GIS-based predictive mapping
for aquamarine-bearing pegmatites,
Lundazi area, northeast Zambia.
Ezekiah Mweetwa Chikambwe
September 2002
GIS-based predictive mapping
for aquamarine-bearing pegmatites,
Lundazi area, northeast Zambia.
by
Ezekiah Mweetwa Chikambwe
Thesis submitted to the International Institute for Geo-information Science and Earth
Observation in partial fulfillment of the requirements for the degree in Master of
Science in Mineral Resources Exploration and Evaluation.
Degree Assessment Board
Thesis advisers
Dr. E. J. M. Carranza
Drs. J. B. de Smeth
Thesis examiners
Prof. Dr. F.v.d. Meer (ITC, TUD), Chairman, External Examiner
Dr. T. Woldai (ITC), Member
Drs. J.B. de Smeth (ITC), Member
Dr. E.J.M. Carranza, (Member)
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION
ENSCHEDE, THE NETHERLANDS
Disclaimer
This document describes work undertaken as part of the study at the International
Institute for Geo-information Science and Earth Observations (ITC). All views and
opinion expressed herein do not necessarily represent those of the Institute and remain
the sole responsibility of the author.
Abstract
The Irumide belt part of Zambia is endowed with gemstones. These gemstones, aquamarine in
particular, have shown to be a potential contributor to the socio-economic growth of Zambia.
However, there is a lack of exploration criteria to guide the search for potential areas of
aquamarine-bearing rocks to sustain the mineral industry in the country. In response to this
need, this study was conducted in the eastern part of the Irumide belt (Lundazi area) in the
eastern part of Zambia to determine spatial relationships between the aquamarine-bearing
pegmatites and the indicative geological features in the area in order to define guidelines useful
for selection of potential areas for further exploration work.
Based on the geological characteristics of areas with known aquamarine-bearing pegmatities in
Lundazi, several geological features were thought to be indicative of areas with potential for the
occurrence of aquamarine-bearing pegmatites. To determine which of the ‘indicative’
geological features are important spatial predictors of areas with potential for the occurrence of
aquamarine-bearing pegmatites, spatial analysis was undertaken. Quantifying the spatial
association between the ‘indicative’ geological features and training set of locations of known
aquamarine workings were done through weights of evidence modeling method. The
application of weights of evidence modeling has shown that mapped axial fold traces with NE
trend and mapped shear zones have negative spatial association with training set of known
aquamarine workings and that the >75th percentile Pc2 scores of the spatially interpolated
stream sediments data generally show negative or non-positive spatial association with the
training set of aquamarine workings. Metagranites, lineaments (fractures), and circular features
(representing late granites) were found to be positively spatially associated with known
aquamarine workings.
Predictive modeling of areas with potential for occurrence of aquamarine bearing pegmatites
was undertaken through weights of evidence modeling and fuzzy logic approach based on
‘indicative’ geological features with positive spatial associations with the known aquamarine
workins. The best predictive map through weights of evidence modeling demarcates about 36%
of the area as potential zones for aquamarine-bearing pegmatites and delineates correctly at least
73% of the known aquamarine workings. The best predictive map through fuzzy logic approach
outlines about 29% of the area as potential zones for aquamarine-bearing pegmatites and
delineates correctly at least 57% of the known aquamarine workings. The optimal fuzzy
predictive map was considered more adequate for directing future exploration work for
aquamarine-bearing pegmatites in the Lundazi area because it is generated from three indicative
geological features and has smaller percentage of delineatded potential zones for aquamarinebearing pegmatite occurrences. However, it it should be treated with caution because of its low
prediction rates.
Both the best predictive map generated by weights of evidence modeling and the best predictive
map generated by fuzzy logic approach do not predict the known aquamarine workings in the
northern part of the area. These aquamarine workings not delineated correclty by the optimal
predictive maps lie within 6000m of mapped granitic gneisses, which could be metagranites.
This finding implies that highly accurate geological maps and standardized lithological
nomenclature are needed and important predictive modeling of mineral potential.
Acknowledgement
I would like to thank the Netherlands Fellowship Program (NPF) for awarding me the
scholarship to pursue further training and immensely improve my professional understanding
and judgement. I also thank the Geological Survey Department of Zambia for allowing me to
pursue this study.
To Dr. John Carranza, my supervisor, I would like to express my gratitude to you for your
guidance, constructive criticism, invaluable suggestions and, last but not least, your ever-in-time
critical reading of the manuscripts. Your constructive criticism has greatly improved the quality
of this dissertation. I would also like to express my sincere gratitude to Prof. Dr. Martin Hale for
his guidance. Thank you.
My sincere gratitude go to Drs. Boudewijn de Smeth the students adviser for his critical review
of my manuscript and guidance during my stay in the Netherlands. I particularly thank you for
my first day in ITC. My special thanks go Drs. Frank van Reitenbeek for being available and
standing ready to help whenever I needed help. My special thanks also go to Prof. Dr. Colin
Reeves, Dr. Sally Barritt and Dr. Jean Roy for their various contributions during interpretation
of the geophysical data.
My special thanks go to ITC and Dish hotel employees and general Dutch citizens for their
hospitality that made my stay in Netherlands a success.
To my wife Rita and my children Chibotu, Miyoba and Chiyavwula, if this turns out to be an
achievement it is for you. I thank you for your support and appreciate your sacrifices.
Finally, I would like to thank the almighty God to whom I owe all.
vi
Contents
Abstract ........................................................................................................................................ v
Chapter 1: Background to the Research ........................................................................................... 1
1.1 Research Problem .................................................................................................................... 1
1.1.1 Mining dependent economy of Zambia ................................................................... 1
1.1.2 Aquamarine gemstones: potential contributor to the economy of Zambia ............. 1
1.1.3 A need for an exploration model for aquamarine-bearing
granitic-pegmatites ................................................................................................. 2
1.2 Rationale of the Research ........................................................................................................ 2
1.3 Objectives of the Research....................................................................................................... 3
1.4 Research Hypotheses ............................................................................................................... 3
1.5 Research Methodology ............................................................................................................ 3
1.6 Geodata Sets Available to the Research................................................................................... 3
1.6.1 Locations of mine workings .................................................................................... 3
1.6.2 Geological map....................................................................................................... 3
1.6.3 Radiometric data .................................................................................................... 5
1.6.4 Remotely-sensed spectral data................................................................................ 5
1.6.5 Geochemical data ................................................................................................... 5
1.7 Conclusion ............................................................................................................................... 5
Chapter 2: The Study Area................................................................................................................... 6
2.1 Location ................................................................................................................................... 6
2.2 General Geology of Zambia..................................................................................................... 6
2.2.1 Stratigraphy............................................................................................................ 6
2.2.2 Structure and Metamorphism ................................................................................. 8
2.3 Local Geology of Lundazi Area............................................................................................. 10
2.3.1 Lithology............................................................................................................... 10
2.3.2 Structure and metamorphism................................................................................ 11
2.4 Exploration History of Lundazi Area..................................................................................... 13
2.5 Conclusion ............................................................................................................................. 14
Chapter 3: The Geology and Exploration of (Aquamarine-Bearing) Granitic-Pegmatites 15
3.1 Aquamarine-Bearing Granitic-Pegmatites in General ........................................................... 15
3.1.1 Introduction .......................................................................................................... 15
3.1.2 Geological and structural settings........................................................................ 15
3.1.3 Classification of granitic-pegmatites.................................................................... 16
3.1.4 Regional scale exploration criteria for granitic-pegmatites................................. 17
3.2 The Aquamarine-Bearing Pegmatite Belt of Lundazi Area ................................................... 17
3.2.1 Geological and structural setting of granitic-pegmatite belt................................ 17
3.2.2 Classification of aquamarine-bearing granitic-pegmatites
of Lundazi area..................................................................................................... 18
3.3 Possible Genetic Model of Aquamarine-Bearing Pegmatites
of the Lundazi Area ............................................................................................................... 19
3.4 General Characteristics of Geological Environments of
(Aquamarine-Bearing) Granitic-Pegmatites .......................................................................... 21
3.5 GIS-Based Geological Exploration for Granitic-Pegmatites ................................................. 22
3.6 Conclusion ............................................................................................................................. 22
Chapter 4: Extraction of Spatial Indicative Features .................................................................. 24
4.1 Structures and lithological units extracted from geological map ........................................... 24
4.1.1 Lithological units.................................................................................................. 24
4.1.2 Faults.................................................................................................................... 25
4.1.3 Shear zones........................................................................................................... 25
4.1.4 Axial traces of folds .............................................................................................. 25
4.2 Structural features extracted from ASTER imagery .............................................................. 27
4.2.1 General ................................................................................................................. 27
Contents
vii
4.2.2 Extraction of lineaments ....................................................................................... 28
4.2.3 Extraction of shear zones...................................................................................... 30
4.2.4 Interpretation of circular features ........................................................................ 31
4.2.5 Extraction of alteration zones............................................................................... 33
4.2.6 Extraction of silicic rocks ..................................................................................... 34
4.3 Spatial indicative features from stream sediments geochemical data .................................... 36
4.3.1 Uni-element analysis ............................................................................................ 36
4.3.2 Correlation matrix of background geochemical data........................................... 38
4.3.3 Spatial distribution of the geochemical data ........................................................ 39
4.3.4 Extraction of a multi-element signature indicative of granitic areas ................... 46
4.4 Geochemical evudence from radiometric data....................................................................... 49
4.5 Conclusions............................................................................................................................ 50
Chapter 5: Spatial Data Analysis and Integration ........................................................................ 52
5.1 Introduction............................................................................................................................ 52
5.2 Analysis using weights of evidence method .......................................................................... 52
5.2.1 Creating binary predictor patterns....................................................................... 52
5.2.2 Combining binary predictor patterns ................................................................... 61
5.2.3 Validation of predictive maps ............................................................................... 63
5.3 Analysis by fuzzy logic method............................................................................................. 66
5.3.1 Creating fuzzy predictive patterns ........................................................................ 68
5.3.2 Combining fuzzy predictor patterns...................................................................... 69
5.3.3 Validation of fuzzy predictive map........................................................................ 70
5.4 Conclusion ............................................................................................................................. 72
Chapter-6: Conclusions and Recommendations............................................................................ 74
6.1 Conclusions ............................................................................................................................ 74
6.2 Recommendations .................................................................................................................. 75
References ............................................................................................................................................... 72
Chapter 1: Background to the Research
1.1 Research Problem
1.1.1
Mining dependent economy of Zambia
It is every government’s wish, especially in developing countries, to explore and exploit
its natural resources to provide economic and social development for its people. Socioeconomic development, however, is hampered due to under-utilization of their natural
resources mostly because of lack of sufficient and appropriate geosciences data.
Another factor that hampers socio-economic development is giving much emphasis to
one mineral commodity as a source of revenue and give less significance to other
potential resources.
A real case example is Zambia, whose economy depends mainly on one mineral
commodity, copper. The Zambian copper deposits are being depleted and the mines are
closing due high costs of re-capitalization. Kabwe zinc-lead and the Luanshya copper
mines closed in the early 1990 and in 2000, respectively, due to high re-capitalization
demands. There are also other large copper mines on the verge of collapse (e.g., the
Konkola Copper Mine). In order to sustain Zambia’s socio-economic growth, there is
need to explore and exploit new mineral deposits, although not necessarily copper.
1.1.2
Aquamarine gemstones: potential contributor to the economy of Zambia
Gemstones have shown to be a potential contributor to the socio-economic growth of
Zambia in the 1970’s after the discovery of emeralds in the Copperbelt province. Smallscale mining for aquamarine, tourmaline and red garnet from granitic-pegmatites in
Lundazi area in the Eastern province of Zambia started in the late 1970’s. The
pegmatites were exploited for mica as early as 1930’s. Recoveries of 19 tonnes of beryl
were recorded in 1955. In 1960 one pit, the Aries pit, produced between 40 and 50
tonnes of beryl (some of which are of gem quality), small crystals of excellent amethyst,
rose-quartz, yellow-green chrysoberyl of up to 0.6m, tourmaline and uranium minerals
that include uranophane, torbenite, and rutherfordite in addition to muscovite production
(O’Connor, 1997).
Prospecting for aquamarine-bearing granitic-pegmatites in the Lundazi area was started
en masse by villagers who became aware that fortunes could be made by digging after
the discovery of emeralds in the Copperbelt province (Tether and Partney, 1988).
Prospecting for gemstones then took place in most parts of the country and led to the
discovery of other types of gemstones. Amethyst was discovered in Southern and
Central provinces of the country while aquamarine, garnet, pink, and green tourmaline
was discovered in the central part of the country. Most of the discoveries were made by
villagers who were guided by schorl and quartz, both in outcrops and in floats.
In 1984 and 1985, the Geological Survey Department of Zambia realized that
gemstones were a national asset and undertook field investigations in gem bearing
pegmatites in the Eastern province. The field investigations followed local guides and
Tether and Partney (1988) observed that there were several false trails and some
aquamarine locations were undoubtedly missed.
2
Background to the Research
In the mid 1980’s, Zambia Consolidated Copper Mines limited (ZCCM) opened a
number of pits in the Lundazi area and between 1987 and 1989 about 528,190g of beryl
and aquamarine were produced. Watts et al. (1991) observed that the overall reserves
for the Lundazi aquamarine are unknown, but are undoubtedly large in terms of smallscale mining. O’Connor (1997) estimated the gross value of rough aquamarine currently
being mined at 20 to 40 million US dollars per annum and further observed that good
estimates were hampered by unrecorded production.
1.1.3
A need for an exploration model for aquamarine-bearing granitic-pegmatites
Exploration needs a management of a wide choice of geological models in order to find
ore with minimum costs but with maximum results in the shortest possible time
(Westerhof, 1992). For any meaningful investment in mining to be undertaken,
sufficient and up-to-date geoscientific information related to mineral resource
assessment is required. Since the 1970’s, 90% of the discoveries of aquamarine and
other gemstones in the Lundazi area have been by villagers who were only using ‘trial’
and ‘error’ wherever there was quartz and/or schorl. The study area has remained
underdeveloped despite being endowed with abundant gemstones reserves because of
lack of any gemstones geoscientific information to guide exploration. Geoscientific
information is important to determine which areas are favorable for the occurrence of a
particular type of mineral deposit (Carranza, 2002).
Part of the study area lies in a Game Management Area where entry is restricted and
many times conflicts have arisen between villagers and the game management over
entries to dig and/or prospect for aquamarine. Mineral resource assessment information,
therefore, would provide another tool in the land-use database for planners to make a
compromise between different land uses.
1.2 Rationale of the Research
The research is essential in the study area because of the following reasons.
• Aquamarine gemstones offer high potential for income generation for both the
local people and the country.
• The known aquamarine-bearing pegmatites zones cover quite a large area, about
4% of the total area of the country.
• The aquamarine mostly occurs with other gemstones like amethyst and both pink
and green tourmaline that can be mined as by products.
• The price for aquamarine is second to that of emerald, which is the most
expensive gemstone of the gemstones of Zambia.
1.3 Objectives of the Research
The objectives of the research are as follows.
• Develop a GIS-based exploration model for identifying areas with potential for
aquamarine-bearing pegmatites.
• Determine spatial relationships between aquamarine-bearing pegmatites and
geological features in the area.
• Delineate areas with potential for aquamarine-bearing pegmatites.
Chapter 1
3
1.4 Research hypotheses
Because of constancy in composition of granitic-pegmatites intruded in different rock
types (Nash, 1962), it is hypothesized that granitic-pegmatites are derived from a single
source, i.e., highly differentiated batholiths of granitic character below the present
erosion levels. It is also hypothesized that the presence of concealed batholiths of
granitic character can be deduced from mappable circular features, which represent
structures resulting from cooling of the batholiths or small granites associated with the
batholiths (Rolet et al., 1995). It is further hypothesized that the intrusion of both
aquamarine-bearing pegmatites and the parental granites were along weaker zones
(faults, joints and shear zones).
1.5 Research Methodology
The study was carried out in four stages (see Fig. 1.1).
• Extraction, from available geodata sets, of geological features indicative of
zones with potential for aquamarine- bearing granitic-pegmatites.
• Generation of evidence maps through the quantification of spatial associations
between geological features and known workings for the aquamarine gemstones.
• Integration of evidence maps to generate a predictive map showing zones with
potential for aquamarine-bearing pegmatites.
• Validation of predictive map(s).
1.6 Geodata Sets Available to the Research
1.6.1
Locations of mine workings
The locations of aquamarine workings were used as training data for the predictive
mapping of zones with potential for aquamarine-bearing granitic pegmatites. The
locations of workings were divided randomly into two sets; one set was used to quantify
the spatial relationships with the different geological features while the other set was
used to validate the predictive map of zones with potential for aquamarine-bearing
granitic-pegmatites.
1.6.2
Geological map
The geological map shows lithological units, faults, and shear zones. The lithological
units were digitized as polygons while faults, and shear zones were digitized as
segments. The faults digitized from the geological maps were combined with faults
interpreted from the ASTER imagery (see below). The spatial association of these faults
with the known aquamarine-bearing pegmatites were quantified to derive a lineament
evidence map used in the creation of a predictive map of zones with potential for the
occurrence of aquamarine-bearing pegmatites. The spatial association of the shear zones
with the known aquamarine-bearing pegmatites was quantified to create a shear zone
evidence map and combined with other evidence maps to produce a predictive map of
zones with potential for aquamarine-bearing granitic-pegmatites. The lithological units
were reclassified to extract the granitic bodies that are thought to be spatially associated
with the aquamarine-bearing granitic-pegmatites in the area.
4
Background to the Research
ASTER IMAGE
GEOLOGY
BAND
COMBINATION
ALTERATION
ZONES
MAP
RADIOMETRICS
DATA
FAULTS
AND
FRACTURES
MAP
PRINCIPAL
COMPONENT
ANALYSIS
DATA
GRIDING
DIGITIZE
CIRCULAR
FEATURES
MAP
GEOCHEM.
DATA
SHEAR
ZONES
MAP
INTRUSIVES
MAP
MINES
LOCATIONS
DIVIDE INTO
TWO
SETS
PRINCIPAL
COMPONENT
SCORES
QUANTIFY SPATIAL RELATIONSHIP BETWEEN GEOLOGICAL FEATURES
MAP AND KNOWN MINERAL LOCATIONS
MAP
BEFFERED
ALTER.
ZONES
MAP OF
BUFFERED
CIRCULAR
FEATURES
MAP
MAP OF
BUFFERED
FAULTS/
FRACTURE
MAPS
MAP FO
BUFFERED
SHEAR
ZONES
MAP OF
BUFFERED
INTRUSIVES
MAP OF
BUFFERED
PRINCIPAL
COMPONENTS
SCORES
INTEGRATION OF BUFFERED EVIDENCE MAPS AND VALIDATION
PREDICTIVE MAP FOR ZONES WITH POTENTIAL
FOR OCCURRENCE OF AQUAMARINE-BEARING
GRANITIC-PEGMATITES
Fig. 1.1. Flow chart showing methodology for predictive mapping for zones of
aquamarine-bearing granitic-pegmatites.
1.6.3
Radiometric data
Digital radiometric data for potassium, uranium and thorium was generated by digitising
intersections of flight lines and data contours. Ternary and ratio maps were generated to
identify features indicative of granitic rocks. Features indicative of granitic rocks were
Chapter 1
5
digitized and combined with those features derived from the geological maps to
generate a source rock binary map.
1.6.4
Remotely-sensed spectral data
Remotely-sensed spectral data were obtained from ASTER imagery (see below). Five
sub-scenes were obtained. The sub-scenes were georeferenced using control points
identified both on the images and on 1:50 000 scale topographic maps. The sub-scenes
were corrected for atmospheric effect and then mosaicked to create one scene. Image
enhancement was performed to extract lineaments that would not have been apparent
from the images. The lineaments were digitized and combined with faults digitized from
the geological map, after which the spatial relationship with the known aquamarinebearing pegmatites was quantified to derive a lineament evidence map for predictive
mapping of zones with potential for the occurrence of aquamarine-bearing graniticpegmatites. Shear zones and circular features were digitized. The ASTER imagery
spectral data were used to identify alteration zones associated with weathering of
aquamarine-bearing granitic-pegmatites. A detailed discussion of data processing is
given in section 4.3.
1.6.5
Geochemical data
Stream sediments geochemical data for Zn, Pb, Cu, Co, Mn, Fe, and Ni are available in
digital format. Uni-element and multi-element analysis were conducted to identify
correlations between the elements and to identify multi-element association that could
be indicative of granitic-environment.
1.7 Conclusion
Zambia has mainly been exploiting copper, cobalt, zinc and lead deposits. Recently,
however, low prices for these commodities and a need for heavy re-capitalization of the
mines, has led to closure of some of these mines. Gemstones, however, have shown to
be a potential contributor to Zambia’s socio-economic development. Aquamarine,
derived from aquamarine-bearing granitic-pegmatites occurs in considerable amounts
especially in the eastern part of the country. To effectively contribute and sustain its
contribution to the socio-economic development of Zambia, zones favorable for the
occurrence of aquamarine-bearing granitic-pegmatites need to be outlined for further
exploration. A GIS-based predictive mapping of the zones favorable for occurrences of
aquamarine-bearing granitic-pegmatites is tested in the Lundazi district of Zambia. The
area is a Proterozoic intracratonic basin with metasediments intruded by the
aquamarine-bearing granitic-pegmatites.
Chapter 2: The Study Area
2.1 Location
Zambia is located centrally at the intersection of Latitude 15o 00 S and Longitude 30o 00
E in Southern Africa. It shares borders with Democratic Republic of Congo, Angola,
Namibia, Botswana, Zimbabwe, Mozambique, Malawi and Tanzania (see Fig. 2.1).
The study area is located in Lundazi District in the Eastern Province of Zambia (Fig.
2.1). The Lundazi area is about 650 km east of the capital city, Lusaka, and about 75km
north of Chipata town the provincial headquarters. In the western part, the area extends
to Luangwa River, while the eastern part extends to the Zambian border with Malawi.
The area extends to about 32 km north of Lundazi Boma and about 73 km south of
Lundazi Boma. The total aerial coverage is approximately 10,000 sq. km.
Fig 2.1. Location map of Zambia and the study area (yellow area in small box).
2.2 General Geology of Zambia
2.2.1
Stratigraphy
The geology of Zambia can be broadly described as consisting of Pre-Cambrian
Basement Complex unconformably overlain by Katangan metasediments, Karoo
sediments and Kalahari sands (Fig. 2.2). The Basement Complex is exposed mainly in
the eastern and southeastern parts of the country and forms undulating hills and valleys.
The Basement Complex consists of granites, altered volcanics, schists and gneisses
uncomfortably overlain by quartzites, schists and limestones of the Muva Super group.
These rocks have been involved in several tectono-thermal episodes. In few localities, it
is possible to distinguish between individual units but, generally, it is virtually
impossible due to the thermal and dynamic metamorphism differentiation, (GSD, 1989).
Some of these metasediments have retained their sedimentary structures in some places
despite the advanced recrystallization indicating that most of the Basement Complex
Chapter 2
7
gneisses are paragneisses, (Hickman 1982). The meta-igneous rocks also retain some of
their original texture.
Fig. 2.2. Lithological stratigraphic column of Zambia.
Lying unconformably on the Basement Complex rocks are four groups of sedimentary
formations within the Katanga Super group (that cover almost all of the central,
northern, and north-western parts of the country), Karoo sediments (that cover mostly
the major river valleys), and Kalahari Sands and alluvial deposits (that cover the
western part of the country). The Katangan Super group, or the Katanga as it is
sometimes called, is a pile of beds of more than 10000m thick, which lie
stratigraphically between the Basement Complex and the Phanerozoic cover rocks that
overly it (between 1310 to after 656 Ma) (GSD, 1989). The Katanga has been divided,
based on lithostratigraphic groups from bottom to top, into Upper and Lower Roan, the
Mwanshya, and the Upper and Lower Kundelungu groups. The Lower Roan group is
composed of continental and platform deposits representing four superimposed
8
The Study Area
deposition cycles each showing passage from continental to marine or marine to
continental or both. This group is the main host of the copper mineralization being
exploited in Zambia and the Democratic Republic of Congo. It consists of arenaceous
arkoses that grade into argillaceous rocks of the Upper Roan and into carbonaceous
shales of Mwanshya group. The Lower Roan may therefore be considered to represent a
marine transgression encroaching on to a topographically irregular continent. The
overlying Kundelungu is characterized by an increase in thickness from the edges of the
basin to its centre and by corresponding facies change. The Lower Kundelungu
commences with a tilloidal conglomerate locally known as Grand Conglomerate and
grades into Kankontwe limestones and into a very thick sequence of shale.
The Karoo Supergroup consists of basal conglomerates, Madumabisa sandstones, the
Escarpment Grit, and basalts. It covers mostly the major river valleys and the plateau
part in the southern part of Zambia while the Kalahari sands cover mostly the western
and southern parts of the country.
2.2.2
Structure and Metamorphism
Zambia has been divided into five main orogenic belts - the Irumide belt, the
Mozambique and Zambezi belts, the Pan African Lufilian Arc and the Bangweulu block
(Fig. 2.3). The Irumide and the Zambezi orogenic belts define an area between the
Bangweulu block and Lufilian arc to the north and the Zimbabwe Craton in the south.
The Irumide orogeny is dated at around 1350 to 1100 Ma. The orogenic belt covers the
central part, part of southern and most of the eastern parts of Zambia. It is bounded by
the Bangweulu block in the north and the Mozambique Belt in the southeast (GSD,
1989). This orogenic belt or province is composed of granites, gneisses and mainly
quartzites and pelitic metasediments of the Basement Complex and part of Katangan
rocks characterized by northeast to east-northeast foliation trends (Namateba 1986).
These foliation trends continue southwestwards where they are cut into two portions by
the Luano-Lukusashi-Luangwa valley. Around and on the southeast of Lusaka (to the
east of Choma-Kalomo block on Fig. 2.3, the capital city, the trend is cut by easterly to
west-northwesterly Katangan trend linking the Zambezi belt and the Lufilian arc in the
south and north, respectively. The general northeast to north-northeast trend shifts as it
continues into the southern part of the country. This NNE trend continues even to areas
around Lake Kariba near the border with Zimbabwe. The regional strike is deduced
from quartzite horizons and foliation expressed by parallel layers of biotite and
orientation of feldspar porphyloblasts in the gneisses.
The structural features of the Zambezi Orogenic belt were first recognized by McGregor
(GSD, 1989). This orogenic belt covers a narrow zone of the country between the
northern margin of the Zimbabwe Craton and the Karoo rocks of the Zambezi rift
valley. This zone is distinguished by high-grade metamorphic rocks of different ages
and composition overprinted by the tectono-thermal events that gave rise to the present
form of the belt. In some parts of the belt, the rocks have been strongly deformed and at
times show sillimanite metamorphic grade. This metamorphic grade seem to have been
reached during the last major orogenic event, which together with the rift valley type of
fracturing are responsible for the present form of the belt. In the eastern part of the
orogen, the southern part tends to swing southwards to merge with the western margin
Chapter 2
9
of the Mozambique belt. On its western border, the orogen begins to develop a
northwest trend that disappears under the Karoo rocks of the Zambezi valley. The
northwest trend, however, reappears again in the Katangan rocks in the southern part of
the country and in the southwest of Lusaka and structurally links the belt with the
Lufilian Arc in the north.
Study area
Fig. 2.3. General geologic map showing the orogenic mobile belts of Zambia.
10
The Study Area
The Lufilian Arc is a large arcuate Pan African fold belt, which extends along the
Democratic Republic of Congo (DRC) and Zambian border from the extremity of the
southeast DRC pedicle to the triple border joint between Zambia, Angola and
Democratic Republic of Congo (Fig.2.3). This arc continues westwards into Angola
while its southeastern part meets with the NE to ENE trends of the Irumide belt (GSD,
1989). The Lufilian Arc occupies mainly the area in which the Katangan Supergroup
rocks are exploited for copper and other mineral riches in both the Democratic Republic
of Congo and Zambia. In Zambia, this area is usually referred to as the Copperbelt. The
Katanga in this area is characterized by a thickness in excess of 10000 m.
2.3 Local Geology of Lundazi Area
2.3.1
Lithology
The study area covers the eastern part of the Irumide orogenic belt. Precambrian
granites and granitic gneisses overlain by a thick sequence of alternating quartzites and
pelitic metasediments of the Muva Supergroup underlie the area (Fig. 2.4). The Katanga
Supergroup schists unconformably overly the Muva metasediments (Daly et al., 1984).
In the west near the Luangwa valley, the area is underlain by Karoo sandstone and
Madumabisa mudstone, both of the Karoo Supergroup.
Gneisses of upper amphibolite facies with local metabasic granulites, mafic intrusives
and metagranites outcrop in much of the eastern and central parts of the area.
Metasediments, notably schists, a range of leucocratic gneisses and quartzites of the
Muva Supergroup crop out in the central and mid-west parts of the area. A small outlier
of supracrustal quartzites and metacarbonates of greenschist facies (the Mparanza
formation) unconformably overly the older gneisses. These metasediments are thought
to be of Katangan (Neoproterozoic) age (Hickman, 1998). Occassional amphibolites,
marbles and calc-silicates occur. Although recrystallization is advanced,
metasedimentary resisters are present throughout the area indicating that most of the
basement gneisses are paragneisses of sedimentary origin (Harding, 1982).
Igneous rocks consist of micro-granites, pegmatites, syenites, metagranites,
charnockites, enderbites, andesinites, gabbros, dolerites and amphibolites. Several small
granites, which lie parallel to the Lukusuzi anticline fold axis, outcrop in the central part
of the area. In the south of the area, Tether et al. (1988) reported a deep-seated granite
and syenite (dated at about 1000 Ma) around Chipata and a younger set of granites (800
Ma and 650 Ma) to the west.
Pegmatites are widespread and can be broadly classified into two groups based on age.
A simple older generation of pegmatites occurs as lenses associated with areas of
granulitic facies metamorphism. This generation of pegmatites is thought to be a result
of partial anatexis (Tether et al., 1988). This suit of pegmatites contains mainly feldspar,
muscovite, quartz and schorl. No gemstones have been observed in this suit of
pegmatites. The later generation of pegmatites is intrusive into the crystalline Pre-Karoo
rocks. These pegmatites are classified into two sub-groups, simple and complex. The
simple sub-group of pegmatites is unzoned and comprises quartz, muscovite, schorl and
feldspar. The complex sub-group of pegmatites is zoned with a massive quartz core
while the different zones consist of quartz, muscovite, schorl, beryl, feldspar,
Chapter 2
11
aquamarine, pink and green tourmaline and rose quartz in variable proportions.
Amethyst and garnet occur occasionally. Snelling et al. (1972) dated one pegmatite of
the complex sub-group near Lundazi at 485 Ma. Both O’Connor (1998) and Hickman
(1998) alluded to the fact that the complex sub-group of pegmatites may be a result of a
concealed pluton associated with micro-granites, which outcrop in the area. The zoned
type i.e., the complex sub-group of pegmatites is being exploited for aquamarine, beryl
and occasionally pink and green tourmaline.
The sedimentary rocks of the Karoo Supergroup include the Madumabisa mudstone and
the Escarpment Grit of the Upper Karoo. These rocks are confined to the western
margin of the area around Luangwa Valley.
2.3.2
Structure and metamorphism
Table 2.1 shows the generalized geological events that affected the Lundazi area. The
most pervasive structural trend in the study area is the northeast to north-northeast
foliation. This structural trend is dated back to Kibaran metamorphism. The earliest
recognizable event is D2, which obliterated older fabrics and produced isoclinal folding
with approximately NNW-trending axes. Harding (1982) observed that conditions
during D2 caused anatexis with in situ melting and probably intrusive emplacements of
acidic rocks. Polyphase deformation involving five phases of folding has been
recognized in the gneisses. Two other different phases of later folding were recognized
in the Mparanza Formation unconformably overlying the gneisses. The most prominent
of the metamorphic events is the deformation phase that controlled the general structural
trend of northeast trending foliation in the area. After this event and possibly
contemporaneous with it, is the activation of major northeast trending faults and shear
zones. The fracturing that followed later was localized and Harding (1982) suggested
that this could be related to the intrusion of late pegmatites in the area. Granulite facies
surrounded by amphibolite facies metamorphism is attained in most parts of the area.
Migmatization is well developed in pelitic gneisses and a late K-feldsparthization
phenomenon is common in several areas probably overlying large shallow granite
bodies (O’Connor, 1998). Fig. 2.4 shows the geological map of the Lundazi area.
12
The Study Area
Fig. 2.4. Geological map of the study area, showing geological units, faults and locations of
aquamarine workings.
Chapter 2
13
Table 2.1. Geological events in the Lundazi Area (modified after Hickman 1998).
2.4 Exploration History of Lundazi Area
The earliest geological record available in the area is that of an outcrop map by
Loangwa Concession Limited geologists in the 1930’s. Prior to this, however, the
pegmatites in Eastern Province were already being exploited for mica with beryl as a
by-product. In 1947, the first geological map of Zambia was compiled by Bancroft. The
geological map shows most of the area to be underlain by Basement Complex rocks
unconformably overlain by what is currently known to be metasediments of the Muva
Supergroup. In 1952, Guernsey listed the mineral occurrences known at that time
including gold, iron and mica. Nash (1962) gathered enough data for a PhD thesis on
the then mica-producing pegmatites. In 1964, Mossman studied the pegmatites of
Southern Province and included part of the Lundazi area. Hickman (1998), O’Connor
(1998) and Harding (1982), all then working as geologists for the Geological Survey of
Zambia, mapped the areas around the Lukusuzi National Park, Lumezi and Lundazi
areas and around Mwanya in the west, respectively. The trio, in their individual
geological reports, observed that the geology in the area comprised of Basement
14
The Study Area
Complex gneisses unconformably overlain by metasediments of the Muva Supergroup
that are in turn unconformably overlain by the Katangan Metasediments. Conglomerates
and sandstones of the Karoo Supergroup were identified in the west. O’Connor (1998)
and Harding (1982) realized the economic potential of beryl-bearing pegmatites which
were not recognized by Hickman (1998).
In the mid 1980’s, Mineral Exploration Department geologists worked in the area to
map in detail the pegmatites that were then being exploited for gemstones. Almost at the
same time, Zambia Consolidated Copper Mines operated a number of pits for
aquamarine exploitation. Several other workings by artisan miners were opened. A part
from aquamarine, tourmaline, and other gemstones the area has also iron, vein copper
and gold, graphite, and barite occurrences.
2.5 Conclusion
The geology of the study area indicates intracratonic metasediments, which have
undergone polyphase metamorphism and intruded by granitoids of variable ages. The
granitoids include syn-tectonic metabasics and metagranites. Post-tectonic intrusives
include the late granites, granitic-pegmatites, charnockites, enderbites, and syenites. The
late granites are thought to be on the peripherals of older metagranitoids in the area.
Several pegmatite episodes have been distinguished in the area but only the graniticpegmatites associated with margins of late granites are mineralized with aquamarine
and other rare-element minerals. They are also considered the top of an underlying
granitoid that is spatially related to the older granitoids. The border zone between the
granitic-pegmatites and the host rocks are chilled in some localities implying forceful
injection of REE rich fluids on fracture zones of the host rocks. The aquamarine-bearing
granitic-pegmatites are wide spread in the area and in some localities they occur on the
peripherals of late granites parallel to a northeast trending synform axial trace. The
aquamarine-bearing granitic-pegmatites outcrop as hillocks with either a northeast or
northwest trend parallel to the two main fault direction in the area. On the plateau and
low-lying areas, the aquamarine-bearing pegmatites weather into a mass of Kaolinite
with muscovite and schorl as float. The aquamarine in granitic-pegmatites is being
exploited for gemstones. Minor east-west trending granitic-pegmatites also occur.
Chapter 3: The Geology and Exploration of (Aquamarine-Bearing) Graniticpegmatites
3.1 Aquamarine-Bearing Granitic-Pegmatites in General
3.1.1
Introduction
Many rare-element granitic-pegmatites including aquamarine-bearing graniticpegmatites represent the final water-rich, siliceous melts of intermediate to acid magmas
and are thought to be final residual melts rich in silica, alumina, water, halogens, alkalis,
and lithophile elements not readily accommodated in common igneous rocks. Graniticpegmatites have been formed in all tectono-magmatic cycles of the geological history.
The first important and extensive pegmatite formation period, however, was the
Kenoran age (2800-2600 ma) which saw the formation of rare-element pegmatites of
Superior and Slave provinces of Canada, Zimbabwean, Kapvaal, and Tanzanian Cratons
and many other parts of the Western Australia craton. This period was succeeded by
formation of rare-elements pegmatites in various stages of the geological history with
Alpine orogeny (85-20 Ma) pegmatites field being the youngest (Cerny, 1982).
3.1.2
Geological and structural settings
Geological position and structural control of granitic-pegmatites fields has changed
during the evolution of the crust. The late Archean, rare-element pegmatite fields are
generally restricted to linear, geosynclinal greenstone belts down-warped between the
batholith masses of tonalitic and potassic granites. The granitic-pegmatites fields tend to
concentrate on the tectonic boundaries or on the central part of the major sedimentary
troughs crossing the greenstone terrain or forming part of their margins (Trueman,
1982). The early Proterozoic pegmatite fields, however, are generally confined to
graben-type geosynclinal troughs, adjacent to margins of the Archean cratons. Both
Archean and Proterozoic pegmatite fields are mostly controlled by deep faults axial to
greenstone belts or on the flanks of gneissic troughs, or to lithologic contacts along
early batholithic margins. Phanerozoic pegmatites occur mainly in flysch sediments of
folded and metamorphosed eugeosynclinal sequences of the orogenic belts (Cerny,
1982). Rare-element granitic-pegmatites and their parent granite intrusions are often
controlled by large tectonic features or by lithological boundaries separating rocks of
contrasting competence. In terranes with diversified lithology of country rocks, graniticpegmatites are concentrated in more competent rock types that fracture and provide
open space under deformation. Granitic-pegmatites also tend to intrude along contacts
of massive pre- to syntectonic batholiths and some metamorphic rocks where their
differential response to late adjustment stresses loosened their contact paving way for
intrusion of deep-seated magma. In this case, granitic-pegmatites tend to be randomly
distributed in the batholith but are distributed along the contact of these two rock types
in the other metamorphic sequences. Rare-element granitic-pegmatites and their parent
granites tend to be confined to anticlinal structures as small to sizable (10 X 100 km2)
bodies (Trueman, 1982).
16
3.1.3
Geology and Exploration of Aquamarine Bearing Granitic Pegmatites
Classification of granitic-pegmatites
Schaller (1933) classified granitic-pegmatites based on their mineralogy as “Simple”
and “Complex” granitic-pegmatites. “Simple’’ granitic-pegmatites consist of quartz and
microcline and contain no significant quantities of other minerals. “Complex”
pegmatites contain other minerals like albite, beryl, topaz, cassiterite, micas, tourmaline,
garnets, lithium minerals, rare-element minerals, the columbates and tantalates, the
phosphates, and others that contain rare elements (Nash, 1962).
Cameron et al. (1945) in Nash (1962), on the other hand, classified granitic-pegmatites
as “zoned” and “unzoned” pegmatites to distinguish the two major types in England.
They described “zoned” pegmatites as those pegmatite bodies whose minerals are
distinctly arranged into structural units of different composition and texture and
systematically arranged with respect to walls of a given body. They use the term
“unzoned” pegmatites for any pegmatite that appears essentially homogeneous apart
from the presence of the border zone.
Johnston (1945) classified the microcline-quartz–muscovite pegmatites of Brazil into
“homogeneous”, “tabular dykes” and “heterogeneous” pegmatites based on the degree
of internal differentiation. He described “homogeneous” pegmatites as those that have
“a fairly uniform texture from wall to centre and do not contain crystals of remarkable
size” and “heterogeneous” the “lens-shaped dykes showing a high degree of
differentiation with walls of muscovite, gigantic crystals of microcline in the interior of
the dyke, and a central core or nucleus of quartz”.
Ginsburg et al. (1979) classified granitic-pegmatites into four main groups based on
their depth of consolidation, mineralization, and relationship to igneous processes, and
metamorphic environments: (1) miarolitic pegmatites (1.5-3.5 km); (2) rare–element
pegmatites (3.5 – 7km); (3) mica-bearing pegmatites (7-8km); and (4) maximal-depth
pegmatites (>11km). Miarolitic pegmatites are exposed as pods of pegmatites in the
upper parts of granites intrusive into rocks of lowest metamorphic grades with cavities
of piezometric rock-quartz, optical fluorite, and gem-quality beryl, topaz etc. Rareelement pegmatites usually occur as fracture in-filling in cordierite-amphibolite facies
and are usually mineralized with Li, Rb, Cs, Be, Ta and (Sn, Nb). They are a result of
the differentiated allochthonous granites. Mica-bearing pegmatites are hosted by
almandine-amphibolite facies and are mainly mica reserves with less or no rareelements mineralization. They either are direct products of anatexis or separated from
anatectic autochthonous granites. Maximal-depth pegmatites are associated with
granulite facies terranes with no obvious granitic parents and no economic
mineralization but carrying allanite, monazite, and corundum.
Individual hybrid characteristics, however, occur. An example is the extensively beryl
mineralized mica-bearing pegmatites of India (Ginsburg et al., 1979). The beryl
mineralized granitic-pegmatites belt of India would belong to Ginsburg’s (1979) “rareelements” granitic-pegmatites that formed between 3.5 and 7km, while the mica-bearing
granitic-pegmatites belong to the granitic-pegmatites that formed at 7-8 km depth,
which by Ginsburg’s (1979) classification would not be mineralized with rare-elements
minerals.
Chapter 3
17
Johnston’s (1945) “homogeneous” and “heterogeneous” pegmatites are essentially
equivalent to those described as ‘unzoned’ and ‘zoned’ pegmatites, respectively, by
Schaller (1933). Based on the complexity of their mineralogy and structure, the
heterogeneous pegmatites described by Johnston (1945) and the zoned pegmatites
described by Cameron et al. (1945) can be classified as complex pegmatites in
Schaller’s classification. The unzoned and the homogeneous pegmatites would be
equivalents of simple granitic-pegmatites of Schaller’s classification (1933). The zoned
granitic-pegmatites belong to the to Ginsburg’s (1979) first, second and third classes.
3.1.4
Regional scale exploration criteria for granitic-pegmatites
The age range of formation of rare-element granitic-pegmatites varies from Archean to
Tertiary. They are usually associated with Archean Cratons and Phanerozoic orogenic
belts. In Archean Cratons, the granitic-pegmatites are localized along deep fault
systems that in many areas coincide with major metamorphic and tectonic boundaries.
The granitic-pegmatites are also associated with shear zones within these high
metamorphic terranes. The rare-element granitic-pegmatites of Cerny (1982) occur in
less deeply- eroded low-pressure metamorphic terranes, usually of amphibolitecordierite facies. They are mainly late or post-tectonic and are generally peripheral to
larger granitic plutons. They have a spatial association with axial traces of late
synformal structures in the area. In remote sensing, granitic-pegmatites can be
discriminated by their circular features and fracturing during the cooling of the granite.
In aeromagnetic data, they express themselves as horizontal or moderately inclined
plates connected with the sources of felsic magmas by steep feeding channels. Heavy
minerals sampling may identify areas likely to be underlain by granitic-pegmatites. In
some instances, the granitic-pegmatites tend to form geochemical haloes around the
parent granite.
3.2 The Aquamarine-Bearing Pegmatite Belt of Lundazi Area
3.2.1
Geological and structural setting of granitic-pegmatite belt
The aquamarine-bearing granitic-pegmatites belt of Lundazi area is one of the two
aquamarine-bearing granitic-pegmatites belts hosted by a linear intracratonic basin of
Kibaran age (1355Ma), Irumide mobile belt, of Zambia. Granitic-pegmatites intrude
metasediments of high to medium grade metamorphic terranes. Metamorphic rocks are
generally basic and felsic granulites, gneisses and schists of sillimanite and amphibolite
facies in a Proterozoic belt. Other igneous rocks include gabbros, dolerites, enderbites,
charnockites, granites, syenites and pegmatites. Some of these igneous rocks have
suffered folding and foliation concordant with the metamorphic host rocks.
Metagranites seem to preferentially occur at cores of antiformal structures. The
granitic-pegmatites are typically late orogenic to anoregenic. Hickman (1998) reported a
series of micro-granites, with marginal zones of aquamarine-bearing graniticpegmatites, outcropping parallel to the northeast trending anticlinal axial trace in the
study area. Stocks of granites are exposed as ovoid bodies (up to 20m across), aligned
parallel to the axial trace of a northeast trending antiform structure. The marginal
aquamarine-bearing granitic-pegmatites are similar to those that are wide spread in the
area (Harding 1998). During the Irumide orogeny, several faulting phases occurred but
only two fault systems are prominent; i.e. northeast and northwest trending faults. The
18
Geology and Exploration of Aquamarine Bearing Granitic Pegmatites
northwest trending faults laterally displace the northeast trending faults. Minor faults
trending north and east also occur in the study area. Fold interference patterns and local
fracture cleavage formed contemporaneous with the northwest trending faults (Harding,
1998). The granites and the granitic-pegmatites were emplaced syntectonically with the
formation of the NNE trending Lukusuzi antiform (Hickman 1998). The general
sequence of metamorphic events of the area is given in Table 2.1.
3.2.2
Classification of aquamarine-bearing granitic-pegmatites of Lundazi area
By virtue of their complex mineralogy and zoning the aquamarine-bearing pegmatites
belt of Lundazi area can be classified as “zoned” (under Cameron’s nomenclature), and
as “complex” granitic-pegmatites (under Schaller’s nomenclature). They can also be
classified as heterogeneous (under Johnston’s nomenclature) because of their degree of
internal differentiation. The unzoned granitic-pegmatites of Lundazi area would be
classified as simple and homogeneous and since they are currently of no economic
significance would not be discussed further in this chapter.
The aquamarine–bearing pegmatites of the eastern part of the Irumide belt (i.e., in
Lundazi area) can be classified as a hybrid of miarolitic, rare–element, and mica-bearing
pegmatites under the classification of Ginsburg et al’s (1979). This is based on the
aquamarine-bearing pegmatites’ complex mineralogy, cavities with beryl and
muscovite, and content of rare minerals such as aquamarine, beryl, uraninite, mica and
other minerals, and the type of metamorphic rocks they are associated.
One feature that appears to be consistent with the mineralogical composition of the
granitic-pegmatites in the Lundazi area is that potassium feldspar is mostly dominant
than sodium feldspar (Nash, 1962). Gallagher (1959) classified the beryl-bearing
granitic-pegmatites of Uganda and Southern Rhodesia, the present Zimbabwe, into four
groups depending on the dominant feldspar and the presence or absence of lithium
minerals (Table 3.1).
Table 3.1. Gallagher’s (1959) classification of pegmatites.
Type
Average size
Degree of internal zoning
Na-Li pegmatites
K-Na pegmatites
Na pegmatites
K pegmatites
Large
Medium-large
Small or medium
Small or medium
Highly developed and complex
Simple scheme, usually well developed
Very simple scheme usually developed
Very simple scheme usually developed
The aquamarine-bearing pegmatites of the eastern part of Irumide belt belong to the
Gallagher’s (1959) potassium-sodium group. Gallagher (1959) described this group as
(a) medium or large in size, (b) having potassium and sodium feldspars, quartz, and
muscovite as major minerals with minor or absent lithium and other minerals, (c) having
well developed simple scheme of internal zoning, (d) usually having well developed
quartz cores, (e) where potash feldspar predominates over albite, intermediate or wallzones of albite-muscovite pegmatites are developed and these are often associated with
high beryl mineralization, (f) narrow quartz-muscovite units, possibly fracture-filling
units, may carry considerable amounts of beryl and (g) minerals of niobium and
tantalum are particularly common in beryl-bearing pegmatite rich in muscovite and
relatively fine-grained.
Chapter 3
19
Only the first five features of K-Na pegmatites described by Gallagher (1959), however,
characterize the aquamarine-granitic-pegmatites of the Lundazi area. The most abundant
minerals are quartz, muscovite and feldspar. Accessory minerals include tourmaline,
beryl, apatite, chrsoberyl, garnet, magnetite, ilmenite, columbite, biotite, betafite,
uraninite, cassiterite and many more. Most of the beryl however is mainly associated
with the inner zones of the pegmatite especially the zone next to the quartz core.
3.3 Possible Genetic Model of Aquamarine-Bearing Pegmatites of the Lundazi
Area
Fig. 3.1 illustrates the general genesis of granitic-pegmatites related to granitoids. The
diagram was originally designed by Strong (1989) as a model for granophile mineral
deposits. The current illustration is on the granitic-pegmatites formation part of the
model. A rising magma may intrude and solidify at depth resulting into granites and
monzonites, which are usually large and discordant. These granitoids are usually
batholiths of orogenic belts and are generally barren possibly due to low water or lack
of interaction with ground water because of the intrusion at great depth (Westerhof and
Aleva, 1989). A muscovite-bearing pegmatite granite may form as an anatectic melt or
from the differentiation of the granite. The differentiated muscovite-granite may be
enriched in beryllium, boron, lithium and other elements that were not incorporated in
the early forming minerals, typically at the cupolas at the top of large plutons depending
on the metallogen of the area. The presence of these elements and volatiles prolongs the
crystallization of the muscovite-granite and allows intrusion to shallow levels. It may
then allow second boiling and at times brecciation. The highly fractionated muscovitegranite is enriched in BEBLP (beryllium, boron, lithium, pollodium) and other
lithophile elements. The dispersion of fluids and elements through plutons and country
rocks results into various granitoid related deposits of which aquamarine-bearing
pegmatites are a part. The shallow emplacement allows more siliceous and volatile
fluids be injected through the fractures of the country rocks.
The genesis of rare-element granitic-pegmatites associated with batholiths depends on
the interplay of complex petrogenetic processes during the evolution of the batholith.
The mineralization associated with granitoid magmatic activity, which graniticpegmatites are part of, depends primarily on the composition of the parent magma,
speed at which this magma rises in the crust, water content of the magma, lithological
and environment in which the magma intrudes. The late stage of the parent batholith to
rare-element granitic-pegmatites, in general, involves exsolution of volatile-rich
phase(s), dispersion of a rare-element-rich fluid along shear zones and upward
emigration of rare-element rich melt. The granitic-pegmatites of the Lundazi area
formed by the intrusion and crystallization of magmatic rare-element fluids within
fractures in the country rocks. This is evidenced by their zoning characteristics and the
chilled margins of the contacts between the pegmatites and the country rocks.
20
Geology and Exploration of Aquamarine Bearing Granitic Pegmatites
1
2
3
Fig. 3.1. Generalised possible genetic model for aquamarine-bearing pegmatites in Lundazi
area (modified after Strong (1981), Geoscience Canada, 814).
The aquamarine-bearing pegmatites of the Lundazi area occur both as clusters and as
isolated pegmatites in the basement rocks of medium to metamorphic facies. O’Connor
(1998) considers the granitic-pegmatites in the area roofs of a parent granites that are
not exposed at the current erosion levels. He observed several microgranites in the area
but most of them are too small to be shown on at the map scale. He also noted that this
could be another proof that there could be a parent granite under the current erosion
levels. The late metamorphic events may have reactivated the old faults which acted as
channelways for the granites parental to the aquamarine-bearing granitic-pegmatites.
The pegmatitic fluids were then injected through faults and fractures that may have
resulted during metamorphic events.
Hickman (1998) is of the opinion that the granitic-pegmatites were emplaced
syntectonically with the formation of a northeast-trending Lukusuzi antiform. He
observed that the granitic-pegmatites and their parental micro-granites are spatially
distributed along the axial trace of this antiform. The granitic-pegmatites also show
some preference to shear zones in the northeast of the area. Hickman (1998) contended
that the granitic-pegmatite intrusions are syntectonic with the late shearing and faulting
in the area which may be coeval with the formation of the northeast trending fold axis in
this area. Mohan (1982) shares the same view but he further suggests that the graniticpegmatites are also controlled by fractures and foliation planes.
Chapter 3
21
3.4 General Characteristics of Geological Environments of (Aquamarine-Bearing)
Granitic-Pegmatites
In general, the (surface) geological characteristics of zones favorable for emplacement
of aquamarine-bearing pegmatites based on the geological and structural setting of
granitic-pegmatites elsewhere and in Lundazi area are as follows.
a) The granitic-pegmatites occur within metamorphosed eugeosynclinal sequences of
orogenic belt penetrated by batholithic belts of the Proterozoic age (Ginsburg et al.,
1979).
b) The granitic-pegmatites have a spatial association with proximity to faults and
fractures. The faults and fractures may have acted as pathways for the rare-elements
enriched fluids (Harding, 1982).
c) The granitic-pegmatites are spatially associated with proximity to antiformal axial
traces (Hickman, 1998).
d) The granitic-pegmatites tend to occur in foliated metamorphic sequences proximal to
the contact with metagranites. Differential responses of the metamorphic sequences and
the pre-to syntectonic metagranite to the late adjustment stresses may loosen the contact
and allow injection of the deep- seated magma (Trueman, 1982).
e) Granitic-pegmatites have been characterized based on petrographical and
geochemical mapping of hydrothermally altered rocks to reconstruct the hydrothermal
activity events. This approach allows determination of geochemistry of alteration
systems related to ore-bearing granitic-pegmatites and that related to pre-granitic
intrusion country rocks (Komov et al., 1994).
f) The presence of and proximity to late or post-tectonic granitic bodies. These are small
to moderate in size and usually postdate the peaks of regional metamorphism. Such
granitic emplacements are controlled by large-scale tectonic features like deep regional
faulting, re-activation along old tectonic lineaments or faults of a younger date (Cerny,
1982).
g) Presence of radiometric signature of Uranium, Thorium and Potassium. The parent
granite and/ or associated pegmatites may be discriminated from other rocks in the area.
(Subhash et al., 2001).
h) Presence of and proximal to alteration zones. Alteration zones around pegmatites
may be over 150m in magmatic-disseminated rare-earth pegmatites though generally the
zones are in the range of a few metres (Subhash et al., 2001).
i) Presence of and proximity to ductile shear zones. The shear zones are weaker zones
and may provide easy pathway for the granites (Abdalla et al., 1999).
j) Presence of circular features. Simple and circular features in Remote sensing image
data are related to small granitic bodies or to hidden domes that correspond to
22
Geology and Exploration of Aquamarine Bearing Granitic Pegmatites
concentric networks of fractures formed during the cooling of the granite. This is as
opposed to complex features related to larger plutons (Rolet et al., 1993).
k) Geochemically, the rare-elements granitic-pegmatites have high Si, and K/Na ratios,
moderate Al and Na, and low Ti, Fe, Mg, Mn and Ca. Ratios like Li2/(RbxCs) and (Ba x
Sr x Pb)/(Zn x Cr x Cu) are used to determine haloes around pegmatites. The ratio
Li2/(Rb x Cs) decreases with increasing depth of the pegmatite while the ratio
(BaxSrxPb)/(ZnxCrxCu) decreases in negative haloes. Heavy minerals stream sediments
sampling can also be employed (Komov et al., 1994).
l) The high concentrations of beryllium, fluorine, boron, lithium and rubidium in
granitic-pegmatites enable use of lithogeochemical and hydrogeochemical prospecting
methods for granitic-pegmatites. Fluorine and boron form haloes, which propagate in
the form of a train up to 200m from the granitic-pegmatites in the direction of water
flow. This makes hydrogeochemical prospecting a useful tool. Fluorine also forms
haloes of about 5m around a granitic-pegmatite (Komov et al., 1994).
3.5 GIS-Based Geological Exploration for Granitic-Pegmatites
The general geological criteria for exploration of granitic-pegmatites and observations
in the study area form the basis for the extraction of evidence features from the geodata
sets available. Features from geodata sets discussed in chapter 1 will be used to generate
maps showing spatial association with the aquamarine-bearing granitic-pegmatites. The
geological evidences are divided into geochemical, progenitor intrusives, alteration
zones, Lineaments, syn-form axial traces, shear zones, and structural map i.e., showing
circular features hypothesized to be due to either micro-granites or underlying batholith.
The geological evidence maps will be combined to predict zones with potential for the
occurrence of aquamarine-bearing granitic-pegmatites by: a) weights of evidence
method (data-driven) and b) Fuzzy logic method (knowledge-driven).
3.6 Conclusion
Identification of granitic rocks parental to mineralized rare-element granitic-pegmatites
forms an essential starting point for a search of rare-element mineralized graniticpegmatites. This search may combine remotely sensed data, geochemical, geophysical
and geological data to identify the above outlined exploration criteria. The present
research will use only the following criteria from those outlined above because of lack
of adequate data for the rest of the other criteria.
• Presence of or proximity to faults and fractures, which provide pathways for
aquamarine-bearing granitic-pegmatites from progenitor granites. These features
will be extracted from geological maps and ASTER imagery.
• Presence of or proximity to synform axial traces with NE trends, which are
thought to be coeval with late granite intrusion. These features will be extracted
from the geological map.
• Presence of late granites and metagranites related to the aquamarine-bearing
granitic-pegmatites. These features will be extracted from the geological maps.
• Presence of radiometric signature indicative of intrusive rocks. This feature will
be extracted from uranium, thorium and potassium radiometric data.
Chapter 3
•
23
Presence of or proximity to circular features indicative of unexposed late
granites and small exposed intrusives. These features will be analysed from the
ASTER imagery.
• Alteration zones associated with granitic-pegmatites weathering. These features
will be identified using the ASTER imagery.
• Geochemical anomalies. These will be identified from stream sediments
geochemical data.
• Presence of or proximity to ductile shear zones, which provide pathways for
granitic and granitic-pegmatite intrusions. These features will be extracted from
both the ASTER imagery and the geological map.
Chapter 4: Extraction of Spatial Indicative Features
This chapter explains the extraction, from the appropriate exploration datasets, of the
following spatial features considered in the previous chapter to be indicative of zones
with potential for the occurrence of aquamarine-bearing granitic-pegmatites.
• Geological lineaments, (faults and joints), which are considered conduits for
granitic intrusions.
• NE trending synformal axial traces, which are thought to be coeval with the
intrusion of granitic-pegmatites.
• Meta- and late granites, which are thought to be parental to the granitic
pegmatites.
• Radiometric feature indicative of granitic rocks. This feature will identify
granitic intrusions from the undifferentiated gneisses and migmatites.
• Circular features, which are indicative of unexposed and small granitic bodies
probably parental to granitic-pegmatites.
• Alteration zones, which could be associated with the weathering of the graniticpegmatites.
• Silicic rocks (i.e., granitic intrusions).
• Multi-element association of Pb, Cu, and Zn, which is thought to reflect granitic
environments.
• Shear zones that provide weak areas for the granitic intrusions.
Lineaments and shear zones were extracted from both the geological map and the
ASTER imagery. Shear zones were also obtained from interpretations of aeromagnetic
data by ERIPTA (Economic Recovery of Investment Programme for Technical Aid)
project. Meta- and late granites and axial traces were extracted from the geological map
while circular features and alterations zones were extracted from the ASTER imagery.
A multi-element association geochemical signature was extracted from the stream
sediments geochemical data. Extraction of different granitic intrusions was conducted
from the radiometric data.
4.1 Structures and lithological units extracted from geological map
Four geological maps of 1:100 000 scale cover the study area. From the geological
maps, lithological units and structures (faults, shear zones and axial traces) were
digitized into different segment layers.
4.1.1
Lithological units
The boundaries of lithological units were digitized into a segment map, which was then
polygonized. Each polygon was assigned the name of a lithological unit. The polygon
map was rasterized from which a map of classified generalized geological units was
created. A raster map showing metagranites and microgranites was then extracted from
the raster map of classified lithological units.
Chapter 4
4.1.2
25
Faults
Faults were digitized from the geological maps and each fault was labelled by its
general orientation (NE, NW, E and N). Fig. 4.1 shows the digitized faults from the
geological map and a rose diagram, which provides a general view of the orientation of
the faults. The rose diagram shows two main orientation directions, i.e., NE and NW.
There is also a small number of EW and NS trending faults. The NE trending and NW
trending faults were extracted for further use in the analysis of zones with potential for
occurrence of granitic-pegmatites.
Fig. 4.1. Faults digitized from geological map.
4.1.3
Shear zones
Shear zones were digitized from the geological map and the ASTER imagery. Fig. 4.2
shows the shear zones extracted from geological maps. Shear zones interpreted from
aeromagnetics by ERIPTA project (Economic Recovery of Investment Programme for
Technical Aid) were also merged with shear zones extracted from both ASTER imagery
and the geological map. Extraction of shear zones from ASTER imagery is discussed in
section 4.2.3. The digitized shear zones from different data sources were then merged.
4.1.4
Axial traces of folds
Axial traces with a NE orientation were digitized from the geological map. The graniticpegmatites are thought to be coeval with the deformation phase that generated the set of
folds with a NE trending axial traces. Fig. 4.3 shows the axial traces digitized from
geological map.
26
Extraction of Spatial Indicative Features
Fig. 4.2. Shear zones digitized from geological map.
Fig.4.3. Axial traces digitized from geological map.
Chapter 4
27
4.2 Structural features extracted from ASTER imagery
4.2.1
General
Advanced Spaceborne Thermal Emission and Reflectance Radiometer or ASTER is a
high spatial resolution multispectral imaging radiometer on NASA’s Earth Observation
System TERRA platform. It was launched on December 18, 1999. ASTER detects
electromagnetic energy in the VNIR (visible to near infrared), SWIR (short-wave
infrared), and TIR (thermal infrared) regions of the electromagnetic or EM spectrum
(Volesky et al., 2002). There are three VNIR bands, six SWIR bands and five TIR
bands. The spatial resolution ranges from 15m for the VNIR bands, 30m for the SWIR
bands and 90m for the TIR bands. ASTER has a swath width of 60km.
ASTER imagery has been used in discriminating geological units, and minerals
including kaolinite in argillized sandstones, and muscovite in sericitized granites, as
well as commonly occurring illite/muscovite (Rowan, 1997). ASTER imagery has also
been used to map and understand regional structures, like faults and shear zones to help
in mineral exploration (Volesky et al., 2002). For the present research, the ASTER
imagery was used to map lineaments, circular structures, shear zones, silicic zones and
alteration zones, which may be used as spatial guides to zones with potential for
aquamarine bearing pegmatites in the study area.
Six scenes of ASTER imagery were acquired for the study area, but one scene covering
the eastern part has high percentage of cloud cover and could not be of any use so it was
discarded. The scenes were obtained at different periods as shown in Fig. 4.4. The
images were corrected for atmospheric ‘haze’ to enhance image contrast as below.
Light scattered by the atmospheric constituents that reaches the sensor constitutes
‘haze’, which has an additive effect resulting in higher DN values and a decrease in the
overall contrast of the image (Prakash, 2001). The histograms for each band were
checked for atmospheric haze effect. Corrections for the haze effect were made by
subtracting from all the pixels the offset at which the DN-values of the images began.
The result is an image with DN-values starting at zero. This procedure was repeated on
all the images.
No sun angle correction was conducted on the images because the images were obtained
at the same period of the year. According to Prakash (2001), solar elevation angle
changes according to the season of the year and as a result, the image data of different
seasons are acquired under different solar illuminations. As shown in Fig. 4.4, the
images were acquired the same season of the year (summer for southern hemisphere).
The five scenes were georeferenced using tie points identified from the imagery and the
1:50,000 scale topographic maps. To improve the visualization of individual bands,
contrast enhancement was conducted on all SWIR and VNIR bands using histogram
equalization. Histogram equalization stretches the DN values to cover the range of the
256 digital numbers. This approach attempts to put a similar number of digital values in
every portion of the distribution, thus the choice for this approach. Linear stretching
could also be used. The SWIR bands were resampled using the nearest neighbour
28
Extraction of Spatial Indicative Features
algorithm to a spatial resolution of 15 m for integration with the VNIR bands to extract
particular geological features.
11/11/01
05/09/01
02/12/02
05/09/01
11/11/01
Fig. 4.4. Area covered by ASTER imagery (shown as color composite with band 6 in red
channel, band2 in green channel and band 1 in blue channel) and their respective dates of
acquisition.
4.2.2
Extraction of lineaments
A lineament is a mappable, simple or composite linear feature of a surface whose parts
are aligned in a rectilinear or slightly curvilinear relationship and which differs
distinctly from the pattern of adjacent features and presumably represents a subsurface
phenomenon (O’Leary et al., 1976). To detect lineaments from ASTER data, two
procedures were carried out. First, a suitable color composite was generated from which
lineaments were interpreted visually. Second, directional filters were applied to all band
images to enhance detection of lineaments.
Color composites are among the most basic forms of images that can be used for rapid
and first-order analysis of remotely sensed data (Mustard and Sunshine, 1999). Prior to
any band combinations, optimum index factor (OIF) can be calculated to determine
optimum band combinations. This technique can be used to rank R-G-B combinations
of bands and to select bands for principal components analysis, etc., based on the
amount of correlation between bands and total variance within individual bands
(Chavez et al., 1984). Band combinations with high OIF values usually give more
information. Although OIF simplifies the selection of band combinations, it does not
always provide a combination suitable to convey specific information desired by the
user. Table 4.1 shows the OIF values for the SWIR and VNIR band combinations.
Chapter 4
29
However, the band combination found most suitable for visual interpretations of
lineaments, and shear zones was bands 6, 2 and 1 in red, green, and blue channels,
respectively (Fig. 4.4). The band combination that was found suitable for visual
interpretation of circular features was bands 8, 3, and 1 in red, green and blue channels,
respectively.
Table 4.1. Band combinations, OIF value and ranking
Band combination
1-3-8
1-3-9
2-3-8
3-4-8
2-3-9
3-8-9
OIF value
116.09
115.48
114.58
114.45
113.68
113.33
OIF Rank
1
2
3
4
5
6
The filters below (Fig. 4.5) were designed and applied on individual bands to enhance
linear features but the results were not better than the 6-2-1 color composite for visual
interpretation of lineaments.
2
-1
-1
-1
-1
2
-1
2
-1
-1
2
-1
-1
-1
2
2
-1
-1
(a)
(b)
Fig. 4.5. 3x3 filter kernels used to enhance (a) NE trending lineaments and (b) NW trending
lineaments.
The lineaments interpreted on the 6-2-1 color composite were digitized and labelled
according to their orientations (i.e. NS, NE, NW, and EW). Fig. 4.6 shows the
lineaments (and a rose diagram) extracted from the ASTER imagery. The rose diagram
shows four dominant sets of lineaments directions, 0o-60o, 60o-90o, 90o-120o, 120o –
180o. From the lineaments interpretation, four deformation phases are postulated to have
occurred. The relatively early deformation was the major NE faulting. The NE shearing
followed this episode. The NW faulting succeeded the NE shearing. The last episode
seems to have been another NE faulting which could have been contemporaneous or
post NW faulting.
The lineaments were then checked against the faults digitized from the geological maps.
There are more lineaments interpreted from the ASTER imagery compared to those
indicated on the geological map. Duplicate lineaments were then deleted. The
lineaments were then merged with the faults digitized from the geological maps (Fig.
4.7). A rose diagram (Fig. 4.7) for the merged lineaments shows the same general trends
as the one for the lineaments interpreted from ASTER imagery.
30
Extraction of Spatial Indicative Features
Fig. 4.6. Lineaments (and rose diagram) extracted from color composite (R = band 6; G = band
2; B = band 1) of ASTER imagery.
Fig. 4.7. Lineaments (faults) digitized from geological map and interpreted from ASTER
imagery and corresponding rose diagram.
Chapter 4
4.2.3
31
Extraction of shear zones
A ductile shear zone is a zone where the rocks reacted in a ductile manner to stresses
and strains during deformation leading to formation of foliations and lineation with
mylonitic fabric. Mylonites are foliated rocks in the shear zones. They are normally well
foliated, banded and often with linear shape fabric (Jessell et al, 1997). In this research,
the zones of lineation and banding were interpreted as shear zones. Fig. 4.8 shows the
shear zones interpreted from ASTER imagery. The shear zones interpreted from
ASTER imagery, geological map and those interpreted by ERIPTA project from
aeromagnetics were then combined. Shown in Fig. 4.9 are merged shear zones
interpreted from datasets mentioned above.
Fig. 4.8. Shear zones digitized from ASTER imagery
4.2.4
Interpretation of circular features
Rolet et al. (1995) applied remotely sensed data to identify different types of granites
based on structure. They concluded that circular features correspond to concentric
fractures formed during cooling of small granitic bodies or hidden granitic domes. ElRakaiby (1995) used coloured composite ratio images to discriminate younger granites,
which are associated with uranium mineralization from other granitic masses. Each of
the granitic groups possesses certain image characteristics, such as colour and surface
texture. In this research, circular features were interpreted and digitized from the color
composite shown on Fig. 4.4. The circular features were interpreted in relation to
lineaments to establish the relationship with different lineament sets. Lineaments sets
were recognized by the spatial arrangement and overall distribution of the individual
interpreted structures.
32
Extraction of Spatial Indicative Features
Fig.4.9. Merged shear zones interpreted from geological map, ASTER imagery and the
aeromagnetics data. (Aeromagnetics interpretation by ERIPTA).
Fig. 4.10. Circular features and lineaments interpreted from ASTER imagery
Chapter 4
33
Fig. 4.10 shows the circular features map with lineaments. The identified circular
features tend to terminate along the NW trending lineaments but are occasionally cut by
a set of NE trending lineaments. The circular features seem to be controlled by NW
trending faults. The metagranites extracted from the geological map do not exhibit
spatial relation to the circular fracture patterns. The circular features are interpreted as
indicative of hidden granitoids or minor granites and will be used further in this study.
4.2.5
Extraction of alteration zones
ASTER imagery bands in the VNIR and SWIR regions of the EM spectrum can be used
to map clays, carbonates, hydrous sulfates, and iron oxide minerals, which are generally
associated with hydrothermal alterations. These minerals exhibit diagnostic absorption
features in the VNIR and SWIR regions of the EM spectrum (Rowan et al., 2001).
The granitic-pegmatites have been identified to be associated with alteration zones
ranging from a few meters to 150m accompanied by a mass of kaolinitic clay (Subhash
et al., 1998). An attempt was made to interpret areas with kaolinite and those with iron
oxide minerals. This was done to show areas that are hydrothermally altered and those
that are associated with kaolinization due to pegmatites weathering. It is assumed that in
hydrothermally altered areas kaolinite, hydrous iron oxides and iron oxide minerals will
be present while in areas where kaolinization is due to pegmatites weathering will have
only or mainly kaolinite. Kaolinite or clay minerals in general have low reflectance in
the spectral region of 2.08-2.35 µm (bands 5-8). The clay minerals have high
reflectance in the spectral region of 1.55-1.75 µm (band 4). Ferric iron oxides (limonite)
have high reflectance in the spectral region of 0.63-0.69 µm (band 2-3) and low
reflectance in the spectral region of 0.45-0.52 µm (band 1). The wavelength coverage of
each ASTER image band is given in Table 4.2.
Table 4.2 ASTER imagery bands and their respective wavelength
Band
1
2
3b
3n
4
5
6
7
8
9
10
11
12
13
14
Wavelength (µm)
0.556
0.661
0.804
0.807
1.656
2.167
2.209
2.262
2.336
2.400
8.291
8.634
9.075
10.657
11.318
Based on the spectral regions of low and high reflectance a band ratio of 4/6 will
enhance areas rich in clay minerals or sulphates and a band ratio of 2/1 will enhance
areas rich in ferric iron oxides (limonite). According to Lipton (1997) a band ratio of
3 / 4 shows little spectral contrast in areas with either clay minerals or limonite. Band
34
Extraction of Spatial Indicative Features
ratios of 4/6, 2/1, and 3 / 4 , were therefore generated and a colour composite with band
ratios of 4/6 (in red channel), 2/1 (in blue channel), and 3/4 (in green channel) were
made. From such a combination clay rich areas will be red, ferric oxide areas will be
green and where both are present it will be orange or yellow. To determine whether the
Lundazi pegmatites are associated with the interpretable clay minerals areas, the areas
in red colour were compared with known granitic-pegmatite occurrences. The colour
composite band ratio images were not useful to delineate/interpret alteration zones
associated with the granitic-pegmatites. This could be because of the small size of the
granitic-pegmatites, although initially it was thought that it would be possible to
interpret them from colour composites of image ratios because the granitic-pegmatites
occur in clusters.
4.2.6
Extraction of silicic rocks
The first three bands in the TIR regions of the electromagnetic spectrum provide critical
information about hydrous rock forming minerals, such as quartz, feldspars, which lack
diagnostic absorption features in the VNIR and SWIR regions of the electromagnetic
spectrum (Rowan et al., 2001).
The TIR atmospheric window (8-12 µm) provides a feature diagnostic of felsic silicates
- a strong reflectance maximum associated with the stretching motion of the Si-O
bonds. The Reststrahlen emissivity minimum, associated with the Si-O bond stretching
vibration frequency, shifts to shorter wavelengths as the strength of Si-O bond increases
(Sabine et al., 1994). The Reststrahlen emissivity minimum of felsic rocks shifts to
longer wavelengths as compared with that of intermediate and mafic rocks.
In TIR data, there is a high band-to-band correlation due to predominance of
temperature effects resulting into low colour saturation in colour composites of TIR
bands. Decorrelation stretch can be used to enhance variations due to emissivity
differences and suppress the effect of surface temperature (Sabine et al., 1994). A
decorrelation stretch procedure uses Karhunen-Loeve principal component
transformation, in which variances of eigenvectors are not normalized during data
transformation. The data set is rotated and transformed into a new system defined by
statistically independent axes. The transformation further stretches the data along these
axes and transforms it back to the original coordinate system.
An attempt was made to discriminate metagranites and granites from other lithological
units using the TIR bands of the ASTER imagery. The sub scenes were processed and
interpreted separately because they could not be georeferenced because of lack of easily
identified points on the decorrelated images. After the data transformation using
ENVISAT software, the data were imported to ILWIS. As stated above the Reststrahlen
emissivity minimum shifts from longer to shorter wavelength for felsic to mafic rocks
(see also table 4.2). Based on this spectral behaviour of rocks from felsic to ultramafic,
colour composites from bands of longer wavelengths (bands 13 and 14), intermediate
wavelength (11 and 12), and shorter wavelength (band10) were generated and analysed.
Only colour composite of bands 13(red), 12(green), and 10(blue) were able to outline
the granitic rocks in the area. The interpreted areas are, however, coincident with those
indicated as granitic or metagranites on the geological map.
Chapter 4
35
(b
)
(a
)
(c)
(d)
Fig. 4.11. Interpreted silicic rocks based on TIR
color composites of decorrelated TIR bands
(band13=red, band 12=green, band10=blue) for
silicic alteration mapping (for the five scenes, a, b, c,
d, and e, covering the area).
(e)
36
Extraction of Spatial Indicative Features
On two of the subscenes (see Fig 4.11.a, b), granitic gneisses (Grgnss) and metagranites
(Mgrnt) are shown in yellow on the SW and middle parts of the area. The granitic
gneisses and the metagranites appear yellow also in the other subscenes (Fig. 4.11.c, d,
e). An aplite/microgranite was identified with a brown color (see Fig. 4.11.b). Since the
interpretation of TIR data confirms the granitic units indicated on the geological map
the interpreted silicic rocks will not be further used in this research.
4.3 Spatial indicative features from stream sediments geochemical data
Geochemical data have proved useful for mineral potential mapping. Indicator elements
useful for mapping miarolitic cavity type of granitic-pegmatites are K, Na, Li, Rb, Cs,
F, Be, B, P, Nb, Ta, Ti, Zr, Bi, Zn, Pb, and Cu (Komov et al. 1994). In this research,
only stream sediments data for Cu, Ni, Zn, Co, Mn, Fe and Pb are available. Four sets of
analysis were applied to the data - uni-element analysis, correlation analysis, spatial
analysis, and extraction of multi-element signature indicative of granitic rocks.
4.3.1
Uni-element analysis
The geochemical data were inspected for duplicates. When the duplicate analytical
values for each of the elements are equal, one of the duplicate samples was discarded.
When the analytical values are different, the average was calculated and one of the
duplicate samples was discarded. Two thousand nine hundred and seventy-three (2973)
samples were retained and used in the analysis. The sampling density was three (3)
samples per sq. km. Table 4.3 shows the elementary statistics of the geochemical data.
Table 4.3. Elementary statistics of element concentrations (ppm) for samples retained.
Element Observations Max.
Cu
Co
Ni
Zn
Mn
Fe
Pb
2935
2820
2844
2973
2973
2973
1756
290
505
320
600
9550
45000
320
Min.
Mean
3
3
3
5
10
100
5
20
18
23
57
424
2865
20
Std
dev.
14
16
19
41
457
2150
16
Std
Median Mode Skewness Kurtosis
error
0
20
10
5
71
0
15
10
16
463
0
20
20
6
73
1
50
40
4
33
8
320
300
7
102
39
2500
2000
6
85
0
20
20
9
148
One thousand and fifteen (1015) samples had anomalous element content based on
threshold ‘mean + standard deviation’. The anomalous samples were separated from the
rest. Table 4.4 shows the elementary statistics of the background data.
Table 4.4. Elementary statistics of the element concentrations (ppm) for background samples.
Element Observations Max.
Cu
Co
Ni
Zn
Mn
Fe
Pb
1922
1827
1842
1958
1958
1958
1136
30
30
40
95
865
5010
35
Min.
Mean
3
5
5
5
10
100
5
15
14
18
42
300
2212
16
Std
dev.
7
6
9
22
166
1135
7
Std
Median Mode Skewness Kurtosis
error
0.2
15
10
0.6
-0.6
0.2
10
10
0.6
-0.3
0.2
20
20
0.8
0.1
0.5
40
20
0.6
-0.6
3.8
280
300
0.8
0.4
25.7 2000
2000
0.5
-0.4
0.2
20
20
0.4
0.6
Fig 4.12 shows the frequency histograms for normal and lognormal distribution of the
raw background data. There is not much difference between the normal and log normal
Chapter 4
37
distribution and therefore any of the form of distribution of the data can be used for data
analysis. In this research, the normal distribution of data is used in further analysis.
Lognormal values histograms
1000
150%
100%
50%
120%
100%
80%
60%
40%
20%
0%
800
600
400
200
30
0
7
12
19
5
0%
Frequency
1000
800
600
400
200
0
3
Frequency
Normal values histograms
0.
Cu (ppm) values
6
0.
9
1
1.
1.
3
Log-transformed Cu (ppm) values
600
400
200
0
19
30
Frequency
400
200
0.
9
1.
0
1.
2
1.
3
1.
5
120%
100%
80%
60%
40%
20%
0%
Frequency
Frequency
800
12
600
Log-transformed Co (ppm) values
Co (ppm) values
7
120%
100%
80%
60%
40%
20%
0%
800
0
30
19
12
120%
100%
80%
60%
40%
20%
0%
7
1000
800
600
400
200
0
5
Frequency
1000
47
7
0.
Ni (ppm) values
120%
100%
80%
60%
40%
20%
0%
700
600
500
400
300
200
100
0
9
0.
2
1.
4
1.
6
1.
Log-transformed Ni (ppm) values
100%
300
80%
200
60%
40%
100
20%
30
47
74
0%
7
12
19
0
Zn (ppm) values
300
250
200
150
100
50
0
120%
100%
80%
60%
40%
20%
0%
0.
7
0.
9
1.
2
1.
4
1.
6
1.
9
120%
Frequency
Frequency
400
Log-transformed Zn (ppm) values
Fig. 4.12. Normal and lognormal histograms and frequency curves for the background data.
(Continued next page)
Extraction of Spatial Indicative Features
120%
100%
80%
60%
40%
20%
0%
400
300
200
100
Mn (ppm) values
Log-transformed Mn (ppm) values
Frequency
120%
100%
80%
60%
40%
20%
0%
120%
500
100%
400
80%
300
60%
200
40%
100
20%
2
Fe (%) values
0%
.0
3 6 9 3 6
2. 2. 2. 3. 3.
Log-transformed Fe (%) values
500
120%
500
120%
400
100%
400
100%
100
20%
30
19
0%
12
7
0
Pb-(ppm) values
60%
200
40%
100
20%
0
0%
1.
6
40%
1.
3
60%
200
80%
300
1.
1
300
0.
9
80%
Frequency
Frequency
600
0
14
8
29
5
58
9
11
75
23
44
46
77
Frequency
Fe-histogram
350
300
250
200
150
100
50
0
120%
100%
80%
60%
40%
20%
0%
9
8 2 6
0 4
1. 1. 1. 2. 2. 2.
37
74
14
8
29
5
58
9
19
0
300
250
200
150
100
50
0
0.
6
Frequency
500
Frequency
38
Log-transformed Pb (ppm) values
Fig. 4.12. Normal and lognormal histograms and frequency curves for the background data.
4.3.2
Correlation matrix of background geochemical data
The correlation between element concentrations was determined using background
samples. Prior to calculation of the correlation matrix, samples with missing values (i.e.,
below detection limit) for one or more elements were discarded. Table 4.5 shows the
number and percentages of missing values in the background geochemical data. Table
4.6 is a correlation matrix generated after removing samples with missing values.
At the 0.01 probability level, the critical Pearson’s r-value is 0.130. The data reveals
that there is high correlation between Mn, Fe and Ni. This may be attributed to basic
dykes and possibly laterites in the area. The high correlations between Mn and Co and
between Mn and Zn suggest scavenging of Co and Zn by Mn. The same can be said
about the correlation of Fe and Cu, and Fe and Co. The high correlation of Ni-Co-CuZn may be indicative of particular lithological units (e.g., mafic/ultramafic rocks). In
Chapter 4
39
general, the correlation matrix shows that there is high correlation between elements,
except for the correlation of all other elements with Pb.
Table 4.5. Percentages of blanks for each element in the background geochemical data
Element
Number of
Number of observations % of observations with
observations
with missing values
missing values
Cu
1922
35
2
Co
1827
131
7
Ni
1842
116
6
Zn
1958
0
0
Mn
1958
0
0
Fe
1958
0
0
Pb
1136
822
42
Table 4.6. Correlation matrix for background (1520 samples) geochemical data
Cu
Co
Ni
Zn
Mn
Fe
Cu
1.00
Co
0.33
1.00
Ni
0.35
0.35
1.00
Zn
0.30
0.37
0.33
1.00
Mn
0.21
0.38
0.24
0.38
1.00
Fe
0.36
0.36
0.40
0.32
0.44
1.00
Pb
0.07
0.03
0.06
0.06
0.02
-0.06
4.3.3
Pb
1.00
Spatial distribution of the geochemical data
The spatial distribution of each of the uni-element data was modelled by kriging.
Kriging is a linear point interpolation algorithm that estimates an output for a pixel with
unknown value based on known input values and weight, and also optionally calculate
the error in the estimate depending on the input parameters. The estimation of an output
value Zo is calculated as
Z o = S ( Wi * Z i )
Eq. (1)
where, Wi is the weight factor for input value Zi.
Before the spatial data interpolation, the background data was examined by point
pattern analysis and semi-variogram analysis. Point pattern analysis provides
information about the distribution of data in space to enable deduction of certain
patterns. Sawatari et al., (1990) used pattern analysis to analyse the spatial pattern of
REE, Ba and Sr abundances, Sr and Nd isotopic ratios and major element composition
of basalts to determine their genetic spatial association. In this research, pattern analysis
was used to determine the least distance between geochemical samples where there is
0% probability of finding a point next to another. This least distance was used as the lag
distance for the semi-variogram analysis. The semi-variograms for all the uni-element
data assume a spherical model (Fig. 4.13). The semi-variogram analysis enabled
calculation of lag, sill, nugget and range for data, which are required for interpolation by
kriging interpolation method (see Table 4.7). The point distance is in meters.
40
Extraction of Spatial Indicative Features
Cu
Co
Fe
Mn
Pb
Zn
Fig. 4.13. Variograms of stream sediment
geochemical data.
Ni
Chapter 4
41
Table 4.7. Nugget, sill, and range of each element used in kriging of geochemical data.
Co (ppm) Cu (ppm) Zn (ppm) Ni (ppm) Mn (ppm)
Fe (%)
Pb (ppm)
Nugget
54
74
450
98
38000
1310000
79
(ppm)
Sill
70
110
1050
123
51800
200000
169
(ppm)
Range
49000
49000
98000
7500
40000
57000
65000
(m)
The whole data set, anomalous data inclusive, was interpolated by ordinary kriging
method. Ordinary kriging was used because it is the best linear estimator for regional
variables by trying to minimize the error variance. Jimenez-Espinosa et al. (1999) used
ordinary kriging interpolation method for identifying potential anomalous areas for gold
mineralization. Ouyang et al. (2002) used ordinary kriging for the investigation of
characteristics and spatial distribution of sediments and contaminants in Cedar and
Ortega rivers. After kriging, in this research estimated values outside the study area
were masked. Fig 4.14 shows the krigged stream sediments data from Lundazi area.
High iron values are associated with streams draining the southwestern and the far
eastern parts of the area (Fig. 4.14a). Sporadic highs occur in the northern part. The high
values in the east are associated with pelitic granulite while a stretch of high values
from southwest towards north is mainly associated with areas with basic intrusives in
the hornblende gneiss and migmatitic gneiss.
High cobalt values are shown in the streams draining north-northeast and eastern parts
of the area (Fig. 4.14b). The high cobalt values in the north-northeast area are associated
with alluvium sporadically interrupted by leucocratic gneiss interlayered with granulite
and basic dykes (see also Fig. 2.3). The high cobalt values could be interpreted as
resulting from the granulites and the basic dykes. In the east, high cobalt values are
associated with sillimanite gneiss with charnockite intrusions. The cobalt values are
generally low in the study area.
The streams draining the central and eastern parts of the area show high copper values
(Fig. 4.14c). High copper values in the central part are mainly associated with granitic
gneisses and metagranites. In the eastern part, high copper values are associated with
the pelitic granulite and metaquartzite. A moderately high values-stretch occurs in the
northeast in the leucocratic and granitic gneisses.
The streams draining the central and eastern parts of the area have high nickel values
(Fig. 4.14d). The high values in the central part are associated with both migmatitic
gneiss and the granulite while those in the eastern part are associated with pelitic
granulite. In the northeast, high values of nickel occur on a stream draining areas
covered by alluvium with sporadic outcrops of leucocratic gneiss interbanded with
granulite and basic dykes.
Sporadic high values of manganese occur in streams draining the central and eastern
parts of the area. The high values are not associated with any particular lithological unit
(Fig. 4.14e). No much information was extracted.
42
Extraction of Spatial Indicative Features
High lead values occur in the streams draining the southeastern, eastern and
northeastern parts of the area. In the southeastern part, high values are associated with
hornblende gneiss interlayered with amphibolite and pyroxene granulite (Fig. 4.14f).
High values also occur in some streams in the same area associated with coarse-grained
metagranites. In the southeast, east and north areas the high values area associated with
pelitic granulite and leucocratic gneisses.
High zinc values occur in the central and western parts of the area (Fig. 4.14g). The
high values in the central part are mainly associated with metagranites and hornblende
gneiss. In the western part, the high values of zinc are associated with alluvium along a
major river valley. This is a low lying area and the high values of zinc could be because
of high mobility of zinc and the tendency of elements flowing from higher areas to
precipitate in low areas. Some high values occur in the sillimanite gneiss in the
southeastern part.
From the interpolated data some element associations may be indicative of lithological
units, for example the association of cobalt, nickel and copper may indicate basic or
granulitic rocks while copper, zinc and lead associated with low iron values may
indicate granitic terrains. To extract a multi-element association indicative of granitic
terrains, principal component analysis was conducted. The granitic indicative element
association will be used to generate a binary pattern that will be integrated with other
All.
M
Gn
All.
m
m
m
S
Hn
S
m
All- Alluvium
M- Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn- Granulite
Hn- Hornblende gnss
(a) Krigged values for Fe.
Fig. 4.14. Krigged values of uni-element concentrations in stream sediments. (a to g).
binary patterns to generate a predictive map.
Chapter 4
43
M
All.
Gn
m
m
All.
m
S
S
Hn
m
All.-Alluvium
M- Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn- Granulite
Hn- Hornblende gnss
(b) Krigged values for Co.
All.
M
Gn
m
All.
m
S
Hn
(c) Krigged values for Cu.
S
m
All- Alluvium
M- Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn- Granulite
Hn- Hornblende gnss
44
Extraction of Spatial Indicative Features
M
All.
Gn
m
All.
m
t
S
m
S
Hn
m
All.- Alluvium
M- Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn- Granulite
Hn- Hornblende gnss
(d) Krigged values for Ni.
M
All.
Gn
m
All.
m
S
m
S
Hn
(e) Krigged values for Mn.
m
All.- Alluvium
M- Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn- Granulite
Hn- Hornblende gnss
Chapter 4
45
M
All.
Gn
m
All.
m
S
m
S
Hn
m
All.- Alluvium
M- Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn- Granulite
HnHornblende
gnss
(f) Krigged values for Pb.
M
All.
Gn
m
All.
m
m
S
Hn
(g) Krigged values for Zn
S
m
All- Alluvium
M- Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn- Granulite
HnHornblende
gnss
46
4.3.4
Extraction of Spatial Indicative Features
Extraction of a multi-element signature indicative of granitic areas
In the present research, principal component analysis was applied to identify from the
krigged geochemical data a multi-element signature, which reflects granitic
environments where pegmatites may occur. Principal component analysis has been used
to examine element associations and their relationship with the geology. Rogers et al.
(1986) used principal component analysis to asses the association of elements and their
relationship with the geology in the Cobequid Highlands, Nova Scotia. Grunsky et al.
(1999) used principal component analysis to identify element association and
populations from soil samples. Carranza (2002) used the principal component analysis
to extract multi-element associations that characterize known mineralization in
Catanduanes Island, Philippines. The results of the principal components analysis of the
spatially interpolated uni-element data are shown in Table 4.8.
Table 4.8 Principal component analysis of the Krigged values of the uni-element data
PC
Cu
Zn
Pb
Ni
Co
Fe
Mn
Var.%
1
0.456
0.399
0.364
0.285
0.138
0.520
0.360
73.95
2
-0.080
0.303
-0.776
0.000
-0.175
0.506
-0.112
9.46
3
0.004
0.814
-0.045
-0.065
-0.067
-0.570
0.041
7.12
4
0.468
0.045
0.203
-0.010
-0.417
0.048
-0.749
3.60
5
0.663
-0.259
-0.468
0.297
0.159
-0.369
0.156
2.99
6
0.299
-0.095
-0.011
-0.723
-0.437
0.031
0.433
1.73
7
-0.193
-0.095
0.048
0.551
-0.745
-0.095
0.289
1.14
The first principal component, which explains 73.95% of the variance, has positive
loadings for all the elements. The loadings for Fe and Cu are higher as compared to
others, which may be attributed to the vein-copper sulphide and massive iron
mineralization in the area. The lower loadings for Ni and Co reflect the lithological
associations.
The second principal component explains about 9.46 % of the variance. It has positive
loadings on Zn and Fe with high negative loadings for Pb. The Zn-Fe association can be
explained by scavenging of Zn by Fe. The antipathetic relation between Zn and Pb
shows their differences in mobility and indicates that Pb is not scavenged by Fe in the
area. The pathetic association between Cu and Pb could indicate lithologic association,
possibly granitic rocks.
The third principal component explains about 7.12 % of the variance. It has high
positive loadings on Zn and high negative loadings on Fe. The antipathetic association
between Zn and Fe indicates different mineralization in the area. Zinc represents
sulphide deposits while Fe represents iron deposits in the area.
The fourth principal component has high and moderate positive loading for Cu and Pb,
respectively, and high negative loadings for Mn and Co. This principal component
explains about 3.6 % of the variance. The association of Mn and Co indicates
scavenging of Co by Mn.
The fifth principal component explains about 2.99 % of the variance. This principal
component has high positive loadings on Cu and high negative loadings on Zn, Pb and
Fe. The high positive loading for Cu indicates the vein copper sulphide mineralization.
Chapter 4
47
The association between Zn, Pb and Fe indicates areas where Zn and Pb are scavenged
by Fe oxides.
Principal component six explains about 1.73 % of the variance. It has high and
moderate negative loadings on Ni and Co, respectively, and moderate positive loadings
on Mn. The Ni-Co association is indicative of lithologic association possibly basic rocks
in the area. The antipathetic association between Mn and Ni-Co implies areas where
both Ni and Co are not scavenged by Mn.
The seventh principal component explains only 1.14 % of the variance. It has high
positive loadings on Cu and high negative loadings on Co. No meaningful deduction
can be made from this association.
Komov et al. (1994) observed that indicator minerals for miarolitic granitic- pegmatites
included Cu, Zn and Pb among other elements. From the description of principal
components above, Pc2 has high loading on Pb with pathetic low loadings on Cu. This
principal component was interpreted to represent presence of granitic environment. A
map of Pc2 scores was therefore created to delineates areas where granitic pegmatites
may occur. The Pc2 scores were calculated as
S ci =
n
l cj Z ij
j =1
(Eq. 2)
for i=1,2,3,4,…….,n samples, where Sci = score for sample i on component c, lcj =
loading of element j for sample i and Zij = standardized value for element j for sample i.
The Pc2 scores were classified into 0-50, and 50-75 and 75-100 percentiles to indicate
areas of very low, low and high likelihood, respectively, of being a granitic
environment.
Fig. 4.15. Distribution of PC2 scores and metagranites
48
Extraction of Spatial Indicative Features
Fig. 4.15 shows the distribution of Pc2 scores and the metagranites. The spatial
distributions of high Pc2 scores (i.e., >75th percentiles) show spatial association with
metagranites in the central part. The metagranites in the southeast and eastern parts are
spatially associated with low Pc2 scores (<50th percentiles). A map of >75th percentile
Pc2 scores will be used as geochemical factor map for the presence of granites.
4.4 Geochemical evidence from radiometric data
Gamma ray spectrometry has been applied for geological mapping and mineral
exploration since the 1970’s for the reason that absolute and relative concentrations of
radiometric elements, K, U, and Th vary measurably and significantly with lithology.
The concentrations of these radiometric elements can be effective in subdividing acid
igneous and metamorphic rocks; and can be of direct assistance in exploration for U and
Th but also for Sn, W, REE, Nb, and Zr (Darnley et al., 1989). Since U and Th are
lithophile elements, they can serve as pathfinder elements for Li, Cs, Be, Nb, Ta, Zr and
REE, which are concentrated in some pegmatites. Ratio measurements of U, Th, and K
are useful when outlining granitic bodies because they are less affected by the surface
variability than individual elements (Darnley et al., 1989).
The radiometric data available represent concentrations of K, U and Th. The data were
digitized from intersections of flight lines and concentration contour lines for each
element from 1:50000 scale topographic maps. The total sample number is 1195. The
data was interpolated using the minimum curvature algorithm (in Geosoft Oasis Montaj
software), which estimates grid values at the nodes based on inverse distance average of
the data within a specified radius. The algorithm first estimates values for a course grid
and fits a minimum curvature surface (the smoothest possible surface that will fit the
given data values). The grid is then adjusted to fit the actual data points nearest to the
course grid nodes. The grid cell is halved and the same process repeated until minimum
curvature surface is fit at the final grid cell size. A grid cell size of 250m was used. This
is based on that a grid cell size should be at least a quarter of the flightline spacing
(Barritt, 2001). The flightline spacing is 1000 km. A ternary map (with K grid in red, Th
grid in green and U grid in blue) and grid ratios of eU/K, eU/Th and eTh/K were
generated and each compared with geology. The maps were then exported to ILWIS
programme where they were re-georeferenced for integration with other data.
The eU/eTh ratio map shows high ratio values in the southeast part of the area
associated with sillimanite gneiss (Fig. 4.16a). There are sporadic high values in the
western and central northern parts associated with metagranites in the western and
sillimanite gneiss in the central northern part. The map shows low values over
metagranites. The eU/K ratio map shows high values over metagranites in the central
and eastern parts of the area (Fig. 4.16b). The high values, however, go beyond the
metagranites boundaries as indicated on the geological map. The eTh/K ratio map
shows high values in the central part and eastern parts underlain by metagranites (Fig.
4.16c). It also outlines a different boundary from that shown in the geological map but
same as that outlined by the eU/K ratio map. The ternary map outlined the same areas as
eU/K and eTh/K as those with high eU and eTh (Fig. 4.16d). The metagranites outlined
by the eU/K and eTh/K ratio grid maps were digitized and merged with the mapped
metagranites from the geological map.
Chapter 4
49
M
All.
Gn
m
m
All.
m
S
S
m
Hn
All-Alluvium
M-Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn-Granulite
Hn-Hornblende
(a) Ratio grids of eU and eTh, with eU as numerator and eTh as
denominator
M
All.
Gn
m
m
All.
S
m
Hn
S
m
All-Alluvium
M-Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn-Granulite
Hn-Hornblende
(b) Ratio grid of eU and K with eU as the numerator and K
as the denominator
Fig. 4.16. Ratio grids and a ternary map of K, Th and U used in the interpretation of granitic
terrains in the Lundazi area
50
Extraction of Spatial Indicative Features
M
All.
Gn
m
m
All.
m
S
S
m
Hn
All-Alluvium
M-Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn-Granulite
Hn-Hornblende
( C ) Ratio grid of eTh and K with eTh as the numerator and K
as the denominator
All.
M
Gn
m
m
All.
m
S
Hn
S
m
All-Alluvium
M-Migmatite gnss.
m- Metagranite
S- Sillimanite gnss.
Gn-Granulite
Hn-Hornblende
(d) Ternary map with K, Th, and U grids in red, green and blue respectively
Fig. 4.16. continued from page 52.
Chapter 4
51
4.5 Conclusions
Six geological features were extracted from the geological map, ASTER imagery,
geochemical data and radiometric data (metagranites, circular features, lineaments,
shear zones, axial traces and PC2 scores).
An attempt to extract alterations of pegmatites into kaolinite by ASTER imagery was
unsuccessful. This can be attributed to small size (about 70 X 300 m) of the pegmatites
although initially it was thought it could be possible because the pegmatites occur in
clusters.
Siliceous rocks were interpreted using TIR from ASTER imagery. The mapped areas
are the same as those mapped as either metagranites or leucogneisses in the area. This
feature was, therefore, not considered any further.
Parental granites to aquamarine-bearing pegmatites below the current erosional levels
were extracted based on circular features resulting from their consolidation during
cooling.
Same geological features interpreted from different geodata sets (e.g. shear zones
interpreted from ASTER imagery and those interpreted from geological map and
aeromagnetics, (by ERIPTA) were merged to form a geological feature to be used in the
generation of a binary predictor pattern in the following chapter, chapter 5. The
predictor patterns were integrated to generate a predictive map for the occurrence of
aquamarine-bearing pegmatites. The procedure of integration of the binary patterns is
discussed in chapter 5 below.
Chapter 5: Spatial Data Analysis and Integration
5.1 Introduction
This chapter explains predictive mapping of zones with potential for the occurrence of
aquamarine-bearing granitic-pegmatites through integration of indicative geological
features discussed in chapter 4 in Lundazi area. The predictive models were generated
through a data-driven (weights of evidence) approach and through a knowledge-driven
(Fuzzy logic) method. The predictive modelling was performed in ILWIS.
In this research, the spatial data analysis and integration based on weights of evidence
and fuzzy logic methods were performed with raster maps with a pixel size of 100 X
100m. The choice of the pixel size depended on ensuring that each aquamarine-bearing
pegmatite occurrence occupies only one pixel, which is about the average size of the
aquamarine-bearing pegmatite occurrences in the study area.
5.2 Analysis using weights of evidence method
Weights of evidence is a quantitative method that was originally developed for a nonspatial application of medical diagnosis and was adapted in the late 1980s for mineral
potential mapping with GIS (Geographic Information Systems) by Frits Agterberg and
Graeme Bonham-Carter at the Geological Survey of Canada (Bonham-Carter et al.,
1989). Bonham-Carter et al. (1994) applied weights of evidence for potential mapping
for gold in the Nova Scotia. Carranza and Hale (1999) applied weights of evidence
method in predicting potential areas for gold deposits in the Baguio district of the
Philippines. Hale and Asadi (2001) applied the weights of evidence for mapping
potential gold and base metal mineralization in Takab area, Iran.
5.2.1
Creating binary predictor patterns
By weights of evidence modelling, the spatial associations between the indicative
geological features and the occurrences of aquamarine-bearing pegmatites are
quantified. To quantify spatial associations between indicative geological features and
occurrences of aquamarine-bearing pegmatites, each set of indicative geological feature
was buffered at some distance to create a binary map indicating presence or absence of
a geological feature. The probability of finding an occurrence of an aquamarine-bearing
pegmatite given the presence of an indicative geological feature can be estimated by the
equation
P{D B} =
P{B D}
P{D B}
= P{D}
P{B}
P{B}
(5.1)
where P{D/B} is the posterior probability of occurrence of an aquamarine-bearing
pegmatite given the presence of an indicative geological feature B, P{B/D} is the
posterior probability of the indicative geological feature B given the presence of an
aquamarine-bearing pegmatite occurrence, P{D} is the prior probability of an
occurrence of an aquamarine-bearing pegmatite and P{B} is the prior probability of the
indicative geological feature.
Chapter 5
53
Conversely, the posterior probability of finding an occurrence of an aquamarine-bearing
pegmatite given the absence of an indicative geological feature can be estimated by the
equation
P{D B} =
P{D B}
P{B}
= P{D}
P{B D}
P{B}
(5.2)
where P{D B} is the posterior probability of an occurrence of an aquamarine-bearing
_
pegmatite given the absence of an indicative geological feature B , P{B/ D} is the
−
posterior probability of the absence of an indicative geological
feature B given the
_
presence of aquamarine-bearing pegmatite occurrence, P{B} is the prior probability of
the absence of the indicative geological feature.
Equations 5.1 and 5.2 can also be expressed in an odds form, where odds is defined as
O = P /(1 − P) . Expressed in odds form, equations 5.1 and 5.2 become
O{D / B} = O{D}
P( B / D)
and
−
P( B / D)
(5.3)
−
−
O{D / B} = O{D}
P ( B/ D )
.
−
P( B)
(5.4)
In weights of evidence modeling, the natural logarithm of both sides of equations 5.3
and 5.4 are taken. The positive weight of evidence is then defined as
−
W + = loge P{B / D} / P{B / D} ,
(5.5)
and the negative weight of evidence is defined as
−
−
−
W − = loge P{B/ D} / P{B/ D}
(5.6)
where W+ and W- are weights of evidence in the presence and in the absence of the
binary pattern, respectively.
The variances of the positive and negative weights of evidence are calculated as below
(Bonham-Carter, 1989):
1
1
+
−
N {B D} N {B D
}
1
1
+
s 2 (W _ ) = _
_
_
N {B D} N {B D}
s 2 (W + ) =
The contrast C and its standard deviation s(C) are defined as below.
(5.7)
(5.8)
54
C =W + −W −
s (C ) = s 2 (W + ) + s 2 (W − )
Spatial Data Analysis and Integration
(5.9)
(5.10)
The contrast C provides information about the spatial association between the
aquamarine-bearing pegmatite occurrences and the binary pattern of indicative
geological feature. If the value of C is positive, then there exists a positive spatial
association; if it is negative, there is a negative spatial association. The parameter that
aids in determining the statistical significance of the contrast and thus the optimal
spatial association is the studentized C (or sigC ), which is defined as
sigC = C / s (C ) .
(5.11)
The distance in which there is optimal spatial association between the aquamarinebearing pegmatites and a set of indicative geological feature was then used as a buffer to
create a binary predictor pattern of a particular set of indicative geological feature.
For the weights of evidence application in Lundazi area, six (6) evidential maps (axial
traces, circular features, lineaments, metagranites, Pc2 scores and shear zones) were
used. From a total number of 218 aquamarine-bearing pegmatite occurrences in the
area, 109 occurrences were used as training set in the generation of a predictive map
while the other 109 occurrences were used for validation (test set) of the predictive map.
In the tables below, ‘Npixcu’ and ‘Npixmocu’ mean the number of pixels in the
cumulative buffer zones and the number of aquamarine-bearing pegmatite occurrences
in the cumulative buffer zone, respectively; ‘Dist’ means cumulative distance.
Weights of evidence of circular features
Cumulative distances of 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000,
5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000, 10500, and 78040m
were used to create binary maps of circular features. Each binary map was crossed with
the training set of aquamarine-bearing pegmatite occurrences map to determine the
number of aquamarine-bearing pegmatite occurrences in the cumulative buffer zones.
The weights of evidence were then computed using equations 5.5 and 5.6. Table 5.1
shows the results of this analysis. The optimal spatial association occurs at a cumulative
distance of 1500m. Fig. 5.1 shows the resulting binary predictor pattern where zones
within 1500m of circular features are assigned a weight of 1.1068 and zones beyond
1500m of a circular feature were assigned a weight of –0.0796. The binary predictor
pattern of circular features has low predictive strength i.e., the ratio of the number of
deposits within a pattern indicating presence of a geological feature to the total number
of deposits used in the analysis is less than 0.5 (Carranza, 2002). In addition it can be
seen from Table 5.1 that although the contrast C is statistically significant (up to 400m),
the negative weights are not statistically different from zero. The low predictive strength
and the non-statistically significant negative weight of the binary predictor pattern
suggest that the binary predictor pattern is not a useful predictor of zones of
aquamarine-bearing pegmatites. This will be demonstrated later in the chapter.
Chapter 5
55
Table 5.1. Results of weights of evidence analysis of circular features.
Dist.(m) Npixcu Npixmocu
C
W + s(W +) W − s(W −)
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
7500
8000
8500
9000
9500
10000
10500
>10500
14495
28369
42080
55578
69641
83410
97920
112623
127877
143386
159366
175320
192191
209361
227158
245487
263939
281785
300875
320014
339543
1156119
4
8
12
12
13
15
15
17
18
18
20
20
20
22
22
23
25
26
26
30
34
109
1.0740
1.0956
1.1068
0.8286
0.6831
0.6458
0.4854
0.4706
0.4008
0.2863
0.2860
0.1906
0.0987
0.1085
0.0269
-0.0063
0.0046
-0.0216
-0.0871
-0.0057
0.0602
0.0000
0.5001
0.3536
0.2887
0.2887
0.2774
0.2582
0.2582
0.2426
0.2357
0.2357
0.2236
0.2236
0.2236
0.2132
0.2132
0.2085
0.2000
0.1961
0.1961
0.1826
0.1715
0.0958
-0.0248
-0.0514
-0.0796
-0.0674
-0.0649
-0.0732
-0.0596
-0.0671
-0.0633
-0.0481
-0.0544
-0.0383
-0.0209
-0.0257
-0.0067
0.0017
-0.0014
0.0069
0.0289
0.0022
-0.0262
0.0976
0.0995
0.1015
0.1015
0.1021
0.1031
0.1031
0.1043
0.1048
0.1048
0.1060
0.1060
0.1060
0.1072
0.1072
0.1078
0.1091
0.1098
0.1098
0.1125
0.1155
1.0987
1.1470
1.1864
0.8960
0.7479
0.7189
0.5449
0.5377
0.4640
0.3344
0.3404
0.2289
0.1196
0.1341
0.0336
-0.0080
0.0060
-0.0284
-0.1161
-0.0079
0.0864
s (C )
sigC
0.5100
0.3670
0.3060
0.3060
0.2960
0.2780
0.2780
0.2640
0.2580
0.2580
0.2470
0.2470
0.2470
0.2390
0.2390
0.2350
0.2280
0.2250
0.2250
0.2140
0.2070
2.1563
3.1225
3.8768
2.9279
2.5302
2.5858
1.9599
2.0362
1.7988
1.2964
1.3756
0.9250
0.4833
0.5619
0.1408
-0.0341
0.0263
-0.1264
-0.5166
-0.0368
0.4179
Fig.5.1. Binary predictor pattern of circular features buffered at 1500m and the training set of
aquamarine-bearing pegmatites occurrences.
56
Spatial Data Analysis and Integration
Weights of evidence of lineaments
Cumulative buffer distances of 500, 1000, 1500, 2000, 2500, 3000, 3500, and 61080m
were used to create binary maps of lineaments. Table 5.2 shows the results of the
analysis of the spatial association of cumulative distances from lineaments and the
training set of aquamarine-bearing pegmatite occurrences. The cumulative distance
within which there is optimal positive spatial association between the lineaments and
the aquamarine-bearing pegmatites is 2000m. Fig. 5.2 shows the resulting binary
predictor pattern where zones within 2000m of a lineament are assigned a weight of
0.2600 and zones beyond 2000m of a lineament are assigned a weight of –1.3584,
respectively.
Table 5.2. Results of weights of evidence analysis of lineaments.
s (W + )
W+
W − s(W −)
Dist.(m) Npixcu Npixmocu
500
1000
1500
2000
2500
3000
>3500
316607
558517
724351
826039
892117
937725
1156119
38
70
91
101
107
108
109
0.2414
0.2847
0.2871
0.2600
0.2407
0.2002
0.1752
0.1622
0.1195
0.1048
0.0995
0.0967
0.0962
0.0958
-0.1087
-0.3679
-0.8160
-1.3584
-2.5213
-3.0248
0.1187
0.1601
0.2357
0.3536
0.7071
1.0000
C
0.3501
0.6526
1.1031
1.6184
2.7620
3.2250
s (C )
0.2010
0.2000
0.2580
0.3670
0.7140
1.0050
sigC
1.7421
3.2668
4.2766
4.4060
3.8701
3.2102
Fig. 5.2. Binary predictor pattern of lineaments buffered at 2000m and the training set of
aquamarine-bearing pegmatite occurrences.
Chapter 5
57
Weights of evidence of metagranites
The metagranites were buffered using cumulative distances of 500, 1000, 1500, 2000,
2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000,
9500, 10000, 10500, and 73955m. The resulting map was then crossed with training set
of the aquamarine-bearing pegmatite occurrences. From Table 5.3 three observations
can be made; at 500m cumulative distance there is negative spatial association between
the metagranites binary pattern and the aquamarine-bearing pegmatite occurrences,
from 500m to 3000m the positive contrast is not statistically significant implying nonsignificant positive spatial association between the binary pattern and the aquamarinebearing pegmatites, and from 3500m to 10500m the positive contrast C is statistically
significant implying a significant positive spatial association between the geological
feature and the aquamarine-bearing pegmatite occurrences. The cumulative distance
where there is optimal significant positive spatial association of the binary predictor
pattern and the training set of aquamarine-bearing pegmatite occurrences is 7000m
within which the metagranite binary pattern has high predictive strength (Table 5.3).
Table 5.3. Results of weights of evidence analysis of metagranites.
+
s(W + ) W −
s(W −)
Dist.(m) Npixcu Npixmocu W
500
189931
1000
220125
1500
249373
2000
275647
2500
301003
3000
325266
3500
348984
4000
371018
4500
392159
5000
411933
5500
431155
6000
448965
6500
466440
7000
482769
7500
498530
8000
513646
8500
528106
9000
541585
9500
555893
10000 569705
10500 583488
>10500 1156119
17
21
25
29
32
39
43
48
53
61
66
72
76
79
80
80
82
85
86
87
89
109
-0.0520
0.0118
0.0614
0.1097
0.1201
0.2404
0.2677
0.3164
0.3601
0.4515
0.4847
0.5312
0.5471
0.5514
0.5318
0.5020
0.4989
0.5096
0.4952
0.4823
0.4811
0.0000
0.2425
0.2182
0.2000
0.1857
0.1768
0.1601
0.1525
0.1443
0.1374
0.1280
0.1231
0.1179
0.1147
0.1125
0.1118
0.1118
0.1104
0.1085
0.1078
0.1072
0.1060
0.0958
0.0099
-0.0028
-0.0176
-0.0370
-0.0460
-0.1125
-0.1424
-0.1935
-0.2517
-0.3796
-0.4634
-0.5889
-0.6782
-0.7496
-0.7598
-0.7366
-0.7852
-0.8813
-0.9003
-0.9215
-0.9930
0.1043
0.1066
0.1091
0.1118
0.1140
0.1195
0.1231
0.1280
0.1336
0.1443
0.1525
0.1644
0.1741
0.1826
0.1857
0.1857
0.1925
0.2041
0.2085
0.2132
0.2236
C
-0.0619
0.0146
0.0790
0.1467
0.1661
0.3529
0.4101
0.5099
0.6118
0.8311
0.9481
1.1201
1.2253
1.3010
1.2916
1.2386
1.2841
1.3909
1.3955
1.4038
1.4741
s (C )
0.2640
0.2430
0.2280
0.2170
0.2100
0.2000
0.1960
0.1930
0.1920
0.1930
0.1960
0.2020
0.2080
0.2140
0.2170
0.2170
0.2220
0.2310
0.2350
0.2390
0.2470
sigC
-0.2345
0.0601
0.3468
0.6768
0.7896
1.7664
2.0925
2.6435
3.1924
4.3087
4.8376
5.5367
5.8771
6.0660
5.9587
5.7142
5.7866
6.0174
5.9454
5.8827
5.9571
The negative and the non-significant positive spatial association between the mapped
metagranites and aquamarine-bearing pegmatites are because of lack of aquamarinebearing pegmatites within about 3 kilometres from the mapped metagranites.
Geologically, this may be explained as follows. Pegmatites from a parental granitic
source form an aureole of progressively more highly fractionated pegmatites arranged in
a zoned pattern about the granitic source (Trueman 1982). The first few kilometres (in
this case about 3 kilometres) from the parental granitic source will be mainly ‘barren’
pegmatites. The highly fractionated pegmatites consolidate further away from the
58
Spatial Data Analysis and Integration
granitic source while the less fractionated pegmatites consolidate closer to the parental
granitic source. Since the aquamarine-bearing pegmatites are highly fractionated, they
occur further away from the granitic sources (Trueman 1982) and would thus exhibit a
lack of positive spatial association with the parental granitic sources (i.e., metagranites).
Fig. 5.3 shows the resulting binary predictor pattern where zones within 7000m are
assigned a wight of 0.5514 and those beyond 7000m of metagranites are assigned a
weight of 0.7496.
Fig. 5.3. Binary predictor pattern of metagranites buffered at 7000m and the training set of
aquamarine-bearing pegmatite occurrences.
Weights of evidence of axial trace
The axial traces were buffered using cumulative distances of 500, 1000, 1500, 2000,
2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000,
9500, 10000, 10500 and 88611m. Table 5.4 shows the results of the analysis of spatial
association between the mapped axial traces and the aquamarine-bearing pegmatites. It
can be observed from Table 5.4 that the mapped axial traces show statistically nonsignificant negative spatial association with the aquamarine-bearing pegmatite
occurrences. This is because not all geological maps in the study area had axial traces
indicated on them. These mapped geological features were not considered further in this
research because of the negative spatial association with the training set of aquamarinebearing pegmatite occurrences.
Chapter 5
59
Weights of evidence of shear zones
Cumulative distances of 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000,
5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000, 10500, and 57828m
were used to buffer the mapped shear zones. From Table 5.5 it can be seen that the
values of contrast C are negative and mostly statistically significant implying that a
negative spatial association exists between the aquamarine-bearing pegmatite
occurrences and the shear zones. The aquamarine-bearing pegmatite intrusions may not
have been controlled by the shear zones. This geological feature was not considered
further in this research because of the negative spatial association with the aquamarinebearing pegmatite.
Table 5.4. Results of weights of evidence analysis of axial traces
+
s(W + ) W −
s(W −)
Dist (m) Npixcu Npixmocu W
500
14041
1000
29523
1500
46040
2000
62886
2500
80946
3000
100121
3500
120786
4000
142019
4500
164695
5000
187858
5500
212151
6000
236903
6500
263173
7000
289788
7500
317251
8000
343655
8500
368659
9000
391792
9500
415174
10000 437193
10500 458049
>10500 1156119
2
4
6
6
6
7
8
10
12
14
17
19
24
25
29
32
33
38
39
40
43
109
0.4126
0.3626
0.3237
0.0119
-0.2405
-0.2990
-0.3531
-0.2919
-0.2577
-0.2352
-0.1626
-0.1617
-0.0333
-0.0888
-0.0309
-0.0124
-0.0519
0.0283
-0.0037
-0.0300
-0.0043
0.0000
0.7072
0.5000
0.4083
0.4083
0.4083
0.3780
0.3536
0.3162
0.2887
0.2673
0.2425
0.2294
0.2041
0.2000
0.1857
0.1768
0.1741
0.1622
0.1601
0.1581
0.1525
0.0958
-0.0063
-0.0115
-0.0160
-0.0007
0.0160
0.0242
0.0341
0.0348
0.0370
0.0399
0.0332
0.0378
0.0096
0.0280
0.0114
0.0052
0.0234
-0.0148
0.0020
0.0178
0.0028
0.0967
0.0976
0.0985
0.0985
0.0985
0.0990
0.0995
0.1005
0.1015
0.1026
0.1043
0.1054
0.1085
0.1091
0.1118
0.1140
0.1147
0.1187
0.1195
0.1204
0.1231
C
0.4189
0.3741
0.3397
0.0126
-0.2565
-0.3232
-0.3872
-0.3267
-0.2948
-0.2750
-0.1958
-0.1995
-0.0429
-0.1168
-0.0424
-0.0176
-0.0753
0.0432
-0.0057
-0.0478
-0.0071
s (C )
0.7140
0.5090
0.4200
0.4200
0.4200
0.3910
0.3670
0.3320
0.3060
0.2860
0.2640
0.2520
0.2310
0.2280
0.2170
0.2100
0.2080
0.2010
0.2000
0.1990
0.1960
sigC
0.5869
0.7343
0.8088
0.0300
-0.6107
-0.8271
-1.0541
-0.9847
-0.9633
-0.9605
-0.7417
-0.7902
-0.1856
-0.5127
-0.1956
-0.0837
-0.3612
0.2149
-0.0285
-0.2405
-0.0362
Weights of evidence of Pc2 scores
The scores ( ≥ 75th percentiles) of the second principal component representing a multielement geochemical evidence (see subsection 4.3.4) were buffered at cumulative
distances of 500,1000,1500,2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000,
6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000, 10500 and 67954m. Table 5.6 shows
that the values of contrast C are negative within 4000m and are positive and are mostly
statistically non-significant (i.e. C is less than 2) beyond 4000m implying a negative and
a lack of positive spatial association between the Pc2 scores (>75th percentile) and the
aquamarine-bearing pegmatite occurrences. Because of this, the mapped geochemical
evidence of PC2 scores was not considered useful for predictive mapping of zones with
potential for aquamarine-bearing pegmatites.
60
Spatial Data Analysis and Integration
Table 5.5. Results of weights of evidence analysis of shear zones
+
s(W + ) W −
s(W −)
Dist. (m) Npixcu Npixmocu W
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
7500
8000
8500
9000
9500
10000
10500
>10500
201688
382390
538847
653074
748082
826585
887410
934529
975113
1007841
1035155
1056526
1074517
1087777
1097584
1104609
1110330
1115259
1120158
1124261
1128132
1156119
11
27
38
49
52
55
57
61
65
67
72
76
80
83
87
88
89
90
92
94
96
109
-0.5474
-0.2891
-0.2904
-0.2284
-0.3048
-0.3485
-0.3838
-0.3677
-0.3467
-0.3494
-0.3042
-0.2705
-0.2361
-0.2116
-0.1735
-0.1684
-0.1623
-0.1556
-0.1380
-0.1201
-0.1025
0.0000
0.3015
0.1925
0.1622
0.1429
0.1387
0.1348
0.1325
0.1280
0.1240
0.1222
0.1179
0.1147
0.1118
0.1098
0.1072
0.1066
0.1060
0.1054
0.1043
0.1031
0.1021
0.0958
0.0853
0.1170
0.1988
0.2351
0.3932
0.5528
0.7191
0.8318
0.9471
1.1001
1.1769
1.2569
1.3269
1.3950
1.3829
1.4642
1.5332
1.5958
1.6123
1.6082
1.5947
C
0.1010
0.1104
0.1187
0.1291
0.1325
0.1361
0.1387
0.1444
0.1508
0.1543
0.1644
0.1741
0.1857
0.1962
0.2132
0.2183
0.2237
0.2295
0.2426
0.2583
0.2774
-0.6327
-0.4061
-0.4892
-0.4635
-0.6980
-0.9013
-1.1029
-1.1995
-1.2938
-1.4495
-1.4811
-1.5274
-1.5630
-1.6066
-1.5564
-1.6327
-1.6955
-1.7513
-1.7502
-1.7283
-1.6972
s (C )
0.3180
0.2220
0.2010
0.1930
0.1920
0.1920
0.1920
0.1930
0.1950
0.1970
0.2020
0.2080
0.2170
0.2250
0.2390
0.2430
0.2480
0.2530
0.2640
0.2780
0.2960
sigC
-1.9898
-1.8300
-2.4339
-2.4068
-3.6389
-4.7051
-5.7497
-6.2162
-6.6269
-7.3643
-7.3211
-7.3261
-7.2108
-7.1457
-6.5221
-6.7207
-6.8493
-6.9346
-6.6278
-6.2143
-5.7417
Table 5.6. Results of weights of evidence analysis of Pc2scores
Dist.(m) Npixcu Npixmocu
500
144702
10
1000
187855
17
1500
229088
20
2000
266825
22
2500
302583
25
3000
334673
30
3500
366659
32
4000
397312
35
4500
427015
42
5000
455014
44
5500
481564
49
6000
505406
52
6500
528297
55
7000
550255
58
7500
571971
61
8000
593246
63
8500
614190
66
9000
633742
66
10000
671830
74
10500
689985
76
>10500 1156119
109
W
+
-0.3106
-0.0410
-0.0769
-0.1341
-0.1320
-0.0505
-0.0772
-0.0679
0.0423
0.0253
0.0763
0.0874
0.0992
0.1115
0.1233
0.1190
0.1308
0.0995
0.1555
0.1555
0.0000
+
s (W )
0.3162
0.2425
0.2236
0.2132
0.2000
0.1826
0.1768
0.1690
0.1543
0.1508
0.1429
0.1387
0.1348
0.1313
0.1280
0.1260
0.1231
0.1231
0.1163
0.1147
0.0958
W
−
0.0375
0.0078
0.0181
0.0370
0.0429
0.0199
0.0339
0.0338
-0.0256
-0.0168
-0.0582
-0.0735
-0.0918
-0.1134
-0.1375
-0.1429
-0.1725
-0.1357
-0.2659
-0.2865
−
s (W ) C
0.1005
0.1043
0.1060
0.1072
0.1091
0.1125
0.1140
0.1163
0.1222
0.1240
0.1291
0.1325
0.1361
0.1400
0.1443
0.1474
0.1525
0.1525
0.1690
0.1741
-0.3481
-0.0488
-0.0950
-0.1711
-0.1749
-0.0704
-0.1111
-0.1017
0.0679
0.0421
0.1345
0.1609
0.1910
0.2249
0.2608
0.2619
0.3033
0.2352
0.4214
0.4420
s (C )
0.3320
0.2640
0.2470
0.2390
0.2280
0.2140
0.2100
0.2050
0.1970
0.1950
0.1930
0.1920
0.1920
0.1920
0.1930
0.1940
0.1960
0.1960
0.2050
0.2080
sigC
-1.0492
-0.1849
-0.3839
-0.7170
-0.7677
-0.3282
-0.5281
-0.4957
0.3450
0.2156
0.6984
0.8388
0.9971
1.1717
1.3521
1.3506
1.5476
1.2001
2.0541
2.1200
Chapter 5
5.2.2
61
Combining binary predictor patterns
In weights of evidence modeling, two or more (j = 1,2,.,.,n) binary predictor patterns
are combined to generate a map of posterior odds based on the equation
{D B1k
B2k
Bnk =
B3k
n
j =1
W jk + ln O{D}
(5.12)
where k is positive (+) or negative (-) if the binary predictor pattern is present or absent,
respectively.
Prior to combining binary predictor patterns of indicative geological features to generate
a predictive map of aquamarine-bearing pegmatite occurrences, the binary predictor
patterns should be tested for conditional independence between one another with respect
to aquamarine-bearing pegmatites. If the binary predictor patterns are not conditionally
independent, the resultant map will either over- or under-estimate the posterior odd (or
probability) of an aquamarine-bearing pegmatite occurrence. If two or more binary
predictor maps are conditionally independent, then
N {B1
B2
D} =
N {B1 D}N {B2
D}
N {D}
(5.13)
where the left hand side of the equation is the observed number of aquamarine-bearing
pegmatite occurrences in the overlap region where both two binary predictor patterns
are present. The right hand-side is the predicted number of aquamarine-bearing
pegmatite occurrences. To test the conditional independence of two binary predictor
patterns, a contingency calculated table is used. The chi-square value is then calculated
as below and compared with critical chi-square value to test the hypothesis of
conditional independence.
χ
2
=
4
(observed i − predicted
predicted i
i =1
i)
2
(5.14)
In mineral potential mapping, where the prior probability of a deposit is assumed to be
the average known deposit point density, a simple overall test of conditional
independence can be applied by determining the total number of predicted deposit
points. This test can be used after combining the binary predictor patterns. If the total
predicted number of deposits is much larger (i.e., >10%) than the total observed number
of deposits, then the assumption of conditional independence is violated (BonhamCarter, 1994). The total number of predicted mineral deposits N{D}pred is determined by
summing up the product of the area of unit cells, N{A}, and their calculated posterior
probabilities, P, as the equation below.
N{D}pred =
m
k =1
Pk N{A}k
(5.15)
62
Spatial Data Analysis and Integration
The binary predictor patterns generated by weights of evidence in sub-section 5.2.1 and
considered important for predictive mapping for zones with potential for aquamarinebearing pegmatites are those of the metagranites, circular features and lineaments. This
consideration was based on the value of studentized C. The value of studentized C
should be positive and statistically significant, which indicate a significant positive
spatial association. These binary predictor patterns are ranked in Table 5.7 based on the
magnitude of the value of studentized C, which indicates the strength of their spatial
association with the aquamarine-bearing pegmatites.
Table 5.7. Ranking of binary predictor patterns based on the value of studentized C.
Binary predictor patterns
Metagranites
Lineaments
Circular features
Optimal spatial association
distance (m)
7000
2000
1500
W
+
0.5514
0.2600
1.1068
W
−
-0.7496
-1.3584
-0.0796
sigC
6.0660
4.4060
3.8768
Prior to integrating the binary predictor patterns with significant positive spatial
associations with the aquamarine-bearing pegmatite occurrences, a pairwise chi-square
test of conditional independence was conducted using equation 5.14 and the results
compared with tabled χ2. Table 5.8 is the matrix of the χ2 values for the pairs of binary
predictor patterns. Values less than the critical χ2 value of 3.841 at the 95% significance
level with 1 degree of freedom indicate map pairs for which the null hypothesis of
conditional independence is not rejected (Davis, 1973). All the binary predictor map
pairs show they are conditionally independent.
Table 5.8. Calculated χ2 values for pairwise test for conditional independence between binary
predictor maps with statistically significant positive spatial association with the aquamarine–
bearing pegmatite occurrences.
Metagranites
Circular features
Lineaments
0.03
1.72
Circular features
2.49
The conditionally independent binary predictor patterns were combined to generate a
predictor map by applying equation (5.12), which can also be written as:
predic.map=exp(-9.27212+LBR+MBR+CBR)/(1+exp((-9.27212+LBR+MBR+CBR))
(5.16)
where predic. map is the resulting posterior probability map while LBR, MBR and CBR
are binary predictor patterns for lineaments, metagranites and circular features,
respectively. The value -9.27212 is the loge O{D} based on the prior probability of
0.0001.
Fig. 5.4 shows the classified posterior probability map generated by combining the
CBR, LBR and MBR binary predictor maps. The map was classified considering
posterior probability values less than 0.0001 (i.e., the prior probability of the training set
of aquamarine-bearing pegmatite occurrences) as unfavorable, values from 0.00010.0002 as favorable and values between 0.0002-0.0004 as very favorable. This map
indicates that about 37% of the study area is favorable to very favorable for
aquamarine-bearing pegmatite occurrence. The probabilistic map delineates correctly
70% and 73% of training and test sets of aquamarine-bearing pegmatites, respectively.
Chapter 5
63
Fig. 5.4. Classified posterior probability map based on combining binary predictor patterns of
circular features, metagranites and lineaments.
Validation of Predictive maps
5.2.3
The classified map in Fig. 5.4 was validated by conducting an overall test of conditional
independence according to equation 5.15, which indicates the statistical validity of the
posterior probabilistic map. The overall test of conditional independence reveals that,
although there was lack of conditional dependence during pairwise test, the assumption
of conditional independence is violated. The estimated number of training set of
aquamarine-bearing pegmatites is greater by 11% than the observed number of training
set of aquamarine-bearing pegmatite occurrences (Table 5.9). The classified
probabilistic map in Fig. 5.4 was therefore considered not statistically valid based on
over-predicting the number of aquamarine-bearing pegmatite occurrences in the training
set. One of the binary predictor patterns was then eliminated each time to generate
predictive maps based on two binary predictor patterns and to determine the best
predictive map. The procedures that followed were:
•
•
Performing an overall test of conditional independence, according to equation
5.15, to determine which of the predictive maps least violates the overall
conditional independence rule. Maps that violate the overall conditional
independence rule (by either over- or under-estimating the training set of
aquamarine-bearing pegmatite occurrences) were considered non-valid.
Determining the percentage area covered by favorable to very favorable zones
(areas with posterior probabilities greater than the prior probability). In nature,
potentially mineralized areas are rare and thus a smaller percentage of potential
zones for mineralization is appropriate for recommendations for further work.
64
Spatial Data Analysis and Integration
•
Determine the percentage of the training and test sets of aquamarine-bearing
pegmatite occurrences delineated correctly by the each of the generated
predictive maps.
Figs. 5.5, 5.6 and 5.7 show the classified posterior probabilistic maps generated by
combining LBR and MBR, CBR and LBR, and CBR and MBR, respectively, which
were classified in the same way as the classified map in Fig. 5.4. Table 5.9 shows the
results of overall test for conditional independence for each posterior probability map
prior to classification. From Table 5.9 it can be seen that the posterior probabilistic
maps classified into Figs. 5.5, 5.6, and 5.7 do not violate the overall test of conditional
independence because the predicted number of training set of aquamarine-bearing
pegmatites is not greater by 10% than the number of aquamarine-bearing pegmatites
used for training.
Table 5.10 is the summary of the results of validation of the classified posterior
probabilistic maps based on the last two validation methods above. From Table 5.10,
the classified predictive map in Fig. 5.5 correctly delineates about 73% of the training
and about 79% of the test sets of aquamarine-bearing pegmatite occurrences. This
predictive map compared to the other maps delineates the least percentage (36%) of
favorable and very favorable zones for aquamarine-bearing pegmatite occurrences.
Based on the results of overall test of conditional independence and based on the
percentages of correctly delineated training and test sets of aquamarine-bearing
pegmatite occurrences, the classified predictive map in Fig. 5.5 was considered optimal
predictive map of zones with potential for aquamarine-bearing pegmatites in the
Lundazi area.
Table 5.9. Results of overall test of conditional independence of posterior probability maps (by
equation 5.15).
Posterior probability map
LBR+MBR+CBRa
LBR+MBRb
CBR+LBRc
CBR+MBRd
a
c
classified into map in Fig. 5.4;
classified into map in Fig. 5.6;
Predicted number of training set of
aquamarine occurrences
121
118
110
111
b
d
% larger than the number of training
set of aquamarine occurrences
11
9
1
2
classified into map in Fig. 5.5
classified into map in Fig. 5.7
Table 5.10. Results of the validation of classified posterior probability maps
Posterior probability map
a
CBR+LBR+MBR
LBR+MBRb
CBR+LBRc
CBR+MBRd
a
c
% favorable and very
favorable zones
37
36
66
42
classified into map in Fig. 5.4; b classified into map in Fig. 5.5
classified into map in Fig. 5.6; d classified into map in Fig. 5.7
% of training set
delineated
70
73
93
67
% of test set delineated
73
79
90
72
Chapter 5
65
Fig. 5.5. Classified posterior probability map based on combining binary predictor patterns of
metagranites and lineaments.
Fig. 5.6. Classified posterior probability map based on combining binary predictor patterns of
circular features and lineaments.
66
Spatial Data Analysis and Integration
Fig. 5.7. Classified posterior probability map based on combining binary predictor patterns of
circular features and metagranites.
5.2.4
Discussion of classified weights of evidence predictive map
The weights of evidence analysis of the spatial relationships between the aquamarinebearing pegmatites and the indicative geological features in the area have shown that
only lineaments, metagranites and circular features have positive spatial association
with aquamarine-bearing pegmatites. The best predictive map was generated from
integration of binary pattern maps generated from lineaments and metagranites
(classified map in Fig. 5.5). This predictive map predicts 73% and 79% of the training
and test sets of aquamarine-bearing pegmatites, respectively. This predictive map does
not, however, predict the training set and test set of aquamarine-bearing pegmatites in
the northern part, which lie within 6000m proximity of mapped granitic gneisses. This
implies that the mapped granitic gneisses could be metagranites.
5.3 Analysis by Fuzzy logic method
In fuzzy set theory, a fuzzy set is a subset of objects whose membership in a set of
objects is intermediate between complete membership and non-complete membership
(Zadeh, 1965). Fuzzy membership values are chosen, based on subjective judgment, to
reflect the degree of membership of a set. The fuzzy membership values always lie in
the range of (0,1). The degree of membership is large (classically equal to 1) for
objects that completely belong to fuzzy set and it is small (classically equal to 0) for
objects that do not completely belong to fuzzy set. The membership always relates to a
certain proposition, which in mineral exploration is “favorable location for a mineral
deposit”.
Chapter 5
67
An example of a set used in mineral exploration is the set of distances from curvi-linear
or point geological features, for example lineaments (Carranza, 2002). Employing the
fuzzy theory introduced by Zadeh (1965), the class “favorable distance”, d, translates
into a series of measures such that:
d = {(x, µd (x)) x∈X}
(5.17)
where µd(x) defines a grade of membership of x distance in a class “favorable distance”.
Set-theoretic operations can be performed on fuzzy sets, including equality,
containment, union, and intersection. An et al. (1991) discussed five fuzzy operators
that can be used to combine exploration datasets; fuzzy AND, fuzzy OR, fuzzy
algebraic sum, fuzzy algebraic product and fuzzy gamma operator.
The fuzzy AND operation is equivalent to the Boolean AND (logical intersection)
operation on classical set values of 1 and 0. It is expressed as
µcombination = MIN(µA, µB, µC, ., ., .,)
(5.18)
where µA is the fuzzy membership value for map A, µB is the fuzzy membership value
for map B, µC is the fuzzy membership value for map C, and so on, at a particular
location. The minimum fuzzy membership value occurring at each location controls the
output map of such an operation. The AND operator is appropriate for application in
cases where two or more evidences for the hypothesis must be present together for the
hypothesis to be valid.
The fuzzy OR is like the Boolean OR (logical union). The maximum fuzzy membership
value of any of the input maps for any particular location controls the output
membership values. The fuzzy OR is expressed as
µcombination = MAX(µA, µB, µC, ., ., .,).
(5.19)
This operator can be appropriate for mineral potential mapping where favorable
evidences for the occurrence of mineralization are rare and the presence of any evidence
may be sufficient to suggest favorability.
The fuzzy algebraic product is defined as
µ combination
n
=Πµ
i =1
i
(5.20)
where µi are the fuzzy membership values for the i-th (i=1, 2,.,.,.,.,.,n) maps that are to
be combined. The resulting fuzzy membership values by using this operator tend to be
smaller than or equal to the smallest contributing fuzzy membership value (due to
multiplication of several numbers less than 1) and is thus “decreasive”.
The fuzzy algebraic sum operator is defined as
68
Spatial Data Analysis and Integration
n
µ combination = 1 − Π (1 − µ i )
(5.21)
i =1
The resulting fuzzy membership value for this operator is either larger than or equal to
the largest fuzzy membership value of the input maps and is thus “increasive”. Two or
more pieces of evidence that both favor the hypothesis tend to reinforce each other.
The fuzzy gamma operator is defined as
n
1−γ
n
γ
µ combination = (Π µ i) (1 − Π (1 − µ i)) .
i =1
(5.22)
i =1
The fuzzy gamma operator is a combination of the fuzzy algebraic sum and the fuzzy
algebraic product, where gamma is a parameter chosen in the range of (0,1).
A combination of two or more fuzzy sets by using any of the fuzzy operators results
into a fuzzy set. In this research, fuzzy sets of favorable distances to lineaments, fuzzy
sets of favorable distance to parent lithological units (i.e., metagranites) and favorable
distances to circular features were combined to produce a fuzzy set (a map) of zones
with potential for aquamarine-bearing pegmatite occurrence.
5.3.1
Creating fuzzy predictive patterns
The results generated from the spatial association analysis by weights of evidence (see
section 5.2.1) were used to generate fuzzy sets of proximity to indicative geological
features (circular features, metagranites, and lineaments) with significant positive
spatial association with the training set of the aquamarine-bearing pegmatite
occurrences. Fuzzy membership values were assigned to these fuzzy sets of proximity
distances based on subjective judgement. Shown in Table 5.11 are the fuzzy
membership values for different distance classes for the different indicative geological
features.
Table 5.11. Fuzzy scores and distance classes for different geological features.
Metagranite
Distance
Fuzzy score
Class (m)
<500
0.1
500-1000
0.2
1000-1500
0.3
1500-3500
0.4
3500-7000
0.9
7000-9000
0.8
9000-10500
0.7
10500-73955
0.6
>73955*
0.5
Circular features
Distance class
Fuzzy
(m)
score
<500
0.8
500-1500
0.9
1500-3000
0.8
3000-4000
0.7
4000-6000
0.4
6000-7500
0.3
7500-9000
0.1
9000-78040
0.1
>78040*
0.1
Lineaments
Distance class Fuzzy score
(m)
<500
0.6
500-1000
0.7
1000-1500
0.8
1500-2000
0.9
2000-4000
0.8
4000-6000
0.4
6000-8000
0.3
8000-61119
0.2
>61119*
0.1
* distance of aquamarine-bearing pegmatite occurrence farthest to a particular indicative geological feature.
Increasing distances up to 3500m of metagranites were assigned increasing fuzzy scores
of 0.1 to 0.4, based on the increasing but non-significant positive spatial association
between the aquamarine-bearing pegmatite occurrences and the metagranites within this
Chapter 5
69
distance (see Table 5.3). The distance class from 3500 to 7000m (where there is optimal
positive spatial association) were assigned a fuzzy score of 0.9. The distance classes
from 7000 to 10500m were assigned decreasing fuzzy scores of 0.8 to 0.5, based on
significant (yet generally decreasing) positive spatial association between the
aquamarine-bearing pegmatite occurrences and the metagranites. The distance class
farthest from metagranites was assigned fuzzy score of 0.5.
Fuzzy sets of increasing distances within the range of optimal positive spatial
association with circular features (i.e., up to 1550m; see Table 5.1) were assigned
increasing fuzzy scores of 0.8 to 0.9. The fuzzy sets of increasing distance beyond the
range of optimal spatial association up to 4000m were assigned decreasing values of 0.8
to 0.7 while those beyond 4000m were assigned decreasing values of 0.4 to 0.1.
Fuzzy sets of increasing distances within the range of significant positive optimal
spatial association to lineaments (i.e., up to 2000m; see Table 5.2) were assigned
increasing values of 0.6 to 0.9. The distance class 2000 to 4000m were assigned a fuzzy
value of 0.8 while those beyond 4000m were assigned decreasing fuzzy values of 0.4 to
0.1.
5.3.2
Combining fuzzy predictor patterns
The fuzzy sets of proximity classes to the indicative geological features (i.e.,
metagranites, circular features, lineaments) were combined in two steps to represent two
intermediate hypotheses. First, the metagranites were hypothesized to be the relatively
older pegmatite-related intrusives in the area and the intrusions were controlled by
lineaments. Second, granitic bodies causing circular features during consolidation
intruded along the peripherals of the metagranites. The late granites solidified below the
current erosional level and were more differentiated than the earlier metagranite bodies
and had more volatiles and REE, which were injected into fractures (lineaments)
forming aquamarine-bearing pegmatites.
The two intermediate steps of combining the fuzzy sets are as shown in the schematic
inference network in Fig. 5.8. The fuzzy sets of proximity to metagranites and
lineaments were combined first through various fuzzy operators to derive an output
fuzzy map of evidence of geological environment favorable for intrusions of highly
differentiated granitic bodies (i.e., parent rocks of aquamarine-bearing pegmatites).
Next, the resulting fuzzy evidence for environments for intrusion of highly
differentiated granites and the fuzzy evidence of circular features (representing buried
highly differentiated granites) were combined through various fuzzy operators to
generate a fuzzy predictive map of zones favorable for aquamarine-bearing pegmatites.
Several ‘intermediate hypothesis’ maps were generated using different fuzzy operators
to obtain an optimum predictive map based on three criteria. First, the favorable zones
depicted by ‘intermediate hypothesis’ maps are characterized by a fuzzy score of 0.60.
This criterion is based on expectation of fuzzy scores of 0.63 (i.e., 0.7 x 0.9). The
second criterion is that the favorable zones outlined by both the ‘intermediate
hypothesis’ maps and the final predictive map delineates at least 60% of the training set
of aquamarine-bearing pegmatites. The second criterion is to give both the ‘intermediate
hypothesis’ and final predictive maps high predictive strength. The third criterion is that
70
Spatial Data Analysis and Integration
the favorable zones cover at most 30% of the total area. The third criterion is based on
the knowledge that potentially mineralized zones occupy a relatively small percentage
of a certain study area.
After conducting several experimental combinations of fuzzy operators based on the
schematic inference network in Fig. 5.8, the optimum predictive map based on the three
criteria above was the one generated by the inference network in Fig. 5.9. The best
‘intermediate hypothesis’ map was obtained by combining the fuzzy sets of proximity
to mapped metagranites and to lineaments through fuzzy γ operator with γ=0.72. The
‘intermediate hypothesis’ map delineates about 28% of the area as favorable zones for
aquamarine-bearing pegmatite occurrences. The final fuzzy predictive map was
generated by combining the fuzzy set of the ‘intermediate hypothesis’ map and the
fuzzy map of proximity to circular features using a fuzzy γ operator with γ=0.9. The
fuzzy predictive map was classified into a binary favorability map by considering fuzzy
score <0.60 as unfavorable and fuzzy score 0.60 as favorable. This is based on the
expectation of fuzzy scores of 0.63 (i.e., 0.7 x 0.9). The final fuzzy classified predictive
map in Fig. 5.10 indicates that about 29% of the area is favorable for the occurrence of
aquamarine-bearing pegmatites. This predictive map, however, delineates only 59% of
the training set of aquamarine-bearing pegmatite occurrences.
Metagranites
Lineaments
Fuzzy operator
Favorable zones for
intrusion of highly
differentiated granites
Explanation
Circular
features
Fuzzy operator
Input map
Intermediate map
Final map
Favorable zones for aquamarinebearing pegmatites
Fig. 5.8. Schematic inference network for generation of predictive map for
aquamarine-bearing pegmatites, Lundazi area.
5.3.3
Validation of fuzzy predictive map
The classified final fuzzy predictive map (Fig. 5.10) was validated using the test set of
aquamarine-bearing pegmatites to determine whether the predictive map can predict
unknown aquamarine-bearing pegmatite occurrences. The predictive map correctly
delineates 57% of the aquamarine-bearing pegmatite occurrences in the test set. This
Chapter 5
71
predictive map should, therefore, be treated with caution because of the low percentage
(57%) of correctly delineated aquamarine-bearing pegmatites occurrence in the test set.
Lineaments
Metagranite
Fuzzy operator
gamma=0.72
Favorable zones for
intrusion of highly
differentiated granites
Circular
features
Fuzzy operator
Gamma=0.90
Favorable Zones for aquamarinebearing pegmatites
Fig. 5.9. Inference network used in the generation of the best predictive map for
aquamarine-bearing pegmatites in Lundazi area.
Fig. 5.10. Classified final fuzzy predictive map.
72
5.3.4
Spatial Data Analysis and Integration
Discussion of classified fuzzy predictive map
The best fuzzy predictive map was found to be the one generated by integrating
‘intermediate hypothesis’ map with fuzzy map of proximity to circular features by fuzzy
γ operator with γ=0.9. This predictive map has low predictive rate and it also does not
predict the aquamarine-bearing pegmatites in the northern part of the area. The
aquamarine-bearing pegmatites in the northern part are unpredicted for the same reason
as that given in subsection 5.2.4.
5.4 Conclusion
Two methods have been used to generate predictive maps for aquamarine-bearing
pegmatite occurrences in Lundazi area; the weights of evidence (data-driven) modeling
and the fuzzy logic (knowledge driven) approach.
The weights of evidence method adopts a spatial empirical approach, where the known
aquamarine-bearing pegmatite occurrences in the study area are used to quantify the
spatial associations between the indicative geological features and the aquamarinebearing pegmatite occurrences. This approach involves thresholding of maps of
distances from geological features into binary maps using distances within which there
is optimal positive spatial association between indicative geological feature and
aquamarine-bearing pegmatite occurrences. These binary maps were used as predictors
of aquamarine-bearing pegmatites in the study area.
The application of weights of evidence in predictive mapping of aquamarine-bearing
pegmatites in Lundazi area has shown that the mapped axial traces with NE trends have
no spatial association with the aquamarine-bearing pegmatites. The mapped shear zones
also show negative spatial association with the aquamarine-bearing pegmatites. The
>75th percentile PC2 scores generally show negative and lack of positive spatial
association with the aquamarine-bearing pegmatite occurrences. The PC2 scores are
probably inadequate as geochemical evidence of parental granitic bodies of the
aquamarine-bearing pegmatites.
Binary predictor patterns of three geological features (lineaments, circular features and
metagranites) were used to generate aquamarine-bearing pegmatite predictive maps.
The optimum predictive map was obtained through binary predictor patterns of the
lineaments and the metagranites. The binary predictor pattern of the circular features
proved to be not useful, as already suggested earlier. The ASTER imagery data may not
have been adequate to delineate all circular features for the predictive modeling. The
classified probabilistic map derived by combining the binary predictor patterns of
lineaments and metagranites indicate that 36% of the study area has potential for
aquamarine-bearing pegmatite occurrences. It correctly delineates 73% and 79% of the
training and test sets of aquamarine-bearing pegmatite occurrences, respectively. This
classified predictive map should be treated with caution because it is based on only two
predictor geological features, which may not be the case.
The fuzzy logic method is a knowledge-driven approach that utilizes ‘expert’
knowledge and conceptual deposit models to generate a predictive map of mineral
potential. The optimal fuzzy predictive map was generated based on the following
Chapter 5
73
hypothesis. The metagranites are hypothesized to be relatively older granitic bodies
related to aquamarine-bearing pegmatites in the area. These metagranites intruded
through weaker zones provided by fractures (lineaments). A later phase of granitic
bodies intruded along the peripherals of the earlier metagranites. This later phase (late
granites) of intrusions consolidated below the current erosional levels, forming circular
features during cooling, and was more differentiated than the metagranites. The late
granites were also enriched in REE, volatiles, silica and other elements, which led to
formation of aquamarine-bearing pegmatites. The fuzzy predictive map generated based
on this hypothesis indicates that about 29% of the study area has potential for
aquamarine-bearing pegmatites but correctly delineates only 59% and 57% of the
training and test sets of aquamarine-bearing pegmatites, respectively.
The predictive maps generated by the two predictive methods (Fig.5.5 and 5.10) define
almost the same area and both do not predict most of the aquamarine-bearing
pegmatites in the northern part of the area. The notable aspect about the aquamarinebearing pegmatite occurrences that are not delineated correctly by both predictive maps
is that they occur within a distance of 6000m of mapped granitic gneisses in the
northern part of the area. This implies that the mapped granitic gneisses could be
metagranites.
The optimum predictive map generated by weights of evidence modeling demarcates
larger potential zones for aquamarine-bearing pegmatite occurrences and correctly
delineates higher percentages of the training and test sets of aquamarine-bearing
pegmatite occurrences. The optimum fuzzy predictive map, on the other hand,
demarcates smaller potential zones for aquamarine-bearing pegmatites and correctly
delineates lower percentages of the training and test sets of aquamarine-bearing
pegmatites. However, the fuzzy predictive map was considered the more appropriate
predictive map for two reasons. First, it is a result of combining three indicative
geological features making it more reliable than the predictive map generated by the
weights of evidence modeling. Second, it represents a slightly higher chance of finding
an undiscovered aquamarine-bearing pegmatite in the predicted potential zones based
on the ratio of the number of pixels of occurrences delineated correctly to the number of
pixels of potential zones. For the weight of evidence predictive map (Fig.5.5), this
ration is 0.00017; for the fuzzy predictive map (Fig. 5.10), it is 0.00019.
Chapter 6: Conclusions and Recommendations
6.1 Conclusions
1. Artisanal and commercial workings on aquamarine-bearing pegmatites in the study
area indicate the potential contribution of this mineral commodity to the economic
development of Zambia.
2. Inasmuch as the aquamarine-bearing pegmatites in Zambia are less studied, this
study was conducted in response to a need for an exploration model for the
aquamarine-bearing pegmatites in order that guidelines for its exploration can be
established.
3. The geological characteristics of aquamarine-bearing pegmatites in general and the
geological characteristics of the aquamarine-bearing pegmatites in the study area
were used as a basis for the conceptual exploration model implemented in the GISbased predictive mapping of zones with potential for the mineral commodity under
study.
4. ASTER imagery was useful in delineating lineaments (fractures) not previously
mapped in the area.
5. Spatial analysis of the relationships between the mapped geological features and the
known locations of aquamarine-bearing pegmatite workings assist to determine the
adequacy/inadequacy of available geological information in the GIS-based
predictive mapping of zones with potential for the aquamarine-bearing pegmatites.
6. The application of weights of evidence modeling in the spatial analysis has shown
that the mapped axial traces with NE trends and the mapped shear zones have
negative spatial association with the aquamarine-bearing pegmatites. This implies,
however, that the axial traces and the shear zones were inadequately mapped rather
than having no geological significance in the emplacement of the pegmatites.
7. The >75th percentile PC2 scores of the spatially interpolated stream sediments data
(that was interpreted as geochemical indicator for granitic zones) generally show
negative and lack of positive spatial association with the aquamarine-bearing
pegmatite occurrences. The PC2 scores are probably inadequate as geochemical
evidence of parental granitic bodies of the aquamarine-bearing pegmatites. This
further implies that the stream sediments geochemical data was inadequate for
delineating indicators of aquamarine-bearing pegmatites.
8. Weights of evidence modeling has shown that the metagranites, lineaments
(fractures) and circular features (representing sub-outcropping granitic intrusions)
have positive spatial association with aquamarine-bearing pegmatites. However, of
the three sets of indicative geological features the metagranites and the lineaments
were found to be the most useful in predictive mapping (based on weights of
evidence modeling) of zones with potential for aquamarine-bearing pegmatites in
Lundazi area. The predictive map generated from combining the binary predictor
maps generated from these two sets of indicative geological features outlines about
Chapter 6
75
36% of the area as potential zones for aquamarine-bearing pegmatites. This
predictive map correctly delineates 73% and 79% of the training and test sets of the
aquamarine-bearing pegmatites.
9. The results of the spatial association analysis through the weights of evidence
modeling were used as basis for subjective judgment in the application of fuzzy
logic approach in predictive mapping of zones with potential for aquamarinebearing pegmatites in the area. The best fuzzy ‘intermediate hypothesis’ map was
generated through combining fuzzy values of proximity to metagranites and
lineaments with fuzzy γ operator with γ=0.72; the optimal fuzzy predictive map was
then generated through combining the fuzzy ‘intermediate hypothesis’ map and the
fuzzy map of proximity to circular features through fuzzy γ operator with γ=0.90.
This predictive map outlines about 29% of the study area as potential zones for
aquamarine-bearing pegmatites but correctly delineates only 59% and 57% of
training and test sets of aquamarine-bearing pegmatites.
10. The predictive maps generated by weight of evidence modeling and fuzzy sets
theory do not predict most of the aquamarine-bearing pegmatites in the northern part
of the area. These aquamarine-bearing pegmatites are notably within the proximity
of 6000m from mapped granitic gneisses. This implies that the mapped granitic
gneisses could be metagranites.
11. The best fuzzy predictive map is considered more adequate for directing further
exploration for aquamarine-bearing pegmatites in Lundazi area. This is based on
two reasons. First, it is a result of combining three indicative geological features
making it more reliable than the predictive map generated by the weights of
evidence modeling. Second, it represents a slightly higher chance of finding an
undiscovered aquamarine-bearing pegmatite in the predicted potential zones based
on the ratio of the number of pixels of occurrences delineated correctly to the
number of pixels of potential zones. However, this map should be used with caution
because of the low predictive rate.
6.2 Recommendations
1. The quantified negative spatial associations of the aquamarine-bearing pegmatites
with the mapped axial fold traces and shear zones do not necessary imply that these
features have no structural control in the emplacement of the aquamarine-bearing
pegmatites. Rather, the results indicate the need for more accurate geological maps
in the predictive mapping of the aquamarine-bearing pegmatites.
2. Detailed geological mapping should be conducted in the area for correct
identification of lithological units and a standardized lithological nomenclature is
ideally required to optimize the conceptual exploration model applied to the
predictive mapping.
3. The stream sediments geochemical samples should be re-analysed and the analysis
should include Li and Be. The analysis for beryllium should be carried out using
XRF because beryllium occurs in mineral form difficult to decompose. If the
analysis has to be done by AAS or ICP-AES, the sediments should be decomposed
76
Conclusions and Recommendations
in HF-HClO4-HNO3-HCl to break down all silicates. Lithium analysis can be done
by using the regular aqua regia or hot concentrated HCl decomposition.
4. There should be systematic heavy mineral sampling of the study area with a sample
density of one sample per square km. The concentrates should be cleaned-up with
heavy liquid and split with a magnetic separator so that only the non-magnetic
fraction will be studied under the binocular microscope for the presence of
beryl/aquamarine.
References
Abdalla, H.M and Mohamed, F.H., 1999. Mineralogy and geochemical investigation of emerald
and beryl mineralisation, Pan-African belt of Egypt: Genetic and exploration aspect.
Journal of African earth Sciences 28 (3): 581-598.
An, P., Moon, W. and Rencz, A. 1991. Application of fuzzy set theory for integration of
geological, geophysical and remote sensing data. Canadian journal of exploration
geophysics 27: 1-11.
Barritt, S. D. 2001. Geophysical images for geological mapping and regional exploration.
(Exercises).
Bonham-Carter, G.F., 1994. Geographic Information System for Geoscientists. Pergamon.
398pp.
Bonham-Carter, G.F., Wright, D. and Agterberg, F., 1989. Weights of evidence modelling with
GIS: a new approach to mapping mineral deposits. Geological Survey of Canada paper, pp
89-99:171-183.
Cameron, E.M., Jahns, R.H., McNair, A.H., and Page, L.R., 1949. Internal Structure of Granitic
pegmatites. Econ. Geol., Mon. 2.
Carranza, E.J.M., 2000. Geologically-Constrained Mineral Potential Mapping. PhD Thesis. ITC
pub. No.86. ITC Netherlands.
Cerny, P., 1982. Anatomy and Classification of Granitic Pegmatites. Short course in Granitic
Pegmatites in Science and Industry, pp. 1-30.
Chavez, P.S., Guptill, S.C. and Bowell, J.A., 1984. Image Processing Techniques for Thematic
Mapper Data. American Society of Photogrammetry: 728-743.
Chork, C.Y., 1990. Unmasking multivariate anomalous observations in exploration geochemical
data from sheeted-vein tin mineralization near Emaville, NSW, Australia. Journal.
Geochemical exploration. Pp 205-223.
Daly, M.C., Chakraborty, S.K., Kasolo, P., Musiwa, M., Mumba, P., Naidu. B., Namateba, C.,
Ng’ambi, O. and Coward, M.P., 1984. The Lufilian Arc and Irumide belt of Zambia: Results
of a Geotraverse Across their Intersection.
Darnley, A.G. and Ford, K.L., 1989. Regional airborne gamma ray surveys: Review.
Proceedings of exploration ’87. Special vol.3.
Davis, J.C., 1973. Statistics and Data Analysis in Geology, 2nd edition. John Wiley and Sons,
Singapore, 646 pp.
El-Rakaiby, M. L., 1995. The use of enhanced Landsat-TM image in the characterization of
uraniferous granitic rocks in the central Eastern Desert of Egypt. International-Journal-ofRemote-Sensing. 16(6), pp 1063-1074.
Gallagher, M., 1959. Classification of Beryl Pegmatites. Geol. Surv. Great Britain. Atomic
Energy Division. Rept. No.215
GSD, 1989. The Irumide Province of Zambia, the Zambezi belt, and the Lufilian Arc.
Geological Survey Department (GSD) Occ. pap. No.157, pp. 112-131.
Ginsburg, A.I., Timofeyev, I.N. and Feldman, L.G., 1979. Principles of geology of the Granitic
Pegmatites.
Green, A. A. and Huntington, J. F., 1998. Remote sensing for surface Mineralogy
Guernsey, T. D., 1952. The mineral occurrences of the Loangwa Concessions areas, Northern
Rhodesia. British South African Co., Salisbury. Unpublished.
Grunsky, E.C. and Smee, B.W., 1999. The differentiation of soil types and mineralization from
multi-element geochemistry using multivariate methods and digital topography. Journal-ofGeochemical-Exploration. 67(1-3): 287-299
Hale. M and Asadi. H.H., 2001. A predictive GIS model for mapping potential gold and base
metal mineralisation in Takab area, Iran. Computers and Geosciences 27 (2001) pp.901-912
Harding, A.E., 1982. The Geology of Mwanya Area: explanation of degree sheet 1232, SW
quarter. Rep. Geol. Surv. Zambia No.91
78
References
Hickman, A.C.J., 1975. The Geology of the Lukusuzi area: explanation of degree sheets
1232SE quarter and 1233SW quarter. Rep. Geol. Surv. Zambia No.50
Jessell, M., Bons, P. and Rey, P., 1997. Shear zones and kinematic indicators. Lecture 4B.
Internet.
Jimenez-Espinosa, R. and Chica-Olmo, M., 1999. Application of geostatistics to identify goldrich areas in the Finisterre-Fervenza region, NW Spain. Applied geochemistry, pp (133145).
Johnston, W.D., 1945. Beryl-Tantalite Pegmatites of North-eastern Brazil. Geol. Soc. Am. Bull.
Vol. 56
Komov, I.L., Lukashev, A.N. and Koplus, A.V. 1994. Geochemical Methods for Non-metallic
Minerals (new and expanded edition).
Lipton, G., 1997. Spectral and microwave remote sensing: An evolution from small scale
regional studies to mineral mapping and ore deposit targeting. Proceedings of exploration
97: Fourth decennial international conference on mineral exploration. pp 43-58.
Loughlin, W.P., 1991. Principal Components for alteration mapping.
Mohan, M.R. 1981. Preliminary investigations for mica and gemstones from pegmatites,
Lundazi district.
Moorby, S.A., Cronan, D.S., Perissoratis, C., and Sakellariadou, F., 1989. A statistical analysis
of geochemical data in regard to placer mineral exploration in the northern Aegean Sea.
Muller, D. W., McKenzie, J.A. and Weissert, H., 1991. Controversies in modern geology.
Evolution of geological theories in sedimentology, earth history, and tectonics. Academic
press, Harcourt Brace Jovanovich.
Mustard, J.F. and Sunshine, J.M., 1999. Spectral Analysis for Earth Science: Investigations
Using Remote Sensing Data. Remote Sensing for Earth Sciences: Manual of Remote
Sensing. John Wiley and Sons, Inc., pp. 251-306.
Namateba, C., 1986. Irumide- The Kibaran belt of Zambia. UNESCO, Geology for Economic
Development, Newsletter 5, 163-172.
Nash, R.A., 1962. The Geology and Mineralogy of some Northern Rhodesian pegmatites. PhD
Thesis.
O’Connor, E.A., 1998. Geology of and Lundazi -Lumezi Mission area: explanation of degree
sheets1232 NE quarter and Parts of 1232 NW and NE quarters. Rep. Geol. Surv. Zambia
No.71.
O’Leary, D.W., Friedman, J.D., and Pohn, H.A., 1976. Lineaments, Linea, Lineation: some
proposed new standards for old terms. Bull. Geol. Soc. Am. Vol.87, pp. 1463-1469.
Ouyang, Y., Higman, J., O’-Toole, T. and Campbell, D., 2002. Characterisation and spatial
distribution of heavy metals in sediments from Ortega and Cedar rivers subbasin. Jour. of
Contaminant-Hydrology. pp(1—35).
Patney, R.K. and Tether, J., 1988. The Gem Bearing Pegmatites of Eastern Zambia.
Prakash, A., 2001. Radiometric aspects. Principals of Remote Sensing. An introductory
textbook. ITC education series.pp103-107
Rasilainen, K., Nurmi, P., and Bornhorst, T.J., 1993. Rock geochemical implications for gold
exploration in the late Archean hattu schist belt, Ilomants, eastern Finland (special pap.
Geological Survey of Finland. pp 353-362).
Rogers, P.J., Bonham-Carter, G.F., and Ellwood, D.J., 1986. Anomaly enhancement by use of
catchment basin analysis on surficial geochemical data from the Cobequid Highlands, Nova
Scotia, Canada. Prospecting in areas of glaciated terrain. pp 163-174
Rolet. J., Yesou. H., and Besnus, Y., 1995. Satellite image analysis of circular anomalies and
fracturing networks in the Armorican Massif, France. Mapping Sciences and remote sensing
32 (1), pp21-43.
Rowan, L.C. and Mars, J.C., 2001. Initial Lithologic Mapping Results Using Advanced Space
borne Thermal Emission and Reflection Radiometer (ASTER) Data, EOS, Transactions,
American Geophysical Union, Spring Supplement, Abstract U31A-05.
References
79
Sabine, C., Realmuto, V.J., and Taranik, J.V., 1994. Quantitative estimation of granitoid
composition from thermal infrared multispectral scanner (TIMS) data, Desolation
Wilderness, northern Sierra Nevada, California. Journal of Geophysical Research, Vol.99,
NoB3, pages 4261-4271.
Sawatari, H., Kawata, Y., Sugisaki, R., Dunkley, P.N., Shimizu, H., Masuda, A., 1990.
Rectilinear partition coefficient function of REE pertaining to the formation of basaltic
rocks from the New Georgia Group, the Solomon Islands. Geochemical-Journal. 24(3), pp
159-171.
Schaller, W.T., 1933. Pegmatites: Ore deposits of the western States. A.I.M.E. Lindgren
Volume. Pp. 144
Snelling, N.J., Johnson, R.L. and Drysdall, A.E., 1972. The geochronology of Zambia. Records
of Geol. Surv. of Zambia, No.12, 19-30.
Strong, D.F., 1981. A model for granophile mineral deposits. Geoscience Canada, vol. 8(4),
pp.155-161.
Subhash, J. and Hassan, L., 1998. Descriptive model of rare-metal pegmatites: Assessment of
Mineral and hydrocarbon Resources in the South-west Forest Region of Western Australia,
pp60-62. Pub. Commonwealth and Western Australian Regional Forest Agreement (RFA)
Steering Committee.
Tether, J. and Partney, R.K., 1988. The gem bearing pegmatites of eastern Zambia. GSD occ.
pp. 41-52.
Trueman, D.L., 1982. Exploration for Rare-element granitic-pegmatites. Min. Ass. Of Canada
short course handbook 8, 1982, 463-493.
Valois, J.P., 1991. The granitic complex of Brame/Saint-Sylvestre/Saint-Goussaud (French
Massif Central). Geochemical mapping applied to uranium prospecting. (Bull. Centres-deRecherche-Exploration Production-Elf-Aquitaine. pp239-248).
Volesky, J.C., Stern, R.J. and Abdelsalam, M.G., 2002. Mineral Exploration Using ASTER and
Landsat ETM+Data, Wadi Bidar Mineral district, Saudi Arabia.
Watts, Griffis and McQuat, 1991. Assessment of mineral exploration potential of Zambia.
Unpublished
Westerhof, A.B., 1992. Introduction to Exploration Design and Strategy.
Westerhof, A.B. and Aleva, G.J.J., 1989. Geology of Mineral Deposits.
Zadeh, L. A. 1965. Fuzzy sets. IEEE information and control, vol. 8. pp338-353.