938 (Price and others, 

2014). The district contains northeast-striking, very high-grade (13 percent Cu) veins that fill 

either normal and reverse faults that cut the Triassic Chitistone Limestone. Silver was recovered

(65 g/t), as well as Cu (MacKevett and others, 1997; Price and others, 2014). Deposits show 

considerable wall-rock replacement by copper sulfides and broad dolomitized alteration 

envelopes. The deposit bases were along probable bedding-plane faults about 25 m above the 

contact between the Chitistone Limestone and the underlying Nikolai Greenstone. Over nearly 

its entire extent in Alaska, the Nikolai Greenstone contains scattered quartz+Cu-sulfide or native 

Cu±calcite veins or amygdular zones, typically with epidote±calcite±chlorite alteration. 

Although Cu mineral occurrences are very widespread in the Nikolai Greenstone wherever it 

occurs, mining has not succeeded for long at any of these occurrences. However, the copper in 

the Kennecott veins was apparently dissolved from the Nikolai Greenstone during prehnitepumpellyite facies metamorphism in the Early Cretaceous and then moved upwards and away 

from zones of higher grade metamorphism (Silberman and others, 1980; MacKevett and others, 

1997; Price and others, 2014), suggesting that locations where there is epigenetic Cu 

mineralization in the Nikolai predict high potential in the overlying Chitistone Limestone or 
33

other carbonate rocks immediately upsection. The Cu occurrences in the basalt are not 

themselves economically attractive at this time, but the occurrences are surely prospecting guides 

that might lead to the discovery of Kennecott-type deposits in overlying carbonates.

The Ruby Creek deposit (plate 8 for location) is an Alaskan example of Kipushi-type 

deposits, which are named for Kipushi, Democratic Republic of Congo (DRC). These are 

massive base-metal-sulfide replacement bodies or breccia fillings in carbonates, now 

dolomitized, that are either peneconcordant or cut bedding as high-angle breccia bodies, or both. 

All deposits classified as Kipushi type by (Cox and Singer, 1986) have Ge-bearing minerals; this 

appears to be the basis of their classification, as there are other types of Cu-Pb-Zn-sulfide ores in 

carbonate rocks that lack Ge minerals. The Ruby Creek deposit is hosted by the middle Silurian 

to Early Devonian Cosmos Hills Sequence (Till and others, 2008). As of April 2014, Ruby Creek 

had an indicated resource of 14 Mt at 1.08 percent Cu and an additional inferred resource of 165 

Mt at 1.57 percent Cu (Davis and others, 2014). Cu-Co-Ge mineralization occurs as sulfide 

minerals that fill former open space in breccias or replace breccia matrix and wall-rock 

carbonates within a very large halo of hydrothermal dolomite containing minor sphalerite 

(Runnells, 1969; Bernstein and Cox, 1986; Hitzman, 1986). Copper sulfide minerals include 

chalcopyrite, bornite, and chalcocite. Cobalt occurs in carrollite (CuCo2S4) and cobaltiferous 

pyrite, and Ge occurs in sulfides are renierite (Zn3(AsO3)2) and germanite (Cu13Fe2Ge2S16). 

Grades of the trace metals Co, Ge, and Ga in the Ruby Creek deposit have not been published. 

Elsewhere, Kipushi-type ores may contain approximately 1,000 ppm Ge, but calculations from 

production figures from Kipushi, DRC, suggest that recovery there ranged from 23 to 600 ppm 

Ge (Höll and others, 2007).

Mineral Resource Potential Estimation Methodology

For carbonate-hosted Cu deposits, five criteria were used to estimate resource potential—

(1) lithology, particularly the presence of carbonate host rocks, (2) sedimentary rock 

geochemistry, (3) mineralogy of pan concentrates of sediment samples, (4) sediment 

geochemistry, and (5) presence of known (ARDF) deposits and occurrences with copper in 

sedimentary rocks, particularly carbonate rocks.

Lithology

Rock-type information is available from map-unit descriptions for an anticipated geologic 

map of Alaska (Wilson, Frederic H., and others, USGS, written commun.). Using lithologic 

descriptions, map units with carbonate rocks were divided into two groups—(1) “carbonate, 

major” and (2) “carbonate, other”—on the basis of whether carbonate rocks form more or less 

than 50 percent of the map unit, respectively (appendix D). Each HUC that contained any 

“carbonates, major” units was assigned a score of 3; HUCs that lacked “carbonates, major,” but 

contained “carbonates, other” were assigned a score of 2. To account for potential down-dip 

extent of carbonate units, each HUC adjacent to an HUC that scored 2 or 3 was assigned a score

of 1 (table 8). Future work could refine this buffering approach by examining bedding attitudes 

of the carbonate-bearing units and retaining buffer zones only in the downdip direction and 

removing the buffer zone in the updip direction where carbonate rocks have been eroded.

After the above ranking of HUCs on the basis of lithology, all remaining HUCs that did 

not score 1–3 were assigned a total potential score of 0, and they were not included in any further 

scoring procedures. Those HUCs lack the permissive host-rock types for carbonate-hosted Cu 

deposits.
34

Rock Geochemistry

Geochemical data for sedimentary and metasedimentary rocks were included from both 

the AGDB and the ADGGS databases. Three points were assigned to any HUC that contained a 

sedimentary rock sample with ≥5,000 ppm Cu. Two points were assigned for sedimentary rock 

samples with <5,000 ppm Cu but ≥1,000 ppm Cu. One point was assigned for a sample 

containing <1,000 ppm but ≥150 ppm Cu (table 9). To avoid bias where more than one sample 

occurs in a HUC, only the single sample with the greatest measured Cu concentration was 

allowed to score in each HUC.

For sedimentary rocks with ≥150 ppm Cu, 1 point was also awarded to any HUC with 

one or more samples additionally containing ≥45 ppm Co, ≥3 ppm Ge, ≥35 ppm Ga, and (or) ≥1 

ppm Ag. (These values follow the recommendation of Huyck [1990], who examined trace 

elements in black shales, the sedimentary rock type with the highest background values for these 

four elements.) The metals concentration values are chosen to be consistent with a 

“metalliferous” black shale as defined there. The trace-element-scoring thresholds are thus 

designed to award scores only where values anomalous for any sedimentary-rock type are found; 

concentrations that would seemingly require an epigenetic mineralizing process to reach within 

carbonate host rocks. Each HUC can score one additional point for each trace element, and 

therefore, trace element geochemistry of the byproduct metals can contribute as many as 4 points 

for a single HUC. The maximum possible score for each HUC on the basis of rock geochemistry 

is then capped at 5 (table 9), so that the rock geochemistry cannot dominate all other scoring 

factors.

Heavy Mineral Concentrate Mineralogy

An HUC containing one or more samples with an HMC report indicating the presence of 

chalcopyrite, copper cobalt sulfides, copper silicate, copper sulfides/oxides, azurite, cuprite, 

enargite, and (or) malachite (that is, value not null) was assigned a score of 1 for each mineral 

present. The score was additive for each possible mineral present, and therefore the maximum 

possible score for each HUC on the basis of HMC mineralogy was 8 (table 9).

Stream-Sediment Geochemistry

An HUC containing one or more sediment samples with copper values in the 98th 

percentile or greater (≥150 ppm) was assigned a score of 3. An HUC containing one or more 

sediment samples with copper values between the 98th and 91st percentiles (149–76 ppm) was 

assigned a score of 2, and an HUC containing one or more sediment samples with copper values 

between the 91st and 75th percentiles (75–50 ppm) was assigned a score of 1.

For Co, Ge, Ga, and Ag, only sediment samples with copper concentrations of ≥50 ppm 

were considered. For example, this specific scoring procedure worked toward giving no weight 

to Co that may have been sourced in black shale, no weight for Ge sourced in coal underclay, no 

weight for Ga sourced in a sedex deposit, and no weight for Ag sourced in an epithermal vein. 

This approach assigns scoring weight to these trace metals only where they had the proper metal 

association for a copper in carbonates deposit. A HUC containing one or more stream-sediment

samples with ≥50 ppm Cu and Co, Ge, Ga, and (or) Ag concentrations in the 91st percentile or 

greater (≥36 ppm, ≥5.9 ppm, ≥30 ppm, ≥0.4 ppm, respectively) was given a score of 1 for each 

trace element. The maximum possible score for each HUC on the basis of sediment chemistry 

was 7, with 3 possible for Cu and 4 possible for additional trace metals (table 9).
35

ARDF

The ARDF keyword list and scoring criteria for carbonate-hosted Cu deposits was 

designed to (1) maximize the probability of identification of occurrences that are consistent with 

the carbonate-hosted Cu deposits and (2) to minimize the inclusion of unrelated occurrences. 

Copper is present in a wide variety of mineral deposit types, most of those unrelated to Kipushior Kennecott-type deposits. Thus, the ARDF scoring method was designed to account for this 

potential complexity and overlap. The greatest scoring weight was assigned to ARDF records 

that were identified as either basaltic Cu or Kipushi deposits (model numbers 23 and 32c, 

respectively, of Cox and Singer, 1986). Negative scores were assigned if a different deposit type 

or model number (for example, sediment-hosted massive sulfide deposits, volcanic-hosted 

massive sulfide deposits, or skarn deposits) was assigned in the ARDF record. Neither positive 

nor negative scores were assigned for ARDF occurrences that had been classified as polymetallic 

replacement deposits and polymetallic veins (model numbers 19a and 22c, repectively, of Cox 

and Singer, 1986) because of possible ambiguities in their classification. There are a number of 

localities identified where occurrences of polymetallic replacement deposits and (or)

polymetallic veins were described near clusters of Cu mineral occurrences in carbonate host 

rocks. In general, the polymetallic vein and polymetallic replacement classes are not defined in 

language that is sufficient to determine which deposit type is most appropriate or if they are 

related to other deposit types. As a result, deposit models 19a and 22c can include features that 

might be indicative of another type of mineralization but where the specific relations are not 

completely understood. To account for this possible overlap, the scoring was designed to allow 

other criteria to score favorably for HUCs with mineral occurrences described that way, rather 

than to score negatively for such occurrences. A score of 3 was assigned where Cu and (or) Co 

were listed as a main commodity, whereas scores of –3 were given for each element that is 

unlikely to be a main commodity in a carbonate-hosted Cu deposit. Negative scores were also 

given to keywords in the ARDF records that are unlikely to be associated with this type of 

deposit (appendix C), for example, the word, porphyry, as in a porphyry copper or porphyry 

molybdenum deposit. The resulting ARDF word-hit results produced maximum scores of 23, and 

the highest scores typically were for occurrences that were classified either basaltic Cu or 

Kipushi type. A score of 5 was assigned to each HUC that contained one or more ARDF record 

with a word-hit model score that was >4 (that is, 4–23), a score of 2 was assigned to each HUC 

that contained one or more ARDF records with word hit model scores between 3 and 1, and a 

score of 0 was assigned to any HUC that contained only ARDF records with results of 0 or less.

Results and Discussion

From the above, the greatest total score an HUC could receive was 30 points, though the 

maximum HUC score in the CYPA and its buffer was 20 for this deposit type. Table 10 outlines 

the criteria by which resource potential was assigned for each HUC. Those HUCs with scores of 

≤3 were classified as having low potential because, although possible host rocks might be 

present, there was no evidence of any mineralizing process from the remaining 6 types of data. 

The remaining composite scores of 4 and greater were divided at one natural statistical break 

(Jenks method), with the two new categories assigned medium and high potential for score 

ranges of 4–6 and ≥7, respectively. The assigned certainty value for each HUC corresponds to 

the number of data types that contribute to the potential score as outlined in table 10.
36

Results of the scoring analysis for carbonate-hosted Cu deposits are shown in plate 7, 

with accompanying data in appendix E. A total of 141 HUCs (6 percent of 2,460 HUCs in the 

CYPA) are considered to have high potential. Of those, just over half (79) also have high

certainty. There are 425 HUCs (17 percent of the HUCs in the CYPA) with medium potential. 

The remaining 1,882 HUCs (77 percent of CYPA) have low potential, with almost two-thirds of 

these (59.1 percent of the low-potential HUCs) also having high certainty. A total of 1,017 HUCs 

had total scores of 0, but only 12 of these (0.5 percent of CYPA) were classified as having

unknown potential and unknown certainty (plate 7, appendix E).

The 100-km buffer zone included Ruby Creek and two similar ARDF occurrences (plate 

8). All six HUCs in that area have high potential with five of these at medium certainty (plate 8), 

and this result helps to validate the scoring method. 

The results also identified at least three major belts of high potential for carbonate-hosted 

Cu deposits close to the boundaries of the CYPA—(1) Sheep Creek belt, (2) the belt of Nikolai 

Greenstone basaltic Cu occurrences, and (3) the Brooks Range foreland belt. The Sheep Creek 

belt extends along the south flank of the Brooks Range, with additional occurrences on thrust 

plates farther north in the interior of the range (plate 8). A group of 10 ARDF occurrences form 

part of a northeast-southwest belt of high potential in the Wiseman quadrangle, and it may 

extend discontinuously eastward and northeastward across the Brooks Range into the Philip 

Smith Mountains and Mount Michelson quadrangles (plate 8). 

A belt of high potential southeast of the CYPA boundary (plate 8) is related to basaltic 

Cu ARDF occurrences, copper geochemical anomalies principally in stream sediments, and 

some copper minerals in heavy mineral concentrates. This belt is largely identified because of 

basaltic Cu-type ARDF occurrences along exposures of Nikolai Formation greenstone (plate 8), 

and it is somewhat analogous with the geology of the Kennecott deposit. However, slightly less 

than half of those high-potential HUCs also have potential carbonate host rocks upsection from 

the Nikolai Formation. A parallel belt of high-potential HUCs to the northwest of the Nikolai 

basaltic Cu occurrences intersects the southeastern part of the CYPA (plate 8) and is more 

diverse; HUCs along this belt contain basaltic copper occurrences in other greenstones, different 

carbonate host rocks, fewer ARDF occurrences, and more copper minerals in heavy-mineral 

concentrates.

The third belt with high potential extends along the Brooks Range foreland (plate 8), but 

copper concentrations in stream-sediment samples and rocks in this area are typically less than in 

the areas described above, and there are no ARDF occurrences along this flank of the range that 

strongly suggest carbonate-hosted Cu deposits. Anomalies along the Brooks Range foreland are 

mostly associated with the slope facies of the Carboniferous Lisburne Group and with additional 

chert, shale, and thin carbonate units upsection from the Lisburne through rocks of Jurassic age. 

Although copper concentrations are generally lower, the copper anomalies of the Brooks Range 

foreland should be considered because available sampling was sparse and the carbonate rocks, 

particularly the Lisburne Group, are viable host rocks.

Elsewhere, results suggest high potential for carbonate-hosted Cu deposits where this 

deposit group is unlikely, and these results mainly occur in places where “carbonate, other” 

lithologic units contain mineral deposits or prospects with anomalous Cu concentrations or Cu 

minerals. For example, the Ambler schist belt (plate 8) hosts at least six Cu-rich volcanogenic 

massive sulfide deposits (Schmidt, 1986). Similarly, the Medfra quadrangle contains a northeasttrending belt of HUCs in the area around Limestone Mountain with high potential, where 

geochemical anomalies are immediately adjacent to Jurassic intrusions (plate 8). These high

potential HUCs likely reflect either skarn or porphyry mineralization, rather than carbonate-
37

hosted Cu deposits. A group of HUCs with high potential in the northwestern Chandalar 

quadrangle (plate 8) might reflect Cu anomalies from metamorphosed Devonian-age porphyry 

Cu deposits and Cu skarns (Newberry and others, 1986), or these HUCs might reflect high

potential for carbonate-hosted Cu deposits as part of a discontinuous belt in the Chandalar, 

Wiseman, Survey Pass, and Ambler River quadrangles.

Sandstone U (-V-Cu) Deposits

Deposit Group Characteristics

Sandstone uranium deposits (ssU) are epigenetic deposits with a worldwide distribution.

They form in sandstone host rocks that range in age from Carboniferous to Tertiary. Sandstone 

uranium deposits are commonly divided into four types—basal, tabular, roll front, and 

tectonolithologic (table 1; Cuney and Kyser, 2009). Regardless of type, all ssU deposits are 

thought to form by redox-controlled processes that can be generalized as (1) oxidative 

mobilization of the uranium from the source rock, commonly granite or tuff, (2) transport of 

soluble uranyl (U6+) complexes through an oxidized nonmarine-sandstone aquifer, and (3) 

reduction and precipitation as (U4+) minerals by encounter with reduced host rocks that are 

laterally continuous or lying below the aquifer in which uranium is transported (Guilbert and 

Park, 1986; Cuney and Kyser, 2009). Reducing conditions in the host rock are typically a result 

of entrained carbonized plant matter. In this analysis, all types of ssU deposits were initially 

targeted, but basal-type deposits are the most likely to occur in Alaska and the CYPA. Other Uenriched deposits that occur within similar host rocks are sediment-hosted V and sedimenthosted Cu deposits (Turner-Peterson and Hodges, 1986).

The best described ssU deposit in Alaska is the Death Valley deposit (ARDF record 

BN089), located in the Boulder Creek Basin near the eastern end of the Seward Peninsula (see 

plate 10 for location). It was discovered in 1977 by airborne radiometric survey and has an 

average grade of 0.27 percent U3O8 in beds averaging 3 m thick and has calculated reserves of 

about 1,000,000 lbs (~453.59 t) U3O8 (Dickinson and others, 1987). The host rocks are earlyEocene carbonaceous arkosic sandstones of fluvial or colluvial origin deposited in a graben 

formed on granitic bedrock (Dickinson and others, 1987). The underlying Late Cretaceous 

granitic pluton, known as the Darby pluton, forms the western side of the graben. Basalt, coal,

and lacustrine laminated sideritic mudstone and turbidites are interbedded with the sandstone. An 

early-Eocene basalt flow dammed the ancestral river valley, forming the lake in which the 

lacustrine sediments were deposited (Dickinson and others, 1987).

The deposit history comprises an earlier Eocene(?) primary epigenetic mineralization and 

later Holocene secondary supergene enrichment. The Darby pluton is considered the likely 

source for the uranium. Primary mineralization is thought to have developed when uranium was 

dissolved by oxidizing ground water, carried east from the Darby pluton, and deposited in the 

reducing environment of the carbonaceous Tertiary sedimentary rocks (Dickinson and others, 

1987). The mineralized rocks are fairly widespread in the subsurface, occurring both above and 

below the Eocene basalt and lacustrine rocks and extending to a depth of about 91 m. Uranium 

minerals in the primary ore are coffinite (U(SiO4)·nH2O) in the reduced zones and autunite 

(Ca(UO2)2(PO4)2·10-12H2O) where the primary ore has been oxidized. The secondary supergene 

enrichment is related to the present surface exposure and is possibly ongoing, because the 

involvement of recent surficial mudflows and soil formation processes indicated a recent age 

(Dickinson and others, 1987). Uranium minerals in the secondary enrichment include meta-
38

autunite (Ca(UO2)2(PO4)2·6H2O). The uranium-rich rocks of the Death Valley deposit are 

strongly carbonaceous and have anomalously low manganese (Mn) concentrations. The features 

of Death Valley are most consistent with those of a basal-type ssU deposit (Dickinson and 

others, 1987) and the low Mn concentration of the U-bearing rock is inconsistent with roll-fronttype deposits (Harshman, 1972).

Mineral Resource Potential Estimation Methodology

The presence of sandstone is a defining characteristic for sandstone uranium deposits. 

However, because the geologic mapping is generally too coarse and unconsolidated material 

covers many of the Tertiary sedimentary basins in Alaska, the analysis was not simply limited to 

HUCs with sandstone lithology. Instead, differing sedimentary-rock lithologic and coal data 

layers were used to define host rock favorability, and evidence for U-mineralization included (1) 

uranium distribution (stream sediments, sedimentary rock), (2) bedrock with strong radioactive 

signature (aerial gamma-ray survey data), (3) U potentially derived from highly differentiated 

igneous plutons (Fe# displacement), and (or) (4) locations of known or possible ssU occurrences, 

prospects, and mines (ARDF records). During scoring, host-rock favorability and U-mineralizing 

process indicators were considered of equal importance. Data density was also considered as 

discussed below. Scoring components and criteria are listed in table 11.

Lithology

A generalized sedimentary rock layer was derived from an anticipated geologic map of 

Alaska (Wilson, Frederic H., and others, USGS, written commun.) using search terms that were 

based on the map unit descriptions (see appendix D). The generalized sedimentary rock layer 

includes all the mapped units where sedimentary rocks were listed as either a “major” or an 

“other” component of the unit description. Because unconsolidated materials cover many of the 

sedimentary basins, a generalized lithology layer of unconsolidated sediment was also 

developed.

Sandstone U deposits in North America, including the Death Valley deposit in Alaska 

(plate 10), are commonly hosted in Tertiary sandstone, specifically Tertiary arkosic sandstone 

(Guilbert and Park, 1986; Cuney and Kyser, 2009). To capture the high favorability of these 

rocks, map units described as Tertiary arkosic sandstone and Tertiary/Cretaceous sandstone were 

extracted from the generalized sedimentary rock layer, regardless of whether these characteristics 

were a “major” or an “other” component in the unit descriptions. HUCs containing Tertiary 

arkosic sandstone and Tertiary/Cretaceous sandstones were assigned scores 4 and 3, respectively 

(table 11). HUCs containing part of the generalized sedimentary rock layer were given a score of 

2. If an HUC contained unconsolidated sediment that was within a 3-km buffered area 

surrounding favorable sedimentary lithologies, the HUC was assigned a score of 1. For HUCs 

that met more than one of these lithological criteria, only the highest score was used. Thus, the 

maximum possible score for lithology was 4 (table 11).

Coal

Coal-bearing sedimentary sections typically also contain rocks from fluvial, lacustrine, 

and (or) coastal sedimentary environments. The first two environments match the targeted host 

rocks for sandstone uranium deposits. Thus, to capture the favorability of sedimentary rocks that 

contain solid organic matter, Tertiary to Tertiary/Cretaceous coal-bearing rock units were 
39

identified from the published map of Alaska’s coal resources (Merritt and Hawley, 1986). A 

HUC containing one or more coal-bearing units was given a score of 1 (table 11). This score was 

applied to an HUC independent of any other lithology score.

Stream-Sediment and Sedimentary-Rock Geochemistry

The uranium distribution in both sediments and sedimentary rocks was used to define 

areas with anomalously high uranium content. Analyses of sedimentary rock are the most direct 

determination of the uranium content; however, the spatial distribution of available sedimentaryrock data is more limited than that of the stream-sediment data. In addition, rock samples might 

have been collected for a specialized purpose, such as background determination, and such 

information might not be available as metadata together with the analytic result. For these 

reasons, the maximum score for U content in sedimentary rock was 2 and was assigned to HUCs 

containing one or more sedimentary rock samples greater than or equal to the 98th-percentile 

value for uranium (≥40 ppm). A score of 1 was assigned to HUCs with a sedimentary rock 

sample greater than or equal to the 91st-percentile and less than the 98th-percentile values for 

uranium (≥11 and <40 ppm).

The uranium content in stream sediment is a less direct measure in comparison with 

sedimentary rock, because stream-sediment samples can integrate the contributions from 

multiple sources upstream of the collection point. However, because of the dataset’s greater areal 

extent and uniformity of purpose compared to the sedimentary rock data, the maximum possible 

score for stream-sediment uranium data was set at 3. HUCs with a stream-sediment sample 

greater than or equal to the 98th-percentile value for uranium (≥20 ppm) scored 3; those with a 

stream-sediment sample greater than or equal to the 91st-percentile and less than the 98thpercentile values (≥5.8 ppm and <20 ppm) for uranium scored two, and those with a streamsediment sample greater than or equal to the 75th-percentile and less than the 91st-percentile 

values for uranium (≥3.5 ppm and <5.8 ppm) scored 1 (table 11).

Both the sedimentary rock and stream-sediment geochemistry datasets were scored 

independently and, in both instances, a single HUC could contain multiple samples. For HUCs 

containing multiple samples, scoring was limited to the highest scoring single sample within the 

HUC. Because the two geochemistry datasets were scored independently, an HUC could receive 

a score of 2 for uranium content greater than or equal to the 98th percentile in a sedimentaryrock sample and 3 for a uranium content greater than or equal to the 98th percentile in a streamsediment sample, if both were present in the HUC.

Igneous-Rock Geochemistry

Igneous rocks are the likely sources of U in some ssU deposits. Because of the behavior 

of U in magmas, the most likely source plutons for U are highly differentiated igneous rocks. 

There are few U analyses in the igneous-rock dataset and, therefore, an indicator approach was 

used. Uranium is highly incompatible and, as a result, is commonly in higher concentrations in 

late-stage differentiates and more highly evolved, silica-rich compositions. To identify highly 

differentiated and potentially U-rich igneous bodies, we used the Fe#displacement metric described 

above (Frost and Frost, 2008). An HUC containing one or more igneous rock samples with 

SiO2>70 percent and Fe#displacement>0 was assigned a score of 1 (table 11).
40

Aerial Gamma-Ray Survey Data

Data from the aerial gamma-ray surveys include measurement of the naturally occurring 

radioactive element 238U across Alaska (Duval, 2001). Uranium data are reported in equivalent 

ppm (eq. U ppm). HUCs containing data with U equivalents ≥5 (eq. U ppm) received a score of 

2, and HUCs between eq. U ppm ≥2 and <5 scored 1 (table 11). The maximum score for each 

HUC was assigned on the basis of the maximum eq. U ppm value anywhere within the HUC.

ARDF

Alaska Resource Data File records were searched for prospective ssU occurrences using 

the keywords and associated scores for ssU deposits in appendix C. The results from the word-hit 

search were further limited to only those records with U as a “main” or “other” commodity. 

These records were reviewed individually and retained if (1) the deposit-type model type was 

ssU or roll-front, (2) if no deposit type was given and lode, granite, vein, dike, phosphates, or 

skarn were not present in any of the descriptive fields, or (3) if the deposit type was a placer with 

U present as either a major or minor commodity. The retained ARDF records contributed a score 

of 1 for the HUC they occur in (table 11).

Results and Discussion

A score of 14 was the maximum total attainable using the scoring procedure for ssU 

deposits described above (table 11). Table 12 outlines the criteria by which estimated mineral 

resource potential and certainty were assigned. Because the best described ssU deposit in Alaska 

(the Death Valley deposit) is outside of the CYPA (plate 10), our classification of estimated 

mineral resource potential and certainty was informed by initial analysis of HUCs across the 

entire State. In the statewide analysis, HUC scores ranged from 0 to 10, and the HUC containing 

the Upper Tubutulik River and the Death Valley deposit scored seven. Thus, a score of seven and 

higher was assigned high potential. HUCs with scores of 4–6 and 0–3 were assigned medium and

low potential, respectively. Although it was possible for a HUC to receive a total score of 4 

based on lithology alone or a combination of the lithology and coal score, no HUC was allowed 

to receive a medium potential rating based solely on host rock favorability lacking any indication 

of a U-mineralizing process. HUCs with total scores of 4 derived only host-rock favorability 

were assigned low potential (table 12).

The classification of certainty was based on the number of datasets that contributed out of 

a possible seven (table 12). High certainty was assigned when 5–7 datasets contributed to the 

total score, and medium and low certainty were assigned when 3–4 and 0–2 datasets contributed, 

respectively. Two exceptions to this are that (1) an HUC with a score from a reviewed and 

retained ARDF record was always assigned high certainty and (2) an HUC with a total score of 

zero was assigned high certainty. A value of unknown was only assigned to an HUC if it (1) 

lacked stream-sediment data and had a total score of zero or (2) lacked stream-sediment data and 

the total score was completely based on the lithology, lithology + coal, or the aeroradiometric 

data score.

Results for ssU are shown in plate 9, and accompanying data are presented in appendix E. 

Of the 2,460 HUCs in the CYPA, 51 (2 percent of total) have high potential for ssU. Of these, 46 

(90.2 percent of high-potential HUCs) also have high certainty and 5 (9.8 percent of high 

potential) had medium certainty. 543 HUCs (22 percent of total) have medium potential, with 

either high (51 percent of medium potential) or medium (49 percent of medium potential) 
41

certainty. 75 percent of the HUCs in the CYPA (1,847 total) have low potential for ssU, and 

most in this category have either high (36 percent of low potential) or medium (64 percent of low 

potential) certainty. There are 251 HUCs with a total score of zero, but only a small number of 

these (4) were assigned unknown potential and unknown certainty.

The results identified three general areas of high potential for ssU deposits at or within 

the boundaries of the CYPA. high potential HUCs occur within the trend of the Ruby Batholith, 

the northeastern portion of the Hogatza igneous belt (Shungak and Hughes quadrangles), and an 

area bordering the Fairbanks and Healy quadrangles (plate 10). The high potential HUCs 

typically contain favorable lithologies such as sedimentary rock and Tertiary/Cretaceous 

sediments or arkose sandstone. Coal is present in 33 percent of the high potential HUCs. These

high potential HUCs also commonly contain one or more of the following: (1) favorable ARDF 

records, (2) sediment data above the 98th percentile value for U content (≥ 20 ppm), (3) 

radiometric uranium equivalents >2 eq. U ppm, and (or) (4) a highly differentiated and 

potentially U-rich igneous body.

Within the CYPA, medium-potential HUCs cluster around the high-potential HUCs,

except for a cluster of 16 medium-potential HUCs that cross the Hughes/Melozitna quadrangle 

boundary and another cluster of 42 HUCs that cross the Kateel River, Nulato, and Norton Bay 

quadrangle boundaries in the southwestern portion of the CYPA (plate 10). The host-rock setting 

for the cluster of 16 medium-potential HUCs is the generalized sedimentary lithologic unit and 

coal is not present. Within this grouping, all of the 16 HUCs have stream-sediment samples at or 

above the 75th percentile, and 7 have U content between the 91st and 98th percentile value. All 

16 HUCs also have radiometric uranium equivalents >2 and ≤5 eq. U ppm. 

The host rock setting for the cluster of 42 medium-potential HUCs is the generalized 

sedimentary class and 41 of 42 have coal present. Thirty-seven of these HUCs have a streamsediment sample above the 75th percentile value for U content, with six between the 91st and 

98th percentile value for U content. Half of the 42 HUCs in the belt have radiometric uranium 

equivalents >2 and ≤5 eq. U ppm.

To better characterize the areas of high and medium potential for the ssU deposit model, 

future work might include rock and stream-sediment geochemical and mineralogical sampling in 

these areas to better determine the U source(s) and geologic mapping might be refined to better 

delineate extent and characteristics of the sedimentary deposits.

Sn-W-Mo (-Ta-In-fluorspar) Deposits Associated with Specialized Granites

The mineral commodities Sn, W, Mo, Ta, In, and fluorspar (industry term for fluorite, 

cubic CaF2) do not always occur as a group, but they are commonly found in various 

combinations closely associated with highly evolved felsic intrusive rocks that are grouped 

herein as specialized granites (for example, Reed, 1986). Specialized granites include granite 

porphyries, peraluminous granites, and high-silica (>73 percent SiO2; Rogers and Greenberg, 

1990; Blatt and others, 2006) granites. Such granites tend to be found in tectonic environments 

that relate to thickening, contraction, or extension of continental crust. With some exceptions, the 

common petrogenetic characteristics of specialized granites and their associated dikes and sills

are that they (1) are late-stage magmatic phases containing elevated concentrations of 

incompatible elements as a result of fractional crystallization and (2) have sources that include 

continental crust. Mineral deposits associated with specialized granites mainly occur in dikes, 

veins, stockworks, and greisens or hydrothermally altered zones near the top of granite intrusions 

or in skarns in carbonate wall rock adjacent to the other features just named near the top of the 
42

granite. In systems associated with specialized granites, Sn ore minerals are mainly cassiterite 

and less commonly stannite, W is mainly found in scheelite and wolframite, and Mo is mainly 

found in molybdenite. Tantalum occurs mainly in columbotantalite and less often in wodginite or 

microlite. Indium typically occurs as a substitution in sphalerite, cassiterite, and (or) stannite, and 

is found rarely as roquesite.

Specialized granites in this model include a range of compositions because many factors 

can influence production and crystallization of silicic magma. The formation of specialized 

granites can include late phases associated with large magma systems or localized anatectic 

melts, and a common characteristic of these diverse granites is their relatively small volume. For 

example, granite porphyries that contain Sn, W, and Mo mineralization form at lower 

temperatures and lower sulfidation states and also tend to be smaller in size than Cu and Cu-Au 

porphyries (Seedorff and others, 2005). Most economic concentrations of Sn, W, Mo, Ta, and In, 

with or without fluorspar, are associated with peraluminous granites. Peraluminous granites have 

high ASI values, are corundum normative, and commonly contain aluminous minerals such as 

cordierite, garnet, muscovite, andalusite, sillimanite, topaz, tourmaline, and biotite. Pegmatites, 

veins, and dikes that contain elevated concentrations of Sn, Ta, W, Mo, Cs, and Li are classified 

as lithium-cesium-tantalum (LCT) pegmatites (Černý and others, 2005), which commonly occur 

within and peripheral to the cupolas of peraluminous and highly evolved granites (Černý, 1991; 

Černý and others, 2005; Johan and others, 2012). Indium is a common impurity in Zn-, Sn-, and 

Cu-sulfides, and in Sn-oxides (cassiterite) in Sn-W veins, pegmatites, and skarns associated with 

evolved granitic rocks (Černý and others, 2005; Ishihara and others, 2006; Sinclair and others, 

2006). Fluorine and H2O are important agents for transport of Sn, In, Ta, and W in fluids 

exsolved from peraluminous granite melts (Dobson, 1982; Swanson and others, 1990; Černý and 

others, 2005; Johan and others, 2012). Highly fractionated, LCT granites and pegmatites are also

thought to be enriched in F by anatectic crustal melts (Černý, 1991). This possibility is consistent 

with the high initial 87Sr/86Sr ratios for peraluminous Sn-granites in Australia (Clemens and 

Wall, 1984; Blevin and Chappell, 1992) and in Alaska (see table 1 for localities; Hudson and 

Arth, 1983; Arth and others, 1989). 

Specialized granites associated with Sn deposits generally have elevated Sn

concentrations relative to average granite (Černý and others, 2005). However, W, Mo, Ta, and In 

do not always vary directly with Sn concentration in granite. In some systems, Ta varies 

inversely with Sn and increases upward in the mineral columbotantalite (Puchner, 1986). 

Porphyry-Sn granites are not common and are only well documented in Bolivia (Grant and 

others, 1980).

Mineable deposits of tin are mainly found in skarns, greisens, simple Sn-veins and Snpolymetallic veins (table 1). Greisens associated with evolved granites consist of quartz, white 

mica, and cassiterite, with or without scheelite, molybdenite, pyrite, and (or) fluorite, that form in 

the upper roof zones of hydrothermally altered granite (endogreisen), or as alteration of wall 

rocks of the intrusions (exogreisen). Additional minerals found less commonly in greisen include 

tourmaline, topaz, garnet, axinite, arsenopyrite, sphalerite, and chalcopyrite. A characteristic 

mineral that occurs in greisens in Sn-granites is lithium (Li) rich annite (formerly “zinnwaldite”), 

where annite is the iron-rich end member of the biotite-phlogopite micas. Alaska has a wellknown province of Sn granites and associated Sn occurrences on the northwestern Seward 

Peninsula (Hudson and Arth, 1983), as well as widely scattered Sn occurrences in interior Alaska 

between the central Alaska Range and the central Brooks Range and to the west of the Alaska 

Range (fig. 1; Hudson and Reed, 1997). Key deposits and occurrences in Alaska are summarized 

in table 1. Tin has also been mined from alluvial placers, mainly as cassiterite.
43

Concentrations of tungsten are recognized in both oxidized (magnetite-series) and 

reduced (ilmenite-series) granites; porphyry-W deposits are rare (table 1; Seedorff and others, 

2005). Granite porphyries that contain W are described in the Fairbanks area in Alaska (fig. 1; 

Newberry and others, 1990). Tungsten is typically concentrated in veins and greisens around the 

margins of highly evolved granites (Werner and others, 1998), and is mined mainly from skarns 

in carbonate wall rocks adjacent to the granites and from veins (table 1). Although high W 

contents result from extended melt fractionation, W contents of granites associated with scheelite 

skarns are not commonly elevated relative to other igneous rocks (Newberry and Swanson, 1986; 

Černý and others, 2005). It is common for W-skarns to contain Sn, for Sn-skarns to contain W, 

and for W-skarns to contain some Mo. Fluorspar is typically found as either a principal product 

with Sn and W in vein, greisen, and skarn deposits associated with highly evolved granites or as 

an unrecovered accessory mineral in rocks mined for W or Sn (Richards and others, 2003). Late 

stage Sn-, Zn-, and Cu-sulfide veins and breccias that post-date W-Mo mineralization can 

contain significant concentrations of indium (Sinclair and others, 2006). In southeastern Japan, 

ilmenite granites contain late-stage cassiterite-wolframite (Sn-W) quartz veins that contain as 

much as 0.8 percent Indium, in association with scheelite-molybdenite skarn deposits (Ishihara 

and others, 2006). Scheelite-skarns and -veins associated with ilmenite-series granites within the 

CYPA near Fairbanks (fig. 1; Newberry and Swanson, 1986) might have similar potential for In 

concentrations. 

Magmatic-associated Mo deposits include a broad range of compositions for shallowly 

emplaced to hypabyssal intrusive rocks, but molybdenum is mined principally from two types of 

deposits—(1) Climax-type porphyry Mo systems, where it is the principal product (White and 

others, 1981; Seedorff and Einaudi, 2004), and (2) porphyry Cu (-Mo) deposits, where it is a 

byproduct of mining large-tonnage, low-grade copper deposits (Seedorff and others, 2005). In 

the range of compositions between those two granite porphyry types is a group of deposits 

classified as low-fluorine porphyry Mo deposits (Theodore, 1986; Ludington and Plumlee, 

2009). In the Climax-type deposits, molybdenum is associated with high-K, aluminous granite 

and rhyolite porphyry with more than 73 weight percent SiO2 (White and others, 1981). The 

specialized granite-scoring model herein is intended to identify Climax-type and low-F porphyry 

Mo-type deposits. It does not target the more prevalent porphyry Cu (-Mo) deposits (Cox, 1986), 

although it might identify areas prospective for porphyry Cu deposits because of associated Mo 

enrichments.

Late intrusive or hydrothermal activity associated with specialized granites can 

remobilize Sn, W, Mo, Ta, and In. However, high-volatile (especially F) contents in near-solidus 

melts make it difficult to distinguish magmatic versus aqueous fluid effects (Černý and others, 

2005, and references therein). 

There are few known Sn-W-Mo deposits in Alaska at present (table 1), and there are no 

known deposits in the CYPA at this time. Most known Sn-W-Mo prospects and occurrences in 

the CYPA are either placer deposits containing cassiterite or other Sn- or Ta- bearing minerals or 

relatively localized features such as veins, skarns, or greisens associated with larger igneous 

bodies (table 1). 

Mineral Resource Potential Estimation Methodology

To estimate mineral resource potential for Sn, W, Mo , Ta, In, and fluorspar deposits in 

specialized granites in the CYPA, the following five criteria were scored: (1) specialized granite 

compositions indicated by element ratios, (2) igneous rock geochemistry, (3) presence or absence 
44

of known economic mineral occurrences, deposits, or mines, (4) stream-sediment geochemistry, 

and (5) aerial gamma-ray survey data. The significance and application of these criteria are 

briefly described below and summarized in table 13.

Igneous-Rock Geochemistry

Igneous-rock geochemical data were used to identify permissive rock types for Sn-W-Mo 

deposits, particularly rocks meeting the classification of specialized granite as described above. 

These distinctive igneous rock compositions are relatively easy to identify using geochemical 

data from individual rock samples or suites of rock samples, and the distribution of igneous rock 

samples across the CYPA provides more detail than geological maps typically portray, especially 

considering the compositional complexity of many igneous systems and a general lack of 

detailed geologic mapping. The method of discriminating the presence of specialized granites

described herein offers three advantages—(1) geochemistry provides an objective test of key 

compositional criteria for identification of permissive rock compositions; (2) geochemical data 

are also spatially referenced to dikes and small satellite bodies that can be associated with Sn-WMo mineralization but are not typically captured in mapped units; and (3) rock-sample data 

correspond to discrete locations within an HUC, which provides greater geospatial precision for 

rock compositions than is provided by granite map units that commonly span several HUCs. For 

these reasons, igneous-rock geochemistry was favored over using geological map units alone to 

better identify the specific compositions of interest across the area.

Two scoring components based on igneous rock geochemical criteria were used to 

identify specialized granite compositions that have potential to host Sn-W-Ta-Mo-In and 

fluorspar mineral deposits (table 13). The first scoring component is defined based on the values 

derived from two ratios—(1) the aluminum saturation index, or ASI (modal Al/[(Ca1.67×P)+Na+K]; Shand, 1947), and modal Al/[Na+K] (ANK), which allows differentiation 

between peraluminous (ASI>1), metaluminous (ANK>1), and peralkaline (ANK>1) rock types 

(Maniar and Piccoli, 1989), and (2) the Ga/Al ratio, which at 10,000×Ga/Al>2.6 can distinguish 

alkaline and subalkaline igneous rock types (Whalen and others, 1987), and which also serves as 

a proxy monitor of fluorine contents because of the higher solubility of GaF6 relative to AlF6.

This ratio also has utility for identifying Sn and In concentrations because these elements are 

positively correlated with F in granite. The Ga/Al ratio has only been calibrated for granitic 

(intermediate and felsic) rock compositions (Whalen and others, 1987), and, because highly 

fractionated igneous rocks were the target of this deposit model, scoring of both the ASI and 

Ga/Al parameters was limited to igneous rocks with >65 weight percent SiO2.

The second scoring component was composed of 6 factors based on contents of key 

elements in the granites, including SiO2 and concentrations of the 5 target metals. Scores were 

assigned for contents of SiO2>73 weight percent in order to identify specialized granites high in 

silica that might, in particular, correspond to siliceous porphyries that contain Mo deposits. The 

cutoff at 73 wteight percent silica was chosen based on the normalized average silica 

composition of four different suites of granite (Rogers and Greenberg, 1990) and based on 

published silica values for granite porphyries that contain Mo at Climax (White and others, 

1981) and at Quartz Hill (Ashleman and others, 1997). Points were assigned for igneous rocks in 

the geochemical database that have In, Mo, Sn, Ta, and W abundances at or above the 91st

percentile, which significantly exceed average upper continental crustal abundances for these 

elements (table 14; Taylor and McLennan, 1995).
45

As with other models, nonsystematic rock sampling and incomplete igneous-rock 

exposure across the CYPA area led to variable coverage of igneous-rock geochemical data, so 

for each chemical scoring component (peraluminous, Ga/Al, and single element concentrations), 

a single point was assigned if a sample within an HUC exceeded cutoff values in table 13, for a 

maximum igneous rock score of 8 points (table 13).

ARDF

Mineral occurrence records compiled in the ARDF database were scored on the basis of 

keywords in the various descriptive fields as described in the introduction of this report. 

Keywords of interest included individual elements and commodities (for example, Sn, W, Ta, In, 

and Mo) and host minerals (for example, fluorite, cassiterite, scheelite, and wolframite) in the 

records (see appendix D). Fluorite described in ARDF records provides the only direct indication 

for its actual presence across the CYPA, because fluorine was not routinely or reliably measured 

for rock or stream-sediment samples in the geochemical databases. The cumulative keyword hits 

contributed to a net ARDF keyword score, with net scores higher than 4 representing favorable 

occurrences based on statistical analysis and verification by inspection of individual records. The 

results of the ARDF scoring output were reviewed for each record to ensure that known 

occurrences received appropriate scores and that occurrences unlikely to contain potential for Sn, 

W, Ta, Mo, In, and (or) F mineralization were not ranked inappropriately. Many ARDF localities 

that received high keyword scores were placer deposits, which were considered useful to include 

in the ranking process because, for example, placer cassiterite or scheelite suggest the presence 

of a nearby igneous source for those minerals. An ARDF keyword score higher than 19 

corresponded to mineral occurrences with keyword hits for multiple categories in an ARDF 

record, suggesting high resource potential. By this reasoning, on the basis of statistical breaks in 

the ARDF score distribution, localities with scores of 4 to 19 were assigned 1 point, and 

localities with scores of 20 or higher were assigned 2 points (table 13). Although some HUCs 

contain more than one ARDF locality, the maximum score for any ARDF locality in the HUC 

provides the maximum score for the HUC, so a single HUC can have a maximum ARDF score 

of 2 points.

Stream-Sediment Geochemistry

Stream-sediment concentrations for five representative elements (In, Mo, Sn, Ta, W)

were scored at two levels for this deposit model (table 13). A score of 1 point was assigned for 

stream-sediment concentrations of the target elements from the 91st to the 98th percentile and a 

score of 2 points for values in the 98th percentile or greater. Each HUC was scored based on the 

maximum possible value of any single stream-sediment sample it contained, such that the 

maximum possible score for a stream-sediment sample that had high values in all the target 

elements is 10.

Aerial Gamma-Ray Survey Data

Equivalent thorium concentrations (eq. ppm Th) in aerial gamma-ray surveys were 

measured by proxy from gamma-rays emitted from radioactive 208Tl, the daughter product of 

232Th decay. These data help to identify igneous rocks associated with potential for incompatible 

elements, such as Sn, W, and Ta, that are commonly accompanied by Th. As stated above, aerial 

radiometric Th values correlate with Th concentrations in stream sediments, suggesting that the 
46

radiometric surveys indicate areas that might be fed by material from Th- and incompatibleelement-bearing rocks. HUCs with Th values ≥75th percentile (~6 eq. ppm Th; Duval, 2001) 

were assigned a score of 1 point, a smaller percentage of the total score than other parameters 

because elevated Th values may, but do not necessarily, correspond to possible enrichments of 

Sn, W and (or) Ta. As with the other datasets, each HUC was limited to a score based on its 

maximum radioactivity, with a maximum value of 1 point (table 13).

Results and Discussion

The results of attribute analyses showed that the variety of rock types that hosted 

enriched concentrations of Sn, W, Ta, Mo, In, and F could not be applied to a discrete map unit, 

and in many cases could be overstated if assigned to a plutonic complex mapped as a single unit.

This is consistent with descriptions of known occurrences at which concentrations of these 

commodities are almost entirely associated with veins and greisens associated with late phases of 

a variety of granitic rocks. These types of localized features are not commonly mapped as 

discrete units in the regional-scale maps available for Alaska. Thus, although “specialized 

granite” defines the best approximation of a host-rock type for a deposit model for theses 

commodities, the location of specialized granites is more precisely designated by analytical-datapoint locations than by map units, and discrete sample locations within HUCs can (1) constrain 

the location of specialized granite phases within a larger generalized intrusive body and (2) 

identify the location of intrusive phases too small to appear at a regional map scale and isolated 

as dikes or veins in the host rocks of an intrusive body.

In the scoring method created for the Sn-W-Mo (-fluorspar-Ta-In) deposit model, streamsediment geochemistry contributed as much as 50 percent of the total HUC score, which is

considered valid for this dataset because it has the most comprehensive and systematic coverage 

for the CYPA and is quantitative evidence for the presence of the target elements. Igneous rock 

geochemistry, the best predictor of appropriate host rock types for undiscovered Sn, W, Mo, Ta, 

In, and (or) fluorspar resources in igneous rocks at a regional scale, accounted for as much as 35 

percent of the total HUC score. ARDF localities, although important for showing locations of 

known Sn, W, Mo, Ta, In, and (or) fluorspar concentrations and indirectly suggesting the 

possibility of additional deposits in the vicinity, require prior knowledge or entry into the 

existing database, do not represent systematic evaluation of the project area, and more 

importantly, do not necessarily predict undiscovered resources in areas that do not contain 

known occurrences; therefore, ARDF localities contributed only as much as 10 percent of the 

total HUC score. Gamma-ray survey data contributed as much as 5 percent of total HUC score,

because these data do not constitute as strong a predictor of the targeted deposits as do the other 

factors.

The total possible score an HUC could receive for Sn-W-Mo (-Ta-In-fluorspar) deposits

was 21 points, and the maximum score for any HUC in the CYPA and buffer was 15 (appendix 

E). Composite scores were subdivided at natural statistical breaks by the Jenks method into three 

categories corresponding to low, medium, and high potential, with score ranges of 0–1, 2–5, and 

6-21 points, respectively (table 15). Certainty values were assigned based on the number of 

datasets that contributed to the total score for each HUC, such that low, medium, and high values 

correspond to contributions of 0–1, 2–3, and 4–5 datasets, respectively (table 15).

Results of the scoring analysis for Sn-W-Mo (-Ta-In-fluorspar) deposits are shown in 

plate 11, and accompanying data are in appendix E. A total of 97 HUCs (4 percent of HUCs in 

the CYPA) are considered to have high potential to contain a mineral occurrence of this type. Of 
47

those, 70 also have high certainty. There are 624 HUCs (25 percent of HUCs in the CYPA) that 

have medium potential. The remaining 1,724 HUCs (70 percent of the CYPA) have low potential

on the basis of our scoring method. A total of 1,215 HUCs have total scores of 0; only 17 of 

these (<1 percent of CYPA) were classified as having unknown potential and certainty (plate 11, 

appendix E) because of a lack of data.

The areas that stand out as having the highest potential for Sn-W-Mo (-Ta-In-fluorspar) 

mineralization in the CYPA and its buffer zone include (1) areas underlain by Devonian felsic 

intrusive rocks in the southern Brooks Range, (2) areas near middle to Late Cretaceous intrusive 

rocks in the Hogatza igneous belt, (3) parts of the middle Cretaceous Ruby batholith, (4) areas 

near Late Cretaceous to early Tertiary intrusive rocks in the Tofty belt and discrete locations in 

the Sischu-Prindle igneous belt, and (5) the southeastern Yukon-Tanana upland (plate 12). Areas 

with moderate potential include (1) the Brooks Range, although generally with low certainty, (2) 

the Hogatza igneous belt, (3) most of the Ruby batholith and its inferred southwestern extension 

in the Kaiyuh Mountains area, (4) isolated HUCs, some of which are known to contain igneous 

rocks in the Yukon-Koyukuk Basin, (5) discrete locations in the Sischu-Prindle igneous belt, (6) 

a broad area of poor rock exposure to the southwest of known intrusive rocks east of Fairbanks, 

and (7) areas near known Cretaceous-Tertiary intrusive suites in the Yukon Tanana uplands and 

the northern foothills of the Alaska Range in the southeastern part of the CYPA (plate 12).

Three areas of interest that contain groups of HUCs that have medium to high potential 

but low certainty (based on limited available data) are (1) the area west of Fairbanks, (2) the area 

in the southeastern CYPA in the northern foothills of the Alaska Range near the Ptarmigan Creek 

deposits, and (3) the area in the southwestern CYPA in the vicinity of the Kaiyuh Mountains 

near the Illinois Creek and Round Top deposits. It is uncertain whether areas with low potential 

might be more favorable if additional data were available, but combinations of available datasets 

in some cases suggest the presence of appropriate rocks in regions of poor exposure. For 

example, in some areas where igneous rocks are not mapped, stream sediments have high values

for Sn, W, Mo, Ta, and (or) In, raising the possibility of potentially permissive rocks in areas 

where they were not previously documented. Specific areas that have high stream-sediment

values for the target elements but lack information with respect to bedrock compositions, and 

lack known occurrences of the target commodities, include the Yukon Tanana uplands near 

Fairbanks and the southeastern corner of the CYPA (plate 12). These results suggest that areas 

with significant potential but low certainty warrant further investigation.

Summary

The method described herein provides means by which to assemble and analyze disparate 

datasets in a geospatial environment to map mineral resource prospectivity across broad areas 

such as the CYPA in Alaska. It is worth noting that the data distribution for the datasets used 

herein is generally not always consistent across the State. In particular, the number and spatial 

distribution of geochemistry sample localities differs significantly among 1:250,000-scale 

quadrangles throughout the State (Granitto and others, 2013). In some areas, ARDF records 

cluster around known occurrences, prospects, or mines and (or) they cluster in areas with easier 

means of access such as along roads or rivers. Attempts were made to equalize scores potentially 

skewed through uneven sampling by limiting the number of scores per database that could be 

applied to each HUC. The ability and opportunity to analyze and query the results has also been 

preserved by providing complete data regarding the individual scoring components and score 

results for each deposit group (appendix E). For those datasets such as the AGDB2, ADGGS 
48

geochemical data, and ARDF that are publicly available, it is possible for an individual user to 

compile the same data and evaluate sampling trends and compare them to our scoring results 

across the CYPA.

The results described above highlight numerous areas, some of them large, rated with 

high potential for one or more of the selected deposit groups within the CYPA. The CYPA has 

recognizable potential for REE deposits associated with the Hogatza plutonic belt and parts of 

the Ruby batholith. Another area of high potential surrounds a known carbonatite occurrence 

near Tofty, Alaska, in the Hot Springs placer district. Placer-gold potential is relatively high 

along drainages scattered across the CYPA. Prospecting for and production from relatively small 

placer-gold deposits were widespread in the past and have continued to the present. Areas with 

high potential for PGE deposits associated with mafic and ultramafic rocks are in the 

northwestern Brooks Range to the west of the CYPA and in the Killik River quadrangle within 

the CYPA. Another area of high PGE potential flanks the Ruby batholith to the northwest, 

generally within and near the Kanuti River drainage. High potential for carbonate-hosted copper 

deposits exists outside the CYPA within the Gates of the Arctic National Park and Preserve 

along the south flank of and into the core of the Brooks Range. That discontinuous belt of high 

potential extends far to the northeast and includes areas within the CYPA in the Wiseman, 

Chandalar, and Philip Smith Mountains quadrangles. Other areas with high potential for copper 

deposits in carbonate rocks are in the southeastern CYPA and in the Brooks Range foreland. 

Sandstone uranium potential within the CYPA appears most closely associated with areas 

proximal to felsic intrusive igneous rocks such as the Hogatza plutonic belt and the Ruby 

batholith; the potential appears highest for a basal-type sandstone uranium deposit analogous 

with the Death Valley (Boulder Creek) deposit adjacent to the Darby pluton on the eastern 

Seward Peninsula. The highest potential for Sn-W-Mo-fluorspar deposits is associated with the 

Hogatza belt and Ruby batholith plutons, but high potential also occurs along the ToftyLivengood belt of intrusive igneous rocks and in scattered areas within the Yukon-Tanana 

uplands.

Future geologic investigations might focus on areas that lack information but have 

relatively high potential scores based on limited available data. Land use planning decisions will 

benefit most from additional data in areas with low degrees of certainty because, in these areas, a 

lack of relevant data precludes sufficient evaluation of mineral potential. Mineral exploration 

targets will be most confidently pursued in areas of known potential.

Data Resources

Alaska Resource Data File (ARDF)—http://ardf.wr.usgs.gov/

Alaska Geochemical Database Version 2.0 (AGDB2)—http://pubs.usgs.gov/ds/759/

National Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment 

Reconnaissance data—http://pubs.usgs.gov/of/1997/ofr-97-0492/

Alaska Division of Geological and Geophysical Surveys (ADGGS) WebGeochem geochemical 

sample analysis search—http://www.dggs.alaska.gov/webgeochem/

Alaska aerial gamma-ray surveys—http://pubs.usgs.gov/of/2001/of01-128/

Map of Alaska’s coal resources (Merritt and Hawley, 1986)—

http://www.dggs.dnr.state.ak.us/pubs/pubs?reqtype=citation&ID=2636
49

References Cited

Ames, D.E., Davidson, A., and Wodicka, N., 2008, Geology of the giant Sudbury polymetallic 

mining camp, Ontario, Canada: Economic Geology, v. 103, no. 5, p. 1057–1077.

Angiboust, S., Fayek, M., Power, I. M., Camacho, A., Calas, G., and Southam, G., 2012, 

Structural and biological control of the Cenozoic epithermal uranium concentrations from the 

Sierra Peña Blanca, Mexico: Mineralium Deposita, v. 47, no. 8, p. 859–874.

Arbogast, B.F., ed., 1990, Quality assurance manual for the Branch of Geochemistry, U.S. 

Geological Survey, U.S. Geological Survey Open-File Report 90–668, 184 p.

Arbogast, B.F., ed., 1996, Analytical methods manual for the Mineral Resource Surveys 

Program, U.S. Geological Survey, U.S. Geological Survey Open-File Report 96–525, 248 p.:

Armbrustmacher, T.J., 1989, Minor element content, including radioactive elements and rareearth elements, in rocks from the syenite complex at Roy Creek: Mount Prindle area, Alaska: 

U.S. Geological Survey Open-File Report 89–0146, 11 p.

Arth, J.G., Criss, R.E., Zmuda, C.C., Foley, N.K., Patton, W.W., and Miller, T.P., 1989, 

Remarkable isotopic and trace element trends in potassic through sodic Cretaceous plutons of 

the Yukon-Koyukuk Basin, Alaska, and the nature of the lithosphere beneath the Koyukuk 

terrane: Journal of Geophysical Research, Solid Earth, v. 94, no. B11, p. 15,957–915,968.

Ashleman, J., Taylor, C., and Smith, P., 1997, Porphyry molybdenum deposits of Alaska, with 

emphasis on geology of Quartz Hill deposit, southeastern Alaska: Economic Geology 

Monograph, v. 9, p. 334–354.

Athey, J.E., Harbo, L.A., Lasley, P.S., and Freeman, L. K., 2013, Alaska’s mineral industry, 

2012: Alaska Division of Geological and Geophysical Surveys Special Report 68, p. 61.

Baker, T., 2002, Emplacement depth and carbon dioxide-rich fluid inclusions in intrusion-related 

gold deposits: Economic Geology, v. 97, no. 5, p. 1111–1117.

Barker, J.C., 1985, Sampling and analytical results of a mineral reconnaissance in the Selawik 

Hills area, northwestern Alaska: U.S. Bureau of Mines Open-File Report 43–85, 67 p.

Barker, J.C., 1991, Investigation of rare-earth elements and zirconium in the Windy Fork 

peralkaline pluton, west-central Alaska: U.S. Bureau of Mines Field Report, 36 p.

Barker, J.C., and Foley, J.Y., 1986, Tin reconnaissance of the Kanuti and Hodzana Rivers 

uplands, central Alaska: U.S. Bureau of Mines Information Circular 9104, 27 p.

Barker, J.C., and Swainbank, R.C., 1986, A tungsten-rich porphyry molybdenum occurrence at 

Bear Mountain, northeast Alaska: Economic Geology, v. 81, no. 7, p. 1753–1759.

Barker, S.L.L., Hickey, K.A., Cline, J.S., Dipple, G.M., Kilburn, M.R., Vaughan, J.R., and 

Longo, A.A., 2009, Uncloaking invisible gold; Use of nanoSIMS to evaluate gold, trace 

elements, and sulfur isotopes in pyrite from Carlin-type gold deposits: Economic Geology, v. 

104, no. 7, p. 897–904.

Barnes, S.-J., and Lightfoot, P.C., 2005, The formation of magmatic nickel-copper-PGE sulfide 

deposits, in Hedenquist, J.W., Thompson, J.F.H., Goldfarb, R.J., and Richards, J.P., eds., 

Economic Geology one hundredth anniversary volume 1905–2005: Littleton, Colorado, 

Society of Economic Geologists, p. 179–213.

Barnes, S.J., Godel, B., Gürer, D., Brenan, J.M., Robertson, J., and Paterson, D., 2013, Sulfideolivine Fe-Ni exchange and the origin of anomalously Ni rich magmatic sulfides: Economic 

Geology, v. 108, no. 8, p. 1971–1982.

Bea, F., 1996, Residence of REE, Y, Th and U in granites and crustal protoliths; Implications for 

the chemistry of crustal melts: Journal of Petrology, v. 37, no. 3, p. 521–552.
50

Beaty, D.W., Landis, G.P., and Thompson, T.B., 1990, Carbonate-hosted sulfide deposits of the 

Central Colorado mineral belt: Economic Geology Monograph 7, 424 p.

Bentzen, E.H., Ghaffari, H., Eng, P., Galbraith, L., Hammen, R.F., Robinson, R.J., Hafez, S.A., 

and Annavarapu, S., 2013, Preliminary economic assessment; Bokan Mountain rare earth 

element project, near Ketchikan, Alaska: Vancouver, British Columbia, Canada, Ucore Metals, 

Inc. [prepared by Tetra Tech, Inc.], document no. 1196000100-REP-R0001-02, 219 p.

Berger, B.R., and Henley, R.W., 1989, Advances in the understanding of epithermal gold-silver 

deposits, with special reference to the western United States, in Kcays, R.R., Ramsay, W.R.H., 

and Groves, D.I., eds., The geology of gold deposits; The perspective in 1988: Economic 

Geology Monograph 6, p. 405–423.

Berger, V.I., Singer, D.A., and Orris, G.J., 2009, Carbonatites of the World, Explored Deposits 

of Nb and REE--database and Grade and Tonnage Models, U.S. Geological Survey Open-File 

Report 2009–1139, 17 p. and database.

Bernstein, L.R., and Cox, D.P., 1986, Geology and sulfide mineralogy of the Number One 

Orebody, Ruby Creek copper deposit, Alaska: Economic Geology, v. 81, no. 7, p. 1675–1689.

Bittenbender, P.E., Bean, K.W., Kurtak, J.M., and Deininger, J., Jr., 2007, Mineral assessment of 

the Delta River mining district area, east-central Alaska, U.S. Bureau of Land Management, 

Alaska Technical Report 57, 676 p.

Blatt, H., Tracy, R.J., and Owens, B., 2006, Petrology: igneous, sedimentary, and metamorphic, 

3d ed.: New York, W.H. Freeman and Company, 533 p.

Blevin, P.L., and Chappell, B.W., 1992, The role of magma sources, oxidation states and 

fractionation in determining the granite metallogeny of eastern Australia: Transactions of the 

Royal Society of Edinburgh; Earth Sciences, v. 83, no. 1-2, p. 305–316.

Bliss, J., ed., 1992, Developments in Mineral deposit modeling: U.S. Geological Survey Bulletin 

2004, p. 168 p.

Bluemel, B., Leijd, M., Dunn, C., Hart, C.J., Saxon, M., and Sadeghi, M., 2013, Biogeochemical 

expression of rare earth element and zirconium mineralization at Norra Kärr, Southern 

Sweden: Journal of Geochemical Exploration, v. 133, p. 15–24.

Blundy, J., and Wood, B., 2003, Mineral-melt partitioning of uranium, thorium and their 

daughters: Reviews in Mineralogy and Geochemistry, v. 52, no. 1, p. 59–123.

Borg, G., Frotzscher, M., and Ehling, B., 2005, Metal content and spatial distribution of Au and 

PGE in the Kupferschiefer of the Mansfeld/Sangerhausen mining district, Germany: Mineral 

Deposit Research; Meeting the Global Challenge, proceedings of the eighth biennial SGA 

meeting, p. 885–888.

Boyle, D.R., 1982, The formation of basal-type uranium deposits in south central British 

Columbia: Economic Geology, v. 77, no. 5, p. 1176–1209.

Burleigh, R.E., 1992, Tin mineralization at the Won Prospect, west-central Alaska: U.S. Bureau 

of Mines Open-File Report 85–92, p. 21 p.

Burton, P.J., 1981, Radioactive mineral occurrences, Mount Prindle area, Yukon-Tanana 

uplands, Alaska: Fairbanks, University of Alaska, M.S. thesis, 72 p.

Castor, S.B., 2008, The Mountain Pass rare-earth carbonatite and associated ultrapotassic rocks, 

California: The Canadian Mineralogist, v. 46, no. 4, p. 779–806.

Černý, P., 1991, Rare-element granitic pegmatites, Part I; Anatomy and internal evolution of 

pegmatitic deposits: Geoscience Canada, v. 18, p. 49–67.

Černý, P., Blevin, P.L., Cuney, M., and London, D., 2005, Granite-related ore deposits, in

Hedenquist, J.W., Thompson, J.F.H., Goldfarb, R.J., and Richards, J.P., eds., Economic 
51

Geology one hundredth anniversary volume 1905–2005: Littleton, Colorado, Society of 

Economic Geologists, p. 337–370.

Chao, E.C., Back, J., Minkin, J., Tatsumoto, M., Wang, J., Conrad, J., McKee, E., Hou, Z., 

Meng, Q., and Huang, S., 1997, The sedimentary carbonate-hosted giant Bayan Obo REE-FeNb ore deposit of Inner Mongolia, China; A cornerstone example for giant polymetallic ore 

deposits of hydrothermal origin, U.S. Geological Survey Bulletin 2143, n.p.

Cheilletz, A., Clark, A.H., Farrar, E., Pauca, G.A., Pichavant, M., and Sandeman, H.A., 1992, 

Volcano-stratigraphy and 40Ar/39Ar geochronology of the Macusani ignimbrite field; monitor 

of the Miocene geodynamic evolution of the Andes of southeast Peru: Tectonophysics, v. 205, 

no. 1, p. 307-327.

Clemens, J.D., and Wall, V.J., 1984, Origin and evolution of a peraluminous silicic ignimbrite 

suite; the Violet Town Volcanics: Contributions to Mineralogy and Petrology, v. 88, no. 4, p. 

354–371.

Cobb, E.H., compiler, 1972, Metallic mineral resources map of the Nulato quadrangle, Alaska: 

U.S. Geological Survey Miscellaneous Field Studies Map MF-423, 1 sheet, scale 1:250,000.

Cox, D.P., 1986, Descriptive model of porphyry Cu in Cox, D.P., and Singer, D.A., eds., Mineral 

deposit models: U.S. Geological Survey Bulletin, 1693, p. 76.

Cox, D.P., and Singer, D.A., eds.,1986, Mineral deposit models: U.S. Geological Survey Bulletin 

1693, 379 p.

Cuney, M., and Kyser, T.K., 2009, Recent and not-so-recent developments in uranium deposits 

and implications for exploration: Mineralogical Association of Canada, Short Course 39, 257 

p.

Czamanske, G.K., Haffty, J., and Nabbs, S.W., 1981, Pt, Pd, and Rh analyses and beneficiation 

of mineralized mafic rocks from the La Perouse Layered Gabbro, Alaska: Economic Geology, 

v. 76, no. 7, p. 2001–2011.

Dashevsky, S.S., 2002, Alaska Resource Data File, Norton Bay Quadrangle, Alaska: U.S. 

Geological Survey Open-File Report 02–75, 27 p.

Davis, B., Sim, R., and Austin, J., 2014, Technical Report on the Bornite Project, Northwest 

Alaska, USA: Vancouver, British Columbia, Canada, NovaCopper Inc. [Report prepared by 

BD Resource Consulting, Inc., SIM Geological Inc., and International Metallurgical & 

Environmental Inc.], NI 43-101, 142 p.

Dickinson, K.A., Cunningham, K.D., and Ager, T.A., 1987, Geology and origin of the Death 

Valley uranium deposit, Seward Peninsula, Alaska: Economic Geology, v. 82, no. 6, p. 1558–

1574.

Dobson, D.C., 1982, Geology and alteration of the Lost River tin-tungsten-fluorine deposit, 

Alaska: Economic Geology, v. 77, no. 4, p. 1033–1052.

Dostal, J., Karl, S. M., Keppie, J.D., Kontak, D.J., Shellnutt, J.G., and Murphy, B., 2013, Bokan 

Mountain peralkaline granitic complex, Alexander terrane (southeastern Alaska): evidence for 

Early Jurassic rifting prior to accretion with North America: Canadian Journal of Earth 

Sciences, v. 50, no. 6, p. 678–691.

Dostal, J., Kontak, D.J., and Karl, S.M., 2014, The early Jurassic Bokan Mountain peralkaline 

granitic complex (southeastern Alaska); Geochemistry, petrogenesis and rare-metal 

mineralization: Lithos, v. 202, p. 395–412.

Duval, J.S., 2001, Aerial gamma-ray surveys in Alaska: U.S. Geological Survey Open-File 

Report 01–128.

Eby, G.N., 1990, The A-type granitoids; A review of their occurrence and chemical 

characteristics and speculations on their petrogenesis: Lithos, v. 26, p. 115–134.
52

Eckstrand, O.R., ed., 1985, Canadian mineral deposit types; a geological synopsis: Geological 

Survey of Canada, Economic Geology Report 36, 68 p.

Eckstrand, O.R., and Hulbert, L. J., 2007, Magmatic nickel-copper-platinum group element 

deposits in Goodfellow, W.D., ed., Mineral deposits of Canada; A synthesis of major deposittypes, district metallogeny, the evolution of geological provinces, and exploration methods: 

Geological Association of Canada, Mineral Deposits Division, Special Publication no. 5, p. 

205–222.

Flanigan, B., 1998, Genesis and mineralization of ore deposits in the Illinois Creek region, west 

central Alaska: Anchorage, University of Alaska, M.S. thesis, 125 p. 

Foley, J., and Barker, J., 1986, Uranium occurrences in the northern Darby Mountains, Seward 

Peninsula, AK: U.S. Bureau of Mines Information Circular 9103, 27 p.

Foley, J.Y., and Barker, J.C., 1984, Chromite deposits along the Border Ranges fault, southern 

Alaska (in two parts); 1. Field investigations and descriptions of chromite deposits, Avondale, 

Maryland: U.S. Bureau of Mines Information Circular 8990, 58 p.

Foley, J., Light, T., Nelson, S., and Harris, R., 1997, Mineral occurrences associated with maficultramafic and related alkaline complexes in Alaska: Economic Geology, v. 9, p. 396–449.

Force, E.R., 1991, Geology of titanium-mineral deposits: Geological Society of America Special 

Papers, v. 259, p. 1–112.

Frimmel, H., Groves, D., Kirk, J., Ruiz, J., Chesley, J., and Minter, W., 2005, The formation and 

preservation of the Witwatersrand goldfields, the world’s largest gold province in Hedenquist, 

J.W., Thompson, J.F.H., Goldfarb, R.J., and Richards, J.P., eds., Economic Geology one 

1905–2005 anniversary volume: Littleton, Colorado, Society of Economic Geologists, p. 769–

797.

Frost, B.R., and Frost, C.D., 2008, A geochemical classification for feldspathic igneous rocks: 

Journal of Petrology, v. 49, no. 11, p. 1955–1969.

Frost, C.D., and Frost, B.R., 2011, On ferroan (A-type) granitoids; their compositional variability 

and modes of origin: Journal of Petrology, v. 52, no. 1, p. 39–53.

Frost, B.R., Barnes, C.G., Collins, W.J., Arculus, R.J., Ellis, D.J., and Frost, C.D., 2001, A 

geochemical classification for granitic rocks: Journal of Petrology, v. 42, no. 11, p. 2033–2048.

Garnett, R.H.T., and Bassett, N. C., 2005, Placer deposits, in Hedenquist, J.W., Thompson, 

J.F.H., Goldfarb, R.J., and Richards, J.P., eds., Economic Geology one hundredth anniversary 

volume 1905–2005: Littleton, Colorado, Society of Economic Geologists, p. 813–843.

Ghaffari, H., Morrison, D.R.S., Ruijter, M.A. d., Živković, A., Hantelmann, T., Ramsey, D., and 

Cowie, S., 2011, Preliminary assessment of the Pebble project Southwest Alaska: Vancouver, 

British Columbia, Canada, Northern Dynasty Minerals, Ltd. [produced by Terra Tech, Inc.], 

document no. 1056140100-REP-R0001-00, 529 p.

Goldfarb, R.J., Baker, T., Dubé, B., Groves, D.I., Hart, C.J.R., and Gosselin, P., 2005, 

Distribution, character, and genesis of gold deposits in metamorphic terranes, in Hedenquist, 

J.W., Thompson, J.F.H., Goldfarb, R.J., and Richards, J.P., eds., Economic Geology one 

hundredth anniversary volume 1905–2005: Littleton, Colorado, Society of Economic 

Geologists, p. 407–450.

Granitto, M., Bailey, E.A., Schmidt, J.M., Shew, N.B., Gamble, B.M., and Labay, K.A., 2011, 

Alaska Geochemical Database (AGDB)—Geochemical Data for Rock, Sediment, Soil, 

Mineral and Concentrate Sample Media: U.S. Geological Survey Data Series 637, 31 p. and 

database, 1 DVD.

Granitto, M., Schmidt, J.M., Shew, N.B., Gamble, B.M., and Labay, K.A., 2013, Alaska 

Geochemical Database, Version 2.0 (AGDB2)—including “best value” data compilations for 
53

rock, sediment, soil, mineral, and concentrate sample media: U.S. Geological Survey Data 

Series 759, 20 p. and database, 1 DVD.

Grant, J.N., Halls, C., Sheppard, S.M., and Avila, W., 1980, Evolution of the porphyry tin 

deposits of Bolivia, in Ishihara, S., and Takenouchi, S., eds., Granitic magmatism and related 

mineralization: The Society of Mining Geologists of Japan, Mining Geology Special Issue, no. 

8, p. 151–173.

Groves, D., Goldfarb, R., Gebre-Mariam, M., Hagemann, S., and Robert, F., 1998, Orogenic 

gold deposits; A proposed classification in the context of their crustal distribution and 

relationship to other gold deposit types: Ore Geology Reviews, v. 13, p. 7–27.

Guilbert, J.M., and Park, C.F., 1986, The geology of ore deposits: Long Grove, Illinois, 

Waveland Press, Inc., 985 p.

Hamilton, N.T.M., 1995, Controls on the global distribution of coastal titanium-zirconium 

placers: International Geology Review, v. 37, no. 9, p. 755–779.

Harris, D.C., and Cabri, L.J., 1991, Nomenclature of platinum-group-element alloys; review and 

revision: The Canadian Mineralogist, v. 29, no. 2, p. 231–237.

Harshman, E.N., 1972, Geology and uranium deposits, Shirley Basin area, Wyoming: U.S. 

Geological Survey Professional Paper 745, 82 p.

Hart, C.J.R., 2007, Reduced intrusion-related gold systems, in Goodfellow, W.D., ed., Mineral 

deposits of Canada; A synthesis of major deposit-types, district metallogeny, the evolution of 

geological provinces, and exploration methods: Geological Association of Canada, Mineral 

Deposits Division, Special Publication no. 5, p. 95–112.

Helsel, D.R., 2012, Statistics for censored environmental data using Minitab and R: New York, 

John Wiley and Sons, 344 p.

Himmelberg, G.R., and Loney, R.A., 1995, Characteristics and petrogenesis of Alaskan-type 

ultramafic-mafic intrusions, southeastern Alaska: U.S. Geological Survey Professional Paper 

1564, 47 p.

Hitzman, M.W., 1986, Geology of the Ruby Creek copper deposit, southwestern Brooks Range, 

Alaska: Economic Geology, v. 81, no. 7, p. 1644–1674.

Höll, R., Kling, M., and Schroll, E., 2007, Metallogenesis of germanium—A review: Ore 

Geology Reviews, v. 30, no. 3–4, p. 145–180.

Hudson, T., and Arth, J.G., 1983, Tin granites of Seward Peninsula, Alaska: Geological Society 

of America Bulletin, v. 94, no. 6, p. 768–790.

Hudson, T., and Reed, B., 1997, Tin deposits in Alaska in Goldfarb, R.J., and Miller, L.D., eds., 

Mineral deposits of Alaska: Economic Geology Monograph, no. 9, p. 450–465.

Hulbert, L.J., 1997, Geology and metallogeny of the Kluane mafic-ultramafic Belt, Yukon 

Territory, Canada; eastern Wrangellia; a new Ni-Cu-PGE metallogenic terrane: Geological 

Survey of Canada Bulletin 506, 265 p.

Hulbert, L.J., Carne, R.C., Gregoire, D.C., and Paktunc, D., 1992, Sedimentary nickel, zinc, and 

platinum-group-element mineralization in Devonian black shales at the Nick Property, Yukon, 

Canada; a new deposit type: Exploration and Mining Geology, v. 1, no. 1, p. 39–62.

Huyck, H.L, 1990, When is a metalliferous black shale not a black shale, in Grauch, R.I., and 

Huyck, H.L.O., compilers, Metalliferous black shales and related ore deposits––Proceedings, 

1989 United States Working Group Meeting, International Geological Correlation Program 

Project 254: U.S. Geological Survey Circular 1058, p. 42–56.

Irvine, T.N., and Baragar, W.R.A., 1971, A guide to the chemical classification of the common 

volcanic rocks: Canadian Journal of Earth Sciences, v. 8, no. 5, p. 523–548.
54

Ishihara, S., Hoshino, K., Murakami, H., and Endo, Y., 2006, Resource evaluation and some 

genetic aspects of indium in the Japanese ore deposits: Resource Geology, v. 56, no. 3, p. 347–

364.

Johan, Z., Strnad, L., and Johan, V., 2012, Evolution of the Cínovec (zinnwald) granite cupola, 

Czech Republic; Composition of feldspars and micas, a clue to the origin of W, Sn 

mineralization: The Canadian Mineralogist, v. 50, no. 4, p. 1131–1148.

Kamona, A., and Günzel, A., 2007, Stratigraphy and base metal mineralization in the Otavi 

Mountain Land, Northern Namibia—a review and regional interpretation: Gondwana 

Research, v. 11, no. 3, p. 396–413.

Kampunzu, A., Cailteux, J., Kamona, A., Intiomale, M., and Melcher, F., 2009, Sediment-hosted 

Zn–Pb–Cu deposits in the Central African Copperbelt: Ore Geology Reviews, v. 35, no. 3, p. 

263–297.

Keith, T.E.C., Page, N.J., Oscarson, R.L., and Foster, H.L., 1987, Platinum-group element 

concentrations in a biotite-rich clinopyroxenite suite, Eagle C-3 quadrangle, Alaska, in

Hamilton, T.D., and Galloway, J.P., eds., Geologic studies in Alaska by the U.S. Geological 

Survey during 1986: U.S. Geological Survey Circular 998, p. 62–68.

Kelley, K.D., Lang, J.R., and Eppinger, R.G., 2013, The giant Pebble Cu-Au-Mo deposit and 

surrounding region, southwest Alaska; Introduction: Economic Geology, v. 108, no. 3, p. 397–

404.

Kimball, A.L., Still, J.C., and Rataj, J.L., 1978, Mineral resources, in Brew, D.A., Johnson, B.R., 

Grybeck, D., Griscom, A., Barnes, ed., Mineral resources of the Glacier Bay National 

Monument Wilderness Study Area: U.S. Geological Survey Open-File Report 78–494, p. 1C–

375C.

Kogarko, L., Lahaye, Y., and Brey, G., 2010, Plume-related mantle source of super-large rare 

metal deposits from the Lovozero and Khibina massifs on the Kola Peninsula, Eastern part of 

Baltic Shield; Sr, Nd and Hf isotope systematics: Mineralogy and Petrology, v. 98, no. 1–4, p. 

197–208.

Kooiman, G., McLeod, M., and Sinclair, W., 1986, Porphyry tungsten-molybdenum orebodies, 

polymetallic veins and replacement bodies, and tin-bearing greisen zones in the Fire Tower 

Zone, Mount Pleasant, New Brunswick: Economic Geology, v. 81, no. 6, p. 1356–1373.

Kwak, T., and Askins, P., 1981, Geology and genesis of the F-Sn-W (-Be-Zn) skarn (wrigglite) 

at Moina, Tasmania: Economic Geology, v. 76, no. 2, p. 439–467.

Kwak, T.A.P., 1987, W-Sn skarn deposits and related metamorphic skarns and granitoids: 

Amsterdam, Netherlands, Elsevier, 468 p.

Le Bas, M., Kellere, J., Kejie, T., Wall, F., William, C., and Peishan, Z., 1992, Carbonatite dykes 

at Bayan Obo, Inner Mongolia, China: Mineralogy and Petrology, v. 46, no. 3, p. 195–228.

Le Bas, M.J., Le Maitre, R.W., Streckeisen, A., and Zanettin, B., 1986, A chemical classification 

of volcanic rocks based on the total alkali silica diagram: Journal of Petrology, v. 27, no. 3, p. 

745–750.

Le Maitre, R.W., ed., 2002, Igneous Rocks; A Classification and Glossary of Terms: A 

Classification and Glossary of Terms; Recommendations of the International Union of 

Geological Sciences Subcommission on the Systematics of Igneous Rocks, 2d ed.: Cambridge, 

United Kingdom, Cambridge University Press, 236 p.

Li, C., Ripley, E.M., and Naldrett, A.J., 2009, A new genetic model for the giant Ni-Cu-PGE 

sulfide deposits associated with the Siberian flood basalts: Economic Geology, v. 104, no. 2, p. 

291–301.
55

Linnen, R.L., Samson, I.M., Williams-Jones, A.E., and Chakhmouradian, A.R., 2014, 

Geochemistry of the Rare-Earth Element, Nb, Ta, Hf, and Zr Deposits, chap. 21 in Holland, 

H.D., and Turekian, K.K., eds., Geochemistry of mineral deposits: Treatise on Geochemistry 

(2d ed.), vol. 13, p. 543–568.

Loney, R.A., and Himmelberg, G.R., 1989, The Kanuti ophiolite, Alaska: Journal of Geophysical 

Research, Solid Earth, v. 94, no. B11, p. 15,869–815,900.

Long, K.R., Van Gosen, B.S., Foley, N.K., and Cordier, D., 2010, The principal rare earth 

elements deposits of the United States—A summary of domestic deposits and a global 

perspective: U.S. Geological Survey Scientific Investigations Report 2010–5220, p. 96.

Ludington, S., and Plumlee, G.S., 2009, Climax-type porphyry molybdenum deposits: U.S.

Geological Survey Open-File Report 2009–1215, 16 p.

Ludington, S.D., Hammarstrom, J., and Piatak, N., 2009, Low-fluorine stockwork molybdenite 

deposits: U.S. Geological Survey Open-File Report 2009–1211, 9 p.

MacKevett, E.M., Jr., Cox, D.P., Potter, R.W., II, and Silberman, M.L., 1997, Kennecott-type 

deposits in the Wrangell Mountains, Alaska; High-grade copper ores near a basalt-limestone 

contact, in Goldfarb, R.J., and Miller, L.D., eds., Mineral deposits of Alaska: Economic 

Geology Monograph 9, p. 66–89.

Maniar, P.D., and Piccoli, P.M., 1989, Tectonic discrimination of granitoids: Geological Society 

of America Bulletin, v. 101, no. 5, p. 635–643.

Mernagh, T.P., Wyborn, L.I.A., and Jagodzinski, E.A., 1998, ‘Unconformity-related’ U ± Au ± 

platinum-group-element deposits: Australian Geological Survey Organization, Journal of 

Australian Geology & Geophysics, v. 17, no. 4, p. 197–205.

Merritt, R.D., and Hawley, C.C., 1986, Map of Alaska's coal resources: Alaska Dept. of Natural 

Resources, Division of Mining and Geological and Geophysical Surveys Special Report 37, 1 

sheet, scale 1:2,500,000.

Middlemost, E., 1990, Mineralogy and petrology of the rauhaugites of the Mt Weld carbonatite 

complex of Western Australia: Mineralogy and Petrology, v. 41, no. 2–4, p. 145–161.

Miesch, A.T., 1976, Sampling designs for geochemical surveys—Syllabus for a short course, 

U.S. Geological Survey Open-File Report 76–772, 127 p.:

Miller, J.D., Jr., Green, J.C., Severson, M.J., Chandler, V.W., Hauck, S.A., Peterson, D.M., and 

Wahl, T.E., 2002, Geology and mineral potential of the Duluth complex and related rocks of 

northeastern Minnesota: Minnesota Geological Survey Report of Investigations 58, 207 p.

Miller, T.P., and Ferrians, O., Jr., 1968, Suggested areas for prospecting in the central Koyukuk 

River region, Alaska: U.S. Geological Survey Circular 570, 12 p.

Miller, T.P., Moll, E., and Patton W.W., Jr., 1980, Uranium- and thorium-rich volcanic rocks of 

the Sischu Creek area, Medfra quadrangle, Alaska: U.S. Geological Survey Open-File Report 

80–803, 9 p.

Minter, W.E.L., 2006, The sedimentary setting of Witwatersrand placer mineral deposits in an 

Archean atmosphere, in Kesler, S.E., and Ohmoto, H., eds., Evolution of early Earth's 

atmosphere, hydrosphere, and biosphere—Constraints from ore deposits: Geological Society 

of America Memoir 198, p. 105–119.

Moroni, M., Girardi, V., and Ferrario, A., 2001, The Serra Pelada Au-PGE deposit, Serra dos 

Carajás (Pará State, Brazil); geological and geochemical indications for a composite 

mineralising process: Mineralium Deposita, v. 36, no. 8, p. 768–785.

Naldrett, A., 2004, Magmatic sulfide deposits; Geology, geochemistry and exploration: Berlin, 

Springer, 727 p.
56

Naldrett, A.J., Asif, M., Krstic, S., and Li, C., 2000, The composition of mineralization at the 

Voisey’s Bay Ni-Cu sulfide deposit, with special reference to platinum-group elements: 

Economic Geology, v. 95, no. 4, p. 845–865.

National Research Council, 2008, Minerals, Critical Minerals, and the U.S. Economy: 

Washington, D.C., The National Academies Press, 264 p.

Newberry, R.J., and Brew, D.A., 1989, Epigenetic hydrothermal origin of the Groundhog BasinGlacier Basin silver-tin-lead-zinc deposits, southeastern Alaska, in Dover, J.H., ed., Geologica 

studies in Alaska by the U.S. Geological Survey, 1988: U.S. Geological Survey Bulletin 1903, 

p. 113–121.

Newberry, R., and Solie, D.N., 1995, Data for plutonic rocks and associated gold deposits in 

Interior Alaska, Division of Geological & Geophysical Surveys Public Data File 95–25, 62 p.

Newberry, R.J., and Swanson, S.E., 1986, Scheelite skarn granitoids; An evaluation of the roles 

of magmatic source and process: Ore Geology Reviews, v. 1, p. 57–81.

Newberry, R., Burns, L., Swanson, S., and Smith, T., 1990, Comparative petrologic evolution of 

the Sn and W granites of the Fairbanks-Circle area, interior Alaska: Geological Society of 

America Special Papers, v. 246, p. 121–142.

Newberry, R.J., Dillon, J.T., and Adams, D.D., 1986, Regionally metamorphosed, calc-silicatehosted deposits of the Brooks Range, northern Alaska: Economic Geology, v. 81, no. 7, p. 

1728–1752.

Nokleberg, W.J., Bundtzen, T.K., Berg, H., Brew, D., Grybeck, D., Robinson, M., Smith, T., and 

Yeend, W., 1987, Significant metalliferous lode deposits and placer districts of Alaska, U.S. 

Geological Survey Bulletin 1786, 104 p.

Northrop, H.R., Goldhaber, M.B., Landis, G.P., Unruh, J.W., Reynolds, R.L., Campbell, J.A., 

Wanty, R.B., Grauch, R.I., Whitney, G., and Rye, R.O., 1990, Genesis of the tabular-type 

vanadium-uranium deposits of the Henry Basin, Utah: Economic Geology, v. 85, no. 2, p. 

215–269.

Notholt, A., Highley, D., and Deans, T., 1990, Economic minerals in carbonatites and associated 

alkaline igneous rocks: Transactions of the Institution of Mining and Metallurgy. Section B.,

Applied Earth Science, v. 99, p. 59–B124.

Nyman, M.W., Sheets, R.W., and Bodnar, R.J., 1990, Fluid-inclusions evidence for the physical 

and chemical conditions associated with intermediate-temperature PGE mineralization at the 

New Rambler Deposit, southeastern Wyoming: The Canadian Mineralogist, v. 28, no. 3, p. 

629–638.

Patton, W.W., Jr., compiler, 1992, Ophiolitic terrane of the western Brooks Range, Alaska: U.S. 

Geological Survey Open-File Report 92-20D, 7 p.

Patton, W.W., Jr., and Moll-Stalcup, E.J., 2000, Geologic map of the Nulato Quadrangle, westcentral Alaska: U.S. Geological Survey Geologic Investigations Series I–2677, 41 p., 1 sheet, 

scale 1:250,000.

Patton, W.W., Jr., Wilson, F.H., Labay, K.A., and Shew, N., 2009, Geologic Map of the YukonKoyukuk Basin: U.S. Geological Survey Scientific Investigations Map 2909, 30 p., 3 sheets, 

scale 1:500,000.

Pearce, J.A., 1996, A user’s guide to basalt discrimination diagrams, in Wyman, D.A., and 

Bailes, A.H., eds., Trace element geochemistry of volcanic rocks; applications for massive 

sulphide exploration: Geological Association of Canada, Short Course Notes 12, p. 79–113.

Pearce, J.A., Harris, N.B.W., and Tindle, A.G., 1984, Trace element discrimination diagrams for 

the tectonic interpretation of granitic rocks: Journal of Petrology, v. 25, no. 4, p. 956–983.
57

Philpotts, J., Taylor, C., Evans, J., and Emsbo, P., 1993, Newly discovered molybdenite 

occurrences at Dora Bay, Prince of Wales Island, southeast Alaska, and preliminary scanning 

electron microscope studies in Dusel-Bacon, C., and Till, A.B., eds., Geologic studies in 

Alaska by the U.S. Geological Survey: U.S. Geological Survey Bulletin 2068, p. 187–196.

Philpotts, J.A., Taylor, C., Tatsumoto, M., and Belkin, H., 1998, Petrogenesis of late-stage 

granites and Y-REE-Zr-Nb-enriched vein dikes of the Bokan Mountain stock, Prince of Wales 

Island, Southeastern Alaska: U.S. Geological Survey Open-File Report 98–459, 71 p.

Pollard, P.J., 1995, A special issue devoted to the geology of rare metal deposits; geology of rare 

metal deposits; an introduction and overview: Economic Geology, v. 90, no. 3, p. 489–494.

Portnov, A., 1987, Specialization of rocks toward potassium and thorium in relation to 

mineralization: International Geology Review, v. 29, no. 3, p. 326–344.

Preto, V., 1978, Setting and genesis of uranium mineralization at Rexspar: Canadian Institute of 

Mining and Metallurgical Bulletin, v. 71, no. 800, p. 82–88.

Pretorius, D.A., 1981, Gold and uranium in quartz-pebble conglomerate, in Skinner, B.J., ed., 

Economic Geology, seventy-fifth anniversary volume, 1905–1980, El Paso, Texas, Society of 

Economic Geologists, p. 117–138.

Price, J.B., Hitzman, M.W., Nelson, E.P., Humphrey, J.D., and Johnson, C.A., 2014, Wall-rock 

alteration, structural control, and stable isotope systematics of the high-grade copper orebodies 

of the Kennecott district, Alaska: Economic Geology, v. 109, no. 3, p. 581–620.

Puchner, C.C., 1986, Geology, alteration, and mineralization of the Kougarok Sn deposit, 

Seward Peninsula, Alaska: Economic Geology, v. 81, no. 7, p. 1775–1794.

Raimbault, L., Cuney, M., Azencott, C., Duthou, J.-L., and Joron, J.L., 1995, Geochemical 

evidence for a multistage magmatic genesis of Ta-Sn-Li mineralization in the granite at 

Beauvoir, French Massif Central: Economic Geology, v. 90, no. 3, p. 548–576.

Rankin, A., 2005, Carbonatite-associated rare metal deposits; composition and evolution of oreforming fluids—the fluid inclusion evidence, in Samson, I. and Linnen., R., eds., Rare-element

geochemistry and ore deposits: Geological Association of Canada-Mineralogical Association 

of Canada Short Course Series, v. 17, p. 299–314.

Reed, B.L., 1986, Descriptive model of Sn greisen deposits in Cox, D.P., and Singer, D.A., eds., 

Mineral deposit models: U.S. Geological Survey Bulletin 1693, p. 70.

Reifenstuhl, R., Dover, J., Newberry, R., Clautice, K., Liss, S., Blodgett, R., and Weber, F., 

1998, Interpretive geologic bedrock map of the Tanana A-1 and A-2 quadrangles, central 

Alaska: Alaska Division of Geological & Geophysical Surveys Public Data File 98–37B, v. 

1.1, 17 p., 1 sheet, scale 1:63,360.

Richards, J., Dang, T., Dudka, S., and Wong, M., 2003, The Nui Phao tungsten-fluorite-coppergold-bismuth deposit, northern Vietnam; An opportunity for sustainable development: 

Exploration and Mining Geology, v. 12, no. 1–4, p. 61–70.

Richardson, D., and Birkett, T., 1996, Peralkaline rock-associated rare metals: Geology of 

Canadian Mineral Deposit Types, p. 523–540.

Richter, D. H., 1967, Geology of the Upper Slana-Mentasta Pass Area, Southcentral Alaska, 

Alaska Division of Mines and Minerals Geologic Report 30, 30 p., 2 sheets, scale 1:63,360

Rogers, J.J., and Greenberg, J.K., 1990, Late-orogenic, post-orogenic, and anorogenic granites; 

distinction by major-element and trace-element chemistry and possible origins: The Journal of 

Geology, v. 98, no. 3, p. 291–309.

Rollinson, H.R., 1993, Using geochemical data; Evaluation, presentation, interpretation: New 

York, Routledge, Longman Scientific and Technical series, 352 p.
58

Roy, P.S., 1999, Heavy mineral beach placers in southeastern Australia; their nature and genesis: 

Economic Geology, v. 94, no. 4, p. 567–588.

Runnells, D.D., 1969, The mineralogy and sulfur isotopes of the Ruby Creek copper prospect, 

Bornite, Alaska: Economic Geology, v. 64, no. 1, p. 75–90.

Saltus, R.W., Riggle, F.E., Clark, B.T., and Hill, P. L., 1999, Merged aeroradiometric data for 

Alaska; a web site for distribution of gridded data and plot files: U.S. Geological Survey OpenFile Report 99–0016, 13 p.

Schmidt, J.M., 1986, Stratigraphic setting and mineralogy of the Arctic volcanogenic massive 

sulfide prospect, Ambler District, Alaska: Economic Geology, v. 81, no. 7, p. 1619–1643.

Schulz, K.J., Chandler, V.W., Nicholson, S.W., Piatak, N., Seal, R.R., II, Woodruff, L.G., and 

Zientek, M.L., 2010, Magmatic sulfide-rich nickel-copper deposits related to picrite and (or) 

tholeiitic basalt dike-sill complexes—A preliminary deposit model: U.S. Geological Survey 

Open-File Report 2010–1179, 25 p.

Seedorff, E., Dilles, J.H., Proffett Jr., J.M., Einaudi, M.T., Zurcher, L., Stavast, W.J.A., Johnson, 

D.A., and Barton, M.D., 2005, Porphyry deposits; Characteristics and origin of hypogene 

features, in Hedenquist, J.W., Thompson, J.F.H., Goldfarb, R.J., and Richards, J.P., eds., 

Economic Geology one hundredth anniversary volume 1905–2005: Littleton, Colorado, 

Society of Economic Geologists, p. 251–298.

Seedorff, E., and Einaudi, M.T., 2004, Henderson porphyry molybdenum system, Colorado; I. 

Sequence and abundance of hydrothermal mineral assemblages, flow paths of evolving fluids, 

and evolutionary style: Economic Geology, v. 99, no. 1, p. 3–37.

Shand, S.J., 1947, Eruptive rocks, their genesis, composition, and classification, with a chapter 

on meteorites: New York, J. Wiley & Sons, 444 p.

Sheard, E.R., Williams-Jones, A.E., Heiligmann, M., Pederson, C., and Trueman, D.L., 2012, 

Controls on the concentration of zirconium, niobium, and the rare earth elements in the Thor 

Lake Rare Metal Deposit, Northwest Territories, Canada: Economic Geology, v. 107, no. 1, p. 

81–104.

Silberman, M.L., MacKevett, E.M., Connor, C.L., and Matthews, A., 1980, Metallogenic and 

tectonic significance of oxygen isotope data and whole-rock potassium-argon ages of the 

Nikolai Greenstone, McCarthy Quadrangle, Alaska: U.S. Geological Survey Open-File Report 

80–2019, 31 p.

Sinclair, W., Kooiman, G., Martin, D., and Kjarsgaard, I., 2006, Geology, geochemistry and 

mineralogy of indium resources at Mount Pleasant, New Brunswick, Canada: Ore Geology 

Reviews, v. 28, no. 1, p. 123–145.

Sinclair, W.D., 2007, Porphyry deposits, in Goodfellow, W.D., ed., Mineral deposits of Canada; 

A synthesis of major deposit-types, district metallogeny, the evolution of geological provinces, 

and exploration methods: Geological Association of Canada, Mineral Deposits Division, 

Special Publication no. 5, p. 223–243.

Singer, D.A., 1993, Basic concepts in three-part quantitative assessments of undiscovered 

mineral resources: Nonrenewable Resources, v. 2, no. 2, p. 69–81.

Singer, D.A., and Menzie, W.D., 2010, Quantitative mineral resource assessments: An integrated 

approach: Oxford, United Kingdom, Oxford University Press, 232 p.

Slingerland, R., and Smith, N.D., 1986, Occurrence and formation of water-laid placers: Annual 

Review of Earth and Planetary Sciences, v. 14, no. 1, p. 113–147.

Solie, D., Bundtzen, T., Bowman, N., and Cruse, G., 1993, Land selection unit 18 (Melozitna, 

Ruby, Nulato, and Kateel River quadrangles); References, DGGS sample locations, 
59

geochemical and major oxide data: Alaska Division of Geological & Geophysical Surveys 

Public Data File 93–18, 20 p., 1 sheet, scale 1: 250,000. 

Staatz, M., Sharp, B., and Hetland, D., 1979, Geology and mineral resources of the Lemhi Pass 

thorium district, Idaho and Montana: U.S. Geological Survey Professional Paper 1049-A, 90 

p., 2 pls.

Staatz, M.H., 1983, Geology and description of thorium and rare-earth deposits in the southern 

Bear Lodge Mountains, northeastern Wyoming, U.S. Geological Survey Professional Paper 

1049-D, 52 p., 2 pls.

Still, J.C., 1984, Copper, gold, platinum, and palladium sample results from the Klukwan 

mafic/ultramafic complex, southeast Alaska: U.S. Bureau of Mines Open-File Report 84–21, p. 

53 p.

Still, J.C., Hoekzema, R.B., Bundtzen, T.K., Gilbert, W.G., Wier, K.R., Burns, L.E., and 

Fechner, S.A., 1991, Economic geology of Haines-Klukwan-Porcupine area, southeastern 

Alaska: Alaska Division of Geological & Geophysical Surveys Report of Investigation 91–4, 

156 p.

Swanson, S.E., Newberry, R.J., Coulter, G.A., and Dyehouse, T.M., 1990, Mineralogical 

variation as a guide to the petrogenesis of the tin granites and related skarns, Seward 

Peninsula, Alaska, in Stein, H.J., and Hannah, J.L., eds., Ore-bearing granite systems; 

petrogenesis and mineralizing processes: Boulder, Colorado, Geological Society of America 

Special Paper 246, p. 143–160.

Sweetapple, M.T., and Collins, P.L., 2002, Genetic framework for the classification and 

distribution of Archean rare metal pegmatites in the North Pilbara Craton, Western Australia: 

Economic Geology, v. 97, no. 4, p. 873–895.

Taggart, J.E., ed., 2002, Analytical methods for chemical analysis of geologic and other 

materials, U.S. Geological Survey Open-File Report 2002–223 [variously paged].

Taylor, B.E., 2007, Epithermal gold deposits, in Goodfellow, W.D., ed., Mineral deposits of 

Canada; A synthesis of major deposit-types, district metallogeny, the evolution of geological 

provinces, and exploration methods: Geological Association of Canada, Mineral Deposits 

Division, Special Publication no. 5, p. 113–139.

Taylor, S.R., and McLennan, S.M., 1995, The geochemical evolution of the continental crust: 

Reviews of Geophysics, v. 33, no. 2, p. 241–265.

Theodore, T., 1986, Descriptive model of porphyry Mo, low-F, in Cox, D.P., and Singer, D.A., 

Mineral deposit models: U.S. Geological Survey Bulletin, 1693, p. 120.

Thompson, T.B., 1988, Geology and uranium-thorium mineral deposits of the Bokan Mountain 

granite complex, southeastern Alaska: Ore Geology Reviews, v. 3, no. 1, p. 193–210.

Thorne, R.L., and Wells, R.R., 1956, Studies of the Snettisham magnetite deposit, southeastern 

Alaska: U.S. Bureau of Mines Report of Investigations 5195, 41 p.

Till, A.B., Dumoulin, J.A., Harris, A.G., Moore, T.E., Bleick, H.A., and Siwiec, B.R., 2008, 

Bedrock geologic map of the southern Brooks Range, Alaska, and accompanying conodont 

data, U.S. Geological Survey Open-File Report 2008–1149, 88 p., 2 sheets, scales 1:500,000 

and 1:600,000.

Tolstykh, N.D., Foley, J.Y., Sidorov, E.G., and Laajoki, K.V.O., 2002, Composition of the 

platinum-group minerals in the Salmon River placer deposit, Goodnews Bay, Alaska: The 

Canadian Mineralogist, v. 40, no. 2, p. 463–471.

Turner-Peterson, C., and Hodges, C., 1986, Descriptive model of sandstone U, in Cox, D.P., and 

Singer, D.A., Mineral deposit models: U.S. Geological Survey Bulletin1693, p. 209–210.
60

Upton, B., Emeleus, C., Heaman, L., Goodenough, K., and Finch, A., 2003, Magmatism of the 

mid-Proterozoic Gardar Province, South Greenland; chronology, petrogenesis and geological 

setting: Lithos, v. 68, no. 1, p. 43–65.

Van Gosen, B.S., Fey, D.L., Shah, A.K., Verplanck, P.L., and Hoefen, T.M., 2014, Deposit 

model for heavy-mineral sands in coastal environments: U.S. Geological Survey Scientific 

Investigations Report 2010–5070-L, 51 p.

Vasyukova, O., and Williams-Jones, A.E., 2014, Fluoride–silicate melt immiscibility and its role 

in REE ore formation; Evidence from the Strange Lake rare metal deposit, Québec-Labrador, 

Canada: Geochimica et Cosmochimica Acta, v. 139, p. 110–130.

Verplanck, P.L., Van Gosen, B.S., Seal, R.R., and McCafferty, A.E., 2014, A deposit model for 

carbonatite and peralkaline intrusion-related rare earth element deposits: U.S. Geological 

Survey Scientific Investigations Report 2010-5070-J: 58 p.

Wall, F., 2013, Rare earth elements, in Gunn, G., ed., Critical Metals Handbook: Oxford, United 

Kingdom, J. Wiley & Sons, p. 312–339.

Warner, J.D., Mardock, C.L., and Dahlin, D.C., 1986, A columbium-bearing regolith on Upper 

Idaho Gulch, near Tofty, AK: U.S. Bureau of Mines Information Circular 9105, 29 p.

Wenrich, K.J., 1985, Mineralization of breccia pipes in northern Arizona: Economic Geology, v. 

80, no. 6, p. 1722–1735.

Werner, A.B.T., Sinclair, W.D., and Amey, E.B., 1998, International strategic minerals summary 

report—tungsten: U.S. Geological Survey Circular 930-O, 74 p.

Whalen, J., Currie, K., and Chappell, B., 1987, A-type granites; geochemical characteristics, 

discrimination and petrogenesis: Contributions to Mineralogy and Petrology, v. 95, no. 4, p. 

407–419.

Whalen, J.B., Anderson, R.G., Struik, L.C., and Villeneuve, M.E., 2001, Geochemistry and Nd 

isotopes of the François Lake plutonic suite, Endako batholith; host and progenitor to the 

Endako molybdenum camp, central British Columbia: Canadian Journal of Earth Sciences, v. 

38, no. 4, p. 603–618.

White, W., Bookstrom, A., Kamilli, R., Ganster, M., Smith, R., Ranta, D., and Steininger, R., 

1981, Character and origin of Climax-type molybdenum deposits, in Skinner, B.J., Economic 

Geology Seventy-Fifth Anniversary Volume (1905–1980): Lancaster, Pennsylvania, The 

Economic Geology Publishing Company, p. 270–316.

Winchester, J.A., and Floyd, P.A., 1977, Geochemical discrimination of different magma series 

and their differentiation products using immobile elements: Chemical Geology, v. 20, p. 325–

343.

Yang, K.-F., Fan, H.-R., Santosh, M., Hu, F.-F., and Wang, K.-Y., 2011, Mesoproterozoic 

carbonatitic magmatism in the Bayan Obo deposit, Inner Mongolia, North China; Constraints 

for the mechanism of super accumulation of rare earth elements: Ore Geology Reviews, v. 40, 

no. 1, p. 122–131.

Yeend, W.E., 1986, Descriptive model of placer Au-PGE, in Cox, D.P., and Singer, D.A., eds., 

Mineral deposit models: U.S. Geological Survey Bulletin 1693, p. 261–263.

Yefimov, A., Borodayev, Y.S., Mozgova, N., and Nenasheva, S., 1990, Bismuth mineralization 

of the Akchatau molybdenum-tungsten deposit, central Kazakhstan: International Geology 

Review, v. 32, no. 10, p. 1017–1027.

Zientek, M.L., 2012, Magmatic ore deposits in layered intrusions—Descriptive model for reeftype PGE and contact-type Cu-Ni-PGE deposits: U.S. Geological Survey Open-File Report 

2012–1010, p. 44 p.
61

Zientek, M.L., Causey, J.D., Parks, H.L., and Miller, R.J., 2014, Platinum-group elements in 

southern Africa—Mineral inventory and an assessment of undiscovered mineral resources, in

Zientek, M.L., Hammarstrom, J.M., and Johnson, K.M., eds., Global mineral resource 

assessment: U.S. Geological Survey Scientific Investigations Report 2010–5090-Q, 126 p.

Zientek, M.L., Cooper, R.W., Corson, S.R., and Geraghty, E.P., 2002, Platinum-group element 

mineralization in the Stillwater Complex, Montana, in Cabri, L.J., ed., The geology, 

geochemistry, mineralogy and mineral beneficiation of platinum-group elements: Canadian 

Institute of Mining, Metallurgy and Petroleum Special Volume 54, p. 459–481.
62

Tables

Table 1. Mineral deposit groups and types considered in this study, and their commodities, characteristics, and representative localities.

Mineral deposit group Commodities (critical 

elements in bold)

Deposit types World examples Alaska deposits*

and 

occurrences

Ore deposit model references

Deposit and occurrence 

references

#

REE-Th-Y-Nb(-U-Zr) deposits associated 

with peralkaline to carbonatitic igneous 

rocks

REE, Th, Y, Nb, U, Zr

Carbonatite Bayan Obo, China; Mount Weld, Australia; 

Mountain Pass, California Tofty 

Verplanck and others (2014); model 10 in 

Cox and Singer (1986); model 16 in 

Eckstrand (1985); Berger and others 

(2009)

Yang and others (2011); Middlemost (1990); 

Castor (2008); Reifenstuhl and others 

(1998)

Alkaline intrusive Thor Lake, Canada; Lovozero, Russia Windy Fork, Ruby batholith, Darby 

Mountains

Long and others (2010); Verplanck and 

others (2014)

Sheard and others (2012); Kogarko and 

others (2010); Barker (1991); Arth and 

others (1989); Foley and Barker (1986)

Syenite or peralkaline granite REE-Zr-U-Nb Strange Lake, Canada; Ilimaussaq, 

Greenland; Norra Kärr, Sweden Bokan Mountain*, Dora Bay, Roy Creek Linnen and others (2014); Verplanck and 

others (2014)

Vasyukova and Williams-Jones (2014); 

Upton and others (2003); Bluemel and 

others (2013); Dostal and others (2014); 

Philpotts and others (1993); 

Armbrustmacher (1989)

Thorium-REE Veins Lemhi Pass, Idaho-Montana; Bear Lodge, 

Wyoming Bokan Mountain* Staatz and others (1979); Bliss (1992)

Staatz and others (1979); Staatz (1983); 

Dostal and others (2014)

Plutonic-volcanic U-REE Nopal, Mexico; Rexspar, Canada Selawik Hills; Sischu Mountain Cox and Singer (1986) 

Angiboust and others (2012); Preto (1978);

Barker (1985); Miller and others (1980)

Placer and paleoplacer Au Au, PGE, Ag, Sn, W, Cr, Ti

Alluvial placer Thousands Hundreds Model 39a in Cox and Singer (1986) Nokleberg and others (1987)

Alluvial paleoplacer Tofty district Frimmel and others (2005) Garnett and Bassett (2005)

Coastal placer Richards Bay, South Africa Model 39c in Cox and Singer (1986) Van Gosen and others (2014)

PGE (-Co-Cr-Ni-Ti-V) deposits associated 

with mafic and ultramafic igneous rocks PGE, Cu, Ni, Ti, V

Alaska-Ural type PGE Goodnews Bay; Union Bay Model 9 in Cox and Singer (1986) Naldrett (2004)

Duluth-Noril'sk Cu-Ni-PGE Noril'sk, Russia; Wellgreen, Yukon, Canada Spirit Mountain (ARDF VA 080) * Models 5a & 5b in Cox and Singer (1986); 

Zientek (2012)

Hulbert (1997); Li and others (2009)

Synorogenic -synvolcanic Ni-Cu-PGE Selebi-Phikwe, Botswana Brady Glacier Models 5a & 5b in Cox and Singer (1986); 

Schulz and others (2010)

Barnes and Lightfoot (2005)

Fe-Ti-V-rich mafic-ultramafic rocks with 

PGE; (see, also, table 6)

Klukwan; Snettisham Still (1984); Thorne and Wells (1956)

Carbonate-hosted Cu(-Co-Ag-Ge-Ga) 

deposits

Cu, Ag Kennecott type; related to basaltic copper Keweenawan native copper district, 

Michigan Kennecott veins* Model 23 in Cox and Singer (1986); model 10 in Eckstrand (1985)

MacKevett and others (1997): Price and 

others (2014)

Cu, Co, Ge, Ga Kipushi type Kipushi, Democratic Republic of Congo; 

Tsumeb, Namibia Ruby Creek* Model 32c in Cox and Singer (1986)

Kampunzu and others (2009); Kamona and 

Günzel (2007)

Sandstone U (-V-Cu) deposits U, V, Cu

Roll-front Shirley Basin district, Wyoming

Model 30c in Cox and Singer (1986) 

Harshman (1972)

Tabular Grants Belt, New Mexico.; Uravan district, 

Colorado

Northrop and others (1990)

Basal Okanagan highlands, B.C., Canada Death Valley (Boulder Creek)* Model 6.4 in Eckstrand (1985) Boyle (1982)

Tectono-lithologic Orphan mine, Arizona Model 32e in Bliss (1992) Wenrich (1985)
63

Sn-W-Mo (Ta-In-fluorspar) deposits 

associated with specialized granites Sn, W, Mo, F, In, Ta

Sn porphyry, greisen, skarn, peraluminous 

granite

Podlesi and Cinovec, Czech Republic; 

Beauvoir, France; Chlorolque, Bolivia; 

Mancusani, Peru; Moina, Australia; 

Cinovec, Czech Republic; Akchatau and 

Kara Oba, Kazakhstan

Ruby batholith, Rocky Mountain, Mount 

Prindle, Groundhog Basin; Lost River*, 

Kougarok*, Sleitat*, Sithylemenkat

Linnen and others (2014); Models 14b, 15c 

in Cox and Singer (1986); Kwak (1987)

Cérny and others (2005); Raimbault and 

others (1995); Cheilletz and others (1992); 

Hudson and Arth (1983); Newberry and 

others (1990); Newberry and Brew 

(1989); Kwak and Askins (1981); Cérny 

and others (2005); Yefimov and others 

(1990); Dobson, 1982; Hudson and Arth 

(1983); Puchner (1986); Hudson and Reed 

(1997); Barker and Foley (1986)

Sn veins, Sn-polymetallic veins, pegmatites Wodgina, Australia; Tanco, Canada Waterpump Creek*,Won, McIntyre,

Groundhog Basin*

Models 15b and 20b in Cox and Singer

(1986); Linnen and others (2014)

Sweetapple and Collins (2002); Cérny and 

others (2005); Flanigan (1998); Burleigh 

(1992); Reifenstuhl and others (1998); 

Newberry and Brew (1989)

W porphyry, greisen, skarn

Pine Creek, California; Mactung, Canada; 

Mount Pleasant, Canada Stepovich*; Spruce Hen*; Tanana*; 

Gillmore Dome

Models 14a and 15a in Cox and Singer 

(1986); Kwak (1987)

Kooiman and others (1986); Newberry and 

others (1990); White and others (1981); 

Barker and Swainbank (1986); Barker 

(1985)

Climax-type Mo Climax, Henderson Bear Mountain, Zane Hills

Model 16 in Cox and Singer (1986); Seedorff 

and others (2005); Ludington and Plumlee 

(2009)

White and others (1981); Barker and 

Swainbank (1986); Barker (1985)

Porphyry Mo, low-F Endako, Canada Quartz Hill*, Nunatak Model 21b in Cox and Singer (1986); 

Ludington and others (2009)

Whalen and others (2001); Ashleman and 

others (1997); Kimball and others (1978)

* Deposits (indicated by asterisk) are localities with reported inventory or past production.

# References in this column contain maps or coordinates for specific deposits, occurrences, and (or) prospects worldwide and in Alaska. References are in the same order as localities are listed in columns four and five.
64

Table 2. Scoring template for analysis of REE-Th-Y-Nb (-U-Zr) potential within each hydrologic unit code 

in the Central Yukon Planning Area. 

[ARDF, Alaska resource data file; REE, rare earth elements; AGDB2, Alaska geochemical database version 2.0; ADGGS, 

Alaska Division of Geological and Geophysical Surveys; NURE, National uranium resource evaluation database; MALI 

displacement, MALI deviation from a published geochemical threshold; Fe# displacement, Fe# deviation from a published 

geochemical threshold; ppm, concentration in parts per million; HUC, hydrologic unit code]

Category Dataset/layer Component Selection and score

ARDF records ARDF REE model keywords1 2 points if REE keyword score >5

1 point if REE keyword score >2 and <5

Igneous-rock 

geochemistry2

AGDB2 + ADGGS + 

literature 

MALI displacement31 point if MALI value >0

10,000×Ga/Al41 point if 10,000×Ga/Al >2.6

Fe# displacement51 point if Fe# displacement >0.05

Nb/Y ratio 1 point if Nb/Y≥1

Sediment 

geochemistry6 AGDB2 + ADGGS + NURE

Nb ppm 91st and 98th percentile 2 points if Nb ppm >23

1 point if Nb ppm >17 and <23

Ce ppm 91st and 98th percentile 2 points if Ce ppm ≥166

1 point if Ce ppm >103 and <166

Yb ppm 91st and 98th percentile 2 points if Yb ppm ≥8.4

1 point if Yb ppm ≥5.6 and <8.4

Th ppm 91st and 98th percentile 2 points if Th ppm ≥28.1

1 point if Th ppm >13.4 and <28.1

Aeroradiometric 

data7 Aerial gamma-ray survey data Th/K ratio 2 points if Th/K value >121 point if Th/K value >5 and ≤12

1

See appendix C for list of REE keywords and scoring template for ARDF; maximum single score for an HUC contributes to the 

total score, although any ARDF score >1 results in an assignment of high potential.

2

Igneous-rock geochemistry scores are additive for a total possible score of 4 for each HUC.

3MALI, modified alkali-lime index (Na2O+K2O-CaO). Score applied only to igneous rocks with SiO2>56 weight percent.

4

10,000×Ga/Al scores applied only to igneous rocks with SiO2>60 weight percent.

5

Fe# (FeO/[FeO + MgO]) displacement calculated using the Fe# versus SiO2 array proposed by Frost and Frost (2008).

6Maximum single score for each element in HUC is used. Element scores are additive for possible total of 8.

7Mean score for an HUC contributes to the total score. Data from Duval (2001).
65

Table 3. Matrix of mineral resource potential versus certainty classification for REE-Th-Y-Nb (-U-Zr) 

deposits associated with peralkaline-to-carbonatitic intrusive rocks.

[REE, rare earth elements; ARDF, Alaska resource data file; HUC, hydrologic unit code]

REE-Th-Nb-Y-(Zr-U)

Estimated certainty1

Low Medium High

Unknown

Total score = 0 and no 

sediment data 

points in HUC or

aerial gamma-ray 

survey is only data 

set represented and

no sediment data 

points in HUC

ARDF score >1 or

total score >6 (p)

1 dataset not null(c)

ARDF score >1 or

total score >6 (p)

2–3 datasets not null (c)

ARDF score >1 or 

total score >6 (p)

4 datasets not null (c)

High

Estimated potential

1

Total score 3–5 (p)

1 dataset not null (c)

Total score 3–5 (p)

2–3 datasets not null (c)

Total score 3–5 (p)

4 datasets not null (c)

Medium

Total score 1–2 (p)

1 dataset not null (c)

Total score 1–2 (p)

2–3 datasets not null (c)

Total score 1-2 or

Total score = 0 and 

sediment data points in 

HUC (p, c)

Low

1Abbreviations (p) and (c) in cells denote which components contribute to assignment of potential and certainty, 

respectively.
66

Table 4. Scoring template for analysis of placer and paleoplacer Au potential in hydrologic unit codes 

within the Central Yukon Planning Area.

[ARDF, Alaska resource data file; AGDB2, Alaska geochemical database version 2.0; ADGGS, Alaska Division of Geological 

and Geophysical Surveys; NURE, National uranium resource evaluation database; ppm, concentration in parts per million; HUC, 

hydrologic unit code; NHD, National Hydrologic Dataset]

Category Dataset/layer Component Selection and score

ARDF records ARDF Placer model keywords110 points if total >0

Pan Concentrate 

Mineralogy2 AGDB2

Gold 10 points if Au is present

Cassiterite 1 point if cassiterite is present

Powellite 1 point if powellite is present

Scheelite 1 point if scheelite is present

Cinnabar 1 point if cinnabar is present

Monazite 1 point if monazite is present

Thorite 1 point if thorite is present

Sediment 

geochemistry3

AGDB2 + ADGGS + 

NURE

Au ppm 75th percentile 3 points if Au ppm ≥0.007

Ag ppm 75th percentile 3 point if Ag ppm ≥0.16

Ti ppm 75th percentile 1 point if Ti ppm ≥0.57

W ppm 75th percentile 1 point if W ppm ≥2.0

Lithology4

Digital geologic map 

of Alaska

Plutonic major component of map unit 3 points if present

Plutonic minor component of map unit 2 points if present

Hypabyssal igneous map unit 2 points if present

Volcanic igneous map unit 1 point if present

Metaigneous map unit 1 point if present

Downstream of

high potential5 NHD

River/stream <25 km downstream of HUC with high

potential 6 points if present

River/stream >25 km downstream of HUC with high

potential 3 points if present

River/stream downstream of HUC with high potential 

past intersection with equal or higher order segment 1 point if present

1

See appendix C for list of placer model keywords and scoring template for ARDF.

2

Pan Concentrate Mineralogy—If Au is not null, only use the highest value of 10; otherwise, score is additive for other possible 

minerals.

3

Stream-sediment geochemistry—Scores are additive for possible total of 8.

4Lithology—if more than one lithologic type is present, only use the type with the highest value.

5Downstream analysis excludes HUCs with high potential; if more than one type of segment is present only use the type with the 

highest value
67

Table 5. Matrix of mineral resource potential versus certainty classification for placer and paleoplacer Au 

deposits.

[ARDF, Alaska resource data file; HUC, hydrologic unit code]

Placer Au

Estimated certainty1

Low Medium High

Unknown

Total score = 0 and no 

sediment data 

points in HUC

n/a

Total score ≥16 (p)

2–3 datasets not null (c)

ARDF score = 10 or pan 

concentrate score = 10 or

total score ≥16 (p)

ARDF score = 10 or pan 

concentrate score = 10 or 

4–5 datasets not null (c)

High

Estimated potential

1

Total score 6–15 (p)

1 dataset not null (c)

Total score 6–15 (p)

2–3 datasets not null (c)

Total score 6–15 (p)

4–5 datasets not null (c)

Medium

Total score 1–5 (p)

1dataset not null (c)

Total score 1–5 (p)

2–3 datasets not null (c)

Total score 1–5 (p) and 

4–5 datasets not null (c) or

Total score = 0 and sediment 

data points in HUC (p,c)

Low

1Abbreviations (p) and (c) in cells denote which components contribute to assignment of potential and certainty, 

respectively.
68

Table 6. Platinum group element (PGE) ore deposit types.

PGE deposit models Example / locations References

PGE ore deposit models considered in mineral potential evaluation

Alaska-Ural type Goodnews Bay, southwest Alaska

Union Bay, southeast Alaska

Foley and others (1997)

Himmelberg and Loney (1995)

Ophiolites Angayucham-Tozitna terranes, northern Alaska Loney and Himmelberg (1989); 

Patton (1992)

Layered magmatic PGE Stillwater, Montana Zientek and others (2002)

Magmatic-sulfide PGE Wellgreen, Yukon and Fish Lake Complex, 

Alaska, both in the Wrangellia terrane

Hulbert (1997)

Synorogenic Ni-Cu-PGE Brady Glacier, Mount Fairweather trend, 

southeast Alaska Czamanske and others (1981)

Large-igneous-province/floodbasalt and feeder-zone Ni-Cu-PGE Norilsk, Russia3

; Duluth Complex, Minnesota4

3Li and others (2009)

4Miller and others (2002)

Troctolite-anorthosite-granitehosted Ni-Cu-Co±PGEVoisey’s Bay, Newfoundland and Labrador, 

Canada Naldrett and others (2000)

PGE found with chromite in islandarc crustal sections Red Mountain, Kenai Peninsula, Alaska Foley and Barker (1984)

Fe-Ti±V-rich mafic/ultramafic 

(MUM) rocks with PGE Klukwan, Snettisham, southeast Alaska; Still and others (1991)

PGE ore deposit models known in Alaska but not factored into mineral potential assessment

PGE-enriched porphyry Cu-Mo-Au 

deposits Pebble, Alaska Kelley and others (2013)Ghaffari and others (2011)

PGE-enriched composite plutons Butte Creek, Alaska Keith and others (1987)

Differentiated igneous complexes Mentasta Pass, Alaska Range, Alaska Richter (1967)

PGE-enriched skarns adjacent to 

PGE-bearing mafic-ultramafic

intrusions

Unnamed skarn occurrence, West Fork Rainy 

Creek, Mount Hayes quadrangle, Alaska Bittenbender and others (2007)

PGE-bearing placer deposits in 

unconsolidated sediments Goodnews Bay, Alaska Tolstykh and others (2002)

PGE ore deposit models not presently known in Alaska and not optimized for in this assessment

Hydrothermal PGE deposits New Rambler, Wyoming

Kupferschiefer, Germany

Nyman and others (1990)

Borg and others (2005)

Black shale-hosted PGE Nick Property, Yukon Hulbert and others (1992)

Unconformity related U±Au±PGE Rum Jungle, Australia Mernagh and others (1998)

Supergene PGE Serra Pelada, Brazil Moroni and others (2001)

Meteorite impacts Sudbury, Ontario Ames and others (2008)

Komatiites Kambalda, Australia Barnes and others (2013)
69

Table 7. Scoring template for analysis of PGE (-Co-Cr-Ni-Ti-V) potential in hydrologic unit codes within 

the Central Yukon Planning Area.

[PGE, platinum group elements; MUM, mafic-ultramafic; ARDF, Alaska resource data file; AGDB2, Alaska geochemical 

database version 2.0; ADGGS, Alaska Division of Geological and Geophysical Surveys; HMC, heavy metal concentrate; ppm, 

concentration in parts per million; HUC, hydrologic unit code]

Category Dataset/layer Component Selection and score

Lithology1 Digital geologic 

map of Alaska

MUM rocks, major 

component 2 points if present

MUM rocks, minor or 

incidental component 1 point if present

ARDF records3 ARDF PGE model keywords2

3 points if PGE reported as major or minor commodities

2 points if chromite and favorable geology

1 point if permissible geology but no direct evidence for 

PGE

Pan concentrate 

Mineralogy3

AGDB2 + 

ARDF

ARDF placers

3 points if PGE reported as major or minor elements

2 points if chromite or other PGE-related minerals, or 

questioned PGE identifications

1 point if mineralogy for MUM rocks in a drainage, but no 

direct evidence for PGE

Ore-related mineral 

comment

2 points if chromite (‘CHR’) is present

2 points if copper cobalt sulfide (‘Cu_Co sulfs’) is present

2 points if nickel cobalt sulfide (‘Ni_Co sulfs’) is present

2 points if nickel sulfide (‘Ni sulfs’) is present

Rock-forming mineral 

comment

2 points if chromium nickel silicate (‘Cr-Ni silicates’) is 

present

1 point if serpentinite (‘SRP’) is present

1 point if chrome diopside (‘Cr-D’) is present

Chalcopyrite 1 point if not null

Pan concentrate

geochemistry3 AGDB2 HMC geochemistry

3 points if Ir_ppm >0

3 points if Os_ppm >0

3 points if Pd_ppm >0

3 points if Pt_ppm >0

3 points if Rh_ppm >0

3 points if Ru_ppm >0

2 points if Co_ppm >500

1 point if Co_ppm >150 and <500

2 points if Cr_ppm >7000

1 point if Cr_ppm >2000 and < 7000

2 points if Ni_ppm >700

1 point if Ni_ppm >200 and <700

1 point if Ti_pct >5.0

2 points if V_ppm >1500

1 point if V_pp >700 and <1500
70

Sediment 

geochemistry3 AGDB2 Sediment geochemistry

2 points if Co_ppm ≥70 (98th percentile)

1 point if Co_ppm ≥36 and <70 (91st percentile)

2 points if Cr_ppm ≥500

1 point if Cr_ppm ≥200 and <500

2 points if Ni_ppm ≥150

1 point if Ni_ppm ≥84 and <150

2 points if Ti_pct ≥1.11

1 point if Ti_pct ≥0.79 and <1.11

2 points if V_ppm ≥500

1 point if V_ppm ≥299 and <500

3 points if Os_ppm ≥0.016 (91st percentile)

3 points if Pd_ppm ≥0.005 (91st percentile)

3 points if Pt_ppm >0.015 (91st percentile)

Rock 

geochemistry3

AGDB2 Rock geochemistry

2 points if Co_ppm >200

1 point if Co_ppm >100 and <200

2 points if Cr_ppm >2000

1 point if Cr_ppm >700 and <2000

2 points if Ni_ppm >1500

1 point if Ni_ppm >500 and <1500

2 points if TiO2_pct >3.0

1 point if TiO2_pct >2.0 and <3.0

2 points if V_ppm >1000

1 point if V_ppm >500 and <1000

3 points if Ir_ppm >0

3 points if Pd_ppm >0.005

3 points if Pt_ppm >0.004

3 points if Rh_ppm >0

3 points if Ru_ppm >0

DGGS Rocks geochemistry

2 points if Co_ppm >70

1 point if Co_ppm >30 and <70

2 points if Cr_ppm >2000

1 point if Cr_ppm >700 and <2000

2 points if Ni_ppm >500

1 point if Ni_ppm >50 and <500

2 points if TiO2_pct >3.0

1 point if TiO2_pct >2.0 and <3.0

2 points if V_ppm >100

1 point if V_ppm >50 and <100

3 points if Ir_ppm >0

3 points if Pd_ppm >0

3 points if Pt_ppm >0

3 points if Rh_ppm >0

3 points if Ru_ppm >0

1Digital geologic map of Alaska (Wilson, Frederic H., and others, USGS, written commun.) is source of lithology layers. 

Maximum HUC score is 2 points.

2

See appendix C for list of PGE model keywords and scoring template for ARDF; maximum HUC score is 3 points.

3Maximum HUC score is 3 points.
71

Table 8. Matrix of mineral resource potential versus certainty classification for PGE (-Co-Cr-Ni-Ti-V) 

deposits.

[ARDF, Alaska resource data file; HUC, hydrologic unit code]

PGE (Co, Cr, Ni, Ti, V)

Estimated certainty1

Low Medium High

Unknown

Total score = 0 and no 

sediment data 

points in HUC or 

no sediment data 

points in HUC and

Lithology is only 

dataset >0

Total score >5

1– datasets not null (c)

Total score >5 (p)

4–5 datasets not null (c)

ARDF score ≥ 3 or

total score >5 (p)

ARDF score ≥ 3 or

6 datasets not null (c)

High

Estimated potential

1

Total score 2–5 (p)

1–3 datasets not null (c)

Total score 2–5 (p)

4–5 datasets not null (c)

Total score 2–5 (p)

6 datasets not null (c)

Medium

Total score = 1 (p)

1–3 datasets not null (c)

Total score = 1 (p)

4–5 datasets not null (c)

Total score = 1 or

total score = 0 and

sediment data points in 

HUC (p,c)

Low

1Abbreviations (p) and (c) in cells denote which components contribute to assignment of potential and certainty, 

respectively.
72

Table 9. Scoring template for analysis of carbonate-hosted Cu (-Co-Ag-Ge-Ga) potential in the 

hydrologic unit codes within the Central Yukon Planning Area.

[ARDF, Alaska resource data file; AGDB2, Alaska geochemical database version 2.0; ADGGS, Alaska Division of Geological 

and Geophysical Surveys; HMC, heavy metal concentrate; ppm, concentration in parts per million; HUC, hydrologic unit code]

Category Dataset/Layer Component Selection and score

Lithology1 Digital geologic 

map of Alaska

Carbonate rocks, major component 3 points if present

Carbonate rocks, minor or incidental 

component

2 point if present

ARDF records2 ARDF Cu carbonate model keywords2 5 points if keyword score ≥4

2 points if keyword score >0 and <4

Pan concentrate

Mineralogy3 AGDB2 

Ore-related mineral comment

1 point if copper cobalt sulfide minerals (‘Cu-Co 

sulfs’) are present

1 point if copper silicate minerals (‘Cu SiO4 mnrls’)

are present

1 point if copper sulfide and (or) copper oxide 

minerals (‘CuS/O’) are present

1 point if azurite (‘AZR’) is present

1 point if cuprite (‘CUP’) is present

1 point if enargite (‘ENG’) is present

1 point if malachite (‘MAL’) is present

Chalcopyrite 1 point if present

Sediment 

geochemistry3 AGDB2 Sediment geochemistry

3 points if Cu_ppm ≥ 150 (98th percentile)

2 points if Cu_ppm ≥76 and <150 (91st percentile)

1 point if Cu_ppm ≥ 50 and <76 (75th percentile)

Sediment trace 

geochemistry5 AGDB2 Sediment trace geochemistry

1 point if Cu_ppm ≥50 and Co ppm ≥36

1 point if Cu_ppm ≥50 and Ge_ppm ≥5.9

1 point if Cu_ppm ≥50 and Ga_ppm ≥30

1 point if Cu_ppm ≥50 and Ag_ppm ≥0.4

Rock 

geochemistry4

AGDB2 Cu ppm

3 points if Cu_ppm ≥5000

2 points if Cu_ppm ≥1000 and <5000

1 point if Cu_ppm ≥150 and <1000

DGGS Cu ppm

3 points if Cu_ppm ≥5000

2 points if Cu_ppm ≥1000 and <5000

1 point if Cu_ppm ≥150 and <1000

Rock trace 

geochemistry5

AGDB2 Co ppm, Ge ppm, Ga ppm, Ag ppm, 

with Cu ppm

1 point if Cu_ppm ≥150 and Co ppm ≥45

1 point if Cu_ppm ≥150 and Ge_ppm ≥3

1 point if Cu_ppm ≥150 and Ag_ppm ≥1

1 point if Cu_ppm ≥150 and Ga_ppm ≥35

DGGS Co ppm, Ge ppm, Ga ppm, Ag ppm, with

Cu ppm

1 point if Cu_ppm ≥150 and Co_ppm ≥45

1 point if Cu_ppm ≥150 and Ge_ppm ≥3

1 point if Cu_ppm ≥150 and Ag_ppm ≥ 1

1 point if Cu_ppm ≥150 and Ga_ppm ≥35

1Digital geologic map of Alaska (Wilson, Frederic H., and others, USGS, written commun.) is source of lithology layers.

Maximum HUC score is 3 points. Only HUCs that contain carbonates or are in a one HUC ring around these HUCs will be 

scored for the other layers. All unscored HUCs will have a total score of null, low potential, and high certainty.

2

See appendix C for list of Cu carbonate model keywords and scoring template for ARDF; maximum HUC score is 5 points.

3Maximum HUC score is 8 points.

4Maximum HUC score is 3 points.

5Maximum HUC score is 4 points.
73

Table 10. Matrix of mineral resource potential versus certainty classification for carbonate-hosted Cu (-CoAg-Ge-Ga) deposits.

[ARDF, Alaska resource data file; HUC, hydrologic unit code]

Cu (Co-Ag-Ge-Ga)

Estimated certainty1

Low Medium High

Unknown

Total score = 0 and no 

sediment data 

points in HUC or 

no sediment data 

points in HUC and

Lithology is only 

dataset >0

Total score >7 (p)

1-2 datasets not null (c)

Total score >7 (p)

3-4 datasets not null (c)

Total score >7 (p)

5-7 datasets not null (c)

High

Estimated potential

1

Total score 4–6 (p)

1-2 datasets not null (c)

Total score 4–6 (p)

3-4 datasets not null (c)

Total score 4–6 (p)

5-7 dataset not null (c)

Medium

Total score 1-3 (p)

1-2 datasets not null (c)

Total score 1-3 (p)

3-4 datasets not null (c)

Total score = 0 and sediment 

data points in HUC or

Total score is null (p,c)

Low

1Abbreviations (p) and (c) in cells denote which components contribute to assignment of potential and certainty, 

respectively.
74

Table 11. Scoring template for analysis of sandstone U (-V-Cu) potential in the hydrologic unit codes 

within the Central Yukon Planning Area.

[ARDF, Alaska resource data file; AGDB2, Alaska geochemical database version 2.0; ADGGS, Alaska Division of Geological 

and Geophysical Surveys; NURE, National uranium resource evaluation database; ppm, concentration in parts per million; HUC, 

hydrologic unit code]

Category Dataset/layer Component Selection and score

ARDF records ARDF Reviewed records from ssU model keywords11 point if present

Lithology2 Digital geologic map of 

Alaska

Arkose sandstone 4 points if present

Tertiary/ Cretaceous sedimentary rocks 3 points if present

Sedimentary rocks 2 points if present

Unconsolidated geologic units within 3km 

buffer around sedimentary rocks 1 point if present

Coal

Map of Alaska's coal 

resources (Merritt, and 

Hawley, 1986)

Upper Cretaceous to Tertiary coal 1 point if present

Igneous-rock geochemistry AGDB2 + ADGGS + 

literature Fe# displacement 3

1 point if Fe# displacement >0

Sedimentary-rock 

geochemistry4

AGDB2 + ADGGS + 

NURE

U_ppm 98th percentile 2 points if U_ppm ≥40

U_ppm 91st percentile 1 point if U_ppm ≥11 and <40

Sediment geochemistry4 AGDB2 + ADGGS + 

NURE

U_ppm 98th percentile 3 points if U_ppm ≥20 ppm

U_ppm 91st percentile 2 points if U_ppm ≥5.8 and

<20

U_ppm 75th percentile 1 point if U_ppm ≥3.5 and <5.8

Aeroradiometric data5 Aerial gamma-ray survey 

data U equivalent ppm

2 points if mean _ppm value >5

1 point if mean U_ppm value

>2 and ≤5

1

See appendix C for list of ssU keywords and scoring template for ARDF; see text for reviewing criteria.

2 Lithology scores are the highest ranking sedimentary lithology present anywhere in the HUC.

3

Fe# (FeO/[FeO + MgO]) displacement calculated using the Fe# versus SiO2 array proposed by Frost and Frost (2008); applied 

only if SiO2 >70 percent.

4 Maximum single score for each element in HUC is used.

5 HUC score based on mean eU value. Data from Duval (2001).
75

Table 12. Matrix of mineral resource potential versus certainty classification for sandstone U (-V-Cu) 

deposits.

[ARDF, Alaska resource data file; HUC, hydrologic unit code]

ssU

Estimated certainty1

Low Medium High

Unknown

No sediment data 

points in 

HUC and total 

score = 0; or no 

sediment data 

points in 

HUC and the total 

score entirely 

derived from the 

lithology, coal, or 

aerorad scores

Total score >7 (p)

1-2 datasets not null (c)

Total score >7 (p)

3–4 datasets not null (c)

Total score >7 (p)

5–7 datasets not null or an 

ARDF record meeting the 

reviewed criteria for ssU is 

present2(c)

High

Estimated potential

1

Total score 4-6 (p)

1-2 datasets not null (c)

Total score 4–6 (p)

3–4 datasets not null (c)

Total score 4 - 6 (p)

5-7 datasets not null or an 

ARDF record meeting the 

reviewed criteria for ssU is 

present2(c)

Medium

Total score 0-3 (p)

1-2 datasets not null (c)

Total score 0–3 (p)

3–4 datasets not null (c)

Total score 0–3 (p)

5-7 datasets not null or an 

ARDF record meeting the 

reviewed criteria for ssU is 

present2(c)

Low

1Abbreviations (p) and (c) in cells denote which components contribute to assignment of potential and certainty, 

respectively.

2

ssU ARDF recorded review criteria. Records with U as a “main” or “other” commodity were retained if (1) the 

deposit-type model type was sandstone uranium or roll-front, (2) if no deposit type was given and the “lode,”

granite, vein, dike, phosphates, or skarn were not present in any of the descriptive fields, (3) if the deposit type was 

a placer with U present as either a major or minor commodity.

3

For a HUC to receive a medium potential the total score could not arise solely from the lithology or the lithology 

+ coal; such HUCs were assigned low potential.
76

Table 13. Scoring template for analysis of Sn-W-Mo (-Ta-In-fluorspar) potential in the hydrologic unit 

codes of the Central Yukon Planning Area.

[ARDF, Alaska resource data file; AGDB2, Alaska geochemical database version 2.0; ADGGS, Alaska Division of Geological 

and Geophysical Surveys; NURE, National uranium resource evaluation database; ppm, concentration in parts per million; HUC, 

hydrologic unit code]

Category Dataset/Layer Component Selection and score

ARDF records ARDF Sn-W-Mo-F keywords1 2 points if Sn-W-Mo-F keyword score ≥20

1 point if Sn-W-Mo-F keyword score >4 and <20

Igneous-rock 

geochemistry

ratios2

AGDB2 + 

ADGGS + 

literature 

Peraluminous31 point if classified as peraluminous

10,000×Ga/Al41 point if 10,000×Ga/Al ≥2.6

Igneous rock 

geochemistry2

AGDB2 + 

ADGGS + 

literature

In ppm 91st percentile 1 point if In_ppm ≥0.4387

Mo ppm 91st percentile 1 point if Mo_ppm ≥50

Sn ppm 91st percentile 1 point if Sn_ppm ≥70

Ta ppm 91st percentile 1 point if Ta_ppm ≥2.5206

W ppm 91st percentile 1 point if W_ppm ≥50

High silica 1 point if SiO2_pct >73

Sediment 

geochemistry5

AGDB2 + 

ADGGS + 

NURE

In_ppm 91st and 98th percentiles

2 points if In_ppm ≥0.17

1 point if In_ppm >0.08 and <0.17

Mo_ppm 91st and 98th percentiles

2 points if Mo_ppm ≥10

1 point if Mo_ppm >5 and <10

Sn_ppm 91st and 98th percentiles

2 points if Sn_ppm ≥39

1 point if Sn_ppm >4 and <39

Ta_ppm 91st and 98th percentiles

2 points if Ta_ppm ≥2.2

1 point if Ta_ppm >1 and <2.2

W_ppm 91st and 98th percentiles

2 points if W_ppm ≥21

1 point if W_ppm >5.2 and <21

Aeroradiometric 

data6

Aerial gamma-ray 

survey data Th7

75th percentile 1 point if Th value ≥6

1

See appendix C for list of Sn-W-Mo-F keywords and scoring template for ARDF; maximum single score for a HUC is used as 

the total score.

2

Igneous rock geochemistry scores are additive.

3

See text for description of peraluminous classification criteria. Score applied only to igneous rocks with SiO2 >65 weight

percent.

4

10,000×Ga/Al scores applied only to igneous rocks with SiO2 >65 weight percent.

5Maximum single score for each element in HUC is used. Element scores are additive for possible total of 10.

6Data from Duval (2001).

7Apparent Th as 208Thallium (parts per million equivalent thorium).
77

Table 14. Average element concentrations in the upper continental crust.

Element Average concentration in the upper 

continental crust, in parts per million*

In 0.05 

Mo 1.5 

Sn 5.5 

Ta 2.2 

W 2.0 

*

From Taylor and McLennan (1995)
78

Table 15. Matrix of mineral resource potential versus certainty classification Sn-W-Mo (-Ta-In-fluorspar) 

deposits in specialized granites.

[ARDF, Alaska resource data file; HUC, hydrologic unit code]

Sn-W-Mo (Ta-Influorspar)

Estimated certainty1

Low Medium High

Unknown

Total score = 0 and no 

sediment data 

points in HUC

ARDF score >1 or total 

score >6 (p)

1 data set not null (c)

ARDF score >1 or total 

score >6 (p)

2–3 data sets not null (c)

ARDF score >1 or total score 

>6 (p)

4–5 datasets not null (c)

High

Estimated potential

1

Total score 3–4 (p)

1 data set not null (c)

Total score 3–4 (p)

2–3 data sets not null (c)

Total score 3–4 (p)

4–5 datasets not null (c)

Medium

Total score 1 (p)

1 data set not null (c)

Total score 1 (p)

2–3 data sets not null (c)

Total score = 0 and

sediment data points in 

HUC (p,c) or

Total score 1 (p) and 4-5 data 

sets not null (c)

Low

1Abbreviations (p) and (c) in cells denote which components contribute to assignment of potential and certainty, 

respectively.