VISUAL–STATISTICAL CLASSIFICATION OF As–Fe …rruff.info/doclib/cm/vol41/CM41_1135.pdfThe dark...

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The Canadian MineralogistVol. 41, pp. 1135-1146 (2003)

VISUAL–STATISTICAL CLASSIFICATION OF As–Fe-RICH PRODUCTSOF ALTERATION OF TAILINGS FROM THE ENGUIALÈS TUNSGTEN MINE, FRANCE

JEAN-FRANÇOIS LENAIN§ AND ALEXANDRA COURTIN-NOMADE

Laboratoire HYDRASA (UMR 6532 CNRS), Faculté des Sciences et Techniques,123, avenue Albert Thomas, F-87060 Limoges Cedex, France

ABSTRACT

We have studied the products of alteration of sulfides contained in the tailings of a former tungsten mine at Enguialès,Aveyron, France. Most of these products are poorly crystalline and contain high concentrations of arsenic. A preliminary classi-fication based on visual observations was carried out, and five products were distinguished according to their color and texture.The data obtained by electron-microprobe analysis were submitted to different statistical approaches in order to discriminateamong the various materials and to test the efficiency of the visual classification. The statistics involved rely on logratios of thechemical elements because the ratio of any two elements is independent of the perturbations caused by other elements, andremains unchanged whatever the choice of subcompositional space. The statistical discrimination of the groups indicates thatlog(Fe/As) and log(S/As) best express the chemical differences. Both of these logratios correspond to the system Fe–As–S. In thisstudy, the two approaches, visual observations and statistics, are complementary, and one method without the other could giveonly partial information or misinterpretations. A hierarchical clustering applied to the whole dataset confirms the existence of fivegroups, but visual and statistical classifications differ. On one hand, by using the logratio method, different subgroups with thesame color were identified. On the other hand, some groups with different colors correspond to the same mineral, which washighlighted by Raman microspectrometry. These observations (concerning As-rich jarosite, for example) are explained mainly bydifferences in composition.

Keywords: mining environment, As-bearing products, iron oxyhydroxides, Raman microspectroscopy, compositional data,logratios, multivariate statistical analysis, Enguialès mine, France.

SOMMAIRE

Les haldes d’une ancienne mine de tungstène à Enguialès, Aveyron, en France, présentent des produits d’altération de sulfures,pour l’essentiel faiblement cristallins, et contenant de fortes teneurs en arsenic. Ces produits d’altération ont été préalablementclassés de manière visuelle, c’est-à-dire en fonction de leur couleur et de leur texture, et cinq groupes ont été ainsi définis. Lesdonnées d’analyses en microsonde électronique obtenues sur ces matériaux ont été soumises à différentes approches statistiquesdans le but de les discriminer de manière plus précise, et de tester l’efficacité de la classification visuelle. Les méthodes mises enoeuvre utilisent le log des rapports entre éléments chimiques, car tout rapport de deux éléments est indépendant des perturbationscausées par les autres éléments, et demeure inchangé quelle que soit la sous-composition considérée. L’analyse discriminante desgroupes visuels a montré que ce sont log(Fe/As) et log(S/As) qui expriment le mieux les différences chimiques entre groupes. Cesdeux logs de rapports correspondent au système Fe–As–S. Dans cette étude, nous montrons que les deux approches (visuelle etstatistique) sont complémentaires, et qu’une méthode sans l’autre peut conduire à des interprétations partielles ou érronées. Uneclassification hiérarchique réalisée sur l’ensemble des données a aussi mis en évidence l’existence de cinq groupes; cependant, lesclassifications visuelle et statistique diffèrent. Différents sous-groupes de même couleur ont été différenciés de manière statistique,tandis que certains groupes de couleurs différentes correspondraient en fait à un même minéral, ce qui a été confirmé parmicrospectrométrie Raman. Ces observations (concernant la jarosite riche en As par exemple) s’expliquent en grande partie pardes différences de composition.

Mots-clés: environnement minier, produits arséniés, oxyhydroxydes de fer, microspectroscopie Raman, données de composition,log de rapports, analyse statistique multivariée, mine d’Enguialès, France.

§ E-mail address: [email protected]

1136 THE CANADIAN MINERALOGIST

INTRODUCTION

The characterization of minerals or phases is usu-ally carried out via classical techniques, such as X-raydiffraction (XRD), scanning electron microscopy(SEM), electron microprobe (EMP) analysis. Thechemical data obtained by EMP allow geoscientists todetermine the precise composition and also provide in-formation on the homogeneity (or lack thereof) of thematerials. However, several problems can be encoun-tered with EMP analytical data depending on the choiceof subcompositional space, substitutions, solid solu-tions, and even window dimensions that exceed crystalsize. Furthermore, simple consideration of the raw EMPdata is difficult in the case of amorphous or poorly crys-talline products and unidentified minerals. Thus thereare problems in correctly interpreting EMP data.

In this study, the logratio statistical methods, devel-oped by Aitchison (1986) for compositional data, areapplied to some by-products of mining. The alterationof sulfides contained in the tailings can generate amor-phous or poorly crystalline products that cannot be wellidentified by direct methods of mineralogical charac-terization. In previous studies of the alteration productsresulting from the weathering of tailings, we pointed outproblems concerning the precise definition of their com-position (Courtin-Nomade et al. 2003, Courtin-Nomade2001). These studies allowed us to distinguish productsof alteration according to visual criteria (e.g., color, tex-ture). Electron-microprobe analyses indicated widechemical variations within a single visual object. Fur-thermore, the analytical total generally never reaches100% owing to a high degree of hydration. The aim ofthis study is (i) to show the advantages of a statisticalapproach applicable to compositional data of heteroge-neous, amorphous or poorly crystalline products, (ii) toprovide more information on a previous visual classifi-cation by testing the homogeneity of the different groupsaccording to the chemical composition of the micro-metric products of alteration, and (iii) to give an over-view of the links among these materials in theirevolution at the expense of the sulfide minerals.

MATERIALS AND METHODS

Characterization

Chemical analyses were carried out with aCAMECA SX50 electron microprobe equipped withfour wavelength-dispersion spectrometers (WDS) andan energy-dispersion spectroscopy system (EDS;CAMPARIS, Paris). The acceleration voltage used was15 kV, with a beam current of about 4 nA. The elementssought were: As, Fe, S, Si, Al, K, Ca, Mn, Mg, and Na.The materials were also characterized by Ramanmicrospectroscopy with a XY 800 Dilor instrument(CRSCM, Orléans). As a source of radiation for Ramanspectroscopy, we used a green laser (� = 514.5 nm) with

a low output of energy, about 5 to 10 mW, to avoid lo-cal heating of the sample. About 10% of the energy ofthe laser reached the sample through an Olympus mi-croscope using a �100 objective. The scans were usu-ally collected from 60 to 1579 cm–1 and from 3000 to3900 cm–1, with a scan time of 15 minutes (300 s perwindow). These two methods provide complementaryinformation: EMP data for the chemical composition,and Raman microspectroscopy for the precise mineral-ogical identification of the species.

Materials

The materials studied are the result of the alterationof sulfide-rich tailings from an abandoned tungsten mineat Enguialès, Département de l’Aveyron, France. Themain sulfide minerals are pyrite and arsenopyrite. Fivedifferent As- and Fe-rich products of alteration wereidentified (Courtin-Nomade et al. 2003) according totheir color and luster: dark red (DRD), red (RED), fleecy(FLE), yellow (YEL) and resinous (RES). That studypointed out the heterogeneity of some of the species inthese groups, especially the red (goethite, lepidocrociteor amorphous iron oxyhydroxides), the fleecy (jarositeor clay minerals), and the yellow materials (goethite oramorphous iron oxyhydroxides). The dark red materi-als correspond to hematite, and the resinous materials,to As-rich amorphous products with a well-definedchemical composition, close to Fe7(AsO4)4(OH)9 as afirst approximation.

The data used in this study come from analyses car-ried out on several thin sections. In the EMP analyses,we detected no significant concentrations of Mn, Na,Mg and Ca in the materials considered, or these con-centrations were below the limit of detection. Therefore,these elements were not taken into account in the fol-lowing study. Concerning Si, it was very commonlydetected in high concentrations. However, the presenceof Si in our case is due to the presence of primary sili-cate minerals, and the As–Fe-rich by-products we in-tend to study invariably coat these silicates. Thus weconsider Si as a “pollutant” element, i.e., the measuredconcentrations do not reflect the presence of Si in thesecondary products, but the beam used to analyze theproducts is too wide or penetrates too deeply. Thus Simeasured only corresponds to the adjacent silicates. Inorder to avoid the influence of Si on the data during thestatistical procedures, we chose to consider only thesubcomposition [As, S, Al, K, Fe], according to thedefinition of Aitchison (1986), which represents 565data. Among them, 21 were found to be very scatteredand far from the remaining values. These outliers werediscarded before carrying out the satistical analysis.

Statistical procedures

The particular nature of compositional data and thestatistical methods used in this study are presented here

PRODUCTS OF ALTERATION OF MINE TAILINGS, ENGUIALÈS, FRANCE 1137

and summarized in Figure 1. The logratio transforma-tions enable the use of standard multivariate methodsthat all statistical packages contain: we employed S–PLUS (Insightful Corp. 2002) for the clustering of allthe data, and SYSTAT® (SPSS 2000) in the other cases.We also used some personal routines in MATLAB®

language (The MathWorks 2000) largely based on theNEWCODA toolbox provided by Aitchison (2000).

Nature of the compositional data. The compositionaldata are closed; this mathematical dependence leads tospurious correlations and makes covariance matricesunexploitable (Aitchison 1986, Hinkle & Rayens 1995,Pawlowsky-Glahn & Egozcue 2001). In fact, the truecharacteristic of the compositional data is their scaleinvariance (Aitchison 2001, Barceló-Vidal et al. 2001),so that the data are still compositional even if their sumis not constant. This may happen for analytical reasons(hydration of the sample, incompatibility in modes ofmeasurement) or because of the choice of elements(subcomposition). Normalization allows all composi-tions to be represented on the same plane, the simplex.Other drawbacks are the non-linear relationships in tri-angular diagrams, squeezed data and skewed distribu-tions near edges, and proportional errors (Aitchison1986, 2001, Eynatten et al. 2002). Moreover, Euclidiandistances and linear methods are not valid in a simplex

because results may lie outside this closed space (Weltje2002).

The logratio transformations. They offer severalbenefits; distributions become more symmetrical, vari-ances become independent of means, ratios become dif-ferences, and proportional errors become additive.Consequently, different scales of proportions can bejointly used, so that major, minor, and trace elementsand their variances may be compared in a single analy-sis. The mean of logs corresponds to the geometricalmean of original proportions: it better describes the cen-ter of a group than the arithmetic mean (Philip & Watson1988). Only Euclidian and Mahalanobis distances cal-culated after these transformations have the requiredproperties for compositional data (Martín-Fernández etal. 1998). Three logratios matrices contain the sameinformation: the matrix Z of all pairwise logratios, thematrix Y of additive logratios (ALR), and the matrix Xof centered logratios (CLR), with covariance matrices�, �, and �, respectively. For example, from a four-ele-ment composition [A, B, C, D], we get: Z = log([A/D,B/D, C/D, A/C, B/C, A/B]), Y = log([A/D, B/D, C/D]),X = log([A/g, B/g, C/g, D/g]), where g is the geometricmean of [A, B, C, D]. The choice of the working matrixdepends on some properties like invariance, distances,and inversibility (Aitchison 1986).

FIG. 1. Summary of the main statistical methods that can be used on the EMP data. Themethods chosen for this study are in bold.


Exploratory methods. With Principal ComponentAnalysis (PCA), or Singular Value Decomposition(SVD), compositions and logratios may then be dis-played on a single “biplot” (Aitchison 1986, Aitchison& Greenacre 2001). Such biplots are useful in the choiceof some ways to display the data, e.g., a triangular dia-gram representation that best shows the variability inthe data. Here, Z and X give equivalent biplots, but X isthe working matrix; the CLR biplot only displays Dvariables, and logratios are the links between the CLRapices (Aitchison & Greenacre 2001). Biplots have beenapplied, for example, to fluid variation in petroleum(Whelan et al. 2001) and marine micropaleontology(Kucera & Malmgren 1998). Hinkle & Rayens (1995)extended the method to Partial Least-Squares Regres-sion and applied it to organic chemistry and sedimen-tology. Based on the example of olivine compositions,Aitchison & Thomas (1998) have also shown the im-portance of near-zero eigenvalues; they correspond toalmost-constant eigenvectors. Returning to proportions,they can reveal typical relations between elements un-der the form of pseudo-equilibrium constants (Aitchison1999).

Methods of classification. No difficulty arises in us-ing rules of classification found in standard statisticalpackages; the Aitchison distance between compositionsis the Euclidian distance between CLR. The Wardmethod of Hierarchical Clustering (HC) gives compactclusters and clear hierarchies (Martín-Fernández et al.1999, Martín-Fernández 2001). Pawlowsky-Glahn &Buccianti (2002) applied cluster analysis to the chemi-cal composition of a fumarole on the Island of Vulcano,Italy. Mixture models also give excellent results, withoverlapping groups (Barceló-Vidal et al. 1999, Martín-Fernández et al. 1997).

Analysis of Variance (ANOVA) and Linear Discrimi-nant Analysis (LDA). Y is the working matrix and pro-duces identical results whatever the choice of thereference element (Aitchison 1986). In most cases, amajor element or oxide is chosen as the reference, e.g.,SiO2 (Clarke et al. 1989, Verrucchi & Minissale 1995).Ridenour (1999) applied these methods to hydrology,and Guitart et al. (1999) made biochemical comparisonsof two marine vertebrates. Only multivariate tests mustbe considered, because individual tests only concern thecorresponding binary subcompositions. As a conse-quence, the selection of variables must be replaced by achoice of subcomposition. This choice was done usingdiscriminant biplots. The correlations between the vari-ables (here the CLR) and the discriminant factors aresuperimposed on the compositional plot. The best rep-resented CLR on the correlation circle are candidatesfor formation of a discriminant subcomposition.

Confidence regions (CR). A recent review coversthis subject extensively (Weltje 2002). CR can be drawnfrom Y by using multivariate normal models (Weltje2002) or kernel estimates (Aitchison & Lauder 1985,Aitchison 1986). For a three-element subcomposition,

the CR can be displayed in a triangular diagram usingthe inverse ALR transformation (Weltje 2002).Pawlowsky-Glahn & Buccianti (2002) used this repre-sentation with data on fumaroles.

RESULTS

Bulk compositions

The first attempt was to compare visual and statisti-cal classifications based on electron-microprobe data.Unlike Linear Discriminant Analysis (LDA), Hierarchi-cal Clustering (HC) is not based on a predefined classi-fication. It is an adequate method of evaluation if some“natural” populations can be distinguished, without pre-conceived ideas. Euclidian distances between composi-tions were calculated following the Ward method inS–PLUS from the five centered logratios X = CLR(S,As, Al, K, Fe), and used to construct the dendrogram inFigure 2. A distance equal to 17 enabled the creation offive clusters that were compared with the five predefinedvisual groups by cross-tabulation (Table 1a). It seemedfirst that the three visually most important groups, FLE,RED and RES, were split into several clusters. This in-dicates that the one color may correspond to manychemical entities, and that the homogeneity of visuallyrecognized groups has to be verified. Cluster 1, an iron-rich composition, groups together DRD, the greatest partof RED and a small part of FLE. Cluster 4 groups to-gether another major part of RED and FLE, which seemsto correspond to As-rich compositions.

The second stage was to determine the most discri-minant ratios as well as the most discriminant triangu-lar diagram with the help of LDA and of a discriminantbiplot. The variables involved for LDA are the fourlogratios Y = ALR(S, As, Al, K, Fe), whereas the corre-lations between the CLR X and the discriminant factors(scores) are calculated and superimposed to form thebiplot (Fig. 3a). The links S–As and As–Fe are nearly

FIG. 2. Hierarchical clustering of the 544 EMP data (usingWard method and Euclidian distance on the five centeredlogratios). The dotted line defines five clusters.


orthogonal in As and best express the variance betweenthe groups. In this way, we have considered the orthogo-nal logratios log(S/As) and log(Fe/As) (Fig. 3b), and thecorresponding triangular diagram Fe–As–S (Fig. 3c).The subcomposition [S, As, Fe] best explains the visualclassification. The Ward method applied to the threecentered logratios X = CLR(S, As, Fe) shows that theRES group is highly distinct from the others (Table 1b).

This visual classification, which seemed satisfactoryon the whole as a first approach, remains too imprecise.The presence of very different groups having the samecolor is now clearly observed. This is the case for twoof the five groups: the YEL and the RED groups. Twosets could be identified for each of them: YEL1 andYEL2, and RED1 and RED2, respectively. A clusteringcarried out on these sets confirmed this first observa-tion (Fig. 4). Furthermore, with the five visually definedgroups, this global approach allows one to identify twomain end-members, an “iron” and an “arsenic” one.According to these previous results the RED2, YEL2 andDRD can already be classified in the “iron” end-mem-ber, whereas the RED1, YEL1, FLE and RES belong tothe “arsenic” end-member.

Group-by-group analysis

The “iron” end-member. ANOVA and tests use theALR matrix Y, as does LDA (Table 2a). Consideringall the variables, the chemical identity of the three sub-groups RED2, YEL2 and DRD was confirmed: all dif-ferences were found to be significant. However, RED2and YEL2 are not significantly different, considering thesubcomposition [As, S, Fe] (Table 2b). The LDA of thethree groups (not shown here) indicates that the link As–Fe is the most discriminant variable, and that the sec-ond factor corresponds to the variation of K/S and Al/Slogratios in YEL2. A second test was performed, but thistime only with the binary composition [As, Fe], i.e., with

log(Fe/As) variable. Tukey pairwise comparisons wereused in this univariate case (Table 2c), allowing a re-covery between the RED2 and YEL2 groups to be ob-served; RED2 and YEL2 show no difference, whereasthe test is significant in comparison to DRD. This re-covery can be explained in mineralogical terms. Analy-ses by Raman microspectroscopy show that RED2 andYEL2 both correspond to goethite (�-FeOOH) (Fig. 5a),whereas the DRD group corresponds to hematite (�-Fe2O3; spectrum not shown). These results indicate thattwo compounds can have the same mineralogy, a closechemical composition, but a different color. The mainchemical difference between the RED2 and YEL2groups is due to K/S and Al/S variations, whereas themain difference among the three groups of the “iron”end-member is explained by variations in Fe/As.

The “arsenic” end-member. This end-member com-prises distinct populations, each of which contains vari-ous amounts of arsenic. RES and YEL1 groups seemhomogeneous and well defined if we consider thesubcomposition [As, S, Fe] (Fig. 3a). These groups arealso the richest in arsenic. However, YEL1 and alsoRED1 are superimposed on the remaining and widelydispersed group FLE. Thus the major questions involvethe homogeneity of FLE and its relation to RED1 andYEL1. Mineralogical studies indicate that both RED1and YEL1 correspond to Fe-amorphous products en-riched in As (am-FeOOH ± As). However, they arechemically very different (Table 3). An average of theircomposition in terms of iron and arsenic content, themain components of these two groups, can be estimatedby their Fe/As values, which indicate, respectively, forRED1 and YEL1: Fe/As ≈ 10.43 (geometric mean As(g)≈ 7.24% in the composition [Al, K, S, As, Fe]) and Fe/As ≈ 4.08 (As(g) ≈ 18.46%) (Table 4). Thus the maindifference between RED1 and YEL1 is chemical and notmineralogical, which can be explained by two different


components from distinct sources. RED1 is probably aresult of the alteration of pyrite, whereas YEL1 comesfrom the alteration of arsenopyrite.

The dispersion of the FLE group was analyzed byclustering. The dendrogram obtained for this groupclearly shows four subgroups (Fig. 6), and the PCAenables the main axes of variation (Fig. 7) to be deter-mined. The two major subgroups FLE1a and FLE1b arenoted, whereas the less important are FLE2 and FLE3.

FIG. 4. Cluster tree of (a) the YEL (yellow) group indicatingthe presence of two subgroups, and (b) the RED (red) groupshowing also a division into two subgroups.

FIG. 3. (a) Linear Discriminant Analysis (LDA) biplot of thefive visual groups FLE (fleecy), YEL (yellow), RED (red),DRD (dark red), and RES (resinous), with 80% normalconfidence region. LDA is based on additive logratios,whereas elements are displayed as correlations betweencentered logratios and discriminant factors. (b) Represen-tation of the logratios log(S/As) / log(Fe/As), with 80%kernel confidence region. (c) Corresponding triangular dia-gram Fe–As–S.


The subgroup FLE1 shows a slight dispersion, whichessentially reflects a chemical variation due to As con-centrations. FLE1a and FLE1b correspond to two end-members, one rich in As, with the As geometric meanAs(g) ≈ 8.3% (FLE1b), and an other with smaller amountsof As, with As(g) ≈ 6.0% (FLE1a) (Fig. 7, Table 4). Min-eralogically, according to Raman microspectroscopy,we determined that FLE1a and FLE1b both correspondto jarosite (Fig. 5b) that contains adsorbed or copre-cipitated As (substitution of AsO4 for SO4 can easilyoccur in the structure of jarosite: Paktunc & Dutrizac2003). No Raman spectra were obtained for the secondsubgroup, FLE2, involving poorly crystalline (e.g., clayminerals) or non-crystalline (amorphous) products. Ac-cording to a previous study (e.g., XRD), the FLE2 sub-group corresponds to clay minerals; they are known tohave an affinity for As, as is observed here with an av-erage concentration of As(g) ≈ 9.9% (Table 4). Finally,the FLE3 subgroup corresponds to misclassified data andcould preferentially be classified with the YEL1 group.

The test indicates no significant differences betweenthese two groups (Table 3).

Statistical observations carried out on the FLE groupallow three different subgroups to be clearly identified,whereas the visual classification does not distinguishthem. Furthermore, the cluster analysis performed onthe RED1, YEL1 and FLE (1, 2 and 3) groups indicatesthat they are well classified except for the RED1, whichcould easily be confused with the FLE1 group (Table 5).However, a visual classification enabled discriminationbetween these two groups. Thus visual classificationappears essential in this case. The study of these threegroups shows the complementarity of the two ap-proaches (statistical and visual); one method without theother would likely give partial interpretations or misin-terpretations.

Contrary to the two previous groups, RES is, afterYEL1, the least scattered group in terms of the [As, S,

FIG. 5. Raman spectrum of (a) the YEL2 (yellow subgroup 2)and the RED2 (red subgroup 2) products, both correspond-ing to goethite; (b) the FLE1a,b (fleecy subgroup 1) productidentified as jarosite, more or less enriched in arsenic.

FIG. 6. Cluster tree on the FLE (fleecy) group. The subdivi-sion defines four subgroups.


Fe] subcomposition (Fig. 3). This fact is confirmed byPCA (or SVD) analysis of the group. There is no evi-dence for a subgroup structure in spite of the chemicalvariations due to Al and K.

A principal component analysis reveals a near-zeroeigenvalue (the last one), which corresponds to an al-most constant eigenvector with equal contributions ofAs and Fe (Table 5) and a negligible contribution of S.Following Aitchison (1999), we deduce that the ratioFe/As is the fundamental characteristic of this group.RES is the richest in arsenic, with an average Fe/As ra-tio of 1.9 (Table 4); RES is also the only group of the“arsenic” end-member where S varies independently ofAs (and Fe). This characteristic is better displayed witha centered triangular diagram, which prevents a largepart of the data squeeze near the triangle edges (Fig. 8).This transformation retains straight lines (Eynatten etal. 2002).

Evolutionary model

The precise definition of all of these groups enablesus to identify their source components, pyrite or arse-nopyrite. It is well known that these minerals can gen-erate secondary products such as jarosite, goethite andpoorly crystalline materials, such as ferrihydrite(Blowes et al. 1994, Jambor & Dutrizac 1998, Savageet al. 2000). All of these products have greater or lesseraffinities with arsenic and can trap it (adsorption orcoprecipitation: Manceau 1995, Waychunas et al. 1995,Lumsdon et al. 2001). A statistical analysis coupled witha microspectroscopy technique allow us to evaluate thealteration of the primary minerals and to specify whattype of secondary products are generated.

From the previous results, we contend that the FLE1aand RED1 subgroups correspond to the first products ofalteration of pyrite, whereas the FLE1b, YEL1 and RESgroups (“arsenic” end-member) result from the primary

stage of alteration of arsenopyrite (Fig. 9). Furthermore,these products have themselves been altered and havegenerated more “evolved” materials, which representthe “iron” end-member corresponding to crystalline end-products. Thus this evolution indicates a subsequentrelease of As with time and degree of alteration.

FIG. 7. Principal Component Analysis (PCA) covariancebiplot of the FLE (fleecy) group, with representation of thefour subgroups defined by Hierarchical Clustering and theircorresponding 80% normal confidence regions. The foursubgroups are therefore well identified: FLE1a,b, FLE2 andFLE3. Clusters: • FLE3, � FLE1b, + FLE1a, � FLE2.


CONCLUSIONS

In this study, we have shown that a preliminary vi-sual classification of the materials is necessary to sepa-rate the major products of alteration. Nevertheless, astatistical analysis enables this classification to be re-fined in order to obtain more homogeneous chemicaland mineralogical groups. This approach is validated byconfirming some of the groups identified using special-ized techniques of characterization such as Ramanmicrospectroscopy. The complementarity of the twoapproaches is remarkable: only the visual classificationallows one to define the YEL1 and the RED1 groups(both corresponding to am-FeOOH but with differentconcentrations of As), which are chemically very closeto some other minerals groups. A statistical analysiseasily allows us to differentiate some groups with thesame color and to detect the presence of misclassifiedindividuals.

As already observed in some other studies,Aitchison’s logratio method appears to be very efficient.This model allows most of the usual methods of statis-tical analysis to be used, provided that all kinds of lin-ear relationships between proportions are forgotten. Itallows a general model of the different As-bearing prod-ucts of evolution to be established and a discriminationof the precursor minerals (arsenopyrite and pyrite).

ACKNOWLEDGEMENTS

We gratefully acknowledge the PDZR program forfinancial support. Special thanks to Dr. J.M. Bény ofthe CRSCM–CNRS, ISTO/Orléans, for the Raman

FIG. 8. Original (a) and centered (b) triangular diagrams ofthe RES (resinous) group. The centering moves the geo-metric center [g(As), g(S), g(Fe)] / �j gj of the group to thegeometric center of the triangle (g = geometric mean). Theprincipal factors defined in the logratio space are also dis-played by inverse transformation. The independent varia-tion of S is clearly revealed by centering.


microspectroscopy analyses, and to Dr. J. Aitchison,who kindly provided us the NewCoda toolbox and pre-prints at the beginning of this study. Professor H. Briland Dr. K. Ludwig also provided very helpful commentson an early draft of this paper. Constructive reviews andnumerous comments from Drs. R.F. Martin and M.Raudsepp were gratefully appreciated, as well as thosefrom Dr. Chang-jo Chung and an anonymous reviewer.

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Received January 7, 2003, revised manuscript acceptedAugust 12, 2003.

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