Point-based Model for Predicting Mineral Deposit Using GIS and Machine Learning

Adamu M. IbrahimSchool of ComputingUniversity of Leeds

Leeds, UKEmail: scami@leeds.ac.uk

Brandon BennettSchool of ComputingUniversity of Leeds

Leeds, UKEmail: b.bennett@leeds.ac.uk

Abstract—In this paper we present a novel Point-based pat-tern analysis for predicting Cassiterite (tin-ore) as a secondarymineral deposit in the Plateau Younger Granite Region ofNigeria (PYGR) using statistics, spatial analysis and machinelearning techniques. Existing mining data points collected fromfield survey in the PYGR and the geological map of the regionwere digitized and converted into shape files using Arc-GIS.Spatial analysis was conducted using a distance distributionmethod to investigate the spatial mineral distribution patternsas well as correlations between mineral deposit points and ge-ological features. A combination of spatial evidence map layerscreated using GIS represent the structure of the mineralisationindicators or attributes. Binary indicators are used to build apredictive model using machine learning techniques to predictthe presence or absence of mineral deposits in the PYGR.

Key words—GIS, machine learning, spatial analysis, youngergranites, mineral deposit.

I. INTRODUCTION

Known mineral deposits are generally represented as pointsin a particular region on a map and the spatial patterns of aparticular set of points can be characterised and analysed bypoint pattern analysis [7][14]. The manner in which mineraldeposit occurrences are distributed is of great interest to bothgeoscientists and explorationists. The distribution pattern isgenerally considered to be non-random, since the occurrenceof mineral is a result of the interplay of some geologicalfeatures [6]. In addition, there is a spatial association betweenoccurrence of mineral deposits and certain geological features[28]. The understanding of spatial distribution patterns is onestep towards understanding the relationship between mineraldeposit locations and their geological features. Analysis ofspatial associations of known particular mineral deposits withcertain geological features is very useful in weighing therelative importance of each type of geological feature andtheir effects on mineral deposits [6]. A conceptual modelcan be developed through quantitative or qualitative methodsto predict mineral occurrence in a given area based on someattributes/features associated with mineralisation of the area.

A quantitative method is a data-driven approach to mod-elling, where evidential weights are quantified with respectto locations of known target deposit [1][6].

This paper examines computer-based techniques for pre-dicting mineral occurrence based on the association betweengeological features and mineral deposit of the region. It usesstatistics and artificial intelligence (machine learning) as tools

for spatial analysis and predictive model building of mineralpotential in a given area. This is based on already discoveredmineral deposit data (mining location) represented as Pointsin a district-scale unit on a map and the data relating togeological and geographic features. Spatial point analysiswill be employed to determine the distribution pattern andquantify the spatial association between mineral depositsand geological features. The statistical techniques will usethe measure of dispersion (nearest neighbour distance andKolmogorov-Smirnov tests) to determine the distributionpattern as well as correlation between the various featuresof mineralisation while machine learning approaches willinvolve classification of spatial data attributes which willset up data-driven modelling approach to predict possiblemineral occurrence points. We present machine learning(ML) as a tool for modelling geo-spatial data by buildinga spatial predictive expert model capable of learning patternsin geospatial attributes and making predictions. We will notgo deeply into the details of ML in this paper but ratherdemonstrate how we can use it to build a predictive modelfor mineral potential of the Plateau Younger Granite Region(PYGR) in Nigeria.

II. BACKGROUND AND PREVIOUS WORK

Mineral deposits are the concentration or existence of oneor more useful substances that are for the most part sparselydistributed in the Earth’s Crust [2]. Mineralisation consistsof a set of processes that lead to the formation of mineraldeposits. A secondary deposit which is the focus of ourstudy originates from superficial processes caused by theenvironment and physical or chemical phenomena therebycausing ore materials to concentrate at the regolith (layersof weathered rock). Physical components include erosionand weathering. To discuss mineral formation, we recall thetheory of ore genesis which describes ore formation in threedifferent components- namely; source, transport (conduit)and trap (deposit Point). Mineral deposits rarely fit snuglyinto boxes in which geologist expect them to because of themultiple causes of their formation, they are often classifiedbased on their type [9][17]. In mineral prospecting, one ofthe major goals is discovering new mineral deposits. This canbe done by predicting their occurrence using spatial analysisof the distribution of known mineral deposits [13][6]. Asthe concept of mineral potential becomes more established,

2014 First International Conference on Systems Informatics, Modelling and Simulation

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2014 First International Conference on Systems Informatics, Modelling and Simulation

978-0-7695-5198-2/14 $31.00 © 2014 Crown Copyright

DOI 10.1109/SIMS.2014.22

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2014 First International Conference on Systems Informatics, Modelling and Simulation

978-0-7695-5198-2/14 $31.00 © 2014 Crown Copyright

DOI 10.1109/SIMS.2014.22

65

2014 First International Conference on Systems Informatics, Modelling and Simulation

978-0-7695-5198-2/14 $31.00 © 2014 Crown Copyright

DOI 10.1109/SIMS.2014.22

65

2014 First International Conference on Systems Informatics, Modelling and Simulation

978-0-7695-5198-2/14 $31.00 © 2014 Crown Copyright

DOI 10.1109/SIMS.2014.22

65

several methods of predicting hidden mineral deposit throughthe aid of geographic information systems (GIS) have beendeveloped.

Recently, there is a paradigm shift towards research indata mining and ML, this is motivated by the increase involume of heterogeneous data and the need to make sense ofit. This includes the application of ML to model geographicaland geological data for the purpose of predicting someuncertainties (such as the presence or absence of mineralsin a given area). Research in spatial data analysis has beenconsiderably active over the last two decades, it has helpedimprove different kinds of computer applications, such asGIS, computer aided design (CAD), multimedia informationsystems, data warehousing and earth observation systems[25]. The traditional method employed by geologists is thegeo-statistical method of krigging, which considered mainlyspatial correlation in the data sets but violating the aspect ofobserving independence of attributes and randomness in datadistribution.

Previous work has been employed for predicting mineralpotential mapping such as in the expert system namedProspector developed at Stanford Research Institute for eval-uating mineral prospects [16], Duda [16] for example, usedProspector for combining predictor patterns in a study of theIsland Copper deposits in British Columbia and Campbell[10][6], published the results of applying prospector to mapthe potential for molybdenum deposits in Mt.Tolman areaof Washington State. Others are Reddy [23] who used GISmethods to map base metal potential in Manitoba. Yatabeand Febbri, Bonham-Carter [29][6] also did a review of theProspector. Porwal in 2006, developed some mathematicalmodels for mineral potential mapping and made some com-parison in terms of performance and how well the modelfits the various mineral attributes [21]. Zhou and Civcoalso mentioned problems affecting knowledge-driven dataintegration models [21], [11].

It is important to note that geochemical data will not beconsidered as a predictive attribute in this research paper dueto the complexity associated with chemical composition ofdifferent mineral data sets and also the lack of such data atour disposal. Even though geochemical data analysis is usedin mineral potential mapping however, it is complementaryin the case of heavy metals such as cassiterite. We shall useML as a data-driven model thereby eliminating subjectivityassociated with most expert opinion as adopted in variousliteratures and check the possibility of getting spurious resultsdue spatial autocorrelation in data.

III. SPATIAL POINT-BASED MODEL

A predictive model is a form of computational process orsets of mathematical equations that takes descriptor variablesand calculates estimates for responses [6][21]. We defineSpatial Point-based Model (SPM) for mineral potential ofa given area using a mathematical equations representing

relations between recognition criteria (descriptors/attributes),spatial elements and the target mineral deposits as:

SPM =(

(Elevation) + (Distance to Rocks Units)

+ (Hillshade) + (Lithology) + (Slope)

+ (Curvature))

IV. STUDY AREA AND DATA COLLECTION

The Younger Granite province of Nigeria constitutes anigneous province which is one of the best examples of mid-plate magmatic in the world, mainly due to the presenceof aluminous biotite- granites which are the source of richalluvial tin and columbite [27]. The ring-complexes thatformed the province, is made of a high level sub-volcanicanorogenic intrusions exposed over a distance of about400km [8]. The mineral deposits associated with the ringcomplexes of the province have been the chief motivatingforce behind geological research in the province, ever sincedeposits of alluvial tin were first discovered [17]. The tindeposits that formed the basis for the Nigerian tin miningindustry are mostly secondary alluvial deposits that wereshaded from the weathering and transportation by river orstream to form the alluvial placer (cassiterite) [27]. On thebasis of the availability of these rich alluvial deposits, theInternational Tin council named Nigeria as the 6th largestproducer of tin in the 1970s [20]. More than 98% of thecassiterite produced from Nigeria is mined in the area aroundJos Plateau and the surrounding younger granite rocks inBauchi, Nasarawa and Kano states.

A. Field Work Survey of Mining Sites

The survey team covered an area between Latitude 900’00” N to 10 30’00”N and Longitude 8 30’00”E to9 30’00”E. This corresponds to the area covered by theavailable geological map of the area, at scale of 1cm to0.5km of approximately 16,650km2. In this area hundreds ofpast and current mining sites were visited and their accurateposition measured (lat, long and elevation above sea level)using GPS. For ease of survey the mining sites within theJos Plateau were divided into eight mining districts. Eachof these districts holds several dozens of mining sites anda data collection sheet was designed to capture the relevantinformation such as latitude and longitude position, elevationand other ancillary information about the mining sites suchas sizes, status was established and tabulated in an excelsheet. We also considered data density as sampling valuesdepending on the number of mining sites in a giving miningdistrict (i.e. areas with larger number of mining site has adenser sampling interval).

V. METHODOLOGY

The general methodology adopted for this research paperinvolves three processes of mineral potential mapping asshown in Figure 1 [6].

6666666666

Fig. 1: Tabular structure of the methodology adopted for theresearch

VI. EXPERIMENTAL DATA PRESENTATION ANDANALYSIS

In Figure 2, a total number of 690 labelled data points withother predictive attributes are classified as 420 mineralisedand 260 non-mineralised points, the mineralised and non-mineralised points are given a binary indicators of 1 and 0respectively. Other predictive attributes include 15 digitisedlithological components of rock types forming 204 poly-gon units, a digital elevation map (SRTM-DEM), the geo-referenced TM LANDSAT data map of the area and spatialdistance of each mineral and non-mineral points to rock unitswere carefully overlaid to perform spatial analysis using GIS.The attribute values were extracted from GIS as attributetable representing all data sets in a spatial frame of referenceinto matlab or weka for computational modelling (ML). Asummary of list of datasets used for this work indicated intable I.

A. Statistical Analysis

Setting complete spatial randomness (CSR) as a bench-mark, patterns such as random, unknown, clustered or regularcan be identified by the techniques employed for point patternanalysis. Using the technique of Measure of dispersion byway of examining the location of points relative to the studyarea, we use distance distribution method to analyse thepoint pattern and the characteristics separating individualpoints. A common approach is to calculate distance tonearest neighbour for each point through crude search or

Fig. 2: Digitized geological map of PYGR showing mineraland non-mineral points with attributes

TABLE I: DATASETS USED IN THE EXPERIMENTS.

Dataset Types NumbersMineral Mining Points Points 690Mineralised mining Points Points 460Non-Mineralsed MiningPoints

Points 240

15 Digitised Rock Layers Polygons 204Predictive Attributes ofpoints

Numeric 24

1965 Map of PYGR Cartographic Map (scan) 11975 TM-LANDSATMap

Landsat Map 1

Digitised Map of PYGR Points and Polygon(Shape file)

1

SRTM DEM Elevation Map 1Mineralization Class Binary (0 & 1) 2

constructing dirichlet tessellation of number of points usingGreen-Sibson Algorithm [19] to obtain the Z-Test value to aset of hypothesis given by:

Z =d− E(d)

(V ar(d))1/2. . . (2)

If the value of mean nearest neighbour distance d is lessthan Expected value of the nearest neighbour distance E(d)(for a P = 0.05) in a random pattern which is approximatedaccording to Donelly [15], the Zcalculated < 0 or negativethen Ho hypothesis of CSR pattern is rejected and we acceptthe alternative hypothesis that confirms non-random patterndistribution. We applied Z-test formula available in ArcGISstatistical tool box to obtain the average nearest neighbourdistance of each point using our data points and obtainedthe Z-score value of Z = -50.18182 as shown in Figure3, which concludes that the distribution of points (mineral

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points) in an area of 16,650km2 in PYGR is less than 1%chance that it is random and data points tend to be closer thanwould be in CSR and as such it is said to be in clustereddistribution[18]. This is an indication that indeed certainprocess or phenomenal controls the occurrence of mineraldeposits represented as points.

Fig. 3: Normal curve graph showing clustered distribu-tion pattern

B. Geospatial analysis of points with geological features

The Distance Distribution Method is the comparison ofa cumulative frequency distribution of distance from a setof geological features relative to mineral deposit locationrepresented as D(PM) and a cumulative relative frequencydistribution of distances from same set of geological featuresto a non-mineral deposit locations represented as D(AM)[12][5] [3]. Two sets of opposing hypothesis may be madeas follows:

• H0: Mineral locations are spatially independent of theset of geological features.

• H1: Mineral locations are spatially dependent of set ofgeological features.

and• H0: Both Points samples are from same continuous

distribution.• H1: Both Points samples are not from same continuous

distribution.D= D(PM) - D(AM) If D ≡ 0 it means spatial in-

dependent while if the value of D is positive (D > 0) itdenotes that the graph of D(PM) plots above the graph ofD(AM) and therefore we have a positive spatial associationbetween mineral points locations and geological fetures. IfD < 0, it means graph of D(PM) plots below D(AM)

and therefore indicates a negative spatial association betweenmineral location and geological feaures.

To perform distance distribution analysis we recall Tobler’sfirst law of geography which states that everything is relatedto everything else but nearby things are more related thandistant things [26]. In spatial data analysis, Tobler’s interde-pency between spatial data cannot be ignored [25] and this isa highly important in determining spatial autocorelation anddata attributes.

Looking at Kolmogorov-Smirnov test using matlab code,the result is as shown in table II where all the parameters’D’, ’p’, ’k’, D(AM) and D(PM) values are indicated.

TABLE II: KOLOMOGOROV-SMIRNOV TEST AND CUMULA-TIVE DENSITY VALUES RESULT

.S/No. Parameter name Value

1 D 12 p 1.4929 exp−873 k 0.47964 D(AM) 257.00205 D(PM) 258.0020

So we reject the two H0 and accept the two alternativeH1 by concluding that the value of D = 1 (D > 0) it denotesthat the graph of D(PM) plots above the graph of D(AM)as shown in Figure 4 and say there is a positive spatialcorrelation or association between mineral deposit (miningpoints) and the geological features (Rocks Units) representedas polygons and say that both points samples are not fromsame continuous distribution. A positive spatial correlationbetween mineral deposit point and the geological featuresis very important in mineral potential mapping as it helpsto evaluate the quantity of mineral found at a given location[4][3]. The ’k’ statistics is the maximum difference betweeencurves in figure 4.

VII. MACHINE LEARNING AND IMPLEMENTATION

ML is a field of study which enables computers to learnwithout being explicitly programmed [24]. ML is closely re-lated to artificial intelligence (AI), but with machine learningemphasizing more on using data to drive and adapt the model.There are available classification algorithms in MATLABand WEKA. In order to select the best algorithm to adopt,there is a trade-off between complexity and restrictivenessin the underlying assumptions associated to the algorithms.Simple models are preferred over a complex one because thecomplex ones are most times difficult to manage and maytend to overfit the data while simple models are easier tomanage or maintain. The experimental approach was point-based approach, the aim is to deal with the ground truthdata points as presented (as against other methods such asgrid method where so many assumptions and generalizationsare involved about the predictive target) thereby minimizingerror and increasing precision. The data points with 430mineralized and 260 non- mineralized points were split in theratio of 60% for training and 40% for testing using stratified

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Fig. 4: Kolmogorov-Smirnov test for spatial correlation

random holdout selection. Two supervised machine learningalgorithms were considered, we used Naive Bayesian (NB)and Bagged Decision Tree or Tree bagging (TB) for trainingand testing the model. The results of model performance wereobtained as preseneted in tables III - V. We also plot thereceiver operating characteristic (ROC) to show the accuracyin determining how well the model works as shown in figure5.

VIII. DISCUSSION AND RESULT PRESENTATION

Mineral Potential of PYGR is modelled using a supervisedML technique to predict presence or absence of cassiterite inthe PYGR given all predictive attributes of mineralisation andthe results are presented in tables II-V.

TABLE III: CONFUSION MATRIX TABLE FOR TREE BAG-GING ALGORITHM

.Class Actual Class (0) Actual Class (1)

Predicted Class (0) 94 15Predicted Class (1) 10 157

TABLE IV: CONFUSION MATRIX TABLE FOR NAIVE BAYESAPPROACH

.Class Actual Class (0) Actual Class (1)

Predicted Class (0) 68 43Predicted Class (1) 36 129

Although nature is never the same, given a different areaof study where similar predictive attributes can be extractedand distribution patterns established, we can adopt this modelfor the purpose of predicting the presence or absence ofsecondary cassiterite deposits in that area. In overall, we sayhere that the model can be generalised to the extent that

we can extract the geographical, geophysical, geological andspatial attributes of such area as predictive indicators.

TABLE V: MODEL PERFORMANCE TABLE FOR CLASSI-FIERS

.Classifiers Accuracy Error Sensitivity Specificity

Tree bagging 90% 10% 90% 91%Naive Bayes 70% 30% 65% 75%

Fig. 5: ROC curve plot for Naive Bayes and Tree Bagging

IX. CONCLUSION

We conclude that mineral deposit of the PYGR of Nigeriahas a non-random distribution pattern. A positive spatialcorrelation between the geological features (granite rocks)and mineral deposit points of the PYGR indicated that thegranite rock layers have spatial connection the mineral loca-tion and may likely be primary source of cassiterite depositsin the area as suggested by Turaki [27] and therefore a goodpredictive attributes for modelling mineral deposits. Basedon the results obtained, supervised ML algorithms are goodclassifiers for modelling mineral prediction of cassiterite inthe PYGR of Nigeria and elsewhere. We also conclude thatsince the occurrences of cassiterite are represented by pointson a map, the data analysis and predictive modelling ofcassiterite occurrence can be achieved using point based anal-ysis approach. The Naive Bayes classifier and Tree baggingperformed well as shown is tables IV-V with about 70% and94% accuracy respectively. Area representing the the ROCcurve as shown in Figure 5 shows a trade off between truepositive and false positive rate for both models.We suspecteda clear case of overfitting in the case of Tree bagging whichmay be attributed to high spatial data correlation associatedwith environmental and geo-spatial data [22].

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X. PROPOSED FUTURE WORK

Future work for this research will involve investigatingthe effect of spatial autocorrelation among environmentaland geo-spatial data to overfitting in ML algorithms inorder to check model overfitting as in the case of Treebagging. We will also like focus more on implementing otherML alogorithms and improving their performance throughselection of attribute most important in modeling in orderto simplify models and reducing errors attributed to data inpredicting mineral occurrence.

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