Ressources naturelles

Canada

Natural Resources

Canada

Geological Survey of Canada

Bulletin 593

U.S. Geological Survey

Professional Paper 1721

R. Bernknopf, A. Wein, M. St-Onge, and S. Lucas

2007

ANALYSIS OF IMPROVED GOVERNMENT

GEOLOGICAL MAP INFORMATION FOR

MINERAL EXPLORATION: INCORPORATING

EFFICIENCY, PRODUCTIVITY, EFFECTIVENESS,

AND RISK CONSIDERATIONS
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i

Geological Survey of Canada

Bulletin 593

U.S. Geological Survey

Professional Paper 1721

ANALYSIS OF IMPROVED GOVERNMENT

GEOLOGICAL MAP INFORMATION FOR

MINERAL EXPLORATION: INCORPORATING

EFFICIENCY, PRODUCTIVITY, EFFECTIVENESS,

AND RISK CONSIDERATIONS

R.L. Bernknopf, A.M. Wein, M.R. St-Onge, and S.B. Lucas

2007
ii

All requests for permission to reproduce this work, in whole or in part, for

purposes of commercial use, resale, or redistribution shall be addressed to:

Earth Sciences Sector Information Division, Room 402, 601 Booth Street,

Ottawa, Ontario K1A 0E8.

R.L. Bernknopf (rbern@usgs.gov),

A.M.Wein (awein@usgs.gov)

United States Geological Survey

345 Middlefield Road, MS-531

Menlo Park, 94025-3561

California, U.S.A.

M.R. St-Onge(mstonge@nrcan.gc.ca),

Geological Survey of Canada

Earth Sciences Sector

Natural Resources Canada

601 Booth Street

Ottawa, Ontario

Canada K1A 0E8

S.B. Lucas (slucas@nrcan.gc.ca),

Minerals and Metals Sector

Natural Resources Canada

580 Booth Street

Ottawa, Ontario

Canada K1A 0E4

Authors

Critical reviewers

R. Araucibia

A. Galley

W. Holtz

G. Raines

E. Schwere

D. Singer

Cover illustration

Portion of one of the Geological Survey of Canada's finer resolution bedrock maps for

southern Baffin Island (Nunavut, Canada). The map published at 1:100 000 scale

documents the distribution of principal tectonostratigraphic units of the Lake Harbour

Group: psammite and pelite (PLHp map unit) in yellow, marble (PLHc map unit) in blue,

gabbro (PLHm map unit) in dark green, diorite (PLHd map unit) in pale green, peridotite

(PLHu map unit) in purple, and leucogranite (PLHw map unit) in reddish brown. The

stratigraphic basement (PRm map unit) to the Lake Harbour Group is shown in pink.

Two generations of thrust faults (open and closed teeth on hanging wall of individual

faults), post-thrusting folds (F3 fold axes), and structural point data (strike and dip of

planar fabric; trend and plunge of linear fabric) are also shown.

©Her Majesty the Queen in Right of Canada 2007

ISSN 0068-7626

Catalogue No. M42-593E

ISBN 978-0-660-19721-0

Available in Canada from the Geological Survey of Canada Bookstore

(see inside front cover for details)

A copy of this publication is also available for reference in depository

libraries across Canada through access to the Depository Services Program's

Web site at http://dsp-psd.pwgsc.gc.ca

A free digital download of this publication is available from GeoPub:

http://geopub.nrcan.gc.ca/index_e.php

Click on "Free Download".
CONTENTS

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

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

The geological map as a public good . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

The role of geoscientific information in attracting exploration investment . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Step 1: favourability indices and domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Step 2: target densities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Point estimate of target density per search unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Uncertainty about the estimates of target density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Step 3: mineral exploration efficiency and productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Model 1: minimizing the search area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Model 2: maximizing the expected number of exploration targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Step 4: economic model (model 3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Exploration campaign statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Constrained optimization model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

The objective functionV

x

x pf

f

F

f

θ θ

θ

= ⎛

⎝

⎜

⎜

⎞

⎠

⎟

⎟ =

∑ max $

0

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Budget constraint Q I I M I u Iu d M st s x t x x (,, ) =+ ≤ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Risk constraint RP t B t P u d x x

( ,) ( ) = − ≤− , 1 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Campaign target density constraint TD E d E x ( ) = ≥ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Feasible and optimal solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Relative effectiveness of the maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Step 5: value of improved map information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Application of the model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Assumptions of the analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Case studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Flin Flon Belt case study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Geological maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Favourability indices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Flin Flon Belt favourability domains and target densities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Efficiency and productivity of Flin Flon Belt maps for mineral exploration . . . . . . . . . . . . . . . . . . . . . . . . 22

Flin Flon Belt mineral exploration campaign risk and benefit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

iii
South Baffin Island case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Geological maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Cape Smith Belt favourability domains and exploration target densities . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

South Baffin Island favourability domains and adopted exploration target densities . . . . . . . . . . . . . . . . . . 28

Mineral exploration efficiency and productivity of south Baffin Island maps . . . . . . . . . . . . . . . . . . . . . . . 33

Results for model 1: minimizing the search area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Results for model 2: maximizing the mineral target potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

South Baffin Island exploration economic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

South Baffin Island economic model constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Analysis of south Baffin Island exploration campaigns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Preferred solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Estimated net economic value of the updated, finer resolution maps for south Baffin Island . . . . . . . . . . . 38

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Geoscientific information and mineral exploration investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Applications of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Shadowing geological information for inaccuracies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Alternative economic model formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Other improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Appendix A: GIS method to delineate contacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Appendix B: Kuhn-Tucker conditions for the optimal solution to model 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Figures

1. Illustrative expected efficiency versus expected productivity curves . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2. Exploration preference and feasibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3. Sensitivity of the risk constraint to the minimum number of targets . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4. Sensitivity of risk constraint to the probability of finding at least 20 targets. . . . . . . . . . . . . . . . . . . . . 14

5. Comparison of optimal exploration campaigns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

6. Simplified geological map of Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

7. Volcanic-hosted massive-sulphide favourability domains from the old,

coarser resolution Flin Flon Belt map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

8. Volcanic-hosted massive-sulphide favourability domains from the updated,

coarser resolution Flin Flon Belt map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

9. Volcanic-hosted massive-sulphide favourability domains from the updated,

finer resolution Flin Flon Belt map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

10. Flin Flon Belt mineral exploration efficiency and effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

11. Flin Flon Belt exploration campaigns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

12. Magmatic massive-sulphide favourability domains from the old,

coarser resolution Cape Smith Belt map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

iv
13. Magmatic massive-sulphide favourability domains from the updated,

finer resolution Cape Smith Belt map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

14. Magmatic massive-sulphide favourability domains from the old,

coarser resolution south Baffin Island map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

15. Magmatic massive-sulphide favourability domains from the updated,

finer resolution south Baffin Island map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

16. Comparison of south Baffin Island favourability maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

17. Results of models 1 and 2 for the south Baffin Island maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

18. Economic model for the south Baffin Island maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

19. Investment advantage of the south Baffin Island updated, finer resolution map . . . . . . . . . . . . . . . . . . 39

20. Exploration preference, feasibility, and constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Tables

1. Summary of case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2. Geospatial data used for the Flin Flon Belt case study area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3. Favourability indices for the old, coarser resolution Flin Flon Belt map. . . . . . . . . . . . . . . . . . . . . . . . 18

4. Geological unit coding scheme for the old, coarser resolution

Flin Flon Belt map favourability indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5. Favourability indices for the updated, coarser resolution Flin Flon Belt map . . . . . . . . . . . . . . . . . . . . 18

6. Favourability indices for the updated, finer resolution Flin Flon Belt map . . . . . . . . . . . . . . . . . . . . . . 19

7. Geological unit coding scheme for the updated, finer resolution

Flin Flon Belt map favourability indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

8. Search units, exploration targets, and target density for the old,

coarser resolution Flin Flon Belt map area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

9. Search units, target density, and expected number of targets

per exploration campaign for the Flin Flon Belt maps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

10. Geospatial data used for the Cape Smith Belt area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

11. Geospatial data used for the south Baffin Island case study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

12. Favourability indices for the Cape Smith Belt old, coarser resolution maps . . . . . . . . . . . . . . . . . . . . . 25

13. Geological unit coding scheme for the Cape Smith Belt old,

coarser resolution map favourability indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

14. Favourability indices for the Cape Smith Belt updated, finer resolution maps.. . . . . . . . . . . . . . . . . . . 26

15. Geological unit coding scheme for the Cape Smith Belt updated,

finer resolution map favourability indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

16. Empirical mineral exploration target densities for the

Cape Smith Belt updated, finer resolution map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

17. Empirical mineral exploration target densities for the

Cape Smith Belt old, coarser resolution map. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

18. Favourability indices for the south Baffin Island old, coarser resolution map . . . . . . . . . . . . . . . . . . . 29

19. Geological unit coding scheme for the south Baffin Island old,

coarser resolution map favourability indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

20. Favourability indices for the updated, finer resolution south Baffin Island map . . . . . . . . . . . . . . . . . . 29

21. Geological unit coding scheme for the updated,

finer resolution south Baffin Island map favourability indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

22. South Baffin Island updated, finer resolution map search statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

23. South Baffin Island old, coarser resolution map search statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

24. Minimization of search unit examinations utilizing the updated,

finer resolution and old, coarser resolution maps for south Baffin Island . . . . . . . . . . . . . . . . . . . . . . . 34

v
25. Maximization of the target potential, using the updated,

finer resolution and the old, coarser resolution maps for south Baffin Island . . . . . . . . . . . . . . . . . . . . 34

26. Search units, target density, and expected number of targets

for exploration campaigns derived from the south Baffin Island maps . . . . . . . . . . . . . . . . . . . . . . . . . 37

27. Optimal exploration and investment solutions to the simplified

constrained optimization economic model for the south Baffin Island area . . . . . . . . . . . . . . . . . . . . . 38

28. Return on investment, south Baffin Island area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

vi
ANALYSIS OF IMPROVED GOVERNMENT

GEOLOGICAL MAP INFORMATION FOR

MINERAL EXPLORATION: INCORPORATING

EFFICIENCY, PRODUCTIVITY, EFFECTIVENESS,

AND RISK CONSIDERATIONS

Abstract

This bulletin/professional paper focuses on the value of geoscientific information and knowledge, as

provided in published government bedrock geological maps, to the mineral exploration sector. An economic model is developed that uses an attribute-ranking approach to convert geological maps into

domains of mineral favourability. Information about known deposits in these (or analogous)

favourability domains allow the calculation of exploration search statistics that provide input into measures of exploration efficiency, productivity, effectiveness, risk, and cost stemming from the use of the published geological maps. Two case studies, the Flin Flon Belt (Manitoba and Saskatchewan) and the south

Baffin Island area (Nunavut), demonstrate that updated, finer resolution maps can be used to identify more

exploration campaign options, and campaigns thats are more efficient, more effective, and less risky than old,

coarser resolution maps when used as a guide for mineral exploration. The Flin Flon Belt study illustrates that

an updated, coarser resolution bedrock map enables improved mineral exploration efficiency, productivity,

and effectiveness by locating 60% more targets and supporting an exploration campaign that is 44% more

efficient. Refining the map resolution provides an additional 17% reduction in search effort across all favourable domains and a 55% reduction in search effort in the most favourable domain. The south Baffin Island

case study projects a 40% increase in expected targets and a 27% reduction in search effort when the updated,

finer resolution map is used in lieu of the old, coarser resolution map. On southern Baffin Island, the economic

value of the updated map ranges from CAN$2.28 million to CAN$15.21 million, which can be compared to the

CAN$1.86 million that it cost to produce the map (a multiplier effect of up to eight).

Résumé

Le présent document paper porte sur la valeur au secteur de l’exploration des ressources minérales de

l’information et des connaissances géoscientifiques fournies par les cartes géologiques du socle publiées

par le gouvernement. Les auteurs ont créé un modèle économique qui utilise une approche de classement

hiérarchique des attributs permettant de transformer les cartes géologiques en domaines de favorabilité

pour les ressources minérales. Le recours à l’information sur des gîtes connus à l’intérieur de ces domaines

de favorabilité (ou de domaines analogues) permet le calcul de statistiques ayant trait aux recherches liées

aux activités d’exploration. Ces statistiques fournissent les éléments permettant de mesurer les taux

d’efficience et de productivité, les risques et les coûts liés aux activités d’exploration, résultant de

l’utilisation des cartes géologiques publiées. Deux études de cas, celle de la ceinture de Flin Flon (au

Manitoba et en Saskatchewan) et celle de la région sud de l’île de Baffin (Nunavut), démontrent que des

cartes à plus grande résolution mises à jour, opeuvent servir à cerner un plus grand choix de campagnes

d’exploration caractérisées par un plus haut taux d’efficience et d’efficacité et qui présentent moins de

risques que les anciennes cartes à moindre résolution lorsqu’elles sont utilisées comme guide aux fins de

l’exploration minérale. L’étude de cas de la ceinture de Flin Flon permet d’illustrer que le recours à une

carte géologique du socle à moindre résolution mise à jour, favorise une amélioration des taux

d’efficience, de productivité et d’efficacité en localisant 60 % plus de cibles et en augmentant de 44 % le

taux d’efficience de la campagne d’exploration. Une résolution encore plus élevée permet de réduire de 17

% supplémentaires les efforts consacrés à l’exploration dans tous les domaines favorables et de 55 % les

efforts consacrés à l’exploration dans le domaine le plus favorable. L’étude de cas de la région sud de l’île

de Baffin prévoit une augmentation de 40 % dans le nombre de cibles prévues et une réduction de 27 % des

efforts consacrés à l’exploration, lorsqu’une carte à plus grande résolution mise à jour est utilisée plutôt

que l’ancienne carte à moindre résolution. Dans le cas de la région sud de l’île de Baffin, la valeur

économique de la carte mise à jour se situe entre 2,28 millions de dollars canadiens et 15,21 millions, et peut

être comparée au coût de 1,86 million de dollars canadiens associé à la production de la carte (soit un effet

multiplicateur de huit).

1
2

SOMMAIRE

L’exploration minérale représente l’une des nombreuses

activités qui ont recours aux cartes géologiques offertes à titre de

bien public au Canada et aux États-Unis. Un bien public est

disponible et facilement utilisable par la collectivité à de

nombreuses fins, sans que son utilisation par une personne ne

réduise son utilité pour d’autres utilisateurs, ni ne les prive de son

utilisation. Ces cartes s’avèrent utiles pour d’autres activités,

notamment l’aménagement du territoire, l’évaluation des

incidences environnementales, ainsi que l’évaluation des risques.

Le présent document porte sur la valeur, au secteur de l’exploration

des ressources minérales, de l’information et des connaissances

géoscientifiques fournies par les cartes géologiques du socle

publiées par le gouvernement. L’hypothèse adoptée veut que le

gouvernement considère souhaitables les investissements en

matière d’exploration minérale et la détermination de cibles de

prospection minière puisqu’ils peuvent, en fin de compte, faire

bénéficier la société d’une approche durable, sans effet sur

l’environnement. Les auteurs proposent un modèle économique qui

lie les décisions en matière d’investissements liés à l’exploration

minérale à l’information représentée sur les cartes géologiques du

socle. Le modèle utilise une approche de classement hiérarchique

des attributs permettant de transformer les cartes géologiques en

domaines de favorabilité (la probabilité qu’un cadre géologique

renferme une cible d’exploration) pour les ressources minérales,

qui sont considérés comme d’éventuelles régions de recherche lors

de campagnes d’exploration sur le terrain. Le recours à

l’information sur des gîtes connus à l’intérieur de ces domaines de

favorabilité (ou de domaines analogues) permet le calcul

rétrospectif (ou prospectif), pour les domaines en question, des

statistiques de recherche ayant trait aux activités d’exploration. Ces

statistiques fournissent les éléments permettant de mesurer les taux

d’efficience, de productivité et d’efficacité, ainsi que les risques et

les coûts liés aux activités d’exploration résultant de l’utilisation

des cartes géologiques publiées.

Il est plus efficient d'examiner un secteur de superficie plus

limitée par cible et plus productif de trouver plus de cibles que

prévu par secteur examiné. On juge qu’une carte est plus efficace si

elle peut servir à identifier les campagnes d’exploration réalisables

susceptibles de hausser les attentes quant au nombre de cibles à

découvrir. Les risques liés à une campagne d’exploration sont

contrôlés par la probabilité de découvrir au moins un certain

nombre prédéterminé de cibles. Les coûts d’une campagne

d’exploration représentent la somme des coûts liés à l’étude des

unités d’exploration et des coûts relatifs aux essais de forage.

Le modèle décisionnel est élaboré à partir de la mesure des taux

d’efficience, de productivité et d’efficacité, ainsi que des risques et

des coûts liés aux activités d’exploration, dans le but d’indiquer

comment les sociétés d’exploration auraient pu investir de manière

optimale dans les activités d’exploration si des cartes, des

statistiques et des mesures avaient été disponibles au moment de la

prise de décisions. Les auteurs ont ensuite comparé les décisions en

se basant sur l’information représentée sur des cartes de diverses

époques et à diverses résolutions (échelles) dans deux études de cas.

SUMMARY

Mineral exploration is one of the many uses of

geological maps that are provided as a public good in

Canada and the U.S.A. A public good is defined as something that is readily available to all for numerous purposes, and its use by one user does not degrade its utility

nor does it exclude use by others. Other uses of geological maps include land-use planning, environmentalimpact assessments, and hazard evaluation. This

bulletin/professional paper focuses on the value of

geoscientific information and knowledge, as provided

in published government bedrock geological maps, to

the mineral resource exploration sector. It is assumed

that the government considers mineral exploration

investment and location of exploration targets as desirable in that they can ultimately benefit society in an

environmentally neutral and sustainable way. The

authors propose an economic model that links mineral

exploration investment decisions to information contained in bedrock geological maps. The model uses an

attribute-ranking approach to convert the geological

maps into domains of mineral favourability (the likelihood of a geological setting containing an exploration

target), which are treated as the potential search areas

of exploration campaigns. Information about known

deposits in these (or analogous) favourability domains

allows one to retrospectively (or prospectively) calculate exploration search statistics for these domains.

These statistics provide input into measures of

exploration campaign efficiency, productivity,

effectiveness, risk, and cost stemming from the use of

the published geological maps.

Examination of less area per target is more efficient

and more expected targets per are examined is more

productive. One says a map is more effective if it can be

used to identify feasible exploration campaigns that

yield higher expectations of the number of targets to be

found. The risk of an exploration campaign is monitored by the probability of finding at least a predetermined number of targets. The cost of an exploration

campaign is the sum of search unit examination costs

and target drill-testing costs.

The decision model is constructed from the measures of exploration efficiency, productivity, effectiveness, risk, and cost to suggest how companies might

have optimally invested in exploration if the maps,

statistics, and measures had been available when exploration decisions were made. The authors then compared the decisions based on information contained in

maps of different ages (vintage) and resolutions

(scales) in two case studies.
3

Les études de cas, celle de la ceinture de Flin Flon au Manitoba

et en Saskatchewan et celle de la région sud de l’île de Baffin dans

l’est du Nunavut (respectivement, un district minier parvenu à

maturité et une région pionnière au Canada), démontrent que des

cartes à plus grande résolution mises à jour, offrent de l’information

plus détaillée et plus exacte que les anciennes cartes à moindre

résolution lorsqu’elles sont utilisées comme guide aux fins de

l’exploration minérale. En règle générale, l’intégration de nouvelles

connaissances permet d’améliorer la qualité de l’information

représentée sur les cartes, alors qu’une plus grande résolution

(c.-à-d. une échelle plus grande) permet d’augmenter la quantité

d’information représentée. Comparées aux anciennes cartes à

moindre résolution, les cartes à plus grande résolution mises à jour

permettent de mieux délimiter tant les domaines favorables (où on

s’attend à de fortes densités de cibles) que les domaines non

favorables (où on s’attend à des densités plus faibles de cibles). La

présente analyse appuie l’énoncé affirmant que : « comme

conséquence à l’augmentation de la qualité et de la quantité

d’information, on peut soit obtenir le même résultat en utilisant

moins de ressources, soit accroître les résultats en utilisant les

mêmes ressources » [traduction]. Ainsi, les cartes à plus grande

résolution mises à jour permettent d’améliorer la recherche des

gîtes minéraux, la rendant plus efficiente et plus productive. En

outre, la productivité accrue combinée au nombre d’unités de

recherche (c.-à-d. les régions de recherche), délimitées par les

domaines favorables sur les cartes à plus grande résolution mises à

jour, permettent de réaliser des campagnes d’exploration moins

risquées et plus efficaces en fonction d’un budget établi.

On suppose que la quantité et la qualité de l’information dérivée

de la carte à plus grande résolution mise à jour permettent d’attirer

davantage les investissements du secteur de l’exploration minérale.

Les auteurs estiment que la valeur de l’information géologique mise

à jour de la carte à plus grande résolution correspond aux

investissements additionnels attendus pour l’exploration (en accord

avec les meilleures pratiques de gestion), par rapport aux

investissements réalisés suite au recours uniquement sur

l’information représentée sur l’ancienne carte à moindre résolution.

Les investissements additionnels peuvent être comparés aux coûts

de production de la carte mise à jour. Dans cette comparaison, les

auteurs supposent implicitement qu’il est souhaitable de susciter

des investissements supplémentaires dans des activités

d’exploration et que chaque dollar additionnel investi en exploration représente un avantage équivalent pour la société, d’abord

comme compensation pour la production de la carte et ensuite

comme moyen de stimuler l’activité économique.

Dans l’étude de cas de la ceinture de Flin Flon, les campagnes

d’exploration sont modélisées de manière rétrospective. Ainsi, cela

signifie que l’information sur des occurrences minérales connues

est utilisée pour estimer a priori les densités des cibles que les

prospecteurs auraient associées aux domaines de favorabilité.

L’étude de la ceinture de Flin Flon permet d’illustrer que des

campagnes s’appuyant sur une carte géologique du socle à moindre

résolution mise à jour, entraîne une amélioration des taux

d’efficience, de productivité et d’efficacité liés aux activités

d’exploration minérale. La campagne d’exploration optimale,

déterminée à l’aide de la carte à moindre résolution mise à jour,

permet de localiser 60 % plus de cibles prévues et d’augmenter de

The case studies, the Flin Flon Belt of Manitoba and

Saskatchewan, and the south Baffin Island area of

eastern Nunavut (a mature mining district and a frontier region in Canada, respectively) demonstrate that

updated, finer resolution maps provide more detailed

and accurate information than older, coarser resolution

maps when used as a guide for mineral exploration. As

a general rule, incorporating new knowledge improves

the quality of a map’s information, whereas using a

finer resolution (i.e. larger scale) increases the quantity

of a map’s information. Relative to the old, coarser resolution maps, the updated, finer resolution maps better

delineate both favourable domains (with higher

expected target densities) and unfavourable domains

(with lower expected target densities). The analysis

herein supports the statement that: “as a consequence

of an increase in the quality and quantity of information

either the same output can be achieved for less

resources or for the same input, output can be

increased”. Thus, the updated, finer resolution map

enables an improved mineral search that is more efficient and more productive. In addition, the increased

productivity combined with the number of search units

(i.e. search area) delineated by favourable domains on

the updated, finer resolution map leads to exploration

campaigns that are less risky and more effective for a

set budget.

The authors surmise that the quantity and quality of

the information derived from the updated, finer resolution map increases the attractiveness of investment to

the mineral exploration industry. The authors estimate

the value of the updated, finer resolution geological

information as being the expected additional exploration investment (as per best business practice) relative

to the investment based on information contained in the

older, coarser resolution map only. The additional

investment can be compared to the cost of producing

the updated map. Implicit in this comparison is the idea

that more investment in exploration is desirable and

each additional dollar of exploration investment is

assumed to be of equal benefit to society: firstly, as

recompense for the cost of producing the map; and

secondly, in terms of the economic activity stimulated.

In the Flin Flon Belt case study, exploration campaigns are modelled retrospectively, that is, information about known mineral showings is used to estimate

the a priori target densities explorers would have

attached to the favourability domains. The Flin Flon

Belt study illustrates that campaigns derived from the

updated, coarser bedrock map enables improved mineral exploration efficiency, productivity, and effectiveness. The optimal exploration campaign of the updated,

coarser resolution map locates 60% more expected targets and is 44% more efficient. Increased map resolution also has a positive effect on exploration efficiency
4

44 % le taux d’efficience de la campagne. Une meilleure résolution

de la carte entraîne également des effets positifs sur l’efficience et la

productivité liées aux activités d’exploration, particulièrement dans

les domaines de favorabilité plus élevée où de plus grandes densités

de cibles d’exploration minérale sont attendues. Une meilleure

résolution de la carte permet également de déterminer plus de

classes de favorabilité, offrant ainsi plus d’options pour

l’exploration. Une comparaison des cartes à diverses résolutions

mises à jour démontre qu’une résolution plus élevée permet de

réduire de 17 % supplémentaires les efforts consacrés à

l’exploration dans tous les domaines favorables et de 55 % les

efforts consacrés à l’exploration dans le domaines le plus favorable.

Dans le cas de la région sud de l’île de Baffin, les campagnes

d’exploration sont examinées de manière proactive : la région

demeure en grande partie peu explorée et les statistiques ayant trait

à l’exploration sont basées sur une région analogue. L’étude de cas

de la région sud de l’île de Baffin prévoit une augmentation de 40 %

dans le nombre de cibles prévues et une réduction de 27 % des

efforts consacrés à l’exploration, lorsque tous les domaines

favorables sont explorés et qu’une carte à plus grande résolution

mise à jour est utilisée plutôt que l’ancienne carte à moindre

résolution. Puisque la région sud de l’île de Baffin est une région

pionnière et que des estimations des coûts d’exploration étaient

disponibles, cette étude de cas facilite l’analyse économique des

investissements additionnels aux fins d’exploration qui pourraient

éventuellement être attribuables à la production des cartes à plus

grande résolution mises à jour (publiées en 1999). En supposant, au

minimum, l’exploration des domaines comportant la classe de

favorabilité la plus élevée sur des cartes à plus grande définition

mises à jour, l’estimation des investissements générés se situe entre

2,28 millions de dollars canadiens et 15,21 millions, selon la

tolérance au risque et le besoin d’efficience du décideur. Le taux de

rendement des investissements (nombre de cibles prévues par

million de dollars investis) diminue de 6,6 à 5,1 lorsque

l’investissement dans le domaine le plus favorable est étendu à

l’exploration de tous les domaines favorables représentés sur la

carte mise à jour. Cela peut se comparer à un taux de rendement de

4,7 pour l’exploration des domaines favorables déterminés à l’aide

de l’ancienne carte à moindre résolution. De plus, la valeur

économique de la carte mise à jour se situe entre 2,28 millions de

dollars canadiens et 15,21 millions et peut être comparée au coût de

1,86 million de dollars canadiens associé à la production de la carte

à plus grande résolution mise à jour (soit un effet multiplicateur de

huit). Par conséquent, dans le cas présent, le gouvernement peut

fournir un bien public et démontrer que le coût de cette information

est plus que compensé par l’activité économique qui en résulte au

sein d’un groupe particulier d’utilisateurs. La conversion

proprement dite des investissements liés à l’exploration en termes

d’avantages pour la société dépasse cependant la portée du présent

document.

En résumé, la présente étude a permis d’élaborer une

méthodologie innovatrice pour estimer la valeur des cartes

géologiques du socle dans le contexte de l’exploration minérale et, se

faisant, répond aux besoins du secteur industriel à la recherche de

« méthodologies quantitatives plus robustes permettant de mesurer

l’efficacité de l’exploration et d’informer les gestionnaires, les

and productivity especially in the higher favourability

domains with higher expected mineral target densities. Increasing map resolution also provides the capability to define more favourability classes, thereby

offering more exploration options. A comparison of

updated maps of different resolutions shows that

refining the resolution provides an additional 17%

reduction in search effort across all favourable domains

and a 55% reduction in search effort in the most

favourable domain.

In the south Baffin Island case, exploration campaigns

are considered proactively: the area is still largely unexplored and the exploration statistics are derived from an

analogous area. The south Baffin Island study projects

a 40% increase in expected targets and a 27% reduction

in search effort when all favourable domains are

searched and the updated, finer map is used in lieu of

the old, coarser map. Since south Baffin Island is a

frontier region and exploration cost estimates were

available, this case study facilitates the economic analysis of the additional exploration investment that may

be ultimately attributable to the production of the

updated, finer resolution maps (published in 1999).

Assuming exploration of the highest favourability class

on the updated, finer maps at a minimum, estimation of

the investment generated is within a range from

CAN$2.28 million to CAN$15.21 million depending on the decision maker’s tolerance for risk and need

for efficiency. The return on the investment (expected

number of targets per million dollars spent) declines

from 6.6 to 5.1 when investment within the highest

favourability domain is extended into exploration of

all favourable domains on the updated map. This can

be compared to a return of 4.7 for exploring favourable

domains on the old, coarser map. The economic value

of the updated map also ranges from CAN$2.28 million to CAN$15.21 million and can be compared to the

CAN$1.86 million that it cost to produce the updated,

finer resolution map (a multiplier effect of eight).

Consequently in this case, the government can provide a public good and demonstrate that the cost of providing that information can be more than offset by the

consequent economic activity of a specific user group.

The actual translation of the exploration investment

into societal benefits is beyond the scope of this work.

In summary, this study resulted in an innovative

methodology for estimating the value of bedrock geological maps within the context of mineral exploration

and in doing so, answers industry’s need for “more

robust and quantitative methodologies for measuring

exploration effectiveness, and for informing management,
INTRODUCTION

An explanation of the geological map as a public

good sets the stage for the government’s interest in the

role that geoscientific information can have in

attracting mineral exploration investment.

The geological map as a public good

In Canada, the federal Resources and Technical

Surveys Act (http://laws.justice.gc.ca/en/R-7/100170,

[accessed 2005-09-07]) establishes the basis for considering geological map information as a public good. In

Chapter R-7, section 3, the duties of the Minister of

Natural Resources Canada are specified as including

the following:

“The Minister shall:

(c) make a full and scientific examination

and survey of the geological structure and

mineralogy of Canada; ...(f) prepare and

publish the maps, plans, sections, diagrams,

drawings, documents and data that are necessary to illustrate and elucidate any reports of investigations and surveys made

pursuant to this Act."

In Canada, the mineral exploration industry is

largely dependent on published government maps to

provide the regional geological context for mineral

exploration. As providers of public geoscience information, the Geological Survey of Canada (GSC) and

associated provincial and territorial partners generally bear the

full cost (including all field, compilation, and publication costs) of

producing new geological maps in areas deemed to be in need of

updated and finer resolution geoscientific information. Thus, every

year governments produce new geological maps and their use by

mineral exploration companies can help target less risky investments more efficiently and effectively.

Geological maps have many characteristics of a public good:

they contain general information with multiple applications, have a

long life span (shelf life) with an absence of congestion costs, benefit from a jointness of supply, are publicly available, and are nonexclusive (see explanations below). Discussion of the public good

provided by geological maps begins with the distinction between

‘general’ and ‘specific’ information (Bernknopf et al., 1993). As

applied to geological map information, ‘general’ refers to information collected at a scale pertinent to a variety of regional planning

decisions. Generally, the information can inform and/or influence

land-use choices such as mineral exploration, waste repository site

selection, recreational and conservation designation, establishment

of ecological preserves, residential and commercial construction, or

highway route selection to name a few. Furthermore, this information can be used to present alternatives and assess the impacts of

various land-use decisions including environmental-impact assessments, hazard prevention and protection, engineering studies, and

city planning (Bernknopf et al., 1993, 1997; Bhagwat and Ipe,

2000). Given the slow rate of decay of its usefulness, such information has a long life span or shelf life and there is a lack of congestion

costs (one individual’s use of the information does not degrade its

value to another).

5

investisseurs etles actionnaires sur les risques, le rendementetla valeur de

l’exploration,ainsiquesurlaprogressionversd’éventuellesdécouvertes»

[traduction]. Cette méthodologie peut s’appliquer en d’autres

circonstances, notamment pour des évaluations environnementales

et des décisions sur l’utilisation des terres, soulignant à la fois

l’utilité du modèle ainsi que la valeur de l’information représentée

sur la carte géologique à titre de bien public.

Les deux études de cas ont permis de valider le modèle et de

démontrer les éventuels avantages économiques des cartes

géologiques mises à jour. L’analyse, à l’aide du modèle, des régions

visées par les études de cas a montré que les nouvelles cartes

offraient plus d’options pour l’exploration, réduisaient les risques

liés à l’exploration et amélioraient l’efficience et la productivité. En

d’autres termes, les cartes mises à jour favorisaient une exploration

plus rentable, puisque les comportements simulés en matière de

prise de décision dans le domaine de l’exploration préfèrent

toujours avoir recours à l’information cartographique la plus

actuelle et la plus détaillée, et visent, en premier lieu, les zones où la

favorabilité est la plus élevée afin de porter au maximum le rendement

des investissements.

investors, and shareholders of exploration risk, reward,

value, and progress to discovery”. The methodology can

be applied to other circumstances such as environmental assessments and land-use decisions, highlighting

both the utility of the model and the value of the geological

map information as a public good.

The two case studies validated the model and

demonstrated the potential economic benefits of the

updated geological maps. Analysis of the case study

areas using the model showed that the new maps provided more exploration options, reduced exploration

risk, and improved efficiency and productivity. In

other words, the updated maps supported more

cost-effective exploration as the simulated exploration decision-making behaviour always prefers the

most up-to-date and most detailed map information,

and targets the highest favourability areas first to maximize return on investment.
Conversely, ‘specific’ information tends to possess fewer

of these characteristics. In the context of maps, ‘specific’

refers to information that is much more localized and narrower in focus. Groundwater monitoring of mitigation measures for acid leaching at a coal strip mine provides a good

example. As the information becomes more specific, the

range of applications becomes limited, and the number of

actual and potential users becomes smaller. In another example, the collection of site-specific geological information to

determine the economic and environmental feasibility of

extracting a localized porphyry copper deposit would be of

little use in road planning. In compiling ‘specific’ information for efficient mineral exploration, ‘general’ information is

often necessary to provide background data for the search and

complement relevant planning decisions, whereas ‘specific’

information is often necessary to locate and prioritize specific

mineral targets.

Another characteristic of a public good that is satisfied by

a geological map is that the per-unit production and distribution costs of regional geological map information are near

zero, whereas the cost of collecting the information makes up

almost 100 per cent of total costs. The jointness of supply

characteristic occurs because the bulk of the costs of producing such maps are borne ‘up front’, whereas the actual production and distribution costs are relatively small, and the

cost of serving an additional customer is small. For example,

the per-unit cost of information collection and synthesis for a

set of seven 1:100 000 scale maps of south Baffin Island is

CAN$1.86 million (M.R. St-Onge, unpub. data, 2000),

whereas the cost of production and distribution for the seven

published maps are CAN$106 (2005 price) in paper format,

and CAN$110 (2005 price) in digital format (Geological

Survey of Canada, http://gsc.nrcan.gc.ca/book

store/index_e.php, [accessed 2005-06-05]).

Maps are nonexclusive in use, that is, individuals are able

to obtain map information for a nominal fee (paper or digital

format), or often as free downloads from the World Wide

Web. In the current study, the government bedrock geological maps are publicly available, are readily available in

certain repositories or off government web sites, and are

reproducible, so there is little reason to believe that any individual could be restricted from use.

The role of geoscientific information in

attracting exploration investment

The 2002 World Mines Ministries Forum conference

(J. Macdonald, unpub. manuscript, 2002; http://www.wmmf.org/

historical, [accessed 2005-10-15]) discussed the role of geoscientific information in attracting mineral exploration investment.

Finer map resolution and advances in mapping technology and

geoscientific understanding have increased the accuracy and

precision of geological maps that are available to various

users including mineral explorers. Anecdotal evidence suggests that updated geoscience information attracts investment, especially from junior exploration companies, and

reduces risk at the earliest and riskiest stages of exploration

when generally only smaller budgets are available. Empirical evidence was provided by Scott et al. (2002), who applied

a revealed preference technique using multiple regression to

model the effect of age, type, and detail of government scientific data on total exploration dollars spent, to conclude that

upgraded data sets accounted for 10% of the variance in proposed mineral exploration expense compared to a 5% contribution from old data sets. In the same study, an expressed

preference procedure concluded that the perception of

prospectivity at least doubled with the provision of the

upgraded data sets.

The ultimate value of geoscientific information to the

mineral explorer is different from the value to society

(Gilbert, 1981). Scott et al. (2002) addressed the societal

value of updated geological information derived from royalties and reinvestment. Reedman et al. (2002) observed that

geological field surveys required for the production of new

geological maps provide benefits from employment, charitable contributions, and their multiplier effects in developed

countries.

In the present bulletin/professional paper, the authors

propose a hypothetical model of private-sector investment

that directly links exploration decisions to information

contained in bedrock geological maps, as an attempt to

understand how an updated geological bedrock map would

affect investment decisions. It assumes that exploration investment decisions are based on assessments of the likelihood of

geological settings containing an exploration target (i.e. a

mineralized feature or showing identified in the early stages

of an exploration program, which might become a drilling

target in a subsequent, more mature stage of exploration)

and the resulting estimation of the efficiency, productivity,

risk, cost, and effectiveness (number of targets) of mineral

exploration reconnaissance campaigns. The optimal

exploration campaigns suggested by maps of different ages

and scales for the same region are compared to investigate

the effect on exploration investment decisions of

higher quality (updated) information and a greater

quantity (finer resolution) of information. The quality of

information has improved with recent advances in the

understanding of fundamental earth science processes,

including plate tectonics and mineralization, and the

incorporation of more sophisticated and precise

geothermal, geophysical, and geochronological information. The authors are able to quantify Reedman et al.’s

(2002) essential argument that “as a consequence of an

increase in the quality and quantity of information either the

same output can be achieved for less resources or for the same

input, output can be increased”. Less resource per unit of

output is more efficient and more output per unit of resources

is more productive. The results of the present study are also

consistent with Swinden’s (1993) conclusion that the availability of recent maps and studies allowed BP-Selco to focus

their exploration efficiently and effectively in Newfoundland.

One says that a map is more effective if it can be used to

identify feasible exploration campaigns that yield higher

6
expectations for the number of targets to be found. In addition, the authors observe the effect of map age and scale on

exploration risk expressed as the probability of finding a

predetermined minimum number of targets.

The authors define the advantage (or value) of the updated

geological information to the industry in a region as the difference between the exploration investments stimulated by

the updated, finer resolution (i.e. large scale) geological map

and those stimulated by the older map with a coarser resolution (i.e. smaller scale). The investment advantage to the

exploration industry is equated to the benefit to society and

compared to the cost of producing the map. The actual translation of the exploration investment into societal benefits is

beyond the scope of this work.

The authors’ analysis of bedrock maps used for exploration of a mineralization type proceeds as follows:

1) The geological (i.e. tectonostratigraphic) units and

associations documented by each map are assigned mineralization favourability indices. The higher the index for

geological favourability, the more likely mineralization is

considered to have occurred in the geological units and

associations assigned to it. A geographic information system

is utilized to create a coverage (map) of favourability

domains.

2) The search unit for the mineralization (an average projected surface area that uniquely hosts an exploration target)

is determined. It is the area that could encompass one mineral

deposit, as defined by the industry, but no more. Favourability

domain target density (the proportion of search units containing a target) is estimated for each favourability domain using

archived assessment reports and studies for a reference region.

Some discussion is provided regarding uncertainty about the

estimates of target densities.

3) The old, coarser resolution; updated, coarser resolution

(if available); and updated, finer resolution maps are compared in terms of the optimal obtainable expected efficiency

and productivity.

4) A representative optimal exploration campaign is

found for each map using an economic decision-making

model that maximizes effectiveness (target discovery) subject to efficiency, risk, and budget constraints.

5) The advantage or value of the updated, finer resolution

map relative to the older, coarser resolution map is expressed

as the difference in the expected exploration investments

implied by each map’s representative optimal campaign.

Finally, the exploration investment advantage of the updated

map is compared to the production costs of the map (total cost

of associated new fieldwork and subsequent map

publication).

The authors apply this approach in two case studies: the

mature Flin Flon Belt area of Manitoba and Saskatchewan,

and the frontier region of south Baffin Island in eastern

Nunavut. The results from the Flin Flon Belt area study are

counterfactual since the case considers the investment that

the updated map might have inspired, if it had been available

when the Flin Flon Belt region was explored. For the south

Baffin Island case study the analysis is performed without

knowledge of any prior mineral exploration.

In both case studies it is demonstrated that an updated,

finer resolution map enables identification of exploration

campaigns that enhance the feasibility or attractiveness of

exploring a region by:

• reducing the number of expected search unit examinations

(i.e. size of search area) to locate any fixed number of targets (increasing the efficiency of the search),

• increasing the expected number of targets for any given number of search unit examinations (increasing the productivity

of the search),

• increasing the probability of locating a predetermined minimum number of targets (satisfying a risk criterion), and

• increasing the expected number of targets to be located

(increasing the effectiveness of an exploration campaign).

For the south Baffin Island case study only, the authors

project exploration investment under various decisionmaking criteria and produce a range of exploration investment outcomes. All of the exploration investment outcomes

exceed the cost of producing the updated, finerresolution map.

STEP 1: FAVOURABILITY INDICES

AND DOMAINS

Mineral exploration decisions are commonly made with

some level of uncertainty about the general geological context and the occurrence or distribution of geological units

considered most favourable for the type of mineralization of

interest. Generally, exploration geoscientists use both physical and economic information to evaluate opportunities for

mineral development (Singer and Kouda, 1999a).

Singer and Kouda (1999b) and Harris et al. (2003) compared a variety of favourability mapping techniques that

associate geoscientific variables (lithology or rock type,

structure, geophysics, geochemistry, etc.) with mineral

occurrences. Any such technique could be employed to provide the favourability domain input into the present decision-making model. This study uses expert opinion in lieu of

a mathematical method. For each map, an expert assigns a

favourability index to each rock unit or contact between rock

units depending on how pertinent, in their knowledge and

experience, the rock unit or contacts might be for a given mineralization type (M. Einaudi, pers. comm., 1999). A geographic information system is used to convert the geological

maps into coverages (maps) of the favourability domains prescribed by experts. Thus, contiguous rock units and/or contacts assigned the same favourability index are aggregated

into a polygon and the collection of all the polygons for the

7
same favourability index represents the domain of the

favourability index with finite area. The procedures used to

identify and buffer contacts are described in Appendix A.

In this study, no attempt is made to estimate potential geological endowment — number of deposits, grade, or tonnage,

as discussed in Harris (1984). Geological endowment models

can serve the purpose of more confidently and definitively

choosing verified targets for mining and economic planning.

In contrast, this study’s favourability assignments assist with

the selection of areas for further investigation in the early

stages of an exploration program and the assessment of the

associated investment.

STEP 2: TARGET DENSITIES

For each favourability domain the authors calculate a

point estimate of the mineral target density, defined as the

proportion of search units containing an exploration target,

and attempt a measure of uncertainty about the point estimate.

Point estimate of target density

per search unit

The following steps are used to produce the point estimate of mineral target density (the proportion of search

units containing a target) for each favourability domain:

• A reference region is chosen that is either the study area

itself when it is a mature mining region, or a geologically

analogous region (based on established regional tectonostratigraphic correlations) when the study area is a frontier

region with little mining history.

• The mineral exploration literature and/or an expert are

consulted to determine the average aerial extent of a

unique mineral exploration target for the given mineralization

type. This defines the area of a search unit such that multiple mineral deposits within a search unit are counted, in the

industry, as one deposit, and in the present study as one

exploration target. Thus, a search unit can contain either 0

targets or 1 target. The authors recognize, however, that

exploration targets and economic deposits are threedimensional entities, a complexity not addressed in the

current analysis.

• Using a geographic information system, the area of each

favourability domain is calculated. The number of search

units contained within the favourability domain f on map θ

is denoted n f

θ .

• Known mineral deposits and showings are overlain on a

favourability domain map to count t f

θ , the number of exploration targets contained in favourability domain f on

map θ.

• If possible, k f

θ , the proportion of the surface area of

favourability domain f actually explored to produce the

target information, would be determined. Since it is

unlikely that land has been uniformly searched across all

favourability domains it would be more realistic to factor

in the extent of area searched when calculating exploration

target densities. In the absence of further information k f

θ is

assumed to equal 1. The authors suspect that it is more

likely that lower percentages of land have been searched in

lower favourability domains. For example, half of the highest favourability domain may have been explored for targets

compared to 10% of a less favourable domain.

• The empirical target density for each favourability domain

f for map θ, p t kn $ / f f ff

θ θ θθ = is derived as the empirical

proportion of search units that contain an exploration target in favourability domain f, with 0 ≤ ≤ p$ f

θ 1. Note that

the empirical target density is expressed in terms of a

search unit so estimates will differ from those based on

square kilometres.

Uncertainty about the estimates

of target density

Ideally, a mineral exploration decision depends not only

on the point estimates for target densities, but also on the

uncertainty of those estimates as a function of the quality and

quantity of information. Singer et al. (2005) observed that

little published information is available concerning the

variability of deposit densities within deposit types. They

investigated deposit density variability in nineteen porphyry

copper control permissive areas (where permissive area is

akin to an aggregation of all favourable domains). In the present study some of the exploration target density variability is

explained by using more than one favourability class.

Another source of variability relates to the quantity of information (the sample size) and the magnitude of the target density: the greater the sample size and the smaller the target

density, the more statistically confident the estimate.

Regarding search units within a favourability domain as

independent Bernoulli trials with true (but unknown) probability pf

θ of containing a target and probability 1− p fθ of not

containing a target, an approximate large-sample

100 1( ) − α 0

0 confidence interval for p f

θ is given by

$ $ $ $ $ $ $ p

p q

k n p p

p q

k n f

f f

f f

f f

f f

f f

θ

θ θ

θ θ

θ θ

θ θ

θ θ − <<+ z z α α 2 2

where q p $ $ f f

θ θ = −1 (Walpole and Meyers, 1989). In other

words, this confidence interval can be interpreted as having a

100 1( ) − α 0

0 chance of containing pf

θ . A confidence interval

bounded only from below has a100 1

2 − ⎛

⎝

⎜ ⎞

⎠

⎟

α 0

0 chance of

containing p f

θ . Confidence intervals are a function of the

observed target density and the number of search units

within the favourability domain on the map providing the

estimate.

8
The confidence-interval approximation procedure is

unreliable for cases with n pf f

θ θ $ < 5. In addition, the method is

only partially satisfactory, even if the independence

assumption holds, due to the general lack of information

about k f

θ (the proportion of a favourability domain actually

explored to produce the target information). Both the estimate and the confidence surrounding it are distorted by using

k f

θ =1, causing one to underestimate the target density and

overestimate the confidence in one’s estimates. This is particularly true for the larger, less favourable domains that will, in

all likelihood, have been less intensively explored.

STEP 3: MINERAL EXPLORATION

EFFICIENCY AND PRODUCTIVITY

The authors measured expected mineral exploration productivity and efficiency to compare two geological maps

covering the same region. Although the mineral exploration

literature makes reference to the efficiency of exploration

(Reedman et al., 2002), the authors know of no formalization

of this term in the context of information provided by bedrock

geological maps. Consequently, in this section, the related

measures of exploration efficiency and productivity are

defined, discussed, and modelled.

Exploration efficiency is the expected number of search

unit examinations (i.e. search area) required to find a target,

and is the inverse of the target density. Since exploration

efficiency changes across favourability domains, expected

exploration campaign efficiency depends on the extent of the

campaign. The more search unit examinations required to

find a target, on average, the less efficient the exploration

campaign. The expected efficiency of the campaign can be

computed, a priori, from the target densities. Model 1,

defined below, solves for the most efficient search campaign

that minimizes the expected number of search unit examinations to locate a fixed number of targets. It provides the means

to compare the exploration efficiency of campaigns derived

from different geological maps of the same region.

Expected mineral exploration productivity is the expected

number of targets to be found per search unit examination and

therefore, it is the target density at that location. Since target

density (productivity) changes across favourability domains,

exploration campaign productivity depends on the extent of

the campaign. Model 2, defined below, solves for the most

productive exploration campaign given a fixed number of

search unit examinations. Thus, the expected exploration

productivity of campaigns derived from different geological

maps of the same region can be compared.

Model 1: minimizing the search area

The most efficient exploration campaign is described by

the number of search unit examinations in each favourability

domain that minimizes the total number of search unit examinations to find a specified expected number of exploration

targets, t.

S t

x

x f

f

F θ

θ

( ) min = =

∑0

(1)

subject to xp t f f

f

F

$ θ

θ

=

∑ = 0

0 ≤≤ ∀ xn f f f

θ ,

where

designates the map being used,

t is a specified expected number of targets to be found,

S t θ ( )is the minimum expected number of search unit examinations to find the specified number of exploration targets t

using map θ,

x f is the decision variable representing the number of

search unit examinations in favourability domain f,

Fθ is the highest favourability index and 0 is the lowest

favourability index on map ,

x is the vector of decision variables( ,..., ) x x 0 Fθ ,

n f

θ is the number of search units available in

favourability domain f on map , and

p$ f

θ is the point estimate of the target density for

favourability domain f on map .

The optimal decision for map θ is a vector of the optimal

number of search unit examinations in each favourability

class to find the expected number of exploration targetst,

xt xt x t θθ θ ( ) ( ( ),..., ( )) = 0 θ F . The optimal decision is to search

in the highest favourability domains until the specified number of targets is attained. The search effort savings of using

the updated map to find an expected number of t targets is a

function of the difference between the optimal objective

function values, ( ( ) ( )) St St 0 U − , where θ = U for the

updated, finer resolution map and θ = O for the original old,

coarser resolution map.

Model 2: maximizing the expected number

of exploration targets

The most productive exploration campaign is described

by the number of search unit examinations in each

favourability domain that maximizes the expected number

of targets to be found in a specified number of search unit

examinations, s.

9
T s

x

pf

F θ θ

θ

( ) max = ∑x $ f

f =0

(2)

subject to x s f

F

f =0

θ

∑ =

0 ≤≤ ∀ xn f ff

θ θ ,

where

designates the map being used,

s is a specified number of search unit examinations,

T s θ ( ) is the maximum expected number of targets found

in s search unit examinations,

x f is the decision variable representing the number of

search unit examinations in favourability domain f,

x f

θ is the decision variable specifying the number of search

unit examinations in favourability domain f on map ,

Fθ is the highest favourability index and 0 is the lowest

favourability index on map ,

x is the vector of decision variables( ,..., ) x x 0 F θ ,

n f

θ is the number of search units available in favourability

domain f on map , and

p$ f

θ is the point estimate of the target density for

favourability domain f on map .

The optimal decision for map is a vector of the

optimal number of search units examined in each

favourability class to find the most exploration targets

using a specified total number of search unit examinations

s, x s θ ( ) = ( ( ),..., ( ) xs x s 0 F

θ θ

θ The most productive exploration campaign is to search favourability classes with the

highest target density until the designated number of search

units is exhausted. The gain in expected productivity of s

search unit examinations using the updated map is the

difference between the optimal objective function

values, ( ( ) ( )) TsTs U O − , where the updated and old maps

are designated U and O, respectively.

The solutions from the two models are related. If s is the

expected optimal efficiency for t targets then t is the expected

optimal productivity for s search unit examinations. Figure 1

illustrates the optimal efficiency versus productivity curves

for hypothetical old and updated maps of the same region.

The updated, finer resolution map identifies three favourable

domains, whereas the old, coarser resolution map identifies

only one favourable domain. Each map has a favourability 0

domain that is not represented in Figure 1 because it is

assumed to be of no interest. If favourability 0 domains were

explored in addition to the more favourable domains, the

whole region would be under exploration with the same outcome for both maps; it would be as if there were no geological

maps available to guide exploration. Exploring favourability

domains in descending order of favourability is always the

optimal strategy. The slopes of the line segments in Figure 1

10

0

10

20

30

40

50

60

0 1000 2000 3000 4000 5000

Expected number of search unit examinations ( ) s

Expected number of targets ( )t

UF321

UF32

UF3

OC1

Curve of expected

exploration outcomes

using the old, coarser

resolution map with one

favourability domain

Curve of expected

exploration outcomes

using the updated, finer

resolution map with three

favourability domains

Figure 1. Illustrative expected efficiency versus expected productivity curves;

UF3 corresponds to examination of all search units in favourability domain 3,

UF32 in favourability domains 3 and 2, UF321 in favourability domains 3, 2, and

1 on the updated, finer resolution map, whereas OC1 corresponds to examination

of all search units in favourability domain 1 on the old, coarser resolution map.

The horizontal axis, s is the expected number of search units examined and the

vertical axis, t is the expected number of targets to be found.
are the target densities corresponding to the favourability

domains. Hence, the decreasing slope of the lines for the

updated map as exploration exhausts one favourability

domain and moves to the next most favourable domain. The

labelled points in Figure 1 show the efficiency and productivity of the campaigns that fully search a set of favourability

domains. For example, U32 is described by the vector of

search examinations undertaken in each favourability

domain (, , ) 0 2 3 n n U U denoting complete exploration of

favourability domains 3 and 2 of the updated map. The horizontal difference between the two curves at target level t is the

expected number of search unit examinations avoided by

using map U to find t targets as opposed to map O. It is the

gain in expected campaign efficiency given a goal of t

expected targets. The vertical difference between the two

curves at search unit examination levelsis the increase in the

expected number of targets found when examining s search

units on map U instead of on map O. It is the gain in expected

exploration campaign productivity for a given number of

search unit examinations, s.

A high favourability domain that has a higher target density will have fewer expected searches to yield one target and

consequently, will be more efficient. The same domain will,

however, be limited in terms of the total number of targets

(due to the smaller area generally associated with high

favourability domains). A lower favourability domain will be

less efficient to explore, but in some cases may yield a greater

number of exploration targets due to a larger domain area.

A third model, discussed next in step 4, allows for the

more likely outcome of each map suggesting an exploration

campaign that does not have the same expected number of

search unit examinations or the same expected number of

exploration targets to be found. This third model is an

‘economic’ model that expands the decision framework to

include exploration risk and budget concerns. The notion of

productivity is replaced by effectiveness, which encompasses

the number of expected targets to be found by an exploration

campaign in the absence of a constraint on the number of

search unit examinations.

STEP 4: ECONOMIC MODEL

(MODEL 3)

Information is only of value if it affects a decision.

Stanley (1994) presented a comprehensive review of the

literature, spanning a 30 year period, that evaluates resource

information according to its effects on societal costs and

benefits, budget planning, and government policies.

Stanley (1994) ranked various geoscientific survey

methods including aeromagnetic, geochemical, and geological surveys. Herein the present authors propose a model in

which exploration investment responds to the provision of

geological map information from the public sector to the

exploration industry. In the present analysis, the exploration

geoscientist designs and evaluates regional exploration

campaigns on the basis of expected exploration efficiency,

effectiveness, risk, and cost.

Exploration campaign statistics

The exploration campaign, x, for map θ, is described by

specifying the number of search unit examinations x f

θ in each

favourability domain 0 ≤ ≤f Fθ , with, x (x ,...,x ) = 0 Fθ . The

total number of search units involved in the campaign is

u x x f

f

F

= =

∑0

θ

and the campaign target density or expected proportion of

campaign search unit examinations containing a target is the

weighted sum of target densities of the favourability domains

included in the campaign:

d

x

u

p x

f

x f

F

= =

∑ θ

θ

θ

0

$ f .

Constrained optimization model

The collective exploration industry is modelled as a conventional economic agent who seeks to make investment decisions that maximize economic benefits indicated by a map

V θ (the expected number of targets to be found) subject to an

exploration risk constraint (R), an industry guideline for exploration efficiency (target density constraint : TD), and a budget

constraint (Q; Lichtenberg, 1991; Bernknopf et al., 1997):

V

x

x pf

f

F

f

θ θ

θ

= ⎛

⎝

⎜ ⎞

⎠

⎟ =

∑ max $

0

subject to 0 ≤ ≤ x n f f

θ

u x x f

f

F

= =

∑0

θ

d x p

u x E f f

f x

F

= ≥ =

∑ $ θ θ

0

(3)

I u Iu d M s x + ≤ t x x

Bt P u d x x , ( ) − ≤− 1 1

where

θ designates the map being used,

V θ is the optimal expected benefit (number of targets) of

exploration for map θ,

x f is the number of search units examined in favourability

domain f,

Fθ is the highest favourability index and 0 is the lowest

favourability index,

x is the vector of decision variables( ,..., ) x x 0 F θ ,

p$ f

θ is the point estimate of the target density for

favourability domain f on map ,

11
dx is the target density per search unit of campaign x

using map θ,

ux is the number of search units examined in campaign x

using map ,

M is the financial budget available,

I s is the cost of examining a search unit,

It is the cost of pursuing a target,

B t u d x x , ( ) −1 is the cumulative binomial distribution that is

the probability of finding no more than t −1 targets,

P is the minimum allowed probability of finding at least t

targets or an industry risk standard, and

E is a minimum allowed campaign target density, or

industry efficiency standard.

The objective function V

x

x pf f

f

F

θ θ

θ

= ⎛

⎝

⎜

⎜

⎞

⎠

⎟

⎟ =

∑ max $

0

The objective function of the third model maximizes the

expected number of targets found by an exploration campaign. An isobenefit curve of value B is prescribed by the

number of searches u and target density d that have a constant

product, B= ud. The curve represents an equal amount of benefit resulting from trading off the number of exploration campaign search units examined for campaign target density.

Two levels of expected benefit of exploration are shown as

‘Isobenefit curve 1’ and ‘Isobenefit curve 2’ in Figure 2. Each

of the constraints is discussed in turn below.

Budget constraint QI I M s t (,, ) =

Iu Iud M sx txx + ≤

Exploration cost per search unit examination, I s multiplied by the number of search units examined yields the total

campaign unit search exploration costs. Target exploration

cost, It multiplied by the expected number of targets (number

of search units examined multiplied by the expected campaign target density) yields the total expected campaign target exploration cost. The sum of these investment terms must

be less than or equal to the total budget M, that has been allocated for exploration. The budget constraint is the curve

labelled ‘Exploration budget (,, ) I IM s t ’ in Figure 2.

Risk constraint RPt B t P u d x x ( ,) ( ) = − ≤− , 1 1

In a specific exploration effort, risk arises from the possibility of failure to find, and investment loss from not finding a

deposit (Singer and Kouda, 1999a). Discovery risk is particularly high in unexplored areas that lack field verification of

hypothetically suitable areas for a given type of mineralization (Cheng and Agterberg, 1999). There are, however, a

number of ways to reduce discovery risk in mineral exploration including searching for deposit types that have a high

probability of being located (Singer and Kouda, 1999a) and

incorporating multiple deposit types into the search.

The economic model presented here treats target discovery as successful if at least one target (or a minimum number

of targets) is found (target discovery success is different from

commercial success, which of course depends on finding

12

0

500

1000

1500

2000

2500

3000

3500

4000

0 0.1 0.2 0.3 0.4 0.5 0.6

Expected campaign target density

Number of campaign search unit examinations

Exploration budget ( ) I , I , M s t

Target density ( ) E

Isobenefit curve 1

Isobenefit curve 2

C1: infeasible campaign

(too expensive)

C2: infeasible campaign

(too risky)

C3: infeasible campaign

(too ineffecient)

C4: feasible campaign

(most preferred)

C6: feasible campaign

(Least preferred)

C5: feasible campaign

Risk criterion ( ) P, t

Figure 2. Exploration preference and feasibility: unfeasible, feasible, and

preferred feasible exploration campaigns. The three constraints utilized are the

risk criterion, the exploration budget, and efficiency (target density), which

enclose a feasible decision space. Campaigns (C) are labelled 1 through 6.

Campaigns C5 and C4 lie on isobenefit curves 1 and 2, respectively, with 1<2.
economic deposits). The likelihood of target discovery is a

function of the favourability of the geology or campaign

target density explored, and the number of search unit examinations undertaken. Singer and Kouda (1999a) used the

probability of finding at least one deposit as a measure of risk.

The present authors adopt and generalize this risk expression. The approximate probability that an exploration campaign finds at least one target is:

P ux ( ) z d ≥ x 1) =1– (1– (4)

where z is the number of targets found.

The risk criterion is:

1 1 −− ≥ ( ) d P x

ux (5)

where P is an industry threshold probability for success.

The authors think of it as the minimum risk that investors will

tolerate.

Alternatively, for a given probability of success P, the

number of search unit examinations ux required to find at

least one target is:

log( ) / log( ) 1 1 − −≤ P du x x (6)

If this criterion is not satisfied, the campaign target density is too low and/or not enough search area is contained in

the exploration campaign.

More generally, particularly in a frontier region, the

risk criterion may require a minimum number of targets t

greater than 1 to be found with probability P. In this case,

the authors embellish the above risk criteria and use the

cumulative binomial distribution,

B t u

t

d d u d

j

t j uj

x x

() ( ) = ⎛

⎝

⎜ ⎞

⎠

⎟ − =

−

∑0

1

to derive the probability of success as:

P t ( ( z t) = – P(z t) = – B ) ≥≤ − u,d 1 11 (7)

For a particular campaign, x the risk constraint becomes:

Bt P u d x x , ( ) − ≤− 1 1 . (8)

More realistically, the risk criterion must operate with

other economic objectives and constraints because a campaign with a very low target density can satisfy the risk criterion if a large enough area is contained in the campaign. In

Figure 2, the line labelled ‘Risk criterion (P, t)’, which represents the combinations of target density and number of search

unit examinations that yield a probability P of finding at least

t targets, illustrates the sensitivity of the number of search unit

examinations required to satisfy the risk criterion as the target

density moves towards 0.

Figures 3 and 4 explore the sensitivity of the risk constraint to the minimum number of targets required and to the

probability of finding the minimum number of targets,

respectively. A higher risk constraint reduces the space of

feasible solutions; the higher the constraint, the more stringent the condition. In Figure 3 it is evident that the risk constraint rises more slowly as the minimum number of targets is

increased. In contrast, Figure 4 exhibits increasing sensitivity

to the probability of success: a move from 0.81 probability to

0.95 probability is of a similar magnitude to the move from

0.95 probability to 0.99 probability.

13

0

5000

10 000

15 000

20 000

25 000

30 000

35 000

40 000

45 000

0 0.005 0.010 0.015 0.020 0.025 0.030 0.035

Exploration campaign target density

Number of search unit examinations

Risk criterion (0.95 probability of

finding at least 1 target)

Risk criterion (0.95 probability of

finding at least 10 target)

Risk criterion (0.95 probability of

finding at least 20 target)

Risk criterion (0.95 probability of

finding at least 30 target)

Risk criterion (0.95 probability of

finding at least 40 target)

Figure 3.Sensitivity of the risk constraint to the minimum number of targets.

10, 20, 30, and 40) to be found with probability 0.95.
Campaign target density constraint

TD E d E x ( ) = ≥

The efficiency constraint requires the target density to be

greater than or equal to E, an industry or regional minimum

return on exploration investment. The campaign target density constraint is the vertical line labelled ‘Target density (E)’

in Figure 2. It is a simple constraint, but it is interesting to look

at it in the context of the risk constraint and whether it is stated

to protect the decision maker from exploration campaigns

that are placed near the steeper slope of the risk constraint

where the number of search unit examinations required to satisfy the risk constraint is very sensitive to the target density

estimate.

Feasible and optimal solutions

Based on a geological map, an exploration campaign x can

be defined as a spatial allocation (by favourability class) of

search unit examinations. In Figure 2, the authors plot six sample exploration campaigns to illustrate feasible and infeasible

campaigns with respect to the three constraints discussed

above and to demonstrate a preferred exploration campaign

that sits on the highest isobenefit curve. The campaigns are

plotted against target density, risk, and budget constraints. The

feasible region of the graph that satisfies all three constraints is

the region circumscribed by the three constraint curves. The

exploration campaign labelled C3 is infeasible because it fails

to satisfy the target density (efficiency) and budget constraints.

The exploration campaign labelled C2 is infeasible because it

does not satisfy the risk constraint. Despite an attractive target

density there are not enough search units contained in this

exploration campaign to assure a high probability of success.

In the C1 campaign, exploration is infeasible because the

investment required exceeds the budget. The feasible campaigns are C4, C5, and C6, but some feasible allocations are

more effective than others and sit on higher isobenefit curves.

Plotting isobenefit curve 1 and isobenefit curve 2 that fit the C4

and C5 feasible campaigns reveals that the C4 exploration

campaign is preferred over C5.

Optimization of the economic model identifies the exploration campaign that promises the most benefits (targets) out

of all the feasible solutions that meet the risk, budget, and

expected efficiency constraints. The decision constraints and

the optimization of the expected number of targets determine

simultaneously the number of search unit examinations in

each favourability class and the average target density of the

optimal exploration campaign xx x F

θθ θ = ( ,..., ) 0 θ .

Similar to the first two models, the optimal campaign for

model 3 exhausts favourability domains in descending order

of favourability until one of the constraints is binding, if a feasible solution exists. If there is no feasible optimal solution to

the third model for map θthen it is interpreted to mean that the

information contained in map θ does not stimulate any mineral exploration in the region. Experience with the geological

data suggests that it is most likely that if feasible solutions

exist, then either the efficiency or budget constraints become

binding. Although the risk constraint could theoretically be

14

0

5000

10 000

15 000

20 000

25 000

30 000

35 000

40 000

45 000

0 0.005 0.010 0.015 0.020 0.025 0.030 0.035

Exploration campaign target density

Number of campaign search unit examinations

Risk criterion (0.99 probability of

finding at least 20 targets)

Risk criterion (0.95 probability of

finding at least 20 tagets)

Risk criterion (0.81 probability of

finding at least 20 target)

Figure 4. Sensitivity of risk constraint to the probability (0.81, 0.95, and 0.99) of finding at least

20 targets.
binding, the decline in the campaign target density tends to be

overwhelmed by the increase in the number of search unit

examinations available as the area of search is expanded. Thus,

the authors observe that the risk (probability of finding at least

a given number of targets) and effectiveness considerations

favour exploration, whereas the efficiency and budget considerations impose limits on the exploration. These opposing

forces in the objective function and constraint set ensure that

the form of the optimal solution identifies exploration campaigns that search the higher favourability classes for as long as

the efficiency and budget constraints are satisfied. The conditions for the optimal solution are contained in Appendix B.

Relative effectiveness of the maps

Figure 5 illustrates the case of both the old and the updated

maps providing feasible optimal campaigns. The optimal

solutions of each map can be compared to determine if the

improved map information has improved effectiveness that is

illustrated by the shift of the isobenefit curve with the updated

map and if V V U O − > 0. As indicated in Figure 5, an

improvement is achieved with a movement from the optimal

campaign with the old map (OCC) to the optimal campaign

with the updated map (UFC). In this example, the coarser

map exploration decision is bound by the efficiency constraint, whereas the updated map exploration decision is

bound by the budget constraint. If a map does not have a feasible solution, its isobenefit curve is at the origin.

STEP 5: VALUE OF IMPROVED

MAP INFORMATION

The exploration activity of examining search units and pursuing targets is translated into investment dollars to provide a

value for the map information. For each map, the optimal or most

preferred exploration campaign x θ contains a total of ux θ search

unit examinations with an average target densitydx θ such that

the total advantage of updated information relative to the old

information (for example) is the difference in exploration

investment stimulated by the geological maps:

V V V Iu u

Iu d u d

U O

s x x

t x x xx

U O

U U OO

1 =− = − +

−

( )

( )

(10)

where ux θ = 0 if there are no feasible solutions to the economic model for map θ. Although a finer resolution map may

reduce search investment (due to more efficient mineral

exploration), the increase in target investment is likely to be

greater. The authors note that once the budget constraint is

binding, then although improved information would increase

the effectiveness of the optimal exploration campaign, the

investment will have run out.

The investment advantage of the improved information

can be compared with the production cost of the updated map.

Thus, each expected additional dollar of exploration

investment is assumed to be of equal benefit to society:

firstly, as recompense for the cost of producing the map; and

secondly, in terms of the economic activity stimulated.

15

0

500

1000

1500

2000

2500

3000

3500

4000

0 0.1 0.2 0.3 0.4 0.5 0.6

Expected campaign target density

Number of campagin search unit examinations

Updated and finer resolution map optimal campaign (UFC)

Old and coarser resolution map optimal campaign (OCC)

Risk criterion ( , ) P t

Exploration budget ( , , ) IIM s t

Target density ( ) E

Isobenefit curve 1

Isobenefit curve 2

Figure 5. Comparison of optimal exploration campaigns derived from two

different maps. The three constraints (risk criterion, exploration budget, and

target density (efficiency)) enclose a feasible decision space. Campaigns OCC

(based on the old, coarser resolution map) and UFC (based on the updated, finer

resolution map) lie on isobenefit curves 1 and 2, respectively, with 1<2. Constraint

parameters defined in the text.
APPLICATION OF THE MODEL

Assumptions of the analysis

A number of assumptions are inherent to the analysis presented in this paper:

• Mining companies collectively choose exploration programs by considering risk, efficiency, cost, and benefits

(number of targets) in the particular way modelled here.

• The geological maps and other geophysical, geochemical,

and geochronological data are representative of the knowledge at the time of map making and sufficient to determine

favourability domains for each map.

• In the absence of government-produced geological maps,

firms would not — of their own volition — create

comparable maps of any significant portion of the south

Baffin Island or Flin Flon Belt case study areas.

• For statistical purposes, it is assumed that the regions analyzed are unbiased samples of broader geographic regions

that share the same geology and that search results within

each favourability class are independent.

• Mineral occurrences found to date are comprehensive. In

truth, regions such as the Cape Smith Belt remain to be

fully explored. Consequently, the estimation of the target

densities for the corresponding favourability domains is

conservative.

• The assignment of known mineral occurrences to older

maps may overestimate the performance of those maps.

Subsequent knowledge may have assisted in the discovery

of some of the mineral occurrences incorporated into the

analysis of the older maps.

• The analysis uses surface geology only. Consequently, the

information utilized could lead to inaccuracies in the

analysis, as the three-dimensional geometry of lithological

units and structures is not addressed.

• The analysis assumes that all exploration targets are

equally desirable to find; however, it is likely that more

favourable regions not only have higher target densities as

per the authors’ analysis (see ‘Case studies’), but also that

these same regions contain mineral occurrences with

higher grades and tonnages (e.g. Carlson, 1991, and references therein; Singer et al., 2001, and references therein).

If so, this implies that the economic benefits of finding targets in more favourable areas have been underestimated in

the present analysis.

• Exploration under hydrographic features (lakes, rivers) is

allowed.

CASE STUDIES

In the remainder of the bulletin/professional paper, the

authors model efficiency, productivity, risk, and cost of mineral exploration campaigns derived from bedrock geological

maps in the mature Flin Flon Belt and the frontier south

Baffin Island exploration regions. Table 1 summarizes the

analyses that depended on the age and resolution of bedrock

geological maps available in each case study area. The Flin

Flon Belt case study in Manitoba and Saskatchewan provides the opportunity to separate both the effects of updating

a map and of refining the map resolution on exploration efficiency, effectiveness, and risk. Because the old map covers a

smaller area than the updated map, the analysis is limited to

the area common to both. The analysis of the Flin Flon Belt

maps is carried out as though all the potential exploration

benefit of the updated maps has already been realized. The

authors did not have the exploration history to pursue estimates of future discoveries in the region. The analysis

serves to test the method for the second case study, south

Baffin Island, an underexplored frontier area of eastern

Arctic Canada. The south Baffin Island case study compares an old, coarser resolution geological map to an

updated, finer resolution geological map. Because there

has been no significant mineral exploration or resource

extraction in south Baffin Island, the analysis depends on

the crucial assumption that the geology of parts of south

Baffin Island is comparable to that of northern Quebec (a

more mature mining district) as has been documented by

recent fieldwork and tectonic studies (see ‘South Baffin

Island case study’). The analysis of south Baffin Island

proceeds through all five steps of the authors’ approach

including an assessment of the value of the information.

16

Flin Flon Belt South Baffin Island 

Exploration

status Mature Frontier 

Analytical

strategy 

Hindsight

retrospective 

and/or ex post 

Predictive and/or 

forward looking and/or 

ex ante 

Value of map 

information in 

terms of: 

Exploration

efficiency,

effectiveness,

and risk 

Exploration efficiency, 

effectiveness, risk, 

and economic value 

of updated map 

information

Map

comparisons 

Old, coarser 

resolution vs. 

clipped, updated, 

coarser 

resolution vs. 

clipped, updated, 

finer resolution 

Old, coarser 

resolution vs. 

updated, finer 

resolution

Exploration

target data Flin Flon Belt Cape Smith Belt analogue

Table 1. Summary of case studies.
Figure 6 provides the locations of the Flin Flon Belt, the

Cape Smith Belt, and south Baffin Island within a broad

geological context for Canada.

FLIN FLON BELT CASE STUDY

Introduction

This application of the model is based on geological mapping of the Flin Flon Belt that was compiled in the NATMAP

Shield Margin Project completed in 1998 (Lucas et al., 1999).

The National Geoscience Mapping Program (NATMAP)

aimed to foster a multidisciplinary team approach to bedrock

and surficial mapping and related research; combine the

efforts of the federal, provincial, territorial, and university

scientists; and utilize digital information technology for more

efficient interdisciplinary research and map production. Flin

Flon is one of the largest Proterozoic volcanic-hosted massive-sulphide (VMS) districts in the world, from which more

than 107 million tonnes of sulphide-rich ore have already

been extracted from 25 deposits, with a further 58 million

tonnes remaining in 43 subeconomic deposits (Syme and

Bailes, 1993; Syme et al., 1999, and references therein). The

district also contains productive gold deposits; however, the

future of the Flin Flon and Snow Lake mining communities

and the Flin Flon smelter facility was uncertain in the early

1990s due to a lack of defined long-term ore reserves. All

mining ceased in the Snow Lake camp in late 1993, only to

strongly rebound in 1995 with the reopening of the old Nor

Acme gold mine (now called the New Britannia mine) and the

discovery of the Photo Lake Cu-Zn-Au deposit.

Geological maps

Prior to the NATMAP project, a compilation of the Snow

Lake–Flin Flon–Sheridon area existed (Bailes, 1971). The

original Bailes (1971) map was prepared as a 1 inch to 1 mile

compilation (1: 63 360 scale) and subsequently reduced to a

1 inch to 4 miles map (1: 253 440 scale) for publication. The

latter map was digitized for this project and used as the old,

coarser resolution geological map for the Flin Flon area

(Table 2). The NATMAP project produced the updated,

coarser resolution and updated, finer resolution maps as

described below for the Flin Flon Belt (Table 2).

Three fundamental objectives for the NATMAP project

were defined at its onset (Lucas et al, 1999): 1) bedrock and

surficial mapping supported by thematic geological, geochronological, and geophysical studies; 2) development of an

17

PHANEROZOIC

ARCHEAN CRATONS

Thrust fault, teeth

on hanging wall . . .

Continental magmatic

arcs

Orogenic belts

Wopmay

Cordilleran

Innuitian

Grenville

Appalachian

SUPERIOR

HEARNE NAIN

RAE

0 km 1000

Strike-slip fault . ....

FFB

CSB

SBI

MESOPROTEROZOICNEOPROTEROZOIC

PALEOPROTEROZOIC

Trans-Hudson

Figure 6. Simplified geological map of Canada, modified after Hoffman (1988, 1989), Ross et al.

(1995), and Wheeler et al. (1996). Blue diagonal stripes represent areas where the Precambrian

basement is covered by the Phanerozoic platformal cover, which is not illustrated. Upper-cased

names are Archean cratons; lower-cased names are Proterozoic and Phanerozoic orogens.

Locations of case study areas discussed in the text are indicated with the yellow symbols (CSB =
interpretive map of pre-Phanerozoic geology immediately

south of the exposed Precambrian Canadian Shield margin;

and 3) development of a digital geoscience database housing

an extensive set of both existing and new data, including

regional compilation maps.

The updated, coarser resolution map is a set of synoptic

maps of the shield margin area that were produced at a scale

of 1:325 000 (Table 2). These document the bedrock geology

of the exposed Precambrian Shield, the Western Canada

Sedimentary Basin, and the pre-Phanerozoic Precambrian

basement. The updated, finer resolution map (Table 2) is a

1:100 000 scale bedrock geology compilation based on more

than 70 sources ranging in publication date from 1944 to

1997 (NATMAP Shield Margin Project Working Group,

1998). The recently mapped areas (e.g. Flin Flon area, Snow

Lake area, Kisseynew Domain) are shown in considerably

more detail than areas that are documented with information

derived from the older maps. The 1:100 000 scale compilation maps were based on fieldwork carried out at 1:20 000

scale to 1:50 000 scale. Considerable effort was made to produce a seamless geological coverage, but the limitations

inherent in any compilation apply.

The following sections present the analysis of the Flin

Flon maps of different ages and scales illustrating the efficiency, effectiveness, and risk improvements stemming from

the production of an updated map with finer resolution.

Favourability indices

This study evaluates the available bedrock geological

maps for the Flin Flon Belt in terms of their usefulness in mineral exploration for volcanic-hosted massive-sulphide mineral targets that are hosted by juvenile arc rocks (Syme and

Bailes, 1993; Lucas et al., 1996; Syme et al., 1999). The

volcanic-hosted massive-sulphide exploration target

favourability indices for different ages and scales of maps are

based on the different geological interpretations that are

inherent to or limited by the resolution of the different ages of

mapping efforts.

For the old, coarser resolution (Tables 3, 4) and updated,

coarser resolution (Table 5) maps, θ =OC and UC, a binary

system of favourability indices is created where:

• Favourability 0 = geology in search unit is not likely to

contain an exploration target, and

• Favourability 1 = geology in search unit is favourable for

containing an exploration target.

For the updated, finer resolution map analysis, θ = UF, a

more complex favourability ranking is possible as the

geological information presented on the maps allows for

increased distinction of rock types. The index is based upon

the likelihood of volcanic-hosted massive-sulphide exploration targets being hosted by subsets of juvenile arc rocks, as

well as being localized at or near contacts between key units

(see below):

• Favourability 0 = geology in search unit is not likely to

contain an exploration target,

• Favourability 1 = geology in search unit is somewhat

favourable for containing an exploration target,

• Favourability 2 = geology in search unit is favourable for

containing an exploration target, and

• Favourability 3 = geology in search unit is most favourable

for containing an exploration target.

18

Favourability index Rock units on map 

0 All rock units except A1 and A2

1 A1, A2 

Table 3. Favourability indices for the old, coarser

resolution Flin Flon Belt map (Bailes, 1971).

Geological unit code Geological unit Rock type 

A1 Amisk Group 

Pillowed basalt and 

andesite; includes 

many small, related 

intrusions and 

interbedded basic 

to intermediate 

fragmental volcanic 

rocks. 

A2 Amisk Group 

Rhyolite, dacite, 

quartz, porphyry; 

includes acidic 

crystal tuff and 

siliceous, 

carbonate-rich tuff. 

Table 4. Explanation of geological unit coding scheme for

the old, coarser resolution Flin Flon Belt map favourability

indices (Bailes, 1971).

Favourability index Rock units on map 

0 Nonjuvenile arc rock 

1 All juvenile arc rock 

Table 5. Favourability indices for the

updated, coarser resolution Flin Flon

Belt map (NATMAP Shield Margin

Project Working Group, 1998).

Coverage Format Scale Area (km2

)

Old, coarser resolution map 

of bedrock geology Polygon 1:253 440 11 197 

Updated, coarser resolution 

map of bedrock geology Polygon 1:325 000 17 288 

Updated, finer resolution 

map of bedrock geology Polygon 1:100 000 17 288 

Mineral showings Point n/a n/a 

n/a = not applicable 

Table 2. Geospatial data used for the Flin Flon Belt case

study area.
19

Favourability index Rock units on map Rock contacts on map 

0

Nonjuvenile arc 

rocks, other juvenile 

rocks 

None

1 J1(b, d), J2(a–c), 

J3a, J6(a, b), J13c None

2 J1a, J4c, J5(a–d), 

J7(a, b), J12c None

3 J4(a, b) J4a or J4b or J4c + J1a or 

J1b or J1d or J12c 

Table 6. Favourability indices for the updated, finer resolution Flin

Flon Belt map (NATMAP Shield Margin Project Working Group,

1998). For the geological contacts in the third column, there are a

variety of alternative scenarios represented by the coding scheme.

The “or” indicates that the units in the series are substitutable, and

the “+” refers to a contact between two series of units. For the

favourability 3 index, possible contacts between units include J4a

and J1a, J4a and J1b, J4a and J1d, J4a and J12c, J4b and J1a, J4b

and J1b, J4b and J1d, J4b and J12c, J4c and J1a, J4c and J1b, J4c

and J1d, and/or J4c and J12c.

Geological unit code Geological unit Rock type 

J1a 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Tholeiitic basalt, basaltic andesite, gabbro 

J1b 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Calc-alkaline basalt, basaltic andesite 

J1d 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity 

Basalt, basaltic andesite (geochemical affinity 

unknown) 

J2a 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Ferrobasalt, rhyolite, crystal tuff 

J2b 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Basalt, synvolcanic dykes and sills 

J2c 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Gabbro, ferrogabbro, quartz ferrodiorite sills 

J3a 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Andesite

J4a 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Rhyolite to dacite 

J4b 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Felsic gneiss 

J4c 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Felsic to intermediate gneiss ± garnet 

J5a 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Mafic volcaniclastic rocks (tuff, breccia, wacke)

J5b 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Pillow fragment breccia 

J5c 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity 

Mafic volcaniclastic rocks (heterolithological 

breccia, predominantly mafic) 

J5d 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Undivided mafic volcaniclastic rocks and flows 

J6a 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Intermediate tuff, lapilli tuff, breccia 

J6b 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity 

Intermediate to felsic volcaniclastic rocks and 

flows, derived gneiss 

J7a 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity 

Felsic tuff, lapilli tuff, breccia, heterolithologic 

breccia

J7b 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Dacite tuff, lapilli tuff 

J12c 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity Tonalite, quartz diorite 

J13c 1.92–1.87 Ga volcanic, intrusive, and sedimentary rocks 

of juvenile arc affinity 

Rhyolite, dacite; quartz porphyry, feldspar 

porphyry, quartz-feldspar porphyry 

Table 7. Explanation of geological unit coding scheme for the updated, finer resolution Flin Flon Belt map favourability

indices (NATMAP Shield Margin Project Working Group, 1998).
The highest favourability index includes felsic rocks (rhyolite or dacite) in contact with mafic units (basalt or basaltic

andesite). The proximity to synvolcanic felsic plutons,

considered to be a key element of volcanic-hosted massivesulphide deposit models, is not considered in this study. Table 6

explains the details of the favourability index for the updated,

finer resolution map. Table 7 provides a description of the

coding scheme for individual geological map units.

One caveat of the analysis is that it is focused on surface

geology only. The information used could lead to inaccuracies in the analysis, as the three-dimensional geometry of

lithological units and structures was not addressed.

Flin Flon Belt favourability domains and

target densities

The favourability domains of the old, coarser resolution

( ) θ =OC ; updated, coarser resolution ( ) θ = UC ; and

updated, finer resolution ( ) θ = UF Flin Flon Belt maps are

depicted in Figures 7, 8, and 9. The search unit (projected

surface area extent for a unique exploration target) size of

0.45 km2 for a massive-sulphide exploration target was

adopted from Singer et al. (2001). Information on the known

exploration targets and their distribution in the study area was

compiled within the NATMAP project. In this analysis,

exploration targets are associated with the highest

favourability domain identified within 100 m of the target due

to the shallow to moderate angle at which the mineralized

zones and geological boundaries can lie within the study area.

20

101°30'W

101°30'W

100°30'W

100°30'W

99°30'W

99°30'W

54°30'N

54°30'N

55°00'N

55°00'N

km

Legend

Old, coarser resolution map favourability 1

volcanogenic massive-sulphide mineral

exploration target

Old, coarser resolution map favourabilitiy 1 domain

Old, coarser resolution map favourability 0 domain

0 10

Old, coarser resolution map favourability 0 

volcanogenic massive-sulphide mineral

exploration target

Figure 7. Old, coarser resolution Flin Flon Belt map highlighting volcanic-hosted massive-sulphide

favourability domains (as defined in Tables 3 and 4) and mineral exploration targets. Hydrographic

features (lakes, rivers) are omitted for clarity.

Favourability 

domain of 

the map 

Number of 

search

units 

Number 

of

targets

Target 

density 

 per 0.45 km2

Old, coarser resolution map 

OC0 21247 24 0.0011 

OC1 3636 28 0.0077 

Updated (clipped), coarser resolution map 

UC0 21616 7 0.0003 

UC1 3267 45 0.0138 

Updated (clipped), finer resolution map 

UF0 22155 6 0.0003 

UF1 1008 5 0.0050 

UF2 998 19 0.0190 

UF3 722 22 0.0305 

Table 8. The number of 0.45 km2search units, exploration

targets, and the target density for each favourability

domain per map covering the old, coarser resolution Flin

Flon Belt map map area, calculated according to step 2.
21

103°00'W

103°00'W

102°00'W

102°00'W

101°00'W

101°00'W

100°00'W

100°00'W

54°30'N

54°30'N

55°00'N

55°00'N

Legend

Updated, coarser resolution map favourability 1 

volcanogenic massive-sulphide

mineral exploration target

Updated, coarser resolution map favourability 0 

volcanogenic massive-sulphide

mineral exploration target

Old, coarser resolution map extent

Updated, coarser resolution map favourability 1 domain

Updated, coarser resolution map favourability 0 domain

0 km 20

Figure 8. Updated, coarser resolution Flin Flon Belt map highlighting volcanic-hosted

massive-sulphide favourability domains (as defined in Table 5) and mineral exploration targets. Old,

coarserresolutionmapareaoutlinedinred.Hydrographicfeatures(lakes,rivers)areomittedforclarity.

103°00'W

103°00'W

102°00'W

102°00'W

101°00'W

101°00'W

100°00'W

100°00'W

54°30'N

54°30'N

55°00'N

55°00'N

Legend

Updated, finer resolution map 

favourability 3 volcanogenic massive-sulphide

mineral exploration target

Updated, finer resolution map 

favourability 2 volcanogenic massive-sulphide

mineral exploration target

Updated, finer resolution map 

favourability 1 volcanogenic massive-sulphide

mineral exploration target

Updated, finer resolution map 

favourability 0 volcanogenic massive-sulphide

mineral exploration target

Old, coarser resolution map extent

Updated, finer resolution map favourability 3 domain

Updated, finer resolution map favourability 2 domain

Updated, finer resolution map favourability 1 domain

Updated, finer resolution map favourability 0 domain

0 km 20

Figure 9. Updated, finer resolution Flin Flon Belt map highlighting volcanic-hosted massive-sulphide

favourability domains (as defined in Tables 6 and 7) and mineral exploration targets. Old, coarser resolution

map area outlined in red. Hydrographic features (lakes, rivers) are omitted for clarity.
The target densities by favourability class listed in Table 8

are derived using the method described in step 2. The target

estimates are conservative since they are based on a compilation of deposits and showings from 1998 (not including

exploration targets subsequently identified) and are based on

total (versus explored) area for each favourability domain

(i.e. k f

θ is unknown).

As is obvious from Table 8, the updated maps are more discerning at providing a context for volcanic-hosted massivesulphide mineralization than the old map. This is illustrated by a

higher target density for the favourability 1 domain on the

updated, coarser resolution map (UC1) compared to that for the

old, coarser resolution map (OC1). Comparison of the updated

maps in Table 8 shows that the finer resolution map further supports the delineation of two additional favourability domains

with higher target densities. Consequently, the target densities

for the updated map 0 favourability domains (UF0) are lower

than the target densities for the 0 favourability domains on the

old maps (OC0 and UC0).

Efficiency and productivity of Flin Flon Belt

maps for mineral exploration

The results of applying the first two models for the

minimization of the mineral exploration search (maximizing

exploration efficiency) and the maximization of mineral

exploration productivity for each map age and resolution are

presented in Figure 10 for maps covering the old, coarser resolution map area. As demonstrated above, it is most efficient,

productive, and effective to explore the highest favourability

areas first. This is reflected in the progressively decreasing

slope for segments of the line for a map containing geology that

is interpreted to have more than one favourable domain (e.g.

the line for the updated, finer resolution map on Fig. 10). The

horizontal distance between the graphed lines on Figure 10

represents the number of additional searches required by the

older or coarser resolution maps to yield the same expected

number of targets (and is a measure of the comparative efficiency of the different maps). The vertical distance between

the graphed lines on Figure 10 shows the additional expected

number of targets for the same search effort across the maps

(and is a measure of the comparative productivity of the different maps). The updated, finer resolution map is superior in

both dimensions. This result is consistent with the accepted

geological interpretation for this area and the assumptions

utilized in the authors’ model for an exploration campaign.

A comparison of coarser resolution maps of two different

ages suggests that substantial gains are made from updating a

coarser resolution map. The vertical difference between the

two curves for the coarser resolution maps on Figure 10 portrays up to an 80% increase in expected targets found for a

given number of search unit examinations. If all favourable

land is explored on both maps, the updated, coarser resolution

map is expected to locate 60% more targets than the old,

coarser resolution map (Fig. 10). Therefore exploration companies can expect to achieve 160–180% of the results they

would expect if they had based their exploration campaigns

on the old, coarser resolution map only. Likewise exploration

efficiency is increased due to an up to 44% reduction of

search effort for a given number of expected targets (indicated

by the horizontal difference between the old and updated,

coarser exploration trajectories in Fig. 10). Refining the resolution of a map from the updated, coarser resolution to the

updated, finer resolution map provides for a 2% increase in

the expected total number of targets found by exploring all

22

0

5

10

15

20

25

30

35

40

45

50

0 1000 2000 3000 4000

Optimal expected number of search units examined,

searching from highest to lowest favourability

Optimal expected number of exploration targets

Continuum of expected exploration

outcomes using the old, coarser resolution

map

Continuum of expected exploration

outcomes using the updated, coarser

resolution map

Continuum of expected exploration

outcomes using the updated, finer resolution

map

UF321

UF32

UC1

OC1

UF3

Figure 10. Flin Flon Belt retrospective, mineral-exploration efficiency

and effectiveness using old, coarser resolution; updated, coarser

resolution; and updated, finer resolution maps covering the old map

area. Label OC1 denotes the exploration campaign that examines

favourability 1 domains on the old, coarser resolution map; UC1, the

exploration campaign that examines favourability 1 domains on the

updated, coarser resolution map; UF3, the exploration campaign that

examines favourability 3 domains on the updated, finer resolution map;

UF32 the exploration campaign that examines favourability 3 and 2

domains on the updated, finer resolution map; and UF321, the

exploration campaign that examines favourability 3, 2, and 1 domains

on the updated, finer resolution map.
favourable regions on both maps; however, the efficiency

gains indicated by the horizontal differences across the

range of outcomes are more substantial with a minimum

23% reduction in search effort for a given number of targets.

The most impressive gains in efficiency and productivity

occur when the highest favourability domain (favourability

3) on the updated, finer resolution map is compared to the

highest favourability domain on the updated, coarser

resolution map (favourability 1 domain). Utilization of

the favourability 3 domain on the updated, finer resolution

map results in a 55% reduction of search effort to locate an

expected 22 or fewer targets, and more than doubles the productivity. These results support the hypothesis that updated

geological maps lead to more productive and efficient exploration relative to pre-existing information.

Flin Flon Belt mineral exploration

campaign risk and benefit

The authors use the Flin Flon Belt data with the economic

model efficiency and risk constraints to illustrate the relative

advantages of updated maps and finer resolution maps. The

following possible (retrospective) exploration campaigns are

considered: favourability 1 domains as defined by the old,

coarser resolution map (OC1); favourability 1 domains as

23

Retrospective 

exploration 

campaign 

Number of 

search

units 

Campaign 

target

density per 

0.45 km2

Expected 

number 

of targets 

Old, coarser resolution map 

OC1 3636 0.0077 28 

Updated, coarser resolution (clipped to old map area) 

UC1 3267 0.0138 45 

Updated, finer resolution map (clipped to old map area) 

UF3 722 0.0305 22 

UF32 1720 0.0238 41 

UF321 2728 0.0169 46 

Table 9. Number of search units, target density, and expected

number of targets contained in the exploration campaigns

examined for each available Flin Flon Belt map. Label OC1

denotestheexplorationcampaignthatexaminesfavourability1

domains on the old, coarser resolution map; UC1 denotes the

explorationcampaignthatexaminesfavourability1domainson

the updated,coarser resolution map;UF3 denotes the exploration

campaign that examines favourability 3 domains on the

updated, finer resolution map; UF32 denotes the exploration

campaign that examines favourability 3 and 2 domains on the

updated, finer resolution map; and UF321 denotes the

exploration campaign that examines favourability 3, 2, and 1

domains on the updated, finer resolution map.

0

5000

10 000

15 000

20 000

25 000

30 000

35 000

40 000

45 000

0 0.005 0.010 0.015 0.020 0.025 0.030 0.035

Expected campaign target density

Expected number of search unit examinations

OC1

UC1 UF321 UF32 UF3

Target density (0.005)

Isobenefit curve (46 expected targets)

O

Risk criterion (0.95 probability of

finding at least 10 targets)

Figure 11. Flin Flon Belt exploration campaigns within the context of selected risk

(0.95 probability of finding at least 10 targets) and target density (campaign target

density is at a minimum 0.005) constraints and an isobenefit curve of 46 expected

targets. Label OC1 represents exploration of the favourability 1 domains on the

old, coarser resolution map; UC1 represents exploration of the favourability 1

domains on the updated, coarser resolution map; UF3 represents exploration of

the favourability 3 domains in the updated, finer resolution maps; UF32 represents

exploration of favourability 3 and 2 domains on the updated, finer resolution map;

UF321 represents exploration of the favourability 3, 2, and 1 domains on the

updated, finer resolution map; and O represents exploration of the whole area.
defined by the updated, coarser resolution map (UC1);

favourability 3 domains as defined by the updated, finer resolution map (UF3); favourability 3 and 2 domains as defined

by the updated, finer resolution map (UF32); and

favourability 3, 2, and 1 domains as defined by the updated,

finer resolution map (UF321). This choice of exploration

campaigns is consistent with the above observation that geological map information assists in ordering the examination of

search units from highest to lowest favourability. Each campaign target density, expected number of search unit examinations, and expected number of targets is detailed in Table 9.

In Figure 11 the campaigns are plotted with respect to a

risk and a target density constraint and an isobenefit curve.

Exploration campaign UC1 plots farther away from the risk

constraint than does exploration campaign OC1. Although the

use of a finer resolution map to explore all favourable area

(UF321) versus exploration campaign UC1 has less dramatic

effects in terms of reducing risk, the gain is clearly an

improved efficiency and an improved campaign target

density. Whereas exploration of favourability 3 or

favourability 3 and 2 domains on the finer resolution map are

more resilient to the target density constraint, they are least

resilient to the risk constraint simply because exploration of

less land will yield fewer targets when compared to other,

bigger campaigns. Exploration campaign UF321 is the most

effective campaign as it sits on the highest isobenefit curve.

Whereas exploring the whole map area (represented by ‘O’

in Fig. 11) also yields a result that sits on the isobenefit curve

of 46 targets, the dramatic difference in the number of

searches required to find these targets demonstrates the

value of having the updated map from the perspective of

exploration efficiency and productivity.

In summary, the Flin Flon Belt case study confirms the

utility of the model and supports the underlying assumptions.

The updated maps provide more exploration options, reduce

exploration risk, and improve efficiency and productivity. In

other words, the updated map provides for more cost-effective

exploration. Furthermore, the case study also demonstrates

the productivity and efficiency gains for exploration in using

the finer resolution, updated map as opposed to the coarser

resolution, updated map (as evidenced in Fig. 10 and Table 9).

SOUTH BAFFIN ISLAND CASE STUDY

Introduction

The second case study illustrates the full implementation

of the five-step approach developed in this study and

provides an example of a forward-looking analysis. South

Baffin Island in Nunavut is a frontier region of the eastern

Canadian Arctic without a previous history of significant

mineral exploration or mining. Consequently, the south

Baffin Island case study depends on a crucial geological correlation: that the regional magmatic event, with which

magmatic massive-sulphide mineralization is associated

(St-Onge and Lucas, 1994), led to the formation and isolation of a microcontinent approximately 1.92–1.87 billion

years ago (St-Onge et al., 2000), and is documented both in

the Ni-Cu-PGE-producing Cape Smith Belt of northern

Quebec and in the underexplored, but prospective south

Baffin Island area (case study area; Fig. 6). In other words, the

correlation of the geology on both sides of Hudson Strait as

demonstrated by St-Onge et al. (1999a, b, 2001, 2002) is a

basic premise of the present study, and includes the following

five critical assemblages of rocks from different stages in the

tectonic evolution of the Hudson Strait area:

• The lower plate Archean Superior Craton,

• The 2.04–1.96 billion-year-old Povungnituk Group

(siliciclastic rocks and tholeiitic flood basalts),

• The 1.88–1.87 billion-year-old Chukotat Group (layered

mafic-ultamafic sills and komatiitic to tholeiitic basalts),

• The 1.93–1.88 billion-year-old Lake Harbour Group

(continental clastic wedge and carbonate strata) and the

Blanford Bay assemblage (siliciclastic rocks), and

• The 1.86–1.82 billion-year-old calc-alkaline Narsajuaq

magmatic arc.

The following sections provide information on the ages

and resolutions of available geological maps for the Cape

Smith Belt and south Baffin Island areas, definitions of the

respective favourability indices, and geographic information

system representation of the favourability domains. The

empirical exploration target densities and their statistical

uncertainties are derived for the Cape Smith Belt area and utilized in the subsequent analysis of the south Baffin Island

maps, hinging on the geological equivalence between

regions as outlined above. Similar to the Flin Flon Belt case

study, exploration campaigns are analyzed with respect to

efficiency (and productivity), effectiveness, and risk. In addition, a budget constraint is constructed and the economic

model is solved for various exploration efficiency and risk

conditions. A range of exploration campaigns is then evaluated to determine the possible additional exploration investments stimulated by the production of the updated, finer

resolution maps for the south Baffin Island area. The investment results are then compared to the map production costs

incurred by the GSC to estimate the net economic benefit to

the country of the updated information.

Geological maps

The GSC provided the following maps: the older, coarser

resolution (1:250 000 scale) Cape Smith Belt maps (Taylor,

1982); the updated, finer resolution (1:50 000 scale) maps for

the eastern Cape Smith Belt (St-Onge and Lucas, 1990); the

older, coarser resolution (1:506 000 scale) south Baffin Island

maps (Blackadar, 1967); and the updated, finer resolution

(1:100 000 scale) south Baffin Island maps (St-Onge et al.,

1999b). The relative resolution between old and updated maps

in both the Cape Smith Belt area and the south Baffin Island

24
area is consistent at 5:1. Magmatic massive-sulphide deposits

as a whole are the elements of interest in this case study, and are

relevant to areas of and contacts between sulphidic metasedimentary units and layered ultramafic-mafic sills (St-Onge and

Lucas, 1994), that are found both in the Cape Smith Belt and

the south Baffin Island map areas (St-Onge et al., 1999a,

2002). Map details are summarized in Tables 10 and 11.

The updated, finer resolution maps for south Baffin

Island are comparable to the updated and finer resolution

maps for the Cape Smith Belt because each one of the

updated maps is not simply the result of an incremental technological advance in map making, but, in fact, represents a

new synthesis of geological knowledge and information following new integrated fieldwork in each respective area.

Therefore, in this study, the authors assert an equivalence

of tectonic and lithological context for magmatic massivesulphide mineralization between the Cape Smith Belt and the

south Baffin Island area (St-Onge et al., 1999a), as well as an

equivalence in geological knowledge as contained in

updated map information in both areas (St-Onge and Lucas,

1990; St-Onge et al., 1999b). This allows the present authors

to apply information on the mineral exploration target densities derived from the extensively explored Cape Smith

Belt area to the frontier south Baffin Island area where such

information is not yet available.

Cape Smith Belt favourability domains and

exploration target densities

Four favourability classes were identified for both the finer

and coarser resolution maps available for the Cape Smith Belt.

Favourability in this analysis is based upon the likelihood of

magmatic massive-sulphide targets being hosted by

sulphidic sedimentary rocks, as well as the proximity to key

contacts between units:

• Favourability 0 = geology in search unit is not likely to

contain an exploration target,

• Favourability 1 = geology in search unit is somewhat

favourable for containing an exploration target,

• Favourability 2 = geology in search unit is favourable for

containing an exploration target, and

• Favourability 3 = geology in search unit is most favourable

for containing an exploration target.

Table 12 lists the diagnostic rock units and/or contacts

between units as assigned to each favourability index for the

analysis of the old, coarser resolution Cape Smith Belt geological map( ) θ =OC , and Table 13 provides a description of

the coding scheme for individual geological map units.

Table 14 lists the rock units and contacts allocated to each

favourability index for the Cape Smith Belt updated, finer

resolution maps ( ) θ = UF , and Table 15 details the related

geological coding scheme. The consequent geographic

information system favourability maps, resulting from the

translation of the map rock units and contacts into their

favourability indices for the old, coarser resolution and

updated, finer resolution maps for the Cape Smith Belt, are

shown in Figures 12 and 13. The Raglan panels in the Cape

Smith Belt, which are the focus of the magmatic massive-sulphide

mineralization in this area (St-Onge and Lucas, 1994), were

captured manually using a geographic information system.

25

Coverage Format Scale Area (km2

)

Coarser resolution 

bedrock geology Polygon 1:250 000 11 349 

Finer resolution 

bedrock geology Polygon 1:50 000 11 349 

Magmatic

massive-sulphide 

showings 

Point n/a n/a 

n/a = not applicable 

Table 10. Geospatial data used for the Cape Smith

Belt area.

Coverage Format Scale Area (km2

)

Coarser resolution 

bedrock geology Polygon 1:506 880 31 256 

Finer resolution 

bedrock geology Polygon 1:100 000 31 256 

Table 11. Geospatial data used for south Baffin Island

case study area.

Favourability index Units Contacts 

0 All other units None 

1 Asc, Aqz, Ash, 

Asl, Aub, Agb None

2 None Aub or Agb + Asc or 

Aqz or Ash or Asl 

3 None Same as favourability 

2, but in Raglan panels1

1

Raglan panels are geographically located in the centre of the 

Cape Smith Belt, and have a different tectonostratigraphic 

context that leads to a higher favourability index (St-Onge 

and Lucas, 1994). 

Table 12. Favourability indices for the Cape Smith Belt

old,coarserresolutionmaps(Taylor,1982).Forthegeological

contacts in the third column, there are a variety of

alternative scenariosrepresentedbythecodingscheme. The

“or” indicates that the units in the series are substitutable,

and the “+” denotes a contact between two series of

units. For example in the favourability 2 domain, possible

contacts between units include Aub and Asc, Aub and

Aqz, Aub and Ash, Aub and Asl, Agb and Asc, Agb and

Aqz, Agb and Ash, and/or Agb and Asl.

Geological unit code Geological unit Rock type 

Asc Povungnituk Group Schist of 

sedimentary origin 

Aqz Povungnituk Group Quartzite 

Ash Povungnituk Group Shale 

Asl Povungnituk Group Slate 

Aub Chukotat Group Ultramafic rock 

Agb Chukotat Group Gabbro 

Table 13.Explanation of geological unit coding scheme for

the Cape Smith Belt old, coarser resolution map favourability

indices (Taylor, 1982).
26

Favourability index Units Contacts 

0 All other units None 

1 PPse, PPv, Ga, 

Pe None

2 None Ga or Pe + PPse or 

PPv 

3 None 

Same as favorability 

2, but in Raglan 

panels1

1

Raglan panels are geographically located in the centre of the 

Cape Smith Belt, and have a different tectonostratigraphic 

context that leads to a higher favourability index (St-Onge 

and Lucas, 1994). 

Table 14. Favourability indices for the updated, finer

resolution maps, Cape Smith Belt (St-Onge and Lucas,

1990). For the geological contacts in the third column,

there are a variety of alternative scenarios represented

by the coding scheme. The “or” indicates that the units in

the series are substitutable, and the “+” denotes a

contact between two series of units. For example, in the

favourability 2 index, possible contacts between units

are Ga and PPse, Ga and PPv, Pe and PPse, and/or Pe

and PPv.

Geological unit code Geological unit Rock type 

PPse Povungnituk Group

Semipelite, pelite, 

micaceous 

sandstone, 

ironstone, dolomite 

PPv Povungnituk Group

Volcaniclastic 

sedimentary rock, 

minor sandstone 

Ga Chukotat Group 

Gabbro, layered 

peridotite-gabbro 

sills 

Pe Chukotat Group 

Peridotite, layered 

peridotite-gabbro 

sills 

Table 15. Explanation of geological unit coding scheme for

the updated, finer resolution map favourability indices,

Cape Smith Belt (St-Onge and Lucas, 1990).

74°00'W

74°00'W

73°00'W

73°00'W

72°00'W

72°00'W

61°00'N

61°00'N

61°30'N

61°30'N

Legend

Old, coarser resolution map 

favouability 3 magmatic massive-sulphide 

mineral exploration target

Old, coarser resolution map 

favourability 1 magmatic massive-sulphide

mineral exploration target

Old, coarser resolution map 

favourability 0 magmatic massive-sulphide

mineral exploration target

Old, coarser resolution map favourability 3 domain

Old, coarser resolution map favourability 1 domain

Old, coarser resolution map favourability 0 domain

0 km 20

Old, coarser resolution map favourability 2 domain

Figure 12. Old, coarser resolution Cape Smith Belt map highlighting magmatic massive-sulphide

favourability domains (as defined in Tables 12 and 13) and mineral exploration targets. Hydrographic

features (lakes, rivers) are omitted for clarity.
27

74°00'W

74°00'W

73°00'W

73°00'W

72°00'W

72°00'W

61°00'N

61°30'N

61°00'N

61°30'N

Legend

Updated, finer resolution map 

favourability 3 magmatic massive-sulphide

mineral exploration target

Updated, finer resolution map 

favourability 2 magmatic massive-sulphide

mineral exploration target

Updated, finer resolution map 

favourability 1 magmatic massive-sulphide

mineral exploration target

Updated, finer resolution map 

favourability 0 magmatic massive-sulphide

mineral exploration target

Updated, finer resolution map favourability 3 domain 

Updated, finer resolution map favourability 2 domain

Updated, finer resolution map favourability 1 domain

Updated, finer resolution map favourability 0 domain

0 km 20

Figure 13. Updated, finer resolution Cape Smith Belt map highlighting magmatic massive-sulphide

favourability domains (as defined in Tables 14 and 15) and mineral exploration targets. Hydrographic

features (lakes, rivers) are omitted for clarity.

Favourability 

index

Number of 4 km2

 search 

units contained in the 

favourability domain 

Number of 

targets

Mineral exploration target 

density per 4 km2

(90% confidence interval) 

0 1870 5 0.0026

(0.0007–0.0046) 

1 410 15 0.0366

(0.0213–0.0518) 

2 106 7 0.0662

(0.0264–0.1059) 

3 26 10 0.3774

(0.2225–0.5322) 

Table 16. Empirical mineral exploration target densities for the favourability domains on

the updated, finer resolution Cape Smith Belt map.
In order to factor in the change of map scale in the present

analysis, the delineation of a zone of contact between two

units is defined as being 100 m on either side of the contact

line for the finer resolution maps and 500 m on either side of

the contact line for the coarser resolution maps. Given the use

of field systems to capture geological information digitally,

and the geographic information system environment that

characterizes the map production process from the initial

field compilation to the final publication stage for updated

maps, a ±100 m buffer for contacts on a 1:50 000 scale map

(or a 1:100 000 scale map, see below) is thought to be quite

realistic.

The expected surface extent of magmatic massive-sulphide

exploration targets in the Cape Smith Belt varies between

0.1 km2 (approximately 300 m x 300 m) and 0.3 km2

(approximately 550 m x 550 m) (Barnes et al., 1992); however, mineralization within about 800 m is treated as one

mineral deposit (Avramtchev, 1982), thus uniquely defining areas between 3.6 km2 (1900 m x 1900 m) and 4.6 km2

(2150 m x 2150 m). Consequently, the present authors use

4 km2 as the search unit area to define a typical magmatic

massive-sulphide mineral target in the vicinity of Hudson

Strait. The full sample of 37 magmatic massive-sulphide

showings and deposits is utilized to derive the empirical exploration target densities for each favourability index within the

eastern Cape Smith Belt and populate Tables 16 and 17 for the

updated, finer resolution and old, coarser resolution maps,

respectively. The target densities in Tables 16 and 17 will

appear higher than might be expected since the mineral exploration target densities are calculated with respect to the search

unit as defined above and which, for the Cape Smith Belt, has

a surface area of 4 km2.

As can be seen in Table 16, the estimated mineral exploration target density for the updated, finer resolution Cape

Smith Belt maps increases with each higher favourability

index. Whereas the expected mineral exploration target density for the favourability 2 domain is almost twice that of the

expected exploration target density for the favourability 1

domain, the expected target density for the favourability 3

domain is an order of magnitude greater than that of the

favourability 2 domain. Likewise, the expected exploration

target density for the favourability 1 domain is an order of

magnitude greater than that of the favourability 0 domain.

Considering the full range, expected exploration target densities for favourability 0 versus favourability 3 domains are

distinguished by two orders of magnitude. In contrast, the

exploration target density estimates for the old, coarser resolution favourability 3 and 0 domains only differ by one order

of magnitude and the expected target densities forfavourability

1 and favourability 0 indices are only weakly distinguishable.

Compared to the updated, finer resolution map, it is evident

that the old, coarser resolution map identifies only a fraction

of favourable areas.

South Baffin Island favourability domains

and adopted exploration target densities

Four favourability classes were identified for both the

finer and coarser resolution maps available for the south

Baffin Island area. As was the case for the eastern Cape

Smith Belt area, favourability for the south Baffin Island

area in this analysis is based upon the likelihood of

magmatic massive-sulphide targets being hosted by

sulphidic sedimentary rocks, as well as the proximity to key

contacts between units:

• Favourability 0 = geology in search unit is not likely to

contain an exploration target,

• Favourability 1 = geology in search unit is somewhat

favourable for containing an exploration target,

• Favourability 2 = geology in search unit is favourable for

containing an exploration target, and

• Favourability 3 = geology in search unit is most favourable

for containing an exploration target.

Table 18 lists the diagnostic rock units and/or contacts

between units as assigned to each favourability index for the

analysis of the old, coarser resolution south Baffin Island

geological map( ) θ =OC and Table 19 provides a description

of the coding scheme fwhooly cowor individual geological

map units.

28

Favourability 

index

Number of 4 km2

 search 

units contained in the 

favourability domain 

Number of 

targets

Mineral exploration target 

density per 4 km2

(90% confidence interval) 

0 2156 25 0.0116

(0.0078–0.0153) 

1 238 9 0.0378

(0.0175–0.0580) 

2 11 0 0 

3 7 3 0.4138

(0.1129–0.7146)*

* Sample size is not large enough for the confidence interval estimate to be reliable. 

Table 17. Empirical mineral exploration target densities for the favourability domains on

the old, coarser resolution Cape Smith Belt map.
Table 20 lists the rock units and contacts allocated to each

favourability index for the south Baffin Island updated, finer

resolution maps ( ) θ = UF and Table 21 explains the related

geological coding scheme. The derivative geographic information system favourability maps, resulting from the translation of

the map rock units and contacts into their favourability indices

for the old, coarser resolution and updated, finer resolution

maps for the south Baffin Island area, are shown in Figures

14 and 15. In order to factor in the change of map scale in

the present analysis, the delineation of a zone of contact

between two units is defined as being 100 m on either side of

the contact line for the finer resolution maps and 500 m for the

coarser resolution maps. Given the use of field systems to

capture geological information digitally, and the geographic

information system environment that characterizes the map

production process from the initial field compilation to the

final publication stage for updated maps, a ± 100 m buffer for

contacts on a 1:100 000 scale map (or a 1:50 000 scale map,

see above) is thought to be quite realistic.

In order to document how the updated, finer resolution

map allows for a better definition and resolution of

favourability domains when compared to the old, coarser

resolution map in the south Baffin Island area, the higher

favourability domains on the old, coarser resolution map

(favourability indices 1 and 3; Table 18) are overlain on the

favourability domains on the updated, finer resolution map

(favourability indices 0–3; Table 20) in Figure 16. Juxtaposition of the two sets of favourability domains illustrates the

extent of higher favourability (indices 2 and 3) domains on

the updated, finer resolution map not captured by a favourable domain on the old, coarser resolution map. Over half of

updated, finer resolution map favourability domains (indices

1–3) lie outside the boundaries of the old, coarser resolution

map favourability domains (indices 1 and 3).

29

Favourability index Units Contacts 

0 All other units None 

1 5, 6, 7, 10, 15 None 

2 None None 

3 None 15 + 5 or 6 or 7 or 10

Table 18. Favourability indices for the south Baffin

Island old, coarser resolution map (Blackadar,

1967). For the geological contacts in the third

column, there are a variety of alternative scenarios

represented by the coding scheme. The “or”

indicates that the units in the series are substitutable,

and the “+” denotes a contact between two series of

units. For example in the favourability 3 index,

possible contacts between units are 15 and 5, 15

and 6, 15 and 7, and/or 15 and 10.

Geological unit code Geological unit Rock type 

5 Lake Harbour Group Sillimanite gneiss 

and schist 

6 Lake Harbour Group Rusty paragneiss 

7 Lake Harbour Group

Garnet-biotitequartz-feldspar 

gneiss

10 Lake Harbour Group Quartzite 

15 n/a Utramafic rocks 

n/a = not applicable 

Table 19. Explanation of geological unit coding scheme for

the south Baffin Island old, coarser resolution map

(Blackadar, 1967) favourability indices.

Geological 

unit code 

Geological 

unit Rock type 

PLHp Lake Harbour 

Group 

Dominantly psammite, 

semipelite, quartzite, 

minor marble, calcsilicate rocks 

PBBq Blanford Bay 

Assemblage 

Feldspathic quartzite, 

pelite, semipelite 

PBBm Blanford Bay 

Assemblage 

Metaperidotite,

metagabbro, 

metapyroxenite 

PLHd Lake Harbour 

Group 

Metaleucodiorite, 

metatonalite

PLHm Lake Harbour 

Group 

Metagabbro, 

amphibolite

PLHu Lake Harbour 

Group 

Metaperidotite,

metapyroxenite, 

metadunite

Table 21. Explanation of geological unit coding

scheme for the updated, finer resolution Baffin Island

map favourability indices (St-Onge et al., 1999b).

Favourability index Units Contacts 

0 All other units None 

1 PLHp, PBBq, 

PBBm, PLHd None

2 PHLm, PLHu PLHp or PBBq + 

PBBm or PLHd 

3 None PLHp + PLHm or 

PLHu

Table 20. Favourability indices for the updated, finer

resolution south Baffin Island maps (St-Onge et al., 1999b).

For the geological contacts in the third column, there are a

variety of alternative scenarios represented by the coding

scheme. The “or” indicates that the units in the series are

substitutable, and the “+” denotes a contact between two

series of units. For example in the favourability 2 index,

possible contacts between units are PLHp and PBBm,

PLHp and PLHd, PBBq and PBBm, and/or PBBq and

PLHd.
30

Water 

Legend

Old, coarser resolution map 

favourability 3 domain

Old, coarser resolution map 

favourability 1 domain

0 km 25

72°00'W

72°00'W

70°00'W

70°00'W 68°00'W

68°00'W

62°00'N 62°00'N

63°00'N 63°00'N

64°00'N 64°00'N

Old, coarser resolution map 

favourability 0 domain

Figure 14. Old, coarser resolution south Baffin Island map highlighting magmatic massive-sulphide

favourability domains (as defined in Tables 18 and 19).
31

72°00'W

72°00'W

70°00'W

70°00'W 68°00'W

68°00'W

62°00'N 62°00'N

63°00'N 63°00'N

64°00'N 64°00'N

Updated, finer resolution map 

favourability 2 domain

Legend

Water 

Updated, finer resolution map

favourability 3 domain

Updated, finer resolution map 

favourability 1 domain

Updated, finer resolution map

favourability 0 domain

0 km 25

Figure 15. Updated, finer resolution south Baffin Island map highlighting magmatic massive-sulphide

favourability domains (as defined in Tables 20 and 21).
32

72°00'W

72°00'W

70°00'W

70°00'W 68°00'W

68°00'W

62°00'N 62°00'N

63°00'N 63°00'N

64°00'N 64°00'N

Legend

Updated, finer resolution map 

favourability 2 domain

Water 

Updated, finer resolution map

favourability 3 domain

Updated, finer resolution map 

favourability 1 domain

Old, coarser resolution map 

favourability 1 and 3 domains 

Updated, finer resolution map

favourability 0 domain

0 km 25

Figure 16. South Baffin Island old, coarser resolution favourability map overlain onto updated, finer

resolution favourability map.
As previously outlined, the equivalence of the lithological

context for magmatic massive-sulphide mineralization in the

Cape Smith Belt and the south Baffin Island areas (St-Onge et

al., 1999a), as well as the equivalence in geological knowledge as contained in updated map information for both areas

(St-Onge and Lucas, 1990; St-Onge et al., 1999b), allows the

present authors to apply information on mineral exploration

target densities derived from the extensively explored Cape

Smith Belt area to the frontier south Baffin Island area where

such information is not yet available. Consequently, the

empirical mineral exploration target densities and statistics

derived for the various Cape Smith Belt favourability

domains (Table 16, 17) are adopted for the south Baffin

Island analysis and are presented in Tables 22 and 23. The

number of search units contained in each south Baffin Island

favourability domain is calculated and utilized to yield the

estimate of expected number of targets per favourability

domain shown in Tables 22 and 23. As can be noted in Table

17, the Cape Smith Belt analysis did not provide an estimate

for the mineral exploration target density for favourability 2

domains on the old, coarser resolution map; however, this

was of no consequence for the present analysis, as the

assignment of favourability domains to the old, coarser

resolution map for south Baffin Island does not yield

favourability 2 domains (Table 18).

The updated, finer resolution map and the old, coarser resolution map do not predict the same expected number of mineral exploration targets for the area as a whole (sum of the

fourth column in Tables 22 and 23). The predictions would

have been consistent if the same proportions of each

favourable domain on the coarse resolution and finer resolution Cape Smith Belt maps had been preserved on the south

Baffin Island maps. The main source of the discrepancy is

that relatively less favourable 2 and 3 land is prescribed on the

updated, finer resolution south Baffin Island map than its

Cape Smith Belt counterpart, producing a relatively lower

estimate of targets for the south Baffin Island updated, finer

resolution map. In addition, the south Baffin Island coarser

resolution map represents a disproportionate amount of

favourability 1 domain land, that is partially compensated for

by relatively less favourability 3 domain land, but still results

in a relatively higher estimate of targets for the south Baffin

Island coarser resolution map. Despite the greater number of

total targets predicted by the old, coarser resolution south

Baffin Island map, the updated, finer resolution south Baffin

Island map promises to locate more targets within

favourability domains 1 to 3 than does the old, coarser resolution map within favourability domains 1 and 3.

Mineral exploration efficiency and

productivity of south Baffin Island maps

Tables 24 and 25 present the results of the authors’ first

two models that minimize mineral exploration search unit

examinations (maximizing exploration efficiency) for given

numbers of exploration targets, and maximize mineral

exploration productivity for given numbers of search unit

examinations for the various south Baffin Island maps.

Figure 17 summarizes the results across the continuum of

possible exploration outcomes.

33

South Baffin 

Island

favourability index 

Number of 

south Baffin 

Island 4 km2

search units 

Expected target density per 

4 km2

 (extrapolated from 

Cape Smith Belt analysis) 

Expected number of 

south Baffin Island 

mineral exploration 

targets

0 6885 0.0026 19 

1 1514 0.0366 55 

2 121 0.0662 8 

3 40 0.3774 15 

Table 22. South Baffin Island updated, finer resolution map search statistics using the Cape

Smith Belt updated, finer resolution map mineral exploration target densities.

South Baffin 

Island

favourability index 

Number of 

south Baffin 

Island 4 km2

search units 

Expected target density per 

4 km2

 (extrapolated from 

Cape Smith Belt analysis) 

Expected number of 

south Baffin Island 

mineral exploration 

targets

0 7141 0.0116 83

1 1414 0.0378 53 

2 0 0 0 

3 4 0.4138 1–2

Table 23. South Baffin Island old, coarser resolution map search statistics using the Cape

Smith Belt old, coarser resolution map mineral exploration target densities.
Results for model 1: minimizing

the search area

Table 24 presents the results of minimizing the number of

search unit examinations to yield a selected expected target

potential, t, by applying model 1 to the south Baffin Island

area. By systematically searching selections of successively

higher to lower favourability domains as defined by the

updated, finer resolution map and the old, coarser resolution

map, the number of search units required in each case can be

calculated and compared for the two maps of different ages

and scales.

As can be seen in Table 24, the number of search unit

examinations needed using the old, coarser resolution map

ranges from 1.4 times to 9 times greater than the expected

number of search unit examinations needed using the

updated, finer resolution map. The larger ratio occurs when

the favourability 3 domain is searched on the updated, finer

resolution map compared to searching the favourability 3

domain and a fraction of the favourability 1 domain on the

old, coarser resolution map. The smaller ratio occurs when

both maps are used to explore all favourable domains. When

the old, coarser resolution map runs out of favourable

domains, the old map search has to continue into the

favourability 0 domain in order to reach the stated expected

target potential, thus significantly increasing the ratio of old

map to updated map search unit examinations.

Results for model 2: maximizing the

mineral target potential

Exploring a fixed number of search areas, s, on both the

old, coarser resolution map and the updated, finer resolution

map yields different expected numbers of targets, as per

Table 25.

The expected target discovery for a fixed number of

search unit examinations (a measure of productivity) using

the updated, finer resolution map ranges from 1.25 times to 5

times more than the results using the old, coarser resolution

map (Table 25). The larger ratio occurs for 40 search unit

examinations when the updated, finer resolution map

favourability 3 domain is examined and the old, coarser resolution map favourability 3 and a fraction of the favourability 1

domain are examined (Table 25). The smaller ratio occurs

when favourability domains 1–3 are searched on both maps

(the number of search units equal 1418 and 1675 in Table 25).

These conditions for the biggest and smallest differences (in

terms of ratios of expected results) between the two maps

mirror those observed for the first model.

The two maps are compared in terms of exploration efficiency and productivity across all optimal exploration

outcomes involving favourability domains 3, 2, and 1 in

Figure 17. Since the optimal solution to models 1 and 2

explores the highest favourability areas first, the slope of the

line segments for a map containing geology that is interpreted to have more than one favourable domain (e.g. the

34

Expected 

target

potential, 

t

Updated, finer 

resolution map 

campaign: expected 

number of searches 

Old, coarser 

resolution map 

campaign: expected 

number of searches 

15 UF3: 40 OC3q: 357 

23 UF32: 161 OC3q

:569

55 UF32q: 1033 OC31: 1418 

78 UF321: 1675 OC310q

: 3395 

Subscript q denotes that a fraction of the lowest favorability class 

is explored. 

Table 24. Solution to minimizing the search unit

examinations utilizing updated, finer resolution and old,

coarser resolution maps for south Baffin Island. Label OC3

denotes the exploration campaign that examines favourability

3 domains on the old, coarser resolution map; OC31 denotes

the exploration campaign that examines favourability 3 and

1 domains on the old, coarser resolution map; OC310

denotes the exploration campaign that examines

favourability 3, 1, and 0 domains on the old, coarser

resolution map; UF3 denotes the exploration campaign that

examines favourability 3 domains on the updated, finer

resolution map; UF32 denotes the exploration

campaign that examines favourability 3 and 2 domains

on the updated, finer resolution map; and UF321 denotes

the exploration campaign that examines favourability 3, 2,

and 1 domains on the updated, finer resolution map.

Number 

of

searches,

s

Updated, finer 

resolution map 

campaign: expected 

number of targets 

Old, coarser 

resolution map 

campaign: expected 

number of targets 

40 UF3: 15 OC3q

: 3 

161 UF32: 23 OC3q

: 8 

1418 UF32q: 69 OC31: 55 

1675 UF321: 78 OC310q

: 58 

Subscript q denotes that a fraction of the lowest favorability 

class is explored. 

Table 25. Solution to maximizing the target potential,

using the updated, finer resolution and the old, coarser

resolution maps for south Baffin Island. Label OC3

denotes the exploration campaign that examines

favourability 3 domains on the old, coarser resolution

map; OC31 denotes the exploration campaign that

examines favourability 3 and 1 domains on the old,

coarser resolution map; OC310 denotes the

exploration campaign that examines favourability

3, 1, and 0 domains on the old, coarser resolution

map; UF3 denotes the exploration campaign that

examines favourability 3 domains on the updated, finer

resolution map; UF32 denotes the exploration

campaign that examines favourability 3 and 2 domains on

the updated, finer resolution map; and UF321 denotes the

exploration campaign that examines favourability 3, 2, and

1 domains on the updated, finer resolution map.
line for the updated, finer resolution map on Fig. 17) progressively decreases. The decreasing positive slope of the

updated, finer resolution map exploration campaign exhibits increasing expected target discovery at the expense of

efficiency, when expanding the search into lower

favourability domains. The horizontal distance between the

graphed lines on Figure 17 represents the number of additional search unit examinations required by the older, coarser

resolution map to yield the same expected number of targets

as the updated, finer resolution map (and is a measure of the

relative efficiency of the different maps). The vertical distance between the graphed lines on Figure 17 shows the

additional expected number of targets for the same search

effort across the maps (and is a measure of the comparative

productivity of the different maps). When all favourable

domains are examined on both maps the analysis projects that

the updated, finer resolution map enables a 40% increase in

the number of targets found and a 27% reduction in search

effort relative to the old, coarser resolution map exploration

outcome.

South Baffin Island exploration

economic model

In the Flin Flon Belt case study, exploration campaigns

were plotted against a target density, a risk constraint, and an

isobenefit curve. The development of a budget constraint for

mineral exploration in the south Baffin Island area is the point

of departure from the preceding Flin Flon Belt analysis. The

derivation of the constraints for the south Baffin Island economic model is explained below, followed by analysis of

exploration campaign feasibility and preference.

South Baffin Island economic

model constraints

Figure 18 illustrates the target density, risk, and budget

constraints utilized for the south Baffin Island economic

model. Since this area is a frontier region in terms of mineral

exploration, the authors assume that mineral explorers will

demand to be well assured about the likelihood of success of

locating a minimum number of targets to justify the set-up

costs of going into a new region. For example, the authors

assume the risk criterion for an exploration campaign will

require a probability of 0.95 of finding at least 10 targets,

although later the minimum number of targets are allowed to

vary. The target density constraint is set at E = 0.02 and again

is later varied to show its influence on feasible and preferred

exploration campaigns.

At the time of this study, mineral exploration of the south

Baffin Island region was very recent and of a reconnaissance

nature (as stated in the ‘Introduction’, south Baffin Island

could be considered devoid of any previous history of significant mineral exploration or mining). Consequently, the costs

associated with a full-scale exploration campaign were not

publicly known. To populate the budget constraint, the

authors describe a hypothetical four-year exploration campaign below with the expert knowledge of Natural Resources

Canada (D.J. Scott, pers. comm., 2000). This campaign

assumes that exploration is being conducted in a so-called

greenfield or frontier region of Baffin Island, Nunavut.

Year 1: acquire 800 000 ha for CAN$750 000.

• Phase 1: staking and ground acquisition based on an interpretation of the mineral potential associated with geological elements and units as documented on newly released

government maps.

35

UF321

0

10

20

30

40

50

60

70

80

90

0 200 400 600 800 1000 1200 1400 1600 1800

Optimal expected number of search unit examined,

searching from highest to lowest favourability

Optimal expected number of exploration targets

OC3

UF32

UF3

OC31

Continuum of exploration

outcomes using the updated,

Finer resolution map

Continuum of exploration

outcomes using the old,

coarser resolution map

Figure 17. Results of models 1 and 2 for the south Baffin

Island maps. Label OC3 denotes the exploration campaign

that examines favourability 3 domains on the old, coarser

resolution map; OC31 denotes the exploration campaign

that examines favourability 3 and 1 domains on the old,

coarser resolution map; UF3 denotes the exploration

campaign that examines favourability 3 domains on the

updated, finer resolution map; UF32 denotes the

exploration campaign that examines favourability 3 and 2

domains on the updated, finer resolution map; and UF321

denotes the exploration campaign that examines

favourability 3, 2, and 1 domains on the updated, finer

resolution map.
• Phase 2: reconnaissance geology plus geochemical sampling, leading to prioritization for geophysical surveys

(ground and airborne magnetic and electromagnetic surveys).

• Phase 3: geophysical surveys leading to identification of

drill targets. Phase 3 leads to the prioritization of 15 anomalies for drill testing. Refine area to 60% of original land

position.

Year 2: commence drilling on geophysical targets.

• Using newly released government maps, repeat phases 1–3

of year 1.

• Identify 15 additional drill targets for testing.

• Refine land position to 20% of original area.

Year 3: continue drilling on geophysical targets

• Drill year 2 anomalies and define at least one anomalous

intersection from year 1 or year 2.

• Repeat phases 2 and 3 from year 1, but without additional

government map information.

• Identify 10 new drill targets for testing in year 4. Refine

land position to 10% of original area.

Based upon these activities, the exploration project

budget would be approximately CAN$10.5 million with the

specific exploration investment being variable with respect

to both search area examined and mineral targets identified.

Search-area–dependent exploration costs (e.g. ground

acquisition, geological reconnaissance, geochemical

sampling, ground and airborne magnetic testing, and

electromagnetic surveys) were extracted from the above

budget and determined to be CAN$572/km2 or

CAN$2288/search unit of 4 km2. The assumed expected

investment required to explore a target (follow-through

drill testing) is CAN$147 875/target. These numbers are

used to produce the budget constraint in Figure 18.

Analysis of south Baffin Island

exploration campaigns

Figure 18 also provides the context to discuss the various

constraints faced by the mineral exploration geologist.

Exploration campaigns that explore all or nothing of a

favourability domain in descending order are described in

Table 26 and plotted in Figure 17. By definition, favourability

domains characterized by higher target densities require

fewer searches to find a minimum number of targets, and consequently, to achieve target exploration success; however, for a

0.95 probability of success and minimum number of 10 targets,

the risk criterion (red line on Fig. 18) eliminates the old, coarser

resolution map favourability 3 domain (OC3) as a stand-alone

campaign, despite its high target density, due to the small number of search units defined within that favourability domain. In

fact, the old, coarser resolution map favourability 3 exploration

campaign does not satisfy the risk criterion requiring a probability

of 0.95 of finding at least one target.

36

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0 0.1 0.2 0.3 0.4 0.5 0.6

Expected campaign target density

Number of campaign search unit examinations

Target density (0.02)

Exploration budget

Isobenefit curve (55 expected targets)

Isobenefit curve (78 expected targets)

UF321

OC31

UF32

UF3 OC3

Risk criterion (0.95 probability of

finding at least 10 targets)

Figure 18. Economic model for the south Baffin Island maps

illustrating exploration campaigns in the context of risk (0.95

probability of finding at least 10 targets), target density

(at least 0.02) and budget (described in the text) constraints,

and isobenefit curves (for 55 and 78 expected targets). Label

OC3 denotes the exploration campaign that examines

favourability 3 domains on the old, coarser resolution map; OC31

denotes the exploration campaign that examines favourability

3 and 1 domains on the old, coarser resolution map; UF3

denotes the exploration campaign that examines favourability 3

domains on the updated, finer resolution map; UF32 denotes

the exploration campaign that examines favourability 3 and 2

domains on the updated, finer resolution map; and UF321

denotes the exploration campaign that examines favourability

3, 2, and 1 domains on the updated, finer resolution map.
If the campaign target density constraint (blue line on Fig. 18)

is moved to the right (effectively requiring the successful

identification of a greater number of expected exploration

targets per area searched) the constraint becomes more

difficult to satisfy and first eliminates the exploration campaign based on the old, coarser resolution map and focused on

favourability 3 and 1 domains (OC31). A furthermovement of

the campaign target density makes the exploration

campaign based on the updated, finer resolution map and

focused on favourability 3, 2, and 1 domains (UF321) unattractive to the exploration investor. It is the first exploration

campaign based on the updated map depicted to become

infeasible; however, the updated, finer resolution map offers

alternative, more focused exploration options that remain

attractive and satisfy higher campaign target density requirements. For example, exploration campaigns based on the

updated, finer resolution map and focused on favourability 3

domains (UF3) or favourability 3 and 2 domains (UF32)

would be the more preferred feasible option with respect to target density; but the UF3 and UF32 campaigns cover more limited areas such that they are the second and third campaigns to

get culled by the risk constraint if the minimum number of targets and/or the probability of finding the minimum number of

targets are increased. The UF321 exploration campaign is the

most resilient option in terms of more demanding risk

constraints.

A continuum of exploration campaigns exists between the

campaigns plotted in Figure 18 that partially explore the lowest favourability domain of the campaign. It can be shown

that the continuum of exploration campaign options provided

by the coarser resolution map always lies beneath those of the

updated, finer resolution map, displaying consistently the

greater sensitivity of old, coarser resolution map campaigns

to the risk and target density constraints.

If budget constraint is the determining factor (green line

on Fig. 18) then the exploration campaigns based on the finer

resolution map will be cut into before those based on the old,

coarser resolution map; however, exploration campaigns

based on the finer resolution map will still reap more benefits

(find more targets) than campaigns based on the alternative

old, coarser resolution map. For the constraints depicted in

Figure 18, the UF321 exploration campaign provides the

greatest expected benefit of 78 expected targets versus 55

expected targets from the OC31 exploration campaign.

Preferred solution

Leaving the budget constraint aside, the authors studied

the effects of the campaign target density and risk constraints

on the viability of exploration campaigns suggested by maps

of different age and resolution. The authors sought a range

of exploration outcomes to illustrate the decision criteriadependent value of an updated, finer resolution map. Towards

this goal the economic model (equation 3) is employed with an

additional simplifying constraint that binds the decision variables to search all or nothing of a favourability domain:

xx n ff f

θθ θ ( ) − = 0

Table 27 reports the results of the simplified economic

model regarding the number of search unit examinations and

the required investment for each selected possible optimal

exploration campaign. Two exploration campaigns UF3 and

UF32 are suggested by the updated, finer resolution map for

higher values of E (minimum campaign target density) and

lower values of t (minimum expected number of exploration

targets assured with 0.95 probability) in the south Baffin

Island area. Since there is no feasible, old, coarser resolution

map campaign for the conditions satisfied by these campaigns,

the updated, finer resolution map generates CAN$2.3 million

and CAN$3.7 million exploration investment, respectively,

whereas the old, coarser resolution map is not expected to attract

any exploration investment at all.

In the fourth row of Table 27 the conditions are such that,

again, the old map does not stimulate investment whereas

the exploration campaign based on the updated, finer resolution map and focusing on favourability 1, 2, and 3 domains

(UF321) satisfies the constraints. This set of decisionmaking criteria highlight the capacity of the updated, finer

resolution map stimulating as much as CAN$15 million in

exploration investment (that would not have occurred

without publication of the updated, finer resolution map).

As shown in the third row of Table 27, for one set of determining conditions investigated (expected number of exploration

targets≤ 44 and minimum campaign target density≤ 0.038) the

old, coarser resolution map is able to stimulate investment with a

campaign based on favourability domains 3 and 1 (OC31). For

37

Exploration 

campaign 

Number 

of search 

units 

Campaign 

target

density per 

4 km2

Expected 

number 

of targets 

Old, coarser resolution map 

OC3 4 0.414 1–2 

OC31 1418 0.039 53 

Updated, finer resolution 

UF3 40 0.377 15 

UF32 161 0.143 23 

UF321 1676 0.047 78 

Table 26. Number of search units examined, target

density, and expected number of targets for

exploration campaigns derived from the south Baffin

Island maps. Label OC3 denotes the exploration

campaign that examines favourability 3 domains on

the old, coarser resolution map; OC31 denotes the

exploration campaign that examines favourability 3

and 1 domains on the old, coarser resolution map;

UF3 denotes the exploration campaign that

examines favourability 3 domains on the updated,

finer resolution map; UF32 denotes the exploration

campaign that examines favourability 3 and 2

domains on the updated, finer resolution map; and

UF321 denotes the exploration campaign that

examines favourability 3, 2, and 1 domains on the

updated, finer resolution map.
the same conditions, the UF321 campaign based on the updated,

finer resolution map stimulates an additional four million dollars

of investment. For all other determining conditions investigated,

the old, coarser resolution map does not stimulate investment

(either because t or E is too high).

The promise of success and the expected efficiency of an

exploration campaign focused on the favourability 3 domain

of the updated, finer resolution map (UF3) is unmatched —

inviting prospectors to a new region, backed up with more

potential from finer resolution, favourability 2 and 1

domains. In contrast, exploration of the favourability 3

domain on the old, coarser resolution map (OC3) is risky and

indicates few targets (i.e. Fig. 18). The mineral exploration target densities of the favourability 1 area on the old, coarser resolution map would not seem impressive enough to vie for

frontier competition (Table 27). This has historically been the

case.

Figure 19 is a graph of the difference between the two last

columns of Table 27 showing the differential investment

advantage provided by the updated, finer resolution map

information for four alternative exploration campaign outcomes. When favourability 3, 2, and 1 domains based on the

updated, finer resolution map are explored (campaign UF321)

and the coarser resolution map is not used, the payoff from the

producing the updated, finer resolution map is the greatest.

Additionally, the results shown on Figure 19 illustrate that

it is difficult to calculate a single value of information without knowing the specific decision context and understanding

how the information affects a decision.

Estimated net economic value of the

updated, finer resolution maps for

south Baffin Island

Application of equation 10 yields the estimated economic

value of the updated, finer resolution map in terms of exploration investment; in the simplest terms, the authors assume that

the net value of the updated maps to society is the difference

between the expected exploration investment stimulated by

the updated maps and the public sector’s cost of producing

the maps such that there are dollar-for-dollar benefits to

society from exploration investments.

There are both fixed (capital) costs and variable (operating) costs in a public bedrock geological mapping campaign. In general, as the location of the mapping effort moves

northward geographically, costs increase dramatically due to

38

Determining

conditions 

Finer resolution 

map, optimal 

exploration 

campaign 

Coarser

resolution map, 

optimal 

exploration 

campaign 

Expected updated, 

finer resolution 

map exploration 

investment 

(million $CAN) 

Expected old, 

coarser resolution 

map exploration 

investment 

(million $CAN) 

t ื 10

E ืื 377.144.

UF3 with 

0==

40=

2 1

3

U U

U

xx

x 3 1 0== O O xx 2.3 0 

t ื 16

E ืื 143.039.

UF32 with 

0=

121=

40=

1

2

3

U

U

U

x

x

x

3 1 0== O O xx 3.7 0 

t ื 44

E ื 038.

UF321 with 

1514=

121=

40=

1

2

3

U

U

U

x

x

x

OC31 with 

1 1414= O x

3 4= O x

15.2 11.3 

45 t ืื 65 E ื 038.

OR

t ื 65

E ืื 047.039.

UF321 with 

1514=

121=

40=

1

2

3

U

U

U

x

x

x

3 1 0== O O xx 15.2 0 

Table 27. Optimal exploration and investment solutions to the simplified constrained optimization economic model

for the south Baffin Island area with respect to t and E keeping the budget constraint constant. xf

θ is the optimal

number of search units examined in favourability domain f on map θ for the ranges of target density and minimum

targets required by the risk constraint.
the scarcer transportation infrastructure and the harsher climatic conditions. The list of cost sources, however, is fairly

straightforward. These can be listed as follows: salaries,

operating costs (including food, lodging, transportation,

materials), contracted surveys (e.g. geophysical surveys),

laboratory expenses (e.g. geochemistry, geochronology),

data acquisition (e.g. topographic base maps, remotely sensed

imagery), and geographic information system development

and map production.

The total map production costs (including all relevant

sources listed above) for the updated, finer resolution map for

the south Baffin Island area were provided by the mapping

agency involved. The GSC provided the cost of making the

updated, finer resolution map as CAN$1.86 million over a

three-year period (M.R. St-Onge, unpub. data, 2000).

Figure 19 shows that the expected additional exploration

investments for all four mineral exploration campaigns investigated and based on the updated, finer resolution map, exceed the

CAN$1.86millioncostofproducingtheupdated,finerresolution

map. The final specific value of the updated map depends on its

use in the mineral exploration campaign, the extent of the campaign, and whether or not the original old, coarser resolution map

offered exploration-stimulating information. The net value of the

updated, finer resolution map thus range from CAN$0.42 million

up to CAN$13.35 million (depending on the exploration campaign implemented; Table 26), when the costs associated with

producing the new map are compared to the potential exploration

investments stimulated by the release of the map in the public

domain. In other words, the south Baffin Island case study demonstrates that the stimulated private-sector exploration investment can exceed the original government expenditure by as much

as a factor of eight.

DISCUSSION

A range of discussion topics is covered in this section. The

authors recognize the role of geoscientific information in a

broader context and identify applications of the methodology

used. Some extensions to the work are considered that

included shadowing favourability polygons and switching

the economic model constraints with the objective.

Geoscientific information and

mineral exploration investment

The authors are aware that the contribution of geoscientific

information to the mineral exploration investment decision is

not the only information available and applied to the search

effort, and that such a decision is complicated by national legislation and policies, political stability and infrastructure, and

39

0

2

4

6

8

10

12

14

16

UF3 UF32 UF321 vs. OC31 UF321

Exploration campaign comparison

Differential exploration investment

Figure 19. Investment advantage of the south Baffin Island updated, finer

resolution map relative to that stimulated by the old, coarser resolution map.

Label OC31 denotes the exploration campaign that examines favourability 3

and 1 domains on the old, coarser resolution map; UF3 denotes the exploration

campaign that examines favourability 3 domains on the updated, finer resolution

map; UF32 denotes the exploration campaign that examines favourability 3 and

2 domains on the updated, finer resolution map; and UF321 denotes the

exploration campaign that examines favourability 3, 2, and 1 domains on the

updated, finer resolution map.
global market trends. Neither do the authors intend to imply

that exploration decisions are based solely on available public

geological map information as companies will have their own

sources of ancillary data, expertise, and in some cases specific field data. Overall it remains difficult to assess a relationship between geoscience information and exploration

investment given that the potential relationship is often blurred

by lags between publication of information and exploration

activities. Others, including Scott et al. (2002) and Bhagwat

and Ipe (2000) surveyed users to estimate the utility of

geological information in hindsight. The challenge of this

time-consuming approach lies in the framing of surveys and

questions to elicit unbiased results (Mitchell and Carson,

1989; OXERA, 1999). To complement other efforts, the

present authors took a different approach that seeks to relate

the quantity and quality of information to decision making.

Specifically in this study, the authors examined the influence

that geological map information could have on exploration

investment decisions as a means to indicate potential benefits

of publicly funded geological map information to society via

mineral exploration industry expenditures.

Assessments of the economic stimulus provided by

updated, finer resolution maps are conservative in that the

benefits of the updated information are restricted to the next

step of the exploration phase in terms of stimulating investment. The models herein do not incorporate grade and tonnage considerations or the ultimate value of an economical

discovery that results from investment in an exploration campaign based on information contained in newly released bedrock maps. Quite obviously the discovery and development

of an economic deposit would multiply by several orders of

magnitude the value of the geological information contained

in an updated map utilized by industry for the discovery.

The actual exploration investment depends on the

number of search units explored and the number of targets

located, which, in turn depend on the disparity between the

target densities explorers use to choose their exploration

strategy and the actual densities they find in the field. As previously noted, very little information is available about the

variability of target densities.

Applications of this work

This work contributes to private and public decisionmaking. The authors have defined and estimated measures of

efficiency, productivity, risk, cost, and effectiveness. This

contribution addresses a need identified in the literature by

Lord et al. (2001) that

“The industry needs more robust and quantitative methodologies for measuring exploration

effectiveness, and for informing management,

investors, and shareholders of exploration risk,

reward, value and progress to discovery”.

In the public arena, an application of the methodology

developed in this paper could assess how much better updated

geological map information needs to be to justify the expense

of providing it. A government not only supplies basic information

in the form of a geological map, which can stimulate potentially profitable exploration as documented here, but in addition the map information could also highlight how inviting an

area might be by publishing the authors’ type of analysis.

Additionally, viewing the government as an honest broker, it

supplies geological data not only to the mineral exploration

industry, but also to society as a whole, thus enabling more

informed public decision making for planning and development with respect to social, economic, and environmental

issues, and improving scientific knowledge (Nielsen, 2004).

Shadowing geological information

for inaccuracies

Because the accuracy of favourability domain boundaries

depends on the accuracy of the geological rock unit boundaries, analysis (that is not presented in detail here) was carried

out that shadowed favourability polygons by a buffer of size

related to the map resolution. The use of shadowing to compensate for inaccuracies reduced target density estimates due

to the increase in area of nonzero favourability domains, but

did not change the nature of the results.

Alternative economic model formulations

In the economic model used (model 3), it is assumed that

the decision maker is primarily interested in maximizing the

number of targets located with the condition that efficiency,

risk, and budget constraints are satisfied. If the decision

maker were primarily concerned with maximizing efficiency,

minimizing risk, or minimizing cost, the relevant constraint

would be switched with the effectiveness objective.

If the decision maker maximizes exploration efficiency

subject to the same constraint set for the economic model

except that the effectiveness constraint replaces the efficiency

constraint, the form of the optimal solution will be the same:

the higher favourability domains will be searched until the

effectiveness or risk constraint is reached. Minimizing search

area and maximizing the expected number of targets (models 1

and 2, respectively) are a subset of this problem in that they

maximize efficiency subject to an effectiveness constraint

(Fig. 20).

In another model, the decision maker could minimize risk

(maximize the probability of finding at least a certain number

of targets) subject to the same constraint set for the economic

model except that the effectiveness constraint replaces the

risk constraint. In this model, the outcome will depend on

whether or not increasing the number of campaign searches

compensates for the decreasing campaign target density as

the search is expanded from higher to lower favourability

domains. An examination of the data in the authors’ case

studies reveals that the expansion of the campaign search area

40
dominates the decrease in campaign target density, such that

risk decreases as the search is expanded until either the efficiency or budget constraint is reached.

If the decision maker minimizes the money spent subject

to the same constraint set for the economic model except that

the effectiveness constraint replaces the budget constraint,

then the optimal solution will again be to search the highest

favourability domains first until the effectiveness and risk

constraints are satisfied. The optimal solution is the campaign

that finds just enough targets most efficiently to satisfy these

constraints, since there are costs related to the number of

search units examined and the number of targets found.

In all of these models, the rational decision maker would

search the highest favourability domains first (Table 28).

Furthermore, the decision maker would always use the most

up-to-date and most detailed map information for exploration to maximize his return on investment.

Other improvements

Further refinement of the analysis could embrace other

techniques to identify the key geological and mineralization

associations (e.g. Nielson, 2004), in addition to utilizing

expert opinion as the present authors have done, or to expand

the perspective to three-dimensional information. Exploration is a three-dimensional process and experience says most

areas that are well explored are not well explored in three

dimensions (G. Raines, pers. comm., 2005).

The benefits could also capture broader implications of

updated information such as the outcome of disturbing less of

the environment. A map that better delineates exploration

areas suggests less damage to the environment on a regional

scale relative to mineral exploration success.

CONCLUSIONS

The development of a model of mineral exploration

investment decisions contains many assumptions. Whereas

the model is a simplification of actual investment and risk

preferences, the results are intuitively appealing as a representation of the economic incentives. That is, they represent a

behavioural approach to exploration decisions. The authors

demonstrate that updated geological information, as contained in maps, can extend the area of exploration interest by

assigning more land to the favourable domains (as in the Cape

Smith Belt and south Baffin Island case studies) or significantly narrow the search to increase the target density (as in

the Flin Flon Belt case study). Either way, exploration risk is

reduced and exploration campaign efficiency, productivity,

and effectiveness increased, and consequently updated, finer

resolution map information is more likely to attract exploration

41

Number of search unit examinations

Risk criterion

Exploration budget

Target density

Isobenefit curve 1

Minimize risk Isobenefit curve 2

Minimize cost

Maximize effectiveness

Maximize efficiency

Expected target density

Figure 20. Exploration preference, feasibility, and constraints.

Exploration campaign Expected targets/million dollars 

UF3 6.6 

UF32 6.2 

UF321 5.1 

OC31 4.7 

Table 28.The return on investment, south Baffin Island area

(the number of expected targets per million dollars spent).

Label OC31 denotes the exploration campaign that examines

favourability 3 and 1 domains on the old, coarser resolution

map; UF3 denotes the exploration campaign that examines

favourability 3 domains on the updated,finerresolution map;

UF32 denotes the exploration campaign that examines

favourability 3 and 2 domains on the updated, finer

resolution map; and UF321 denotes the exploration

campaign that examines favourability 3, 2, and 1 domains on

the updated, finer resolution map.
investment to the region than old, coarser resolution map

information. This explains why explorationists will preferentially invest in jurisdictions with updated public geoscience

information. The south Baffin Island case study demonstrates

that the stimulated private sector exploration investment can

exceed the original government expenditure by as much as a

factor of eight.

In summary, this study resulted in an innovative methodology for estimating the value of bedrock geological maps

within the context of mineral exploration. This methodology

can be applied to other circumstances such as environmental

assessments and land-use decisions, highlighting both the

utility of the model and the value of the geological map information as a public good. The two case studies validated the

model and demonstrated the potential economic benefits of

the updated geological maps.

ACKNOWLEDGMENTS

The following people are recognized for their contributions to

the project. Don Singer (USGS) was generous with his time and

knowledge of mineral exploration research. Will Forney and

David Halsing (USGS) were involved in an earlier stage of the

project. Will Forney developed early versions of the favourability conceptand statisticalanalyses. David Halsing was supportiveduring

the project transition. Murray Duke (GSC) provided a thoughtful

review that motivated the use of the old, coarser resolution Flin

Flon Belt map. Janet Goodman (USGS) persisted with the

digitization of the old Flin Flon Belt map with willing assistance

by Christian Raumann (USGS). Marco Einaudi (Stanford University) assisted the authors in defining categories of favourable

geology. Dave Scott (NRCan) provided exploration campaign

costs for south Baffin Island. Rodrigo Arancibia (NRCan), Alan

Galley (GSC), Willard Holtz (NRCan), Gary Raines (USGS), and

Elizabeth Schwere Duffie (USGS) are thanked for their insightful

reviewsthatclarified many of theideaspresented in thispaper and

improved the presentation of the work.

REFERENCES

Avramtchev, L.

1982: Cartes des gîtes minéraux du Québec, Région de la Fosse

de L’Ungava; Ministère de L’Energie et des Ressources

du Québec, DPV-897, Maps M327 to M336,

scale 1:250 000.

Avriel, M.

1976: Nonlinear Programming Analysis and methods;

Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 512 p.

Bailes, A.H.

1971: Preliminary compilation of the geology of the Snow

Lake–Flin Flon–Sherridon area; Manitoba Mines Branch,

Geological Paper 1/71, 27 p.

Barnes, S.J., Picard, C.P., Giovenazzo, D., and Tremblay, C.

1992: The composition of nickel-copper sulphide deposits and

their host rocks from the Cape Smith Fold Belt, northern

Quebec; Australian Journal of Earth Sciences,

v. 39, p. 335–347.

Bernknopf, R.L., Brookshire, D.S., McKee, M.J., and

Soller, D.R.,

1997: Estimating the social value of geologic map information: a

regulatory application; Journal of Environmental

Economics and Management, v. 38, p. 204–218.

Bernknopf, R.L., Brookshire, D.S., Soller, D.R., McKee, M.J.,

Sutter, J.F., Matti, J.C., and Campbell, R.H.

1993: The societal value of geologic maps; United States

Geological Survey, Circular 1111, 53 p.

Bhagwat, S.B. and Ipe, V.C.

2000: Value of geologic quadrangle maps of Kentucky; Illinois

State Geological Survey, Special Report 3, 39 p.

Blackadar, R.G.

1967: Geology, Frobisher Bay, District of Franklin; Geological

Survey of Canada, Map 18-1966, scale 1:506 880.

Carlson, C.A.

1991: Spatial distribution of ore deposits; Geology, v. 19,

p. 111–114.

Cheng, Q. and Agterberg, F.P.

1999: Fuzzy weights of evidence method and its application in

mineral potential mapping; Natural Resources Research,

v. 8, p. 27–35.

Gilbert, R.J.

1981: The social and private value of exploration information; in

The Economics of Exploration for Energy Resources,

(ed.) J.B. Ramsey; Contemporary Studies in Economic

and Financial Analysis, v. 26, JAI Press Inc., Greenwich,

Connecticut, p. 173–215.

Harris, D.P.

1984: Mineral resources appraisal, mineral endowment,

resources, and potential supply: concepts, methods, and

cases; Claredon Press, Oxford, United Kingdom, 445 p.

Harris, D.P., Zurcher, L., Stanley M., Marlow, J., and Pan, G.

2003: A comparative analysis of favorability mappings by

weights of evidence, probabilistic neural networks,

discriminant analysis and logistic regression; Natural

Resources, v. 12, p. 241–255.

Hoffman, P.F.

1988: United plates of America, the birth of a craton: Early

Proterozoic assembly and growth of Laurentia; Annual

Reviews of Earth and Planetary Sciences, v. 16,

p. 543–603.

1989: Precambrian geology and tectonic history of North

America; in The Geology of North America – an

Overview (ed.) A.W. Bally and A.R. Palmer; Geological

Society of America, The Geology of North America, v. A,

p. 447–551.

Lichtenberg, E.

1991: Determination of regional environmental policy under

uncertainty: theory and case studies; in The Economics

and Management of Water and Drainage in Agriculture,

(ed.) A. Dinar and D. Zilberman; Kluwer Academic

Publishers, Boston, Massachusetts, p. 701–716.

Lord, D., Etheridge, M., Willson, M., Hall, G., and Uttley, P.

2001: Measuring exploration success: an alternative to the

discovery-cost-per-ounce method of quantifying

exploration effectiveness; Society of Economic Geologists

Newsletter, v. 45, April, p. 10–16.

42
Lucas, S.B., Stern, R.A., Syme, E.C., Reilly, B.A.,

and Thomas, D.J.

1996: Intraoceanic tectonics, and the development of continental

crust: 1.92-1.84 Ga evolution of the Flin Flon Belt

(Canada); Geological Society of America Bulletin, v. 108,

p. 602–629.

Lucas, S.B., Syme, E.C., and Ashton, K.E.

1999: New perspectives on the Flin Flon Belt, Trans-Hudson

Orogen, Manitoba and Saskatchewan: an introduction to

the special issue on the NATMAP Shield Margin Project,

Part 1; Canadian Journal of Earth Sciences, v. 36,

p. 135–140.

Mitchell, R.C. and Carson, R.T.

1989: Using surveys to value public goods: the contingent

valuation method; in Washington, DC, Resources for the

Future Symposium; John Hopkins University Press,

Baltimore, Maryland, 488 p.

NATMAP Shield Margin Project Working Group

1998: Geology, NATMAP Shield Margin Project area (Flin Flon

Belt), Manitoba-Saskatchewan; Geological Survey of

Canada, Map 1968A (also Manitoba Energy and Mines

Map A-98-2, sheets 1 to 7, Saskatchewan Energy and

Mines Map 258A-1) scale 1:100 000.

Nielsen, B.M.

2004: Crustal architecture and spatial distribution of mineral

occurrences in the Precambrian shield of central West

Greenland based on geophysical and geological data;

Danmarks og Grønlands Geologiske Undersøgelse Rapport

2004/26, 63 p., 8 appendices. Ph.D. thesis, Department of

Earth Sciences, University of Aarhus, Denmark.

OXERA

1999: The Economic Contribution of Ordinance Survey GB;

<http://www.ordnancesurvey.co.uk/aboutus/reports/oxera/

oxera.pdf.> (accessed 2005-10-01).

Reedman, A.J., Calow, R., Johnson, C.C., Piper, D.P.,

and Bate, D.G.

2002: The value of geoscience information of less developed

countries; British Geological Survey, Research Report

CR/02/087N, 43 p.

Ross, G.M., Milkereit, B., Eaton, D., White, D., Kanasewich,

E.R., and Burianyk, M.J.A.

1995: Paleoproterozoic collisional orogen beneath the western

Canada sedimentary basin imaged by LITHOPROBE

crustal-reflection data; Geology, v. 23, p. 195–199.

Scott, M., Dimitrakopoulos, R., and Brown, R.P.C.

2002: Valuing regional geoscientific data acquisition

programmes: addressing issues of quantification,

uncertainty and risk; Natural Resources Forum,

v. 26, p. 55–68.

Singer, D.A. and Kouda, R.

1999a: Examining risk in mineral exploration; Natural Resources

Research, v. 8, p. 111–122.

1999b: A comparison of the weights-of-evidence method and

probabilistic neural networks; Natural Resources

Research, v. 8, p. 287–298.

Singer, D.A., Berger, V.I., Menzie, W.D., and Berger, B.R.

2005: Porphyry copper deposit density; Economic Geology,

v. 100, p. 491–514.

Singer, D.A., Menzie, W.D., Sutphin, D.M., Mosier, D.L.,

and Bliss, J.D.

2001: Mineral deposit density – an update; United States

Geological Survey, Professional Paper 1640-A, 13 p.

Stanley, M.C.

1994: A quantitative estimation of the value of geoscience

information in mineral exploration: optimal search

sequences; Ph.D. dissertation, University of Arizona,

Tucson, Arizona, 398 p.

St-Onge, M.R. and Lucas, S.B.

1990: Geology, Ungava Peninsula, Quebec; Geological Survey

of Canada, Maps 1721A–1735A, scale 1:50 000.

1994: Controls on the regional distribution of

iron-nickel-copper-platinum-group element sulfide

mineralization in the eastern Cape Smith Belt, Quebec;

Canadian Journal of Earth Sciences, v. 31, p. 206–218.

St-Onge, M.R., Lucas, S.B., Scott, D.J., and Wodicka, N.

1999a: Upper and lower plate juxtaposition, deformation and

metamorphism during crustal convergence, Trans-Hudson

Orogen (Quebec-Baffin segment), Canada; Precambrian

Research, v. 93, p. 27–49.

St-Onge, M.R., Scott, D.J., and Lucas, S.B.

2000: Early partitioning of Quebec: microcontinent formation in

the Paleoproterozoic; Geology, v. 28, p. 323–326.

St-Onge, M.R., Scott, D.J., and Wodicka, N.

1999b: Geology, Meta Incognita Peninsula, Nunavut; Geological

Survey of Canada, Maps 1979A–1985A, scale 1:100 000.

2001: Terrane boundaries within Trans-Hudson Orogen

(Quebec-Baffin segment), Canada: changing structural

and metamorphic character from foreland to hinterland;

Precambrian Research, v. 107, p. 75–91.

2002: Review of crustal architecture and evolution in the

Ungava Peninsula–Baffin Island area: connection to the

LITHOPROBE ECSOOT transect; Canadian Journal of

Earth Sciences, v. 39, p. 589–610.

Swinden, H.S.

1993: The impact of provincial government geoscience

programs on mineral, petroleum and industrial

development and land use planning in Newfoundland and

Labrador; Government of Newfoundland and Labrador,

Open File NFDL/2254, 62 p.

Syme, E.C. and Bailes, A.H.

1993: Stratigraphic and tectonic setting of volcanogenic massive

sulfide deposits, Manitoba; Economic Geology,

v. 88, p. 566–589.

Syme, E.C., Lucas, S.B., Bailes, A.H., and Stern, R.A.

1999: Contrasting arc and MORB-like assemblages in the

Paleoproterozoic Flin Flon Belt, Manitoba, and the role of

intra-arc extension in localizing volcanic-hosted massive

sulphide deposits; Canadian Journal of Earth Sciences,

v. 36, p. 1767–1788.

Taylor, F.C.

1982: Reconnaissance geology of a part of the Canadian Shield,

northern Quebec and Northwest Territories; Geological

Survey of Canada, Memoir 399 and maps 1538A–1544A,

scale 1:250 000.

Walpole, R.E and Meyers, R.H.

1989: Probability and Statistics for Engineers and Scientists;

MacMillan Publishing Company, New York, New York,

76 p. (fourth edition).

Wheeler, J.O., Hoffman, P.F., Card, K.D., Davidson, A.,

Sanford, B.V., Okulitch, A.V., and Roest, W.R.

1996: Geological Map of Canada; Geological Survey of Canada,

Map 1860A, scale 1:5 000 000.

43
Appendix A

GIS method to delineate contacts

The method to find razor sharp (‘unshadowed’) contacts

for rock unit A and rock unit B is as follows, the implication

being that the unit boundaries are shown with complete accuracy on the geological map:

• Select by location the polygons of rock unit A touching a

polygon of rock unit B.

• Apply a 100 m (for a 1:50 000 scale map) or 500 m (for a

1:250 000 scale map) buffer around the polygons of unit A

selected in step 1.

• Select by location the polygons of rock unit B touching a

polygon of rock unit A.

• Apply a 100 m (for a 1:50 000 scale map) or 500 m (for a

1:250 000 scale map) buffer around the polygons of unit B

selected in step 3.

• Take the intersection of steps 2 and 4.

44
Appendix B

Kuhn-Tucker conditions (Avriel, 1976) for the optimal solution to model 3

45

x f f ≥ ∀0

y i i ≥ ∀0

nx f f f

θ − ≥∀0

− + ≥

=

=

∑

E ∑

x

x p

f

f

F f

f

F

f

1 0

0

0 θ

θ

θ $

M I x I xp s f

f

F

t f

f

F

−− ≥f

= =

∑ ∑ 0 0

0 θ θ

θ $

1 1 1 0

0

0

0 −−− ≥

=

=

∑

∑

∑ = P

x

x p

f

f

F f

f

F

f

x f f

F

( $ ) θ

θ

θ

θ

yn x f s f f ( ) θ − =∀0

y E

x

x p e

f

f

F f

f

F

f ( − = $ )

=

=

∑

∑ 1 0

0

0 θ

θ

θ

y M I x I xp b s f

f

F

t f

f

F

f ( −+ = $ ) = =

∑ ∑ 0 0

0 θ θ

θ

y P

x

r x p

f

f

F f

f

F

f

x f f

F

( ( 1 1 $ ) ) 1 0

0

0

0 −−− =

=

=

∑

∑

∑ =

θ

θ

θ

θ

p y $ ( $ $ ) y

x

p

x

xp y f s

e

f

f

F f

f

f

F f

f

F

f f b

θ θθ

θ θ

θ

−+ − +

= =

=

∑ ∑

∑

0 0

0

1 (−− + I Ip$ ) s t f

θ

y x

x

x p

x

r f

f

F

f

f

F f

f

F

f

x

f

f

f f

F

( ( $ ) ( =

=

=

∑ −

=

∑

∑

∑ =

0

0

0

1 1 1 0

θ

θ

θ

θ

θ

0 0

0

1

F f

f

f

F f

f

F

f p

x

x p θ θ

θ

θ θ

∑ ∑

− + ∑

=

=

( $ $ ))

( $ ) 1 1

0

0

0

0

0

x

x p

x

x

f

f

F f

f

F

f

x

f

f

F f

f

F f f

F

=

=

∑

=

=

∑

∑

∑

∑ =

θ

θ

θ

θ

θ

θ In p f $ )f

θ ≥ ∀0
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Recommended minimum hardware/software:

PC - Pentium® processor with Windows® 98

MAC - Mac® OS X v. 10.2.2

UNIX - Go to adobe.com to download appropriate reader for your system

Monitor - 17" colour monitor, video resolution of 1280 x 1024

RAM - 64 MB

Adobe® Acrobat® Reader® v. 6.0 is required to view the contents of this CD-ROM. It is included on the CD-ROM

in the \APPS directory.

Ceci est un disque à lancement automatique pour les systèmes d’exploitation Windows®. Si le lancement

automatique ne fonctionne pas, allez au répertoire principal du CD-ROM et cliquez deux fois sur le fichier

autoplay.exe. Si vous lisez ce disque à l’aide d’un système d’exploitation Mac® ou UNIX®, le lancement

automatique ne fonctionnera pas.

Des fichiers PDF renfermant le contenu intégral du bulletin sont situés dans le dossier \PDF sur ce disque.

Configuration minimale recommandée :

PC - processeur Pentium® avec Windows® 95

MAC - Mac® OS X v. 10.2.2

UNIX - Connectez-vous au site « www.adobe.com» pour télécharger la version du logiciel dont

vous avez besoin pour votre système

Moniteur - moniteur couleur de 17 po, avec résolution vidéo de 1280 x 1024

RAM - 64 Mo

Le logiciel Acrobat® Reader® v. 6.0 d’Adobe® est requis pour visionner le contenu de ce CD-ROM. Il est fourni

sur le disque dans le répertoire \APPS.