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Integrated approach to diamond exploration in the north-central United States
Memmi, John Michael, Ph.D.
The Ohio State University, 1993
Copyright ©1993 by Memmi, John Michael. All rights reserved.
UMI 300 N. Zeeb Rd. Ann Arbor, MI 48106
INTEGRATED APPROACH TO DIAMOND EXPLORATION
IN THE NORTH-CENTRAL UNITED STATES
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the
Degree Doctor of Philosophy in the Graduate School of
The Ohio State University
by
John Michael Memmi, B.S., M.S.
*****
The Ohio State University
1993
Dissertation Committee: Approved by
G.P. Murdock
H.C. Noltimier Douglas El Pride, Adviser R.R.B. von Frese Department of^Geological Sciences Copyright by John Michael Memmi 1993 ACKNOWLEDGMENTS
Numerous individuals and organizations contributed to the
research discussed in this dissertation, and I am grateful to all
of them.
David Campbell, Minor Davis, Malcolm McCallum, Richard Rapp,
John Reed, Jr., Charles Robinson, BMP Minerals, and Halliburton
Geophysical Services, plus the state geological surveys of
Colorado, Illinois, Indiana, Iowa, Kansas, Kentucky, Michigan,
Minnesota, Missouri, Montana, Nebraska, North Dakota, South Dakota,
Wisconsin, and Wyoming provided information and datasets that were incorporated into the database developed for this investigation.
Decision Images donated a beta version of the MapBox GIS package to this project, through the efforts of Dana Tomlin. Bram
Janse supplied pre-prints of papers on the geologic settings of primary diamond deposits that were helpful in formulating the empirical exploration model.
BMP Minerals and the NASA-sponsored Center for Mapping at The
Ohio State University provided partial financial support for the project. I especially thank Ken Witherly for his strong interest in my work.
ii The Department of Geological Sciences at The Ohio State
University was a source of both financial and logistical backing.
I thank Gary Murdock, Hallan Noltimier, and Ralph von Frese for serving on my dissertation committee and for their critical reading of this manuscript. Douglas Pride, my doctoral adviser, has been helpful throughout my graduate term; I greatly appreciate his many efforts on my behalf.
My parents, Kathryn and August Memmi, furnished unconditional support, as always, and to them I extend my deep gratitude. Sean and Ian Memmi, my sons and future prospectors, helped me to keep all matters in perspective. Last but by no means least, I am indebted to Molly Memmi, my wife, whose personal and professional sacrifices have enabled me to pursue my goals and dreams.
m VITA
April 12, 1957 .... Born - Hershey, Pennsylvania
1978 ...... Geologic Aide Pennsylvania Geological Survey Harrisburg, Pennsylvania
1979 ...... Bachelor of Science West Chester University West Chester, Pennsylvania
1981 ...... Engineering Aide B Colorado Geological Survey Denver, Colorado
1982 ...... Geologic Worker 11 Wyoming Geological Survey Laramie, Wyoming
1982 ...... Laboratory Technician lA Colorado State University Fort Collins, Colorado
1985 ...... Technical Chief Remote Sensing and Geophysics Compania Minera Autlan Mexico City, Mexico
1985 ...... Biological Aide and Software Specialist USDA Forest Service Fernan Ranger District Coeur d'Alene, Idaho
1986 ...... Summer of Applied Geophysical Experience Los Alamos National Laboratory Los Alamos, New Mexico
1986-1987 Energy Development Specialist Pennsylvania Energy Development Authority Harrisburg, Pennsylvania
IV VITA (Continued)
1987-1989 Associate Director Pennsylvania Energy Office Harrisburg, Pennsylvania
1989 ...... Master of Science Colorado State University Fort Collins, Colorado Thesis: Enhancement and modeling of geophysical data for kimberlite exploration in Colorado and Wyoming
1990-Present ...... Graduate Associate Department of Geological Sciences The Ohio State University Columbus, Ohio
PUBLICATIONS
Memmi, J.M., McCallum, M.E. and Hausel, W.D., 1983, Preliminary results of resistivity investigations of Colorado-Wyoming kimberlite diatremes: Geological Society of America Abstracts with Programs, v. 15, no. 5, p. 317.
Lider, E.L. and Memmi, J.M., 1986, User's guide for program EMBRYO, in Fisheries habitat evaluation: USDA Forest Service, Idaho Panhandle National Forests.
Memmi, J.M., 1986, Pennsylvania Energy Development Authority Annual Report for Fiscal Year 1985-86, contributor, 31 p.
Memmi, O.M., 1987, Pennsylvania Energy Development Authority: an overview: Proceedings of the Fourth Annual Pittsburgh Coal Conference, p. 332-346.
Memmi, J.M., 1987, Pennsylvania Energy Development Authority Annual Report for Fiscal Year 1986-87, main contributor, 35 p.
Memmi, J.M., 1988, Clean coal technology developments: Pennsylvania Energy, v. 2, no. 1, p. 5.
Meirani, J.M., 1988, Pennsylvania coal database and market analysis system: Pennsylvania Energy, v. 2, no. 1, p. 6-7.
Memmi, J.M., 1988, Fourth Annual Pittsburgh Coal Conference: Pennsylvania Energy, v. 2, no. 1, p. 8. VITA (Continued)
Memmi, J.M., 1988, PEDA funds new energy development: Pennsylvania Energy, v. 2, no. 2, p. 6,13.
Memmi, J.M., 1988, Low volatile coal project attracts utility interest: Pennsylvania Energy, v. 2, no. 2, p. 7.
Memmi, J.M., 1988, Developments in clean coal technology: Pennsylvania Energy, v. 2, no. 2, p. 8.
Memmi, J.M., 1988, Pennsylvania Energy Development Authority: 1988 update: Proceedings of the Fifth Annual International Pittsburgh Coal Conference, p. 275-284.
Memmi, J.M., 1988, Pennsylvania Energy Development Authority Annual Report for Fiscal Year 1987-88, main contributor, 44 p.
Memmi, J.M., 1988, PEDA update: Pennsylvania Energy, v. 2, no. 3, p. 6-7.
Memmi, J.M., 1988, Energy research, in An energy policy for Pennsylvania: Pennsylvania Energy Office, p. 163-168.
Memmi, J.M., 1988, PEDA funds innovative wood waste utilization project: Pennsylvania Energy, v. 2, no. 4, p. 6.
Memmi, J.M., 1988, PEDA update: Pennsylvania Energy, v. 2, no. 4, p. 6.
Memmi, J.M., 1989, Pennsylvania's unconventional natural gas resources: Pennsylvania Energy, v. 3, no. 1, p. 10-11.
Memmi, J.M., 1989, PEDA update: Pennsylvania Energy, v. 3, no. 1, p. 14-15.
Memmi, J.M., 1989, Remine: software to assess remining costs: Pennsylvania Energy, v. 3, no. 2, p. 6-7.
Memmi, J.M., 1989, PEDA update: Pennsylvania Energy, v. 3, no. 2, p. 8-9.
Memmi, J.M., 1989, Low volatile coal project looks promising: Pennsylvania Energy, v. 3, no. 2, p. 12-13.
Memmi, J.M., 1989, Pennsylvania Energy Development Authority Annual Report for Fiscal Year 1988-89, main contributor, 29 p.
Memmi, J.M., 1989, PEDA update: Pennsylvania Energy, v. 3, no. 3. vi VITA (Continued)
Memmi, J.M., 1989, LICADO: an innovative fine coal cleaning technology: Pennsylvania Energy, v. 3, no. 3.
Meirani, J.M., Pride, D.E. and Krumm, C.W., 1991, Investigation of porphyry, base, and precious metal anomalies over the Crow Springs igneous complex, Nevada: Ohio Journal of Science, V. 91, no. 2, p. 34.
Memmi, J.M., Pride, D.E. and Krumm, C.W., 1991, Rapid manipulation and summary of geochemical data using a PC-based GIS: an example from Crow Springs, Nevada: Geological Society of America Abstracts with Programs, v. 23, no. 3, p. 48.
Memmi, J.M. and McCallum, M.E., 1991, Enhancement of geophysical data for kimberlite exploration at Iron Mountain, Wyoming, USA: Extended Abstracts, Fifth International Kimberlite Conference, Araxa, Minas Gerais, Brazil, Companhia de Pesquisa de Recursos Minerais, Special Publication 2/91, p. 273-275.
Memmi, J.M. and McCallum, M.E., 1991, Finite element modeling of resistivity data from kimberlites in Colorado-Wyoming, USA: Extended Abstracts, Fifth International Kimberlite Conference, Araxa, Minas Gerais, Brazil, Companhia de Pesquisa de Recursos Minerais, Special Publication 2/91, p. 276-278.
Pride, D.E. and Memmi, J.M., 1992, Development of a geoscientific information system for diamond exploration in the north- central United States: National Aeronautics and Space Administration, Center for the Commercial Development of Space, The Ohio State University, Quarterly Report (1 October- 31 December 1991), p. 23-25.
Pride, D.E. and Meirani, J.M., 1992, Development of a geoscientific information system for diamond exploration in the north- central United States: National Aeronautics and Space Administration, Center for the Commercial Development of Space, The Ohio State University, Quarterly Report (1 January - 31 March 1992), p. 24-26.
Pride, D.E. and Meirani, J.M., 1992, Development of a geoscientific information system for diamond exploration in the north- central United States: National Aeronautics and Space Administration, Center for the Commercial Development of Space, The Ohio State University, Quarterly Report (1 April - 30 June 1992), p. 17-18. vii VITA (Continued)
Pride, D.E. and Memmi, J.M., 1992, Development of a geoscientific information system for diamond exploration in the north- central United States: National Aeronautics and Space Administration, Center for the Commercial Development of Space, The Ohio State University, Annual Report for Fiscal Year 1992 (1 October 1991 - 30 September 1992), p. 10-11.
Memmi, J.M. and McCallum, M.E., 1993, Finite element modeling of resistivity data from kimberlite intrusions in Wyoming, USA, in Meyer, H.O.A., and Leonardos, O.H., editors. Diamonds: characterization, genesis and exploration: Proceedings of the Fifth International Kimberlite Conference, v. 2: Brasilia, Brazil, Companhia de Pesquisa de Recursos Minerais.
Memmi, J.M. and Pride, D.E., 1993, An integrated approach to diamond exploration in the north-central United States, in Romberger, S.B., and Fletcher, D.I. editors. Integrated methods in exploration and discovery, extended abstracts: Society of Economic Geologists, p. 71-72.
FIELDS OF STUDY
Major Field: Geological Sciences
Studies in Economic Geology: Malcolm E. McCallum Douglas E. Pride Tommy B. Thompson
Studies in Geophysics: Shawn Biehler Harold S. Boyne David L. Campbell Jeffrey J. Daniels Hallan C. Noltimier Ralph R.B. von Frese
Studies in Remote Sensing: Eugene Maxwell Jan Cipra
Studies in Geographic Information Systems: Duane Marble C. Dana Tomlin Gary P. Murdock
v m TABLE OF CONTENTS
ACKNOWLEDGMENTS ...... ii
VITA...... iv
LIST OF TABLES...... xiii
LIST OF FIGURES ...... xvi
CHAPTER PAGE
I. INTRODUCTION ...... 1
Purpose and Objectives ...... 2
Diamond Exploration Models ...... 5
Kimberlite, Lamproite, and Diamonds ...... 8
Kimberlites in the Study Region ...... 10
Lamproites in the Study Region ...... 12
Diamonds in the Study R e g io n ...... 13
II. METHODS...... 16
DEGIS D esign ...... 16
DEGIS Implementation...... 17
Thematic Data Layers ...... 18
Evaluation of Parametric Surfaces . . 18
Crustal Thickness Map ...... 38
Lithospheric Thickness Map ...... 38
Diamond Exploration Models ...... 43
Empirical Diamond Exploration M o d e l ...... 43 ix TABLE OF CONTENTS (Continued)
CHAPTER PAGE
Pre-Modeling Processing ...... 48
M odeling ...... 49
Stacked Diamond Exploration Model . . 53
Pre-Modeling Processing ...... 56
M odeling ...... 61
Proximity Diamond Exploration Model ...... 69
Parametric Surface Processing . . 70
Pre-Modeling Processing ...... 76
M odeling ...... 87
Integrated Diamond Exploration M o d e l ...... 96
M odeling ...... 100
III. RESULTS AND INTERPRETATIONS ...... 106
Classification of Independent Diamond Exploration Models ...... 107
Empirical Diamond Exploration Model .... 107
Stacked Diamond Exploration Model ...... 113
Proximity Diamond Exploration Model .... 115
Cross-Tabulation of Known Occurrences with the Independent Diamond Exploration Models . . . 119
Alluvial Diamonds ...... 119
Diamondiferous Kimberlites ...... 122
Barren Kimberlites ...... 124
L am p ro ites ...... 128
X TABLE OF CONTENTS (Continued)
CHAPTER PAGE
Carbonatites ...... 130
Cryptoexplosion Structures ...... 132
Analysis of Anomalies Identified by the Independent Diamond Exploration Models ...... 135
Anomalies in the Empirical Diamond Exploration Model ...... 135
Anomalies in the Stacked Diamond Exploration Model ...... 135
Anomalies in the Proximity Diamond Exploration Model ...... 139
The Integrated Diamond Exploration Model .... 142
Exploration Potential of Wisconsin- Upper Peninsula of Michigan ...... 143
Exploration Potential of Southeastern Wyoming ...... 153
Exploration Potential of North Dakota- Minnesota-Ontario ...... 161
Exploration Potential of Northwestern Iow a ...... 166
IV. CONCLUSIONS...... 171
Diamond Potential of the Study Region ...... 171
DEGIS Methodology ...... 176
V. RECOMMENDATIONS...... 178
Diamond Exploration in the Study Region ...... 178
Diamond Exploration Models ...... 178
DEGIS D a ta b a s e ...... 181
Diamond Exploration Practices ...... 183 xi TABLE OF CONTENTS (Continued)
CHAPTER PAGE
Other Applications ...... 185
APPENDICES
A. Descriptions of Precambrian Basement Rocks in the StudyRegion ...... 186
B. Flowcharts of Diamond Exploration M o d e ls ...... 190
B.Cartographic ModelingProcedures ...... 198
LIST OFREFERENCES ...... 211
x n LIST OF TABLES
TABLE PAGE
1. Alluvial Diamond Occurrences in the Study Region . . . 14
2. Thematic Data Layers in the DEGIS Database ...... 19
3. Sources and Types of Thematic Data Used to Create the Thematic Data Layers in the DEGIS Database .... 20
4. Processing Techniques Applied to Thematic Data to Produce Thematic Data Layers of the DEGIS Database . . 22
5. Summary Statistics of Original and Final Datasets Used to Generate Full Parametric Surfaces, and One-Tailed F Value Resulting from the Ratio of Their Variances ...... 37
6. Summary Statistics of Standardized Original and Final Datasets Used to Generate Full Parametric Surfaces, and One-Tailed F Value Resulting from the Ratio of Their Variances ...... 37
7. Summary Statistics of Low-Pass Filtering and Regional/Residual Analysis of Parametric Surfaces 72
8. Correlation Matrix of Low-Pass Filtering and Regional/Residual Analysis of Parametric Surfaces 72
9. Summary Statistics of Original Proximity Datasets 91
10. Summary Statistics of Nlog Transformed Proximity Datasets ...... 95
II. Summary Statistics of Standardized Proximity Datasets ...... 95
12. Classification of Grid Cells in the Empirical Diamond Exploration Model 108
13. Scenarios of Terranes Permissive for the Occurrence of Primary Diamond Deposits ...... 112
xiii LIST OF TABLES (Continued)
TABLE PAGE
14. Classification of Grid Cells in the Stacked Diamond Exploration Model ...... 114
15. Classification of Grid Cells in the Proximity Diamond Exploration Model ...... 116
16. Correlation Statistics of Each of the Input Standardized Proximity Datasets versus the Proximity Diamond Exploration Model ...... 118
17. Cross-Tabulation of Grid Cells That Contain Known Alluvial Diamonds with the Empirical, Stacked, and Proximity Diamond Exploration Models . . 120
18. Cross-Tabulation of Grid Cells That Contain Known Diamondiferous Kimberlites with the Empirical, Stacked, and Proximity Diamond Exploration Models . . 123
19. Cross-Tabulation of Grid Cells That Contain Known Kimberlites with the Empirical, Stacked, and Proximity Diamond Exploration Models ...... 125
20. Cross-Tabulation of Grid Cells That Contain Known Lamproites with the Empirical, Stacked, and Proximity Diamond Exploration Models ...... 129
21. Cross-Tabulation of Grid Cells That Contain Known Carbonatites and the Empirical, Stacked, and Proximity Diamond Exploration Models ...... 131
22. Cross-Tabulation of Grid Cells That Contain Known Cryptoexplosion Structures with the Empirical, Stacked, and Proximity Diamond Exploration Models . . 133
23. Cross-Tabulation of Stacked Anomalies with the Empirical, Stacked, and Proximity Diamond Exploration Models ...... 137
24. Cross-Tabulation of Proximity Anomalies with the Empirical Diamond Exploration Model ...... 141
25. Classification of Permissive Areas in the Integrated Diamond Exploration Model ...... 144
XIV LIST OF TABLES (Continued)
TABLE PAGE
26. Statistics of Geochemical Data Associated with Permissive Areas in the Study Region ...... 148
27. Descriptions of Archean Units ...... 187
28. Descriptions of Early Proterozoic Units ...... 188
29. Descriptions of Middle and Late Proterozoic Units . . 189
30. Procedure to Produce Empirical Diamond Exploration Model ...... 204
31. Procedure to Produce Stacked Diamond Exploration Model ...... 205
32. Procedure to Produce Horizontal Flexure Axes from Parametric Surfaces ...... 206
33. Procedure to Separate Positive Horizontal Flexure Axes from Negative Horizontal Flexure Axes ...... 207
34. Procedure to Produce Proximity Diamond Exploration Model ...... 208
35. Procedure to Produce Integrated Diamond Exploration Model ...... 210
XV LIST OF FIGURES
FIGURE PAGE
1. Map of the study region with known occurrences of alluvial diamonds, diamondiferous kimberlite, barren kimberlite, lamproite, cryptoexplosion structures, and carbonatite ...... 4
2. Locations from which stream sediment samples are known to contain cobalt in concentrations of at least 1pp m ...... 25
3. Locations from which stream sediment samples are known to contain nickel in concentrations of at least 1 pp m ...... 26
4. Geomorphic lineaments ...... 27
5. Geologic map of Archean basement rocks ...... 28
6. Geologic map of Early Proterozoic basement rocks . . . 29
7. Geologic map of Middle Proterozoic and Late Proterozoic basement rocks ...... 30
8. Portion of study region glaciated during the P le is to c e n e ...... 31
9. Crustal thickness ...... 32
10. Lithospheric thickness ...... 33
11. Elevation of the su rfa c e ...... 34
12. Elevation of the top of the Precambrian basement surface ...... 35
13. Cross-section illustrating the relationship between primary diamond deposits and diamond source rock(s) ...... 45
14. Thickness of post-Precambrian sedimentary rocks . . . 50
XVI LIST OF FIGURES (Continued)
FIGURE PAGE
15. General age categories of cratonic Precambrian basement rocks ...... 51
16. Empirical diamond exploration model ...... 52
17. Intersections of geomorphic lineaments ...... 58
18. Intersections of faults in Precambrian basement rocks ...... 59
19. Intersections of lineaments and basementfaults . . . 62
20. Anomalous concentrations of cobalt in stream sediments ...... 63
21. Anomalous concentrations of nickel in stream sediments ...... 64
22. Locations in the stacked diamond exploration model with at least one favorable attribute ...... 65
23. Locations in the stacked diamond exploration model with at least two favorable attributes ...... 66
24. Locations in the stacked diamond exploration model with at least three favorable attributes ...... 67
25. Locations in the stacked diamond exploration model with four favorable attributes ...... 68
26. Positive residual component of elevation of the su rfa ce ...... 74
27. Positive residual component of elevation of the top of the Precambrian basement surface .... 75
28. Aspect of the s u r f a c e ...... 78
29. Aspect of the Precambrian basement surface ...... 79
30. Horizontal flexure axes derived from the surface . . . 82
31. Horizontal flexure axes derived from the Precambrian basement surface ...... 83
x v n LIST OF FIGURES (Continued)
FIGURE PAGE
32. Positive horizontal flexure axes on low-pass filtered elevation of the surface ...... 84
33. Close-up of Wisconsin-Upper Peninsula of Michigan, featuring positive horizontal flexure axes on low-pass filtered elevation of the surface .... 85
34. Positive horizontal flexure axes on low-pass filtered elevation of the top of the Precambrian basement surface ...... 86
35. Close-up of Nebraska, featuring positive horizontal flexure axes on low-pass filtered elevation of the top of the Precambrian basement surface . . 88
36. Proximity of grid cells to geomorphic lineaments . 90
37. Histogram of proximity to geomorphic lineaments . 93
38. Histogram of standardized proximity to geomorphic lineaments ...... 94
39. Unstandardized proximity diamond exploration model 97
40. Standardized proximity diamond exploration model . 98
41. Histogram of standardized proximity diamond exploration model ...... 99
42. Locations from the empirical model that are permissive for the occurrence of economic primary diamond deposits ...... 101
43. Anomalies in the stacked diamond exploration model 102
44. Anomalies in the proximity diamond exploration model ...... 104
45. Integrated diamond exploration model ...... 105
46. Wisconsin-Upper Peninsula of Michigan diamond exploration area ...... 145
x v m LIST OF FIGURES (Continued)
FIGURE PAGE
47. Precambrian geology of the Wisconsin- Upper Peninsula of Michigan diamond exploration area ...... 147
48. Linear and punctual features associated with the Wisconsin-Upper Peninsula of Michigan diamond exploration area ...... 150
49. Cobalt and nickel anomalies in the Wisconsin- Upper Peninsula of Michigan diamond exploration area ...... 151
50. Southeastern Wyoming diamond exploration area .... 154
51. Precambrian geology of the southeastern Wyoming diamond exploration area ...... 154
52. Linear and punctual features associated with the southeastern Wyoming diamond exploration area .... 155
53. Distributions of chromian diopside and pyrope garnet in the southeastern Wyoming diamond exploration area ...... 157
54. Elevation of the southeastern Wyoming diamond exploration area ...... 157
55. Cobalt and nickel anomalies in the southeastern Wyoming diamond exploration a r e a ...... 159
56. North Dakota-Minnesota-Ontario diamond exploration area ...... 162
57. Precambrian geology of the North Dakota-Minnesota- Ontario diamond exploration area ...... 162
58. Linear and punctual features associated with the North Dakota-Minnesota-Ontario diamond exploration area ...... 164
59. Cobalt and nickel anomalies in the North Dakota- Minnesota-Ontario diamond exploration area ...... 165
60. Northwestern Iowa diamond exploration area ...... 167
XIX LIST OF FIGURES (Continued)
FIGURE PAGE
61. Precambrian geology of the northwestern Iowa diamond exploration area ...... 168
62. Linear and punctual features associated with the northwestern Iowa diamond exploration area ...... 169
63. Flowchart of the development of the Empirical Diamond Exploration Model ...... 193
64. Flowchart of the development of the Stacked Diamond Exploration Model ...... 194
65. Flowchart of the development of the Proximity Diamond Exploration Model -- Part 1 .... 195
66. Flowchart of the development of the Proximity Diamond Exploration Model -- Part 2 .... 196
67. Flowchart of the development of the Integrated Diamond Exploration Model ...... 197
XX CHAPTER I
INTRODUCTION
North America is a promising diamond exploration frontier,
despite the fact that sustained commercial diamond mining has yet
to become a reality. The Prairie Creek, Arkansas, lamproite pipe
remains the only primary diamond deposit ever worked as a business
enterprise, with historical production in the first half of this
century amounting to 400,000 stones and 200,000 carats (Jackson and
Cairns, 1993). At present, the Prairie Creek body is open to the
public for prospecting under the moniker, "Crater of Diamonds State
Park." Diamond-bearing kimberlite was discovered in the State Line
field of Colorado and Wyoming in 1975 (McCallum and Mabarak, 1976;
Hausel and others, 1985; McCallum, 1991; McCallum and Waldman,
1991); and at Lake Ellen in the Upper Peninsula of Michigan in the
late 1980's (Cannon and Mudrey, 1981; SEG Newsletter, January 1993;
Jarvis, 1993). Currently, "diamond fever" is running extremely
high in Canada with the discovery of possibly economically diamondiferous kimberlite in the Lac de Gras area, near Yellowknife
in the Northwest Territories (Danielson, 1993; Jennings, 1993a,b;
Pell and Atkinson, 1993).
Aggressive diamond exploration is underway both in the United
States and Canada. Canadian programs include the aforementioned
1 2
Lac de Gras diamond play; and kimberlite exploration and evaluation
Initiatives 1n several regions of Alberta and Saskatchewan (Gent,
1991; Morton and others, 1993; Swanson and Gent, 1993), and In
northeastern Ontario (Brummer, 1993). In the United States,
kimberlite exploration and evaluation continue In the Lake Ellen,
Michigan, and Colorado-Wyoming provinces (Coopersmlth, 1991,
1993a,b; Ouskin and Jarvis, 1993; Jarvis, 1993); and the lamproltes
at Prairie Creek, Arkansas, continue to undergo assessment (Jackson
and Cairns, 1993).
Widespread diamond finds In the United States (Sinkankas,
1959, 1976; Gunn, 1967, 1968a,b; Kopf and others, 1990), and Canada
(Sinkankas, 1959, 1976; Strnad, 1991) have suggested that
additional primary diamond deposits may be present In North
America. From an exploration standpoint, area selection --
applying knowledge of the basic controls governing the location and
emplacement of diamondiferous rocks to select areas that are
permissive for their occurrence (Atkinson, 1989) -- Is paramount In
framing a successful diamond exploration program.
Purpose and Objectives
The purpose of this research Is to develop technology that
can be used effectively (1) to select areas permissive for economic
diamond-bearing rocks, specifically, kimberlite and lamproite; and
(2) to delineate zones within permissive areas that are most prospective for primary diamond deposits. Heretofore, area selection has been an analog process, confined to the study and 3
overlay of small-scale maps, by which terranes permissive for the
occurrence of diamond-bearing rocks have been identified.
Furthermore, continent-scale exploration for permissive terrane has
been limited by the availability of digital data, and the means to
manipulate, analyze and display them. Currently accessible
continent-scale datasets, and PC-based geographic information
system software afford a singular opportunity to design and
implement a user-directed automated system for area selection.
Area selection can be performed quickly and elegantly with a
Diamond Exploration Geoscientific Information System (DEGIS). To
this end, a raster DEGIS has been developed for a 2.9 million km*
(2000 km X 1450 km) region of the north-central United States that
comprises all or part of the states of Montana, Wyoming, Colorado,
North Dakota, South Dakota, Nebraska, Kansas, Minnesota, Iowa,
Missouri, Wisconsin, Illinois, Michigan, Indiana, and Kentucky
(Fig. I). Despite sporadic diamond finds in the last ISO years
(Gunn, 1968a,b) and the presence of favorable geological conditions
(Janse, 1991), no economic concentrations of diamonds have been discovered in the north-central United States. Accordingly, the objectives of this research are:
1. to establish a user-adaptable area selection
modeling methodology;
2. to develop diamond exploration models that
integrate punctual, linear, polygonal, and
parametric attributes with the expertise of the
explorationist; Diamond Find □ D Kiwbarlite m rqi4 Hi',i ' ■ Kimbmrlit# n Lamproite Cr«jptoex Str m Carbonatite
County Line
State Border qÏÏTSÎfW - -4 ""»:ookwt N
Figure 1. Map of the study region with known occurrences of alluvial diamonds ("Diamond Find"), diamondiferous kimberlite ("0 Kimberlite"), "barren" kimberlite, lamproite, cryptoexplosion structures ("Cryptoex Str"), and carbonatite. The symbols are not to scale, and represent one or more occurrences. 5
3. to delineate zones within permissive terrane in
which economic kim berlite and lamproite bodies
might be expected to occur; and
4. to isolate specific targets for follow-up study.
The goal of this research is to show that the DEGIS can be
used to develop exploration models that help to ascertain the
potential of the north central United States for economic
kimberlites and lamproites. Locations within permissive terrane
that are prospective for primary diamond deposits can then be
studied in detail using large-scale remote sensing technologies
(including high-resolution airborne geophysics), together with
ground-based geological, geophysical, and geochemical exploration
techniques. The technology that has been developed during this
project can be readily adapted to diamond exploration in other
parts of the world, and beyond its promise as a diamond prospecting
tool, generic parts of the DEGIS should be useful in geoscientific
and geotechnical venues ranging from mineral and hydrocarbon
exploration to mapping of regional geologic hazards.
Diamond Exploration Models
Present diamond exploration models are based on decades of
careful study of the environments that host economic, sub-economic
and barren kimberlites, and recently, potentially diamondiferous
lamproites have been added to the l i s t (Garlick, 1979; Gold, 1984,
Janse, 1984; Nikulin and others, 1988; Kirkley and others, 1991; Mitchell, 1991; Orris and Bliss, 1991; Helmstaedt, 1993). These
models include the following important elements: (1) economic
kimberlite intrusions occur in old (>2.5 Ga) stable continental
cratons, whereas economic lamproite occurrences are found in
younger (1.6-2.5 Ga) areas of ancient mountain building;
(2) kimberlite and lamproite bodies intrude along deep crustal
fractures; and (3) kimberlite and lamproite fields correlate with
regions of low geothermal gradient (interpreted to indicate the
presence of thick lithosphere and crust).
This research has produced three independent diamond
exploration models, the favorable aspects of which are combined to
yield an integrated model. The independent models are created from
(1) empirical^ user-defined knowledge; (2) coincident {stacked)
linear and punctual features genetically associated with kimberlite
and lamproite intrusions; and (3) objective and quantitative proximity measures that gauge the potential of each location in the
study area for kimberlite and lamproite bodies. The integrated model is the user-determined synthesis of the most significant
information from each of the three independent models, and it yields prospective locations within permissive terrane.
Empirical diamond exploration criteria include the following areal characteristics:
1. age of Precambrian basement rocks (Archean, Early
Proterozoic);
2. lithospheric and crustal thicknesses; and 7
3. presence or absence of glacial deposits and
sedimentary rocks.
Linear and punctual features include:
1. geomorphic lineaments and th e ir intersections;
2. faults in Precambrian basement rocks and their
intersections;
3. intersections of lineaments and basement faults;
4. geochemical anomalies in stream sediments (Co,
Ni);
5. cryptoexplosion structures; and
6. bedrock and loose diamond occurrences.
Proximity measures incorporate nearness to linear and punctual features, plus:
1. nearness to the center lines or "hinges" of
crustal upwarps, herein termed positive
horizontal flexure axes ("lineaments" resulting
from alignments of local maxima in a horizontal
plane), derived from surface and basement
topography (crustal upwarps appear to be
favorable sites throughout the world for
emplacement of primary diamond deposits); and
2. distance from carbonatite intrusions.
(Diamondiferous lamproites apparently intrude
within ancient mobile b e lts, whereas economic
kimberlites occur within intra-cratonic settings. 8
Carbonatites generally occur in rift-type
settings (Woolley, 1987); hence, the further a
location is from a carbonatite intrusion, the
greater the likelihood that it is in either a
mobile belt or craton.)
Kimberlite, Lamproite, and Diamonds
To date, economic concentrations of diamonds are known from only two types of primary deposits: kimberlite and lamproite
intrusions. Kimberlite is an ultramafic, inequigranular rock composed of mantle-derived ferromagnesian minerals that include mainly olivine, plus varying quantities of phlogopite, picroilmenite, chromian spinel, pyrope garnet, chromian diopside, and enstatite. Kimberlite may also contain (1) alteration minerals such as serpentine and c a lc ite , plus (2) xenoliths and xenocrysts
(crystals that resemble phenocrysts but are foreign to the rock in which they are found) derived from the crust and upper mantle
(Clement and others, 1984). Eclogite and p erid o tite are two important xenoliths found in kimberlite, as they are recognized as diamond source rocks (Kirkley and others, 1991; Helmstaedt, 1993).
Eclogite is a coarse-grained ultram afic metamorphic rock that is derived from basalt and is composed dominantly of almandine and pyrope garnet plus pyroxene. The great pressures associated with eclogite formation are similar to those proposed for diamond formation. The presence of eclogite xenoliths in kimberlite may mean that eclogite is the product of subducted oceanic crust (a possible source of organic carbon). Peridotite is a phaneritic
igneous rock consisting chiefly of olivine, with accessory garnet
and spinel. Peridotite is considered to be the most abundant
constituent of the mantle, and is the parent material for both kim berlite and lamproite melts (Kirkley and others, 1991).
The term lamproite refers to a group of potassium-rich ultramafic rocks that contain the same mineral assemblage and suite of xenoliths and xenocrysts as kimberlite, plus minerals such as
leucite, sanadine, and p rid e rite (Bergman, 1987; Kirkley and others, 1991). Kimberlites occur mainly as "carrot-shaped" diatremes (breccia pipes that penetrated the crust as low- temperature, gas-rich fluids) and dikes, whereas lamproites occur chiefly as "champagne glass-shaped" vents and dikes.
Diamond is a rare xenocryst in both kimberlite and lamproite magma, and both types of magma are the vehicles by which diamonds are brought to the surface (Meyer, 1985). These magmas must transport diamonds to the surface in a short period (<24 hours), because diamonds apparently are stable within the earth only at temperature and pressure conditions found below depths greater than
100 km. Diamonds are envisioned to form from both organic and primordial carbon in "pools" of peridotite and eclogite in the upper mantle; the recent discovery of solid CO, in a natural diamond by Schrauder and Navon (1993) strongly suggests that diamonds c ry sta lliz e at depths of a t least 220 km. Preservation of 10
diamonds during and a fte r th e ir ascent to the surface is a function
of the rates of decrease in temperature and pressure within the
melt (Kirkley and others, 1991).
ILMer,litas. Kimberlites are known to occur in four localities within the
study region: (1) Colorado-Wyoming; (2) Lake Ellen, Michigan; (3)
Riley County, Kansas; and (4) Missouri Breaks, Montana (Fig. 1).
The State Line district of the Colorado-Wyoming kimberlite province
and the Lake Ellen kimberlite province contain diamonds, but thus
far in sub-economic quantities. Kimberlite magmatism at these
lo c a litie s occurred sporadically from the Devonian through the
Eocene (Meyer, 1976; Jarvis and Kalliokoski, 1988). Locally,
emplacement of kim berlite likely is related to stru ctu ral trends;
however, the apparently random distribution of kimberlite provinces
argues against the presence of continental-scale structural
controls on their localization.
More than 100 Devonian kim berlite intrusions have been
discovered in two main fie ld s within the Colorado-Wyoming kimberlite province (Fig. 1): (1) the State Line field, which
straddles the border between Colorado and Wyoming; and (2) the Iron
Mountain fie ld in southeastern Wyoming. At present, these occurrences represent the greatest concentration of known kimberlite bodies in North America. The kimberlites intrude along a 200 km north-south trend and are hosted by Precambrian granitic and metamorphic rocks of the Front Range and the Laramie Range 11
(Smith and others, 1979). This trend is roughly parallel to the
eastern mountain fro n t, which indicates that kimberlite emplacement
may have been controlled by a deep-seated fracture system that also
dominated the structural evolution of the ranges (McCallum and
others, 1975).
Ten kimberlite occurrences are known from northern Riley
County, Kansas (Fig. 1). The bodies were emplaced in limestone and
shale during the Cretaceous (Brookins, 1970). Each of the
intrusions is oriented normal to the trend of the Abilene
anticline, which is the major structural feature in northern Riley
County. Brookins and Meyer (1974) observed that the kimberlites
are aligned structurally with Precambrian basement rocks, which
suggests that the emplacement of the bodies was controlled by
fractures in these rocks.
Approximately 20 intrusions have been found to date within
the Lake Ellen kimberlite province in the Upper Peninsula of
Michigan (Jarvis, 1993), near the Wisconsin-Michigan border (Fig.
1). These bodies are Jurassic in age and were emplaced southeast
of the Amasa dome, where they are hosted by country rocks that
include Archean gneiss. Early Proterozoic sedimentary and volcanic
rocks, and Early Paleozoic sedimentary rocks (McGee and Hearn,
1984; Jarvis and K alliokoski, 1988).
The Williams kimberlites of the Missouri Breaks area of north-central Montana (Fig. 1) are present as four closely spaced
intrusions of Eocene age th at occur as part of an east-northeast trending swarm of ultramafic alkalic diatremes, dikes, and plugs. 12
Three of the Intrusions are diatremes, and one appears to be a
linear "dike-blow" complex (intrusions breach the surface but with
too little energy to sustain fluidization). The Williams bodies
were emplaced near Thornhill Butte, a dome of uplifted Cretaceous
sedimentary rocks; and at least two of the intrusions appear to
have been localized by fa u lts (Hearn, 1968; Hearn and McGee, 1984).
LamproitGs. in t hfl....S.tud.y...Re.g.l9n
Lamproites within the study region include the Leucite Hills
province of southwestern Wyoming, the Smoky Butte and Froze-to-
Death Butte fie ld s of northeastern Montana, the Yellow Water Butte
fie ld of east-central Montana, and the H ills Pond fie ld of
southeastern Kansas (Fig. 1).
The Leucite Hills province lies at the northern tip of the
Rock Springs Uplift and is Late Pliocene in age. Lamproite there
intruded and extruded upon gently dipping sedimentary rocks that
range from Cretaceous to Eocene. Twenty-two occurrences of cinder
cones, plugs, volcanic necks, lava flows, dikes, intrusive sheets,
and rare pyroclastic deposits have been identified in the area
(Mitchell and Bergman, 1991). The trend of the province is
northwest, which suggests th at emplacement of the lamproites was
controlled by one or more major structures.
The Smoky Butte, Yellow Water Butte and Froze-to-Death complexes (all Tertiary in age) are hosted mainly by sandstones and shales that range from Cretaceous to Tertiary in age. The
lamproites all occur near structures such as synclines, anticlines. 13
and domes (with no evident preference), some of which are cut by
northeast-trending faults. The Hills Pond lamproite cluster in
southeastern Kansas is Late Cretaceous in age, and it also intruded
folded sedimentary rocks, in this case a gently domed sequence of
sandstone, shale, and limestone of Pennsylvanian age (Mitchell and
Bergman, 1991).
D„1 Miands,J.n-t,hq .Study.,R^g Ian
Both in situ and allu v ial diamonds have been recovered from
various localities within the study region (Fig. 1). A summary of
known alluvial stones from the region of interest is presented in
Table 1. Morphologically, the stones range from simple octahedra
to complex hexoctahedra. Complex crystal shapes such as those
described apparently form (1) through resorption during emplacement
of diamond-bearing magma, and (2) by abrasion during transport by
ice or running water (Gurney, 1989).
The vast m ajority of the allu v ial diamonds have been found in
the Great Lakes area, prim arily in Wisconsin and Indiana (Table 1).
A comparison of stones discovered in Wisconsin with those from
Indiana reveals that total stone weight (62.51 ct versus 31.37 ct)
and average stone weight (2.60 ct versus 1.57 ct) are greater in
Wisconsin.
The size d istrib u tio n s of allu v ial diamonds from Wisconsin and Indiana appear bimodal, with many small stones and a few very
large ones (Table 1), which may indicate th at there were two sources for the stones. Until recently, the Great Lakes diamonds 14
Table 1. Alluvial Diamond Occurrences in the Study Region
Name SL. LaL. Lonq? N&i. ÊJL Crystal Form Ref,*
- - - - lA 42.52 -90.67 1 ? ? 1 IL 40.46 -90.67 22 ?? 2 - • - - IL 38.32 -89.12 1 7.38 ? 2
IN 41.07 -86.22 1 <1 ? 3 — IN 40.75 -86.07 1 3.93 ? 2 Stanley IN 39.54 -86.43 1 4.88 octahedron 2 " IN 39.54 -86.43 1.50 ? 2 — — — — IN 39.54 -86.43 1 0.69 dodecahedron 2 — — — — IN 39.54 -86.43 1 ? dodecahedron 2 — — — " IN 39.53 -86.43 1 2.28 hexoctahedron 2 — - - — IN 39.53 -86.43 1 0.14 dodecahedron 2 — - - IN 39.52 -86.45 1.62 ? 2 — - — — IN 39.43 -86.43 1 3.64 ? 2 — — — — IN 39.36 -86.27 1 3.00 ? 2 Maxwell IN 39.35 -86.47 1 3.00 ? 2 Young IN 39.29 -86.29 1 1.66 dodecahedron 2 — — — — IN 39.29 -86.29 1 1.48 hexoctahedron 2 — IN 39.29 -86.29 1 0.19 hexoctahedron 2 — — — — IN 39.29 -86.29 1 0.16 hexoctahedron 2 — — — — IN 39.29 -86.29 1 0.13 hexoctahedron 2 — - — — IN 39.29 -86.29 ? ? 2 - - “ * IN 39.27 -86.15 1 3.07 ? 2 MI 42.88 -85.69 ??? 2 Dowagiac MI 41.98 -86.11 1 10.88 hexoctahedron 2 — — - — WI 45.17 -89.17 0.05 octahedra 4 - * - WI 44.70 -92.18 10.00 ? 2 — — — — WI 44.70 -92.18 1 0.78 hexoctahedron 2 — — — — WI 44.70 -92.18 0.75 ? 2 — — — — WI 44.70 -92.18 1 0.58 hexoctahedron 2 — — — — WI 44.70 -92.18 1 0.09 hexoctahedron 2 “ “ - - WI 44.08 -87.98 ?? 5 Theresa WI 43.52 -88.45 i 21.50 dodecahedron 2 Saukville WI 43.42 -87.94 1 6.47 trisoctahedron 2 — — — — WI 42.93 -89.38 1 3.87 dodecahedron 2 Eagle WI 42.88 -88.47 1 16.25 dodecahedron 2 - - - - WI 42.68 -88.28 1 2.17 tetrahedron 2
WY . 44.15 -105.50 ? ? octahedra 6,7 — — - — WY 41.40 -106.48 2 0.14 octahedra 8
1: Zeitner (1991) Cannon and Mudrey (1981) 2: Gunn (1968a) Finkelman and Brown (1989) 3: H ill (1988) Finkelman (oral commun., 1992) 4: Bendheim (1984) Hausel and others (1985) 15
were assumed to have been derived from as-yet undiscovered
diamondiferous bodies in the James Bay Lowland of Ontario, Canada,
and from those sources they were dispersed southward during the
Pleistocene glaciations (Gunn, 1967). However, the discovery of
diamond-bearing kimberlite in the Upper Peninsula of Michigan (SEG
Newsletter, 1993a) suggests that at least some of the stones may
have come from local sources.
Most of the known kimberlites in the State Line district of the Colorado-Wyoming province (Fig. 1) contain diamonds
(Coopersmith, 1993a). Several of the intrusions have been evaluated for th e ir economic potential as reported by McCallum and
Waldman (1991), including the Schaffer complex and Aultman pipes
(Lincoln, 1982), the George Creek dikes (Carlson and Marsh, 1986,
1989), the Sloan diatremes (McCallum, 1991), and the Kelsey Lake complex (Coopersmith, 1991, 1993a,b). Grades range from 0.005 to
0.2 carat per ton for the State Line kimberlites, with an exceptional grade of 0.181 to 1.351 carats per ton for the George
Creek dikes (McCallum and Waldman, 1991). Economic grades for viable kimberlite mining operations range from 0.1 carat per ton, to several carats per ton. However, grade is not the lone yardstick of economic p o ten tial. Factors such as percentages of gem and near-gem m aterial and volume of ore also are c ritic a l in making judgements of economic v ia b ility (Lincoln, 1982). In fa c t, the value of a diamond deposit is largely a function of the quality, quantity, and size of gemstones. CHAPTER II
METHODS
DEGIS Design
The design of the Diamond Exploration Geoscientific
Information System (DEGIS) was guided by the goals of the research,
namely, (1) to delineate permissive areas within the study region
that may contain economic primary diamond deposits; and (2) to
identify zones within permissive areas that are most prospective
for kimberlite and lamproite intrusions. A raster format was
selected for the DEGIS for the following reasons:
• several of the pertinent datasets are more
readily amenable to raster formatting than to a
vector scheme;
• cartographic modeling operations such as
proximity assessment ("spreading") are much more
efficient with raster datasets than with vector
d atasets; and
• raster GIS software is more affordable than
vector software for PC systems.
A 4 kmX 4 km grid cell was chosen because i t complements the goals of the research, and it adequately represents the quality and coverage of the datasets. The DEGIS database is 362 rows deep by
16 17
498 columns wide, to ta lin g 180,276 grid c e lls; with th is volume of
data, calculations can be performed rapidly, and both primary and
derivative datasets can be stored and retrieved quickly and easily.
The second major factor in database design was cartographic
projection. To preserve intra- and inter-dataset integrity, all
data must share the same projection. An Albers Equal Area
projection was selected because it is well-suited to areas in the middle latitudes that are broader east-west than north-south
(Robinson and others, 1978; Snyder, 1987).
DEGIS Implementation
The DEGIS database was developed to be compatible with two
PC-based raster GIS software packages: MapBox (Decision Images,
1991) and IDRISI (Clark University, 1992). These packages handle datasets as "n" rows by "m" column matrices, with locations (1,1),
(l,m), (n,l), and (n,m) corresponding respectively to the northwest, northeast, southwest, and southeast corners of the study area. Accordingly, the thematic datasets that compose the database are maintained as an ASCII text file in a row-by-row format. The first and last rows of the file delimit the northern and southern edges of the study region; sim ilarly, the f i r s t and last columns define the western and eastern edges of the study region.
The computing platform for this project is an IBM-compatible
PC equipped with 8 MB RAM, an Intel 486 CPU operating at 33 MHz,
212 MB hard drive, 120 MB tape drive, 1.44 MB and 1.2 MB diskette drives, SVGA 1024 x 768 x 256 monitor, and Hewlett-Packard PaintJet 18
and DeskJet 500 printers. A PC with CD-ROM reader in the
Geological Sciences Computing Laboratory was used to access
datasets on CD-ROM and to copy selected datasets to diskette for
subsequent processing.
Thematis .Data...Uygr$ The DEGIS database contains four types of thematic data
layers (Table 2): punctual, linear, polygonal, and parametric.
Sources of thematic data include maps, digital datasets, published
lite ra tu re , and personal communications (Table 3). These data
required varying degrees of processing to convert them to the DEGIS
format, for example, gathering data from maps and literature
followed by keyboard entry, forward and inverse cartographic
projection, linear interpolation, "bin" averaging, and nearest-
neighbor averaging (Table 4). In addition to Figure 1, maps of
thematic data layers are presented in Figures 2-12.
Evaluation of Parametric Surfaces
O ne-tailed F te s ts (Davis, 1986, p. 67-74) were performed on the surface and basement elevation parametric layers to assess the extent to which the original parametric data may have been adulterated in generating the full parametric surfaces. A normal distribution is necessary for the F test to be accurate (Afifi and
Azen, 1979) and the datasets were assumed to have a normal distribution. In an attempt to validate this assumption, the 19
Table 2. Thematic Data Layers in the DEGIS Database
Lam: Description ly p aU I O a U i . ALLPOINT occurrences of allu v ial diamond, punctual 1 diamondiferous kimberlite, kimberlite, lamproite, and carbonatite, plus cryptoexplosion structures
COBALT cobalt concentrations in stream punctual 2 sediments
NICKEL nickel concentrations in stream punctual 3 sediments
LINEARS Landsat lineaments linear 4
GEOLOGY geology of Precambrian basement linear and 5-7 rocks polygonal
GLANOGLA furthest advance of latest polygonal 8 Pleistocene ice sheet
CRUSTTHK crustal thickness polygonal 9
LITHOTHK lithospheric thickness polygonal 10
EARTHSRF elevation of surface of the earth parametric 11
BASESURF elevation of top of Precambrian parametric 12 basement surface 20
Table 3. Sources and Types of Thematic Data Used to Create the Thematic Data Layers In the DEGIS Database
Laver* Source!s) lypÊ ALLPOINT allu v ial diamonds: Gunn (1968a) analog Cannon and Mudrey (1981) analog Bendheim (1984) analog Hausel and others (1985) analog H ill (1988) analog Zeitner (1991) analog R.B. Finkelman (oral commun., 1992) analog diamondiferous kimberlite: Hausel and others (1985) analog Jarvis and Kalliokoski (1988) analog Carlson and Marsh (1986) analog Coopersmith (1991) analog McCallum (1991) analog kimberlite: Smith (1977) analog Cannon and Mudrey (1981) analon Carlson (1983) analog Hausel and others (1981) analog Hearn and McGee (1983) analog Hausel and others (1985) analog Padgett (1985) analog Rogers (1985) analog Nixon (1987) analog Jarvis and Kalliokoski (1988) analog Relnkensmeyer (1988) analog lamproite: Mitchell and Bergman (1991) analog carbonatite: Woolley (1987) analog cryptoexploslon structures: Kidwell (1947) analog Snyder and Gerdemann (1965) analog Freeberg (1966, 1969) analog Bayley and Muehlberger (1968) analog McCall (1979) analog Cannon and Mudrey (1981) analog Grieve (1987) analog Hills and others (1991) analog W.D. Hausel (oral commun., 1992) analog
GLANOGLA Hunt (1979) analog
LINEARS Saunders and Hicks (1976a,b) analog 21 Table 3 (Continued). laysr* $g.»r.ç.fi(.?.), lyca BASESURF Bayley and Muehlburger (1968) analog Sims (1990) analog Sims and others (1991) analog
CRUSTTHK Braile (1989) analog
LITHOTHK National Geophysical Data Center (1989) analog
COBALT Zinkl and Brock (1984), and Averett (1984), d ig ita l as compiled by BHP Minerals personnel
NICKEL Zinkl and Brock (1984), and Averett (1984), d ig ita l as compiled by BHP Minerals personnel
GEOLOGY Reed (1989), and Reed and others (1989) d ig ita l
EARTHSRF National Geophysical Data Center (1989) digital
* Refer to Table 2 for descriptions of layers. 22
Table 4. Processing Techniques Applied to Thematic Data to Produce Thematic Data Layers of the DEGIS Database
layer* Processing Techniques
ALLPOINT a) geodetic coordinates fo r allu v ial diamond, diamond-bearing kimberlite, kimberlite, lamproite, and carbonatite occurrences, plus cryptoexplosion structures forward transformed into IDRISI coordinates.
COBALT a) geodetic coordinates for cobalt concentrations in stream sediments forward transformed into IDRISI coordinates; b) mean values computed for those data sharing the same IDRISI coordinates.
NICKEL a) geodetic coordinates for nickel concentrations in stream sediments forward transformed into IDRISI coordinates; b) mean values computed for those data sharing the same IDRISI coordinates.
LINEARS a) lineaments across study area copied onto stable base from Landsat lineament map; b) geodetic coordinates of lineament "heads and tails," or straight-line segments of arcuate lineaments, determined and recorded; c) geodetic coordinates forward transformed into IDRISI coordinates; d) IDRISI coordinates line-rasterized to generate layer.
GEOLOGY a) digital geodetic coordinates defining lithology and structure of Precambrian basement forward transformed into IDRISI coordinates; b) IDRISI coordinates line-rasterized to define layer of blank lithologie polygons and faults; c) polygons from step 'b' assigned lithologie attributes; d) layer from step 'c' edited to produce final layer that contains faults, contacts, and 23 generalized rock types. 23 Table 4 (Continued).
Layer* Processing Techniques
GLANOGLA a) line defining furthest advance of latest Pleistocene ice sheet across study area copied onto stable base from map of surficial glacial deposits; b) line from step 'a' divided into straight-line segments, with the geodetic coordinates of the ends of these segments determined and recorded; c) geodetic coordinates forward transformed into IDRISI coordinates; d) IDRISI coordinates polygon-rasterized to generate layer.
CRUSTTHK a) contour lines from map of crustal thickness across study area copied onto stable base; b) contour lines divided into straight-line segments, with the geodetic coordinates of the ends of these segments determined and recorded; c) geodetic coordinates forward transformed into IDRISI coordinates; d) IDRISI coordinates polygon-rasterized to generate layer.
LITHOTHK a) 170 surface heat flow values plotted on geodetic grid and manually contoured; b) contour lines divided into straight-line segments, with the geodetic coordinates of the ends of those segments determined and recorded; c) geodetic coordinates forward transformed into IDRISI coordinates; d) IDRISI coordinates polygon-rasterized to generate layer; e) heat flow layer converted to lithospheric thickness based on relationship between lithospheric thickness and continental surface heat flow developed by Chapman and Pollack (1977), and Pollack and Chapman (1977).
EARTHSRF a) surface elevation data (on a 5 min grid of latitude and longitude) forward transformed into IDRISI coordinates; b) missing data (60%) in IDRISI grid filled-in via iterative nearest-neighbor averaging. 24 Table 4 (Continued).
Layer* Processing Techniques
BASESURF a) 0.2 deg grid drafted onto basement elevation maps; b) elevations at grid intersections determined and recorded; c) data merged with surface elevation data to assign elevations to locations where basement rocks exposed; d) grid expanded to 0.1 deg by ite ra tiv e nearest- neighbor averaging; e) geodetic coordinates forward transformed into IDRISI coordinates; f) missing data (83%) in IDRISI grid filled-in via iterative nearest-neighbor averaging.
* Refer to Table 2 for descriptions of layers. 25
Co Pr»i#rn
Sl#t* Border
— 100 km N
Figure 2. Locations from which stream sediment samples are known to contain cobalt in concentrations of at least 1 ppm. 26
Ni Prasant lü%j
State Border H i
■■■IDDkin N
Figure 3. Locations from which stream sediment samples are known to contain nickel in concentrations of at least 1 ppm. 27
Lin*4v«nt
state Border
lOOkm N
Figure 4. Geomorphic lineaments. 28
Mi wing Data I I
Non-Mr chaan
Mrchtan ? H i
Mrchaan « H
Mrchaan 6 H
Mrchaan 4 H i
Mrchtan 3 I I
Mrchaan 2
Mrchtan 1
Contact Hi
Fault H I
State Border IH
mm 100 km
N
Figure 5. Geologic map of Archean basement rocks. See Table 27 in Appendix A for descriptions of the units. 29
m u in g Data
Non- E Prol
E rt-ottro 9
E Protaro 9
E fVotaro ?
E Protaro s
E Protaro 8 % E Protaro 4 E Protaro 3 I— i
E Protaro 2
E Protaro I
State Border
IQDhin N
"-■* '• s s 'S',; k s - m .e w descriptions of the units. 30
Mi «sing Data I I
Non- M/L Prt
L«t« Protaro | H
t1 Protaro 6 H
M fVotaro B H
M Protaro 4 H
M (Votaro 3 1 1 % H Protaro 2 0 # M fVotaro 1
Contact
Fault
State Bordar
100km \ \\* ' ' N
Figure 7. Geologic map of Middle Proterozoic ("M Protero") and Late Proterozoic ("Late Protero) basement rocks. See Table 29 in Appendix A for descriptions of the units. 30
Hi!t M Protaro < M Prot#ro 6 (1 Protaro 4 ■ M Protaro 3 □ % M Protaro 2 H (Votaro I HR Contact ■ Fault ■ 1 Stala Bordar " " " l O D k w i J r.v ,v .v .t N Figure 7. Geologic map of Middle Proterozoic ("M Protero") and Late Proterozoic ("Late Protero) basement rocks. See Table A3 in Appendix A for descriptions of the units. 31 Nen-Glactd I I eiaci«4«<) I # # State Border H " 1 0 0 km N Figure 8. Portion of study region glaciated during the Pleistocene. 32 ?CvK‘>»»W5ww ?»« •••••'V A W AV.V.UV> " Tm a m------* # Slat» Border • w%« %«MAWAV! :m#:a ^^«AVAVAvKvAvffî W,V.V.V& 100 hm = # = # # # # # w .v.»«vi AV.V,V.«V.V.V, »e:as ‘.VAVAVAV, mmNBmm ï^K^KSSwXSXvW. Figure 9. Crustal thickness in kilometers. 33 >180 ■ 1 >100 ... 160 g yAVVMVAV.V.WAWWAVMVAVlTAy.V.V.W.WAW.'J «iViivAVAV*i*Vt VAVAVaV >60 ... 100 m ' VAVA«VAV,«'AVA«VA'A»«*'A»%'%««VM >28 ... 60 □ Hr Sit* ■ HDD km N Figure 10. Lithospheric thickness in kilometers, estimated from surface heat flow ("HF Site” represents location of heat flow measurement(s), solid squares not to scale) 34 1366 .. 3790 8:6 ... :3«Q 615 ... «6 □ 383 . . . 614 ■ 306 ... 392 ■ 221 ... 304 Hi -30 ... 220 H i 8i«« Border Hi 'ion km, N Figure 11. Elevation of the surface, in meters with respect to mean sea level. 35 W ... 3M7 ■■ 109 . . . 396 ^ -300 . . . 104 □ -993 .. -301 B i -1097.. -994 ■ -1929..-1099 H i -7925..-1927 H i Border '100 km N Figure 12. Elevation of the top of the Precambrian basement surface, in meters with respect to mean sea level. 36 datasets were standardized [(observation - mean) / standard deviation)] to enable comparison of the F ratios between the assumed normal datasets and their normalized (standardized) counterparts (Tables 5, 6). The appropriate F ratio is: F = variance/variance, (variance, > variance,) The hypothesis tested was: Hg: variance^,, = variance, against H,: variance,,,, / variance,,,, For the F distribution, as the size of both sample populations approaches infinity, the critical F value approaches unity (Huntsberger and Billingsley, 1977). The result of the F test for each before-and-after pair of datasets strongly suggests the variances of the datasets are equal, because the F value is close to unity (Table 5). The null hypothesis fails to be rejected in favor of the alternative hypothesis. Hence, the processing procedures employed to produce full parametric surfaces do not appear to have significantly altered the character of the original datasets. Furthermore, no appreciable differences in F value are noted between the assumed normal datasets and their normalized counterparts (compare Tables 5 and 6), supporting the assumption that the unstandardized datasets are normal distributions. Despite the apparent lack of difference between the original and final parametric datasets, changes in the data may have occurred on a feature-by-feature basis, as a result of aliasing 37 Table 5. Summary Statistics of Original and Final Datasets Used to Generate Full Parametric Surfaces, and One-Tailed F Value Resulting from the Ratio of Their Variances Full Vacant Dataset* Cell; Cell; Milk Maik Mean Variance F Val, T0P0_0 72296 107980 -30 3790 705 426405 1.04 TOPO_F 180276 0 -30 3790 715 443326 BASE_0 7128 173148 ■7925 3667 -717 1650215 1.03 BASE_F 180276 0 -7925 3667 -634 1692405 * T0P0_0 and TOPO_F: Original and final datasets of elevation of the surface of the earth with minimum, maximum, and mean in meters, and variance in meters* BASE_0 and BASE_F; Original and final datasets of elevation of the top of the Precambrian basement surface with minimum, maximum, and mean in meters, and variance in meters* Table 6. Summary Statistics of Standardized Original and Final Datasets Used to Generate Full Parametric Surfaces, and One-Tailed F Value Resulting from the Ratio of Their Variances Full Vacant Datasat* Cell; Cell; Milk Mâlk Mean Yarian-ce F Val, TOPO os 72296 107980 -1.13 4.72 0.00 1.00 1.00 TOPO.FS 180276 0 -1.12 4.62 0.00 1.00 BASE OS 7128 173148 -5.61 3.41 0.00 1.00 1.00 BASE.FS 180276 0 -5.60 3.31 0.00 1.00 * TOPO_OS and TOPO_FS; Standardized original and final datasets of elevation of the surface of the earth BASE_OS and BASE_FS: Standardized original and final datasets of elevation of the top of the Precambrian basement surface 38 (frequency ambiguity). Aliasing is an inherent property in the surface generation scheme employed herein and in all sampling, gridding, and analytical techniques where continuous phenomena (such as topography) are represented as discrete points (Sheriff, 1991). Crustal Thickness Map The crustal thickness map used in this research (Fig. 9) was digitized from the crustal thickness map of midcontinental North America given by Braile (1989, Fig. 23). This map reflects the synthesis of information from 122 seismic refraction surveys conducted between 1950 and 1985, and from 17 deep seismic reflection profiles assembled between 1975 and 1988, Correlations are not obvious between crustal thickness (Fig. 9) and basement geology (Figs. 5-7) in the study region. For example, thick crust (>50 km) in Montana occurs in conjunction with Archean basement, whereas crust of similar thickness in Colorado and in the Lake Superior region is associated with Proterozoic rocks. Lithospheric Thickness Map According to Bott (1982), five factors contribute to continental heat flow: (1) radiogenic sources in the upper crust; (2) cooling of orogenically-heated continental lithosphere in the aftermath of magmatism and tectonism; (3) uplift and erosion, which cause hot rocks from depth to be brought closer to the surface. 39 and, In the case of erosion, also removes upper crustal radiogenic sources; (4) background heat flux from radiogenic sources In the lower crust and lithospheric mantle; and (5) flow of heat Into the lithosphere from the underlying mantle. The relative contributions of these factors cannot be determined with certainty for a given point on the earth's surface. However, the radiogenic heat component becomes less Important, relatively, for rocks of potential significance In the present study (I.e., old rocks), and this fact together with knowledge of the configuration of geotherms for continents (Chapman and Pollack, 1977), make possible estimates of the thickness of lithosphere beneath the region of Interest. Figure 10 Is a map of estimated lithospheric thicknesses throughout the study region that is based on the relationship between surface heat flow and lithospheric thickness developed by Chapman and Pollack (1977) and Pollack and Chapman (1977). The map was produced from 170 terrain-corrected surface heat flow measurements (National Geophysical Data Center, 1989); If distributed evenly across the region, the measurements would form a grid with 130 km spacing. Elliptical thickness anomalies with at least one axis smaller than 130 km are evident In several states (e.g., in southeastern Iowa and eastern Nebraska), but these anomalies were retained In view of the possibility that similar size anomalies In southeastern Wyoming may reflect lithosphere that extends well Into the diamond stability field. The heat flow values were manually contoured and then digitized to form heat flow polygons. The polygons were converted 40 to estimates of lithospheric thickness as predicted by the intersection of geotherms with a mixed-volatile solidus thought to represent peridotite in the Earth's mantle (Chapman and Pollack, 1977; Fig. 1). The geotherms, which are parametric in surface heat flow, are based on a model that consists of four components: radiogenic upper crust (thickness b « 8 km), granulite facies lower crust {h » 32 km), depleted ultrabasic zone in the uppermost mantle (h = 80 km), and pyrolite mantle (as defined by Ringwood (1962), pyrolite is a hypothetical upper mantle material composed of 80% dunite and 20% basalt). Comparisons of the map of estimated lithospheric thickness (Fig. 10) with maps of Precambrian basement geology (Figs. 5-7) reveal that Archean and Early Proterozoic lithologies generally correlate with lithosphere that is at least 100 km thick -- low heat flow is associated with old rocks and thick lithosphere. The area of high heat flow (and lithosphere estimates that may be too thin) in south-central North Dakota is attributed to the flow of ground water in large aquifers such as the Dakota sandstone (Morgan and Gosnold, 1989; Blackwell and others, 1991). However, Archean and Early Proterozoic granitic plutons also are present in this area, as well as in other areas of presumably anomalous lithosphere such as northwestern and eastern Nebraska, and southeastern Iowa; and these plutons may contain high concentrations of potassium, uranium, and thorium, and thus may not be adequately accounted for in the model employed by Chapman and Pollack (1977). 41 Additional localities where the estimated lithospheric thicknesses may not be properly represented Include the area just north of Lake Superior, the Steamboat Springs area of north-central Colorado, and the vicinity of the Rio Grande rif t In central Colorado (Fig. 10). The locality north of Lake Superior Is centered on Lake Nipigon In south-central Ontario and correlates with an approximately 17,500 km* and 300 m thick complex of Late Proterozoic gabbros and diorltes (Douglas, 1970), and some of the diorltes may contain higher than normal concentrations of uranium. The Steamboat Springs anomaly may be related to the presence of Cenozolc plutonism In this part of north-central Colorado. The lithosphere evidently Is thin (<50 km) In the vicinity of the Rio Grande rift -- with crust essentially riding atop asthenospheric mantle -- based on a cross-section of Interpreted crustal structure along the 37th parallel between longitudes of -80 and -105 deg (Braile, 1989, Fig. 21). In this cross-section, compresslonal seismic velocities decrease from 8.23 km/s to 7.90 km/s and from east-to-west, between -104 and -105 deg, which Is where the eastern edge of the r if t apparently Intersects the Colorado-New Mexico border (37th parallel). The roughly four percent decrease In velocity Is thought to reflect the upper boundary of the low- veloclty zone (transition from lithospheric to asthenospheric mantle) In the upper mantle; velocities In the low-velocity zone typically are about six percent lower than In the outermost mantle (Sheriff, 1991). 42 Overlay of the maps of crustal thickness and lithospheric thickness (Figs. 9, 10) reveals that areas of thick crust (>50 km) also are underlain by lithosphere that is thick (>100 km), except for the elongate zone of thick crust in central Colorado (Fig. 9). Laramide tectonism and subsequent magmatism may be responsible for the apparent thinning of the lithosphere in Colorado (Eggler and others, 1988). Also, high heat flow related to the possible presence of near-surface magma chambers beneath the San Juan volcanic field (Reiter and others, 1991) may result in an underestimation of lithospheric thickness in Colorado and northern New Mexico. In summary, the lithospheric thickness map used in this research (Fig. 10) is based on thickness estimates derived from surface heat flow measurements. Heat flow was chosen as the parameter for estimating thickness because (1) it is the only reasonably continuous pertinent dataset available for the study region, and (2) good and very good quality measurements are available for the region. Seismic tomography also is available for the region (Grand, 1987), but coverage and spatial resolution limit its applicability to only very broad estimates of thickness. Overall, the lithospheric thickness map correlates positively with the ages of basement rocks, with the oldest rocks underlain by the thickest lithosphere. This agreement is particularly important because economic primary diamond deposits throughout the world are found in old rocks (Archean, Early Proterozoic) where the lithosphere is thick (>150 km). 43 Diamond Exploration Models Empirical Diamond Exploration Model The empirical diamond exploration model reflects the wisdom and intuition of diamond explorationists (see Figure 63 in Appendix B for flowchart). The model is the product of five areal characteristics that have been recognized through time as being important in defining terrane that is permissive for primary diamond deposits and, thereafter, amenable to diamond prospecting: 1. age of Precambrian basement rocks, 2. lithospheric thickness, 3. crustal thickness, 4. presence or absence of post-Precambrian sedimentary rocks, and 5. presence or absence of glacial deposits. The relationship between economic diamond-bearing kimberlite and basement age was first articulated by Clifford (1966), who maintained that such occurrences were limited to cratonic areas in which the geologic basement rocks are older than 1.5 Ga. More recently, Janse (1984, 1991) has refined "Clifford's rule" by further constraining the known global occurrences of diamondiferous kimberlite to "Archons" (Archean cratons in which the rocks are >2.5 Ga in age). The database on economic lamproite occurrences is much more limited; the fabulously rich Argyle lamproite pipe is in "Proton" (Proterozoic craton in which the rocks are between 1.6 and 2.5 Ga), as is the Prairie Creek lamproite vent (Janse, 1991). 44 Thus, Archons and Protons, respectively, are where economic kimberlite and lamproite intrusions most likely will be found. Economic primary diamond deposits correlate with thick lithosphere (>150 km) and crust (>40 km); increased lithospheric thickness is reflected by the deepening of isotherms (stable cratons are typified by low geothermal gradients) and the shallowing of the diamond stability field (Fig. 13). The two lens shaped areas at the center of the cross-section are particularly important. The large lens, formed by the intersections of the diamond-graphite equilibrium boundary and the lithosphere- asthenosphere boundary, is believed to be a diamond source region where diamonds of both peridotitic and eclogitic origin are concentrated (Helmstaedt, 1993). Through unknown processes, the diamonds migrate into the smaller lens, outlined by the 1200 deg C isotherm and the diamond-graphite transition, where they are stable, having been stored and preserved for as long as 3.2 Ga (Kirkley and others, 1991). Magmas originating in the upper mantle, most notably kimberlite and lamproite, will transport diamonds to the surface, provided they travel through one or both of these lenses and that the lenses contain diamonds. Accordingly, lithosphere that extends well into the diamond stability field is an essential element in the empirical diamond exploration model. Thick crust generally correlates with old, stable cratons (Fig. 13) that undergo b rittle deformation, that is, they fracture under tectonic stress as opposed to ductile responses such as 45 I— Primary Deposits— j Surface Crust 50 Transport 100 Asthenosphare 150 Diamond^ ^ Source Rock 20 0 L km Figure 13. Cross-section illustrating the relationship between primary diamond deposits and diamond source rock(s). The abbreviations, K, and K„, correspond to economically diamondiferous kimberlite intrusions emanating from the asthenosphere and near the base of the lithosphere, respectively. The designation, L, represents an economic lamproite body with a minimal depth of origin at the base of the lithosphere. The letter, K, depicts a non-economic kimberlite occurrence, whose origin is near the margin of diamond source rock. (Modified from Helmstaedt, 1993, p. 32.) 46 folding. Fractures in cratons act as conduits for the emplacement of diamond-bearing kimberlite and lamproite magmas. Clearly, the best areas for diamond exploration are those underlain by thick lithosphere and thick crust, especially the former. However, important points to remember when considering the lithosphere and crust with respect to diamond exploration include: • the present thicknesses of the lithosphere and crust may not be the same as they were at the time of emplacement of diamond-bearing rocks, for example. Late Mesozoic-Early Cenozoic magmatism may have "thinned" the lithosphere beneath the State Line field of the Colorado-Wyoming kimberlite province (Eggler and others, 1988); and • lithospheric structure may be complex -- Eggler and others (1988) submit that Archean lithosphere may occur beneath non-Archean lithosphere and, as a result, economic kimberlites can occur in areas characterized by non-Archean terranes at or near the surface. The relationship between sedimentary rock cover and economic kimberlites and lamproites is not well understood. According to Janse (1991), the richest primary diamond deposits have been discovered in areas with thick strata. The Argyle lamproite pipe was emplaced into a sedimentary sequence more than 1500 m thick (Boxer and others, 1989). Economic kimberlite fields in the 47 Siberian Platform occur in piles of sedimentary rocks that range from 4000 m to 5000 m in thickness (Frantsesson, 1970); and the country rocks surrounding the famous diamondiferous kimberlite intrusions near Kimberley, South Africa, comprise 600 m of strata (Hawthorne, 1975). Apparently, the sedimentary rocks act as insulators, protecting diamond-bearing intrusions from erosion. For this reason, the presence of sedimentary host rocks is a desirable characteristic in diamond exploration, although primary deposits may be more difficult to detect in such an environment. Intrinsically, the presence or absence of glacial cover has no impact on the occurrence of diamond-bearing rocks. However, glaciated terrane is a "double-edged sword" from an exploration standpoint: glacial processes can broadcast kimberlite and lamproite indicator minerals across a vast area, thereby increasing the likelihood that they will be found (K.E. Witherly, oral commun., 1993), yet simultaneously glaciation will (a) dilute and disperse anomalies by mixing indicator minerals with large volumes of non-anomalous material, and (b) complicate the tracking of indicator minerals to their source(s) because most glacial episodes are characterized by multiple advances. For example, discovery of the first kimberlite intrusion in the Northwest Territories of Canada was the result of nearly a decade of effort by G.E. Fipke, who traced indicator mineral evidence f ir s t recognized in the Mackenzie River across approximately 700 km of the province to its source in the glaciated Lac de Gras area northeast of Yellowknife (Richards, 1992; Wickens, 1993). On the other hand, kimberlites in 48 the State Line field of the Colorado-Wyoming province, where no glacial deposits are present, were located within a few months of the discovery of indicator minerals in stream sediments (Mabarak, 1975; Carlson, 1983). For the purposes of this study, non glaciated terrane has been deemed preferable to glaciated terrane only to the extent that indicator minerals found in non-glaciated terrane should be more readily traceable to their source(s) than their counterparts in glaciated areas. Pre-Modeling Processing In addition to layers derived directly from the thematic data layers of glaciated terrane and lithospheric and crustal thicknesses (Figs. 8-10), the empirical diamond exploration model required input layers for the presence of post-Precambrian sedimentary rocks and for the general ages of Precambrian basement rocks throughout the study region. The thematic data layers of surface elevation and top of Precambrian basement elevation (Figs. 11, 12) were low-pass (mean) filtered to lessen the impact of high- frequency perturbations in these parametric datasets. Filtering was accomplished with the following 3 x 3 grid operator: 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 The correlation coefficients between the original and low-pass filtered layers of surface elevation and basement elevation are 0.9995 and 0.9981, respectively, suggesting that the effect of 49 filtering was minimal. The difference between the low-pass surface elevation layer and basement elevation layer was computed, to create a layer of the thickness of sedimentary rocks across the study area (Fig. 14). The thematic data layer of Precambrian basement geology (illustrated by Figs. 5-7) was reclassified and edited to produce a map of age categories (Fig. 15): Archon (Archean craton >2.5 Ga old), Proton (Early Proterozoic craton >1.6 Ga to 2.5 Ga in age), and Tecton (Middle Proterozoic craton >0.8 Ga to 1.6 Ga old). The transitional categories, Archon/Proton and Proton/Tecton, were employed where rock units with differing ages (for example, Archean and Proterozoic) were grouped together by Reed (1989). Modeling The empirical diamond exploration model (Fig. 16) is the combination of the five factors described above (Fig. 63, Appendix B). To generate the model, the five factors were reclassified in a "power-of-two" scheme (model input reclassified as 1, 2, 4, etc.) and then summed (Table 30 in Appendix C) -- the sums are unique combinations of the five empirical attributes. These sums were reclassified into 15 categories that range from Excellent Archon (grid cells where all five favorable attributes are present) to Possible Proton (grid cells where the only favorable attribute is the presence of Precambrian basement rocks of acceptable age). The empirical model will be discussed fully in Chapter III. 50 2 7 6 6 .. 9S«S 1070 .. 2 707 1273 .. 1849 O 861 ... 1272 ■ W ... 800 21 ... 404 ■ 0 .. . 20 Stat* Border N Figure 14. Thickness of post-Precambrian sedimentary rocks, in meters. Horizontal and vertical seams in northwestern Minnesota reflect (1) transition from generally exposed basement rocks to unexposed basement rocks, and (2) progression from older basement elevation map (Bayley and Muehlburger, 1968) to newer basement elevation maps (Sims, 1990; Sims and others, 1991) 51 Ml»»inq D#t#I I Non-APT ■ Toclon 0 0 Prot/T«ct ^ 0 Proion n Arch/Proi Archon Slot* Bordor :00km ^ f N 'Jiu wm Figure 15. General âgé categories of cratonic Precambrian basement rocks. Categories and ages are: Non-APT: Precambrian rocks, 0.8 Ga or younger Tecton: Middle Proterozoic craton, >0.8 to 1.6 Ga Prot/Tect: Early to Middle Proterozoic craton Proton: Early Proterozoic craton, >1.6 to 2.5 Ga Arch/Prot: Archean to Early Proterozoic craton Archon: Archean craton, >2.5 Ga 52 Ex Archon ■ VG Archon F Archon □ VG A/P B F A/P B Ex P ro to n m VG P ro to n m 6 P ro to n m F P ro to n m P» P ro to n I# Stkto Border B ■ M l D D k l n N Figure 16. Empirical diamond exploration model. The five model attributes are: (1) basement rocks of appropriate age, (2) lithosphere >150 km thick, (3) crustal thickness >40 km, (4) presence of sedimentary rocks, (5) absence of glacial deposits. Potential economic diamondiferous rock types are kimberlite (K) and lamproite (L). Categories; model attributes (1-5); and rock types (K,L) are as follow: Ex Archon; Excellent Archean terrane; 1-5; K VG Archon: Very Good Archean terrane; 1-3; K F Archon: Fair Archean terrane; 1,3; K VG A/P: VGood Archean/Proterozoic terrane; 1-3; K,L F A/P: Fair Archean/Proterozoic terrane; 1,3; K,L Ex Proton: Excellent Proterozoic terrane; 1-5; VG Proton: Very Good Proterozoic terrane; 1-3; G Proton: Good Proterozoic terrane; 1,2; L F Proton: Fair Proterozoic terrane; 1,3; L Ps Proton: Possible Proterozoic terrane; 1; L 5 3 Stacked Diamond Exploration Model The stacked diamond exploration model is the additive overlay of punctual and linear features that are important to the emplacement and discovery of diamond-bearing kimberlite and lamproite bodies (see Figure 64 in Appendix B for flowchart). These features occur primarily on the present surface, with additional structural information obtained from the layer of Precambrian basement geology in the DEGIS database, and they include: • geomorphic lineaments and their intersections; • basement faults and their intersections; • intersections of geomorphic lineaments and basement faults; • anomalous cobalt and nickel in stream sediments; • bedrock and alluvial diamond occurrences; and • occurrences of cryptoexplosion structures. The proximity of kimberlite and lamproite occurrences to major geomorphic lineaments is recognized in virtually all geologic settings where these intrusive bodies are known (Dawson, 1980; Mitchell, 1986; Mitchell and Bergman, 1991), which strongly suggests that the lineaments are deep fractures that act as conduits for migration of kimberlite and lamproite magmas. The presence of lineament (fracture) intersections may be even more important, as they appear to focus the magmatism (Dawson, 1980). Faults and fault intersections in the Precambrian basement surface hold special promise as conduits for possibly diamond-bearing 54 magmas. Locations where both lineaments and basement faults are present have great potential as sites for kimberlite and lamproite intrusions because they represent crustal pathways for magmatism that may conceivably extend to the base of the lithosphere. In this research, geomorphic lineaments (Fig. 4) and basement faults (Figs. 5-7) are features that were mapped independently by Saunders and Hicks (1976a) and Reed (1989), respectively. Geomorphic lineaments are defined by Saunders and Hicks (1976a, p. 326) as " ... 1 inears or groups of aligned 1 inears which are interpreted to have tectonic significance," with 1 inears being "linear or gently curved alignments of topographic features or tones identified on satellite imagery." The basement faults recorded by Reed (1989) include exposed, concealed and submerged faults. Accordingly, the geomorphic lineaments and basement faults are both considered to correlate with ruptures in the lithosphere that are favorable for emplacement of kimberlite and lamproite intrusions. Grid cells where lineaments and basement faults occur together result from the overlay of the two separate layers; such locations are viewed as particularly attractive within the diamond exploration context because two independent lines of evidence reflect the possible presence of very deep fractures for the upward migration of kimberlite and lamproite magmas. Anomalous concentrations of cobalt and nickel in stream sediment samples (Co >50 ppm, Ni >150 ppm), herein defined as more than double the average abundance in crustal rocks (according to Mason (1966), Co = 25 ppm. Ni = 75 ppm), suggest that ultramafic 55 rocks such as kimberlite or lamproite may be present in the drainage basins from which the samples were collected. These anomalies provide a "real-world" component to the stacked diamond exploration model. Primary diamond deposits are "proof-positive" of the presence of appropriate conditions for the emplacement of diamondiferous rocks; and alluvial diamonds are interpreted to have been derived from diamond-bearing rocks in the vicinity. In cases where diamonds have been recovered from till-derived alluvium, the supposition is that the stones have not been transported great distances from their source(s) (tens of kilometers versus thousands of kilometers). Based on years of kimberlite prospecting experience in Canada, Craigie (1993, p. 239) states: Dispersal of minerals by glacial and water erosion has resulted in mineral "halos" around kimberlite pipes or pipe clusters that are several orders of magnitude larger than the bedrock sources of the minerals. Most of the debris eroded from kimberlites by ice was deposited in till within a few kilometres of the kimberlites. Though this supposition is not without risk, the morphology and size of some of the larger (>1 ct) alluvial diamonds discovered in the study region (Table 1) suggest they were not transported long distances. The 28 cryptoexplosion structures (Fig. I) used as input to the stacked model include 16 structures classified by Freeberg (1966, 1969) or McCall (1979) as (1) deeply eroded or buried structures possibly of meteorite impact origin, (2) features 5 6 probably of non-impact origin, (3) structures for which more data are required for classification, and (4) structures incorrectly attributed to extraterrestrial processes; other sources for the locations of cryptoexplosion structures are listed in Table 3. The cryptoexplosion structures are thought to represent sites of "dry" volcanism, the gases involved having been derived from "blind" mafic to ultramafic magmatic intrusions at great depths (Bucher, 1963; Snyder and Gerdemann, 1965). Hence, the sites of the cryptoexplosion structures are interpreted to indicate that the geologic conditions were conducive to the emplacement of potentially diamond-bearing magmas. Pre-Modeling Processing The key part of the pre-model processing was the isolation of lineament intersections and basement fault intersections from the thematic data layer of geomorphic lineaments (Fig. 4), and the layer of faults in Precambrian basement rocks. The latter layer was extracted from the Precambrian basement geology (Figs. 5-7). Possible intersections used in this work are: NW-NS, NW-NE, NW-EW, NS-NE, NS-EW, and NE-EW. Intersections were isolated by applying the following user-defined 3 x 3 filters to the lineaments and basement faults layers: NW-NS NW-NE NW-EW NS-NE NS-EW NE-EW 1 1 0 1 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 57 The value of a given grid cell "GC" in each of the intersection layers is the sum of the products of GC and each of its eight neighbors, with the filte r centered on GC. Grid cells with values of five mark intersections of at least two lineaments or faults that are at least 12 km in length. The six layers that resulted from filtering of each of the two input layers were summed to yield two composite layers of intersections -- one for geomorphic lineaments (Fig. 17) and another for basement faults (Fig. 18). The above filters were not completely successful in detecting lineament intersections (compare Figs. 4, 17) and basement fault intersections (compare Figs. 5, 18). For example, the star-shaped pattern formed by the intersections of three lineaments in south- central North Dakota was not recognized, nor were the two intersections of northwest-northeast basement fault pairs in central South Dakota. Based on visual inspection of the layers of lineaments and basement faults, the performance of the filters with respect to detecting intersections is summarized as follows: l ayer Detected Undetected Mistaken* Total Lineaments 165 (34%) 324 (66%) 5 (03%) 489 Faults 16 (59%) 11 (41%) 11 (41%) 27 * Total lineament intersections detected = 170; total fault intersections detected = 27 The filte rs were able to detect only about 35% of the lineament intersections, with minor confusion; and although 60% of the basement fault intersections were detected, the level of 58 Linnnt Inter State Border ^ 0 • a » ; e 100km 9 ## N # « # # # . # ■ •a <• # ; • •€ % • • . • » Figure 17. Intersections of geomorphic lineaments ("Linmnt Inter"). Dots showing intersections are not to scale. 59 Fault Inter WÊÊ St at* Border 1%:# HOD km N # f t • Figure 18. Intersections of faults ("Fault Inter") in Precambrian basement rocks. Dots showing intersections are not to scale. 60 confusion was very high (41%). The chief drawback of the filte rs, with respect to lineament intersections, was their inability to handle the step-like character of some of the intersections of the rasterized lineaments. With regard to the fault intersections, the main problem of the filters was their inability to distinguish closely-spaced faults (within one or two grid cells of each other), such as those associated with the Midcontinent Rift System (Figs. 5-7), from fault intersections. Clearly, the filters employed in this study need to be augmented with a suite of filters that are sensitive to the step like character of some intersections of rasterized linear features, while simultaneously not identifying step-like individual linear features as intersections. The filters also must be able to distinguish intersections of linear features from closely-spaced linear features. Several alternative approaches may be: (1) digitizing intersections directly, obviating the need for automated detection; (2) using a vector package for processing linear features, in which intersections should be identified as nodes; (3) rasterizing each linear feature as a separate binary layer, which, upon addition of the binary layers, should return values of at least two for those grid cells that are characterized as intersections. Ultimately, digitizing may be the preferred method for capturing intersections, as it provides the opportunity for interpretation of factors such as cross-cutting relationships 61 of linear features, the importance of "T" intersections versus "X" intersections, and the significance of linear features that nearly intersect. Intersections of geomorphic lineaments and basement faults were obtained by (1) adding the binary layers of lineaments and basement faults; and (2) reclassifying the resulting sum layer to extract the locations of intersections, that is, those grid cells with values of two (Fig. 19). To recap, five layers of structural information were included as part of the input for the stacked model; geomorphic lineaments, intersections of geomorphic lineaments, basement faults, intersections of basement faults, and intersections of geomorphic lineaments and basement faults. Four additional layers of data were used as input to the stacked diamond exploration model (Fig. 64, Appendix B). The layer of punctual information (Fig. 1) was reclassified to isolate occurrences of (1) diamonds, and (2) cryptoexplosion structures; and the thematic layers of (3) cobalt and (4) nickel in stream sediments (Figs. 2, 3) were reclassified to yield layers of anomalous concentrations of these elements (Figs. 20, 21). Modeling The stacked diamond exploration model (Fig. 64, Appendix 8) combines the nine elements listed above, after reclassifying the nine elements in a power-of-two format (Table 31 in Appendix C). The stacked model is presented in four figures (Figs. 22-25) that 62 Lin + nt XX H SI ai* Border ^ 0 \ # • • • ilDDkin N : i % . . * !»■•■>•■■■■ r . ^ .. T r •V* /M • ; • # V ' .. # Figure 19. Intersections of lineaments and basement faults ("Lin + Fit XX"). Dots showing intersections are not to scale. 63 Co Anomalg H i Sttio Border E l 11 0 0 k m IM "• » Figure 20. Anomalous concentrations of cobalt (>50 ppm) in stream sediments. Dots showing locations of anomalies are not to scale. 64 Ni Anomaly Slat* Border ' 1 1 100 km N Figure 21. Anomalous concentrations of nickel (>150 ppm) in stream sediments. Dots showing locations of anomalies are not to scale. 65 1 Attribulv Statt Border > 100km IM Figure 22. Locations in the stacked diamond exploration model with at least one favorable attribute. 66 2 A ttribut*» St at* Border • • 1100km %»V # • v ’ # I* { « * # » e ' '••••• • > • • 1 « • Figure 23. Locations in the stacked diamond exploration model with at least two favorable attributes (locations not to scale). 67 S Mtrlbutn Stat* Border H " " 1 0 0 km ▲ IM r •• ■I X . .• . ] %3 W / f Figure 24. Locations in the stacked diamond exploration model with at least three favorable attributes (locations not to scale). 68 4 A ttrib u t» Stat* Border ^ 0 lOQkm N Figure 25. Locations in the stacked diamond exploration model with four favorable attributes (locations not to scale and may represent more than one grid cell with four attributes). 6 9 illustrate an increasing number of coincident attributes (four being the maximum). Graphics production capabilities preclude the display of the 22 combinations of the nine input factors that are present in the stacked model. However, these combinations are discussed in detail in Chapter III. Proximity Diamond Exploration.Model The proximity diamond exploration model assesses the proximity of each grid cell in the region of interest to the linear and punctual features associated with the emplacement and manifestation of primary diamond deposits that were used to develop the stacked diamond exploration model. The proximity model is more inclusive than the stacked model in that it defines zones that appear to be conducive for the emplacement of kimberlite and lamproite bodies. In addition to the features used in creating the stacked model, the proximity model incorporates "distance from carbonatite," plus nearness to the "hinges" or center lines of upwarps in the crust (herein referred to as positive horizontal flexure axes -- PHFA). Crustal upwarps such as domes, anticlines, and horsts are recognized as favorable sites for emplacement of kimberlites and lamproites (Dawson, 1964; Gold, 1984; Mitchell and Bergman, 1991). This observation is not meant to imply that crustal downwarps are unfavorable sites for emplacement of kimberlite and lamproite intrusions; but rather only that erosion of and, perhaps, limited deposition on upwarps increases the likelihood of finding primary 70 diamond deposits in them (Gold, 1984). Upwarps are interpreted to be expressed as positive relief in one or both the earth's surface and the Precambrian basement surface (Figs. 11, 12). Horizontal flexure axes mark diametric changes in the aspect (down-hill direction faced by a location on a surface) of the parametric surfaces, for example, a switch from northwesterly to southeasterly aspect. Vertical flexures (defined by non-diametric changes in aspect such as from north to northeast) define vertical morphology in structures, for example, the sides of a horst block or the non-diametrically opposed faces of a hill or mountain. PHFA are associated with the peaks of domes and crests of upwarps in the surface and basement topographic surfaces; domes are rendered as points and upwarps as lines. Parametric Surface Processing Inclusion of PHFA in the proximity diamond exploration model required processing of the surface and basement topographic surfaces to increase the likelihood of recognizing PHFA. The surface and basement topographic layers were (1) low-pass filtered to reduce the influence of high-frequency perturbations, and (2) separated into their regional and residual components using a 3 x 3 grid operator, as reviewed by Dobrin (1976), and Telford and others (1976) for potential field geophysical data. The grid operator used to generate the regional parametric surfaces is: 0.125 0.125 0.125 0.125 0,000 0.125 0.125 0.125 0.125 71 Residuals were computed by subtracting each of the regional parametric surfaces from its corresponding total surface. The low- pass filtered surfaces were used in computing aspect surfaces for subsequent delineation of horizontal flexure axes, and the positive residual surfaces were used to separate positive from negative horizontal axes. Both applications are discussed in detail in the upcoming section on pre-modeling processing. The summary statistics and correlation matrix of the original, low-pass, regional, and residual parametric datasets are given as Tables 7 and 8. This information indicates that the low- pass filtered and regional datasets are virtually identical for both parametric surfaces, and that the datasets are very representative of the original surfaces. Further, the statistics suggest that the effects of low-pass filtering and regional surface generation were benign with respect to the surface topographic dataset, because virtually all of the variance in the original dataset was preserved in the low-pass filtered and regional datasets. Only 60 percent of the original variance was retained by the low-pass and regional basement datasets; the loss of variance is attributed to filter-induced attenuation of the high-frequency components in the original basement elevation dataset. The direct correlation between the surface and basement datasets (Table 8) is attributed to the fact that basement rocks are exposed in certain parts of the study region (for example, along the Rocky Mountains and across fairly large parts of Wisconsin and Minnesota). However, the correlation also suggests 72 Table 7. Summary Statistics of Low-Pass Filtering and Regional/Residual Analysis of Parametric Surfaces Std. Percent Pa.t,a?ç.t* Min. MaiS-t. Mean BeiL .Yanlanae. ORESRF -30 3790 715 666 443556 M M B LPESRF -17 3683 715 665 442225 >99 RGESRF -16 3683 715 665 442225 >99 RSESRF -568 672 0 22 484 <1 ORBASE -7925 3667 -634 1663 2765569 LPBASE -7672 3626 -634 1298 1684804 61 RGBASE -7656 3624 -634 1293 1671849 60 RSBASE -2674 2561 0 89 7921 <1 * ORESRF, LPESRF, RGESRF, and RSESRF : original, low-pass > regional, and residual datasets of elevation of the surface of the earth with minimum, maximum, mean, and standard deviation in meters, and variance in meters* ORBASE, LPBASE, RGBASE, and RSBASE: original, low-pass, regional, and residual datasets of elevation of the top of the Precambrian basement surface with minimum, maximum, mean, and standard deviation in meters, and variance in meters* Table 8. Correlation Matrix of Low-Pass Filtering and Regional/Residual Analysis of Parametric Surfaces* ORESRF LPESRF RGESRF RSESRF ORBASE LPBASE RGBASE RSBASE ORESRF 1.000 LPESRF 1.000 1.000 RGESRF 0.999 1.000 1.000 RSESRF 0.072 0.043 0.039 1.000 ORBASE 0.234 0.234 0.234 0.018 1.000 LPBASE 0.235 0.235 0.235 0.018 0.998 1.000 RGBASE 0.235 0.235 0.235 0.018 0.998 1.000 1.000 RSBASE 0.008 0.008 0.008 0.000 0.129 0.068 0.061 1.000 Refer to Table 7 for explanation of layers 73 that surface topography mimics basement relief, and by implication supports the hypothesis that areas of positive surficial relief may be favorable sites for diamond exploration. The residual surface elevation dataset accounts for essentially none of the variance in the original dataset (Table 7), which is not surprising in view of the extremely good correlation between its corresponding regional and original datasets. The residual basement elevation dataset correlates in a positive sense with the original dataset of basement elevation, which likewise is expected because the regional basement dataset, although highly correlated with its corresponding original dataset, does not mirror all the variance in the original dataset. Grid cells in the map of positive residual elevation (Fig. 26) are typified by values that range from one meter to ten meters. The higher residual elevations of the Rocky Mountains are apparent in the western one-third of the study region, whereas the lower residual elevations characterize the Great Plains and mid-western states. The map of the positive residual component of the Precambrian basement surface (Fig. 27) suffers from "ringing" (processing- induced oscillations). Generally, ringing results from filtering; however, this explanation seems to be too simple in this instance. If the regional filter was the cause of the ringing, the positive residual component of the earth's surface (Fig. 26) should also ring -- but ringing is not apparent. The proposed explanation for the oscillations is fundamental: the original basement elevation 7 4 I d . . « 7 2 9 a ■ a 1 9 < a . a S □ 4 a i a 9 ■ 3 ■ 2 ■ tÊ Ê Ê Ê Ê Ê m 6t»t* Border HDD km N Figure 26. Positive residual component of elevation of the surface, in meters with respect to mean sea level (blank areas represent locations with residuals <= 0). 75 □ 49 ... 28«1 2 4 . . . 4 6 1 6 . . . 2 3 □ 8 . . . 1 4 B ... S 3 . . . 4 1 ... 2 8tat* Border ■ IQOkm N Figure 27. Positive residual component of elevation of the top of the Precambrian basement surface in meters with respect to mean sea level (blank areas represent locations with residuals <= 0). 76 dataset contained too few data for derived products such as regional and residual surfaces to be free from potential problems. The parametric basement surface Is predicated on 180,276 values that were generated from an Initial dataset of 7128 basement elevations (Table 5). Hence, the attempt to produce a residual surface from an essentially regional one (as opposed to a surface with 180,276 measured values) "pushes the envelope" of regional/residual analysis with a grid operator. However, use of such an operator Is a straightforward way to distinguish positive from negative residuals, In deference to least-squares regression procedures, which require possibly problematic focal (neighborhood) operations to distinguish local maxima from minima. Despite Its shortcomings, the positive residual basement map (Fig. 27) does contain recognizable features such as the Midcontinent Rift System In central Iowa, southeastern Nebraska, and northeastern Kansas. Overall, residual values are higher In the western one-third of the study region, which Is also the case with the positive residual map of surface elevation (Fig. 26). This correspondence Is expected because basement rocks are exposed throughout much of that part of the study region. Pre-Modeling Processing The main challenge In developing the proximity diamond exploration model was to produce the PHFA layers from the low-pass earth surface and Precambrian basement parametric layers. To this end, the first task was to distinguish horizontal from vertical 77 flexures in the surfaces. Grid cells typified by diametric changes in aspect (e.g., NW to SE) define the hinges or axes of horizontal flexures. Development of the aspect surfaces essentially involved computing horizontal gradients on a directional basis -- flexures in the low-pass surfaces correspond to inflections (zero points) in the gradient surfaces. The techniques used herein are similar to those described by Blakely and Simpson (1986) for deducing the edges of source bodies from magnetic and gravity anomalies. However, these investigators were interested in delineating the edges of bodies from potential fields data, and this research focuses on locating the centers of features, namely, the zeniths of upwarps in the parametric surfaces that are interpreted to reflect crustal upwarps (favorable sites for kimberlite and lamproite emplacement). For the purposes of this research, the four possible diametric changes in aspect are: NW to SE, N to S, NE to SW, and E to W. Horizontal flexure axes (HFA) were obtained by executing the following procedure (Table 32 in Appendix C): 1. The aspect (Figs. 28, 29) of each grid cell in the two low-pass parametric surfaces was calculated. 2. Each of the aspect layers was reclassified into nine power-of-two categories that correspond to the following orientations and values: horizontal = 0, NW = 1, N = 2, NE = 4, E = 8, SE = 16, S = 32, SW = 64, and W = 128. 78 Norihww, m Norih w Nor»h««»^ c« n B SoulheaM # '< 5 » vX w A ’AV. South %aw n «Ni»*/* SouthuMt ■ / !(%({*=: B i i We#t ■ H oriionui v^KS\\yWfMS a ,r / X' State Border □ 'lOOhn. N ""'W%WM m a 28. ,,pect Of t,o 79 Northwm m W Ï iiitS i North a mm Northsast # %:œ<% a # # # : # East s Southeast B South m Southwest g # West Horizontal □ f/%%% State Border □ V . ¥¥tWt¥i!i " f 'wmmà — 1 0 0 km urm, N tt fri. m r - Figure 29. Aspect of the Precambrian basement surface. The "wedge-like" appearance of the northern edge of the map is an artifact of data processing (original basement elevation data were extrapolated northward from the U.S. border and produced spurious aspects in the Canadian portion of the study area). 80 This classification strategy produced unique sums that indicate specific changes in aspect. For example, a northerly aspect combined with a southerly aspect equals the unique sum of 34 (2+32). This characteristic is essential in recognizing diametric changes in aspect, and hence HFA. 3. The power-of-two aspect layers were processed with the following 3 x 3 filters: NW-SE N-S NE-SW W-E ICO 010 001 000 000 000 000 101 001 010 100 000 Grid cells with one of the following sums were recognized as locations of diametric changes in aspect and, therefore, HFA: 17 (NW-SE), 34 (N-S), 68 (NE-SW), and 136 (E-W). Each power-of-two aspect layer was filtered four times, producing four component layers for each of the original two power-of-two aspect layers (8 layers total). 4. Each of the four component layers derived from the two power-of-two aspect layers from step 3 was reclassified to isolate HFA. 5. The four reclassified component layers for each of the two power-of-two aspect layers from step 4 81 were added together to yield two HFA layers (Figs. 30, 31), one for each original parametric surface. The final task was to separate positive from negative horizontal flexure axes (Table 33 in Appendix C describes this procedure). Positive horizontal flexure axes (PHFA) were recovered by examining the values in the corresponding residual components of the two parametric surfaces at the locations where HFA are present -- PHFA are defined as HFA with positive residual values -- and they are thought to denote the crests of crustal upwarps. Inspection of the PHFA derived from the low-pass filtered earth's surface reveals that good agreement occurs between the centers of positive-relief features and the locations of PHFA (Fig. 32). It is important to keep in mind that local maxima occur within the range of values depicted by shades on the map; for example, the cluster of PHFA in north-central Iowa and the many PHFA throughout the Rocky Mountains in the western part of the study region. A close-up of the Wisconsin-Upper Peninsula of Michigan area (Fig. 33) clearly shows that PHFA correlate with positive relief in surface topography. Examination of the PHFA derived from the low-pass filtered Precambrian basement surface (Fig. 34) also indicates that good agreement occurs between the PHFA and positive-relief features. PHFA do not occur in the major basins (Michigan, Illinois, Williston, Powder River, Denver) throughout the region of interest, with the possible exception of intra-basin structural features such 82 HFA Slat* Border ■nlQ DIdn Jk. N Figure 30. Horizontal flexure axes ("HFA") derived from the surface. 83 UFA & iij State Border ■IDOkm N 1 -e y. .1 % .' *■/' ..>• .V «• :7vi A, , .V , S 7 1 7 : j I ... I' / ... i ‘ f I .i:‘ \è \.“ Æ :•. I Figure 31. Horizontal flexure axes ("HFA") derived from the Precambrian basement surface. The HFA at the northern edge of the map are artifacts of data processing (see Figure 29 for explanation). 84 1367 .. 3d83 I H 817 . .. 1366 ^ 0 614 . .. 816 □ 383 . . . 613 ■ 306 ... 393 ■ 221 ... 304 ■ -17 ... 220 ■ ■ Stats Bordar 1 I •100 km N Figure 32. Positive horizontal flexure axes ("PHFA") on low-pass filtered elevation of the surface, in meters with respect to mean sea level. 85 1367 .. 3693 B17 ... 1366 383 ... 613 306 ... 393 St«t* Border I I Figure 33. Close-up of Wisconsin-Upper Peninsula of Michigan region, featuring positive horizontal flexure axes ("PHFA") on low-pass filtered elevation of the surface, in meters with respect to mean sea level. Wisconsin-Illinois border is 232 km in length. 86 $07 ... $020 106 . .. 366 -290 ... 105 □ -662 .. -288 ■ -1097.. -663 ■ -1826..-1098 ü -7672..-1925 H i PHTA H Slat* Border I I 100k*, N Figure 34. Positive horizontal flexure axes ("PHFA") on low-pass filtered elevation of the top of the Precambrian basement surface, in meters with respect to mean sea level. 87 as anticlines and horsts (for example, note PHFA in the Williston basin in western North Dakota, eastern Montana and northeastern Wyoming). A close-up of Nebraska (Fig. 35) accentuates the relationship between PHFA and the centers of positive-relief features in basement topography; numerous PHFA occur on basement "highs," most notably on the northwest-trending ridge in the western half of the state that shallows to the northwest and, where exposed, forms the Black Hills of southwestern South Dakota and northeastern Wyoming (Fig. 34). Modeling The proximity diamond exploration model is the most complex, quantitative, and objective of the models developed herein (see figures 65 and 66 in Appendix B for flowcharts). The basic steps in the formation of the proximity model are: (1) generation of proximity surfaces; (2) natural logarithmic (nlog) transformation of these surfaces; (3) standardization of the nlog surfaces; (4) summation of the standardized proximity surfaces, and (5) standardization of the total proximity surface. This procedure is enumerated in Table 34 of Appendix C. Each grid cell in the proximity model has a Z-score that reflects its overall proximity to attributes that are believed to be favorable for the emplacement and discovery of potentially diamond-bearing kimberlite and lamproite bodies. 88 367 ... 3626 H 106 ... 366 1 ^ -298 ... 106 □ -662 .. -288 ■ -1087.. -663 ■ -1926..-1088 ■ -7672..-1926 ■ pm ■ Stmt# Border I I Figure 35. Close-up of Nebraska, featuring positive horizontal flexure axes ("PHFA") on low-pass filtered elevation of the top of the Precambrian basement surface, in meters with respect to mean sea level. Nebraska-Wyoming border is 224 km in length. 89 Twelve proximity surfaces formed the input to the proximity diamond exploration model. Production of a proximity surface was straightforward, for example, grid cells that define a lineament are zero distance from the lineament; grid cells immediately adjacent to the lineament correspond to a distance of one cell to or from the lineament and were assigned a value of one (Fig. 36). More distant grid cells were assigned higher values, based on the concept of Euclidean (straight line) distance between these cells and the closest "lineament cell." All of the proximity surfaces in the proximity model were generated this way. The weighting of distances is not the same among the proximity layers and, therefore, they cannot be simply added to construct the proximity model. As an example, a distance of 10 cells in the layer of proximity to lineaments is not necessarily equivalent to a distance of 10 cells in the layer of proximity to nickel anomalies. The mean and standard deviation of the data in the two layers are not necessarily equal; and hence, distances in the proximity layers had to be converted into a common measurement. Standardization [(observation - mean) / standard deviation] was the transformation used to produce the common measurement. Ideally, this transformation will convert data to values centered on a mean of zero with unit dispersion about the mean. Two-tailed t tests of the means were used to evaluate the normality of the original proximity datasets (Table 9); extreme t-values strongly indicate that the datasets do not conform to a normal distribution 90 LinvMvnt □ 1 • I ■ 2 3 4 28 3 naokM N Figure 36. Proximity of grid cells to geomorphic lineaments (distance values are in cells). 9 1 Table 9. Summary Statistics of Original Proximity Datasets Standard Datasgt* MilL. Max, Mean Dev.latipn .t.zy.a.l,ue LINEARS 0 29 2.8128 3.0093 396.8645 XXLINEAR 0 84 20.0589 12.7216 669.4749 BASEFLTS 0 94 13.1637 14.2502 392.2165 XXBFLTS 0 175 59.2777 36.1807 695.6380 XXLINFLT 0 96 18.4944 I7.I7I8 457.2917 ESRFPHFA 0 44 5.9570 5.1832 487.9761 BASEPHFA 0 67 15.4462 12.2984 533.2636 COANOM 0 179 72.0496 38.9124 786.1628 NIANOM 0 192 58.9683 38.4782 650.6880 DIAMOND 0 198 76.2102 45.4171 712.4635 CRYPTOEX 0 141 53.9487 30.4586 752.0383 CARBNVRT 0 248 155.3884 49.8195 1324.3055 LINEARS Landsat lineaments XXLINEAR intersections of lineaments BASEFLTS faults in Precambrian basement rocks XXBFLTS intersections of faults in Precambrian basement rocks XXLINFLT intersections of lineaments and faults ESRFPHFA positive horizontal flexure axes (PHFA) in the surface of the earth BASEPHFA: PHFA in the Precambrian basement surface COANOM: anomalous cobalt concentrations in stream sediments NIANOM: anomalous nickel concentrations in stream sediments DIAMOND: alluvial and in place diamond occurrences CRYPTOEX: cryptoexplosion structures CARBNVRT: distance from carbonatite occurrences 92 (critical t value = 3.2910 at infinity and significance level of 0.001). Visual inspection of histograms of the original proximity datasets indicated that they approximate lognormal frequency distributions (Fig. 37), and thus the 12 proximity layers were transformed to their natural logarithmic (nlog) value prior to standardization (Fig, 38; one added to each value in original proximity datasets to avoid having to take the nlog of zero). The summary statistics of the original, nlog transformed and standardized proximity layers are reported in Tables 9, 10, and 11. Dispersion about the mean (standard deviation) in the original proximity layers is least for the lineaments and PHFA layers, and greatest for the basement fault intersections, and for the geochemical anomaly (Co and Ni), diamond occurrence, and distance from carbonatite layers. This is not surprising, as the lineaments and PHFA layers have the most "targets" from which distances are measured, whereas the basement fault intersections, geochemical anomaly, diamond occurrence, and distance from carbonatite layers contain the fewest targets. The nlog transformation (Table 10) was effective in moving the means of the datasets closer to zero, and the standard deviations closer to one. The standardization procedure was generally successful in transforming the nlog proximity layers into layers with standardized normal distributions (Table 11). The means of all the standardized proximity datasets are now zero or very close to zero, and the standard deviations are essentially one, except for the distance from carbonatite layer. And even in 93 6 0 0 0 0 5 0 0 0 0 4 0 0 0 0 >. ü c 3 3 0 0 0 0 O" (D 20000 10000 0 I I I I I I I____ I I_____I 1------1------1------1------1 -5 0 5 10 15 20 25 30 Distance Figure 37. Histogram of proximity to geomorphic lineaments (distance in cells). 94 6 0 0 0 0 5 0 0 0 0 g " 4 0 0 0 0 c g 3 0 0 0 0 ^ 20000 10000 j I I I I I I I I I I I I I I I I I I I I I I I -200 -100 0 100 200 300 400 Stnd_Dist Figure 38. Histogram of standardized proximity ("StndJDist") to geomorphic lineaments (distances have been multiplied by 100 for illustrative purposes). 95 Table 10. Summary Statistics of Nlog Transformed Proximity Datasets Standard Dataset" Minimum Maximum Mean Deviation LINEARS 0 3.4012 1.1148 0.7656 XXLINEAR 0 4.4427 2.8484 0.8864 BASEFLTS 0 4.5539 2.1713 1.1266 XXBFLTS 0 5.1705 3.8678 0.9491 XXLINFLT 0 4.5747 2.6096 1.0384 ESRFPHFA 0 3.8067 1.7185 0.8310 BASEPHFA 0 4.2195 2.5122 0.9627 COANOM 0 5.1930 4.0946 0.9007 NIANOM 0 5.2627 3.8440 0.9647 DIAMOND 0 5.2933 4.1009 0.9815 CRYPTOEX 0 4.9558 3.8139 0.8929 CARBNVRT 0 5.5175 4.9812 0.7015 * Refer to Table 9 for descriptions of layers. Table 11. Summary Statistics of Standardized Proximity Datasets Standard Dataset* Minimum Maximum Mean Deviation LINEARS -1.4561 2.9864 0.0009 1.0527 XXLINEAR -3.2134 1.7986 -0.0003 0.9447 BASEFLTS -1.9273 2.1148 0.0004 1.1115 XXBFLTS -4.0752 1.3725 -0.0003 0.9906 XXLINFLT -2.5131 1.8924 -0.0000 0.9729 ESRFPHFA -2.0680 2.5128 0.0004 1.0076 BASEPHFA -2.0695 1.7735 -0.0001 0.9772 COANOM -4.5460 1.2194 -0.0001 0.9896 NIANOM -3.9847 1.4706 -0.0002 0.9920 DIAMOND -4.1782 1.2149 -0.0002 1.0123 CRYPTOEX -4.2714 1.2789 -0.0002 0.9633 CARBNVRT -7.1008 0.7644 -0.0002 0.7993 * Refer to Table 9 for descriptions of layers. 9 6 this case, Improvement occurs, that is, the standard deviation is closer to one (0.7993) than the corresponding value for the nlog dataset (0.7015). The final step in generating the proximity diamond exploration model was to standardize the "raw" proximity model (Fig. 39) that was assembled by summing the 12 standardized proximity layers. The raw proximity model has a mean and standard deviation of 0.0001 and 4.2340, respectively, and standardization of the raw model produced a final proximity model (Fig. 40), with a mean of 0.0000 and standard deviation of 1.1482. The mean of zero, standard deviation of nearly one, and bell-shaped character of the histogram of data (Z-scores, Fig. 41) that compose the final model plainly suggest that the final proximity model has a normal distribution. Therefore, normal probability theory (Huntsberger and Billingsley, 1977) can be applied to assess which grid cells in the study region have anomalous Z-scores -- Z-scores that reflect the anomalous proximity of these cells to characteristics associated with kimberlites and lamproites. Integrated DiamondJExploratipn Model The integrated diamond exploration model is the synthesis of the empirical, stacked, and proximity diamond exploration models. It combines subjective, user-specified diamond exploration criteria with (1) exploration targets that are defined by the coincidence of punctual and linear attributes favorable for the occurrence of potentially diamond-bearing rocks, and (2) objective, quantitative 97 <-2.0 □ -2.0 .. <0.0 >■0.0 Slat* Border i 1100km N Figure 39. Unstandardized ("raw") proximity diamond exploration model (values are total proximity scores). 98 2.0 .. .rsatAw >-0.0 iÜ SS 8tat* Bordtr VVW.V.V.VA '100 km iW.V.VAWAWA*, •• AV.VAV.W.V.V.V.' ü# „ VtWtVtVAWAV«> .. A avav.C%w.’H * H { w H * a' SHwM N M AV.WW^^g.V.V, WAVAW AVAV/.IV a r {‘.VAV.VAW WVWAVWAJ\J'*ÂV«V«V>V«V.Vi{ AVAWAVAV.WA1 WAA'AVAVS’W WA*V«V«VAVAVc Figure 40. Standardized proximity diamond exploration model (values are Z-scores). 99 8 0 0 0 7 0 0 0 6 0 0 0 5 0 0 0 >- ü c ® 4 0 0 0 c r 0) L. ^ 3 0 0 0 2000 1000 0 ülll II 1111111111111111111111111111111 i-j -400-300-200-100 0 100 200 300 400 Z-Score Figure 41. Histogram of standardized proximity diamond exploration model (Z-scores have been multiplied by 100 for illustrative purposes). 1 0 0 measures of proximity to punctual and linear features that are known to be associated with the emplacement and manifestation of kimberlite and lamproite bodies. Modeling The integrated diamond exploration model required little preparation beyond that applied in the development of the empirical, stacked, and proximity models. The following steps were employed in the creation of the integrated model (see Figure 67 in Appendix B for flowchart, and Table 35 in Appendix C for syntax): 1. The empirical model (Fig. 16) was reclassified to create a layer of excellent to good locations in Archon, Archon/Proton, and Proton (Fig. 42). These locations are permissive for the occurrence of economic kimberlite and lamproite intrusions. Prospective locations are those permissive cells that additionally are classified as hosting a stacked anomaly, or as having anomalous proximity. Incorporation of the stacked and proximity models is described in steps 2 and 3. 2. The stacked model (Figs. 22-25) was reclassified to generate a layer of stacked anomalies (Fig. 43). Stacked anomalies occur where four of the punctual and linear attributes that are associated with kimberlite and lamproite bodies coincide (four being the maximum number). 1 0 1 Ex Archon ■1 VG Archon s VO A/P n Ex Proton ■ VO Proton ■ 6 Proton ■ Stat* Bordar ■ ""lOOkh, N Figure 42. Locations from the empirical model that are permissive for the occurrence of economic primary diamond deposits. Minimal attributes are (1) basement rocks of appropriate age and (2) lithosphere >150 km thick; Other attributes may include (3) crustal thickness >40 km, (4) presence of sedimentary rocks, and (5) absence of glacial deposits. Potential economic diamondiferous rock types are kimberlite (K) and lamproite (L). Categories; attributes (1-5), and rock types (K,L) are: Ex Archon: Excellent Archean terrane; 1-5; K VG Archon: Very Good Archean terrane; 1-3; K VG A/P: VGood Archean/Proterozoic terrane; 1-3; K,L Ex Proton; Excellent Proterozoic terrane; 1-5; L VG Proton: Very Good Proterozoic terrane; 1-3; L G Proton: Good Proterozoic terrane; 1,2; L 1 0 2 I+IX+F+XX L+r+FK+XX ■ L+F*XX+D □ L+F+XX+CS ■ St at* Bordar " 1 0 0 km ▲ N Figure 43. Anomalies in the stacked diamond exploration model (squares showing anomalies are not to scale and represent one or more locations). Categories and their explanations are: L+LX+F+XX: lineament, lineament intersection, basement fault, and intersection of lineament and basement fault L+F+FX+XX: lineament, basement fault, basement fault intersection, and intersection of lineament and basement fault L+F+XX+D: lineament, fault, intersection of lineament and basement fault, and diamond occurrence L+F+XX+CS: lineament, fault, intersection of lineament and basement fault, and cryptoexplosion structure 103 3. The proximity model (Fig. 40) was reclassified to produce a layer of anomalous proximity (Fig. 44). Anomalous locations are those with absolute Z- scores that are greater than two standard deviations from the mean -- in the negative half of the standard normal distribution. This layer of anomalous proximity holds 3810 cells, roughly two percent of the original population of 180,276 grid cells. The extreme negative Z scores for the anomalous proximity cells mean that they are much closer to features known to correlate with kimberlite and lamproite intrusions than cells with Z-scores between 0 and -2 standard deviations. Cells with positive Z-scores are not close to favorable features and thus are not considered to be anomalous. 4. The layers generated in steps 1, 2, and 3 were combined and reclassified to produce the integrated diamond exploration model (Fig. 45), which highlights permissive terrane and prospective grid cells within such terrane. The four permissive areas that are present in the integrated model will be closely examined in Chapter III. 104 Prox Anomaly Slat* Bordtr ilDDIcm N f . ff Figure 44. Anomalies in the proximity diamond exploration model (locations with Z-scores < -2.00). 105 8P U8 Proton BH SP 0 Proton C Z] P Ex Archon P W0 Archon P 09 Proton p 0 Proton Ex Archon Ex Proton US Archon VG A/P VG Proton 6 Proton m St#t* Gordon lio a k in IM Figure 45. Integrated diamond exploration model, featuring permissive terrane and prospective locations within such terrane (P: Prospective, SP: Stacked and Prospective; SP locations not to scale). See Figure 42 for explanation of categories. CHAPTER III RESULTS AND INTERPRETATIONS The Empirical, Stacked, and Proximity diamond exploration models are independent assessments of the potential for diamond- bearing rocks in the study region. The empirical model (Fig. 16) is the fusion of five factors thought globally to be important in the exploration for economic kimberlites and lamproites; the stacked model conjoins linear structural features and geologic and geochemical punctual elements that commonly are associated with the emplacement of kimberlite and lamproite intrusions (Figs. 22-25); and the proximity model uses the concepts of (1) closeness to desirable punctual and linear attributes, and (2) distance from an undesirable feature to assign standardized proximity values to each grid cell in the region of interest (Fig. 40). The Integrated Diamond Exploration Model (Fig. 45) is the synthesis of the three independent models. The empirical model is the foundation of the integrated model, because it focuses on the factors that are most germane to diamond exploration, and the stacked and proximity models aid in refining the empirical model by isolating grid cells that exceed minimum area selection criteria. 106 107 The results of the exploration models and their interpretations are presented in the following order: 1. classification of grid cells in the empirical, stacked, and proximity models; 2. cross-tabulation of known occurrences of alluvial diamonds, diamondiferous kimberlite, kimberlite, lamproite, carbonatite, and cryptoexplosion structures with the three independent models; 3. analysis of anomalies in the independent diamond exploration models; and 4. analysis of the Integrated Diamond Exploration Model. C lassification of Independent Diamond Exploration Models Emp..ir.1çal D iamaod. ExoJoration Model The Empirical Diamond Exploration Model (Fig. 16) identifies many locations in the study region that may be favorable for economically diamondiferous kimberlite and lamproite (Table 12). Grid cells classified as Excellent Archon or Very Good Archon^ and Excellent Proton through Good Proton have the highest potential for the occurrence of kimberlite and lamproite, respectively, and the transitional category. Very Good Archon/Proton also may contain kimberlite and lamproite bodies. These categories are considered to be permissive for economic primary diamond deposits, as discussed in Chapter II (Fig. 42), and they will be addressed in detail in this chapter. 108 Table 12. Classification of Grid Cells in the Empirical Diamond Exploration Model No. of Percent Area Class Attributes* Cells of Total fkm^ Exlnt Archon la ,2-5 163 <1 2608 VGood Archon la , 2, 3 9437 5 150992 Fair Archon la,3 35836 20 573376 VGood Arch/Prot lb,2, 3 614 <1 9824 Fair Arch/Prot lb,3 3836 2 61376 Exlnt Proton lc,2-5 36 <1 576 VGood Proton lc,2,3 6476 4 103616 Good Proton lc,2 3862 2 61792 Fair Proton lc,3 41797 23 668752 Poss Proton Ic 1 « 1 16 Other variable 78218 43 1251488 Other.01 2-5 63 <1 1008 Other.02 2-4 13911 8 222576 Other.03 2,3 8895 5 142320 Other.04 2,4 4039 2 64624 Other.05 2 604 <1 9664 Other.06 3-5 22418 12 358688 Other.07 3,4 12373 7 197968 Other.08 3,5 881 <1 14096 Other.09 3 217 <1 3472 Other.10 4,5 1993 1 31888 Other.11 4 12804 7 204864 Other.12 5 20 <1 320 * la: Archean basement rocks lb: Archean/Early Proterozoic basement rocks Ic: Early Proterozoic basement rocks 2: Lithosphere >150 km thick 3: Crust >40 km thick 4: Presence of post-Precambrian sedimentary rocks 5: Absence of glacial deposits variable: non-qualifying attributes 109 Excellent locations in Archon and Proton (Table 12) are those where basement rocks of requisite age for primary diamond deposits, "diamond-friendly lithosphere and crust" (respective thicknesses >150 km and >40 km), and post-Precambrian sedimentary rocks are present, and where the surface rocks are not obscured by glacial cover (Fig. 16). l^ery Good locations in Archon, Archon/Proton, and Proton are typified only by basement rocks of requisite age for primary diamond deposits, plus the presence of diamond-friendly lithosphere and crust. Locations characterized as Good Proton are associated with Proterozoic basement rocks (favorable for economic lamproite intrusions) and diamond-friendly lithosphere. These six categories compose 11 percent of the study region (almost equally divided between Archean and Proterozoic terranes), and they represent 329,408 km' of acceptable real estate for economic primary diamond deposits. Economic kimberlites and lamproites are believed to come from lithospheric depths in excess of 150 km (Boyd and Gurney, 1986; Mitchell, 1991; Levinson and others, 1992); therefore, according to these workers, a prerequisite for the occurrence of primary diamond deposits is the presence of lithosphere that extends well into the diamond stability field (herein referred to as diamond-friendly lithosphere, Fig. 13). This convention has been adopted in the current work, and grid cells underlain by diamond-friendly lithosphere encompass 27 percent (769,600 km') of the study region (Table 12), more than doubling the area identified in the preceding paragraph. This may mean that at least some of the alluvial 1 1 0 diamonds recovered from the Great Lakes part of the study region are of local origin -- diamond-friendly lithosphere (Fig. 10) underlies eastern and northern Minnesota, the entire state of Wisconsin, the northern half of Iowa, the Lake Michigan basin and all of the Upper Peninsula of Michigan, plus a wedge-shaped portion of northwestern Indiana. The presence of appropriate lithospheric thickness is important in the search for primary diamond deposits, yet it must be noted that some researchers believe that lithospheric thickness may have changed through time. For example, Eggler and others (1988) contend that the thickness of the lithosphere can change as a consequence of tectonomagmatism. Hence, the presence of primary diamond deposits cannot be discounted in places such as southern Michigan, central Indiana, and western and southern Illinois, where alluvial diamonds have been found (Fig. 1), but where the present lithosphere (Fig. 10) does not appear to extend deeply into the diamond stability field (Fig. 13). Economic kimberlites and lamproites also are emplaced in what is herein termed "diamond-friendly crust" (Fig. 9) -- crust that typically is old, cool, brittle, and thick (generally considered to be >40 km), according to Mitchell (1991). However, the thickness requirement for diamond-friendly crust is unclear; research on the evolution of Archean and Proterozoic crust by Durrheim and Mooney (1991) reveals mean crustal thicknesses of 35 km and 45 km, respectively. The diamond-rich Archean Kaapvaal craton in South Africa, with an average crustal thickness of 37 km, was one of the Ill regions investigated by these workers. Hence, the age and brittleness of the crust may be more important characteristics than thickness for the emplacement of economic primary diamond deposits. The older and more rigid the crust the more susceptible it is to fracturing; and deep crustal fractures are imperative for the ascent of potentially diamondiferous magmas. Locations classified as Excellent Archon through Fair Proton (Fig. 16) contain rocks of appropriate age coupled with diamond- friendly crust (Table 12). These categories represent 54 percent of the study region (1,571,120 km'). All grid cells in the region of interest that are underlain by Archean basement rocks also are associated with diamond-friendly crust, but not always with diamond-friendly lithosphere (e.g.. Fair Archon). Of the 52,136 grid cells within areas of Proterozoic basement rocks, 3863 (7%) do not correlate with diamond-friendly crust, yet, except for one possibly misclassified cell, the 3863 locations do correlate with diamond-friendly lithosphere. In this research, the moderately conservative combination of basement rocks of appropriate age and the presence of diamond- friendly lithosphere, which underlies 11 percent of the study region, is viewed as the essential empirical criterion for the emplacement of economic diamond deposits (Fig. 42). However, several combinations of age of basement rocks, presence of diamond- friendly lithosphere, and presence of diamond-friendly crust can be considered as appropriate for the occurrence of diamonds (Table 13, Fig. 16). The joint occurrence of basement rocks of appropriate 1 1 2 Table 13. Scenarios of Terranes Permissive for the Occurrence of Primary Diamond Deposits No. of Percent Area Scenario* Cell; Of Total (km') 1-3 16726 9 267616 1,2 20588 11 329408 2,3 27075 15 433200 2 39595 22 633520 1,3 98195 54 1571120 1 102058 57 1632928 2 156953 87 2511248 * 1: Basement rocks of requisite age (either Archean or Early Proterozoic) 2: Diamond-friendly lithosphere (>150 km thick) 3: Diamond-friendly crust (>40 km thick) 113 age with diamond-friendly lithosphere and diamond-friendly crust renders only nine percent of the study region "in the ball park," whereas, if one believes only diamond-friendly crust is required, 87 percent of the study area is "in play." Stacked ,D.1,an)Q,nd,.Expl9ratian_Madel The Stacked Diamond Exploration Model (Figs. 22-25) yielded 22 combinations of the nine punctual and linear features used as input (Table 14). One or more of these features occur in 15 percent of the grid cells across the study region, and virtually 100 percent of these cells are cut by either a surface geomorphic lineament or a basement fault. "Stacked" features (at least two coincident attributes) are present in 1137 cells (<1%) throughout the region of interest, with 791 (70%) of those cells corresponding to concurrent lineaments and basement faults, followed by lineament intersections (233 cells or 20%), and basement fault intersections (61 cells or 5%). The most significant anomalies recognized in the stacked model (Fig. 43) are 20 grid cells where four punctual and linear attributes occur together (Table 14). They represent approximately two percent of the 1137 stacked grid cells, and are described as follows: • lineament intersections coincident with basement faults (attributes 1,2,3,5), 14 cells; • lineaments in conjunction with basement fault intersections (1,3,4,5), four cells; 114 Table 14. Classification of Grid Cells in the Stacked Diamond Exploration Model No. of Percent Area C,las.§ Attnibute(s)* Cells Qf Total XKdÜ Aa 1,2,3,5 14 <1 224 Ab 1,3,4,5 4 « 1 64 Ac 1,3,5,6 1 « 1 16 Ad 1,3,5,7 1 « 1 16 B 1,3,5 791 <1 12656 Ca 1,2 233 <1 3728 Cb 1,6 3 «1 48 Cc 1,7 6 « 1 96 Cd 1,8 6 « 1 96 Ce 1,9 11 <1 176 Da 3,4 61 <1 976 Db 3,6 2 « 1 32 Dc 3,7 1 « 1 16 Dd 3,8 1 « 1 16 De 3,9 1 «1 16 E 8,9 1 « 1 16 F 1 19513 11 312208 G 3 5625 3 90000 H 6 23 <1 368 I 7 20 <1 320 J 8 15 <1 240 K 9 64 <1 1024 Z none 153879 85 2462064 * 1; Geomorphic lineament 2: Lineament intersection 3: Basement fault 4: Fault intersection 5: Intersection of lineament and basement fault 6: Diamond occurrence 7: Cryptoexplosion structure 8: Cobalt anomaly 9: Nickel anomaly 115 • an intersection of a lineament and basement fault, along with a diamond occurrence (1,3,5,6), one c e ll; and • an intersection of a lineament and basement fault, plus a cryptoexplosion structure (1 ,3 ,5 ,7 ), one c e ll. £r.ftidnii.tx..D.1amQnd .Explora.t.1,Qn. Madgl The Proximity Diamond Exploration Model returns a standardized, aggregate score (Z-score) for each grid cell in the region of interest that defines its nearness to punctual and linear features that are thought to be important in the emplacement and discovery of kimberlites and lamproites (Fig. 40). The distribution of Z-scores is reported in 15 classes (Table 15); locations that are closer than the mean distance to favorable features correspond to classes Aa through E, whereas locations that are equal to or farther than the average distance to favorable attributes correspond to classes F through L. Anomalous locations are grid cells with Z-scores less than -2.00 (classes Aa-Ad of Table 15, Fig. 44). Z-scores of less than -2.00 occur in 3810 c e lls (60,960 km'), a to ta l th at is approximately two percent of the cells in the study region. The probability of finding locations with more anomalous proximity values than those identified is less than 0.03. 116 Table 15. Classification of Grid Cells in the Proximity Diamond Exploration Model No. of Percent Area Class Z-Score Ranae Cells Of Total (km') Aa -4.00 to -3.51 8 « 1 128 Ab -3.50 to -3.01 80 «1 1280 Ac -3.00 to -2.51 664 <1 10624 Ad -2.50 to -2.01 3058 2 48928 B -2.00 to -1.51 7576 4 121216 C -1.50 to -1.01 16475 9 263600 0 -1.00 to -0.51 27625 15 442000 E -0.51 to -0.01 36448 20 583168 F 0.00 to 0.50 34201 19 547216 G 0.51 to 1.00 24430 14 390880 H 1.01 to 1.50 16462 9 263392 I 1.51 to 2.00 9665 5 154640 J 2.01 to 2.50 3051 2 48816 K 2.51 to 3.00 531 <1 8496 L 3.01 to 3.50 2 « 1 32 1 1 7 In an effort to understand the contribution of each of the 12 standardized proximity layers that form the input to the proximity model (Fig. 40), correlation analysis was performed on a layer-by- layer basis (Table 16). The (-values associated with the correlation coefficients (r) strongly suggest that they are directly correlated with the proximity model, with the exception of the distance from carbonatite layer (critical (-value = 3.2910 at infinity and significance level of 0.001). With respect to the normalized coefficient of determination (norm, r'), the intersections of lineaments and basement faults, intersections of basement faults, and basement faults proximity layers account for 50 percent of the variation in the proximity model, emphasizing the importance of structural controls in the model, and by implication, on the localization of kimberlites and lamproites. The degree of correlation of a given proximity layer with the model is not influenced by the number of cells occupied by a given feature; the intersections of basement faults layer, with 65 occupied cells, returns the second-highest correlation, whereas, the lineaments layer, with 20,583 occupied cells, ranks ninth in terms of correlation. In summary, (I) proximity to joint structural features (lineament intersections, basement fault intersections, intersections of lineaments and basement faults), (2) proximity to punctual features (diamonds, geochemical anomalies, cryptoexplosion structures), (3) proximity to linear structural features (lineaments and faults), and (4) proximity to PHFA in surface and 118 Table 16. Correlation Statistics of Each of the Input Standardized Proximity Datasets versus the Proximity Diamond Exploration Model r' Norm, r' No. of Dataset* r t-value m (%) Cells XXLINFLT 0.6830 397.0103 46.65 18.22 811 XXBFLTS 0.6417 355.2621 41.18 16.09 65 BASEFLTS 0.6298 344.3029 39.67 15.50 6502 DIAMOND 0.5192 257.9355 26.96 10.53 29 COANOM 0.4686 225.2004 21.96 8.58 23 BASEPHFA 0.4021 186.4650 16.17 6.32 1022 XXLINEAR 0.3886 179.0520 15.10 5.90 247 CRYPTOEX 0.3862 177.7892 14.92 5.83 28 LINEARS 0.3729 170.6522 13.91 5.43 20583 ESRFPHFA 0.3490 158.1115 12.18 4.76 3415 NIANOM 0.2702 119.1652 7.30 2.85 77 CARBNVRT -0.0012 -000.4929 0.00 0.00 12 100.01 * XXLINFLT intersections of lineaments and fau lts XXBFLTS intersections of faults in Precambrian basement rocks BASEFLTS faults in Precambrian basement rocks DIAMOND alluvial and in place diamond occurrences COANOM anomalous cobalt concentrations in stream sediments BASEPHFA PHFA in the Precambrian basement surface XXLINEAR intersections of lineaments CRYPTOEX cryptoexplosion structures LINEARS Landsat lineaments ESRFPHFA positive horizontal flexure axes (PHFA) in the surface of the earth NIANOM: anomalous nickel concentrations in stream sediments CARBNVRT: distance from carbonatite occurrences 119 basement topography are responsible for 40 percent, 28 percent, 21 percent, and 11 percent, respectively, of the variation in the proximity diamond exploration model (Table 16). Accordingly, locations near to joint structural features and to punctual features associated with kimberlites and lamproites that are also underlain by terrane th a t is permissive for economic primary diamond deposits should be priority candidates for diamond exploration. Cross-Tabulation of Known Occurrences with the Independent Diamond Exploration Models Alluvial Diamonds Grid cells th at contain known allu v ial diamonds (Fig. 1) coincide with three classes of the Empirical Diamond Exploration Model and three sub-categories of other empirical attributes (Table 17). Five cells correlate with diamond-friendly lithosphere and appropriate age basement rocks for economic intrusions of kim berlite (Very Good Archon) and lamproite (Very Good Proton, Good Proton), and five additional cells overlie diamond-friendly lithosphere (Other.02, Other.04). Both sets of occurrences suggest that there may be local sources for the stones. The source of the a llu v ia l diamonds found in Wyoming (Fig. 1, Table 17) may be kim berlite emplaced in Archean basement; the more southerly of the two locations is in a region with very good potential for economic kimberlite, based on both its empirical characteristics (Very Good Archon) and its very anomalous proximity 1 2 0 Table 17. Cross-Tabulation of Grid Cells That Contain Known Alluvial Diamonds with the Empirical, Stacked, and Proximity Diamond Exploration Models* Stacked Proximity Name LaL. Long, EmRicical IN P t/L ia ild „ /.C l,) - * - " lA 42.52 -90.67 VGood Proton 1/H -1.24/C IL 40.46 -90.67 Other.11 1/H -0.55/D ---- IL 38.32 -89.12 O ther.11 1/H -1.03/C IN 41.07 -86.22 Other.11 1/H -1.61/C — — — — IN 40.75 -86.07 O ther.11 1/H -1.31/C Stanley IN 39.54 -86.43 O ther.11 1/H -1.03/D - - - - IN 39.52 -86.45 O ther.11 1/H -1.13/C — * “ - IN 39.43 -86.43 O ther.11 1/H -1.00/D — - - — IN 39.36 -86.27 O ther.11 1/H -1.27/C Maxwell IN 39.35 -86.47 O ther.11 1/H -1.13/C Young IN 39.29 -86.29 Other.11 1/H -1.06/D - - - - IN 39.27 -86.15 O ther.11 1/H -1.13/D ■. M w IB MI 42.88 -85.69 Other.11 2/Cb -0.05/F Dowagiac MI 41.98 -86.11 O ther.11 1/H -0.39/F WI 45.17 -89.17 O ther.02 1/H -2 .38/Ac * - - - WI 44.70 -92.18 Other.02 1/H -0.70/D - - - - WI 44.08 -87.98 Good Proton 2/Db -2.28/Ad Theresa WI 43.52 -88.45 Good Proton 1/H -1.47/B Saukville WI 43.42 -87.94 Good Proton 1/H -1.18/C WI 42.93 -89.38 O ther.04 2/Cb -1.41/C Eagle WI 42.88 -88.47 Other.04 2/Cb -1.81/8 - " - - WI 42.68 -88.28 O ther.04 1/H -1.03/D WY 44.15 -105.50 Fair Archon 1/H -1.68/C - - — WY 41.40 -106.48 VGood Archon 1/H -3.06/Ab * For explanation of the attribute classes of the empirical, stacked, and proximity models, refer to Tables 12, 14, and 15. 1 2 1 value (Z score = -3.06). Diamondiferous lamproite may be the source of three of the allu v ial diamonds found in Wisconsin, including the famous Theresa and Saukville stones (Table 1); and the single find in Iowa may also have come from lamproite. All of these occurrences are in Proterozoic terrane, which is classified as good to very good in terms of its potential for economic lamproites. The suite of grid c ells th at contain allu v ial diamonds in Illinois, Indiana, and Michigan (Fig. 1) correlate with terrane of apparently no potential for economic diamond-bearing rocks, as determined in this study (Table 17). These locations lack the minimal requirements of basement rocks of eith er Archean or Early Proterozoic age (Figs. 5-7) and diamond-friendly lithosphere (Fig. 10). The stones apparently were transported to their point of discovery during Pleistocene glaciations from Canada or possibly the Upper Peninsula of Michigan (where diamondiferous kim berlites are known). It is possible, however, that the diamonds could have come from (1) as yet undiscovered local kimberlite or lamproite bodies that were emplaced in a diamond-friendly environment that no longer is present in the area; (2) from local kimberlites and lamproites with characteristics that do not conform to current knowledge regarding the emplacement and m anifestation of primary diamond deposits; or (3) from an as yet undiscovered (non- kimberlite or lamproite) type of primary diamond deposit that is not constrained by present theories of formation. 1 2 2 The source(s) of the allu v ial diamonds found in Wisconsin is intriguing (Fig. 1). The simple explanation is that they are exotic stones from Canada or the Upper Peninsula of Michigan, brought to the area by glaciers during the Pleistocene. Curiously, however, a ll of the locations in which stones have been found are underlain by diamond-friendly lithosphere (Table 17). Three of the eight grid c ells from which allu v ial diamonds have been recovered also correlate with e ith e r a geomorphic lineament or a basement fault, and two of these locations also are characterized by anomalous proximity values (Z-score < -2.00). These findings support the proposition that at least some of the known alluvial diamonds in Wisconsin are from local diamond-bearing rocks. Interestingly, at least one kimberlite pipe has been recently discovered in Wisconsin (SEG Newsletter, April 1993). In sum, (1) five of the 24 grid cells (21%) with known alluvial diamonds (Table 17) are permissive for economic primary diamond deposits, (2) none of the cells also are a stacked anomaly, and (3) three of the c e lls (13%) are characterized by anomalous proximity. Two of the three cells with anomalous Z-scores are also underlain by permissive terrane, and all three cells are underlain by diamond-friendly lithosphere. P i am o n d If er o u .§.. K 1 mb e r .l 1 t.g.§ Known diamondiferous kimberlites occur in only five of the grid cells within the study region (Fig. 1, Table 18), and four of the five locations are in the State Line district of the Colorado- 1 2 3 Table 18. Cross-Tabulation of Grid Cells That Contain Known Diamondiferous Kimberlites with the Empirical, Stacked, and Proximity Diamond Exploration Models* Stacked Proximity Name/State+ l & L . Laoa,. Empirical INp ,,/C.L.), aa.l,/Cl.) Lk Ellen/MI 46.21 -88.17 VGood Proton 1/H -2.46/Ad Aultman/WY 41.03 -105.49 O ther.08 1/H -2.20/0 Kelsey Lk/CO 40.99 -105.50 Other.08 1/H -2.21/B George Cr/CO 40.90 -105.70 Fair Proton 4/Ac -2 .92/Ac Sloan/CO 40.86 -105.45 O ther.08 2/Db -2.01/0 * For explanation of the attribute classes of the empirical, stacked, and proximity models, refer to Tables 12, 14, and 15. + Lk: Lake Cr: Creek 1 2 4 Wyoming kimberlite province, with a single additional location in the Lake Ellen, Michigan, kimberlite province, which parallels the Wisconsin-Upper Peninsula of Michigan border. Nonetheless, the empirical attributes of these locations offer insight into why they are not economic under current market conditions. Known economic kim berlites are confined to regions of the earth that are underlain by Archean cry stallin e basement rocks (Janse, 1991); however, all five of the grid cells that contain diamondiferous kimberlite in the study region (Fig. 1) are in Proterozoic or younger terranes (Figs. 5-7, Table 18). Diamond- friendly crust (Fig. 9) is present beneath all five of the locations, but diamond-friendly lithosphere (Fig. 10) is found at only the site of the Lake Ellen pipe. One of the five grid cells (George Creek dikes, Colorado) is classified as a stacked anomaly, and all five of the locations are quantitatively anomalous with respect to their proximity scores. These findings suggest that the punctual and linear attributes known to correlate with kimberlites and lamproites and employed in developing the stacked and proximity models are appropriate. The apparent lack of economic v ia b ility of known diamond-bearing kimberlites within the study region may be due, at least in p art, to the absence of Archean basement rocks and the general absence of diamond-friendly lithosphere. B arren K.1niber.l.ite,§ Barren kimberlites occur in five states within the study region (Fig. 1, Table 19): Montana, Wyoming, Colorado, Kansas, and 125 Table 19, Cross-Tabulation of Grid Cells That Contain Known Diamond Exploration Models* Stacked Proximity Name/Stat@+ L a L Long,, Empirical (No,/Cl,) U a , L i C l , ) Williams/MT 47.85 -108.70 Fair Archon 0/Z 0.44/F LE(Bacon)/MI 46.17 -88.14 VGood Proton 2/Da -2.61/Ad LE(#69)/MI 46.00 -87.83 VGood Archon 0/Z -1.34/C LE(#70a)/MI 45.95 -87.90 VGood Archon 0/Z -1.67/B LE(#70b)/MI 45.95 -87.88 VGood Archon 0/Z -1.46/C LE(#73)/MI 45.86 -87.60 VGood A/P 0/Z -0.98/D Sheep Rock/WY 41.69 -105.40 Other.01 0/Z -1.09/C IM(S2)/MY 41.65 -105.22 Other.06 0/Z -0.91/C IM(S7,8)/WY 41.65 -105.20 Other.06 0/Z -0.96/D IM(S3,9,10)/WY 41.64 -105.28 Other.01 0/Z -0.89/C IM(S17,18)/WY 41.62 -105.26 Exlnt Proton 0/Z -0.88/C IM/(S15,16)/WY 41.62 -105.22 Fair Proton 0/Z -0.90/C Maxwell/co 40.99 -105.53 Other.08 0/Z -2.37/Ad Nix/co 40.94 -105.44 Other.08 0/Z -1.85/B Chicken Pk/CO 40.86 -105.54 Other.08 1/G -2.06/Ad Estes Pk/CO 40.40 -105.53 Other.06 0/Z -1.42/C Green Mt/CO 40.00 -105.28 Fair Proton 1/F -2.02/B Fancy Cr/KS 39.56 -96.73 Other.06 0/Z -0.27/E SwCr,Rph 2/KS 39.52 -96.72 Other.06 0/Z -0.39/E Randolph l/KS 39.49 -96.73 Other.06 0/Z -0.40/E Winkler/KS 39.48 -96.82 Other.06 0/Z 0.00/F Lone Bush/KS 39.35 -96.73 Other.06 0/Z -0.24/E Lnrdville/KS 39.34 -96.86 Other.06 0/Z -0.01/F Stock,LT/KS 39.34 -96.73 Other.06 0/Z -0.29/E Bala/KS 39.31 -96.92 Other.06 0/Z -0.05/F * For explanation of the attribute classes of the empirical, stacked, and proximity models, refer to Tables 12, 14, and 15. + LE: Lake Ellen IM(S#): Iron Mountain (Section #) Pk: Park SwCr: Swede Creek Rph 2: Randolph 2 Lnrdville: Leonardville Stock: Stockdale LT: Lone Tree 126 Michigan. Of the 25 grid cells with barren kimberlites, (1) three (12%) are permissive fo r economic primary diamond deposits, but (2) none correlate with the 20 recognized stacked anomalies, and (3) only three (12%) are typified by anomalous proximity scores. None of the 25 cells are prospective (cells with anomalous proximity values in permissive terrane). According to the exploration models proposed herein, these kimberlites are barren or sub-economic because they are hosted by post-Archean basement rocks (Figs. 5-7), and by lithosphere that is not diamond-friendly (Fig. 10). Exceptions are the three cells that contain some of the Lake Ellen occurrences (identified as #69, #70a, #70b in Table 19), where both Archean basement and diamond- friendly lithosphere are present, but other criteria may be important at Lake Ellen. For example, deep erosion of the intrusions may have removed higher-grade kim berlite, and we may never know for sure the influences that upper mantle composition has on the economic v ia b ility of primary diamond deposits. In addition, the tectonic setting of the Lake Ellen kimberlite bodies is complex (Figs. 5-7), and the Archean basement rocks at or near the surface could overlie Proterozoic or younger rocks. As an analogy, Eggler and others (1988) contend that Archean basement rocks are present beneath overthrust Proterozoic terrane that hosts the State Line intrusions of the Colorado-Wyoming kimberlite province. Barren kimberlites in the study region occur along recognized major lineaments and basement faults in only 3 of 25 grid cells. 127 based on stacked attributes (Table 19). The apparent lack of direct association of the barren intrusions with deep lithospheric fractures may be a facto r in explaining the paucity of diamonds in these bodies (assuming th at diamonds were present in lithospheric mantle during emplacement of the kimberlites). Fractures that extend through the entire lithosphere may be an essential requirement in the formation of economic primary diamond deposits; where the "plumbing" is more complex, diamonds entrained in ultramafic magmas could be resorbed during their tortuous ascent to the surface. Proximity Z-scores for known kimberlites in the study region (Table 19) generally are less than the mean distance to linear and punctual features that are believed to be related to the occurrence of kimberlite. Z-scores range from -2.61 for the Bacon intrusion in the Lake Ellen Province to 0.44 for the Williams kimberlites in Montana. Taken as groups, the mean Z-scores for the kim berlites in the five states where they are known, from best to worst, are: Colorado (-1.94), Michigan (-1.61), Wyoming (-0.94), Kansas (-0.21), and Montana (0.44). Keeping in mind possible biases from the presence of diamonds nearby, surface exposures of basement rocks that permit re la tiv e ly d irect mapping of fa u lts, and the absence of geochemical anomalies that may be due to a lack of sampling, one could (cautiously) interpret the proximity scores to be an independent indicator of diamond p o ten tial. For example, the State Line kimberlite field in Colorado, which contains the largest number of known diamond-bearing kimberlites, has the best proximity 128 score, whereas the Riley County occurrences In Kansas and the Williams cluster In Montana, with no known diamond-bearing bodies, have the poorest proximity scores. Therefore, areas In the study region with anomalous Z-scores may be promising targets for possibly economic primary diamond deposits, p articu larly If the explorationist Is willing to consider areas that are not wholly In accord with the conventional wisdom regarding environments for such deposits. Lamp£Q.1l£g. Known lamproltes In the study region (Fig. 1) are barren of diamonds for two reasons, according to empirical c rite ria espoused by Janse (1991) and verified In this research (Table 20): (1) they Invade Archean terrane (Figs. 5-7), and (2) they are emplaced In lithosphere that Is not diamond-friendly. As with known kimberlites In the region of Interest (Tables 18, 19), all of the grid cells with known lamproltes penetrated diamond-friendly crust, but for primary diamond deposits to form, diamond-friendly crust must be coupled with diamond-friendly lithosphere. None of the 22 grid cells that contain lamprolte Intrusions correlate with stacked anomalies (Table 20), and only one of the cells (Leuclte Hills/Spring Butte Intrusion In Wyoming) coincides with the linear and punctual attributes used In this study to mark a channel for upward migration of lamproite magma. Lamproites 129 Table 20. Cross-Tabulation of Grid Cells That Contain Known Lamproites with the Empirical, Stacked, and Proximity Diamond Exploration Models* Stacked Proximity Nam@/State+ U i ^ Long, EmiLirical (Na,/Cl,) m i , / £ . U Smoky B(NE)/MT 47.34 -107.07 Fair Archon 0/Z 0.72/G Smoky B(SW)/MT 47.32 -107.08 Fair Archon 0/Z 0.72/G YWB (NW)/MT 46.97 -108.62 Fair Archon 0/Z 1.50/1 YWB (NE)/MT 46.97 -108.46 Fair Archon 0/Z 1.36/H YWB (SW)/MT 46.91 -108.62 Fair Archon 0/Z 1.38/H YWB (SE)/MT 46.91 -108.46 Fair Archon 0/Z 1.21/H Gold Butte/MT 46.67 -106.93 Fair Archon 0/Z 0.51/F F-to-D B/MT 46.43 -107.30 Fair Archon 0/Z 0.72/G LH(Stmb Mt)/WY 41.97 -108.97 Fair Archon 0/Z -0.60/E LH(Matt H1)/WY 41.95 -109.19 Fair Archon 0/Z -0.42/E LH(N TabM)/WY 41.92 -109.02 Fair Archon 0/Z -0.48/E LH(S TabM)/WY 41.91 -109.01 Fair Archon 0/Z -0.43/E LH(Hag H1)/WY 41.88 -109.00 Fair Archon 0/Z -0.46/E LH(BlackR)/WY 41.87 -108.80 Fair Archon 0/Z -0.51/E LH(Sprng B)/WY 41.85 -108.88 Fair Archon 1/F -0.72/D LH(Deer B)/WY 41.83 -109.04 Fair Archon 0/Z -0.20/F LH(Cabin B)/WY 41.82 -109.00 Fair Archon 0/Z -0.36/E LH(Emmns C)/WY 41.82 -108.97 Fair Archon 0/Z -0.37/E LH(Twin Rs)/WY 41.81 -109.12 Fair Archon 0/Z -0.07/F LH(Zirkl M)/WY 41.80 -108.93 Fair Archon 0/Z -0.55/D HP(Rose Dm)/KS 37.78 -95.71 Other.06 0/Z -0.83/E Hills Pond/KS 37.75 -95.79 O ther.06 0/Z -0.78/E * For explanation of the attribute classes of the empirical, stacked, and proximity models, refer to Tables 12, 14, and 15. + Smoky B: Butte Sprng B: Spring Butte YWB: Yellow Water Butte Deer B: Deer Butte F-To-D B: Froze to Death Butte Cabin B: Cabin Butte LH: Leucite H ills Emmns C: Emmons Cone Stmb M: Steamboat Mountain Twin Rs: Twin Rocks Matt HI: Matthew H ill Zirkl M: Zirkel Mesa TabM: Table Mountain HP; Hill s Pond Hag HI: Hague H ill Rose Dm: Rose Dome Black R: Black Rock 130 apparently were emplaced In the Leucite H ills along a geomorphic lineament, presumably where it is intersected by cross-cutting fractures. The proximity scores of grid cells that contain known lamproites are uniformly no/i-anomalous (Table 20), but the scores may reflect a lack of appropriate structural mapping (including lineaments) and geochemical sampling. Also, the Leucite Hills are at the extreme western edge of the study region, and linear and punctual elements that may be present immediately to the west of these lamproites were not included in the DEGIS database. Carb.o,nat.1tds The known carbonatites in the region of interest (Fig. 1), except for the Firesand River and Prairie Lake, Ontario, intrusions, occur in lithosphere that is not diamond-friendly (Table 21). Carbonatites are present in both Archean and Proterozoic terranes within the study region, and deep crustal fractures appear to have been important for the localization of the known intrusions; 75 percent occur along either a geomorphic lineament or a basement fault, but none of the 12 cells with carbonatite bodies are classified as a stacked anomaly. The close relationship between crustal fractures and carbonatite intrusions may explain the surprisingly good proximity Z-scores of the carbonatites, particularly those in Colorado. However, the six cells in Colorado with anomalous Z-scores do not qualify as 1 3 1 Table 21. Cross-Tabulation of Grid Cells That Contain Known Diamond Exploration Models* Stacked Proximity N,ame/.§,tate+ Jj l L. Lang., Empirical (No,/Cl.) m i , / C l , ) P rairie Lk/ON 49.03 -86.72 other.03 0/Z 0.40/F Firesand Rv/ON 48.00 -84.67 O ther.02 2/Ca 0.11/E Rocky Boy/MT 48.17 -109.73 Fair Archon 1/F -0.11/E Bear Ldg Mt/WY 44.48 -104.45 Fair Archon 1/F -1.79/Ad Elk Creek/NE 40.30 -96.20 Fair Proton 0/Z -0.46/E Amethyst/co 38.51 -105.46 Fair Proton 1/G -2.10/8 Goldie/CO 38.43 -105.51 Fair Proton 1/F -2.25/8 McCoy Gulch/CO 38.41 -105.55 Fair Proton 1/F -2.25/8 McClure Mt/CO 38.35 -105.46 Fair Proton 1/G -2.06/8 Gem Park/co 38.27 -105.55 Fair Proton 1/G -2.04/8 Democrat Cr/CO 38.26 -105.36 Fair Proton 1/G -2.06/Ad Iron Hill/co 38.25 -107.05 Fair Proton 0/Z -1.26/8 * For explanation of the attribute classes of the empirical, stacked, and proximity models, refer to Tables 12, 14, and 15. + Lk: Lake ON: Ontario Rv: River Ldg: Lodge Cr: Creek 1 3 2 significant diamond exploration targets because of their unsatisfactory empirical characteristics. CnxptosxplAsAon-Structwre; Cryptoexplosion structures are enigmatic and may be caused by either or both shallow crustal (<35 km) or deep lithospheric processes. Cryptoexplosion structures with diamonds or diamond- bearing rocks in the vicinity, such as the Popigay structure in the diamond fields of eastern Siberia (Kvet, 1976), and the structures in Wisconsin and Michigan, argue for a deep lithospheric or upper mantle origin. This is particularly true if the ages of the structures and nearby lamproites and kimberlites are similar. The empirical characteristics and proximity scores of the grid cells that contain the Glover Bluff cryptoexplosion structure in Wisconsin and the Brule River O utlier in the Upper Peninsula of Michigan (Fig. 1, Table 22) lend credence to the hypothesis that economic primary diamond deposits may be present there. Both locations correlate with (1) Early Proterozoic terrane with good to very good potential for economic lamproites, and (2) an a ttra c tiv e structural setting. Evidence for the latter include the intersection of a geomorphic lineament and a basement fault at the Brule River Outlier (cell also classified as a stacked anomaly), and the presence of a lineament at the Glover Bluff structure. A third unnamed disturbance in Wisconsin, located 32 km southwest of the Glover Bluff structure, also is in good Proterozoic terrane, but without an associated anomalous proximity value. 1 3 3 Table 22. Cross-Tabulation of Grid Cells That Contain Known Cryptoexplosion Structures with the Empirical, Stacked, and Proximity Diamond Exploration Models* Stacked Proximity Name/.Staie+ LaJL Looa,. Empirical (NW-CIJ i M L / n ,) Crestone C/CO 38.87 -105.65 Fair Proton 2/Cc -3.19/Ab Manson S/IA 42.58 -94.52 Other.02 1/1 -0.96/C Tiffin C/IA 41.80 -91.68 Fair Proton 1/1 -1.78/B Des Pines D/IL 42.03 -87.93 Other.11 l/I -0.47/D Glasford S/IL 40.58 -89.82 O ther.11 l/I -0.23/E ----- /IL 37.62 -88.20 O ther.06 2/Cc -2.72/Ab Hicks Dome/IL 37.54 -88.41 Other.06 2/Cc -2 .76/Ac ----- /IL 37.48 -88.37 Other.06 l/I -2.39/Ad Kentland S/IN 40.75 -87.42 Other.11 l/I -0.58/E Sherman Hl/MI 46.83 -88.67 Other.03 2/Cc -2.28/B ----- /MI 46.82 -88.65 Other.02 l/I -2.00/B Limestone M/MI 46.81 -88.70 Other.02 l/I -2.14/B Brule Riv 0/MI 46.01 -88.67 VGood Proton 4/Ad -2.98/Ab Weaubleau S/MO 37.96 -93.62 Fair Proton l/I -0.94/D -■-“/MO 37.91 -90.74 Other.06 l/I -1.98/Ad Decatrvl D/MO 37.90 -92.72 Fair Proton l/I -1.52/C Crookd Cr S/MO 37.83 -91.38 Other.06 l/I -2.18/Ad Furnce Cr S/MO 37.80 -90.78 Other.06 2/Dc -2 .29/Ac - - - -/MO 37.75 -90.20 Other.06 l/I -2.31/Ad HazelGrn Cr/MO 37.74 -92.52 Fair Proton l/I -1.30/C Avon/MO 37.72 -90.22 Other.06 l/I -2.21/B Red Wing Cr/ND 47.60 -103.55 Fair Proton 2/Cc -0.98/D Glover Bl/WI 44.18 -89.37 Good Proton 2/Cc -2.06/Ad ----- /WI 43.93 -89.56 Good Proton l/I -1.34/B ---/WY 43.17 -106.71 Fair Archon l/I -0.84/D West Hawk L/MB 49.77 -95.20 Other.02 l/I 0.96/G Hartney/MB 49.47 -100.54 O ther.02 l/I 1.43/1 Slate Is/ON 48.67 -87.00 Other.03 l/I -0.92/D * For explanation of the attribute classes of the empirical, stacked, and proximity models, refer to Tables 12, 14, and 15. + C: Crater D: Disturbance M: Mountain Bl: Bluff Is: Islands S: Structure HI: Hill 0:Outlier L: Lake 1 3 4 The Manson structure in Iowa, and structures in the Upper Peninsula of Michigan that include Sherman Hill and Limestone Mountain near Pelkie, together with several cryptoexplosion structures in Manitoba and Ontario occur in diamond-friendly lithosphere and diamond-friendly crust (Table 22). Unfortunately, all of these structures invaded "young" basement terrane that according to experts such as Janse (1991) does not host economic primary diamond deposits. Interestingly, however, the Sherman Hill and Limestone Mountain disturbances are su fficien tly close to punctual and linear elements associated with kimberlites and lamproites to generate anomalous proximity values. One-third of the known cryptoexplosion structures from throughout the study region occur along either a surface geomorphic lineament or basement fault (Table 22). Not surprisingly, proximity scores of the cryptoexplosion structures are variable; however, the highly anomalous Z-scores of grid c ells that contain Crestone Crater, Colorado, and several of the cryptoexplosion structures in Illinois and Missouri support the contention that at least some of these disturbances occur near punctual and linear attributes that coincide with kimberlites and lamproites. Overall, three of the 28 grid c ells th at encompass cryptoexplosion structures (Table 22) are underlain by terrane that is permissive for economic primary diamond deposits, with a to ta l of 10 cells underlain by diamond-friendly lithosphere. The cell that encompasses the Brule River Outlier in the Upper Peninsula of Michigan is the only one also recognized as a stacked anomaly. 135 Twelve c ells contain anomalous proximity scores, yet only two of these cells (Brule River and Glover Bluff) are prospective from a diamond exploration standpoint. Analysis of Anomalies Identified by the Independent Diamond Exploration Models AD.Qina,ljA§..1n..the Em p irical..Diamond. Exploration Mddg] The Empirical Diamond Exploration Model (Fig. 16) identifies anomalies in four areas of the study region: (1) Wisconsin and the Upper Peninsula of Michigan; (2) southeastern Wyoming; (3) northeastern North Dakota, northern Minnesota, and southwestern Ontario; and (4) northwestern Iowa. The anomalies highlight terrane that is permissive for the occurrence of economic primary diamond deposits (Fig. 42). Anomalie...1n_jih9 Stacked.Diamond Exploration Mods! stacked anomalies are defined as grid cells that are the loci of four punctual and linear attrib u tes related to the emplacement and discovery of primary diamond deposits (four being the maximum). Anomalies in th is model (Fig. 43) occur mainly in Colorado (10), where the geology is well exposed (Figs. 5-7); two anomalies are present in Wisconsin, and single anomalies are present in Montana, Wyoming, Minnesota, Iowa, Missouri, Michigan, Kentucky, and Ontario, Canada. 1 3 6 Cross-tabulation of the stacked anomalie&jwith the empirical and proximity models (Table 23) reveals that three anomalies occur in terrane with good to very good potential for economic lamproites: in Iron County, Michigan (at the site of the Brule River O utlier, Table 22), and in Chippewa and Columbia counties, Wisconsin (Fig. 43). The Iron County, Michigan, and Chippewa County, Wisconsin, anomalies are associated with Early Proterozoic basement rocks, and with diamond-friendly lithosphere and diamond- friendly crust. The anomaly in Columbia County, Wisconsin also correlates with basement rocks of Early Proterozoic age and diamond-friendly lithosphere, but not with diamond-friendly crust. Diamonds have been recovered from intrusions in the Lake Ellen kim berlite province (Duskin and Jarv is, 1993), of which the Lake Ellen pipe is located 70 km northeast of the Iron County, Michigan, anomaly; and allu v ial diamonds have been found in Wisconsin (Fig. 1, Table 1) southwest of the Chippewa County anomaly (90 km), and southeast of the Columbia County anomaly (60 km). Anomalies in Freeborn County, Minnesota, and near Thunder Bay, Ontario, correlate with diamond-friendly lithosphere and diamond-friendly crust, but not with basement rocks of appropriate age (Fig. 43, Table 23). Consequently, the anomalies are not likely to be associated with economic kim berlites and lamproites, which according to Janse (1991) are confined to Archean and Early Proterozoic terranes. The Crook County anomaly in Wyoming (Fig. 43) is in Archean terrane, which is appropriate for economic kim berlites (Table 23); 1 3 7 Table 23. Cross-Tabulation of Stacked Anomalies with the Empirical, Stacked, and Proximity Diamond Exploration Models* Stacked Proximity CPa/State Empirical m j n . d Crook/WY Fair Archon 4/Ab -2.52/Ad Iron/Ml VGood Proton 4/Ad -2.98/Ab Chippewa/WI VGood Proton 4/Ab -2.27/Ad Columbia/WI Good Proton 4/Aa -3.06/Ab Saguache/CO Fair Proton 4/Aa -3.59/Aa Grand/co Fair Proton 4/Aa -3.21/Ab Larimer/co Fair Proton 4/Ac -2.92/Ac Osage/MO Fair Proton 4/Aa -2 .62/Ac Jefferson/CO Fair Proton 4/Ab -2.72/Ac Pueblo #2/C0 Fair Proton 4/Aa -2.53/Ad San Juan/co Fair Proton 4/Aa -2.34/Ad Roosevelt/MT Fair Proton 4/Aa -2.31/Ad Thunder Bay/ON+ O ther.02 4/Aa -2 .20/Ac Freeborn/MN Other.02 4/Ab -2.35/Ad Henderson/KY O ther.06 4/Aa -3.51/Ab Pueblo #1/000 O ther.06 4/Aa - 3 .16/Ac Otero/CO O ther.06 4/Aa -2.13/Ad Baca/CO O ther.06 4/Aa -2.02/B Polk/IA O ther.07 4/Aa -1.98/B * For explanation of the attribute classes of the empirical, stacked, and proximity models, refer to Tables 12, 14, and 15. + ON: Ontario 0 This anomaly comprises two adjacent grid c e lls. 138 and the anomaly in Roosevelt County, Montana, and tw o-thirds of the Colorado anomalies are in Early Proterozoic terrane, which favors economic lamproite intrusions. However, none of these anomalies occur in regions of diamond-friendly lithosphere, and this also may explain why the diamond-bearing kimberlites in Larimer County, Colorado (for example, the Sloan diatremes. Fig. 1) are not economic. The chemistry of indicator minerals (pyrope garnet, chromite, and ilmenite) in concentrates of several of the State Line kim berlites also suggests that they are not economic (McCallum and Waldman, 1991); for example, the presence of high Fe^ ilmenites indicates that oxidizing conditions, which foster resorption of diamonds, were extant during emplacement of these intrusions. Eggler and others (1988) presented an alternative view on the economic potential of the State Line kim berlite fie ld , based on lithospheric thickness. These workers contend th at diamond- friendly lithosphere was present during the Devonian when the State Line kimberlites were emplaced, but that it was "thinned" by the movement of mantle material during Cretaceous-Tertiary magmatism in the region. If this hypothesis is correct, the deep level of erosion of the State Line bodies may have removed any economic parts of the pipes. Regardless of the explanation for the lack of economic concentrations of diamonds in the State Line fie ld , the fact remains that the entire Colorado-Wyoming kimberlite province lies within Proterozoic or younger basement terrane and no economic kim berlite body has ever been found in rocks th at are younger than 1 3 9 Archean. However, Proterozoic terrane that is permissive for economic lamproites is present adjacent to the province in southeastern Wyoming. The stacked anomaly in Henderson County, Kentucky, plus the anomaly designated "Pueblo #1," and anomalies in Otero and Baca counties of Colorado (Fig. 43) occur in diamond-friendly crust, but not in diamond-friendly lithosphere (Table 23). The Polk County anomaly in Iowa correlates with none of the major empirical factors th at are thought to be important to the occurrence of economic primary diamond deposits. Interestingly, many of the stacked anomalies (Table 23), though not in terrane favorable fo r economic concentrations of diamonds, do make attractive exploration targets for carbonatite intrusions. For example, the Crook County, Wyoming, and Saguache County, Colorado, anomalies (Fig. 43) are near known carbonatites (Fig. 1). Deep crustal fractures, which are essential for the emplacement of kim berlite and lamproite bodies, also could focus carbonatite magmas. Therefore, diamond exploration models that include the localities of major crustal fractures may pinpoint favorable environments for carbonatite intrusions (and included commodities such as rare earth elements and base m etals). Anomalies in the Proximitv Diamond Exploration Model The Proximity Diamond Exploration Model (Fig. 40) includes the punctual and linear elements that are used to generate the Stacked Diamond Exploration Model, and 18 (95%) of the stacked 140 anomalies exhibit anomalous proximity Z-scores (Table 23). Commonly, the stacked anomalies are the foci of the proximity anomalies. Anomalies in the proximity model (grid cells with Z-scores less than -2.00) cluster in three areas within the study region (Fig. 44): • in a roughly north-south band through central Colorado and into southern Wyoming, -- perhaps associated with abundant geomorphic lineaments and basement faults in the area (Figs. 4-7); • scattered across Wisconsin and into the Upper Peninsula of Michigan, where a llu v ial diamonds have been found and numerous lineaments and basement faults are present (Figs. 1, 4-7); and from east to west across Kentucky, southeastern Illinois, and southeastern Missouri, extending perhaps into east-cen tral Kansas -- th is trend may be associated genetically with the 38th p arallel lineament, where numerous mafic and ultramafic intrusions and cryptoexplosion structures also are present (Snyder and Gerdemann, 1965; Lidiak and others, 1985); Cross-tabulation of the grid cells with anomalous proximity values and the Empirical Diamond Exploration Model (Table 24) reveals that anomalous Z-scores plus good to excellent potential for economic primary diamond deposits are present in 675 grid cells 141 Table 24. Cross-Tabulation of Proximity Anomalies with the Empirical Diamond Exploration Model No. of Percent Area Claçs* ,Cfins_. oL.TotA.1 (km') Excellent Archon 31 <1 496 VGood Archon 29 <1 464 Fair Archon 79 2 1264 Fair Archon/Proton 9 <1 144 VGood Proton 344 9 5504 Good Proton 271 7 4336 Fair Proton 1434 38 22944 Other 1613 42 25808 Other.01 2 « 1 32 Other.02 101 3 1616 Other.03 65 2 1040 Other.04 1 « 1 16 Other.06 1103 29 17648 Other.07 16 <1 256 Other.08 146 4 2336 O ther.10 137 4 2192 O ther.11 42 1 672 * Refer to Table 12 for explanation of the attribute classes of the empirical model. 142 within the region of interest. The 675 locations (<1% of the study region), which are classified as Excellent Archon, Very Good Archon, Very Good Proton, and Good Proton, account for 18 percent of the grid cells th at exhibit anomalous Z-scores. Ninety-one percent of the grid c e lls with anomalous Z scores and good to excellent diamond potential fall within Good Proton and Very Good Proton, which underscores the conclusion that exploration for economic lamproites should be a high p rio rity within these c e lls. Clearly, a combination of stacked anomalies, regions of anomalous proximity Z-scores, and permissive empirical attributes "zero in" on areas for follow-up exploration effort. The Integrated Diamond Exploration Model The Integrated Diamond Exploration Model (Fig. 45) combines anomalies from the em pirical, stacked, and proximity models (Figs. 42-44). The layer of permissive terrane (empirical anomalies. Fig. 42) contains grid cells that have excellent to good empirical diamond exploration potential, and is the underpinning of the integrated exploration model. Permissive terrane is limited to the following categories; Good through Excellent Proton, Very Good Archon/Proton, and Very Good and Excellent Archon. The addition of the layer of proximity anomalies (Fig. 44) refines the permissive locations by isolating those grid cells that have excellent to good empirical diamond exploration potential plus anomalous Z-scores (Table 24) -- such grid cells are classified as prospective. Accordingly, the diamond exploration potential of Prospective 143 Excellent Archon, Prospective Very Good Archon, Prospective Very Good Proton, and Prospective Good Proton is greater than the corresponding permissive categories. Finally, the three stacked anomalies (two grid cells termed Stacked and Prospective Very Good Proton, and one named Stacked and Prospective Good Proton) are superposed to yield the integrated model. The latter three cells have superior potential for economic lamproite intrusions. The Integrated Diamond Exploration Model (Fig. 45, Table 25) highlights four areas for follow-up exploration work (in rank order): 1. Misconsin-Upper Peninsula of Michigan -- for lamproite and kimberlite; 2. southeastern Uyoming -- for kimberlite and possibly lamproite; 3. northeastern North Dakota^ northern Minnesota» and southwestern Ontario -- for kimberlite and lamproite; and 4. northwestern Iowa -- for lamproite and possibly kimberlite. Exploration Potential of Wisconsin-Upper Peninsula of Michigan The Wisconsin-Upper Peninsula of Michigan diamond exploration area (Figs. 45, 46) is the most prospective of the four areas identified in this study, with 10,224 km* of prospective terrane (Table 25), which is more than an order of magnitude greater than the total of prospective terrane in southeastern Wyoming (576 km*). 144 Table 25. Classification of Permissive Areas in the Integrated Diamond Exploration Model No. of Area Region Glasses* Cells (km') Wisconsin-Upper Pen. SP VGood Proton 2 32 of Michigan SP Good Proton I 16 P VGood Archon 24 384 P VGood Proton 342 5472 P Good Proton 270 4320 VGood Archon 781 12496 VGood Archon/Proton 611 9776 VGood Proton 4183 66928 Good Proton m i 57456 9805 156880 Southeastern Wyoming P Exlnt Archon 31 496 P VGood Archon 5 80 Exlnt Archon 132 2II2 Exlnt Proton 576 204 3264 N. Dakota-Minnesota- VGood Archon 8587 137392 Ontario VGood Archon/Proton 3 48 VGood Proton IflZS 17194 9665 154640 Northwestern Iowa VGood Archon 40 640 VGood Proton 13984 914 14624 * Refer to Table 12 for explanation of the empirical categories. P: Prospective SP: Stacked and Prospective 145 SP MS Proion H H SP 6 Proton I 1 P we Archon ^ 0 P we Proton P 0 Proton W0 Archon WO A/P VO Proton 0 Proton W Border Mama# 100 km Figure 46. Wisconsin-Upper Peninsula of Michigan diamond exploration area (P: Prospective, SP; Stacked and Prospective). Locations classified as "SP" are not to scale. See Figure 42 for explanation of categories. 146 which is the only other area with prospective terrane. The Wisconsin-Michigan area is "doughnut-shaped" and covers most of Wisconsin and the Upper Peninsula of Michigan, plus part of the Lake Michigan basin. Precambrian basement lithologies include four Archean and six Early Proterozoic units as illustrated in Figure 47. Good and Very Good Proton are the two major types of permissive terrane, with coverage totalling 134,224 km' (86 percent of the area), along with smaller tracts of Very Good Archon and Very Good Archon/Proton. Such an expanse of diamond-friendly territory reinforces the contention that at least some of the alluvial diamonds found in Wisconsin (Fig. 1, Table 1) come from local sources. The geomorphic lineaments and basement faults, and cobalt and nickel concentrations in stream sediments throughout the Wisconsin- Upper Peninsula of Michigan area were examined in detail to substantiate the integrated model and to identify specific targets within the area (Fig. 46). The geochemical data are expressed in threshold units (tu), with the threshold established as two standard deviations from the mean of values within the area (Table 26). Thus, a grid cell with a cobalt concentration three times as high as the threshold would have a value of 3 tu. The rationale for the treatment of the geochemical data is that extreme positive values may reflect the presence of ultramafic rocks (including kimberlite and lamproite) that typically exhibit high concentrations of cobalt and nickel (Mannard, 1968; Gregory and Tooms, 1969). 1 4 7 E Protaro 8 E Prottro 7 E Protaro 5 E F¥otaro 4 E A-otaro 3 E Prottro 1 Ardtaan 7 Archean 6 □ Archean B Archtan 4 Contact Fault Wl Border 100 km Figure 47. Precambrian geology of the Wisconsin-Upper Peninsula of Michigan diamond exploration area. See Appendix A for descriptions of units. 148 Table 26. Statistics of Geochemical Data Associated with Permissive Areas In the Study Region Region fiüL. MIjl. Mail. Mean st. Dev. Threshold Wisconsln-Mlchlgan Cobalt (ppm) 1249 4 105 11.12 6.83 14 Nickel (ppm) 1641 2 643 15.90 18.38 37 Wyoming Cobalt (ppm) 118 4 30 8.55 4.34 9 Nickel (ppm) 120 5 89 20.83 12.36 25 N. Dakota-Minnesota Cobalt (ppm) 372 4 24 7.68 3.00 6 Nickel (ppm) 391 3 74 23.11 10.46 21 Iowa Cobalt (ppm) Nickel (ppm) 1 4 9 Prospective zones in Wisconsin-Michigan (Fig. 46) correlate with intersections of multiple north-northeast and west-trending crustal fractures (Fig. 48), with the stacked anomalies centered on the intersections. Known occurrences of kimberlite and cryptoexplosion structures throughout the area are within or near geomorphic lineaments and basement faults, supporting the tenet that emplacement likely was along major crustal fractures. Similarly, known occurrences of alluvial diamonds in the area also are within or close to major fractures, which may mean that the fractures act as fluvial traps or that they may host the primary deposits from which the stones were derived. Two clusters of anomalous cobalt and nickel values are present in the Wisconsin-Upper Peninsula of Michigan area: one with a northeasterly trend in the upper part of Figure 49, and the other trending west-northwest from the center to the left-hand edge of the figure. Additional anomalies may be present in the area, but the datasets used in this investigation (Figs. 2, 3) include only the northwest quadrant of the area. Several of the grid cells that compose the northeasterly anomaly may reflect the presence nearby of the Lake Ellen kimberlites (Fig. 48). However, one of the three grid cells classified as stacked and prospective is within this zone (Fig. 46), in Proton that has very good potential for economic lamproites. The west-northwest trending cobalt and nickel anomalies (Co and Ni >1 tu) form a linear zone (Fig. 49) that generally cross cuts the structural grain in the area (Fig. 48) and thus may mark 1 5 0 Stacked Ancm H Kinbcrlita H Diamond Find I I Crt/ploax Str | H Lin or Fit Par» Tarran» HI Border 100 km Figure 48. Linear and punctual features associated with the Wisconsin-Upper Peninsula of Michigan diamond exploration area (punctual features not to scale), Key: Stacked Anom = Stacked and Prospective Cryptoex Str = Cryptoexplosion Structure Lin or Fit = Lineament or Fault Perm Terrane = Permissive Terrane 151 Co>l + Nl>l Ptrn Ttrr«n* 100 km Figure 49. Cobalt and nickel anomalies in the Wisconsin Upper Peninsula of Michigan diamond exploration area, in threshold units (see Table 26). Squares marking locations of anomalies are not to scale; Perm Terrane = permissive terrane. 152 the presence of intrusions along as yet unrecognized east-west basement fractures. The coincidence of elevated levels of cobalt and nickel with intersecting structures (if present) is encouraging with respect to the search for primary diamond deposits because (1) some of the geochemical anomalies correlate with known kimberlites and with one of the stacked and prospective grid cells in the area, and (2) these locations also are within or near intersecting crustal fractures. At least 20 barren to sub-economic kimberlites (Fig. 48) recently have been discovered in the Lake Ellen province of the Upper Peninsula of Michigan (Jarvis, 1993; Duskin and Jarvis, 1993). The Upper Peninsula is a complex mix of Archean and Early Proterozoic rocks (Fig. 47), and consequently is an enigmatic setting for the occurrence of economic kimberlites, which seemingly are restricted world-wide to Archean cratons. Hence, a wise course for exploration in the Upper Peninsula might be to look for both kimberlite and lamproite bodies. Despite the discovery of several notable alluvial diamonds in Wisconsin (Table 1 and Fig. 48), no primary diamond deposits have been found to date in the state. Kimberlite has been identified only recently in northern Wisconsin (SEG Newsletter, April 1993), but because much of the terrane in Wisconsin is Proterozoic (Figs. 46, 47), prospecting for lamproite may be a more fruitful strategy than exploring just for kimberlite. The traditional indicator minerals of kimberlite (picroilmenite, pyrope garnet, and chromian diopside) are present in only trace amounts in lamproite (Fipke, 153 1993), and thus, samples must be carefully processed to optimize the recovery of characteristic lamproite mineral species that include tourmaline, zircon, chromite, almandine garnet, ilmenite, and with good fortune, diamond (Mitchell and Bergman, 1991; Fipke, 1993). Exp1.orat i Qn.ü fllent 1,aL ,Qf , Sout heastern Wyoming The southeastern Wyoming area is the smallest of the four areas with diamond exploration potential in the study region (Figs. 45, 50), with an areal extent of 3264 km* (Table 25). However, it is the only one of the four permissive areas with tracts of Excellent Archon, which is the prime terrane for economic kimberlites. Two "islands" of generally Excellent Archon and one island of Excellent Proton are present in southeastern Wyoming (Fig. 50). The larger of the two islands of Excellent Archon contains 576 km* of terrane that is prospective for economic diamond-bearing kimberlite, Archean and Early Proterozoic basement rocks are found in southeastern Wyoming (Fig. 51). The Cheyenne Belt, a suture zone between Archean basement rocks of the Wyoming craton to the northwest and Proterozoic rocks to the southeast (Karlstrom and Houston, 1984) is manifest as a series of northeast-trending structures near the center of Figure 52. This suture defines the southeastern edge of the two islands of Excellent Archon, and separates the eastern island of Excellent Archon from the island of Excellent Proton immediately to the south. 154 P Ex Archon H i P W0 Archon tÉÉI Ex Archon ü Ex Proton H SE UV Bor dm- ■ 40 km Figure 50. Southeastern Wyoming diamond exploration area (P: Prospective). See Figure 42 for explanation of categories. E Proloro 4 E Pr Otero 2 Archemn ? m Archton c □ Archtan 8 Archtan 4 Contact FaUlt SC UV Bordtr 40 km Figure 51. Precambrian geology of the southeastern Wyoming diamond exploration area. See Appendix A for descriptions of units. 155 D Kinbarlit» Kimbwlitt IH Diamond Find I I Emglt Rk C8 WÊ Lin or Fli H Pdrm Tarran* SE UV Border 40 km Figure 52. Linear and punctual features associated with the southeastern Wyoming diamond exploration area. Key: D Kimberlite = Diamond-Bearing Kimberlite Eagle Rk CS = Eagle Rock Cryptoexplosion Structure Lin or Fit = Lineament or Fault Perm Terrane = Permissive Terrane 156 The eastern mass of Excellent Archon together with the tract of Excellent Proton (Fig. 50) lie immediately northwest and west of the Sheep Rock kimberlite plug and the Iron Mountain field of the Colorado-Wyoming kimberlite province (Fig. 52). Colorado-Wyoming kimberlites, especially the State Line intrusions, are located within and near to major crustal fractures. Additionally, the group of northeast-trending structures that define the Cheyenne Belt together with the nearby alluvial diamond discovery help to define the zone of prospective Excellent to Very Good Archon within the larger of the two tracts of Archean terrane. Extensive stream sediment sampling programs were conducted during the late 1970's and into the late 1980's by personnel from the Wyoming Geological Survey (Hausel and others, 1988) and from Colorado State University (Vos, 1989) in the Laramie Range of southeastern Wyoming in the search for kimberlites. The occurrences and distributions of the traditional kimberlite indicator minerals chromian diopside and pyrope garnet were examined, and the results are presented as a map of chromian diopside and pyrope garnet distribution (Fig. 53). This map was prepared from the maps of Hausel and others (1988) by (1) obtaining the geodetic coordinates of stream sediment samples containing chromian diopside and pyrope garnet, (2) projecting the coordinates into Albers space, and (3) calculating the total number of chromian diops ides and pyrope garnets within each grid cell. 1 5 7 CD>10, P>-1 H i CD-I, P>-1 □ P>-1 ■ Perm Terrane m g 8E UY Border 40 km Figure 53. Distributions of chromian diopside ("CD") and pyrope garnet ("P") in the southeastern Wyoming diamond exploration area ("Perm Terrane" ■ permissive terrane). 3001 .. 3273 2801 .. 3000 2dOI .. 2800 ^ 2401 .. 2800 □ 2201 .. 2400 ^ 0 am...... # 2001 .. 2200 H ..% ..Æ ÏmM 1801 .. 2000 # a # % ™ 4# 1801 .. 1800 # 1 : îâa-.v.rSïÊ t ______# " “ - 1401 .. 1800 H ^ 1235 .. 1400 ■ PI Baunderij ËM3 40 km SE UY Border ■ Figure 54. Elevation of the southeastern Wyoming diamond exploration area, in meters with respect to sea level ("PT Boundary" defines the perimeter of permissive terrane). 158 Chromian diopside and pyrope garnet have been found along the eastern flank of the Laramie Range in southeastern Wyoming (Figs. 53, 54), immediately north and east of the eastern island of Excellent Archon, and southeast of the tract of Excellent Proton (Fig. 50). Some of the minerals can be linked to the known kimberlite plug at Sheep Rock, and the Iron Mountain kimberlites (Fig. 52). In the case of Sheep Rock, however, the abundance and distribution of indicators suggest that additional kimberlites may be present in the vicinity (Hausel and others, 1988). The greatest concentration of chromian diopside occurs just north of the eastern island of Excellent Archon (Figs. 50, 53); and isolated indicators also have been found within the eastern island of Excellent Archon. Elevations of cells within the zone of Excellent Archon are higher than those adjacent to the east and north (Fig. 54), which means that the indicator minerals could easily have been derived from the west and south, from as yet undiscovered kimberlites within the area of Excellent Archon. Although no intrusions of ultramafic affinities are known in the eastern island of Excellent Archon (Fig. 50), two additional factors argue for the occurrence there of potentially diamond- bearing kimberlite: (1) kimberlites nearby prove that kimberlite magmatism is a reality in the area, and their occurrence also suggests that appropriate plumbing is present; and (2) elevated concentrations of cobalt and nickel may reflect ultramafic source rocks (Fig. 55). Microprobe analyses of three of the pyrope garnets from the indicator anomaly immediately north of the eastern 159 Co>2 + Ni>2 ■ Co>l + Ni>t ■ Perm Terrane ËÉ1 SC UY Border ■ 40 km Figure 55. Cobalt and nickel anomalies in the southeastern Wyoming diamond exploration area, in threshold units (see Table 26). Perm Terrane = permissive terrane. 160 island of Excellent Archon revealed that the source(s) of these garnets is not favorable for economic concentrations of diamonds; however, Hausel and others (1988) reported that three analyses are not sufficient to assess the economic viability of potential sources in the area. In spite of the above, the western island of Excellent Archon in southeastern Wyoming (Fig. 50) may be the most attractive zone for diamond exploration in the area. Recovery of two small diamonds during gold placering in the Medicine Bow Mountains (Hausel, 1977), and the detection of chromian diopside and pyrope garnet (Hausel and others, 1988) downstream from the location of the diamonds (Figs. 52-54), provide compelling evidence that potentially economic diamond-bearing kimberlite is present in this zone. Numerous geochemical anomalies (Fig. 55) are supporting evidence for the occurrence of kimberlite bodies. These facts indicate that the zone should be targeted for either high- resolution airborne magnetic and electromagnetic surveys or stream sediment sampling, and if anomalies are detected, to be followed by ground-based reconnaissance. The tract of Excellent Proton is permissive both empirically and tectonically for economic lamproites (Figs. 50, 52). The tectonic setting of the tract is similar to that of the Argyle lamproite vent in northwestern Australia, which was emplaced in a Proterozoic mobile belt adjacent to Archean craton (Mitchell and Bergman, 1991). A recent report of lamproite discoveries in 161 southern Wyoming (Day, 1993) strengthens the hypothesis that the tract is permissive for lamproite. The Eagle Rock cryptoexplosion structure (Fig. 52; Hausel and others, 1988), which lies immediately southeast of the zone of Excellent Proton shown in Figure 50, may be a lamproite vent. Investigation of this structure by Hausel and his co-workers uncovered just one pyrope garnet, but pyrope garnets are found only rarely in lamproite (Fipke, 1993). The Eagle Rock structure probably should be re-evaluated as a possible lamproite occurrence. Stream sediment samples that have been collected within and near to the tract of Excellent Proton (Fig. 53) should be re examined for the presence of lamproite indicator minerals such as tourmaline, zircon, and chromite. Geochemical prospecting for primary diamond deposits in southeastern Wyoming should include elements that are associated specifically with lamproite, elements such as barium and zirconium (Mitchell and Bergman, 1991), in addition to cobalt and nickel which can occur in anomalous concentrations in both kimberlite and lamproite. Explorat ion Potential o f North Dakota-Minnesota-Ontario The North Dakota-Minnesota-Ontario diamond exploration area is a broad, roughly triangular expanse of Archean and Early Proterozoic basement lithologies that encompasses northeastern North Dakota, the northern one-third of Minnesota, and the Thunder Bay region of Ontario (Figs. 45, 56, 57). Very Good Archon and Very Good Proton underlie 89 percent (137,392 km') and 11 percent 1 6 2 V8 Archon VG ft/P IK3 Proton St#t* Border 100 km Figure 56. North Dakota-Minnesota-Ontario diamond exploration area. See Figure 42 for explanation of categories. E Protero 3 E fYotero 1 ■ Archeen ? m Areheen 6 □ Ardwen 6 ArcAeen 4 Cart «et ■ Fault ■ Stale Border ■ 100 km Figure 57. Precambrian geology of the North Dakota-Minnesota- Ontario diamond exploration area. See Appendix A for descriptions of units. 163 (17,164 km') of the area, respectively, and a trace of Very Good Archon/Proton also is present (Table 25). The northeast-southwest orientation of the contact between Very Good Archon and Very Good Proton, and the apparent northeasterly trend of the zone of Very Good Proton (Fig. 56) suggest that much of the lithosphere beneath the western part of Lake Superior also can be characterized as Very Good Proton. The zones of Very Good Archon and Very Good Proton in the North Dakota-Minnesota-Ontario area seem to be fertile ground for economic kimberlites and lamproites. Numerous deep crustal fractures and fracture intersections are present (Fig. 58), along with pipe-like intrusions of possible lamproite-kimberlite affinity (Southwick and Chandler, 1987) that herein are termed the Little Falls Ultramafic Bodies. These bodies provide strong evidence that kimberlite and lamproite magmatism may be present in the area. The geochemical data (Figs. 2, 3, 59) cover only a small part of the North Dakota-Minnesota-Ontario area, but numerous coincident cobalt and nickel anomalies indicate that kimberlites may be present. The area was glaciated several times during the Pleistocene, which can greatly complicate exploration programs, although significant discoveries recently have been made in the Upper Peninsula of Michigan, near Kirkland Lake in Ontario, and in the Lac de Gras area of the Northwest Territories, all of which have been glaciated at least once. The North Dakota-Minnesota-Ontario area undoubtedly merits further study. A stream sediment sampling program is suggested for 164 D Kinbw'Utv ■ ! LF un eed its □ Cnjptovx Str H | Un or Fit Perm Terrene State Border 100 km Figure 58. Linear and punctual features associated with the North Dakota-Minnesota-Ontario diamond exploration area (punctual features not to scale). Key: D Kimberlite = Diamond-Bearing Kimberlite LF UM Bodies = Little Falls Ultramafic Bodies Cryptoex Str = Cryptoexplosion Structure Lin or Fit = Lineament or Fault Perm Terrane = Permissive Terrane 1 6 5 Co>2 * Ni>2 ■ P#rm Term»* WW 100 km Figure 59. Cobalt and nickel anomalies in the North Dakota- Minnesota-Ontario diamond exploration area, in threshold units (see Table 26). Squares marking locations of anomalies are not to scale; Perm Terrane = permissive terrane. 166 the limited zones for which strong geochemical anomalies have been recognized (Fig. 59), particularly where the anomalies correlate with fracture intersections (Fig. 58). The chemistry of indicator minerals should be carefully evaluated to determine if they may have come from bodies with economic quantities of diamonds, and these studies could be followed by detailed geologic, geochemical, and geophysical studies. High-resolution aeromagnetic and electromagnetic surveys should be performed over zones with favorable structure; and if anomalies are identified, they should be further examined following the procedures outlined above, beginning with stream sediment sampling. Exploration Potential of Northwestern Iowa The oval-shaped area in northwestern Iowa (Figs. 45, 60, 61) is composed predominantly of Very Good Proton, with minor Very Good Archon (Table 22). The Very Good Proton may host economic lamproites, whereas the Very Good Archon could host economic kimberlites. The zone of Very Good Proton is cut by geomorphic lineaments and basement faults (Fig. 62), which suggests that possible conduits for lamproite magmatism are present in the area. The nearby Manson cryptoexplosion structure provides evidence that explosive magmatism may have occurred in the area, and the presence of Pleistocene glacial deposits in the area is a formidable but not insurmountable barrier to lamproite exploration. 167 V@ Archon VG Proton NU lA Bordor 40 km Figure 60. Northwestern Iowa diamond exploration area. See Figure 42 for explanation of categories. 168 E Protvro 3 Archtan 7 Contact H NM lA Border ■ 40 km Figure 61. Precambrian geology of the northwestern Iowa diamond exploration area. See Appendix A for descriptions of units. 169 Manmon CS U n or nt P#rm Terran# Ml lA Bordtr 40 km Figure 62. Linear and punctual features associated with the northwestern Iowa diamond exploration area. Key; Manson CS = Manson Cryptoexplosion Structure Lin or Fit = Lineament or Fault Perm Terrane = Permissive Terrane 170 Assessment of the diamond potential of northwestern Iowa suffers from lack of data, particularly geochemical data. However, the small size of the area suggests that high-resolution airborne magnetic and electromagnetic surveys might be useful, perhaps to be followed by stream sediment sampling and ground-based geophysics and geochemistry if the airborne geophysical data are promising. CHAPTER IV CONCLUSIONS Diamond Potential of the Study Region The potential of the north-central United States for economic kimberlites and lamproites has been evaluated using a Diamond Exploration Geoscientific Exploration System (DEGIS). The DEGIS was used to develop independent diamond exploration models that are combined to produce an integrated exploration model (see flowcharts in Appendix B). The Empirical Diamond Exploration Model (Fig. 16) correlates the areal distributions of geologic characteristics that have been determined through decades of study by diamond explorationists to be important for the localization, emplacement, preservation, and discovery of economic kimberlite and lamproite bodies. The attributes of this model are: age of Precambrian basement rocks, lithospheric and crustal thicknesses, and the presence or absence of post-Precambrian sedimentary rocks and Pleistocene glacial cover. The potential for primary diamond deposits is ranked qualitatively as follows: 171 172 1. Excellent • basement rocks of suitable age (Archean for kimberlite, Early Proterozoic for lamproite), • diamond-friendly lithosphere (thickness >150 km), • diamond-friendly crust (thickness >40 km), • presence of post-Precambrian strata, and • absence of glacial cover. 2. Very Good • basement rocks of suitable age, • diamond-friendly lithosphere, and • diamond-friendly crust. 3. Good basement rocks of suitable age, and diamond-friendly lithosphere. 4. Fair • basement rocks of suitable age, and • diamond-friendly crust. 5. Possible • basement rocks of suitable age. Grid cells in the study region that have excellent to good potential for economic kimberlite or lamproite occurrences are termed permissive (Fig. 42). Permissive areas within the region of interest (Table 25) include: Wisconsin-Upper Peninsula of Michigan, 173 southeastern Wyoming, North Dakota-Minnesota-Ontario, and northwestern Iowa. The Stacked Diamond Exploration Model (Figs. 22-25) registers linear and punctual features that may focus kimberlite and lamproite intrusions, or that are associated genetically with such bodies. These features include: surface geomorphic lineaments and their intersections, basement faults and their intersections, intersections of lineaments and basement faults, anomalous cobalt and nickel in stream sediments, and the presence of cryptoexplosion structures, plus alluvial and bedrock diamond occurrences. Grid cells where four such features coincide (four being the maximum) are interpreted to be anomalous, and 20 such locations are present in the study region, in the following states: Colorado (10), Wisconsin (2), and one each in Montana, Wyoming, Minnesota, Iowa, Missouri, Kentucky, Michigan, and Ontario, Canada (Fig. 43). The Proximity Diamond Exploration Model (Fig. 40) ranks grid cells based on (a) their nearness to linear and punctual attributes associated with the emplacement and manifestation of kimberlite and lamproite bodies, and (b) their distance from one undesirable feature (carbonatite intrusions). The lower (more negative) the score, the greater the potential of a location for hosting kimberlites and lamproites. Linear and punctual features include surface geomorphic lineaments and their intersections, basement faults and their intersections, intersections of lineaments and basement faults, stream sediment cobalt and nickel anomalies. 174 occurrences of bedrock and alluvial diamonds, and the presence of cryptoexplosion structures, plus the hinges (positive horizontal flexure axes -- PHFA) of crustal upwarps. Crustal upwarps appear to be favorable sites for invasion by kimberlite and lamproite magmas, and they are interpreted to be reflected by positive relief in surface topography and Precambrian basement topography throughout the study region. Grid cells with standardized proximity Z-scores that fall outside two standard deviations from the mean in the negative tail of the standard normal distribution are considered to be anomalous in this study (Fig. 44). Proximity anomalies group into three areas within the study region: Colorado-Wyoming, Wisconsin- Michigan, and Kentucky-Illinois-Missouri-Kansas. The Integrated Diamond Exploration Model (Fig. 45) combines terrane that is permissive for economic primary diamond deposits (Fig. 42) according to the empirical model with anomalies in the stacked and proximity models (Figs. 43, 44) that occur within permissive terrane. The permissive terrane is the foundation of the integrated model, within which (1) the proximity anomalies highlight zones that are close to linear and punctual attributes viewed to be associated with or genetically linked to potentially diamond-bearing intrusions, and (2) the stacked anomalies are the foci of such attributes. Proximity anomalies in permissive terrane are classified as prospective because they reflect the joint occurrences of apparently favorable emplacement zones and permissive terrane. Stacked anomalies in permissive terrane also 175 yield anomalous proximity Z-scores; hence, they are graded as stacked and prospective because they represent the juxtaposition of both focal and zonal attributes associated with kimberlites and lamproites, within a permissive terrane. The four permissive areas in the study region (Fig. 42, Table 25) are ranked as follows, from most to least potential for economic primary diamond deposits: 1. Uiscansin-Upper Peninsula of Michigan -- this area contains (i) known diamondiferous kimberlites; (ii) all (3) of the stacked and prospective locations in the study area; (iii) roughly 10,000 km' of prospective Good to Very Good Proton that may host economic lamproites, mainly in Wisconsin; (iv) a total of 156,880 km' of terrane that is permissive for economic primary diamond deposits; and (v) two notable alluvial diamond finds within Good Proton. 2. Southeastern Hyoming -- this area is host to (i) two small tracts of Excellent Archon, some of which is prospective, and one zone of Excellent Proton; (ii) kimberlites, some of which contain sub-economic concentrations of diamonds, and lamproites; and (iii) kimberlite and possibly lamproite indicator minerals (Fig. 53) that may be the weathering products of as yet undiscovered 1 7 6 kimberlite and lamproite intrusions in the three permissive zones in the area. 3. North Dakota-Hinnesota-Ontario -- this area comprises a vast expanse of apparently unexplored Archean terrane (Very Good Archon) that may be fe rtile ground for economic kimberlites, plus a large zone of unexplored Very Good Proton that is favorable for economic lamproites. 4. Northwestern Iowa -- this area contains Early Proterozoic terrane that is permissive for economic lamproites. DEGIS Methodology The DEGIS is a rapid, effective, and elegant tool for assessing the potential of the north-central United States for economic kimberlites and lamproites. Important aspects of the modeling strategy developed in the research include: • a power-of-two classification scheme to combine numerous binary layers without information loss in the empirical and stacked diamond exploration models; • focal operations to detect intersections of linear features as part of the stacked and proximity models, plus flexure axes in parametric surfaces for the proximity model; and 177 • standardization of disparate proximity layers to form consistently-scaled input to the proximity model. The modeling techniques are scale-independent and transferable. Within the diamond exploration context, the procedures can be employed to examine an entire continent (for example, Antarctica), or small areas such as the Lac de Gras diamond play in the Northwest Territories of Canada. The DEGIS is (1) a dynamic vehicle for generating diamond exploration models that are responsive virtually in real-time to user-specified criteria; (2) a platform for testing spatial hypotheses on the emplacement of kimberlites and lamproites, and the likelihood that they will contain economic concentrations of diamonds; and (3) a "microscope" for examining the lateral and vertical characteristics of the earth near to known occurrences of kimberlite, lamproite, and diamonds. The results of this research suggest that individual geoscientists can create a small-scale, broad coverage, and useful database within a short time (about two years in the present case), and produce meaningful spatial models from the data. CHAPTER V RECOMMENDATIONS Diamond Exploration in the Study Region Areas within the north-central United States that are permissive for economic primary diamond deposits have been identified in (1) Wisconsin-Upper Peninsula of Michigan, (2) southeastern Wyoming, (3) North Dakota-Minnesota-Ontario, and (4) northwestern Iowa. Based on the exploration criteria employed in this research, the areas highlight locations that are most likely to contain economic kimberlite and lamproite intrusions, and thus merit follow-up evaluation. The follow-up work should involve some blend of satellite imagery and aerial photography, high- resolution aeromagnetic and electromagnetic surveys, ground-based geophysical and geochemical investigations, and classical approaches such as outcrop studies and soil and stream sediment sampling. In such evaluations, prospectors must bear in mind that lamproite as well as kimberlite may be present in all four regions. Diamond Exploration Models The efficacy of the diamond exploration models developed herein should be tested by applying them to regions where economic kimberlite or lamproite intrusions, as well as sub-economic to 178 1 7 9 barren bodies are known to occur. Appropriate regions for kimberlite would include the South African craton and the Sakha (formerly Yakutia) region in eastern Siberia, and northwestern Australia for kimberlite and lamproite. The Lac de Gras area in the Northwest Territories of Canada is a new frontier for diamond exploration in North America, and an additional site in which to test the models. Success in known areas will validate the models, and applying them in a new area may help to focus existing exploration programs. Nuances within individual fields and provinces that may be critical in predicting occurrences of undiscovered deposits can easily be incorporated into the DEGIS. Recommendations to improve the diamond exploration models produced in this work include: 1. Applying a broader suite of filte rs for detecting the intersections of linear features such as geomorphic lineaments and faults, and especially, filters that are sensitive to the step-like character of rasterized linear features (digitizing may prove to be the most desirable technique for capturing intersections). 2. Using different interpolation techniques such as kriging, instead of linear interpolation and nearest-neighbor averaging methods, to generate parametric surfaces from sparse data (e.g, the surface and basement topographic surfaces used to derive input for the proximity model). 180 3. Trying alternative methods of regional/residual separation within parametric surfaces, such as regression analysis or spectral analysis, to develop residual surfaces that may be less susceptible to ringing, although focal operators would likely have to be employed to detect local maxima (or minima) in the residual surfaces. 4. Weighting the components of the models, especially the empirical and stacked models, to impose the expertise of the explorationist. As an example, the presence of diamond-friendly lithosphere may be much more important for economic primary diamond deposits than the presence of sedimentary strata in the region, and the empirical model could be weighted accordingly. 5. Attempting to visualize the diamond exploration models in new ways; for example, draping the empirical model over surface or basement topography to delimit locations where favorable diamond potential correlates with upwarps in these surfaces. 181 DEGIS Database The following measures are recommended for Incorporation into the Diamond Exploration Geoscientific Information System (DEGIS) database, to increase its utility: • anthropogenic and environmental factors that constrain mineral exploration and mining, factors such as the presence of state and national parks, wildlife refuges, recreation areas, lakes, rivers, wetlands, and metropolitan areas, plus roads and railways; • lithospheric thickness data derived from subsurface imaging technologies such as seismic tomography (Grand, 1987), when the study region is well-imaged and tomographic data are widely available, that may be more meaningful than the surface heat flow-based approach used in this research to estimate the thickness of the lithosphere in the region of interest; • tracks of mantle hotspots that may be genetically linked to kimberlite and lamproite magmatism (Crough and others, 1980); • curvilinear structures that may be detected from satellite imagery, high-altitude photography, topography, and regional geophysical data -- major curvilinear features have been recognized within and near kimberlite fields in eastern 182 Siberia (Dukhovskiy and others, 1986; Nikulin and others, 1988), in South Africa (Pretorius, 1979), and in the State Line district of the Colorado- Wyoming kimberlite province where the Virginia Dale ring dike complex is a conspicuous feature (Hausel and others, 1979); • traces of igneous dikes and, especially, dike swarms that reflect regional structures that may be favorable environments for kimberlite and lamproite magmatism (dikes are plainly evident in the Lac de Gras area of the Northwest Territories of Canada (Pell and Atkinson, 1993), and have been noted by Nikulin and others (1988) in the Siberian craton, by Dempster and Richard (1973) in Lesotho, and by Smith (1977) in the Iron Mountain district of the Colorado-Wyoming kimberlite province) • seismic velocities of the uppermost mantle, where locally high values (as much as 8.6 km/s) are reported to correlate with kimberlite fields in eastern Siberia (Nikulin and others, 1988); • locations, types, and chemistry of kimberlite and lamproite indicator minerals to help corroborate other lines of evidence for the presence of these rocks; and 183 • glacier-related information such as directions of ice movement and the syn-glacier surface, to ascertain where kimberlite and lamproite indicator minerals might be expected to be found with respect to their sources. Diamond Exploration Practices Recommendations to advance diamond exploration practices in North America (and elsewhere) include (in rank order): 1. Quantifying the geotectonic controls on the occurrences of kimberlite and lamproite provinces, as well as fields within the provinces (for example, is orientation of major fractures important in localizing kimberlite fields in a given province, and if so, why?) -- this approach has been used to identify promising locations for kimberlites in the Siberian Platform (Dukhovskiy and others, 1986; and Nikulin and others, 1988). 2. Clarifying attributes that cause crustal upwarps (and perhaps even downwarps) to be favorable sites for the emplacement of kimberlite and lamproite. 3. Developing better insight into the spatial and genetic relationships between kimberlites and lamproites -- the two have been found together in Minas Gerais, Brazil (Barron and Barnet, 1993), 184 and a lamproitic kimberlite intrusion has been discovered recently in the northern Northwest Territories, Canada (The Northern Miner, 23 August 1993). To date, all that is known is that economic kimberlites occur within Archean cratons, and economic lamproites are localized in Early Proterozoic terranes around the margins of cratons. 4. Determining what factors constitute minimally- sufficient criteria for diamond exploration (e.g., are basement rocks of requisite age and the presence of diamond-friendly lithosphere the only essential factors in the search for economic kimberlites and lamproites). 5. Supporting the development of public computer databases on the distributions and characteristics of kimberlites and lamproites (Muggeridge, 1989) to facilitate their study - - a link to a geographic information system would produce a formidable diamond exploration tool. 6 . Investigating the potential for using the compositions of (i) chromite inclusions in diamonds from lamproite and (ii) tourmaline from lamproite as predictors of diamond grades in lamproite bodies (Fipke, 1993). 185 Other Applications In a broader context, the DEGIS database may be useful in applications other than diamond exploration in the north-central United States. For example, the generic CIS could be used to search for Mississippi Valley type (MVt) base metal deposits by (a) establishing a spatial model for such deposits; (b) creating pertinent thematic data layers such as known base metal mining districts and pathfinder elements for MVt deposits; and (c) manipulating the layers along with the geologic, geophysical, and remote sensing layers of the GIS. Additional applications might include (1) study of fundamental earth processes such as the formation of large intra-continental basins, within and around the margins of which coal, oil, gas, and many types of metallic and non-metal lie mineral resources occur; (2) assessment of sites being considered for civil works, power plants, and hazardous waste repositories; and (3) regional geological evaluations. APPENDIX A DESCRIPTIONS OF PRECAMBRIAN BASEMENT ROCKS IN THE STUDY REGION 186 187 Table 27. Descriptions of Archean Units from Reed (1989) Archean 7; (1) migmatitic gneiss and associated granitic gneiss, amphibolite, pyroxene granulite, and biotite schist with agesof 2.6-3.6 Ga in Minnesota and South Dakota; (2) rocks of the Wyoming craton in Wyoming and Montana. Archean 6; metasedimentary and metavolcanic rocks, including (1) low-to high-grade metasedimentary rocks and associated gneissic and migmatitic rocks, and metavolcanic rocks (mainly greenstone belts) in Minnesota and Ontario; (2) layered gneisses >2.7 Gaold in western Wyoming, plus >2.7 Ga old greenstone belts in this state; (3) metamorphic rocks with ages of 2.7-2.8 Ga in North Dakota and South Dakota. Archean 5; plutonic rocks, including (1) massive and foliated granite to diorite with ages of 2.6-2.8 Ga in Minnesota, North Dakota, and South Dakota; (2) granitic rocks 2.5-2.7 Ga old in Wyoming and Montana; (3) granite intruding migmatitic schist in Minnesota, South Dakota, and Nebraska; (4) granite, granodiorite, and syenite in small post-tectonic plutons in Minnesota. Archean 4; orthogneiss and paragneiss in Wyoming. Archean 3; metasedimentary and metavolcanic rocks with ages>2.8 Ga in Wyoming. Archean 2; granitic rocks with ages >3.0 Ga in Wyoming. Archean 1; (1) metasedimentary rocks and metavolcanic rocks with ages >3.0 Ga in Montana; (2) orthogneiss and paragneiss >3.0 Ga old in Montana and Wyoming. 188 Table 28. Descriptions of Early Proterozoic Units from Reed (1989) E Protero 9; granitic rocks and mafic plutonic rocks in Colorado, Montana, Nebraska, and New Mexico. E Protero 8; sedimentary and metasedimentary rocks with ages of 1.6-1.8 Ga, including (1) quartzite, phyllite, mica schist, and gneiss in the northern midcontinent region, Colorado, Nebraska, and New Mexico; (2) metavolcanic rocks and plutonic rocks in the northern midcontinent. E Protero 7; volcanic rocks with ages of 1.6-1.8 Ga, including (1) chiefly rhyolite in the northern mid-continent region; (2) bimodal and volcaniclastic sequences in Colorado. E Protero 6; metamorphic rocks with ages of 1.6-1.8 Ga, including (1) felsic gneiss and hornblende gneiss in Colorado and New Mexico; (2) metasedimentary and metavolcanic rocks in New Mexico. E Protero 5; plutonic rocks with ages of 1.6-1.8 Ga, including (1) small granitic plutons in Wisconsin; (2) pre- and syn-tectonic granitic rocks in Colorado and New Mexico; (3) gabbro in Missouri; (4) gabbro and related rocks in Colorado. E Protero 4; rocks of mixed or uncertain origin with ages of 1.6-1.8 Ga, including (1) rhyolite and oogenetic epizonal granite in Wisconsin; (2) metasedimentary and meta-igneous rocks (possibly with younger post-orogenic and anorogenic plutons) in the northern midcontinent region; (3) granitic gneiss in New Mexico. E Protero 3: lithologies with ages of 1.8-2.1 Ga, including (1) epicratonic sedimentary rocks in Michigan and Minnesota; (2) gneiss, amphibolite, foliated granite and tonalité in Wisconsin; (3) basalt, rhyolite, and associated sedimentary rocks and subvolcanic intrusions in Iowa, Minnesota, and Wisconsin. E Protero 2; rocks with ages of 2.1-2.5 Ga, including (1) the Huronian Supergroup and minor basalt, rhyolite, granite, and mafic intrusive rocks in Ontario; (2) the Snowy Pass Supergroup and metasedimentary rocks in Wyoming; (3) interlayered metasedimentary and metavolcanic rocks in South Dakota. E Protero 1: Early Proterozoic and Archean rocks, including (1) gneiss and epicratonic sedimentary rocks in Michigan and Wisconsin; (2) Early Proterozoic rocks and reactivated Archean basement rocks along the western margin of the Trans-Hudson orogen in South Dakota. 189 Table 29. Descriptions of Middle and Late Proterozoic Units from Reed (1989) Late Protero; sedimentary rocks, minor basalt flows, and related mafic and ultramafic intrusions in the New Madrid rif t complex. M Protero 6; sedimentary rocks of the Uinta Mountain Group in Colorado. M Protero 5; plutonic rocks, including (1) anorogenic granitic rocks of 1.3-1.5 Ga age in the midcontinent region and southwestern United States; (2) magnetite-bearing anorogenic granites with ages of 1.3-1.5 Ga in the mid-continent region; (3) anorthosite and associated rocks of the Laramie Anorthosite Complex in Wyoming. M Protero 4; sedimentary and volcanic rocks with ages of 0.9-1.2 Ga, including clastic sedimentary rocks and interlayered mafic volcanic rocks (plus correlative rocks in the subsurface) associated with the Midcontinent Rift system in Michigan, Minnesota, and Wisconsin. M Protero 3; plutonic rocks with ages of 0.9-1.2 Ga, including (1) granitic rocks of the Pikes Peak batholith in Colorado; (2) mafic intrusive rocks of the Duluth Intrusive Complex in Minnesota; (3) the Mellon Intrusive Complex in Wisconsin. M Protero 2; lithologies with ages of 1.2-1.4 Ga, including (1) sedimentary and metasedimentary rocks in Colorado, New Mexico, and Ontario; (2) rhyolite and associated rocks of the granite- rhyolite terrane in the southern midcontinent region, plus magnetite-bearing granitic rocks in this region; (3) granitic plutons in the granite-rhyolite terrane of the midcontinent region, the San Isabel batholith in Colorado, and the Amarillo Granite in New Mexico. M Protero 1; rocks with ages of 1.4-1.6 Ga, including (1) granitic rocks and rhyolite in the mid-continent region, epizonal granitic rocks of the Wolf River Batholith and rocks of the Wausau Syenite Complex in Wisconsin; (2) the Sierra Grande Granite in New Mexico; (3) magnetite-bearing granitic rocks of the lowa-Wisconsin plutonic belt. APPENDIX B FLOWCHARTS OF DIAMOND EXPLORATION MODELS 1 9 0 191 Glossary of Layers* Base Asp: Directional aspect of basement surface Basement Ages: General ages of Precambrian basement rocks B PFA: PHFA in basement surface BPF Px: P to PHFA in basement surface BPF SPx: SP to PHFA in basement surface Carbt: Carbonatite occurrences Co An: Anomalous concentrations of cobalt Co Prx: P to anomalous concentrations of cobalt Co SPx: SP to anomalous concentrations of cobalt Cobalt: Concentrations of cobalt in stream sediments Crust Thick: Crustal thickness Ct Prx: Distance from carbonatite occurrences Ct SPx: Standardized distance from carbonatite occurrences Cx St: Cryptoexplosion structures Cx Prx: P to cryptoexplosion structures Cx SPx: SP to cryptoexplosion structures DF Crust: Diamond-friendly crust (thickness >40 km) DF Lithosphere: Diamond-friendly lithosphere (thickness >150 km) Dimnd: Diamond occurrences Dd Prx: P to diamond occurrences Dd SPx: SP to diamond occurrences Empirical Model: Empirical diamond exploration model FI Prx: P to basement faults FI SPx: SP to basement faults Fits: Basement faults Fit X: Basement fault intersections FX Prx: P to intersections of basement faults FX SPx: SP to intersections of basement faults Geology: Lithology and structure of Precambrian basement rocks Glac Terrane: Glaciated and non-glaciated terrane Integrated Model: Integrated diamond exploration model L&F X: Intersections of lineaments and basement faults LFX Px: P to intersections of lineaments and basement faults LFX SPx: SP to intersections of lineaments and basement faults Linrs: Geomorphic lineaments Lin X: Lineament intersections Litho Thick: Lithospheric thickness Ln Prx: P to lineaments Ln SPx: SP to lineaments LP Base Elev: Low-pass filtered basement surface LP Surf Elev: Low-pass filtered earth surface LX Prx: P to interesections of lineaments LX SPx: SP to intersections of lineaments 192 Glossary (Continued) NI An: Anomalous concentrations of nickel Nickel: Concentrations of nickel in stream sediments Ni Prx: P to anomalous concentrations of nickel Ni SPx: SP to anomalous concentrations of nickel Permissive Terrane: Areas permissive for economic Ks and Ls Proximity Anomalies: Anomalies in proximity model Proximity Model: Proximity diamond exploration model Pt Data: Locations of diamonds, kimberlites, lamproites, etc. Sediment Cover: Thickness of sedimentary rock S PFA: PHFA in earth surface SPF Px: P to PHFA in earth surface SPF SPx: SP to PHFA in earth surface Stacked Anomalies: Anomalies in stacked model Stacked Model: Stacked diamond exploration model Surf Asp: Directional aspect of earth surface Ks = kimberlites Ls = lamproites P = proximity PHFA = positive horizontal flexure axes SP = standardized proximity 1 9 3 Geology — recode Basement Ages — sdd ——I Litho Thick — recode — > DF Lithosphere add Crust Thick — recode — > DF Crust add — LP Surf Elev subtract —> Sediment Cover add LP Base Elev Glac Terrane Empirical Model Figure 63. Flowchart of the development of the Empirical Diamond Exploration Model. Refer to Glossary for definitions of layers. 194 Linrs add — Linrs - filte r ->Lin X add — Geology recode -> Fits Pt Data - recode -> Dimnd add — Pt Data — recode -> Cx St add Cobalt — recode -> Co An add — Nickel — recode -> Ni An add — Fits - filte r ->Fit X — add — Fits addL&F X — add — Linrs Stacked Model Figure 64. Flowchart of the development of the Stacked Diamond Exploration Model. Refer to Glossary for definitions of layers. 195 LP Surf Elev — aspect -> Surf Asp - filte r -> S PFA LP Base Elev - aspect -> Base Asp - filte r -> B PFA Pt Data — recode -> Carbt Figure 65. Flowchart of the development of the Proximity Diamond Exploration Model -- Part 1. Refer to Glossary for definitions of layers. 196 Linrs spread -> Ln Prx - standz ->Ln SPx — add — Lin X - spread -> LX Prx — standz -> LX SPx add — Fits - spread -> FI Prx - standz ->FI SPx add Fit X spread -> FX Prx - standz -> FX SPx — add — L&F X spread ->LFX Px - standz ->LFX SPx — add Dimnd — spread -> Dd Prx standz ->Dd SPx — add Cx St spread ->Cx Prx — standz -> Cx SPx — add — Carbt spread -> Ct Prx - standz -> Ct SPx Co An - spread ->Co Prx - standz -> Co SPx Ni An - spread -> Ni Prx - standz -> Ni SPx add S PFA spread -> SPF Px standz -> SPF SPx add B PFA - spread -> BPF Px - standz -> BPF SPx - add — Proximity Model Figure 66. Flowchart of the development of the Proximity Diamond Exploration Model -- Part 2 (standz=standardize). Note that the model builds on layers created for the Stacked Diamond Exploration Model (Fig. B3). Refer to Glossary for definitions of layers. 197 Empirical Permissive Model - recode -> Terrane add — Stacked Stacked Integrated Model recode ->Anomalies - add Model Proximity Proximity Model -> Anomalies add —recode Figure 67. Flowchart of the development of the Integrated Diamond Exploration Model. APPENDIX C CARTOGRAPHIC MODELING PROCEDURES 198 1 9 9 Footnotes * bfaults a basement faults HFA horizontal flexure axes ND-MND (nlog distance - mean nlog distance) NP-MNP (nlog proximity - mean nlog proximity) PHFA positive horizontal flexure axes SD standard deviation SDT standardized distance SP Standardized Proximity + refer to Glossary for descriptions of layers; modeling language after Tomlin (1990) 2 0 0 Glossary of Data Layers Used in Cartographic Modeling Procedures* Laver Description ILAYER Constant 250LAYER Constant ANOMPROX Anomalies in proximity diamond exploration model ANOMSTCK Anomalies in stacked diamond exploration model APDELTA8 Craton transitional between Archean and Early Proterozoic APTBASE General ages of Precambrian basement rocks ARCH0N16 Archean craton ASPBASE Directional aspect of basement surface ASPESRF Directional aspect of earth surface BASEEW Filter for E-W changes in aspect of basement surface BASEFLTS Basement faults BASEHFA HFA in basement surface BASENESW Filter for NE-SW changes in aspect of basement surface BASENS Filter for N-S changes in aspect of basement surface BASENWSE Filter for NW-SE changes in aspect of basement surface BASEPHFA PHFA in basement surface BFLTDIFF NP-MNP to basement faults BFLTLOG Nlog proximity to basement faults BFLTMEAN Mean nlog proximity to basement faults BFLTPROX Proximity to basement faults BFLTSDEV SD of proximity to basement faults BFLTSTND SP to basement faults BFLXEW Isolated E-W changes in aspect of basement surface BFLXNESW Isolated NE-SW changes in aspect of basement surface BFLXNS Isolated N-S changes in aspect of basement surface BFLXNWSE Isolated NW-SE changes in aspect of basement surface BPFLDIFF NP-MNP to PHFA in basement surface BPFLLOG Nlog proximity to PHFA in basement surface BPFLMEAN Mean nlog proximity to PHFA in basement surface BPFLPROX Proximity to PHFA in basement surface BPFLSDEV SD of proximity to PHFA in basement surface BPFLSTND SP to PHFA in basement surface BTEMPHFA Reclassified HFA in basement surface CARBDIFF ND-MND from carbonatite occurrences CARBLOG Nlog distance from carbonatite occurrences CARBMEAN Mean nlog distance from carbonatite occurrences CARBNITE Carbonatite occurrences CARBNVRT Distance from carbonatite occurrences CARBPROX Proximity to carbonatite occurrences CARBSDEV SD of distance from carbonatite occurrences CARBSTND SDT from carbonatite occurrences COANDIFF NP-MNP to anomalous concentrations of cobalt COANLOG Nlog proximity to anomalous concentrations of cobalt COANMEAN Mean nlog proximity to anomalous concentrations of cobalt 2 0 1 Glossary (Continued) Laysr Dfi.s,çrjp.t l Qn COANOM Anomalous concentrations of cobalt COANPROX Proximity to anomalous concentrations of cobalt COANSDEV SD of proximity to anomalous concentrations of cobalt COANSTND SP to anomalous concentrations of cobalt CRPTDIFF NP-MNP to cryptoexplosion structures CRPTLOG Nlog proximity to cryptoexplosion structures CRPTMEAN Mean nlog proximity to cryptoexplosion structures CRPTPROX Proximity to cryptoexplosion structures CRPTSDEV SD of proximity to cryptoexplosion structures CRPTSTND SP to cryptoexplosion structures CRST40KM Crustal thickness >40 km CRSTTHK Crustal thickness CRYPTOEX Cryptoexplosion structures DIAMOND Diamond occurrences DMNDDIFF NP-MNP to diamond occurrences DMNDLOG Nlog proximity to diamond occurrences DMNDMEAN Mean nlog proximity to diamond occurrences DMNDPROX Proximity to diamond occurrences DMNDSDEV SD of proximity to diamond occurrences DMNDSTND SP to diamond occurrences EFLXEW Isolated E-W changes in aspect of earth surface EFLXNESW Isolated NE-SW changes in aspect of earth surface EFLXNS Isolated N-S changes in aspect of earth surface EFLXNWSE Isolated NW-SE changes in aspect of earth surface EMMASTER Master empirical model with all power of two combinations EMPMODEL Empirical diamond exploration model EPFLDIFF NP-MNP to PHFA in earth surface EPFLLOG Nlog proximity to PHFA in earth surface EPFLMEAN Mean nlog proximity to PHFA in earth surface EPFLPROX Proximity to PHFA in earth surface EPFLSDEV SD of proximity to PHFA in earth surface EPFLSTND SP to PHFA in earth surface ESRFEW Filter for E-W changes in aspect of earth surface ESRFHFA HFA in earth surface ESRFNESW Filter for NE-SW changes in aspect of earth surface ESRFNS Filter for N-S changes in aspect of earth surface ESRFNWSE Filter for NW-SE changes in aspect of earth surface ESRFPHFA PHFA in earth surface ETEMPHFA Reclassified HFA in earth surface topography GLANOGLA Glaciated versus non-glaciated terrane INTMODEL Integrated diamond exploration model 2 0 2 Glossary (Continued) Layer D.e§£r..iptl9.n LINEARS Geomorphic lineaments LINEDIFF NP-MNP to lineaments LINELOG Nlog proximity to lineaments LINEMEAN Mean nlog proximity to lineaments LINEPROX Proximity to lineaments LINESDEV SD of proximity to lineaments LINESTND SP to lineaments LITHOTHK Lithospheric thickness LPBASE Low-pass filtered basement surface LPESRF Low-pass filtered earth surface LTH150KM Lithospheric thickness >150 km NEGLAYER Constant NIANDIFF NP-MNP to anomalous concentrations of nickel NIANLOG Nlog proximity to anomalous concentrations of nickel NIANMEAN Mean nlog proximity to anomalous concentrations of nickel NIANOM Anomalous concentrations of nickel NIANPROX Proximity to anomalous concentrations of nickel NIANSDEV SD of proximity to anomalous concentrations of nickel NIANSTND SP to anomalous concentrations of nickel NOGLACR Non-glaciated terrane PERMAREA Terrane permissive for economic kimb'lites and lamproites PR0T0N4 Early Proterozoic craton RESBASE Residual component of basement surface RESBHFA Residuals at HFA in basement surface RESEHFA Residuals at HFA in earth surface topography RESESRF Residual component of earth surface SEDTHICK thickness of sedimentary rocks SOMESEDS presence of sedimentary rocks SPROXMOD Standardized proximity diamond exploration model STACKP2 Stacked exploration model with all power of two combos STACKSUM Stacked exploration model with coincident attributes TBASEASP Azimuthal aspect of basement surface TEMP128 Temporary layer TEMP16 Temporary layer TEMP2 Temporary layer TEMP256 Temporary layer TEMP32 Temporary layer TEMP4 Temporary layer TEMP64 Temporary layer TEMPS Temporary layer TEMPFILE Temporary layer TESRFASP Azimuthal aspect of earth surface 203 Glossary (Continued) Laver Description UPROXMOD Unstandardized (raw) proximity diamond exploration model UPRXDIFF Difference between raw proximity model and its mean UPRXMEAN Mean of unstandardized proximity exploration model UPRXSDEV SD of unstandardized proximity diamond exploration model XXBFDIFF NP-MNP to intersections of basement faults XXBFLOG Nlog proximity to intersections of basement faults XXBFMEAN Mean nlog proximity to intersections of basement faults XXBFPROX Proximity to intersections of basement faults XXBFSDEV SD of proximity to intersections of basement faults XXBFSTND SP to intersections of basement faults XXLFDIFF NP-MNP to intersections of lineaments and basement faults XXLFLOG Nlog proximity to intersections of lineaments and bfaults XXLFMEAN Mean nlog proximity to intersctns of linments and bfaults XXLFPROX Proximity to intersctns of lineaments and basement faults XXLFSDEV SD of proximity to intersctns of lineaments and bfaults XXLFSTND SP to intersections of lineaments and basement faults XXLINFLT Intersections of lineaments and basement faults XXLNDIFF NP-MNP to lineament intersections XXLNLOG Nlog proximity to lineament intersections XXLNMEAN Mean nlog proximity to lineament intersections XXLNPROX Proximity to lineament intersections XXLNSDEV SD of proximity to lineament intersections XXLNSTND SP to lineament intersections XXXXFLTS Basement fault intersections XXXXLINE Lineament intersections 2 0 4 Table 30. Procedure to Produce Empirical Diamond Exploration Model+ Perform power of two reclassification. NOGLACR = LocalRating of GLANOGLA with I for 0 with 0 for 1 SOMESEDS = LocalRating of SEDTHICK with 0 for -30000 ... 0 with 2 for 1 ... 30000 PR0T0N4 = LocalRating of APTBASE with 0 for 1 ... 8 with 4 for 9 with 0 for 10 ... 20 APDELTAB = LocalRating of APTBASE with 0 for 1 ... 10 with 8 for 11 with 0 for 12 ... 20 ARCH0N16 = LocalRating of APTBASE with 0 for 1 ... 12 with 16 for 13 with 0 for 14 ... 20 CRST40KM = LocalRating of CRSTTHK with 0 for 2 with 32 for 3 ... 15 LTH150KM = LocalRating of LITHOTHK with 0 for 2 ... 7 with 64 for 8 ... 15 Combine reclassified layers for master model. EMMASTER = LocalSum of NOGLACR and SOMESEDS and PR0T0N4 and APDELTAB and ARCH0N16 and CRST40KM and LTH150KM Reclassify master model to isolate excellent, very good, good, fair, and possible locations in Archon, Archon/Proton (A/P), and Proton to obtain the empirical model. EMPMODEL = LocalRating of EMMASTER with 1 for 115 (Excellent Archon) with 2 for 112 ... 114 (Very Good Archon) with 3 for80 ... 83 (Good Archon) with 4 for48 ... 51 (Fair Archon) with 5 for 16 ... 19 (Possible Archon) with 6 for 107 (Excellent A/P) with 7 for 104 ... 106 (Very Good A/P) with 8 for72 ... 75 (Good A/P) with 9 for40 ... 43 (Fair A/P) with 10 for 8 ... 11 (Possible A/P) with 11 for 103 (Excellent Proton) with 12 for 100... 102 (Very Good Proton) with 13 for 68 ... 71 (Good Proton) with 14 for 36 ... 39 (Fair Proton) with 15 for 4 ... 7 (Possible Proton) with 0 for 108 ... Ill with 0 for84 ... 99 with 0 for76 ... 79 with 0 for52 ... 67 with 0 for44 ... 47 with 0 for20 ... 35 with 0 for12 ... 15 with 0 for 1 ... 3 205 Table 31. Procedure to Produce Stacked Diamond Exploration Model+ Perform power of two reclassification (note: LINEARS=1). TEMP2 = LocalRating of XXXXLINE with 2 for 1 TEMP4 = LocalRating of BASEFLTS with 4 for 1 TEMPS = LocalRating of XXXXFLTS with 8 for 1 TEMP16 = LocalRating of XXLINFLT with 16 for 1 TEMP32 » LocalRating of COANOM with 32 for 1 TEMP64 = LocalRating of NIANOM with 64 for 1 TEMP128 = LocalRating of DIAMOND with 128 for 1 TEMP256 = LocalRating of CRYPTOEX with 256 for 1 Combine reclassified layers for power of two stacked model. STACKP2 - LocalSum of LINEARS and TEMP2 and TEMP4 and TEMPS and TEMP16 and TEMP32 and TEMP64 and TEMP128 and TEMP256 Combine reclassified layers for "additive" stacked model. STACKSUM = LocalSum of LINEARS and XXXXLINE and BASEFLTS and XXXXFLTS and XXLINFLT and COANOM and NIANOM and DIAMOND and CRYPTOEX 206 Table 32. Procedure to Produce Horizontal Flexure Axes from Parametric Surfaces+ Compute azimuthal aspect. TESRFASP = IncrementalAspect of LPESRF TBASEASP * IncrementalAspect of LPBASE Perform power of two reclassification. Note: NW=1, N=2, NE=4, E=8, SE=16, S=32, SW=64, W=128. ASPESRF » LocalRating of TESRFASP with 1 for 294 ... 337 with 2 for 1 ... 23 with 2 for 338 ... 360 with 4 for 24 ... 67 with 8 for 68 ... 113 with 16 for 114 ... 157 with 32 for 158 ... 203 with 64 for 204 ... 247 with 128 for 248 ... 293 ASPBASE = LocalRating of TBASEASP with 1 for 294 ... 337 with 2 for 1 ... 23 with 2 for 338 ... 360 with 4 for 24 ... 67 with 8 for 68 ... 113 with 16 for 114 ... 157 with 32 for 158 ... 203 with 64 for 204 ... 247 with 128 for 248 ... 293 Apply 3 x 3 filters to define diametric changes in aspect (filter values in top-down, left-to-right, row-by-row order). ESRFNWSE = FocalFilter of ASPESRF with 1 0 0 0 0 0 0 0 1 ESRFNS = FocalFilter of ASPESRF with 0 1 0 0 0 0 0 1 0 ESRFNESW = FocalFilter of ASPESRF with 0 0 1 0 0 0 1 0 0 ESRFEW = FocalFilter of ASPESRF with 0 0 0 1 0 1 0 0 0 BASENWSE = FocalFilter of ASPBASE with 1 0 0 0 0 0 0 0 1 BASENS = FocalFilter of ASPBASE with 0 1 0 0 0 0 0 1 0 BASENESW = FocalFilter of ASPBASE with 0 0 1 0 0 0 1 0 0 BASEEW = FocalFilter of ASPBASE with 0 0 0 1 0 1 0 0 0 Isolate HFA, by trend. EFLXNWSE = LocalRating of ESRFNWSE with 0 for 1 ... 16 with 1 for 17 with 0 for 18 ... 1000 EFLXNS = LocalRating of ESRFNS with 0 for 1 ... 33 with 2 for 34 with 0 for 35 ... 1000 EFLXNESW = LocalRating of ESRFNESW with 0 for 1 ... 67 with 4 for 68 with 0 for 69 ... 1000 EFLXEW = LocalRating of ESRFEW with 0 for 1 ... 135 with 8 for 136 with 0 for 137 ... 1000 BFLXNWSE = LocalRating of BASENWSE with 0 for 1 ... 16 with 1 for 17 with 0 for 18 ... 1000 BFLXNS =LocalRating of BASENS with 0 for 1 ... 33 with 2 for 34 with 0 for 35 ... 1000 BFLXNESW = LocalRating of BASENESW with 0 for 1 ... 67 with 4 for 68 with 0 for 69 ... 1000 BFLXEW = LocalRating of BASEEW with 0 for 1 ... 135 with 8 for 136 with 0 for 137 ... 1000 Combine component HFA layers for final HFA layers. ESRFHFA = LocalSum of EFLXNWSE and EFLXNS and EFLXNESW and EFLXEW BASEHFA = LocalSum of BFLXNWSE and BFLXNS and BFLXNESW and BFLXEW 207 Table 33. Procedure to Separate Positive Horizontal Flexure Axes from Negative Horizontal Flexure Axes+ Reclassify power of two HFA layers into binary ones. ETEMPHFA = LocalRating of ESRFHFA with 1 for 2 ... 15 BTEMPHFA = LocalRating of BASEHFA with 1 for 2 ... 15 Multiply binary layers by residual component of parametric surfaces. RESEHFA - LocalProduct of ETEMPHFA and RESESRF RESBHFA = LocalProduct of BTEMPHFA and RESBASE Reclassify residual layers to isolate PHFA. ESRFPHFA = LocalRating of RESEHFA with 0 for -10000 ... 0 with 1 for 1 ... 10000 BASEPHFA = LocalRating of RESBHFA with 0 for -10000 ... 0 with 1 for 1 ... 10000 208 Table 34. Procedure to Produce Proximity Diamond Exploration Model+ Generate proximity surfaces. LINEPROX FocalProximity of LINEARS XXLNPROX FocalProximity of XXXXLINE BFLTPROX FocalProximity of BASEFLTS XXBFPROX FocalProximity of XXXXFLTS XXLFPROX FocalProximity of XXLINFLT EPFLPROX FocalProximity of ESRFPHFA BPFLPROX FocalProximity of BASEPHFA COANPROX = FocalProximity of COANOM NIANPROX as FocalProximity of NIANOM DMNDPROX B FocalProximity of DIAMOND CRPTPROX FocalProximity of CRYPTOEX CARBPROX FocalProximity of CARBNITE Add one for subsequent natural logarithm (nlog) transformation. ILAYER = LocalRating of DIAMOND with 1 for 0 LINEPROX = LocalSum of LINEPROX and ILAYER XXLNPROX = LocalSum of XXLNPROX and ILAYER BFLTPROX = LocalSum of BFLTPROX and ILAYER XXBFPROX = LocalSum of XXBFPROX and ILAYER XXLFPROX = LocalSum of XXLFPROX and ILAYER EPFLPROX = LocalSum of EPFLPROX and ILAYER BPFLPROX = LocalSum of BPFLPROX and ILAYER COANPROX = LocalSum of COANPROX and ILAYER NIANPROX = LocalSum of NIANPROX and ILAYER OMNOPROX = LocalSum of DMNDPROX and ILAYER CRPTPROX = LocalSum of CRPTPROX and ILAYER CARBPROX = LocalSum of CARBPROX and ILAYER Invert CARBPROX to create a "distance from carbonatite" layer. 250LAYER = LocalRating of ILAYER with 250 for 1 NEGLAYER = LocalRating of ILAYER with -1 for 1 TEMPFILE = LocalDifference of CARBPROX and 250LAYER CARBNVRT = LocalProduct of TEMPFILE and NEGLAYER Compute nlog transforms LINELOG = LocalNlog of LINEPROX XXLNLOG = LocalNlog of XXLNPROX BFLTLOG = LocalNlog of BFLTPROX XXBFLOG = LocalNlog of XXBFPROX XXLFLOG = LocalNlog of XXLFPROX EPFLLOG = LocalNlog of EPFLPROX BPFLLOG = LocalNlog of BPFLPROX COANLOG = LocalNlog of COANPROX NIANLOG = LocalNlog of NIANPROX DMNDLOG = LocalNlog of DMNDPROX CRPTLOG = LocalNlog of CRPTPROX CARBLOG = LocalNlog of CARBNVRT 2 0 9 Table 34 (Continued). Prepare to compute Z-scores. LINEMEAN = LocalRating of ILAYER with 1.1148 for XXLNMEAN = LocalRating of ILAYER with 2.8484 for BFLTMEAN = LocalRating of ILAYER with 2.1713 for XXBFMEAN = LocalRating of ILAYER with 3.8678 for XXLFMEAN = LocalRating of ILAYER with 2.6096 for EPFLMEAN = LocalRating of ILAYER with 1.7185 for BPFLMEAN = LocalRating of ILAYER with 2.5122 for COANMEAN = LocalRating of ILAYER with 4.0946 for NIANMEAN = LocalRating of ILAYER with 3.8440 for DMNDMEAN - LocalRating of ILAYER with 4.1009 for CRPTMEAN . LocalRating of ILAYER with 3.8139 for CARBMEAN = LocalRating of ILAYER with 4.9812 for LINESDEV = LocalRating of ILAYER with 0.7656 for XXLNSDEV = LocalRating of ILAYER with 0.8864 for BFLTSDEV = LocalRating of ILAYER with 1.1266 for XXBFSDEV = LocalRating of ILAYER with 0.9491 for XXLFSDEV = LocalRating of ILAYER with 1.0384 for EPFLSDEV = LocalRating of ILAYER with 0.8310 for BPFLSDEV « LocalRating of ILAYER with 0.9627 for COANSDEV = LocalRating of ILAYER with 0.9007 for NIANSDEV = LocalRating of ILAYER with 0.9647 for DMNDSDEV » LocalRating of ILAYER with 0.9815 for CRPTSDEV = LocalRating of ILAYER with 0.8929 for CARBSDEV = LocalRating of ILAYER with 0.7015 for Compute Z-scores. LINEDIFF = LocalDifference of LINELOG and LINEMEAN XXLNDIFF = LocalDifference of XXLNLOG and XXLNMEAN BFLTDIFF = LocalDifference of BFLTLOG and BFLTMEAN XXBFDIFF = LocalDifference of XXBFLOG and XXBFMEAN XXLFDIFF = LocalDifference of XXLFLOG and XXLFMEAN EPFLDIFF = LocalDifference of EPFLLOG and EPFLMEAN BPFLDIFF = LocalDifference of BPFLLOG and BPFLMEAN COANDIFF = LocalDifference of COANLOG and COANMEAN NIANDIFF = LocalDifference of NIANLOG and NIANMEAN DMNDDIFF = LocalDifference of DMNDLOG and DMNDMEAN CRPTDIFF = LocalDifference of CRPTLOG and CRPTMEAN CARBDIFF = LocalDifference of CARBLOG and CARBMEAN LINESTND = LocalRatio of LINEDIFF and LINESDEV XXLNSTND = LocalRatio of XXLNDIFF and XXLNSDEV BFLTSTND = LocalRatio of BFLTDIFF and BFLTSDEV XXBFSTND = LocalRatio of XXBFDIFF and XXBFSDEV XXLFSTND = LocalRatio of XXLFDIFF and XXLFSDEV EPFLSTND = LocalRatio of EPFLDIFF and EPFLSDEV BPFLSTND = LocalRatio of BPFLDIFF and BPFLSDEV COANSTND = LocalRatio of COANDIFF and COANSDEV NIANSTND = LocalRatio of NIANDIFF and NIANSDEV DMNDSTND = LocalRatio of DMNDDIFF and DMNDSDEV CRPTSTND = LocalRatio of CRPTDIFF and CRPTSDEV CARBSTND = LocalRatio of CARBDIFF and CARBSDEV 2 1 0 Table 34 (Continued). Combine standardized proximity layers to produce unstandardized model. UPROXMOD = LocalSum of LINESTND and XXLNSTND and BFLTSTND and XXBFSTND and XXLFSTND and EPFLSTND and BPFLSTND and COANSTND and NIANSTND and DMNDSTND and CRPTSTND and CARBSTND Calculate standardized proximity model. UPRXMEAN = LocalRating of ILAYER with 0.0008 for 1 UPRXSDEV = LocalRating of ILAYER with 4.4315 for 1 UPRXDIFF * LocalDifference of UPROXMOD and UPRXMEAN SPROXMOD = LocalRatio of UPRXDIFF and UPRXSDEV Table 35. Procedure to Produce Integrated Diamond Exploration Model+ Reclassify component models. PERMAREA = LocalRating of EMPMODEL with 0 for 4 ... 5 with 0 for 9 ... 10 with 0 for 14 ...15 ANOMSTCK » LocalRating of STACKSUM with 0 for 1 ... 3 with 100 for 4 ANOMPROX = LocalRating of SPROXMOD with 10 for -5.00 ... -2.01 with 0 for -2.00 ... 5.00 Combine component models to yield integrated model. Note: A/P = Archon/Proton, P = prospective, S+P = stacked and prospective. 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