MINERAL POTENTIALPREDICTIVE MODELING

USING GIS

A WHITE PAPER FOR KENYA COUNTY GOVERNMENTS

Presented atESRI EASTERN AFRICA USER CONFERENCE

By

SALOME WABUYELE

CPCS, Greenhouse Building

NAIROBI

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NOVEMBER 7, 2014SAROVA WHITESANDS BEACH RESORT, KENYA

ABSTRACTMineral Potential modeling produces maps showing areas that aremost likely to contain economic concentrations of minerals to beexplored. The maps can also be used to serve other purposes likeshowing where the most prospective areas are relative toresidential areas, existing mine sites, historical exploration, orprocessing facilities. These maps are also known as predictive orposterior probability maps because they show the statisticalprobability of the metal or mineral of interest occurring in apredetermined area.

The Maps present probability data ranked in terms of highprobability of occurrence to the least, which are interpreted as arelative measure of favourability by (e.g. high, moderate, low, orpoor). These classified and ranked maps are then used by explorersto prioritize their investment targeting highly prospective areasand place lesser importance or even completely ignore land that isnot prospective.

The objective of this paper is to introduce Geospatial correlativeintegration (GCI) modeling methodology, a framework of conflatingdifferent GIS based tasks and, using GIS as a basic tool to createmaps and applications for use as prediction documents in differentdisciplines of geosciences. GCI has successfully been used indifferent countries globally for different geo-scientificapplications and the paper explains data requirements, validationtechniques, and finally a discussion / conclusion.

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ContentsABSTRACT..........................................................

1. BACKGROUND.....................................................

2. INTRODUCTION...................................................

3. METHODOLOGY....................................................

3.1.1 Data.......................................................3.1.2 Modeling Techniques........................................

4. GEOSPATIAL CORRELATIVE INTEGRATION (GCI).......................

4.1.1 Spatial Database Compilation...............................4.1.2 Data Processing............................................4.1.3 Generation of Mineral Potential Maps......................104.1.3.1..........................Analysing the Mineral Potential Map

104.1.4. Model Validation..........................................10

5. DISCUSSION AND CONCLUSION.....................................12

References.........................................................13

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Fig 1: Sample Mineral Map................................................

Fig 2: The GCI Model....................................................

Fig 3: Data layer combination in GCI.........................................

Fig 4: Area under curve validation graph.....................................10

Fig 5: The GCI workflow.................................................11

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1. BACKGROUNDA casual look at the fact that Africa is a home for great amounts ofminerals yet it remains one of the poorest continent is one of theworld’s great paradoxes. It appears that abundance in minerals andoil has a symbiotic relationship with conflict- that is, where oil,gold and diamond thrive in plenty, conflict is plenty too.

Kenya recently exported the first batch of base titanium to Chinaand we could soon become an oil exporter. But when we strike the‘black gold’ and it becomes viable, we should be sure that it willwork for us not against us as it has to most African countries.

A lesson to be learnt from happenings in the already oil producingcountries by Kenya is that ‘abundance of minerals and other naturalresources does not automatically make a country rich’. The wealththat the natural resources represent, can be a two sided coinaffair depending on the policies that the country puts in place.While individual countries’ dependence on natural resources on onehand may pose some risks for continued economic growth, theresources generally represents enormous opportunities if managedeffectively. Many developed nations including United States, Norwayand Canada have prospered tremendously from natural resources(Jessica Coria & Thomas Sterner 2011). The secret of success foreconomic prosperity of any country endowed with natural resourceslies in effective management, which in turn, is an aggregate andcorrelation of many factors including Policy, technology andgoodwill of all stake holders.

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Technologically, Kenya can leverage on GIS to effectively manageits newly discovered wealth. Knowing the spatial occurrence,potential and extend of the minerals is a prerequisite tosuccessful management. GIS has potential for, among plenty of othertasks to integrate different geo-spatial knowledge. Spatialrelationships between an event & its spatial variables and how theyhave evolved can be determined by linking a lot of discretetechnologies. Since most of the knowledge related to mineraldeposit exploration activities are geo-spatial, the ‘ResourceCurse’ jinx is just but a fallacy.

Counties, according to Kenya’s constitution bear the fullresponsibility for planning and implementation of the plans onbehalf of the tax payers. Hence county leaders have to ensurefinancial security and independence of all its citizens. That thejob cut out for these leaders is a technical one cannot be overemphasized. They have to mobilize resources and develop throughtheir own initiatives and policies (Stella Kilonzo, 2011). Amongthe natural resources the counties will be relying on are mineralswhich are spread all over the country. Currently, indicative mapsof the mineral occurrence in different parts of the country exist,however, there is no inventory on the mineral distribution. Hence,Prudence is of essence if the mobilization is to be successful.Before the counties decide to invest in mineral exploration, theyshould have evidence of the occurrence of the mineral.

Mineral potential Modeling allows investors evaluate potential ofoccurrence and therefore work out new and or more detailed survey.The models can be rerun to assess the effectiveness of the new datain enhancing the potential of the area being tested.

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2. INTRODUCTIONMany studies have already been carried out in Kenya to identifymineral locations using traditional methods. While in rare casesthere has been some bit of success, these searches can highlybenefit and be accelerated with use of Geographical InformationSystems (GIS). Since early 1990’s, GIS applications have been anintegral part of mineral exploration programs. The systems have thepotential for among plenty of other tasks to integrate differentgeo-spatial knowledge. Spatial relationships between an event & itsspatial variables and how they have evolved can be determined bylinking a lot of discrete technologies. Evaluation of geosciencedata with GIS can provide support for various geologicalinvestigations and aid decision making process such as determiningsuccessful and cost effective exploration or management strategies.

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Furthermore, exploring geo-scientific data using GIS basedtechniques may reveal insights not readily obtained by traditionalmeans of data analysis or representation. If adopted, thequantitative mineral potential modelling methodology discussed andrecommended in this paper will enable county governments in Kenyato determine distribution of the minerals in their regions, hence,save them a considerable amount of money that would have otherwisebeen spend speculatively (Mark J. Mihalasky 2001)

Purpose & Objective of Mineral PotentialModeling

The purpose of mineral potentialmodeling (prospectivitymodeling) is to determine andevaluate spatial associationsbetween the mineral occurrencesand a variety of regional-scalegeo-scientific data.

The objective is to delineatepromising mineral areas, rank

them and produce predictive maps that can be used as mineralpotential evidence in these areas.

3. METHODOLOGY

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Fig 1: Sample Mineral Potential Map

Source: http://kenex.com.au/Predictive/aboutminmodel.asp

Although the recipe for the formation of an ore body can besimplified to geology, geochemistry and geophysics, the combinationof predictive maps that can be created from these base data aremany and varied. The modeling techniques available are unlimitedtoo. They can, each be used as standalone or combined for higheraccuracy. Choice of modeling techniques is highly influenced bydata availability and the technical knowhow of the modeller.

Empirical mineralpotential models can beeither data-driven orexpert-driven dependingon relative deposit-indicators (orpredictor) datasetsused.

The different modelsinclude: Expert OpinionModels, StatisticalModels, Data Mining Modeling and Probability Models.

Techniques availablewith the use of these models are: Expert Opinion, Analytichierarchy Process, Artificial Neural Network, Neuro Fuzzy, DecisionTree, Support vector Machine, Frequency Ratio, Evidential believeFunction, Weight of Evidence, Multiple Regression, LogisticRegression, etc.

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Fig 1: the GCIModel

Source: GIS-based Spatial Analysis forGeosciences

3.1. How Mineral Potential modeling worksIdeally, the mineral potential map will highlight areas of yetundiscovered mineral occurrences. As such, mineral potentialmapping involves the use of predictive models, as opposed toprescriptive models.

Models based on statistical or heuristic relationships, such asFrequency Ratio and weights of evidence, are empirical models. Suchmodels are usually augmented or constrained by relationshipsformulated as conceptual models, since the geologic conditions andprocesses that lead to the formation of mineral deposits is toocomplex to express mathematically (Bonham-Carter, 1994a).

Mineral potential models basically work on the assumption that alldeposits share a common genesis (Ore) and that they are highlyrelated to geophysical, geochemical and geological activities inthe area. Thus, successful Mineral potential modeling depends ondata, modeling technique and model validity. The methodologicalframework consists of three main steps: identification of spatialrelationships between the datasets, quantification of suchrelationships and combination of multiple quantified relationships.

1.1.1 DataData used in Mineral Potential modeling will have either a positiveor a negative association with the mineralization and can bedivided into 2 major groups: Independent and Dependent datasets.Independent datasets are the data layers that do not change over

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time and cannot be moved. For example: Topographic data, Geologic,soil and land use datasets.

Depended datasets are layers that change or move. They include:data on locations of landslides, flooding susceptibility,geochemical data, earth quakes, ground subsidence etc. Theprobability of a deposit occurring in a particular class can bedetermined by correlating independent and dependent datasets usingdata modeling techniques.

Modeling TechniquesMany studies simplify mineralogy as of geology, geochemistry,and/or geophysics. However, the reality is an intricate and morecomplex process. Hence, there exist numerous techniques forcombination of the factors each of which is used depending on dataavailability, and the level of knowledge of the modeller. A listof the techniques available in mineral potential mapping are listedin 3.0 above.

Advantages of Mineral Potential ModelingSpatial data modelling is one of the best techniques to assessmineral potential as it allows the combination of all the importantpredictive variables related to your mineral deposit model into onemap. It is more powerful than just using single predictivevariables such as rock chip geochemistry, contour maps orgeological maps (Kenex, 2007). The model is also based onstatistics, this means that it is not bias to previous ideas,

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current exploration trends, or even the campfire stories of oldminers. It is based on what's been measured on the ground and whichof these measurements are most related to your mineralization model(both positive and negative correlations). 

Apart from maps produced from the mineral potential models, theresults have many more uses other than display and map production.(Kenex, 2007)

i. You can use the probability data from the model to focus yourexploration time and money on highly prospective areas

ii. Acquire or relinquish new tenements based on land potential iii. Use the modelling results to raise capital. iv. Manage your exploration programs.

4. GEOSPATIAL CORRELATIVE INTEGRATION (GCI)

In GIS, one can generate models with built-in function for theinterpretation of multi-dimensional data relating to individualincidents or for the exploration of the spatial patterns associatedwith the likely occurrence of future events. Geospatial CorrelationIntegration (GCI) is one such modeling methodologies that can be

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performed within the GIS environment using GIS tools to providegeospatial scientists with a tool with predictive ability.

GCI is similar to theoverlay function that iscore to GIS. However, GCIis a more comprehensive anddetailed than overlayfunction and itsapplication in thegeosciences field isunlimited. Many previousstudies have used GCI inmany geoscience disciplinesto find spatialcorrelations or otherspatial patterns within thestudy areas & to generate maps for: Landslide and ground subsidencesusceptibility, groundwater productivity (Todd, 1980 n.d.), mineralpotential, habitat Potential (Lee, GIS-based Spatial Analysis forGeosciences: Geospatial Correlative Integration ,2012), etc.

The Expert opinion, probabilistic, statistical and data miningmodels can be used with Remote Sensing data (Lee, GIS-based SpatialAnalysis for Geosciences: Geospatial Correlative Integration ,2012)using a spatial Geodatabase containing, geologic (Lithology),geophysical & geochemical data constructed in ArcMap. Spatialassociation between training deposit locations & related factorsare identified & quantified by the model. The factors areintegrated to generate the maps of the country’s mineral potentialusing the overlay in ArcMap environment. Area under curve (AUC)

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Fig 3: Data layer combination in GCI similar to normal GIS Overlay

Source: kenex.com.au/Predictive/aboutminmodel.asp

(Lee and Sambath 2006) and /or relative operating characteristics(Swets 1988) methods which quantitatively demonstrate the accuracyof maps can be used to validate the maps. These maps do notestimate the size of mineral deposits but indicate on a broad scale(Ordered scale) prospective areas for exploration. Hence, countygovernments can use the maps as a source of basic knowledge formineral resource development.

Steps of GCI Modeling in ArcGIS ( FrequencyRatio technique)

Frequency ratio is a simple and basic technique that can be appliedin GCI to make Mineral potential maps or even susceptibility maps.The spatial relationship between the occurrences of a mineral /event and related factors are considered in the frequency ratiorelationship. (Saro Lee and Hyun-Joo Oh 2011)

Data layers containing scale factors are subdivided into aconvenient number of classes and the class or each type of factoris obtained as an occurrence ratio. Frequency ratio is the ratio ofthe area in which the event occurred in a class for a given factorin the study area (Lee S. & Min K. 2001). The preparation ofmineral potential maps using GIS is accomplished in four majorsteps.

i. Compilation of a spatial database. ii. Processing the data from the data base.

iii. Application of an integration models to generate a mineralpotential maps.

iv. Validation of the mineral relative potential maps using knownmineral deposits.

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4.1.1 Spatial Database CompilationMineral exploration is a multidisciplinary task requiring thesimultaneous consideration of numerous disparate geophysical,geological, and geochemical datasets (Saro Lee and Hyun-Joo Oh2011). The size and complexity of regional exploration dataavailable to geoscientists are increasing rapidly from a variety ofsources such as remote sensing, large commercially availablegeological and geochemical data (Lee, Geological Application ofGeographic Information System 2014). This demands more effectiveintegration and analysis of regional geospatial data with differentformats and attributes. It is important that the data compilationis complete in order to achieve high accuracy results. Ingeniousdatabases are a prerequisite to successful Mineral potentialprediction (Modeling)

To generate predictive maps, database compilation must include acomplete understanding of the factors that influence mineraloccurrence in the study area and source data that defines thedependent and independent factors in cooperated in the database.For example, flood susceptibility is governed by many hydrologicalfactors such as slope, climate, etc. Mineral occurrence is highlydependent on geology and stratigraphy, geochemical data andgeophysical data (i.e. magnetic anomaly)

4.1.2 Data Processing Basically there are two types of data models in GIS: Vector andraster data models. Vector data models are mostly useful in thepresentation of phenomena especially to users who may not

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understand the raster model concept. Vector data representsfeatures as discrete points, lines and polygons thus users caneasily relate it to the reality. On the other hand, raster datamodels represent phenomena as matrix of cells (rows and columns).Raster data models which include Grids, Digital elevation modelsand graphic images are more popular in analysis due to their natureof data storage. (saro Lee 2012)

Data processing in mineral potential modeling involves preparationof digital elevation models (DEM) from contours. Using the DEM,slope gradient and Aspect are calculated. The raster cell size isdetermined by scale of source data. Cell size is important in GISsince if it is too large then there is a risk of some attributesbeing ignored while too small cell sizes mean large files andtherefore longer processing times.

4.1.3 Generation of Mineral Potential MapsFrequency ratio is highly compatible with GIS and can fully becarried out in ArcGIS. It is the ratio between the area in whichthe minerals occur and the factor under consideration. To calculatethe FR for the class or type of each factor, all scale factors inraster type are reclassified using GIS techniques into 7-10 classesbased on equal areas. Cross tabulation in ArcGIS is used tocalculate the number of mineral occurrences (pixels with minerals)in each factor. The FR is then used to calculate the ratio of thecells with minerals occurrence for a reclassified factor (i.e.Geochemical Points and soil type), the ratio is assigned to eachfactor class again and finally, the mineral occurrence probabilitymap is created using overlay function in GIS. The overlay functionmerges different factors assigned to the ratio.

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4.1.3.1 Analysing the Mineral Potential MapFR is the ratio of area where minerals occur to the total area.Hence, 1 is an average value. A value greater than 1 means a highercorrelation and lower than 1 means the vice versa. That is- thehigher the value, the higher the potential of mineral occurrenceand a lower value means the potential is lower.

4.1.4. ModelValidation

Every model, regardless of purpose has to be validated. Modelvalidation is the process of determining the degree to which amodel or simulation fits the data it represents. The residualobtained after a validation is the indicator of such conformity.The mineral potential maps are validated by comparison with knownmineral deposit locations (test set: 30% of total deposit) whichare usually not used during the training of the model.

Success rate curves are calculated for quantitative prediction andarea under the curves are calculated. The rate shows how well themodel and factors predict the mineral deposit occurrence. Thus, thearea beneath the curve qualitatively assesses the prediction

accuracy.

To obtain the relative ranking for each prediction pattern, thecalculated index values of all the pixels in the study area aresorted in descending order. The ordered pixel values are thendivided into classes with accumulated percentage intervals and the

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Fig 2: Area under curve validation graph

Source: Korea Institute of geospatial and Mineral Resources

validation rate appears as a graph. Classes with high mineralpotential index are indicative of high mineral potential occurrence(Saro Lee and Hyun-Joo Oh 2011)

Source: Mineral Potential Modeling notes, Korea Institute of Geospatial and Mineral Resources

Fig 3: The GCI workflow

5. DISCUSSION AND CONCLUSIONMineral Potential, is a set of characteristics attributed to aparticular area that describe the probability for the presence ofmineral deposits or existence of mineralization. The Potential isdetermined by how well the geological and mineral deposits can fitestablished mineral deposit models and existing knowledge about the

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mineralization of a particular area. Thus, the geologically-constrained predictive mineral potential maps generated in suchresearch are based on two factors: Favourability and validity.Favourability is determined by integration of geological variablesthat are considered essential for mineral occurrence while validityis determined by how well the predictive models delineate correctlyknown mineral deposits that were not used to generate themethodologies developed for geologically-constrained predictivemapping of mineral potential

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ReferencesKenex,. 2007. "KENEX- Mineral Prospectivity Modelling." About Mineral

Prospectivity Modelling. Jan 13. Accessed October 29th.kenex.com.au/Predictive/aboutminmodel.asp.

Lee and Sambath, 2006. 2006. "Landslide Susceptibility Mapping inthe Damrei Romel Area, Cambodia using frequency ratio andlogistic regression models." Environment Geology 847-855.

Lee S. & Min K. 2001. Statistical Analysis of Landslide Susceptibility at Youngin,Korea.

Lee, Saro. 2014. Geological Application of Geographic Information System.Daejeon, 305-350 Korea: Korea Institute of Geoscience andMineral Resources.

—. ,2012. GIS-based Spatial Analysis for Geosciences: Geospatial CorrelativeIntegration. Korea Institute of Gescience and Mineral Resource(KIGAM).

Mark J. Mihalasky. 2001. "Mineral Potential Modelling of Gold andSilver Mineralization in the Nevada Great Basin:." Open-File,New Jersey. http://pubs.usgs.gov/of/2001/of01-291/.

Saro Lee and Hyun-Joo Oh. 2011. "Application of Artificial NeuralNetwork for Mineral Potential Mapping." Edited by Yuseong-gu,Daejeon 305-350 Gwahang-no. Geoscience Information Center,Korea Institute of Geoscience and Mineral Resources . April11. Accessed October 29, 2014. http://cdn.intechopen.com/pdfs-wm/14904.pdf.

saro Lee. 2012. GIS-based Spatial Analysis for Geosciences: Geospatial CorrelativeIntegration. daejeon: Korea Institute of Geospatial Sciences andMineral Resources.

Swets, J.A. 1988. "Measuring the accuracy of diagonistic systems."Scienc 240 1285-1293.

Todd, 1980. n.d. Ground Water Hydrology. New York: John wiley & Sons.

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