State-of-the-art analysis of geochemical data for mineral exploration · State-of-the-art analysis of geochemical data for mineral exploration E. C. Grunsky1,2* & P. de Caritat3,4

State-of-the-art analysis of geochemical data for mineralexploration

E C Grunsky12 amp P de Caritat341 Department of Earth amp Environmental Sciences University of Waterloo Waterloo Ontario N2L 3G1 Canada2 China University of Geosciences Beijing China3 Geoscience Australia GPO Box 378 Canberra ACT 2601 Australia4 Research School of Earth Sciences The Australian National University Canberra ACT 2601 Australia

ECG 0000-0003-4521-163X PdC 0000-0002-4185-9124Correspondence egrunskygmailcom

Abstract Multi-element geochemical surveys of rocks soils streamlakefloodplain sediments and regolith are typicallycarried out at continental regional and local scales The chemistry of these materials is defined by their primary mineralassemblages and their subsequent modification by comminution and weathering Modern geochemical datasets represent amulti-dimensional geochemical space that can be studied using multivariate statistical methods from which patterns reflectinggeochemicalgeological processes are described (process discovery) These patterns form the basis from which probabilisticpredictive maps are created (process validation) Processing geochemical survey data requires a systematic approach toeffectively interpret the multi-dimensional data in a meaningful way Problems that are typically associated with geochemicaldata include closure missing values censoring merging levelling different datasets and adequate spatial sample designRecent developments in advanced multivariate analytics geospatial analysis and mapping provide an effective framework toanalyse and interpret geochemical datasets Geochemical and geological processes can often be recognized through the use ofdata discovery procedures such as the application of principal component analysis Classification and predictive procedures canbe used to confirm lithological variability alteration and mineralization Geochemical survey data of laketill sediments fromCanada and of floodplain sediments from Australia show that predictive maps of bedrock and regolith processes can begenerated Upscaling a multivariate statistics-based prospectivity analysis for arc-related CundashAumineralization from a regionalsurvey in the southern Thomson Orogen in Australia to the continental scale reveals a number of regions with a similar (orstronger) multivariate response and hence potentially similar (or higher) mineral potential throughout Australia

Keywords geochemistry analytical methods compositional data multivariate analytics process discovery process validationpredictive mapping machine learning geospatial coherence Melville Peninsula Nunavut Thomson Region New South Wales

Thematic collection This article is part of the Exploration 17 collection available at httpswwwlyellcollectionorgccexploration-17

Received 29 April 2019 revised 6 June 2019 accepted 11 June 2019

What do geochemical data represent

Geochemical datasets can be defined as geochemical data derivedfrom a range of media (eg soil till regolith lake sediments streamsediments bedrock) collected at a spatial scale consistent with thegeologicalgeochemical processes being investigated Continentalregional and local scale surveys reveal increasingly detailedprocesses ranging from the tectonic assemblage of continents tohydrothermal veining for instance

The intent of geochemical surveys is to provide a spatialgeochemical description of the general geology or dominantgeochemical processes as manifested in the medium beingsampled For example the geochemistry of glacial till or regolithover an area may reflect the underlying geology or it may reflect thesource material that has been transported

The value of geochemical datasets

Geochemical surveys generally contribute to the economy environ-ment and society through supporting fact-based decisions in thefollowing applications mineral exploration regional geologicalmapping agriculture and forestry environmental baseline monitor-ing environmental remediation geohealth and general land usestewardship Forexample geochemical surveys can have an impact on

the understanding of human health issues from natural contaminationof the source rock or the effects of the urban environment throughanthropogenic activities that result in local pollution

Survey area and density

Sample density is a critical aspect of geochemical survey design andsubsequent interpretation Sample density generally described interms of the average area or volume (in 3D) that each sample siterepresents will have an influence on the detection and discovery ofgeochemicalgeological processes that have acted at a specific spatialscale Local scale or high-density surveys have sample site densities inthe range of more than 100 sites per km2 to one site per km2 Regionalscale geochemical surveys can vary from one site per km2 to one siteper 500 km2 and continental scale surveys can vary from one site per500 km2 to one site per 5000 km2 (Geological Survey of NorthernIreland 2007 Reimann et al 2009 2010 2014 Caritat amp Cooper2011 Smith et al2011) It is evident that high-density surveys are ableto detect local scale processes which can be associated with mineraldeposits As the density of a survey decreases the likelihood ofrandomly sampling a site that is associated with alteration ormineralization decreases Conversely studying increasing largerareas enables detection of large-scale geological processes such ascontinental accretion collision and major fault and shear zones

copy 2019 Commonwealth of Australia (Geoscience Australia) This is an Open Access article distributed under the terms of the Creative Commons Attribution 40License (httpcreativecommonsorglicensesby40) Published by The Geological Society of London for GSL and AAG Publishing disclaimerwwwgeolsocorgukpub_ethics

Research article Geochemistry Exploration Environment Analysis

Published online July 12 2019 httpsdoiorg101144geochem2019-031 | Vol 20 | 2020 | pp 217ndash232

Geochemical data

The following is a brief summary of the primary considerations thatmust be taken into account when obtaining compiling andsynthesizing geochemical data prior to statistical treatment andinterpretation This is not intended to provide all of the details thatare necessary when obtaining geochemical data

Choosing the sample material

The choice of sample media is a critical part of the strategy of anygeochemical survey Sampling bedrock may reveal lsquoin sitursquo geochem-ical processes pertaining to the underlying geology Sampling regoliththat has been derived by weathering of in situ bedrock may present ageochemical signature that reflects both the protolith and its weath-ering Sampling transported material such as glacial till lakesediments stream sediments overbank sediments colluvial or alluvialmaterial may reflect varying amounts of transport and mixing ofseveral processes which may be desirable It is important to recognizethe nature of the samplemedia and abilities and limitations of what canbe interpreted from the derived geochemical data

Choosing the appropriate size fraction

In samplemedia that are comprised of amix ofmineralorganicmatterthe size fraction of the mineral grains or particles analysed can beimportant in distinguishing between geologicalgeochemical pro-cesses In most sample media a distinction between a coarse-grained(typically gt63 microm and lt2 mm) and fine-grained (lt63 microm) fraction iscommonly used Coarse-grained material can be considered in manycases to represent locally derived particles or minerals that have notundergone weathering comminution or chemical dissolution Thegeochemical signature from fine-grainedmineral matter may representminerals that have undergone weathering comminution and chemicaldissolutionprecipitation The fine-grained size fraction is generallyconsidered to reflect a greater range of geochemical processesalthough this is dependent on the source material and the nature of thesubsequent processes that occurred Another consideration to be awareof relating to particle sizing is that certain sample analysis methods(eg fusion to prepare glass discs for XRF or LA-ICP-MS analysis)may require the sample to be ground or milled to a given specification(eg gtX of mass passing through a 60 microm sieve) If this is the casethe impact of breaking upmineral aggregates andor litho-fragments atthe sample preparation stage as a fit-for-purpose strategy must beborne in mind when interpreting the results

Choosing the appropriate analytical method ndash digestionand instrumentation

The choice of analytical method which includes the method(s) ofsample digestion and subsequent instrumentation for determiningelemental abundances is critical in the interpretation of the results Thechoice of sample digestion is generally the most important Severaltypes of acid digestion including four-acid (HF-HCl-HNO3-HClO4)aqua regia (HCl-HNO3) and numerous weakpartial extractions will(preferentially) dissolve specific mineral organic and amorphousphases or target certain physical sites (eg adsorbed ions exchange-able cations) Four-acid digestion is a lsquonear-completersquo digestion thatdissolves all but themost resistantminerals (egmonazite zircon) Theuse of aqua regia is useful for dissolving sulphideoxide type mineralswhile leaving most silicate minerals unaffected Weak acid leaches(extractions) tend to dissolve the coatings on mineral grains andoradsorbed species that are associated with alterationmineralizationprocesses Other methods of sample preparation include the use of atotal fusion (rather than digestion) whereby finely ground samplematerial is melted with a flux (eg Li2B4O7) to form a homogeneous

glass bead or disk that can then either be analysed directly or taken intosolution with an acid eg HNO3

The resulting acid solution is then presented to an analyticalinstrument after dilution as appropriate Common technologies includeinductively coupled plasma optical emission spectrometry (ICP-OES)and inductively coupled plasmamass spectrometry (ICP-MS) In thesetechniques the acid digest is first aspirated into a chamber andtypically admixed with argon gas before being converted at hightemperature to a plasma Subsequently an optical emission spectrum isproduced where each element has a unique emission spectrum (ICP-OES) Alternatively a mass spectrometer can be used to separate theelements ormolecules based on their uniquemass signature (ICP-MS)Older but still current methods of instrumentation include atomicabsorption spectroscopy (AAS) Fire assay is the preferred method ofpreparation in ore-grade materials for the determination of Au Pt andPd Methods such as X-ray fluorescence (XRF) and instrumentalneutron activation analysis (INAA) have the ability to analyse a samplewithout awet digestion thus delivering a true total analysis The formeris routinely used for the determination of major mineral formingoxides (eg Al2O3 SiO2 etc)

Quality assurancequality control (QAQC)

A critical component of geochemical analysis is the monitoring of allprocedures that result in the analytical values Quality controlmeasures include the use of blanks duplicates and standards to ensurethat the results produced are fit for purpose Two lsquoqualityrsquo parametersare commonly determined precision which measures the repeat-ability of measurements and accuracy which quantifies how close theobtained results are to the lsquorealrsquo values Blanks allow the detection ofcontamination introduced at any stage of the process from samplingto analysis Duplicates can be separated into two types on the basis oftheir purpose field duplicates (collected within a given distance fromthe original sample) which are used to quantify the total (samplingpreparation and analytical) precision and laboratory duplicates (splitin the laboratory under controlled conditions) which test only theanalytical precision Finally standards also come under two guisesfirstly internal project standards (IPSs) which can track drift in thepreparation and analysis steps within and between batches andsecondly certified reference materials (CRMs) which compareresults to certified analytical results and are used to establish accuracyThere is a wide range of CRMs (rocks soils sediments water andvegetal matters) available those chosen should be similar to thematerial that is being analysed

The compositional nature of geochemical data

Geochemical data are by definition compositional in natureElements or oxides of elements are generally expressed as parts permillion (ppm) parts per billion (ppb) weight percent (wt orsimply ) or some other form of lsquoproportionrsquo When data areexpressed as proportions there are two important limitations firstthe data are restricted to the positive number space andmust sum to aconstant (eg 1 000 000 ppm 100) and second when one value(proportion) changes one or more of the others must change too tomaintain the constant sum This problem cannot be overcome byselecting sub-compositions so that there is no constant sum Thelsquoconstant sumrsquo or lsquoclosurersquo problem results in unreliable statisticalmeasures The use of ratios between elements oxides or molecularcomponents that define a composition is essential when makingcomparisons between elements in systems such as igneousfractionation (Pearce 1968) The use of logarithms of ratios orsimply logratios is required when measuring moments such asvariancecovariance (Aitchison 1986 Egozcue et al 2003Buccianti et al 2006 Pawlowsky-Glahn amp Buccianti 2011Buccianti amp Grunsky 2014)

218 E C Grunsky amp P de Caritat

The relationships between the elements of geochemical data arecontrolled by lsquonatural lawsrsquo (Aitchison 1999) In the case ofinorganic geochemistry that law is stoichiometry which governshow atoms are combined to form minerals and thereby defines thestructure within the data Geochemical data are not the only type ofdata to exhibit structure

Methods

Philosophy

To effectively interpret geochemical data a two-phased approach issuggested initial process discovery followed by process validationThis strategy identifies geochemicalgeological processes that existin the data but may not be obvious unless robust statistical methodsare utilized The process discovery phase is most effective whencarried out using a multivariate approach Linear combinations ofelements related by stoichiometry are generally expressed as strongpatterns whilst random patterns and under-sampled processes showweak or uninterpretable patterns If the process discovery phaseprovides evidence that there is structure in the data then models canbe built and tested using the process validation phase If groups ofobservations are associated with specific processes (bedrockalteration mineralization groundwater weathering gravitationalsorting) then the observations can be assembled into training sets inwhich the uniqueness of these groups can be tested

Process discovery and process validation

A two-step approach is recommended for evaluating multi-elementgeochemical data In the first lsquoprocess discoveryrsquo step patternstrends and associations between observations (sample sites) andvariables (elements) are teased out Geospatial associations are alsoa significant part of process discovery Patterns andor processes that

demonstrate geospatial coherence likely reflect an importantgeologicalgeochemical process

Following process discovery lsquoprocess validationrsquo is the step inwhich the patterns or associations are statistically tested todetermine if these features are valid or merely coincidentalassociations Patterns andor associations that reveal lithologicalvariability in surficial sediment for instance can be used to developtraining sets from which these lithologies can be predicted in areaswhere there is uncertainty in the geological mapping andor paucityof outcrop Patterns and associations that are associated with mineraldeposit alteration and mineralization may be predicted in the sameway In low-density geochemical surveys where processes such asthose related to alteration and mineralization are generally under-sampled it may be difficult to carry out the process validation phaserelating to these processes

A multivariate approach is an effective way to start the processdiscovery phase Linear combinations of elements that arecontrolled by stoichiometry may emerge as strong patterns whilstrandom patterns andor under-sampled processes show weak oruninterpretable patterns This approach was successfully used byGrunsky et al (2012) using multi-element lake sediment geochem-ical data from theMelville Peninsula area Nunavut Canada and byCaritat amp Grunsky (2013) using continental scale multi-elementcatchment outlet sediment geochemical data from Australia

Processes are recognized by a continuous range of variableresponses and an associated relative increasedecrease in elementconcentrations The presence of data that are reported at less than thelower limit of detection (LLD) referred to as censored data can affectthe derivation of associations in the process discovery stage Using thedetection limit or some arbitrary replacement value (eg frac12 LLD) asreplacement values for censored data although commonly per-formed may bias any statistical (especially multivariate) calculationTreatment for censored data has been studied within the medicalepidemiology community for a long time and was recognized as a

Fig 1 Regional basement rock type map of Nunavut Canada showing the location of the Melville Peninsula study area

219Advances in geochemical data analytics for mineral exploration

problem for geochemical data in the 1980s (Chung 1985 Campbell1986 Sanford et al 1993) Research by Martiacuten-Fernaacutendez et al(2003) and Hron et al (2010) provided various methods for findingreplacement values for the censored data For instance the R packagelsquozCompositionsrsquo with the function (lrEM) (Palarea-Albaladejo ampMartiacuten-Fernaacutendez 2008 Palarea-Albaladejo et al 2014) can be usedto determine suitable replacement values for several of the elementsEqually important is the distinction between missing values (ie nodata) and censored data Missing values may not be censored valuesrequiring a decision on how they should be replaced or if they shouldbe used at all (Martiacuten-Fernaacutendez et al 2003)

Advanced analytics for process discovery

Process discovery involves the use of unsupervised multivariatemethods such as principal component analysis (PCA) independentcomponent analysis (ICA) multi-dimensional scaling (MDS) orrandom forests (RFs) to name a few Model-based processdiscovery methods can also be used such as model-based clustering(MBC) or RFs As described previously statistical measuresapplied to geochemical data typically reveal linear relationshipswhich may represent the stoichiometry of rock-forming mineralsand subsequent processes that modify mineral structures includinghydrothermal alteration weathering and waterndashrock interactionPhysical processes such as gravitational sorting can effectivelyseparate minerals according to the energy of the environment andmineralgrain density Mineral chemistry is governed by stoichi-ometry and the relationships of the elements that make up mineralsare easily described within the simplex an n-dimensional

composition within the positive real number space It has longbeen recognized that many geochemical processes can be clearlydescribed using elementoxide ratios that reflect the stoichiometricbalances of minerals during formation (eg Pearce 1968)Geochemical data derived from mineral-based materials whenexpressed in elemental or oxide form are a proxy for mineralogy Ifthe mineralogy of a geochemical dataset is known then theproportions of these elements can be used to calculate normativemineral proportions (Caritat et al 1994 Grunsky 2013)

An essential part of the process discovery phase is a suitablechoice of coordinates to overcome the problem of closure Thecentred logratio (clr) transformation (Aitchison 1986) is a usefultransform for evaluating geochemical data The principal compo-nents (PCs) of clr-transformed data are orthonormal (ie statisticallyindependent) and can reflect linear processes associated withstoichiometric constraints The PCs offer a significant advantage forsubsequent process validation

Advanced analytics for process validation

Process validation is the methodology used to verify that ageochemical composition (response) reflects one or more processesThese processes can represent lithology mineral systems soildevelopment ecosystem properties climate or tectonic assem-blages Validation can take the form of an estimate of likelihood thata composition can be assigned membership to one of the identifiedprocesses This is typically done through the assignment of a classidentifier or a measure of probability The prediction of classmembership can be done through techniques such as linear

Fig 2 Geological map of the southern part of the Melville Peninsula Nunavut Canada with lake sediment sample sites (shown as black dots) of Dayet al (2009)


discriminant analysis (LDA) logistic regression (LR) neuralnetworks (NN) support vector machines (SVMs) RFs or othermachine learning procedures

A critical part of process validation is the selection of variablesthat produce an effective classification This requires the selection ofvariables that maximize the differences between the various classes

Fig 3 Mineral occurrences obtained from NUMIN (2017)

Fig 4 Sample quantile v theoretical quantile plot showing the effect onthe data distribution of imputation for Sb in lake sediments MelvillePeninsula

Fig 5 Screeplot of the eigenvalues and cumulative percentage ofeigenvalues derived from a PCA applied to clr-transformed lake sedimentgeochemical data Melville Peninsula


and minimizes the amount of overlap due to noise unrecognized orunder-sampled processes in the data As stated previously becausegeochemical data are compositional in nature the variables that areselected for classification require transformation to logratiocoordinates For the purposes of classification the choice ofcoordinate system based on the additive logratio (alr) or the

isometric logratio (ilr) yields the same results Although thecovariance matrix of clr-transformed data is singular classificationcan still be undertaken using a generalized inverse and yields thesame classification results as the alr and ilr transforms Analysis ofvariance (ANOVA) applied to clr-transformed data enables therecognition of the compositional variables (elements) that are mosteffective at distinguishing between the classes Choosing aneffective alr transform (choice of suitable denominator) or balancesfor the ilr transform can be challenging and requires someknowledge and insight about the nature of the processes beinginvestigated ANOVA applied to the PCs derived from the clrtransform has been shown to be highly effective at discriminatingbetween the different classes (Grunsky et al 2014 ndash MelvillePeninsula Grunsky et al 2017 ndash Australia) Because the dominantPCs (PC1 PC2 hellip) commonly identify active processes asdiscussed above and the lesser components (PCn PCn-1hellip wheren is the number of variables) may reflect under-sampled processesor noise the use of the dominant components can be effectivelyused for classification using only a few variables Classificationresults can be expressed as direct class assignment or posteriorprobabilities (PPs) in the form of forced class allocation or as classtypicality Forced class allocation assigns a PP based on the shortestMahalanobis distance of a compositional observation from thecompositional centroid of each class Class typicality measures theMahalanobis distance from each class and assigns a PP based on theF-distribution (Campbell 1984 Garrett 1990) This latter approachcan result in an observation having a zero PP for all classesindicating that its composition is not similar to any of thecompositions defined by the class compositional centroids

The application of a procedure such as LDA can make use ofcross-validation procedures whereby the classification of the data is

Fig 6 Principal component biplot of clr-transformed lake sedimentgeochemical data Melville Peninsula See text for an explanation of theelement associations

Fig 7 Kriged map of PC2 derived from clr-transformed lake sediment geochemical data Melville Peninsula overlain with mineral occurrences


repeatedly run based on random partitioning of the data into anumber of equal-sized subsamples One subsample is retained forvalidation and the remaining subsamples are used as training setThis approach produces stable results and reduces the influence ofoutliers (Aitchison 1986 Tolosana-Delgado 2006 Pawlowsky-Glahn amp Egozcue 2016) However the subsequent derivation ofmaps displaying PPs which are compositions in themselvesrequires a suitable logratio transformation to deal with the non-negativity and the constant sum constraint of compositional dataPosterior probabilities are transformed using an alr transformfollowed by ordinary co-kriging after which a back transformation iscarried out for geospatial rendering It is important to note that thealr transform cannot be used to estimate kriging variance (Aitchison1986 Tolosana-Delgado 2006) Kriging variance can be estimatedby the calculation of the expected value and error variancecovariance matrix by GaussndashHermite integration (Pawlowsky-Glahn amp Olea 2004) after which a back transform can be applied

Classification accuracies can be assessed through the generationof tables that show the accuracy and errors measured from theestimated classes against the initial classes in the training sets usedfor the classification

Geospatial coherence

The results from the classification of samples gathered in ageochemical survey should bear a geospatial resemblance to the areasampled The creation of maps is part of the process validation

procedure If a geospatial rendering of a posterior probability showsno spatial coherence (ie no structure or a significant amount oflsquonoisersquo) then it is likely that the classification will be difficult tointerpret within a geological context The most effective way to testthis is through the generation andmodelling of semi-variograms thatdescribe the spatial continuity of a specific class based on PPs Ifmeaningful semi-variograms can be created then geospatial mapsof PPs can be generated through interpolation using the krigingprocess Maps of PPs may show low overall values but still bespatially coherent This is also reflected in the classificationaccuracy matrix that indicates the extent of classification overlapbetween classes Geospatial analysis methodology described byBivand et al (2013) and the lsquogstatrsquo package (Pebesma 2004) in Rcan be used to generate the geostatistical parameters and images ofthe PCs and PPs from kriging

Two case studies

Melville Peninsula Nunavut Canada

Process discovery and validation

The Melville Peninsula region Nunavut has been the focus ofgeological mapping and lake sediment and till geochemicalsampling for the past 40 years The example presented herehighlights the value of multi-element geochemical data as an aid toregional geological mapping and exploration targeting for potentialbase and precious metal deposits through the evaluation of regional

Fig 8 Map of the residual values of Au (ppb) estimated from a robust linear regression (Au sim PC1 + PC2 + PC3 + PC4 + PC5) of lake sedimentgeochemical data Melville Peninsula


geochemical survey data in the Melville Peninsula area (Figs 1 and2) Recent work by Grunsky et al (2014) Harris ampGrunsky (2015)andMueller amp Grunsky (2016) has evaluated the lake sediment andtill geochemistry in the context of predictive geological mappingand mineral resource potential Figure 2 shows a generalizedgeological map of the area and Figure 3 shows the principal mineraloccurrences of the area A study in the use of till geochemical datafor predictive geologic mapping using multivariate spatial analysisis summarized by Mueller amp Grunsky (2016) and is not discussedhere for the sake of brevity

The geology is comprised of poly-deformed and poly-metamor-phosed Archean and Paleoproterozoic assemblages (Machado et al2011 2012 Corrigan et al 2013 Grunsky et al 2014) The areawas covered by the Laurentide ice sheet during the Foxe glaciationSandy till covers much of the northern part of the MelvillePeninsula The central part of the area was covered by a cold-basedice cap that preserved much of the pre-glacial landscape which iscomposed of weathered regolith and boulder rubble with only localglacial transport (Dredge 2009 Tremblay amp Paulen 2012)According to Dredge (2009 p 6)

Glacially scoured lake basins and classic glacial erosion formsare absent Apart from a few scattered outcrops the southernplateau surface consists of weathered regolith or boulderyrubble that was glacially transported for short distancesThe main glacial landforms are distinctive subglacial and icemarginal channels associated with wasting phases of the ice

sheet The till on most of the plateau is immature and the matrixtends to be sandy

The lake sediments are the result of reworking and sorting of theglacial till that developed during the retreat of the ice sheet

The lake sediment geochemical data used in the study of Grunskyet al (2014) have been published in the Geological Survey ofCanada Open File 6269 (Day et al 2009) based on earlier studies byHornbrook et al (1978a b) Details on the sampling methodologyand analytical protocols are documented in Open File 6269 Samplepulps collected in the earlier field campaigns were re-analysed usingaqua regia digestion and ICP-MS instrumentation Pulps were alsoanalysed using INAA Where elements have been analysed usingboth methods the elements were evaluated in terms of detectionlimit suitability and visual examination of the correlation of theelement with each method This included the evaluation of thedegree of censoring QAQC protocols and reporting are provided inthe reports by Day et al (2009) and the data were consideredadequate for statistical processing The R statistical package (R CoreTeam 2014) was used to process the data

Following the protocols described above the datawere screened forvalues reported lt LLD Data lt LLD were imputed (estimated) usingthe function lsquoimpKNNarsquo in the R package lsquorobCompositionsrsquo (Hronet al 2010) Figure 4 shows a quantilendashquantile plot of imputed Sbvalues that minimizes bias in calculating statistical moments

After adjusting the censored values to minimize statistical bias aclr transformwas applied to the data These transformed values were

Fig 9 Map of the residual values of Cr (ppm) estimated from a robust linear regression (Cr sim PC1 + PC2 + PC3 + PC4 + PC5) of lake sedimentgeochemical data Melville Peninsula


then used to carry out a PCA on the data A useful tool that is derivedfrom PCA is the screeplot which is shown in Figure 5 The screeplotshows the eigenvalues plotted in descending order The figureindicates that eigenvalues decrease rapidly and that most of thevariation of the data is accounted for by the first five PCs Theremaining PCs can be interpreted as under-sampled or randomprocesses The five largest eigenvalues indicate that there islsquostructurersquo in the data that is controlled by mineral stoichiometryand hence geological processes The structure in the data can bevisualized using a PC biplot (Gabriel 1971) Figure 6 shows a biplotof the first two PCs for the clr-transformed Melville Peninsula lakesediment geochemistry Three generalized features are evident inthis biplot which accounts for 42 of the variability of the dataFirst the plot indicates the relative relationships of the elements(loadings) that highlight the relative affinities of the sample sites andcorresponding geological domains Second scores of the samplesites associated with granitoid and gneissic rocks occur along thepositive PC2 axis Third sample sites associated with the PrinceAlbert Group supracrustal rocks and locally associated granitoidrocks occur along the positive PC1 axis and the sites associated withthe Paleoproterozoic Penrhyn supracrustal rocks occur along thenegative PC1 negative PC2 axes Maps of the first and second PCsare shown in Grunsky et al (2014) The map of PC1 shows aclustering of positive values that correspond with a region ofgranitoid material and rocks associated with the Prince Albertsupracrustal and associated granitoid assemblages The map of PC2(Fig 7) shows positive values associated with granitic and gneissic

rocks in the NW part of the map and negative scores correspondingwith the supracrustal assemblages in the Paleoproterozoic PenrhynGroup in the southeastern part of the map Thus as an initial part ofprocess discovery a PC biplot provides useful information on thegeochemical nature and relationships of the data Grunsky et al(2014) provide more detail on the use of PCA in this area There areseveral other ways that processes can be discovered in geochemicaldata as outlined previously in the process discovery section

Mineral exploration targeting

In the example provided here with Melville Peninsula lake sedimentgeochemical data the underlying geology was tagged to the samplesites which are shown in the PC biplot of Figure 6 An analysis ofthe number of lake sediment sites associated with specificlithologies is summarized in Table 4 of Grunsky et al (2014) Inthis case eight dominant lithologies derived from the revisedgeology of Machado et al (2011 2012) were tagged to the lakesediment sample sites As part of the process discovery phase it isreasonable to test the ability of the lake sediment geochemistry todistinguish between the dominant lithologies This can be done byapplying an ANOVA in which the most significant PCs providemaximum distinction between the lithologies Grunsky et al (2014)demonstrated that PCs derived from clr-transformed geochemicaldata provide an effective and efficient means to demonstratediscrimination between lithologies Since the PCs represent linearcombinations of elements that are mostly controlled by

Fig 10 Map of the residual values of Ni (ppm) estimated from a robust linear regression (Ni sim PC1 + PC2 + PC3 + PC4 + PC5) of lake sedimentgeochemical data Melville Peninsula


stoichiometry more geological information is contained in fewercomponents thus this approach is more parsimonious and effectivethan using the elements In the study byGrunsky et al (2014) it wasfound that the first six PCs accounted for most of the lithologicalseparation of the data In contrast almost all of the 44 elements wererequired to maximize differences between the lithologies

PCA provides insight into processes controlled by mineralstoichiometry Sampling strategies for large-scale geochemicalsurveys are useful for highlighting dominant processes such as theunderlying bedrock but are seldom at a sufficient spatial samplingdensity for detecting processes that have small spatial footprintssuch as veining or mineralization associated with an ore deposit

The following assumptions are made when considering data froma geochemical survey

(1) The dominant PCs reflect linear combinations of elementsand their relative relationships are controlled by mineralstoichiometry Typically rock-forming processes are highlightedin the dominant principal components These processes wereconfirmed in the studies of Grunsky et al (2014) and provide theconfidence that the lesser components likely represent under-sampled processes

(2) Under-sampled processes may represent processes such asalteration and mineralization associated with a specific deposit type

(3) A regression of a commodity of interest (eg Zn) against thedominant PCs will reflect the association of that element with thedominant processes The choice of using the raw values was based

on the assumption that for the sake of regression the commodityelements are independent In fact there is no easy way to create asimple regression of an element in any of the logratio-transformedspaces (alr clr ilr) Each transform presents different problems inany analysis involving regression

(4) High residual values derived from a regression of an elementagainst the dominant PCs may reflect processes that are potentiallyassociated with mineralization

(5) These high residuals when plotted on a map may highlightareas for mineral exploration follow-up

The screeplot of Figure 5 indicates that the first five PCs account for65 of the overall variability in the data The remaining 41 PCsaccount for under-sampled or random patterns in the data On thisbasis parametric linear modelling of specific commodity elementsAu Cu Ni Cr and Zn can be applied on the first five PCs Thedifference between the observed values of these elements and theestimated values derived from a linear regression define theresiduals that may represent under-sampled processes that arepossibly associated with mineralization

Mineral occurrence information was obtained from the NunavutMineral Occurrences database (httpnugeocaappsshowingshowQueryphp) Only selected commodities are presented here todemonstrate the methodology of using residual values for resourceprediction

Figures 8ndash11 show the residuals for linear models applied to thefirst five PCs derived from the clr-transformed lake sediment

Fig 11 Map of the residual values of Zn (ppm) estimated from a robust linear regression (Zn sim PC1 + PC2 + PC3 + PC4 + PC5) of lake sedimentgeochemical data Melville Peninsula


geochemical data for untransformed values of Au Ni Cr and ZnThe function lsquorlmrsquo (robust fitting for linear models) from the MASSlibrary in the R statistical environment (R Core Team 2014Venables amp Ripley 2002) was used Figure 12 shows the results ofmodelling Cu against the first three PCs The choice of threecomponents is based on the observation that most of the variabilityof Cu is contained within the first three components and theresiduals generated based on linear modelling on the first threecomponents may further highlight areas of potential mineralization

Figure 8 shows a map of the residual values of Au Within thePenrhyn Group (Ps12 Ps3) of supracrustal rocks in thesoutheastern part of the map area four lake sediment sites showhigh values of residual Au and are associated with local Auoccurrences Other areas of high residual values are located in thenorthern part and Penrhyn Groups in the eastern part of the areaOne isolated elevated residual Au value is located in thewestern partof the map area within Archean gneisses (APgn Amgn)

Although there are no known Cr occurrences in the area Figure 9shows a map of residual Cr based on the first five PCs Elevated Crvalues appear to be concentrated within the north-central part of themap area possibly associated with the Prince Albert group ofvolcanics (ultramafic mafic and felsic) sediments and associatedmafic gneissic rocks (APwmv APWs APgn) It is unknown if a Cr-steel mill was used in the sample preparation process which couldcontaminate the sample material Alternatively gravitational effectson heavy minerals may have resulted in local accumulation of Cr-rich minerals

Figure 10 shows a map of residual Ni values which are restrictedto the Penrhyn Group supracrustal volcanic and sedimentary rocks(Ps12 Ps3) within the south-central part of the map area One Ni isnoted in the eastern part of the map area with a few isolated residualNi values in the range of 150ndash500 ppm

High residual values of Zn occur throughout the Penrhyn Groupsupracrustal assemblage (Ps12 Ps3) in the southern part of the maparea shown in Figure 11 These values are closely associated withknown Zn occurrences A few isolated lower residual values of Znoccur in the east-central part of the map area

The high residual values of Cu (Fig 12) occur within the westernportion of the Penrhyn Group assemblages (Ps12 Ps3) in thesouthern part of the map area Isolated high residual Cu values occurwithin the K granitoid rocks in the central part of the map area andthe Prince Albert Group supracrustal assemblages (APWmvAPWs) in the east-central part of the area High residual valuesalso occur in the northern part of the area associated with Kgranites mixed gneiss and sediments associated with the PrinceAlbert Group

Thomson region Australia

Resolution of process discovery with regional geochemicalsurvey data

The southern Thomson Orogen region of northern New SouthWales and southern Queensland (Australia) was the subject of two

Fig 12 Map of the residual values of Cu (ppm) estimated from a robust linear regression (Cu sim PC1 + PC2 + PC3) of lake sediment geochemical dataMelville Peninsula


adjacent regional geochemical surveys (Caritat amp Lech 2007 Mainamp Caritat 2016) whose results were combined Here the thicksedimentary deposits of the Mesozoic Eromanga Basin cover theinferred boundary between the largely unknown Paleozoic

Thomson Orogen to the north and the Paleozoic Lachlan FoldBelt to the SE (which includes the Macquarie Arc a fertile provincefor magmatic arc-related mineralization such as porphyry Cudeposits) and the Paleoproterozoic Broken Hill Domain to the SW

Fig 13 Map of PC1 of the MMI extraction of coarse (lt2 mm) surficial sediments from the southern Thomson survey study area in eastern Australia (a)Values at the sampling sites are shown by coloured circles kriging interpolation is shown as a semi-transparent continuous raster based on the same colourscale (eight equi-percentile classes) First vertical derivative of the total magnetic intensity (TMI 1vd) surface (Nakamura 2015) is underlain (as shadedrelief ) as are catchment boundaries (as blackgrey polygons) The green polygon between Brewarrina and Bourke shows the location of ThomsonResources Ltd tenements EL72527253 enlarged in (b) which shows the location of exploration drill holes over a TMI image (httpwwwthomsonresourcescomauprojectsporphyry-copper-gold) Figure from Caritat et al (2017) published with the permission of the Geological Society ofAustralia Map (b) reproduced with the permission of the Chief Executive Officer Thomson Resources Ltd


(which is well known for a multitude of mineral deposits but mostsignificantly its world-class base metal endowment) (Fig 13a)

One hundred and twenty top outlet sediments (0ndash25 cm) similarto floodplain or overbank sediments but with a potentialcontribution of aeolian material in places were sampled by theregional surveys over a combined area of 185 000 km2 (yielding anaverage site density of 1 sample per 1540 km2) and analysed byICP-MS for multi-element geochemistry after weak extraction usingthe Mobile Metal Ionreg (MMI) procedure (Mann 2010 SGS 2017)among other analyses MMI extraction was designed to targetloosely bound elements adsorbed on the surfaces of soil particlesoxyhydroxide coatings and organic matter thereby potentiallyrepresenting elements that moved up in the regolith postmineralization (Mann 2010) PCA carried out after imputation ofcensored values and clr transformation of the data for 30 elementsindicated that the variance explained by PC1 was 493 (Main ampCaritat 2016) The composition of PC1 was dominated by positiveloadings of Ca Sr Cu Mg Au and Mo and negative loadings ofthe rare earth elements (REEs) and Th (Fig 14a) This compositionwas interpreted to dominantly reflect regolith processes (calcreteformation Ca Sr Mg clay minerals REEs and accumulation ofresistate minerals REEs) with a potential overprint of mineraliza-tion expressed by the Cu Au and Mo components

A local exploration company also using soil MMI geochemistryhad coincidentally and independently developed an empirical (notstatistically derived) vector to mineralization consisting of anenrichment in Sr Ca and Au concomitant with a depletion in REEsto successfully site drill holes targeting CundashAumineralization in theregion (J Macauley pers comm 2013) Thus it was postulated thatthe regional map of PC1 from the Thomson regional geochemicalsurvey could serve as a first order mineral potential map forporphyry CundashAu mineralization (Main amp Caritat 2016) This couldpotentially be extended to other CundashAu mineralization types aswell The implications of these findings are that several areas withinthe southern Thomson region were identified to potentially be ofinterest for exploration purposes at the regional scale an areabetween Brewarrina and Bourke and its eastward continuationtowards Walgett and spreading north and south four regions in thecentre of the study area (one c 150 km NW of Cobar one c 50ndash150 km west of Bourke one c 40 km NE of Hungerford and one c40 km NW of Eulo) a region c 80 km east of White Cliffs aroundWilcannia and in the northwestern part of the area stretching fromthe Koonenberry Belt to Tibooburra

A test for this concept emerged when another local explorationcompany reported some historical as well as new drill holeintersections of sub-economic PbndashZnndashCundashAu mineralization atWarraweena between Brewarrina and Bourke in northern NewSouth Wales (tenements EL72527253) (httpwwwthomsonresourcescomauprojectsporphyry-copper-gold) Burtonet al (2008) attributed the mineralization at Warraweena toporphyry-type deposits related to volcanic arcs The boreholeson tenements EL72527253 were sited not based ongeochemistry but on regional and detailed airborne magneticgeophysical surveys (Fig 13b) These tenements happened to be inthe centre of a circular anomalous area of high positive PC1 valuesin the regional MMI survey This was taken as providing supportingevidence for the prospectivity model developed above (Main ampCaritat 2016)

Upscaling from regional to continental scale

Next Caritat et al (2017) tested whether the continental scaleNational Geochemical Survey of Australia (NGSA) data whichalso included MMI data would yield similar PCA results and ifso if the patterns would be potentially useful for prioritizingmineral exploration in certain regions The number of NGSA siteswas c 1200 and the area was gt6 million km2 yielding an averagedensity of one site per 5200 km2 The PCA conducted on the samesuite of 30 elements and following the same procedure(imputation clr transformation) yielded as PC1 (533 ofvariance) the association of CandashSrndashMgndashCundashAundashMo at the positiveend and of REEs-Th at the negative end (Fig 14b) This PCcomposition was noted to be strikingly similar to that obtained forthe smaller southern Thomson regional survey (Caritat et al2017) The resulting national scale patterns of PC1 are shown inFigure 15 The implications of this research (Caritat et al 2017)were that (1) the dominant element associations determined bymultivariate statistics at the regional scale are robust enough toalso emerge at much lower density continental scale surveys andvice-versa and (2) several provinces potentially prospective forCundashAu-base metal mineralization could be determined by the mapof PC1 from the NGSA dataset (Pilbara-Capricorn-northernYilgarn Albany-Fraser western Eucla eastern Eucla westernAmadeus Victoria Adelaide Eromanga southern Georgina-IsaCurnamona-western Murray and southern Surat geologicalregions) This was the first study (to our knowledge) of upscalinga methodology for mineral prospectivity analysis based on thestatistical analysis of surface geochemical surveys from theregional scale to the continental scale

Fig 14 Graphical representation of the scaled and ordered eigenvalues ofPC1 for 30 elements analysed after MMI extraction in both (a) theregional (southern Thomson) and (b) the continental (NGSA) scalestudies Figure from Caritat et al (2017) published with the permission ofthe Geological Society of Australia


Concluding remarks

This paper outlines a systematic approach by which regional andexploration scale geochemical survey data can be evaluated for thepurposes of discovering geologicalgeochemical processes thatdefine underlying geology and features associated with mineralsystems As a contribution to Exploration lsquo17 this papersummarizes advances in the use of statistical and geospatialmethods since the last review of geochemical methods atExploration lsquo07 (Grunsky 2010) The concept of processdiscovery facilitates the construction of geological processmodels that assist in identifying the dominant processes whichin turn assist in unmasking and lsquodiscoveringrsquo under-sampled orlsquorare-eventrsquo processes associated with mineral deposits Themethodology of applying the consecutive steps of processdiscovery followed by process validation provides a systematictransparent defensible and reproducible way for extracting usefulinformation from a range of data that represent geologicalgeochemical processes There are many methods available forboth process discovery and process validation In this paper wehave highlighted only a few of these methods Numerous

references cited in this paper provide further detail on the useand application of other methods

The examples presented in this contribution are based on weak orpartial digestions in which many of the silicate minerals are notdecomposed Despite this limitation the results presented heredemonstrate that both the underlying lithologies and processesrelated to alteration and mineralization can be detected usingmultivariate statistical methods

Geological mapping and mineral exploration programs usuallyhave access to a wealth of digital data that when used effectivelyprovide an enormous amount of information from which mineralexploration and mapping models can be constructed In the authorsrsquoview the use of high quality multivariate and geospatial methodsthrough the application of modern advanced data analytics atdetailed and regional scales is the next step forward in finding thegreenfields mineral provinces of the future

Acknowledgements This manuscript is a minor modification of themanuscript that was published in the Proceedings of Exploration lsquo17 (Grunsky ampCaritat 2017) We gratefully acknowledge Chris Nind for permitting the reprint ofthese proceedings We also would like to acknowledge the support of Charles

Fig 15 Map of PC1 of the MMI extraction of coarse (lt2 mm) surficial sediments from the NGSA project study area Values at the sampling sites areshown by coloured circles kriging interpolation is shown as a continuous raster based on the same colour scale (eight equi-percentile classes) Geologicalregions (Blake amp Kilgour 1998) are overlain (as grey polygons) those discussed in the text are labelled The southern Thomson project area (Fig 13a) isshown by the black polygon Figure from Caritat et al (2017) published with the permission of the Geological Society of Australia


Beaudry and Ken Witherly that enabled us to make this contribution toExploration lsquo17 The predictive mapping studies undertaken over the MelvillePeninsula were supported by the Geo-mapping for Energy and Minerals Program(GEM) of Canada (httpwwwnrcangccaearth-sciencesresourcesfederal-programsgeomapping-energy-minerals18215) Collaboration with the geo-science agencies of all States and the Northern Territory was essential to thesuccess of the NGSA project and is gratefully acknowledged We thank all theland owners and custodians both nationally and within the Thomson region forgranting access to field sites for the purposes of sampling and the laboratory stafffor assistance with preparing and analysing the samples We are grateful to ourinternal Exploration lsquo17 and GEEA reviewers Roger Skirrow Steve Cook BobGarrett Natalie Caciagli and the late Peter Winterburn for their helpful commentsfor improving the manuscript PdC publishes with permission of the ChiefExecutive Officer Geoscience Australia

Funding The National Geochemical Survey of Australia project wassupported by Commonwealth funding through the Onshore Energy SecurityProgram and Geoscience Australia appropriation (httpwwwgagov aungsa)

Scientific editing by Scott Wood

ReferencesAitchison J 1986 The Statistical Analysis of Compositional Data Chapman and

Hall New York 416Aitchison J 1999 Logratios and natural laws in compositional data analysis

Mathematical Geology 31 563ndash580 httpsdoiorg101023A1007568008032Bivand R Pebesma E amp Gomez-Rubio V 2013 Applied Spatial Data

Analysis with R 2nd edn Springer New York 405Blake D ampKilgour B 1998Geological Regions of Australia 15000000 scale

(Data Set) Geoscience Australia Canberra httpsecatgagovaugeonetworksrvengcatalogsearchjsessionid=3BE16A8B235E117FC9F5448B361B954Emetadata32366

Buccianti A amp Grunsky EC 2014 Compositional data analysis ingeochemistry are we sure to see what really occurs during natural processesJournal of Geochemical Exploration 141 1ndash5 httpsdoiorg101016jgexplo201403022

Buccianti A Mateu-Figueras G amp Pawlowsky-Glahn V (eds) 2006Compositional Data Analysis in the Geosciences From Theory to PracticeGeological Society London Special Publications 264 212 httpsdoiorg101144GSLSP2006264

Burton GR Dadd KA amp Vickery NM 2008 Volcanic arc-type rocksbeneath cover 35 km to the northeast of Bourke Quarterly Notes of theGeological Survey of New South Wales 127 1ndash23 httpsdigsopenmineralsnswgovaudigsopen

Campbell NA 1984 Some aspects of allocation and discrimination InHowells WW amp van Vark GN (eds) Multivariate Statistical Methods inPhysical Anthropology D Reidel Dordrecht 177ndash192

Campbell NA 1986 A General Introduction to a Suite of MultivariatePrograms CSIRODivision ofMathematics and Statistics unpublished report

Caritat Pde amp Cooper M 2011 National Geochemical Survey of Australia TheGeochemical Atlas of Australia Geoscience Australia Record 201120 557 p(2 Volumes) httpwwwgagovaumetadata-gatewaymetadatarecordgcat_71973

Caritat Pde amp Grunsky EC 2013 Defining element associations and inferringgeological processes from total element concentrations in Australiancatchment outlet sediments multivariate analysis of continental-scalegeochemical data Applied Geochemistry 33 104ndash126 httpsdoiorg101016japgeochem201302005

Caritat Pde amp Lech ME 2007 Thomson Region Geochemical SurveyNorthwestern New South Wales Cooperative Research Centre for LandscapeEnvironments andMineral Exploration Open File (Report 145) 1021 p+CD-ROM Available at httpcrclemeorgauPubsOFRSindexhtml

Caritat Pde Bloch J amp Hutcheon I 1994 LPNORM a linear programmingnormative analysis code Computers amp Geosciences 20 313ndash347 httpsdoiorg1010160098-3004(94)90045-0

Caritat Pde Main PT Grunsky EC amp Mann A 2017 Recognition ofgeochemical footprints of mineral systems in the regolith at regional tocontinental scales Australian Journal of Earth Sciences 64 1033ndash1043httpsdoiorg1010800812009920171259184

Chung CF 1985 Statistical treatment of geochemical data with observationsbelow the detection limit Geological Survey of Canada Current ResearchPart B Paper 85-1B 141ndash150

Corrigan D Nadeau L et al 2013 Overview of the GEM Multiple Metals ndashMelville Peninsula project central Melville Peninsula Nunavut GeologicalSurvey of Canada Current Research 2013ndash19 17

Day SJA McCurdy MW Friske PWB McNeil RJ Hornbrook EHWamp Lynch JJ 2009 Regional lake sediment and water geochemical dataMelville Peninsula Nunavut (parts of NTS 046N O P 047A and B)Geological Survey of Canada Open File 6269 12

Dredge LA 2009 Till geochemistry and results of lithological carbonate andtextural analyses northern Melville Peninsula Nunavut (NTS 47ABCD)Geological Survey of Canada Open File 6285 11

Egozcue JJ Pawlowsky-GlahnVMateu-FiguerasGampBarcelo-Vidal C 2003Isometric logratio transformations for compositional data analysisMathematicalGeology 35 279ndash300 httpsdoiorg101023A1023818214614

Gabriel KR 1971 The biplot graphic display of matrices with application toprincipal component analysis Biometrika 58 453ndash467 httpsdoiorg101093biomet583453

Garrett RG 1990 A robust multivariate allocation procedure with applicationsto geochemical data In Agterberg FP amp Bonham-Carter GF (eds)Proceedings of Colloquium on Statistical Applications in the Earth SciencesGeological Survey of Canada Paper Ottawa Ontario 89ndash9 309ndash318

Geological Survey of Northern Ireland (GSNI) 2007 Tellus Project Overviewhttpwwwbgsacukgsnitellusindexhtml

Grunsky EC 2010 The interpretation of geochemical survey dataGeochemistry Exploration Environment Analysis 10 27ndash74 httpsdoiorg1011441467-787309-210

Grunsky EC 2013 Predicting Archean volcanogenic massive sulfide depositpotential from lithogeochemistry application to the Abitibi Greenstone BeltGeochemistry Exploration Environment Analysis 13 317ndash336 httpsdoiorg101144geochem2012-176

Grunsky EC amp Caritat Pde 2017 Advances in the use of geochemical data formineral exploration Exploration 2017 Proceedings October 2017 Toronto

Grunsky EC Corrigan D Mueller U amp Bonham-Carter G-F 2012Predictive geologic mapping using lake sediment geochemistry in the MelvillePeninsula Geological Survey of Canada Open File 7171 1 sheet

Grunsky EC Mueller UA amp Corrigan D 2014 A study of the lake sedimentgeochemistry of the Melville Peninsula using multivariate methodsapplications for predictive geological mapping Journal of GeochemicalExploration 141 15ndash41 httpsdoiorg101016jgexplo201307013

Grunsky EC Caritat Pde amp Mueller UA 2017 Using surface regolithgeochemistry to map the major crustal blocks of the Australian continentGondwana Research 46 227ndash239 httpsdoiorg101016jgr201702011

Harris JR amp Grunsky EC 2015 Predictive lithological mapping of CanadarsquosNorth using Random Forest classification applied to geophysical andgeochemical data Computers amp Geosciences 80 9ndash25 httpsdoiorg101016jcageo201503013

Hornbrook EHW amp Lynch JJ 1978a National geochemical reconnaissancerelease NGR 33A-1977 regional lake sediment and water geochemicalreconnaissance data Melville Peninsula Northwest Territories GeologicalSurvey of Canada Open File 522A 1978

Hornbrook EHW amp Lynch JJ 1978b National geochemical reconnaissancerelease NGR 33B-1977 regional lake sediment and water geochemicalreconnaissance data Melville Peninsula Northwest Territories GeologicalSurvey of Canada Open File 522B 1978 14 sheets

Hron K Templ M amp Filzmoser P 2010 Imputation of missing values forcompositional data using classical and robust methodsComputational Statisticsand Data Analysis 54 3095ndash3107 httpsdoiorg101016jcsda200911023

Machado G Houleacute MG Richan L Rigg J amp Corrigan D 2011 Geologysouthern part of Prince Albert Greenstone Belt Prince Albert Hills MelvillePeninsula Nunavut Geological Survey of Canada Canadian GeoscienceMap 44 (preliminary) 1 sheet 1 CD-ROM

Machado G Corrigan D Nadeau L amp Rigg J 2012 Geology northern partof Prince Albert Greenstone Belt Prince Albert Hills Melville PeninsulaNunavut Geological Survey of Canada Canadian Geoscience Map 81(preliminary) scale 125 000

Main P amp Caritat Pde 2016 Geochemical Survey of the Southern ThomsonOrogen Southwestern Queensland and Northwestern New South Wales ndash TheChemical Composition of Surface and Near-Surface Catchment OutletSediments Geoscience Australia Record 201611 136 Available at httpwwwgagovaumetadata-gatewaymetadatarecord83230

Mann AW 2010 Strong versus weak digestions ligand-based soil extractiongeochemistry Geochemistry Exploration Environment Analysis 10 17ndash26httpsdoiorg1011441467-787309-216

Martiacuten-Fernaacutendez JA Barceloacute-Vidal C amp Pawlowsky-Glahn V 2003Dealing with zeros and missing values in compositional data sets usingnonparametric imputation Mathematical Geology 35 253ndash278 httpsdoiorg101023A1023866030544

Mueller UAampGrunsky EC 2016Multivariate spatial analysis of lake sedimentgeochemical dataMelville PeninsulaNunavutCanadaAppliedGeochemistry75 247ndash262 httpsdoiorg101016japgeochem201602007

Nakamura A 2015Magnetic Anomaly Map of Australia (Data Set) GeoscienceAustralia Canberra Available at httpwwwgeosciencegovaugadds

NUMIN 2017 Nunavut Minerals httpwwwnunavutgeosciencecapagesennuminhtml

Palarea-Albaladejo J amp Martiacuten-Fernaacutendez JA 2008 A modified EM alr-algorithm for replacing rounded zeros in compositional data sets Computersamp Geosciences 34 902ndash917 httpsdoiorg101016jcageo200709015

Palarea-Albaladejo J Martiacuten-Fernaacutendez JA amp Buccianti A 2014Compositional methods for estimating elemental concentrations below thelimit of detection in practice using R Journal of Geochemical Exploration141 71ndash77 httpsdoiorg101016jgexplo201309003

Pawlowsky-Glahn V amp Buccianti A (eds) 2011 Compositional Data AnalysisTheory and Applications Wiley amp Sons New York 378

Pawlowsky-Glahn V amp Egozcue JJ 2016 Spatial analysis of compositionaldata A historical review Journal of Geochemical Exploration 164 28ndash32httpsdoiorg101016jgexplo201512010


Pawlowsky-Glahn V amp Olea RA 2004 Geostatistical Analysis ofCompositional Data Studies in Mathematical Geology Oxford UniversityPress New York 7 181

Pearce TH 1968 A contribution to the theory of variation diagramsContributions to Mineralogy and Petrology 19 142ndash157 httpsdoiorg101007BF00635485

Pebesma EJ 2004 Multivariable geostatistics in S the gstat package Computersamp Geosciences 30 683ndash691 httpsdoiorg101016jcageo200403012

R Core Team 2014 The R Project for Statistical Computing R Foundation forStatistical Computing Vienna Austria Available at httpwwwR-projectorg

Reimann C Matschullat J Birke M amp Salminen R 2009 Arsenicdistribution in the environment the effects of scale Applied Geochemistry24 1147ndash1167 httpsdoiorg101016japgeochem200903013

Reimann C Matschullat J Birke M amp Salminen R 2010 Antimony in theenvironment lessons from geochemical mapping Applied Geochemistry 25175ndash198 httpsdoiorg101016japgeochem200911011

Reimann C Birke M Demetriades A Filzmoser P amp OrsquoConnor P (eds)2014 Chemistry of Europersquos Agricultural Soils Part A Methodology and

Interpretation of the GEMAS Data Set Geologische Jahrbuch B102 528 p 1DVD

Sanford RF Pierson CT amp Crovelli RA 1993 An objective replacementmethod for censored geochemical data Mathematical Geology 25 59ndash80httpsdoiorg101007BF00890676

SGS 2017 Mobile Metal Ions (MMI) httpwwwsgscomenminingexploration-servicesgeochemistrymobile-metal-ions-mmi

Smith DB Cannon WF ampWoodruff LG 2011 A national-scale geochemicaland mineralogical survey of soils of the conterminous United States AppliedGeochemistry 26 250ndash255 httpsdoiorg101016japgeochem201103116

Tolosana-Delgado R 2006 Geostatistics for constrained variables positivedata compositions and probabilities Applications to environmental hazardmonitoring PhD thesis University of Girona 215

Tremblay T amp Paulen R 2012Glacial geomorphology and till geochemistry ofcentral Melville Peninsula Nunavut Geological Survey of Canada Open File7115

Venables WN amp Ripley BD 2002Modern Applied Statistics with S 4th ednSpringer Berlin 495


Geochemical data

The following is a brief summary of the primary considerations thatmust be taken into account when obtaining compiling andsynthesizing geochemical data prior to statistical treatment andinterpretation This is not intended to provide all of the details thatare necessary when obtaining geochemical data

Choosing the sample material

The choice of sample media is a critical part of the strategy of anygeochemical survey Sampling bedrock may reveal lsquoin sitursquo geochem-ical processes pertaining to the underlying geology Sampling regoliththat has been derived by weathering of in situ bedrock may present ageochemical signature that reflects both the protolith and its weath-ering Sampling transported material such as glacial till lakesediments stream sediments overbank sediments colluvial or alluvialmaterial may reflect varying amounts of transport and mixing ofseveral processes which may be desirable It is important to recognizethe nature of the samplemedia and abilities and limitations of what canbe interpreted from the derived geochemical data

Choosing the appropriate size fraction

In samplemedia that are comprised of amix ofmineralorganicmatterthe size fraction of the mineral grains or particles analysed can beimportant in distinguishing between geologicalgeochemical pro-cesses In most sample media a distinction between a coarse-grained(typically gt63 microm and lt2 mm) and fine-grained (lt63 microm) fraction iscommonly used Coarse-grained material can be considered in manycases to represent locally derived particles or minerals that have notundergone weathering comminution or chemical dissolution Thegeochemical signature from fine-grainedmineral matter may representminerals that have undergone weathering comminution and chemicaldissolutionprecipitation The fine-grained size fraction is generallyconsidered to reflect a greater range of geochemical processesalthough this is dependent on the source material and the nature of thesubsequent processes that occurred Another consideration to be awareof relating to particle sizing is that certain sample analysis methods(eg fusion to prepare glass discs for XRF or LA-ICP-MS analysis)may require the sample to be ground or milled to a given specification(eg gtX of mass passing through a 60 microm sieve) If this is the casethe impact of breaking upmineral aggregates andor litho-fragments atthe sample preparation stage as a fit-for-purpose strategy must beborne in mind when interpreting the results

Choosing the appropriate analytical method ndash digestionand instrumentation

The choice of analytical method which includes the method(s) ofsample digestion and subsequent instrumentation for determiningelemental abundances is critical in the interpretation of the results Thechoice of sample digestion is generally the most important Severaltypes of acid digestion including four-acid (HF-HCl-HNO3-HClO4)aqua regia (HCl-HNO3) and numerous weakpartial extractions will(preferentially) dissolve specific mineral organic and amorphousphases or target certain physical sites (eg adsorbed ions exchange-able cations) Four-acid digestion is a lsquonear-completersquo digestion thatdissolves all but themost resistantminerals (egmonazite zircon) Theuse of aqua regia is useful for dissolving sulphideoxide type mineralswhile leaving most silicate minerals unaffected Weak acid leaches(extractions) tend to dissolve the coatings on mineral grains andoradsorbed species that are associated with alterationmineralizationprocesses Other methods of sample preparation include the use of atotal fusion (rather than digestion) whereby finely ground samplematerial is melted with a flux (eg Li2B4O7) to form a homogeneous

glass bead or disk that can then either be analysed directly or taken intosolution with an acid eg HNO3

The resulting acid solution is then presented to an analyticalinstrument after dilution as appropriate Common technologies includeinductively coupled plasma optical emission spectrometry (ICP-OES)and inductively coupled plasmamass spectrometry (ICP-MS) In thesetechniques the acid digest is first aspirated into a chamber andtypically admixed with argon gas before being converted at hightemperature to a plasma Subsequently an optical emission spectrum isproduced where each element has a unique emission spectrum (ICP-OES) Alternatively a mass spectrometer can be used to separate theelements ormolecules based on their uniquemass signature (ICP-MS)Older but still current methods of instrumentation include atomicabsorption spectroscopy (AAS) Fire assay is the preferred method ofpreparation in ore-grade materials for the determination of Au Pt andPd Methods such as X-ray fluorescence (XRF) and instrumentalneutron activation analysis (INAA) have the ability to analyse a samplewithout awet digestion thus delivering a true total analysis The formeris routinely used for the determination of major mineral formingoxides (eg Al2O3 SiO2 etc)

Quality assurancequality control (QAQC)

A critical component of geochemical analysis is the monitoring of allprocedures that result in the analytical values Quality controlmeasures include the use of blanks duplicates and standards to ensurethat the results produced are fit for purpose Two lsquoqualityrsquo parametersare commonly determined precision which measures the repeat-ability of measurements and accuracy which quantifies how close theobtained results are to the lsquorealrsquo values Blanks allow the detection ofcontamination introduced at any stage of the process from samplingto analysis Duplicates can be separated into two types on the basis oftheir purpose field duplicates (collected within a given distance fromthe original sample) which are used to quantify the total (samplingpreparation and analytical) precision and laboratory duplicates (splitin the laboratory under controlled conditions) which test only theanalytical precision Finally standards also come under two guisesfirstly internal project standards (IPSs) which can track drift in thepreparation and analysis steps within and between batches andsecondly certified reference materials (CRMs) which compareresults to certified analytical results and are used to establish accuracyThere is a wide range of CRMs (rocks soils sediments water andvegetal matters) available those chosen should be similar to thematerial that is being analysed

The compositional nature of geochemical data

Geochemical data are by definition compositional in natureElements or oxides of elements are generally expressed as parts permillion (ppm) parts per billion (ppb) weight percent (wt orsimply ) or some other form of lsquoproportionrsquo When data areexpressed as proportions there are two important limitations firstthe data are restricted to the positive number space andmust sum to aconstant (eg 1 000 000 ppm 100) and second when one value(proportion) changes one or more of the others must change too tomaintain the constant sum This problem cannot be overcome byselecting sub-compositions so that there is no constant sum Thelsquoconstant sumrsquo or lsquoclosurersquo problem results in unreliable statisticalmeasures The use of ratios between elements oxides or molecularcomponents that define a composition is essential when makingcomparisons between elements in systems such as igneousfractionation (Pearce 1968) The use of logarithms of ratios orsimply logratios is required when measuring moments such asvariancecovariance (Aitchison 1986 Egozcue et al 2003Buccianti et al 2006 Pawlowsky-Glahn amp Buccianti 2011Buccianti amp Grunsky 2014)



Methods

Philosophy




































Two case studies



























































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Methods

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Documents

State-of-the-art analysis of geochemical data for mineral exploration · State-of-the-art analysis of geochemical data for mineral exploration E. C. Grunsky1,2* & P. de Caritat3,4