Predictive Mapping of Gold Potentials Bay of Exploits
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Transcript of Predictive Mapping of Gold Potentials Bay of Exploits
Predictive Mapping of Gold Potentials
Bay of Exploits, Central Newfoundland
GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, P.Geo.
Post-Diploma GIS Applications Specialist Program College of the North Atlantic, Corner Brook Campus
June 16, 2014
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 i
TABLE OF CONTENTS ABSTRACT 1. PREFACE 1. INTRODUCTION 2.
Purpose 2. Background 2. Mineral Predictive Mapping 3.
GEOLOGICAL SETTING 4. Area of Interest 4. Regional Geology 4. Gander Zone 5. Notre Dame Subzone 5. Exploits Subzone 5. Overlap Sequences 6. Intrusive Units 6. Gold Occurrences and Models 7.
METHODOLOGY 7. Data Sources 7.
Software 8. Site Suitability Method 8. Modelling Considerations 9.
EVIDENCE LAYERS 10. 16 Geochemical Layers 10. 3 Geological Layers 10. Gravity Layer 11.
Magnetics Layer 11. Potassic Alteration Layer 12. Lineaments Layer 12.
SITE SUITABILITY PREDICTIVE MAP 12. EVALUATION 13. Gold Mineral Occurrence Files 13. DISCUSSION AND CONCLUSIONS 13. Limitations of Performance 13.
Improving Performance 15. ACKNOWLEDGEMENTS 15. BIBLIOGRAPHY 16.
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 ii
LIST OF FIGURES
Figure 1. Gold Deposit Models 3. Figure 2. Project Location and AOI 4. Figure 3. Lithology of the Bay of Exploits 5. Figure 4. Tectonic Subdivisions and Gold Mineral Occurrences of the Bay of Exploits 6. Figure 5. Geoprocessing Model 8. Figure 6. Stacked Map Illustration of Site Suitability 9. Figure 7. Predictive Map of Gold Potentials for the Bay of Exploits 14.
LIST OF TABLES Table 1. Classification of the Lithology Evidence Layer 10. Table 2. Classification of the Surficial Geology Evidence Layer 11. Table 3. Classification of the Lineaments and Plutonic Haloes Evidence Layers 12. Table 4. List of Gold Hotspots from the Predictive Map. 13.
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 1
Predictive Mapping of Gold Potentials,
Bay of Exploits, Central Newfoundland:
GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, B.Sc., P.Geo.
ABSTRACT
The Bay of Exploits, a region of Notre Dame Bay in north central Newfoundland, contains gold mineralization in at least two
known deposit types; Volcanogenic Massive Sulfide (VMS) related deposits and quartz shear/vein hosted hydrothermal
deposits. The Bay of Exploits covers a large area of Newfoundland’s north coast requiring a large amount of traditional
exploration to evaluate. However, the targeting of prospective areas using GIS to define gold potentials is possible. Modern
GIS methods such as Spatial Analysis and Site Suitability has allowed for the gold potential of this area to be delineated and
provide prospective target areas for renewed mineral exploration. Like many GIS spatial analysis methods this technique is
best applied to areas that have good quality data and allow for the most significant outcome. However, it is understood that
at a regional scale this method does have its limitations and pitfalls. The geoprocessing model developed can be applied to
other areas of similar geology or with modification for other commodities and/or depositional models.
Twenty-three evidence layers, including sixteen geochemistry based pathfinder layers from lake sediment and till surveys,
structures from manually generated lineaments maps, total field magnetics, gravity, potassium alteration from radiometric
data, and three geology layers were utilized. The results were validated by comparison with known surface gold occurrences
to determine, in a spatial sense, what areas are positively predicted to host gold mineralization. Combining these twenty
three evidence layers using the site suitability method within a GIS produced a predictive map for gold within the Bay of
Exploits area.
Ten areas were delineated as having a high potential for gold mineralization. These areas are defined as being within the top
two Standard Deviations of eleven resultant classes. These areas coincide with, or are in close proximity to, over forty of the
one hundred sixteen known gold mineral occurrences. This outcome verifies that the site suitability method of generating a
predictive map can define promising areas for gold deposition and new targets for exploration within the Bay of Exploits.
PREFACE
Prospecting for gold in Newfoundland and
Labrador has traditionally been a boots on the
ground exploration activity. In recent years,
with the increased functionality of both
computers and GIS software packages, the
prospecting focus has shifted, in part, to the
desktop. With the release, by the Newfoundland
and Labrador Geological Survey, of numerous
datasets collected and compiled from private
exploration activities new GIS studies seems
only logical. Any tools of the geosciences that
can assist with identifying new mineral
deposits, at a reduced cost, are most welcome,
especially during times when markets are weak.
In the exploration industry, especially among
prospectors who do grass-roots exploration and
find the majority of deposits, a faster and
cheaper means of isolating exploration targets
is paramount. With this knowledge in mind a
project developed around a single commodity,
gold, for a portion of the province known to be
prospective by traditional means is an
opportunistic test and starting point for this
GIS study.
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 2
INTRODUCTION
Purpose:
This GIS project fulfilled four needs;
1. It completed a requirement of the GIS
Application Specialist program for a final
capstone project highlighting an understanding
of the GIS skills encountered throughout the
course of study.
2. It demonstrated a predictive mapping
methodology, utilizing spatial analysis and site
suitability analysis, as it can be applied within
the realm of exploration geology.
3. It evaluated the GIS site suitability method as
applied to predictive mapping for gold within
the Bay of Exploits, Notre Dame Bay,
Newfoundland and identified the method’s
limitations. Recommendations for more refined
results were discerned.
4. It identified areas within the Bay of Exploits
region that are predicted to have an increased
potential for hosting gold deposits.
Background
Mineral exploration is conducted to discover
deposits with economically viable
concentrations of minerals and metals, for the
end purpose of mining. The four main stages of
mineral exploration are: area selection, target
generation, resource evaluation and the
definition of reserves (Carranza, 2009). GIS can
be a very powerful and useful tool for Geologists
during the target generation phase of
exploration. The focus of this initial phase is to
define prospective areas for further
investigation into the potential of a workable
mineral deposit (Bonham-Carter, 1994). The
required geological mapping process typically
takes place over several scales, ranging from the
regional to the property/deposit scale. The
geological method, often incorrectly applied,
involves a complex modeling process, with
numerous factors controlling if and where a
deposit might form. This inevitably requires the
input and analysis of additional geological,
geophysical and geochemical data (Bonham-
Carter, et al 2000).
This project generated a regional-scale gold
potential map, using the site suitability method
within ArcGIS. Using freely available geological
data from the Newfoundland and Labrador
Department of Natural Resources
(Newfoundland and Labrador Geological
Survey) in conjunction with a current
knowledge of natural controls influencing the
presence of gold deposits in the region a
predictive map for the presence of new gold
mineralization in the Bay of Exploits region was
generated.
Gold deposits and showings in the Central
Newfoundland area are associated with Cu-Pb-
Zn deposits (Volcanogenic Massive Sulfides
(VMS)) such as those of Tally Pond, Buchans,
Rambler and Tilt Cove or hosted in later
occurring shear sets with hydrothermal quartz
veining like those at Nugget Pond, Hope Brook,
Pine Cove and the Hammerdown deposits.
GIS based predictive mapping for the Bay of
Exploits area utilizing GIS and Site Suitability
suggest new areas of exploration interest and
new exploration methodologies. Collecting,
organizing and analyzing currently available
geological, geochemical, geophysical and
structural datasets provided invaluable insight
into the best practices for conducting such a site
suitability analysis. Outcomes will assist in
developing future GIS project plans applied to
other geological regions and for other valued
commodities.
Geological features including lithology,
structure and stratigraphy are known to control
the presence of gold deposits in Newfoundland
(Sandeman, 2010). Each feature layer is given a
weighted “score” based on their ascertained
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 3
influence with respect to known gold deposit
types encountered for the Bay of Exploits
region. Within the predictive map the highest
scores indicate a greater likelihood of gold
mineralization. The final predictive map was
compared to known gold mineralization for the
Bay of Exploits region to gauge whether the GIS
model is successful. A discussion of the GIS
spatial analysis limitations is included following
a review of validation results. The final map and
methodology are useful to prospectors and
exploration companies as a general predictor of
deposit location on the regional scale and allow
for targeted field planning. It must however be
understood that these maps are based on a
simplified conceptual and regional GIS model,
and will need further refining in order to
increase overall accuracy and gold target
definition. Like many tools used in mineral
exploration it cannot be used alone.
Mineral Predictive Mapping
Predictive mapping within a GIS can utilize any
number and combination of spatial analysis
techniques from Weights of Evidence statistical
methods, Fuzzy Logic methods, Site Suitability
methods and a host of others. The basic premise
is to use available spatial data to produce a map
that indicates areas that are most likely to
contain economic concentrations of the metal
or commodity being explored. These predictive
maps, which are a result of spatial data analysis
and modeling provide the explorer sound
statistical information for financial and
tenement management decision making.
Geological, geochemical, and geophysical
exploration data in conjunction with spatial
data modelling techniques were used to create
predictive maps that represent aspects of a
particular mineral system as defined by the
mineral depositional model. Predictive maps
are made up of two or more classes that will
have either a positive or a negative association
with the mineralization.
Spatial data modelling is one of the best
techniques available to assess mineral
prediction, it allows for the combination of all
important predictive variables related to a
mineral deposit model (Figure 1.) into one map.
The model is also based on statistics, this means
that it is not bias to previous ideas or current
exploration trends. Instead the model is based
on what's been measured on the ground and
which of these measurements are most related
to the mineralization model.
Although the genesis for the formation of an ore
body can be simplified to geology,
geochemistry, and geophysics the combination
of predictive maps that can be created from
base datasets are many and varied. The
predictive maps must have some relationship to
the processes that formed the mineral deposits
in question.
Figure 1. Gold Deposit Models (After Sillitoe and
Bonham, 1990 and Hannington et al, 1999).
Regional scale geological mapping and
geophysical data sets are excellent data sources
for modelling as they provide continuous data
coverage, minimizing problems associated with
missing data. Point geochemical data are also
valuable and need to be analyzed for anomalous
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 4
geochemical associations before they can be
used in spatial data modelling. Most of the data
used here is from historical exploration and is
freely available through government Geological
Surveys. This data is often available in a digital
format ready for use in GIS and spatial
modelling.
The geoprocessing model allows you to plan for
the new and or more detailed data that can be
collected for the prospective areas. The model
can be rerun to assess the effectiveness of the
new data in enhancing the prospectivity of the
area being tested.
GEOLOGICAL SETTING
Area of Interest
The area chosen for this study was based upon
nine coincident 1:50,000 scale NTS sheets
(Figure 2.).
These NTS Sheets are;
002E01 (Weir’s Pond)
002E02 (Gander River)
002E03 (Botwood)
002E06 (Point Leamington)
002E07 (Comfort Cove-Newstead)
002E08 (Carmanville)
002E09 (Fogo)
002E10 (Twillingate)
002E11 (Exploits)
This area was chosen for a number of reasons
related to testing the site suitability GIS method
and include; a range of land coverages (forested
areas, islands and ocean), a varied geological
base, and largely because of currently available
spatial data.
Figure 2. Project Location and AOI.
Regional Geology
The underlying regional geology for the Bay of
Exploits area within the project AOI consists of
five broad tectonic subdivisions (Figure 5.);
Dunnage Zone - Notre Dame Subzone
Dunnage Zone - Exploits Subzone
Gander Zone
Overlap Sequences
Intrusive units.
These subdivisions span the Early Cambrian
(542 Ma) to Late Devonian (360 Ma) age with
some later intrusions during the Middle
Jurassic to Early Cretaceous (162-131 Ma).
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 5
Figure 3. Lithology of the Bay of Exploits.
Gander Zone
The Gander Zone represents the Gondwanan
Margin, an ancient continental margin similar
to that seen as our Grand Banks today. The
underlying lithologies consist of siliciclastic
marine sediments of the Jonathan’s Pond
Formation and some very minor intermediate
intrusions. The Gander Zone forms the
southeast portion of the project area and some
962 km2.
Notre Dame Subzone
The Dunnage Zone - Notre Dame Subzone
represents some of the ancient Iapetus Ocean
island arc volcanic sequences like those of
Japan today. The underlying lithologies consist
of mafic and felsic marine volcanics with
siliciclastic marine sediments and minor chert,
felsic plutonics (Twillingate Pluton), mafic
hypabyssal dikes, and a mélange. These
volcanics belong to the Chanceport, Cottrell’s
Cove, Morton’s Harbour and Sleepy Cove
Groups. The Notre Dame Subzone forms the
north edge of the project area where the geology
disappears under Notre Dame Bay with a total
area of 229 km2, 37 km2 of Twillingate Pluton
and 143 km2 of Volcanic units.
Exploits Subzone
The Dunnage Zone - Exploits Subzone
represents another portion of the ancient
Iapetus Ocean island arc volcanic sequences.
The underlying lithologies consist of mafic and
felsic marine volcanics with siliciclastic marine
sediments, minor chert, limestone, ultramafic
to felsic plutonics, and mélange. These marine
volcanics and related marine sediments belong
to the Badger, Davidsville, Duder, Exploits,
Gander, Hamilton Sound, Summerford and
Wild Bight Groups as well as the Boone’s point,
Gander River, Phillip’s Head, and South Lake
Igneous Complexes. The Dunnage Mélange is a
centralized geological feature under the Bay of
Exploits and a number of smaller black shale
and plutonic sequences are noted. The Exploits
Subzone forms the largest portion of underlying
lithologies for the project area with a total area
of 2233 km2. Marine siliciclastics have an area
of 1475 km2, the Dunnage Mélange 210 km2,
marine volcanics 205 km2, plutonics at 188 km2
and black shales at 154 km2.
In geology, a mélange is a large to regional-scale
breccia, a mappable body of rock characterized
by a lack of continuous bedding and the
inclusion of fragments of rock of all sizes,
lithologies and from varied sources. Large-scale
melanges typically form in active continental
margin settings. The mixing mechanisms in
such settings may include tectonic shearing
forces, ductile flow of a water-charged or
deformable matrix (such as serpentinite),
sedimentary action (such as slumping, gravity-
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 6
flow, and olistostromal action), or some
combination of these.
An olistostrome is a sedimentary deposit
composed of a chaotic mass of heterogeneous
material, such as blocks and mud, known as
olistoliths, that accumulates as a semifluid body
by submarine gravity sliding or slumping of the
unconsolidated sediments. It is a mappable
stratigraphic unit which lacks true bedding, but
is intercalated amongst normal bedding
sequences. Olistostromes are mélanges formed
by gravitational sliding under water and
accumulation of flow as a semi fluid body with
no bedding.
Overlap Sequences
The Overlap Sequences represent non-marine
or terrestrial sediments that occur in shallow
marine to non-marine environments. In the Bay
of Exploits project area these sediments
represent the rise of oceanic environments
above sea level during orogenesis (Mountain
Chain Building) and continental collision. The
underlying lithologies consist of non-marine
mafic to felsic volcanics, non-marine
siliciclastics and minor shallow marine
siliciclastics, shales and limestone. Much of
these volcanics and sediments belong to the
Botwood and Indian Islands Groups. The
Overlap Sequences form a central corridor
portion of underlying lithologies for the project
area with a total area of 890 km2 and non-
marine volcanics at 186 km2.
Intrusive Units
Intrusive units are represented by hypabyssal to
small plutonic plugs through to batholith and
layered intrusive scale bodies, typically the
youngest units within the project area. These
lithologies comprise ultramafic to felsic
plutonics with minor hypabyssal equivalents.
Some older units may represent magmas
chambers below volcanic centers while others
may be termed suture plutonics. These plutonic
rocks are seen as the Fogo batholith, Hodges
Hill Intrusive Suite, and Mount Peyton
Intrusive Suite (Layered Mafic Intrusive in
part). Smaller bodies occur throughout the
project area. The youngest units for the project
area are the Budgell’s Harbour and Dildo Pond
plutons at 162-131 Ma. Plutonic bodies are
scattered around the project area with a total
area of 750 km2 with Mount Peyton at 440 km2,
Fogo at 214 km2, Hodges Hills at 14 km2, and all
others combined at 282 km2.
Figure 4. Geology and Gold Mineral Occurrences of the
Bay of Exploits.
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 7
Gold Occurrences and Models
Mineral Occurrence data for the Bay of Exploits
region consists of 240 records with 6 indicated
as past-producers, 2 developed prospects, 85
indications, 21 prospects, and 119 showings. Of
these occurrences 30 occur within the VMS
range of deposit types and 116 occur as gold
occurrences. 35 of these gold hits are described
as having insufficient data to classify; the
remainder, are described as being structurally
controlled vein systems. Within the 116 mineral
occurrences there are 87 noted as being
showings, 17 as prospects, 11 as indications and
one as a past producer (Little Harbour Mine,
Twillingate Island).
The underlying lithologies associated with these
116 gold mineral occurrences include 19 within
the Dunnage Melange, 17 within Badger,
Exploits and Davidsville Group sandstones, 16
within Mortons Harbour and Summerford
Groups, 16 within the Mount Peyton Intrusive
Suite, Thwart Island Gabbro and Gander River
Complex, and 11 within Badger Group
Conglomerates. The remaining occurrences lay
within siliciclastic marine sediments or rarely
within non-marine sediments (Figure 3.).
METHODOLOGY
Geoprocessing spatial data using the Site
Suitability method involved the design of
multiple evidence layers focused on
determining the prospectivity of gold. Each
evidence layer was converted to a raster image
of equal cell size (20m X 20m) and was
classified based on the relevance of each class to
gold deposition. For the Bay of Exploits AOI
this produced 23 evidence layers form 5 vector
datasets and 3 raster/gridded datasets.
Combining evidence layers using weighted sum
geoprocessing tool was based upon weighting
related to gold depositional models, this
produced the final predictive map.
Favorable criteria utilized for the prediction of
gold around the Bay of Exploits was limited to 8
specific pathfinder elements from both lake
sediment and till samples, total field magnetic
data, bouguer gravity data, potassium
radiometric data, and data pertaining to
lineaments (faulting/fracturing), geology-
lithology, surficial geology, and metamorphic
haloes surrounding intrusive bodies.
1. Input Datasets
3 raster images and 6 vector files for the Bay of Exploits.
2. Derived Datasets
Resample Bouguer Gravity, Total Field Magnetics and Potassium Radiometrics to 20m cell size. Lineaments polyline and Plutons polygon vector files are buffered. Geochemical point vector files create 8 elemental files and are Inverse Distance Weighted. Geology and Surface Geology polygon vector files are categorized. All vector files are then converted into raster files with 20m cell size.
3. Reclassified Datasets
Each of the 23 derived datasets are reclassified to 9 classes based on the strength of each class and its importance to gold deposition.
4. Site Suitability Predictive Map
All 23 reclassified datasets are assigned a weight based on the strength of the dataset for predicting gold deposits. These datasets are run through the Weighted Sum tool to result in a final site suitability gold predictive map.
Data Sources
GIS vector data for this predictive study
includes a number of recently updated (2014)
datasets available for download from the
Newfoundland and Labrador Geoscience Atlas
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 8
(geoatlas.gov.nl.ca) and as a minimum
included;
Detailed Bedrock Geology (NFLD2616
v.7)
Regional Surficial Geology
Mineral Occurrences
Regional Lake Sediment Sites
Till Sediment Sites
Topographic vector data was downloaded from
the Natural Resources Canada (CANVEC) site
(geogratis.gc.ca/geogratis) including all 9 NTS
sheets as listed above in the Area of Interest.
The NTS index vector data was acquired from
the College of The North Atlantic datasets.
Raster data was sourced largely from the
GeoBase website (www.geobase.ca) and
included;
CanDigElevData (DEM) for each of the 9
NTS sheets at 20m resolution
LandSat L7 imagery (003025 &
003026) at 30m resolution
SPOT Imagery (05347, 05410, 05418,
05430, 05548, 05455, 05507, and
05515) at 20m resolution.
Gridded spatial data was sourced at Natural
Resources Canada via the Earth Science Sector
webpage
(gdr.agg.nrcan.gc.ca/gdrdap/dap/search-
eng.php). This included;
Gravity data at 2000m resolution for
Island of Newfoundland
Magnetic-Radiometric-EM data for the
North Central Newfoundland area at
200m resolution.
Software
For this project the Windows 7 operating
system was utilized with the Microsoft office
suite. GIS software included ArcGIS 10.2 for
Desktop including ArcCatalog, ArcMap,
ArcGlobe and ArcScene. This was a fully
functional version including all licensed
modules. PCI Geomatica 2013 was used to best
manipulate raster imagery where necessary.
Within ArcGIS 10.2 ModelBuilder is an
interface used to create, edit, and manage
geoprocessing models (Figure 5.) which are
workflows that string together sequences of
geoprocessing tools, feeding the output of one
tool into another tool as input. ModelBuilder
can also be thought of as a visual programming
language for building workflows that can be
shared and modified for use in future projects.
Figure 5. Geoprocessing Model.
Site Suitability Method
Within ArcMap there are numerous tools that
allows for spatial analysis and site suitability to
be represented in the form of a model. This
model visually portrays what spatial files are
being utilized and created and what geospatial
tools are being used in a flow diagram layout.
For this Bay of Exploits gold predictive mapping
project the model started with nine spatial files
(6 vector and 3 raster) and utilized over 15
tools.
In the end the model consisted of over 90
spatial files, both vector and raster and over 85
tool uses to compile the final predictive map.
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 9
Site Suitability Analysis in a GIS context is a
geographic or GIS-based process used to
determine the appropriateness of a given area
for a particular use. The basic premise of GIS
site suitability analysis is that each aspect of the
landscape has intrinsic characteristics that are
in some degree either suitable or unsuitable for
the activities being planned. Site suitability is
determined through systematic, multi-factor
analysis of the different aspect of the terrain.
Model inputs can include a variety of physical,
cultural, and economic factors. The results are
often displayed on a map that is used to
highlight areas from high to low suitability.
A site suitability model typically answers the
question, Where is the best location?—whether
it involves finding the best location for a new
road or pipeline, a new housing development,
or in this case the most prospective ground for
gold deposition. ArcGIS Spatial Analyst derives
new information from the overlay of multiple
layers, which can then be used to determine the
best location.
Modelling Considerations (Favorable
Criteria)
The Site Suitability method utilizes multiple
evidence layers based on a single theme to
determine a final output. These layers are
converted to raster images of equal cell size
then through weighting and addition reveal
areas that have incurred more favorable
evidence responses. Layers used depend on the
project at hand and can consist of spatial data
from just about any source. However, only the
data that is both unique and directly related to
the project characteristics should be used. Too
many redundant or non-related layers will only
create a less than ideal result and poor
outcomes.
Each evidence layer generated is reclassified to
a specific project parameter to include an equal
number of classes. This project utilized a total
of 9 classes. Each class is based on Standard
Deviation statistics and reviewed to be assigned
a best value of 9 downward to a low value of 1
sometimes hosting the No Data as a value of 1.
Favorable criteria for this project was, in part,
limited to specific pathfinder elements from
both Lake Sediment and Till samples. Magnetic
data, Gravity data, Radiometric data and data
pertaining to Lineaments (faulting), Geology-
Lithology, Surficial Geology, and Metamorphic
Haloes surrounding intrusive bodies completed
the evidence layer list.
Figure 6. Stacked Map Illustration of Site Suitability.
With all 23 evidence layers properly classified
into 9 classes each, these images were combined
using the Weighted Sum tool to generate the
final predictive map. Within this Weighted Sum
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 10
tool a weight was assigned to each evidence
layer based on its importance to delineating
gold deposit favorability.
EVIDENCE LAYERS
Predictive mapping of gold potentials in the Bay
of Exploits area using site suitability generated
a total of 23 evidence layers. These evidence
layers are essentially classified raster images
over the same area of interest with the same cell
size. A generalized review of these evidence
layers and their generation are provided below.
Using the NL_NTS50K NTS map index polygon
file an area of interest (AOI) was created to use
for clipping purposes. From this file the NTS
sheets 002E/01, 02, 03, 06, 07, 08, 09, 10, and
11 were selected to create the Exploits_NTS file,
this file was then merged to form the NTS_AOI
file and Dissolved to form the final
Exploits_AOI file used for clipping.
16 Geochemical Layers
Both Till Sediment and Lake Sediment point
data vector files were sourced from the
Geoscience Atlas. These datasets both consisted
of multi-element analysis for each point source.
Following a review of pathfinder elements usage
in gold exploration, and the statistics of these
elements within each dataset, it was decided
that 8 elements would be used from each
dataset. These 8 elements consisted of Gold
(Au), Arsenic (As), Barium (Ba), Copper (Cu),
Lead (Pb), Zinc (Zn), Antimony (Sb), and Iron
(Fe).
The NL_LakeSediments and NL_TillSediments
vector files were first clipped with the
Exploits_AOI to create the EX_LSeds and
EX_Tills files. Using the select tool to isolate
each of the 8 elements LSed and Till elemental
files were generated. All 16 elemental files were
run using the IDW (Inverse Distance Weighted)
tool to create contoured images based on
elemental assay values with a 20m cell size.
Each of the 16 elemental files was reclassified to
9 classes with the highest values based on
standard deviation within class 9. This created
16 reclassified elemental images, with 8
elements for each of the tills and lake sediment,
to be used in final weighting.
3 Geological Layers
From the NFLD2616v7 Newfoundland Detailed
Geology vector file and the NL_Surficial
Geology vector file, both sourced from
Newfoundland and Labrador Department of
Natural Resources via the Geoscience Atlas, 3
geological layers were created.
Table 1. Classification of the Lithology Evidence Layer.
Lit h ology Cla ssifica t ion
Ca r bon a te lim eston e 1
Ch er t 1
Hor n fels 1
Sedim en ta r y 1
Silicicla st ic n on -m a r in e con g lom er a te 1
Hy pa by ssa l m a fic 2
Silicicla st ic a r g illite 2
Silicicla st ic n on -m a r in e 2
V olca n ic in ter m edia te n on -m a r in e 2
Hy pa by ssa l in ter m edia te 3
Hy pa by ssa l u ltr a m a fic 3
Plu ton ic 3
Silicicla st ic con g lom er a te 3
Plu ton ic in ter m edia te 4
Silicicla st ic 4
V olca n ic felsic m a r in e 4
V olca n icla st ic m a fic 4
Silicicla st ic n on -m a r in e sa n dston e 5
V olca n ic felsic n on -m a r in e 5
V olca n ic m a fic n on -m a r in e 5
V olca n ic n on -m a r in e 5
Hy pa by ssa l felsic 6
Plu ton ic felsic 6
Plu ton ic m a fic 6
Plu ton ic u ltr a m a fic 6
Silicicla st ic bla ck sh a le 7
Silicicla st ic m a r in e sa n dston e 7
Silicicla st ic m a r in e sh a le 7
Mela n g e 8
Silicicla st ic m a r in e con g lom er a te 8
Silicicla st ic m a r in e 9
V olca n ic m a fic m a r in e 9
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 11
The Clip tool was used to reduce the size of the
NL_SurficialGeology file to the Exploits AOI
creating the Exploits_SurfaceGeology file. This
Exploits_SurfaceGeology file was converted
into a raster with 20m cell size generating the
Surface_Geo file. The Surface_Geo file was then
reclassified to 9 classes based on the
importance of each of the 11 classifications
provided. (Table 2.)
Table 2. Classification of the Surficial Geology Evidence
Layer.
The NL_Geology vector file (NFLD2616v7) was
used to create 3 additional files; the Lithology
file, a non-plutonic mask, and the buffered
halos around plutons where metamorphic
aureoles are known to generate gold veining
fluids and structures. The NL_Geology is first
clipped with the Exploits_AOI to generate the
Exploits_Geology file. This feature class was
transformed into a 20m cell sized raster of
Lithology. This file is reclassified to 9 classes to
create the Lithology file for use in weighting.
(Figure 3.)
This Exploits_Geology attributes were selected
to isolate non-plutons and create a
PlutonicMask for use in the Alteration file
generation.
Another selection was completed to create a
Exploits_Plutons file that was multi-ring
buffered to create the Plutons_Buffer. This
Plutons_Buffer file was converted to a 20m
raster to create the Pluton_Buff file and finally
reclassified to 9 classes to create the
Pluton_Halo file for use in weighting. (Table 3.)
Gravity Layer
The gravity data was sourced through Natural
Resources Canada via the Geoscience Data
collection. The dataset consisted of 7 gridded
data files covering Newfoundland with a 2000
m (2 km) crude point distribution. For this
study the Bouguer gravity file was utilized.
The Clip tool was used to reduce the size of the
Bouguer gravity file to the Exploits AOI creating
the EX_GRAV file. This EX_GRAV file was
resampled to the project 20m cell size to create
the Boug_20m file which in turn had its
statistics defined with the statistics tool to form
the Boug_20m (2) file. The Boug_20m (2) file
was reclassified to 9 classes with the highest
values making the highest value of 9. This
created the Bouguer file available for final
weighting.
Magnetics Layer
The magnetic data was sourced through Natural
Resources Canada via the Geoscience Data
collection. The Gander-Botwood Radiometric-
Magnetic survey from 1987 was selected to
cover the Bay of Exploits area. The dataset
consisted of 7 gridded data files with a 200 m
crude point distribution. For this study both the
Potassium and Total Field Magnetics were used.
The Clip tool was used to reduce the size of the
Total Field Magnetics file to the Exploits AOI
creating the EX_MAG file. This EX_MAG file
was resampled to the project 20m cell size to
create the MAG_20m file which in turn had its
Su rficia l Geology Cla ssifica t ion
Bog 1
A llu v iu m 2
Ma r in e cla y , sa n d, g r a v el a n d dia m icton 3
Gla cioflu v ia l g r a v el a n d sa n d 4
Hu m m ocky ter r a in 5
Ridg ed t ill 5
Collu v iu m 6
Till bla n ket 7
Till v en eer 7
Con cea led bedr ock 8
Ex posed bedr ock 9
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 12
statistics defined with the statistics tool to form
the MAG_20m (2) file. The MAG_20m (2) file
was reclassified to 9 classes with the highest
values making the highest value of 9. This
created the Total_Field_Mag file available for
final weighting.
Potassic Alteration Layer
From the same source as Total Field Magnetics
noted above the Potassium from the
Radiometric survey of Gander-Botwood area
was used to define potassic alteration.
The Clip tool was used to reduce the size of the
Gander_Botwood_Pot file to the Exploits AOI
creating the Clip_GammaK file. This
Clip_GammaK file was resampled to the project
20m cell size to create the GammaK file. To
reduce the unwanted potassium signature
associated with felsic plutonics a mask file
created from the NL_Geology was used to
remove those areas to create the Alteration file.
This alteration file was reclassified to 9 classes
with the highest values making the highest
value of 9. This created the K_Alteration file
available for final weighting.
Lineaments Layer (Structural)
Several attempts were made to create a
lineaments map from existing DEM data using
both PCI Geomatica and ESRI ArcMap. Both
resulted in data that did not identify obvious
fault sets and generated a large number of short
lineaments including ridgelines unsuitable for
this project. In the end using the hillshade
image generated from the DEM data lineaments
were entered manually where they were known
to occur and postulated to exist.
Table 3. Classification of the Lineaments and Plutonic
Haloes Evidence Layers.
This Exploits_Lineaments vector file was then
buffered with multi-rings to generate 8 rings at
a spacing of 125m each from linear extents and
created the Lineaments_Buff file. This
Lineaments_buff file was then converted into a
raster image with 20m cell size creating the
Buff_Lines file that was reclassified to 9 classes
with closest buffered ring having the highest
value for weighting.
SITE SUITABILITY PREDICTIVE MAP
Weighting of evidence layers was done logically
through characterization of layers as being fixed
or transported, and a further characterization of
their relevance to gold deposition models. The
following schema was used to define the weight
of each layer.
F1 – Fixed High Relevance
F2 – Fixed Moderate Relevance
T1 – Transported High Relevance
T2 – Transported Moderate Relevance
T3 – Transported Low Relevance
The final site suitability predictive map for gold
within the Bay of Exploits area is presented as
Figure 7. There are a total of 10 regions
containing predicted areas for gold each listed
in Table 4.
Ev idence La y er Linea m ent s Plu t onic Ha loes
Unit s m et ers m et ers
Cla ssifica t ion T y pe Dist a nce Dist a nce
Cla ss 1 NoDa ta NoDa ta
Cla ss 2 8 7 5 - 1 000 8 7 5 - 1 000
Cla ss 3 7 5 0 - 8 7 5 7 5 0 - 8 7 5
Cla ss 4 6 2 5 - 7 5 0 6 2 5 - 7 5 0
Cla ss 5 5 00 - 6 2 5 5 00 - 6 2 5
Cla ss 6 3 7 5 - 5 00 3 7 5 - 5 00
Cla ss 7 2 5 0 - 3 7 5 2 5 0 - 3 7 5
Cla ss 8 1 2 5 - 2 5 0 1 2 5 - 2 5 0
Cla ss 9 0 - 1 2 5 0 - 1 2 5
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 13
EVALUATION
Evaluation of the site suitability method and the
generated predictive map for gold in the Bay of
Exploits required a means of validation.
Isolating the mineral occurrence files associated
with gold to see where they plotted in relation
to the predictive map’s prospective areas
(Figure 7.) allowed for this validation. This use
of validation points did not require any ground-
truthing which was not possible for this project
within the given time frame. It is hoped that
maybe an adventurous prospector/geologist
might pick up a prospective lead and discover
some new gold mineralization as a result of the
map and provide additional validation.
Table 4. List of Gold Hotspots from the Predictive Map.
Gold Mineral Occurrence Files
Within the Bay of Exploits AOI there are 240
total mineral occurrence points. Of these 116 are
related to gold directly. One of the occurrences
for gold is a past producer at Little Harbour
Mine, Morton’s Harbour (1897-1900), 11 are
classed as indications(a mineral deposit upon
which no known development work has been
done, and for which there exists only an
"indication" of its existence), 17 as prospects (a
mineral deposit upon which enough
development work has been done to provide
data for the making of a reasonable estimate of
the spatial extent of the deposit, but not enough
to estimate the amount of any commodity
present), and 87 as showings (a mineral deposit
upon which some development work may have
been done, but the extent of such work was not
adequate to provide enough data to estimate its
spatial dimensions).
DISCUSSION AND CONCLUSIONS
There appears to be a moderate to strong
correlation between prospective gold areas on
the predictive map and known gold mineral
occurrences. Of the 116 current gold mineral
occurrences 7 (6%) appear to be related to the
top Classification, 23 (20%) appear to be related
to the second highest Classification, 22 (19%)
appear to be related to the third highest
Classification, and 15 (13%) appear to be related
to the fourth highest Classification.
Limitations of Performance
The generation of the predictive map using the
selected classifications and final weighting does
suggest the methodology is sound, albeit
requiring some modifications for a more refined
result. These modifications would be sourced
from both refined data and expert knowledge of
the gold depositional models for the Bay of
Exploits.
Much of the vector and raster data has
resolution issues that have introduced errors
into the site suitability analysis. Accuracy of the
lithological, surficial geological contacts, point
sources for mineral occurrences, real or
erroneous lineaments all produce
misinformation when geoprocessed. Gridded
data had resolutions greater than that used for
the analysis and needed resampling to be useful
within the site suitability analysis. Resampling
will also produce misinformation especially
when resampling from 2000m to 20m cell
sizes.
Gold Prediction Hot Spots (West to East)
1 Lock’s Harbour – Budgell’s Harbour – Little Northwest Arm
2 Strong Island – Tea Arm – Saunder’s Cove
3 Fortune Harbour New Bay Head
4 Embree – Salt Pond Cove
5 Morton’s Harbour – Hillgrade
6 Duder Lake – Burnt Lake
7 Gander River – Salmon River – Rocky Pond
8 Weir’s Pond
9 Carmanville – Eastern Arm – Shoal Pond
10 Indian Bay – Little Bear Cave Pond – Four Mile Pond
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 14
Figure 7. Predictive map of gold potentials for the Bay of Exploits.
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 15
The bulk information from Radiometric, gravity
and magnetic surveys was useful but would be
more meaningful at a better resolution. In the
case of VMS style deposits the gravity and
magnetic surveys were more important because
of their magnetic and density properties when
discovered, however for the bulk of vein style
hydrothermal gold deposits gravity and
magnetics are not of value since these deposits
have no magnetic or density properties that can
be diagnostic.
From a more generalized point of view these
gold deposits have been dumped into 2 genetic
models where it is likely many occur. Finding
more expert knowledge about these deposits
might shed new light on how the classifications
could be revamped, producing better results.
Similarly, with only limited data for structures
within the Bay of Exploits, more expert
knowledge about where structures occur, how
they are related to gold mineralization and
especially if certain trends are more important
than others would better define the
classification for lineaments overall.
Improving Performance
The regional nature of this study has inherent
spatial and data issues that result in a varied
model performance. For raster images one issue
is the resolution of 2000m for the gravity
survey and 200m for magnetics and
radiometric surveys. This is partially resolved
by resampling the data using a cubic resampling
technique however images with a finer
resolution would no doubt have yielded a better
result.
For the vector point files like till and lake
sediment sites the survey data is again spaced to
approximately 1000m. Like the raster images a
finer spacing between the data would have
yielded a more precise result. Although
structural geology data like faults and contacts
was available it was not complete resulting in
the need to generate lineament maps to cover
the area of interest.
In future studies using this site suitability
method additional evidence layers could be
used or removed depending on the parameters
of that study. Spending more time to delineate
better lithology classifications, using more or
less classifications overall and utilizing data
with a finer resolution would be paramount.
ACKNOWLEDGEMENTS
This report is a result of a capstone project
required to complete the GIS Applications
Specialist program at the College of the North
Atlantic in Corner brook, Newfoundland and
Labrador during 2013-2014. Help with
computer applications and spatial data analysis
was provided by instructors Darin Brooks,
Richard Wheeler, and Neala Griffin. My
classmates, who have taken similar adventures
over the past months, have to be acknowledged
for being available when times were chaotic and
advice was needed. Spatial and research data
was primarily sourced through the
Newfoundland and Labrador Department of
Natural Resources via the Geoscience Atlas and
literature searches. Additional data was
acquired from Natural Resources Canada and
GeoBase.ca. A Thank You to these data sources
for information that made this project possible.
Finally, a huge thank you to my wife and three
wonderful kids, who had to endure my absence
over the past 9 months while I attended this
post-diploma program, it is for them that I have
worked so tirelessly.
Predictive Mapping of Gold Potentials, Bay of Exploits, Central Newfoundland: GIS Spatial Analysis Applied to Exploration Geology
Michael B. Regular, GS3210 Capstone Project Report, June 2014 Page 16
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