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1. Asbestos ore

2. Lead ore, Balmat mine, N. Y.

3. Chromite-chromium ore, Washington

4. Zinc ore, Friedensville, Pa.

5. Banded iron-formation. Palmer, 

Mich.

6. Ribbon asbestos ore, Quebec, Canada

7. Manganese ore, banded 

rhodochrosite

8. Aluminum ore, bauxite, Georgia

9. Native copper ore, Keweenawan 

Peninsula, Mich.

10. Porphyry molybdenum ore, Colorado

11. Zinc ore, Edwards, N. Y.

12. Manganese nodules, ocean floor

13. Botryoidal fluorite ore, 

Poncha Springs, Colo.

14. Tungsten ore. North Carolina
Computer-aided Estimates 

of Concentrating-grade Iron Resources 

in the Negaunee Iron-formation, 

Marquette District, Michigan

By W. F. CANNON, SANDRA L. POWERS, and NANCY A. WRIGHT

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1045

An estimation of the magnitude,

3 O -

quality, and economic potential 

of subeconomic resources of iron 

in an important active mining district

UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON : 1978
UNITED STATES DEPARTMENT OF THE INTERIOR 

CECIL D. ANDRUS, Secretary

GEOLOGICAL SURVEY 

W. A. Radlinski, Acting Director

Library of Congress Cataloging in Publication Data

Cannon, W F

Computer-aided estimates of concentrating-grade iron resources in the Negaunee Iron-formation, Marquette

District, Michigan.

(Geological Survey professional paper ; 1045) 

Bibliography: p. 

Supt. of Docs, no.: I 19.16:1045 

1. Iron ores—Michigan—Marquette Co.—Data processing. I. Powers, Sandra L., joint author. II. Wright,

Nancy A., joint author. III. Title. IV. Series: United States. Geological Survey. 

Professional paper ; 1045. 

TN403.M5C36 553'.4'0977496 77-608120

For sale by the Superintendent of Documents, U.S. Government Printing Office

Washington, D.C. 20402

Stock Number 024-001-03059-4
CONTENTS

Page Page

Abstract —————————————————_____________ 1 Resource data bank—Continued

Introduction ———————————————___________ 1 Variables—Continued

Acknowledgments ——_________________ 2 Deposit characteristics ______________ 7

Negaunee Iron-formation __________________ 2 Reliability of data _________________ 8

Mineralogy and texture _____________ 2 Iron resources __________________ 9

Structural position and thickness _________ 3 Resource classification ________________ 11

Resource data bank —————_______________ 3 Estimates of total recoverable iron ________ 13

Preparation of the data bank ______________ 4 Future of taconite mining in the Marquette district.. 19

Variables ———————————————____________ 5 Effect of technologic advances on iron resources___ 20

Identification and location __________ 5 Conclusions _______________________ 20

Taconite characteristics _____________ 7 References cited __________________________ 21

ILLUSTRATIONS

Page

FIGURE 1. Generalized geologic map of the Marquette district ________ 2

2. Hypothetical cross section of data block showing method by

which variables are measured and assigned to blocks ______ 6

3. Cumulative curve showing relationship between long tons of

Negaunee Iron-formation and depth beneath the surface _____ 9

4. Graph showing long tons of iron-formation in categories described

in the text ___________________________________ 10

5. Histogram showing long tons of the possibly treatable ironformation within 1,000 feet of the surface relative to volume 

of consolidated waste rock overlying it ___________ — __ — _— 11

6. Histogram showing long tons of the possibly treatable ironformation within 1,000 feet of the surface with various ratios 

of volume of iron-formation to volume of interlayered waste 

rock ____.____________________________________ 11

7. Resource classification chart ______________________________ 12

8. Histograms showing results of three types of metallurgical tests 14

9. Geologic map of the Marquette district showing the distribution 

of various mineralogical classes of iron-formation within the 

Negaunee Iron-formation ______________________________ 16

10. Regression lines showing the negative correlation between concentrate-iron percentage and concentrate-silica percentage for 

three types of metallurgical tests _____ —— ____ — __ — — --- 18

11. Curves showing maximum short tons of iron potentially available 

within 1,000 feet of the surface for two metallurgical classes 

of iron-formation at various cutoff grades ____ — _ — _ — ______ 18

12. Graph showing projected production trends for the Marquette 

district from operating mines and announced expansion and 

development plans _________________ — — _____ — _____ 19

TABLES

TABLE 1.

2.

Percentage of variation between original calculations and recalculated values of four variables for i/i-mi 2 (0.65-km 2 ) vertical 

columns through the iron-formation __ — — — — — — -_

Metallurgical data used in simulation model ___ —— ___ ——— _ —

Page

13

in

COMPUTER-AIDED ESTIMATES OF CONCENTRATING-GRADE IRON

RESOURCES IN THE NEGAUNEE IRON-FORMATION,

MARQUETTE DISTRICT, MICHIGAN

By W. F. CANNON,, SANDRA L. POWERS, and NANCY A. WRIGHT

ABSTRACT

The Negaunee Iron-formation of Precambrian age is the 

principal iron-bearing unit in the Marquette iron range, 

Michigan. The Negaunee, along with other sedimentary and 

igneous rocks, is folded into a complex synclinal structure, 

the Marquette trough, and related smaller structures.

The Negaunee contains about 205 billion long tons of rock 

and averages 32 percent iron. By using a computerized data 

bank, we have analyzed the resource potential of the Negaunee, and by using a statistical simulation model, we have 

estimated the maximum amount of iron recoverable under 

present or moderately improved economic and technologic 

conditions.

Within the district is about 91.6 billion long tons of finegrained hematitic and goethitic iron-formation. About 28.4 

billion long tons is within 1,000 feet (304.8 m) of the surface; this material could yield a maximum of about 7 billion short tons of iron in concentrates that contain 60-65 

weight-percent iron. The concentrates could be produced by 

selective flocculation and flotation, or some modification of 

that process, such that recovery of iron is at least 20 weightpercent of the crude ore.

Magnetic iron-formation constitutes about 48 billion long 

tons. About 10.5 billion long tons is within 1,000 feet (304.8 

m) of the surface and could yield a maximum of about 3 

billion short tons of iron in concentrates produced by magnetic separation. The recovery of iron is at least 20 weightpercent of the crude ore.

In addition, about 27.1 billion long tons of iron-formation 

contains coarse-grained hematite. About 4.8 billion long tons 

is within 1,000 feet (304.8 m) of the surface and could 

yield a maximum of about 0.7 billion short tons of iron in 

concentrates produced by froth flotation; the iron recovered 

is at least 20 weight-percent of the crude ore.

Silicate iron-formation constitutes about 37.6 billion long 

tons, about 5.6 billion long tons of which is within 1,000 

feet (304.8 m) of the surface. However, the silicate ironformation is not amenable to concentration by present technology.

INTRODUCTION

The Marquette iron range was the first of the 

great Lake Superior iron-ore districts to be discovered. The iron resources of the district are in the Negaunee Iron-formation, a sedimentary accumulation

of iron minerals. Shortly after the discovery of the 

Negaunee in 1844, iron production began, and since 

that time, the district has been one of the principal 

sources of iron ore for the United States. Most ore 

mined before the 1950's was from high-grade deposits that formed as secondary concentrations in 

the Negaunee Iron-formation. In the 1950's, when 

reserves of high-grade ore were being depleted rapidly, the technology for processing lower grade ore 

was developed, and since that time production from 

the Marquette range has come increasingly from 

concentrating-grade ore, commonly called taconite.

The technology for producing a merchantable 

iron-ore product from taconite is complex and, in 

general, involves open-pit mining, multistage crushing and grinding, a process for separating iron minerals from gangue minerals, and an agglomerating 

process (most commonly, pelletizing) for the ironmineral concentrate. The economic availability of 

iron from a taconite deposit is determined by many 

factors, only some of which are geologic. Hence, by 

using only geologic data as is done in this study, we 

are not able to estimate reserves 1 of taconite. 

Rather, in this paper, we intend to present estimates for the Marquette District of the maximum 

amount of iron that is geologically known as well 

as the amount that is potentially recoverable. The 

estimates are derived by using data from a detailed 

data bank developed for that purpose.

In this paper, iron resources are classified according to the degree to which they approach the geologic characteristics of presently economic deposits and 

the degree of certainty with which the deposit is 

known. Mathematical simulation models are used 

to derive maximum estimates of recoverable iron

1 A reserve is considered to be material from which iron can be economically and legally extracted at the present time. This study is concerned with resources, which are considered to be material from which 

extraction of iron is currently or potentially feasible. Resources therefore include reserves, but also include much more material whose recovery 

may or may not be economically feasible.
ESTIMATES OF NEGAUNEE IRON-FORMATION, MICHIGAN

from material that is geologically most similar to 

presently economic deposits. Finally, the degree to 

which future technologic advances in several fields 

would affect the resource base of the district is discussed.

ACKNOWLEDGMENTS

The data that have made this report possible were 

collected during many years of field and laboratory 

work by U.S. Geological Survey personnel. We wish 

to thank J. E. Gair, W. P. Puffett, G. C. Simmons, 

L. D. Clark, and J. S. Klasner, who, along with the 

senior author, have participated in studies of the 

Marquette district, and who have made available to 

us much original information and material. We are 

also greatly indebted to the mining companies in 

the area who, for many years, have been very cooperative in supplying information from diamond 

drilling and mining operations. This information 

has included subsurface data that have permitted an 

accurate three-dimensional analysis of the district.

Lawrence J. Drew of the U.S. Geological Survey 

designed and wrote a computer program used for 

Monte Carlo simulation of iron resources.

NEGAUNEE IRON-FORMATION

The Negaunee Iron-formation, the principal iron 

resource of the Marquette range, is a stratigraphic 

unit in the complexly folded and faulted Marquette 

trough and adjacent smaller structures—the Palmer 

basin, Republic trough, and Mitchigan River trough 

(see fig. 1). All the structures are synclines complicated by faulting. The iron-formation is part of the 

Marquette Range Supergroup, a thick accumulation 

of sedimentary and volcanic material about 2 billion 

years old.

The mineralogy, texture, and structural position 

of the Negaunee vary greatly within the district and 

have been mapped and described by Simmons

(1974), Puffett (1974), Cannon (1974), Gair

(1975), Clark, Cannon, and Klasner (1975), Cannon (1975), Cannon and Klasner (1974, 1975a, b), 

and Klasner and Cannon (1975a, b). The generalized description that follows is based on those more 

detailed descriptions.

MINERALOGY AND TEXTURE

The sediments of the Negaunee Iron-formation 

were deposited as very fine grained chemical precipi88°10' 50' 45' 40' 35' 87°30'

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MARQUETTE RANGE SUPERGROUP 

(PRECAMBR1AN X)

NEGAUNEE IRON-FORMATION OF 

MENOMINEE GROUP (PRECAMBRIAN X)

CRYSTALLINE ROCK (PRECAMBRIAN 

W)— Mostly granitic gneiss

—— — Isograd, label on side 

of higher metamorphic grade

R. 31 W

01234567 KILOMETERS

FIGURE 1.—Generalized geologic map of the Marquette district showing the distribution of the Negaunee Iron-formation

and the location of metamorphic isograds (modified from James, 1955).
RESOURCE DATA BANK

tates in which siderite, iron-silicate minerals, and 

iron oxides and hydroxides accumulated to form 

iron-rich layers, commonly interbedded with siliceous (cherty) layers. The mineralogy differed from 

place to place, probably as a result of varying physical and chemical environments during or shortly after deposition (James, 1954). The mineralogy was 

further affected by diagenesis, regional metamorphism, and weathering.

Metamorphism has had a very profound effect on 

the mineralogy of the Negaunee, commonly resulting 

in nearly total change in the original mineralogical 

makeup. Metamorphic isograds mapped by James 

(1955) and slightly modified by subsequent work are 

shown in figure 1. In the east end of the Marquette 

district, the Negaunee is least metamorphosed, and 

iron-bearing minerals are chiefly siderite, minnesotaite, stilpnomelane, magnetite, hematite, and goethite. Grain size is very small, generally less than 

0.002 inch (0.05 mm). As metamorphic grade increased, siderite and minnesotaite were converted to 

other minerals, chiefly grunerite, within the biotite 

zone, and grain size became progressively larger. At 

metamorphic grades higher than those in the biotite 

zone, no significant mineralogic changes occurred. 

Grunerite, magnetite, and hematite remained stable 

and are the principal iron-bearing minerals in rocks 

of the highest grade attained (sillimanite zone). 

Chert grain size is as large as 0.02 inch (0.5 mm) 

in rocks of the highest metamorphic grades, and 

some iron minerals are several times larger. In general, the banded cherty nature of the rock is preserved to the highest metamorphic grades. In the 

area shown in figure 1, west of the garnet isograd, 

the relatively coarse grain size has inhibited secondary oxidation, and oxidative weathering has not 

been an important mineral-forming process; east of 

the garnet isograd much of the iron-formation has 

been deeply weathered, and all the goethite and part 

of the hematite were formed by postmetamorphic 

weathering of siderite, minnesotaite, and magnetite.

STRUCTURAL POSITION AND THICKNESS

The Negaunee Iron-formation is folded into a 

doubly plunging synclinorium in the Marquette 

trough (see fig. 1). The iron-formation attains its 

greatest thickness (3,300-3,900 ft; -1,000-1,200 

m) near the west-plunging keel of the synclinorium 

a few miles south of the towns of Negaunee and Ishpeming. Because of limited erosion of the syncline 

and the relatively gentle plunge (20°-30°) and great 

stratigraphic thickness of the folded unit, a very 

large area is underlain by the iron-formation. The

synclinorium generally maintains a westward plunge 

from its eastern end to the vicinity of Humboldt, 

where the deepest parts of the formation are believed to be about 8,200 feet (2,500 m) below the 

surface (Klasner and Cannon, 1974). The formation 

thins abruptly away from the exposed keel, also thins 

along the buried keel, and commonly has a thickness of 250-500 feet (—80-160 m) where exposed 

along the steeply dipping limbs. In much of western 

Marquette district, the Negaunee is absent as a result of truncation along an overlying unconformity. 

The westernmost occurrence of the Negaunee is 

along the north limb of the synclinorium near the 

community of Three Lakes.

In the Palmer basin (see fig. 1), the Negaunee is 

about 1,100 feet (—350 m) thick and is folded into 

a half syncline consisting of a south limb and keel; 

the north limb has been eliminated by faulting. The 

structure is as much as 2,500 feet- (—750 m) deep. 

The Mitchigan River trough is likewise a half syncline because the west limb has been eliminated by 

faulting. The Negaunee there can be as much as 

1,000 feet (—300 m) thick, and the trough is about 

6,500 feet (-2,000 m) deep

The Republic trough is a syncline that plunges 

about 45° NW. at the exposed keel but is believed to 

decrease in plunge toward the northwest. In this 

trough, the Negaunee is generally thin, in several 

areas is absent, and only near the exposed keel 

reaches a significant thickness of as much as 1,000 

feet (-300 m).

RESOURCE DATA BANK

Between 1957 and 1974, the U.S. Geological Survey mapped the Marquette district, including the 

Negaunee Iron-formation, in detail. During that 

work, a vast amount of petrologic and structural 

information was accumulated, and, with the cooperation of the mining companies active in the area, a 

file of diamond-drilling records was compiled. In 

1975, we began a project to organize that information into a computerized data file on iron resources.

The basic element of the data system consists of 

blocks measuring V:>XV:> mile (0.8x0.8 km) horizontally and 500 or 1,000 feet (152.4 or 304.8 m) 

vertically. Thus, the area of the district underlain by 

iron-formation was divided into y±-mi- (0.65-km2 ) 

areas and divided into horizontal slabs from the surface to —500 feet (—152.4 m), —500 feet to —1,000 

feet (—152.4 m to —304.8 m) and then into deeper 

1,000-foot- (304.8-m-) thick slabs to the base of the 

iron-formation. For each three-dimensional block 

thus defined, about 45 variables were measured,
ESTIMATES OF NEGAUNEE IRON-FORMATION, MICHIGAN

calculated, or estimated. All variables were entered 

into a computerized file; by means of the Geologic 

Retrieval and Synopsis Program, GRASP (Bowen 

and Botbol, 1975), the data can be classified and recalled by any of the variables or any combination of 

variables.

The data bank can be used as an information 

system that will answer any question that can be 

phrased in terms of listed variables, as long as the 

questioner considers the limitations imposed by the 

manner in which values were determined and stored.

PREPARATION OF THE DATA BANK

For this resource evaluation, we needed to prepare, as quickly as possible, a computer file of information about the blocks within the Marquette district. In order to determine what variables would 

be included in the file and hence, how the data-input 

form would be designed, we needed to consider first 

what questions would be asked of the final data 

system. Once this was established, a simple input 

form was designed to record the data as efficiently 

and easily as possible.

The next task was to make the data machinereadable. The conventional method is to use punch 

cards, but because of the large volume of data involved in this project, cassette tapes were used to 

record the data. The tapes are small, hold a relatively large amount of data, and are reusable once the 

data contained on them have been transmitted to 

the computer.

A problem in making the data machine-readable 

is the introduction of errors. Each time data are 

translated from one medium to another, errors may 

be introduced. For this project, a programmable 

data-entry station was used, not only because it 

generates cassette tapes, but also because it can do a 

limited amount of data editing. The data-entry station is much like a keypunch machine in concept. As 

data are typed at a keyboard, they appear on a CRT 

(cathode ray tube) screen. After a certain amount 

of data accumulates on the screen, it can be edited, 

corrected if necessary, and then transferred electronically from the screen to the cassette tape, thus 

clearing the screen for more data. For ease of entry 

and editing, a preprogrammed "form" appears on 

the screen; the "form" nas a format similar to the 

sheet on which data are originally recorded. Certain errors are automatically detected. For instance, 

a nonnumeric character cannot be entered in a 

numeric-only field, and vice-versa. If a mistake is 

made, an alarm sounds, and the operator must make 

the correct entry before the machine will continue.

In addition, the data-entry station is programmable 

to the extent that it can generate new numbers for a 

given record on the basis of numbers already entered. Using available arithmetic operators, it is 

possible to generate ratios, sums, products, and so 

on, in various combinations and to incorporate the 

resultant number as part of the record. For this 

discussion, a record is one block in the Marquette 

district and all the data associated with that block. 

Each record is written on the cassette tape in fixed 

format, meaning that in each record, items always 

appear in the same position and have the same number of characters.

After the data are in machine-readable form, they 

are transmitted to the computer for processing. The 

data-entry station is used for this job also. Because 

the data-entry station is programmable it can be 

made to appear to the host computer as a teletype 

terminal. The data can then be transmitted to the 

computer through the use of a voice-grade telephone 

connected to the terminal through an! acoustic 

coupler.

Because the final data file needs to be accessible 

in an interactive mode, the host computer has to be 

a timesharing computer. The computer of a private 

company whose function is to sell computer time 

was deemed the most appropriate, both in cost and 

in speed of processing.

The software system chosen to manage the data 

is known as the Geologic Retrieval and Synopsis 

Program (GRASP) (Bowen and Botbol, 1975). 

GRASP is a set of Fortran IV subroutines and a 

main driver that provides interactive access to geologic data. Because GRASP is a machine-independent interactive system, it was available on the 

chosen computer.

The Marquette district GRASP file actually consists of four files: mask file, definitions file, dictionary file, and numeric master file. The mask file contains a short acronym for each of the 45 variables 

abundant iron mineral or formation name, the mask 

file tells the GRASP system whether a particular 

field will contain a real (decimal) number, an integer (whole) number, or character-string (alphanumeric). For character-string items, such as most 

abundant iron mineral or formation name the mask 

file also has a numeric pointer to the first entry in 

the dictionary file associated with that item. The 

dictionary file is a list of all the possible characterstring items that could appear in the GRASP file. 

At present it has 73 entries. All the items for each 

field are grouped together in the dictionary file, 

which contains a count of how many items there are
RESOURCE DATA BANK

for each field. The definition file's main purpose is 

to provide a full breakdown of the 45 variables into 

various categories of information in the GRASP file 

(such as, taconite characteristics, deposit characteristics), as well as to provide a description of each 

acronym. The numeric master file is the actual data 

file, but it is far different in appearance from the 

raw data as they initially appear on the time-sharing 

computer. Each of the alphanumeric entries has 

been replaced by a number indicating that entry's 

relative position in the dictionary file. An important 

point is that the use of this method of storing dictionary pointers rather than the long characterstring entry saves a considerable amount of space 

in the computer. For instance, instead of having the 

full county name "Marquette" appear in every record in that county, the word is replaced by a number 

indicating its position in the dictionary. Decimal 

number and integer data fields are stored as they 

were entered. For a given record, several fields may 

have been left blank on the original input sheet, 

meaning that the data for a particular item are unavailable. The GRASP software will compress the 

data so that the numeric master file is as small as 

possible.

A Fortran IV program, BUILD 1, changes the raw 

input record into the compressed unformatted binary numeric master file. The BUILD 1 program 

reads in each transmitted record, checks for blank 

numeric fields to ensure that they do not get stored 

as zero, compares each alphanumeric entry in each 

raw-data record with the contents of the appropriate part of the dictionary file in order to determine 

that entry's numeric position in the dictionary, and 

finally writes the transformed record into the numeric master file. The program is run once for each 

cassette file that is transmitted to the timesharing 

computer. BUILD1 has been designed so that records that are already a part of the numeric master 

file may be updated easily. The update part of the 

program applies to numeric fields only. The resultant 

new numeric master file will then contain the old 

numeric master file and any updates that have been 

made, as well as the most recently transmitted cassette file. In addition to the electronic editing performed during the initial data entry and the editing 

functions of the programs, the entire data set was 

edited manually by comparing printouts of the data 

with the original data sheets.

At this point the file can be interrogated by the 

GRASP system. Among the things GRASP can be 

used for is the creation of subfiles. Through use of 

condition, logic, and search commands, these subfiles can be generated according to user-specified criteria. The following are examples of subfiles: (1) all 

the blocks whose most abundant iron mineral is 

magnetite and which contain 15 million to 30 million 

tons of iron ore, and (2) all the blocks that have a 

grain size of less than 0.02 inch (0.5 mm) or a 

maximum Pleistocene thickness of no more than 120 

feet (37 m). Simple statistical operations can be 

performed on the master file or any subfiles. By use 

of the function command, the user can obtain the 

maximum, minimum, mean, variance, and standarddeviation values for any of the variables. Not only 

are they important numbers in themselves, but the 

maximum and minimum values are quite valuable as 

secondary editing numbers. Extremely high or low 

numbers will show up as errors and can be flagged 

and changed. The program will also calculate the 

slope, intercept, and correlation coefficient for a 

least-squares regression line between any two numeric variables.

The final data file contains 1,860 records which 

are all accessible and updatable by the GRASP software system. On the basis of any given set of conditions and associated logic, it takes about 45 seconds 

to search the entire file and to select the appropriate 

records to be stored in a subfile. The total cost of 

this operation averages about $2.75 for each computer run.

VARIABLES

Each of 1,860 blocks containing iron-formation is 

characterized by a set of variables described below. 

A hypothetical cross section and data block are 

shown in figure 2 to illustrate the manner in which 

certain variables were measured and assigned to 

blocks.

IDENTIFICATION AND LOCATION

These variables provide information on the identification and geographic and spatial location of individual blocks.

Unique identification number.—The district was 

divided on a half-mile (0.8-km) grid by north-south 

and east-west lines, and each i/i-mi2 (0.65-km2 ) area 

was assigned a unique identifying number, ID1, that 

corresponds to a number assigned to that block on 

an index map of the district. The district was further divided into horizontal slabs, either 500 feet 

(152.4 m) thick (for the two slabs nearest the surface) or 1,000 feet (304.8 m) thick. The depth below the surface is indicated by ID3, the depth to the 

bottom of the slab. These two are separated by ID2, 

an alpha character coded to indicate the formation
ESTIMATES OF NEGAUNEE IRON-FORMATION, MICHIGAN

a-Unconsolidated stripping required to uncover iron-formation

in block 104-1000 

6-Unconsolidated stripping required to uncover iron-formation

in block 104-2000 

c-Consolidated stripping required to uncover iron-formation in

block 104-1000 

d- Consolidated stripping required to uncover iron-formation in

block 104-2000

e- Iron-formation in block 104-1000 

f- Iron-formation in block 104-2000 

g- Iron-formation in block 104-3000 

/7-lnterlayered waste in block 104-1000 

/- Interlayered waste in block 104-2000 

j- Iron-formation thickness for block 104-1000 

k- Iron-formation thickness for block 104-2000 

/- Formation thickness for blocks 104-1000, 2000, and 3,000

FIGURE 2.—Hypothetical cross section of data block showing method by which variables are measured and assigned to 

blocks. No entry is made for block 104-500 because it contains no iron-formation. No stripping is assigned to block 

104-3000 because all overlying waste would be removed in mining blocks 104-1000 and 104-2000.

or member to which the data apply. Thus, a complete 

identification number, for instance, "104A2000," 

identifies material in the ^4-mi2 (0.65-km2 ) area 

numbered "104" on index sheets, and in the slab 

1,000-2,000 feet (304.8-609.6 m) beneath the surface, and the information pertains to a stratigraphic 

unit "A," a code for the stratigraphic name of the 

unit.

UTM coordinates.—The Universal Transverse 

Mercator grid (UTM) was used to locate each block 

more definitely. The UTM northing and UTM easting for the southeast corner of each block are two 

additional identification and location variables. They 

were measured from 7 y% -minute topographic maps.

County.—The county variable identifies the county in which the block is found.
RESOURCE DATA BANK

TACONITE CHARACTERISTICS

Seventeen variables are included that give information on the nature and magnitude of iron-formation within a block.

Grade.—Five variables give the percentage of 

iron, silica, phosphorous, manganese, and alumina 

in the iron-formation. Values were obtained mostly 

from assays of drill core provided by mining companies but also from analyses by the U.S. Geological 

Survey of drill core and samples from outcrops. Because much of the drilling was done in exploration 

for high-grade ore, many cores are partly in material that has undergone secondary enrichment, and 

thus, the cores are not representative of most of the 

iron-formation. To avoid biasing our values by these 

higher grade cores, any analyses of greater than 40 

percent iron were excluded from our data.

Volume of iron-formation.—The volume of ironformation in millions of cubic feet contained in each 

slab is calculated from cross sections drawn at y^-mi 

(0.8-km) intervals approximately perpendicular to 

the strike of the iron-formation. Where the shape 

of the iron-formation between cross sections is too 

irregular to approximate by considering only end 

areas, the volume is estimated by approximating the 

shape by means of combinations of polyhedral forms 

whose volume can be calculated from simple 

formulas.

Density of iron-formation.—The density of ironformation is recorded in short tons per cubic foot. 

It is determined individually for each slab from a 

graph relating density to iron percentage.

Millions of short tons of iron-formation.—The 

amount of iron-formation in millions of short tons 

is determined by multiplying density by the volume 

of iron-formation in the block.

Millions of short tons of iron.—The amount of 

iron in millions of short tons is determined by multiplying millions of short tons of iron-formation in the 

block by the iron percentage.

Grain size.—The grain-size variable indicates the 

size, in millimeters, of chert grains. The value is 

determined either by direct measurement in thin 

sections or is derived from the location of the block 

with respect to metamorphic isograds by using observed relationships between metamorphic grade and 

grain size.

Most abundant iron mineral.—The most abundant 

iron-bearing mineral within the entire slab is determined. It may represent an average determined 

from two or more lithologic types of iron-formation. 

Mineralogic content is determined from drill records, thin sections, and field observations.

Other iron minerals.—Three variables indicate 

other abundant (more than 10 percent) iron minerals contained in the slab.

Accessory minerals.—The three most common accessory minerals (less than 10 percent), excluding 

quartz, are listed as separate variables.

DEPOSIT CHARACTERISTICS

The remaining variables describe mostly geometric and geologic relationships of the deposit and 

can be used to judge its minability.

Formation name.—The stratigraphic name of the 

formation or member is included here and is an 

alternative means of storing and retrieving the information given by ID2, but here is listed in full in 

alpha characters rather than in coded form.

Formation thickness.—The total stratigraphic 

thickness of the iron-formation of which the material included in the block is a part (I in fig. 2) is 

indicated.

Iron-formation thickness.—The maximum vertical thickness of iron-formation within the block (j 

and k in fig. 2) is defined.

Interlayered waste thickness.—The thickness of 

waste rock, determined largely from drill data, is 

listed.

Iron-formation/waste.—Iron-formation/waste is 

calculated as the ratio of thickness of iron-formation 

to thickness of waste rock.

Dip.—The average dip of bedding of the ironformation estimated to the nearest 5° is listed here.

Thickness of Pleistocene overburden.—The thickness of Pleistocene cover is measured in feet and includes separate variables for maximum, minimum, 

and average thickness. Values are determined from 

diamond-drilling and water-well records.

Stripping required to uncover iron-formation.— 

Separate values are given for consolidated (c and d 

in fig. 2) and unconsolidated (a and b in fig. 2) stripping. Unconsolidated stripping is calculated by using 

the average Pleistocene thickness. Consolidated 

stripping is calculated from the same cross sections 

used to calculate iron-formation volume. Only material within the block is considered, so that figures 

do not reflect the total amount of rock that might 

have to be moved to actually mine the iron-formation 

because mining might also require moving rock in 

adjacent blocks.

Interlayered waste-rock type.—Two variables 

identify the two most common types of rock interlayered with the iron-formation. They are determined largely from drill records.
8 ESTIMATES OF NEGAUNEE IRON-FORMATION, MICHIGAN

Overlying waste-rock type.—Two variables identify the two most common rock types that form overlying waste rock.

Magnitude of deposit.—The total size of the deposit of which the material in the block forms a part 

is identified. Individual deposits are defined primarily by mineralogy, grain size, depth, and waste ratios.

Amount of previous mining.—A coded alpha 

character is used to identify small open-pit mines, 

large open-pit mines, small underground mines, 

large underground mines, and no mines. One million 

long tons of ore shipped is used as the dividing line 

between large and small mines.

Number of data points.—The total number of 

points in the slab at which some information about 

the iron-formation is known is indicated. Points include outcrops, drillholes, and mine workings.

Number of drillholes.—The number of drillholes 

that penetrate the slab are indicated.

Million short tons of iron-formation under water. 

—The amount of iron-formation directly beneath 

lakes or major streams is indicated.

RELIABILITY OF DATA

Data from about 1,600 outcrops and 3,300 drillholes were used to draw cross sections and to calculate values of the variables. The data density varies 

considerably both areally and with depth, and the 

reliability of values likewise varies considerably. To 

test the reproducibility of our figures, an area near 

Ishpeming that has about average data density was 

chosen for recalculation. The ability to reproduce 

values such as those of chemical composition, grain 

size, and mineral content is good because these are 

derived in a methodical manner from available data. 

Barring mechanical errors in calculation or in transscribing data, reproducibility of such data approaches 100 percent. For calculation of other important variables, including volume of iron-formation, thickness of iron-formation, volume of interlayered waste rock, and volume of consolidated rock 

required to be stripped, the ability to reproduce 

values is almost entirely a function of ability to, reproduce cross sections. To test this ability we redrew sections from outcrop and drilling data and 

recalculated these values for 39 blocks in nine i/i-mi2 

(0.65-km2 ) areas to a maximum depth of 5,000 feet 

(1,524 m) below the surface. Comparing these with 

our original calculations, we found that figures for 

individual blocks commonly agreed within 20 percent but that much greater differences were also 

common. Large discrepancies are mostly due to differences in the placement of contacts between ironTABLE 1.—Percentage of variation between original calculations and recalculated value of four variables for y^-mi2 

(0.65-km") vertical columns through the iron-formation

[Positive variations indicate cases in which recalculated values exceeded 

original values. Negative variations indicate cases in which recalculated values were less than original values]

Volume

taconito

(ft;i X10°)

Taconite

thickness 

(ft)

Waste

thickness

(ft)

Consolidated 

stripping

Block

127 _____

115 _____

103 _____

91 _____

84 _____

14,519

12,895

16,190

14,797

4,235

139 __-. 10,696 

151 --.-_ 10,598 

163 __-. 9,223 

175 .__ 633 

Average ______

— 15

— 14

— 2

+ 1 — 28

+ 5

— 5

3,357

2,380

2,757

2,988

2,183

1,938

1,912

1,799

1,188

+ 0.2 — 10

+ 5 

+ 5 

+ .2

943

920

1,243

1,012

467

— 11.6 1,062

— 4.9 437

+ 12.7 201

+ 45.1 12

— 5

— 5

+ 5

— 1

— 3

4,437 +3 

3,147 +8 

2,163 —2 

1,816 +4 

no strijpping

no stripping 

56 +0.2

+ 4.6

— 13 no.strjpping

— 17 no stripping 

— + 2.6 6

1 Percentage of variation of recrlculation from original normalized 

against total volume of block.

- Percentage of variation of recalculation from original normalized 

against total thickness of block.

3 Percentage of variation of recalculation from original normalized 

against volume of block to the maximum depth of consolidated stripping.

formation and interlayered metadiabase sills. Commonly such discrepancies are self-cancelling when 

several adjacent blocks are considered together. For 

instance, a different interpretation of the position of 

a unit of waste rock might decrease the calculated 

volume of iron-formation for one block but would 

increase it by a corresponding amount for an adjacent block. If an entire ^-mi2 (0.65-km2 ) vertical 

column through the iron-formation is considered, as 

in table 1, the variation is generally small, especially 

when normalized against the total volume of the 

block. The average of these variations is well within 

10 percent error for all variables considered. If a 

still larger area were considered, the variation would 

probably be even smaller. In light of these figures, 

we feel that our values for individual blocks should 

be reproducible to within 20 percent about 60 percent of the time, but total figures for five or more 

adjacent blocks are reproducible to within 20 percent about 70 percent of the time, and to within 10 

percent about half the time. Considering the variation normalized against the total volume of rock 

being considered, the observed variation is less than 

10 percent of the total volume about 80 percent of 

the time. Thus, values that are derived simply by 

arithmetic manipulation of data are considered to be 

essentially 100 percent reproducible. Other values, 

derived by calculations from cross sections, are considered to have, on the average, an error of less than 

10 percent of the volume of rock within the area of 

calculation.
IRON RESOURCES

The figures used throughout this report for tons 2 

of iron-formation should be considered maximum 

figures. In calculating the values for tons of ironformation, we calculated a total volume of ironformation and interlayered waste, and then we subtracted the tonnage of identified waste. In many 

areas probably not all waste rock was encountered 

in outcrop or diamond drilling, so our values for 

iron-formation, on the average, may be somewhat 

larger than the actual tonnage. Also, because considerable variation, on a scale too small to differentiate 

with our data, may exist in the iron-formation within a single block, the total calculated tonnage may be 

greater than the tonnage that could be processed successfully by any single concentrating technique. At 

taconite mines, changes in mineralogy or texture, 

some very subtle, render processing of parts of the 

iron-formation uneconomic. No attempt was made to 

exclude any type of iron-formation from our calculated values of tons of iron-formation in each block, 

hence, the tonnage of economically available ironformation in many blocks is probably smaller than 

our calculated values.

IRON RESOURCES

The Negaunee Iron-formation contains a total of 

about 205 billion tons of material in the Marquette 

district. The average iron content 3 is 32 percent, 

and the average for 95 percent of individual blocks 

ranges from 27.5 to 36.5 percent. In order to avoid 

an arbitrary cutoff of resource figures, the entire 

Negaunee is considered a resource even though a 

substantial part of the iron is far from economically 

recoverable at present.

The economic potential of iron-formation is determined largely by the types of processes by which 

iron can be concentrated. At present, iron can be 

recovered only from oxide and hydroxide minerals. 

Three different processes are in use in the district. 

Where iron is largely in magnetite, a magnetic separation is used. Where coarse-grained (>0.002 in.; 

0.05 mm) hematite is predominant, it is concentrated by froth flotation, and where fine-grained 

oxides and hydroxides predominate, they are concentrated by a complex flocculation and flotation process. The potential of iron-formation as mill feed for 

any of these processes is determined largely by mineralogy and grain size rather than by iron content. 

Total iron is not a variable of primary importance 

because almost all the iron-formation contains more

3 Unless otherwise specified, figures are in long tons—2,240 Ib. A long 

ton is about 1.02 metric tons.

"Percent iron is acid-soluble iron, that is, iron in oxide, hydroxide or 

carbonate minerals, except for silicate-rich iron-formation in which 

percent iron is total iron determined by complete chemical analysis

than 27 percent iron. Iron in silicate or carbonate 

minerals cannot be recovered so that total iron is 

commonly substantially greater than recoverable 

iron. Thus, mineralogy and grain size are two of the 

most important variables on which we have based 

our resource classification.

Under present conditions, underground mining of 

taconite is not profitable; thus all taconite mines 

now are open pits. Therefore, depth below the surface is also an important variable for resource classification. Figure 3 shows the relationship between 

tons of iron-formation and depth. We have chosen 

1,000 feet (304.8 m) as the average maximum depth 

of iron-formation that is accessible from open pits. 

In some areas, deeper pits are possible. For instance, 

the Empire pit (see fig. 1) may ultimately reach a 

depth of 1,600 feet (487.7 m), but such deep pits 

require exceptionally favorable conditions. Figure 3 

shows that about 49 billion long tons or about onequarter of the iron-formation is within 1,000 feet 

(304.8m) of the surface.

Figure 4 shows the iron-formation classified by 

mineralogy. Fine-grained hematite and goethite 

iron-formation, part of which can be concentrated 

by selective flocculation and flotation, constitutes the 

largest mineralogic class, totalling about 91.6 billion long tons. About 28.4 billion long tons of this

0 1000 2000 3000 4000 5000 6000 7000 8000 

DEPTH BENEATH SURFACE, IN FEET

FIGURE 3.—Cumulative curve showing relationship between 

billions of long tons of Negaunee Iron-formation and depth 

beneath the surface.
10 ESTIMATES OF NEGAUNEE IRON-FORMATION, MICHIGAN

100 r90

80

70

60 CO

I

z

o

z

o

CO 40

50

30

20

10 -

b

o

ii

LLJ (3

bz

QC 5

LLJ 5

QS$

CO CQ

Q 5 iu £

T cc = 0

bi

S|

tO OQ

FIGURE 4.—Graph showing billions of long tons of Negaunee 

Iron-formation in categories described in the text. Shaded 

part of bars is iron-formation within 1,000 feet (304.8 m) 

of the surface. Smaller bars indicate amount of each class 

that contains more than 10 percent siderite.

material is within 1,000 feet (304.8 m) of the surface. Next in importance is iron-formation containing magnetite as the most abundant mineral. About 

48 billion long tons of this type is present, of which 

about 10.5 billion long tons is within 1,000 feet 

(304.8 m) of the surface. Coarse-grained hematitic 

iron-formation, concentratable by froth flotation, is 

the least abundant type, totalling about 27.1 billion 

long tons, of which only about 4.8 billion long tons 

is within 1,000 feet (304.8 m) of the surface. Silicate 

iron-formation, not amenable to concentration by 

means of present technology, totals about 37.6 billion long tons, about 5.6 billion long tons of which is 

within 1,000 feet (304.8 m) of the surface. About 

92.5 billion long tons of the iron-formation is also 

siderite-bearing. Locally, siderite is the predominant 

iron mineral in the iron-formation, but we have 

identified no blocks in which it is the most abundant 

mineral within an entire ^-mi2 (0.65-km2 ) area. 

Thus, this large tonnage does not represent additional iron-formation over that of the first four 

classes, but is a repetition of the tonnage indicated 

for hematitic, magnetic, and silicate iron-formation 

that also contains siderite as an abundant (> 10 percent) mineral. The amount of each class that contains siderite in quantities greater than 10 percent 

is also shown in figure 4.

These figures indicate that the Negaunee contains 

a maximum of about 167 billion long tons of ironformation that might be treatable by existing concentrating processes or modifications of them. About 

44 billion tons of this material is at depths of less 

than 1,000 feet (304.8 m) beneath the surface and 

might be accessible to open-pit mining. Certainly, 

some percentage of that amount will never be economically available from open pits. However, the 

error introduced by including economically unavailable iron-formation in this assessment is at least 

partly offset because we excluded iron-formation at 

depths greater than 1,000 feet (304.8 m), some of 

which will probably be mined at presently operating 

open pits. Future pits might also be deeper than 

1,000 feet (304.8 m). We emphasize that these figures can in no way be construed to represent economically recoverable ore. They simply indicate the 

maximum amount of material that meets minimum 

requirements to be considered as potential concentrating-grade ore. Many other variables must be defined in order to make a more certain assessment of 

the economic potential of the material. Many of 

these variables are not geologic and cannot be 

treated by means of our data base. Of the geologic 

variables, many require detailed information far in
IRON RESOURCES 11

excess of our data base and cannot be estimated. A 

few additional geologic variables that we can estimate are considered below.

Two important variables in determining the feasibility of open-pit mining are the amount of overlying waste rock that must be stripped and the 

amount of interlayered waste rock that must be 

moved along with ore. Figures 5 and 6 show maximum tonnages of possible treatable iron-formation 

within 1,000 feet (304.8 m) of the surface that can 

be mined relative to the amount of waste rock that 

would be moved. No generalizations can be made for 

the amount of waste that can be moved economically. 

The economics of individual mining plans are governed by a complex set of factors, and there is,

35

z

O 30

? CO = z 

^o

25

20

g

< Z 15 

DC -1

2 10

1 5

10 100 1000 10,000

CONSOLIDATED STRIPPING, IN MILLION 

CUBIC FEET PER %-SQUARE-MILE BLOCK

FIGURE 5.—Histogram showing billions of long tons of the 

possibly treatable Negaunee Iron-formation within 1,000 

feet (304.8 m) of the surface relative to volume of consolidated waste rock overlying it. Volume of waste is calculated only within vertical boundaries of each ^-mi2 

block, not within inward sloping boundaries as would 

probably be required for development of an open pit. 

30

25

Z 20

« 15

O

DC

10

.01 .1 1 10 100 1000 

VOLUME IRON-FORMATION/VOLUME WASTE

FIGURE 6.—Histogram showing billions of long tons of the 

possibly treatable Negaunee Iron-formation within 1,000 

feet (304.8 m) of the surface with various ratios of volume 

of iron-formation to volume of interlayered waste rock.

therefore, a wide range in tolerance for waste rock. 

No attempt is made in this report to estimate 

amounts of iron-formation that could be mined economically relative to amounts of associated waste 

rock that would need to be moved.

Figure 5 shows, however, that development of a 

major part of the resource may not be hindered 

seriously by large amounts of overlying waste. Nearly three-quarters of the material within 1,000 feet 

(304.8 m) of the surface requires stripping of less 

than 1 million ft3 (28,321 m3 ) of consolidated material for each ^-mi2 (0.65-km2 ) area to uncover the 

iron-formation. These figures for overlying consolidated waste must be considered approximate minima for two reasons. First, we have defined overlying 

stripping as being restricted mostly to quartzite and 

conglomerate stratigraphically above the Negaunee, 

whereas an individual mine may be in a stratigraphically medial position in the Negaunee and have a 

metadiabase sill, included in our figures as interlayered waste, as overlying rock. When individual 

mines are considered, therefore, there may in fact 

be material to be stripped, such as metadiabase, that 

in our figures is included as interlayered waste rather than as overlying waste. Second, in calculating 

volumes of stripping, we have calculated only material directly above the iron-formation of interest, 

which, in effect, assumes vertical pit walls if our 

figures are applied to an individual mine. Because 

vertical pits are generally not feasible in actual mining practice, overlying and adjoining waste volumes 

for an individual mine would often be somewhat 

greater than our figures indicate.

Figure 6 indicates that more than 90 percent of 

the 44 billion tons of possibly treatable iron-formation within 1,000 feet (304.8 m) of the surface has 

iron-formation: waste ratios greater than 1:1 and 

that nearly 16 billion tons has a ratio of better than 

10:1. Thus, excessive volumes of interlayered waste 

rock do not seem a major hindrance to development 

of a large percentage of the resources. Much of the 

waste is interlayered metadiabase sills, which are 

generally large discrete bodies (see fig. 1). In mining, much of the sills could be left in place, effectively improving the iron-formation: waste ratios of the 

material actually mined. However, in places, the 

presence of the metadiabase sills would cause mining of some underlying iron-formation to be economically unattractive.

RESOURCE CLASSIFICATION

A classification system for resources based on degree of economic feasibility of mining and process-
12 ESTIMATES OF NEGAUNEE IRON-FORMATION, MICHIGAN

ing the ore and on degree of geologic assurance with 

which the deposit is known was proposed by McKelvey (1973) and has been adopted by the U.S. Geological Survey and the U.S. Bureau of Mines. A 

modified version of that classification is used here. 

Our classification differs from McKelvey's in two 

ways. First, we do not include categories for undiscovered resources. We consider that the Marquette 

district is so thoroughly explored that all the material indicated by our figures is known with adequate degree of assurance to be considered an identified resource. The possibility that substantial additional deposits of iron-formation exist in the district 

is negligible in light of the great amount of geologic 

and geophysical data on the area. Second, we do not 

subdivide reserves as suggested by McKelvey. We 

consider that the definition of economic availability 

of iron ore is dependent on many factors that cannot be estimated without very detailed studies and 

that reserves are restricted to thoroughly tested material at existing mines or in areas where development is known to be feasible in the immediate 

future.

In figure 7, the tonnage that we assign to each of 

seven resource categories is shown. Under economic 

deposits, we include estimates of economically recoverable material at three existing taconite mines 

plus one deposit at which development in the near

\

J

5

0

LJ

L 

J

>

5

7

5

u

0

7

0

t

u

c J

7

o

^

0

z

0

o

111

o

^

0

o 

o

111

CO

3

CO

MEASURED, INDICATED, AND INFERRED

2-3

_i

z

o

DC

^

1

Q.

_,

<^

Z

o

DC

CO

CO

MEASURED

26.1

7.6

INDICATED

7.9

20.9

INFERRED

7.3

133.1

— DECREASING GEOLOGIC ASSURANCE —>• 

[All figures in billion long tons]

FIGURE 7.—Resource classification chart showing billions of 

long tons of Negaunee Iron-formation assigned to categories based on varying degrees to which material approaches the characteristics of presently economic deposits 

and the degree of assurance with which the deposit is 

known.

future has been announced. These estimates are 

based on annual production capacity of concentrating plants and presumed minimum life span of the 

mines, as well as on published reserve figures.

Under subeconomic resources, we have divided 

material into two categories—paramarginal resources and submarginal resources—on the basis of 

mineralogy and depth below the surface. Paramarginal resources include material that has magnetite, 

hematite, or goethite as the most abundant iron mineral and that is within 1,000 feet (304.8 m) of the 

surface. Thus, paramarginal resources include material that is potentially accessible to open-pit mining, and that has mineralogical similarities to material presently being processed commercially. Although large amounts of this material may not be 

processible by techniques currently used, we consider it likely that new processes or modifications of 

current ones could successfully beneficiate much of 

it. All the material has one feature in common— 

a large percentage of the contained iron is present, 

in a mineralogic form (oxide or hydrous oxide minerals), for which beneficiation requires only the 

physical separation of iron minerals from gangue 

minerals.

Submarginal resources include material at depths 

greater than 1,000 feet (304.8 m) and all material, 

regardless of depth, that contains iron-silicate as the 

most abundant mineral. Submarginal resources, 

therefore, include (1) material that to become economically recoverable would require a substantial 

improvement in mining or processing technology or 

a substantial increase in price of iron concentrates, 

or both; and (2) material for which the concentration of much of the contained iron requires a chemical as well as a physical process.

We have rather arbitrarily defined limits on degree of geologic assurance as five or more data points 

(outcrops plus drillholes) per 1/4 mi2 (0.65 km2 ) for 

measured resources, and one to four data points per 

14 mi 2 (0.65 km2 ) for indicated resources. Material 

not known directly but whose presence is predicted 

by projection of data for relatively short distances 

is considered an inferred resource.

Figure 7 shows that for paramarginal resources 

about 34 billion long tons, or more than 80 percent 

of the category, is known from at least one data 

point per i/4 mi2 (0.65 km2 ) and more than 60 percent is known from five or more data points. This 

high degree of geologic assurance reflects the detailed surface mapping of the district as well as the 

great number of drillholes that penetrate the first 

1,000 feet beneath the surface.
IRON RESOURCES 13

For submarginal resources the situation is reversed. Only about 7.6 billion long tons, or about 5 

percent of the category is known from more than 

five data points per ^ mi2 (0.65 km2 ), whereas 

greater than 80 percent or 133.1 billion long tons is 

inferred by projection of data. Because inferred submarginal resources account for about 65 percent of 

the total resource, we would like to consider briefly 

the degree of assurance with which the material is 

inferred. We are dealing with a stratigraphic unit, 

the Negaunee Iron-formation, that is known from 

surface and near-surface data to vary gradually 

along strike in a manner in which the most important variables such as thickness and mineralogy can 

generally be predicted rather well between data 

points 1 mile (1.6 km) apart. Our data bank is not 

constructed so as to identify readily the nearest data 

point to a block, but we believe, from our experience 

in analyzing the data, that seldom is a block more 

than 1 mile from a data point. Therefore, although 

by projecting data we introduce some uncertainty 

into our figures for inferred resources, we feel, from 

the degree and scale of variation seen at the surface, 

that in projecting our data relatively short distances, 

we have not introduced any gross errors.

ESTIMATES OF TOTAL RECOVERABLE IRON

Ultimately the most important values for iron resources are estimates of the amount of metallic iron 

that can be produced from available iron-formation. 

As we have indicated previously, we do not attempt 

to define amounts of economically recoverable iron. 

We have, however, estimated the maximum amount 

of iron that might be produced from known ironformation, given economic conditions that allow its 

profitable mining and processing. These figures indicate the maximum geologic availability of iron under 

existing or somewhat improved technologic and economic conditions and can be considered an upper 

limit for iron reserves.

To define the amount of recoverable iron, we must 

answer two basic questions. First, what percentage 

of the iron-formation is amenable to processing to 

make a concentrate that meets quality standards for 

contained iron and silica? Second, of the amount of 

iron-formation that meets minimum quality standards, what percentage of the iron can be recovered ?

To answer these questions requires data on the 

metallurgical response of iron-formation to various 

concentrating processes. Comprehensive tests for 

the Negaunee as a whole have never been made; 

rather, a few areas, which for a variety of reasons 

have received concentrated study, have been rather 

thoroughly tested, and we are largely without data

for the remainder of the district. Precise estimates 

based directly on empirical measurements cannot be 

made, but the available metallurgical data can be 

used to predict the maximum amount of iron recoverable from untested material. To do this, a Monte 

Carlo simulation model was designed, based on the 

assumption that the material for which test results 

are known is the same as material to which the data 

are being projected. The results provided by the 

model are no more or less valid than that assumption. Possible errors of overestimation introduced by 

this assumption are discussed in more detail below. 

The empirical base for the model was derived from 

metallurgical tests by the U.S. Bureau of Mines 

(Heising and Frommer, 1967) and from data provided by mining companies. In all, more than 36,000 

feet (10,973 m) of drill core is included in the tests; 

an individual test is generally on 5-20 feet (1.5-6.1 

m) of core.

Metallurgical data are available for 41 sample 

blocks. They are divided into three types of tests 

and are shown in figure 8 and table 2. Ten sample

TABLE 2.—Metallurgical data used in simulation model

[Each line represents average results for one data block 1

Concentrate Concentrate Recoverable

Fe SiOa Fe

(in wt. (in wt. (in wt.

percent) percent) percent)

Ironformation 

in block 

(million 

long tons)

Length of

drill core

tested (feet)

Magnetic separation

60.8

67.2

63.4

63.5

66.0

64.7

57.2

55.6

64.1

63.5

60.6

65.7

62.7

66.5

67.1

55.7

58.0

55.9

57.6

12.8

5.9

10.7

9.4

9.8

8.8

17.6

19.1

9.1

9.3

3.8

6.4

8.0

7.2

7.2

20.3

15.7

22.4

16.3

38.2

25.2

24.7

21.0

28.6

25.8

34.2

31.1

17.3

25.7

27.0

27.2

49.5

19.0

43.8

29.4

50.2

44.2

48.2

296.9

179.9

249.3

142.9

332.9-

309.3

69.2

84.7

24.9

52.3

52.4

39.2

13.3

28.8

8.9

47.0

39.2

27.4

9.6

3,320

153

4,842

1,241

1,521

1,706

697

2,388

234

911

5

5

15

10

10

5

60

467

137

Selective flocculation

60.2

66.3

64.1

66.4

66.7

51.1

61.5

57.5

60.5

62.3

18.4

4.0

5.8

5.3

4.4

19.0

11.8

16.0

13.1

10.5

36.0

20.6

29.6

28.7

25.5

18.7

33.9

30.1

33.7

34.8

127.4

345.8

145.8

153.8

205.9

142.2

204.5

182.8

287.3

75.9

715

Trench samples

Do.

Do.

Do.

Do.

3,974

654

3,929

7,948

Froth flotation

64.9

63.0

64.1

59.5

67.9

67.0

66.8

65.5

58.8

52.2

65.5

61.7

4.0 

8.1 

4.3 

6.8 

3.1 

2.1 

4.0 

5.8 

13.4 

9.3 

8.2 

8.8

24.7

27.8

38.7

30.2

39.3

35.5

36.4

41.7

37.0

23.9

19.8

25.2

12.4

20.6

19.3

15.5

28.8

33.1

27.4

8.4

39.2

27.4

135.7

79.0

172

10

5

5

10

30

5

10

207

?

294

335
14 ESTIMATES OF NEGAUNEE IRON-FORMATION, MICHIGAN

LL. 00

TT I

50 55 60 65 70

PERCENT CONCENTRATE

Fe

DAVIS MAGNETIC TUBE TESTS 

[Average results for each %-square-mile block]

J

Jfc

5 - 

4 -

3 -

flI I i

1 2 ~ n nn 1 ~

M I | I I I i | I I n I I n I I I I PI

¥

-~rr 1 1 i i [ 1 1 II i 1 1 i 1 i i 1 i | 1 i i i |

) 5 10 15 20 25 15 20 25 30 35 40 45 50 55

PERCENT CONCENTRATE 

Si02

PERCENT RECOVERABLE 

Fe

V)

LL. 00

4 -

2 _

SELECTIVE FLOCCULATION TESTS 

[Average results for each %-square-mile block]

5 - 

4 -

n n Sn

1 3 ~

2 -

1 -

5 -

4 -

3 -

2 -

nnn

0 55 60 65 70 05 10 15 20 25 15 20 25 30

L

I I II 

35 40 45 50

PERCENT CONCENTRATE 

Fe

PERCENT CONCENTRATE 

Si02

PERCENT RECOVERABLE 

Fe

u_ oo

cc- 1 _

FROTH FLOTATION TESTS 

[Average results for each %-square-mile block]

5 -

4 -

3 -

2 - 

0 -

5

5 -

4 -

3 -

D

2 - 

, , , -i ; °H

0 55 60 65 70 (

5 —

4 -

q

^

) 5

1 3 ~

2 -

——— i n1 ————— o10 15 20 25 1II II I II

5 20 25 30 35 40 45 50

PERCENT CONCENTRATE 

Fe

PERCENT CONCENTRATE 

Si02

PERCENT RECOVERABLE 

Fe

LU

3 c

Q V) 3 —

N^ O

LL. 00 3 —

°m 2 _

uj d 

m ^ 1 -

^

""3 0 ~r"

T

J

'

-

1 '

\

1 '

I

, . , i | , ,

COMBINED FROTH FLOTATION, DAVIS TUBE, 

AND SELECTIVE FLOCCULATION TESTS 

[Average results for each %-square-mile block]

50 55 60 65 70

PERCENT CONCENTRATE 

Fe

6 -

I

n n

1 4 ~

: : _r;;: 32 :

ill |IMI| II M,l I|M M| V,

) 5 10 15 20 25 1

f,

5 20

t

25

si

:;; ; ' : . •:•:•:•:•:! : | j 

:.;,,.; ....;:..:..;:::,;;. :;::;;j ., L:J

1 1 I 1 , 1 . | 1 1 1 1 | 1 M 1 II |l M 1 |

30 35 40 45 50 55

PERCENT CONCENTRATE 

Si02

PERCENT RECOVERABLE 

Fe

FIGURE 8.—Histograms showing results of three types of metallurgical tests. See table 2 for tabulated results. "Percent 

concentrate Fe" is percentage by weight of Fe in final concentrate; "percent concentrate Si02" is percentage by weight 

of Si02 in final concentrate; "percent recoverable Fe" is percentage of crude ore recovered as Fe in final concentrate 

(weight Fe in concentrate/weight crude ore).
IRON RESOURCES 15

blocks represent fine-grained hematitic iron-formation, 19 blocks represent magnetic iron-formation, 

and 12 blocks represent coarse-grained hematitic 

iron-formation. Each value on the histograms and 

in the table represents an average for one of our 

blocks. Each average is derived from one to more 

than 100 individual tests.

The model is designed to test only paramarginal 

resources, because metallurgical data are available 

only for material in which oxides and hydroxides 

are the most abundant iron minerals and that can 

be concentrated by means of current technology. 

Thus, large quantities of iron contained in silicate 

and carbonate minerals are automatically excluded 

because no technology exists to concentrate iron contained in these minerals. Also, iron contained in 

iron-formation at greater than 1,000-foot (304.8-m) 

depths is excluded because we have limited our estimates to material within 1,000 feet (304.8 m) of 

the .surface. Paramarginal resources were divided 

into three categories according to mineralogy and 

grain size for testing against the most appropriate 

metallurgical data. Fine-grained hematitic and goethitic iron-formation was tested against data for i 

selective flocculation tests. Coarse-grained hematitic 

iron-formation was tested against froth-flotation 

data, and magnetitic iron-formation was tested 

against Davis magnetic tube data. The distribution 

of these three types of iron-formation in the Marquette district is shown in figure 9.

When the simulation experiment was performed, 

the quality criteria used for an acceptable concentrate were iron in excess of 60 percent and silica less 

than 10 percent. These values are a few percentage 

points short of currently acceptable standards, but 

were chosen for two reasons. First, metallurgical 

tests are likely to yield results a few percentage 

points poorer than results that can be achieved 

through a concentrating process appropriately adjusted and "fine-tuned" to a particular ore body. 

Second, if a cutoff closer to present standards were 

used—for instance, 65 percent iron and 6-8 percent 

silica—the calculated values would exclude much, 

material that we feel should be included as a paramarginal resource. The data in table 2, for example, 

indicate that only about one quarter of the material 

tested meets current standards. Yet material that 

fails to meet those standards by only 5 percent or 

less requires only a slight improvement in processing. Even though those improvements may be technically difficult to make, we believe that a high potential for such improvements exists and that

material meeting our cutoff criteria is clearly a 

paramarginal resource.

We used the Monte Carlo simulation, a computerized statistical sampling procedure, to test each block 

repeatedly against known laboratory tests. In our 

model, the sampling began by selecting an individual 

block from the array of blocks containing paramarginal resources. A value for concentrate-iron percentage was chosen at random from an empirical 

cumulative distribution curve derived from the concentrate-iron data in table 2. The random selections 

were performed in a manner whereby the results of 

a large number of selections, if plotted as a cumulative frequency diagram, would approximate the 

shape of the empirical cumulative distribution. If 

the concentrate-iron percentage value chosen for the 

first block was less than 60 percent (not acceptable 

quality), 0.0 tons of recoverable iron was assigned 

to that block. The sampling procedure continued by 

again selecting at random a value for concentrate 

iron for a second block. If the .selected value this 

time was greater than 60 percent concentrate iron 

(acceptable quality), the percentage of concentrate 

silica was tested. Figure 10 shows that concentrate 

iron and concentrate silica are not independent variables ; rather, as expected, a strong negative correlation exists between the two because iron and silica 

are the only important constituents of the rock. 

Thus, concentrate-silica percentage cannot be sampled from the empirical distribution as an independent variable. It was derived instead from the regression relationship: SiO2 = A0 + A1 (cone. Fe) ±<rSiO2 , 

where o-SiO2 is the estimate of standard deviation of 

the dependent variable (SiO2 ), and where A0 , the 

intercept, and Ai, the slope coefficient, were determined by least-^squares regression from the data 

shown in table 2. The value for concentrate iron 

(cone. Fe) is the value previously selected from the 

concentrate-iron cumulative curve. If the concentrate-silica percentage was greater than 10 percent 

(not acceptable quality), 0.0 tons of recoverable iron 

was again assigned to the block, and the sampling 

procedure was restarted with a third block. For 

blocks in which quality standards for both iron and 

silica were met, a value for percentage recoverable 

iron was selected at random from the empirical cumulative distribution for recoverable iron. The product of tons of iron-formation in the block and the 

randomly chosen value for percent recoverable iron 

is tons of recoverable iron in the block. Each block 

in the array was tested in this manner to yield a 

single evaluation, which was then repeated 100-200 

times to give an approximation of the expected re-
16 ESTIMATES OF NEGAUNEE IRON-FORMATION, MICHIGAN

R. 31 W. 88°10 R. 30 W. 88°00' R. 29 W. 55'

46°35'

T. 48 N.

T. 47 N

T. 46 N

r,r/ BARAGA COUNTY^

46°20' -

T. 45 N

3 MILES

J

TTTI I 1 

01234 KILOMETERS

Base modified from the ClevelandCliffs Iron Company. 1950
IRON RESOURCES 17

50' R. 28 W. 45' 35' R. 26 W. 87°30'

-, — X ' ''-^'1 /^r'^Ol '' x ^xxs ' N ~;^ U*-^

46°35'

T. 48

— 46°30'

T. 47 N.

DESCRIPTION OF MAP UNITS

.Xn

DIABASE AND METAMORPHOSED DIABASE

MARQUETTE RANGE SUPERGROUP—Including Negaunee Iron-formation (Xn)

CRYSTALLINE ROCKS (PRECAMBRIAN 

W)—Mostly granitic gneiss

Contact—Approximately located; dashed under 

water

Fault—Approximately located; dashed under 

water

MINERALOGICAL CLASSES OF NEGAUNEE 

IRON-FORMATION (Xn)

Magnetic iron-formation—Commonly includes 

iron-carbonate and iron-silicate minerals

Fine-grained hematitic and goethitic ironformation—May include some iron-carbonate 

or iron-silicate minerals

Coarse-grained hematitic iron-formation— 

Commonly includes magnetite and ironsilicate minerals

Silicate iron-formation—Commonly includes 

magnetite or iron-carbonate minerals

FIGURE 9.—Geologic map of the Marquette district showing the distribution of various mineralogical classes of iron-formation within the Negaunee Iron-formation. Because the classes are determined according to the most abundant type 

within each i/i-mi 2 block, the actual distribution of various types is somewhat simplified.

coverable iron. Figure 11 shows the results for magnetic separation and froth flotation in terms of tons 

of recoverable iron compared with recoverable grade 

and can be used to estimate the total tons of iron at 

any given cutoff grade.

The results for selective flocculation are not 

shown because of large uncertainties about how

representative the available data are of untested 

material. If all the material to which we projected 

the data responded as did the tested material, a 

maximum of about 7 billion short tons of iron might 

be recovered. However, because the selective flocculation method is sensitive to changes in mineralogy 

and texture, mostly too subtle to differentiate by
18 ESTIMATES OF NEGAUNEE IRON-FORMATION, MICHIGAN

69

65

60

UJ

O 

cc 

£55

51

i r

DAVIS MAGNETIC 

TUBE TESTS

_ SiO2=83.15-1.159 Fe

4 8 12 16 20 24 

PERCENT CONCENTRATE SiO2

68

65

UJ

<

Z 

O 

O

UJO 

cc 55

51

i r i i r i i

SELECTIVE

FLOCCULATION

TESTS

- SiO2=77.310-1.078 Fe

I I I I

0 4 8 12 16 20 24 

PERCENT CONCENTRATE SiO2

69

65

< 

cc.

LU 

O Rn 2 60

O 

O

UJ

O 

cr 

£55

51

FROTH FLOTATION 

TESTS

- SiO2 =

- 38.514-0.508

I I I I I I I I I I I

0 4 8 12 16 20 24 

PERCENT CONCENTRATE SiO2

co

Z 

O

cc 

O

n:

CO

CO<

cc

LLJ

O 

o

LLJ 

CC

\

Magnetic separation"""^, 

(excluding values for \ 

enriched material) \

Magnetic separation 

/ (all data)

\

\

\

•V

.Flotation (all data) \

Flotation (excluding ^ 

values for enriched material)

I

15 20 25 30 35 40 

PERCENT RECOVERABLE. Fe (GRADE)

45

FIGURE 11.—Curves showing maximum short tons of iron potentially available within 1,000 feet (304.8 m) of the surface for two metallurgical classes of iron-formation at 

various cutoff grades. Estimates are from Monte Carlo 

simulation model based on the data in figures 8 and 10.

means of our data, much of the fine-grained hematitic and goethitic iron-formation may respond more 

poorly than tested material. By using available data 

we can conclude only that when present techniques 

or modifications of them or yet-to-be-developed 

physical-separation techniques are used, a maximum 

of about 7 billion short tons of iron could be recovered from fine-grained hematitic and goethitic 

iron-formation.

Figure 11 shows that at recoverable grades down 

to about 20 percent iron, a maximum of about 3 billion short tons of iron could be separated from magnetic taconite and about 0.7 billion short tons, from 

coarse-grained hematitic iron-formation. These figures are probably too high because of the inclusion 

of tests yielding very high recoverable iron grades 

(see fig. 8), tests that were apparently made on samples of secondarily enriched iron-formation. Although such material is present in and around bodies 

of high-grade ore, now largely mined out, these high

•^FIGURE 10.—Regression lines showing the negative correlation between concentrate-iron percentage and concentrate:silica percentage for three types of metallurgical 

tests. The equations define the least-squares regression 

line for the data points shown. Each point represents average results for one data block. See table 2 for tabulated 

results.
FUTURE OF TACONITE MINING IN THE MARQUETTE DISTRICT 19

values have affected the distribution disproportionately to the abundance of enriched material, so that 

in our judgement, the figures produced from the 

model are perhaps 20 percent too large because of 

this factor alone. The dashed curves in figure 11 

show the approximately adjusted values, which 

largely exclude the effect of the high-yield samples.

Because tests performed by mining companies are 

generally on material judged beforehand to be best 

suited for beneficiation, the test results available to 

us are likely to be somewhat better than those that 

would be obtained on currently untested materials; 

this factor also contributes to overestimation 

through the model. The discrepancy between our 

estimated maximum values and true values is, of 

course, indeterminate by any means short of comprehensive metallurgical testing.

We conclude from the above facts that if continued research can produce modest improvements 

in physical beneficiating techniques, a maximum of 

about 11 billion short tons of iron is geologically 

available in the Marquette district if open-pit mining to an average depth of 1,000 feet (304.8 m) is 

feasible. Several uncertainties in our data result in 

overestimation by an unknown factor. We conclude, 

however, that the true geologic availability of iron, 

including material for which improved processing 

is necessary, is 5 billion to 10 billion short tons. The 

percentage of that amount that will ever be economically available can only be assessed through a 

more detailed evaluation of local geologic data, as 

well as an evaluation of many other technologic, economic, political, and environmental factors. Our purpose is not to make such evaluations here, but we 

would like briefly to discuss some of the more important factors detrimental to the full utilization of 

the resource.

First among these factors is the preemption of 

land for uses other than mining. A very large percentage of the resource is in the exposed keel of the 

Marquette trough, which is also the most heavily urbanized area of the district. Ishpeming and Negaunee, the principal population centers, are situated largely on the Negaunee Iron-formation. The 

economic, geographic, and cultural problems of relocating those towns are likely to prohibit open-pit 

mining in at least part of the area.

Many other environmental and cultural factors 

are involved as well, including what may be largely 

value judgements concerning disruption of recreational and scenic areas, alteration of drainage systems, and problems of tailings disposal. Water supply is also an important consideration. The Marquette district is astride the Michigan-Superior 

drainage divide, and the lack of large streams poses 

a problem of supplying process water to plants from 

the already heavily utilized surficial drainage 

systems.

These and other features together mitigate against 

full utilization of the iron resources of the Marquette 

district and suggest that only a fraction of the 5 billion to 10 billion short tons of geologically available 

iron will ever reach the market.

FUTURE OF TACONITE MINING IN THE 

MARQUETTE DISTRICT

Three taconite operations, the Republic, Empire, 

and Tilden mines, are now in operation in the Marquette district (see fig. 1), and a fourth, the Cascade, 

is likely to be opened by the mid-1980's. Expected 

production in 1976 is about 12 million long tons of 

iron-ore pellets. Figure 12 shows the projected production trends for the district on the basis of annual 

mill capacity at existing mines, announced mine developments and expansions, and estimated longevity 

of individual operations. The longevity is estimated 

by comparing reserves to expected production capacity. Production should reach about 19 million 

long tons per year by the mid-1980's if all plans for

1975 1980 1985 1990 1995 2000 2005 2010 2015 

YEAR

FIGURE 12.—Graph showing projected production trends for 

the Marquette district from operating mines and announced expansion and development plans.
20 ESTIMATES OF NEGAUNEE IRON-FORMATION, MICHIGAN

development and expansion are realized. Although 

the Republic ore body may be mined out about 1990, 

that drop in production will be more than offset by 

the opening of the Cascade deposit and possible expansion of the Tilden mine. Thereafter production 

in the district should remain steady until sometime 

after the turn of the century when the large deposits 

at the Tilden, Empire, and (future) Cascade mines 

may be nearing exhaustion. All presently proven ore 

may be mined out by 2020. This is not to say that 

additional reserves cannot be proven at existing 

mines to prolong their production span.

Although the known reserves seem adequate to 

maintain a high level of production in the district 

for many years, continued research and exploration 

are needed to identify additional reserves in the near 

future to ensure continued production at present or 

increased levels. Long lead times are required simply to prove the existence of and to develop a new 

ore body. At present, 10-15 years may be required. 

As more problematical areas are explored and if 

additional environmental and political restraints are 

imposed, that lead time is likely to grow. If new 

technology is needed to mine new deposits, the problem is compounded. More than 40 years of research 

and development preceded the first magnetic taconite plant, and about 20 years were required to deveop the concentrating process recently used for the 

first time at the Tilden mine.

In view of these long lead times, we believe that 

ore reserves of 30 years are only slightly more than 

adequate to ensure continued mining and should not 

be viewed as a reason for complacency about the 

long-term future of the district.

EFFECT OF TECHNOLOGIC ADVANCES ON 

IRON RESOURCES

The economic availability of iron from taconite is 

largely a function of the technology for mining and 

processing. In the past- 20 years, great strides have 

been made in that technology. Most of the present 

reserves were not feasible to mine and process 20 

years ago, and fine-grained hematitic taconite, which 

forms a major percentage of both reserves and paramarginal resources, has been processed commercially only since early 1975. Further technologic advances will almost undoubtedly improve the resource 

base of the Marquette Range, and we conclude this 

report with a qualitative appraisal of the effect of 

improved technology on iron resources.

The area in which technologic improvement seems 

most likely and in which it would be most beneficial 

is mining techniques that would make possible both

underground mines and deeper open-pit mines. At 

best, only about 20 percent of taconite that might be 

processible by current methods is now accessible to 

mining, so that improving the ability to mine deeper 

could easily increase the paramarginal resources by 

a factor of two or more.

Also, continued metallurgical testing would define 

more certainly the amount and location of iron-formation actually amenable to beneficiation by current 

techniques; it would also define what improvements 

or new techniques are needed. Such research, traditionally conducted by mining companies and the U.S. 

Bureau of Mines, could add greatly to the amount 

of geologically available iron.

Other fields in which new developments might 

take place are in processing of iron-silicate ironformation or sideritic iron-formation. Figure 4 

shows that a great tonnage of taconite contains 

greater than 10 percent siderite. Much of this taconite is already included as a paramarginal resource 

because it also contains abundant magnetite or hematite. At present, siderite is a gangue mineral because the iron contained in it is not recoverable; the 

presence of siderite lowers the recoverable grade. 

The development of a process to recover the iron in 

siderite would increase the total recoverable iron in 

the district and would render presently uneconomic 

parts of the magnetic and hematitic taconite more 

economically attractive. Although high-grade siderite ore is presently mined, roasted, and sintered at a 

commercial scale in other districts, it has not been 

mined commercially from taconite. Because of the 

great amount of energy needed for such a process, 

its development seems unlikely in the energy-short 

economy of the near future.

CONCLUSIONS

The Negaunee Iron-formation in the Marquette 

district of Michigan contains about 205 billion long 

tons of material averaging about 32 percent iron. 

About 49 billion long tons is within 1,000 feet (304.8 

m) of the surface.

Considering only material that might be minable 

from open pits and that has suitable mineralogy and 

grain size to be potentially processible by current 

concentrating processes or by modifications of them, 

a maximum of about 11 billion short tons of iron is 

potentially recoverable from the district. However, 

many geologic, economic, technologic, political, and 

environmental restraints are likely to prevent utilization of much of it.

Even though presently proven reserves are adequate to ensure production of more than 20 million
REFERENCES CITED 21

long tons of iron pellets per year for 30-40 years, 

long lead times in developing new deposits and new 

processing techniques require exploration and research in the near future to ensure continued production beyond the next 30 years, or increased production within the next 30 years.

REFERENCES CITED

Bowen, R. W., and Botbol, J. M., 1975, The geologic retrieval and synopsis program (GRASP) : U.S. Geol. 

Survey Prof. Paper 966, 87 p.

Cannon, W. F., 1974, Bedrock geologic map of the Greenwood quadrangle, Marquette County, Michigan: U.S. 

Geol. Survey Geol. Quad. Map GQ-1168.

———— 1975, Bedrock geologic map of the Republic quadrangle, Marquette County, Michigan: U.S. Geol. Survey 

Misc. Inv. Map 1-862.

Cannon, W. F., and Klasner, J. S., 1974, Preliminary geologic 

map of the Witch Lake 15-minute quadrangle, Michigan: U.S. Geol. Survey open-file report, 7 p., 1 pi., scale 

1:62,500.

———— 1975a, Preliminary geologic map of the southern part 

of the Diorite 7%-minute quadrangle, Marquette 

County, Michigan: U.S. Geol. Survey open-file report 

75-11, 7 p., 1 pi., scale 1:24,000.

1975b, Preliminary geologic map of the southern part

of the Champion 7%-minute quadrangle, Marquette 

County, Michigan: U.S. Geol. Survey open-file report 

75-10, 8 p., 1 pi., scale 1:24,000.

Clark, L. D., Cannon, W. F., and Klasner, J. S., 1975, Bedrock geologic map of the Negaunee SW quadrangle, 

Marquette County, Michigan: U.S. Geol. Survey Quad. 

Map GQ-1226.

Gair, J. E., 1975, Bedrock geology and ore deposits of the 

Palmer quadrangle, Marquette County, Michigan: U.S. 

Geol. Survey Prof. Paper 769, 159 p.

Heising, L. F., and Frommer, D. W., 1967, Lake Superior 

iron resources—Preliminary samples and metallurgical 

evaluation of selected Michigan-Wisconsin iron formations: U.S. Bur. Mines Rept. Inv. 6895, 31 p.

James, H. L., 1954, Sedimentary facies of iron-formation: 

Econ. Geology, v. 49, no. 3, p. 235-293.

———— 1955, Zones of regional metamorphism in the Precambrian of northern Michigan: Geol. Soc. America 

Bull., v. 66, no. 12, p. 1455-1487.

Klasner, J. S., and Cannon, W. F., 1974, Geologic interpretation of gravity profiles in the western Marquette district, northern Michigan: Geol. Soc. America Bull., v. 

85, no. 2, p. 213-218.

———— 1975a, Preliminary geologic map of the southern part 

of the Michigamme 7%-minute quadrangle, Marquette 

and Baraga Counties, Michigan: U.S. Geol. Survey 

open-file report 75-9, 8 p., 1 pi., scale 1:24,000.

1975b, Preliminary geologic map of the southern

part of the Three Lakes 7%-minute quadrangle, Baraga 

County, Michigan: U.S. Geol. Survey open-file report 

75-8, 7 p., 1 pi., scale 1:24,000.

McKelvey, V. E., 1973, Mineral resource estimates and public policy in Brobst, D. A., and Pratt, W. P., eds., United 

States mineral resources: U.S. Geol. Survey Prof. Paper 

820, p. 9-19.

Puffett, W. P., 1974, Geology of the Negaunee quadrangle, 

Marquette County, Michigan: U.S. Geol. Survey Prof. 

Paper 788, 53 p.

Simmons, G. C., 1974, Bedrock geologic map of the Ishpeming quadrangle, Marquette County, Michigan: U.S. Geol. 

Survey Geol. Quad. Map GQ-1130.