Research Article Mineral classiﬁcation from quantitative X ...tsuji/pdf/Tsuji2010_IA.pdf · that...

Research ArticleMineral classification from quantitative X-ray maps using neural

network: Application to volcanic rocksiar_682 105..119

TAKESHI TSUJI,1,* HARUKA YAMAGUCHI,2 TERUAKI ISHII3 AND TOSHIFUMI MATSUOKA1

1Department of Civil and Earth Resources Engineering, Kyoto University, Kyoto, Japan(email: [email protected]), 2Institute for Study of the Earth’s Interior, Okayama University,Tottori, Japan and 3Institute for Research on Earth Evolution (IFREE), Japan Agency for Marine–Earth

Science and Technology (JAMSTEC), Kanagawa, Japan

Abstract We developed a mineral classification technique of electron probe microanalyzer(EPMA) maps in order to reveal the mineral textures and compositions of volcanic rocks.In the case of lithologies such as basalt that include several kinds of minerals, X-rayintensities of several elements derived from EPMA must be considered simultaneously todetermine the mineral map. In this research, we used a Kohonen self-organizing map(SOM) to classify minerals in the thin-sections from several X-ray intensity maps. TheSOM is a type of artificial neural network that is trained using unsupervised training toproduce a two-dimensional representation of multi-dimensional input data. The classifiedmineral maps of in situ oceanic basalts of the Juan de Fuca Plate allowed us to quantifymineralogical and textural differences among the marginal and central parts of the pillowbasalts and the massive flow basalt. One advantage of mineral classification using a SOM isthat relatively many minerals can be estimated from limited input elements. By applyingour method to altered basalt which contains multiple minerals, we successfully classifyeight minerals in thin-section.

Key words: electron probe microanalyzer (EPMA), mineral distribution map, self-organizing map (SOM), volcanic rocks, X-ray intensity maps.

INTRODUCTION

Because mineral textures of rocks as well as theirmodal proportion contain quantitative informationabout rock formation history, it is important toimprove techniques for quantitative determinationof minerals and mineral texture. An electron probemicroanalyzer (EPMA) (e.g. Castaing 1951) hasbeen used to obtain X-ray intensity maps (elemen-tal compositional maps) of thin-sections (e.g.Ozawa 2004), which can then be used to determinethe mineral proportions. Electron microprobe-based techniques have been widely used inmineralogy and petrology because they allownon-destructive in situ analysis with high spatialresolution (e.g. Reed 1993, 1995; Ozawa 2004).

From the X-ray intensity maps obtained byEPMA, mineral distribution maps have been esti-mated by considering the range of X-ray intensi-ties (elemental proportions) of each mineral(e.g. Michibayashi et al. 1999, 2002; Togami et al.2000; Kuroda et al. 2005; Maloy & Treiman 2007;Okamoto et al. 2008). In the case of lithologies suchas basalt, which include several kinds of minerals,however, X-ray intensities of several elementsmust be considered simultaneously to determinethe mineral distribution map. For this purpose, amulti-dimensional modal analysis method for com-paring the X-ray intensities is required (Launeauet al. 1994; Michibayashi et al. 1999, 2002; Togamiet al. 2000; Maloy & Treiman 2007). Furthermore,the backscattered electron image also containsimportant information for the estimation of modalproportions because it relates to mean atomicnumbers (e.g. Robinson & Nickel 1979). However,

*Correspondence.

Received 6 June 2008; accepted for publication 26 December 2008.

Island Arc (2010) 19, 105–119

© 2009 The AuthorsJournal compilation © 2009 Blackwell Publishing Asia Pty Ltd

doi:10.1111/j.1440-1738.2009.00682.x

mailto:[email protected]

it is difficult to use backscattered information formineral identification because the mean atomicnumber (density) of a mineral is determined asan integrated result of its chemical composition.Here, we used a Kohonen self-organizing map(SOM) (Haykin 1994; Kohonen et al. 1996;Kohonen 2001) to map minerals in thin-section onthe basis of X-ray intensity maps as well as back-scattered images constructed by EPMA, becausethe neural network system can classify mineralsby considering several elemental components(X-ray intensity maps) simultaneously.

The SOM is a type of artificial neural networkthat is trained using an unsupervised procedure(Kohonen 2001; Skupin & Agarwal 2007). TheSOM is simple analog of the human brain’s way oforganizing information in a logical manner. TheSOM has been used for the classification of multi-dimensional data as well as geophysical statistics(e.g. lithological classification using several seismicattributes) (Taner 2001; Tsuji et al. 2005). Becausethe SOM constructs two-dimensional (2D) views ofmulti-dimensional data by preserving topologicalproperties of the input multi-dimensional vectorspace, the SOM is an effective tool for clustering

multi-dimensional data. Therefore, the SOM couldbe interpreted as a nonlinear generalization ofprincipal component analysis (Skupin & Agarwal2007). In this study, we applied this classificationtechnique to the basaltic samples obtained on theeastern flank of the Juan de Fuca Ridge duringthe Integrated Ocean Drilling Program (IODP)Expedition 301 (Fisher et al. 2005).

MEASUREMENT WITH EPMA

BASALTIC SAMPLES

IODP Expedition 301 drilled on the eastern flankof the Juan de Fuca Ridge in the Pacific Ocean offWashington State, North America (Fisher et al.2005). Site 1301 (Fig. 1) is on a buried basementridge (second ridge) over crust 3.6 Ma in age and100 km east of the crest of the Juan de Fuca Ridge,which is currently spreading at a rate of about3 cm/year (Davis & Currie 1993). In Hole 1301B,basement samples were recovered from 351.2 to582.8 m below the seafloor, or 86.0 to 317.6 m belowthe sediment–basement interface, and the litho-

Fig. 1 Bathymetric map of the easternflank of the Juan de Fuca Plate. � indi-cates location of IODP Site 1301.

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stratigraphic sequences consist of pillow basalt,massive basalt, and basalt–hyaloclastite breccia(Expedition 301 Scientists 2005). Pillow basalt isthe most abundant recovered lithology and wasidentified by the presence of curved chilledmargins oblique to the vertical axis of the core,with perpendicular radial cooling cracks (Expedi-tion 301 Scientists 2005). The pillow lava frag-ments are aphyric or sparsely to highly plagioclase+ clinopyroxene � olivine phyric, whereas themassive basalts consist of continuous sections0.5–4.4 m thick of similar lithology and are gener-ally characterized by grain-size variations gradingfrom glassy to fine-grained toward the center ofthe flows. Mineralogically, the massive basalts aresimilar to the sparsely to moderately phyric pillowbasalts: they contain phenocrysts of plagioclase,olivine, and clinopyroxene, as well as groundmassand fine-grained phases.

In this study, four thin-sections were preparedfrom marginal part of pillow lavas, three fromcentral part of pillow lavas, and three from massivebasalts. The thin-sections were extracted from thediscrete subsamples used for physical propertymeasurements (Tsuji & Iturrino 2008). The bulkdensities of the discrete samples ranged from 2.69to 2.90 g/cm3, and the porosities ranged from 1.27to 4.84%.

X-RAY INTENSITY MAPS

By scanning samples and displaying the intensitiesof selected X-ray lines, element distributionimages (X-ray intensity maps) are obtained(Fig. 2). The major elements (Mg, K, Fe, Ca, Al, Si,Ti, Na, P, Cr, Mn) and the backscattering values(CP) of the 10 thin-sections were mapped with aJEOL JXA-8900R electron probe microanalyzer.The polished thin-sections were coated with anapproximately 25-nm carbon layer to counteractsample charging. Measurements were performedunder 15 kV accelerating voltage and a 50-nAspecimen current. The diameter of the electronprobe spot was about 20 mm. On each thin-section,the number of mapping points was 600 ¥ 600 andthe mapped area was 1.2 cm ¥ 1.2 cm. When weuse a self-organizing map (neural network withunsupervised training) for mineral classificationfrom X-ray intensities, we do not need to convertthe measured X-ray intensities into absoluteelement concentrations (wt%) by using mineralstandards.

Although the element distributions were clearlymapped by EPMA (Fig. 2), to predict the mineral

distributions, it is necessary to consider X-rayintensities of several elements simultaneouslyusing a SOM. As input variables to the SOM, in thisstudy, we used six-dimensional (6D) vectors con-structed from five elements (Mg, Al, Fe, Si, Ca) andthe backscattering value (CP) (Fig. 2). The SOMsearches, recognizes, and classifies spatial patternsof the 6D elements over the entire EPMA mappingarea on thin-section. There is no restriction to thedimension of the input vector in this algorithm.Although many other chemical elements (e.g. K)could be used as input elements to the SOM, weconsidered these five elements and the backscat-tering value to be sufficient to classify the mineralsin our basaltic samples because other elements arenot decisive for the classification of the minerals inour samples. For lithologies containing many dif-ferent minerals (e.g. altered basalt), more chemicalelements should be used to determine the mineralswith a SOM.

SOM CLASSIFICATION

To classify minerals from 6D X-ray intensities(Mg, Al, Fe, Si, Ca, CP), the SOM operates bythree procedures: (i) unsupervised training proce-dure (Fig. 3a–d); (ii) definition of the classificationboundary in a stabilized neuron map (Fig. 3e); and(iii) mapping procedure (Fig. 3f). In the SOM algo-rithm, we need to prepare the two-dimensionallydistributed neuron map (Fig. 3) in addition to inputmulti-dimensional data. The training procedurebuilds the 2D neuron map, which reflects charac-teristics of input multi-dimensional vectors. Then,the boundary of each class on the stabilized neuronmap is determined by considering the proportionsof elements in each mineral. The mapping pro-cedure classifies minerals by inputting X-rayintensities at each EPMA mapping point to theclassified neuron map. We represent detailed com-putational procedures of the SOM, as follows.

UNSUPERVISED TRAINING PROCEDURE: REFLECTINGTOPOLOGICAL PROPERTIES OF MULTI-DIMENSIONALDATA TO 2D NEURON MAP

The SOM (neuron map) consists of componentscalled neurons which have a weight vector of thesame dimension as the input data vectors (6Dvector in this case). By an unsupervised trainingprocedure, the 2D neuron map will reflect thecharacteristics of multi-dimensional input vectors.This algorithm starts with an unclassified neuron

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map (Fig. 3a). A multi-dimensional input vectorcreated from the selected elements (Mg, Al, Fe, Si,Ca, CP) ‘teaches’ the neuron map; we computedthe Euclidian distance between input vector and

vector of each neuron (Fig. 3b), found the winningneuron, which is the minimum Euclidian distancewith the input vector (Fig. 3b), and updated thevector of the winning neuron and its neighbors to

Fig. 2 Examples of X-ray intensitymaps of (a) marginal part of pillow basalt(24R-1W), (b) central part of pillowbasalt (25R-1W), and (b) massive basalt(18R-2W). The X-ray intensity maps thatwere used for the SOM classification areshown in this figure are Mg, Al, Fe, Si,Ca, and back-scattering strength (CP).The width of each composition map is1.2 cm, and the resolution (pixel inter-val) is about 20 mm.

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have a nearer Euclidian distance with the inputvector (Fig. 3c). All of the neurons within the asso-ciative correction radius from the winning neuronwere modified, and the update (modification)weight decreased as a distance from the winningneuron. At the start of the training procedure, theupdate associative correction radius should be longenough to cover most of the neurons, and theupdate weight should be large. The associative cor-rection radius and update weight were reducedas the iteration number increased. There wereseveral ways to modify the update weight andassociative correction radius (Kohonen 2001). Thisunsupervised training procedure was continueduntil the vector of each neuron stabilized (Fig. 3d).In practical calculations, the number of trainingcycles should be determined considering correc-tion radius and update weight. On the stabilized

neuron map, groups of neurons with similar char-acteristics were identified (Fig. 3d; Fig. 4). Thestabilized neuron map (Fig. 4) represents two-dimensionally the information contained in themulti-dimensional input vectors (Fig. 2) (Kohonen2001).

DEFINITION OF CLASSIFICATION BOUNDARY INSTABILIZED NEURON MAP

The boundary of each class on the stabilizedneuron map was determined (Fig. 3e, black linesin Fig. 4) by considering the proportions of eachelement in each mineral (Table 1), because theSOM merely clusters and classifies the input data(unsupervised training). When we defined theboundaries of each class on the stabilized neuronmap, we did not fix the range of elemental intensi-

Fig. 3 Computational procedure of the Kohonen self-organizing map (SOM). This algorithm starts with (step a) an unclassified neuron map. (steps b,c)Input vectors, each created from the elements of one pixel in the EPMA mapping area, then ‘teach’ the initial classification map (training procedure). Thethin gray arrows from input vector in step (b) represent the computation of the Euclidean distance between the input vector and vector of each neuron. Darkneuron indicated by thick arrow in step (b) represents the winning neuron, whose vector is the minimum Euclidean distance from the input vector. Then,the vector of the winning neuron and those of its neighbors within a correction radius are updated to bring them closer in Euclidean distance to the inputvector (gray neuron in step c). In this example, the associative correction radius is 1; however, we update more neurons in the beginning of trainingprocedure. This training procedure (steps b,c) is continued until (step d) the vector of each neuron has stabilized, and then (step e) groups of similarneurons, ‘classes,’ are identified. (step f) The classified map is then used to define the minerals in the thin-section by finding the winning neuron and itsbelonged class (mapping procedure). This example represents a rectangular grid; however, we used a hexagonal grid in calculation (Fig. 4).



ties of each mineral for all samples, because theelemental intensities (Fig. 2) vary with analyticalconditions. For each sample, therefore, we definedthe relative intensities of each mineral (defined theboundaries among + + +, + +, +, and - in Table 1),based on the range of weight percent (wt%)obtained by quantitative analysis.

Here, we divided the stabilized neuron map intofive classes: (i) pore (po); (ii) glass and clay (gl + cl);(iii) plagioclase (pl); (iv) clinopyroxene (cpx);and (v) magnetite (mt). First, we determinedthe classification boundaries of pores, plagioclase,

clinopyroxene, and magnetite on the stabilizedneuron map (Fig. 4), and then we assumed thatthe remaining unclassified neurons belonged toglassy and clay parts. We could easily determinethe boundary of each class on the stabilizedneuron map (Fig. 4). We did not classify olivinein this study, because the olivine in our sampleshad been completely replaced by a variety ofsecondary hydrothermal alteration phases andwas represented by pseudomorphs of granular tofibrous opaque saponite � celadonite � iddingsite� calcium carbonate minerals (Expedition 301

Fig. 4 Examples of classified neuron maps showing the distribution of each element. These neuron maps represent the classified results of sample11R-1W. In this study, each neuron was a 6D vector (Mg, Al, Fe, Si, Ca, CP). Neurons with similar 6D vectors are adjacent on the maps. The black linesare the interpreted boundaries between classes (minerals). Here, we divided the neuron map into classes of pores (po), glass and clay (gl + cl), plagioclase(pl), clinopyroxene (cpx), and magnetite (mt) by taking into account the elemental proportions in each mineral. Size of white markers on the neuron mapof backscattering value (CP) represents number of the matching (hitting) times for each neuron (Kohonen 2001). Black arrows represent typical neuronsof each mineral which have best-matching characteristics (large number of mapping times).

Table 1 Elemental proportions of each mineral

Si (SiO2 wt%) Al (Al2O3 wt%) Ca (CaO wt%) Fe (FeO wt%) Mg (MgO wt%) CP

Magnetite (mt) - (none) - (none) - (none) + + + (69–70) - (none) + + +Clinopyroxene (cpx) + + + (45–54) + (2–16) + + + (12–21) + (4–19) + + (8–19) + +Plagioclase (pl) + + + (46–61) + + + (23–33) + + (7–17) - (none) - (none) +Glass and clay (gl+cl) + + + (48–56) + (10–17) + + (9–13) + (9–16) + (4–9) +Pore space (po) - (none) - (none) - (none) - (none) - (none) -

The number of + symbols represents the relative intensity of that element in the mineral. A - symbol indicates that the element is absentfrom that mineral. We referred to this table to determine the boundary of each class on the classified neuron map (black lines, Fig. 4). CPrepresents back-scattering strength. We further represent weight percent (wt%) obtained by quantitative analysis of each mineral.

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Scientists 2005). Therefore, the altered olivineshould be classified as clay.

MAPPING PROCEDURE: MAPPING MINERALS WITHCLASSIFIED NEURON MAP

We obtained a mineral distribution map of a thin-section (Fig. 5) by treating the classified neuronmap as a filter. We determined minerals in amapping area by comparing 6D input vectors ateach mapping point (Fig. 2) with the vector of theclassified neuron map (Fig. 4) and finding the class(mineral) of the best match neuron (Fig. 3f). Theclass (mineral) of the neuron with the best matchwas thus determined to be the mineral at themapping point. In this mapping procedure, thevector of each neuron was not updated.

MINERAL DISTRIBUTION MAP

Comparison of the classified mineral maps (Fig. 5)with the backscattering image (CP in Fig. 2)showed that the plagioclase phenocrysts had lowermean atomic numbers (densities) than almost all ofthe surrounding glassy or clay parts. Because clayshould have a lower density than plagioclase(Mavko et al. 1998), most parts identified as glassand clay were likely glassy. The classified mineralsin the thin-sections were confirmed by opticalmicroscope observations (Fig. 6). Because thepixel interval in EPMA analysis is relatively long(~20 mm) compared to the grain size of the basalticsamples, the resolution of the classified mineralmap was low (Fig. 6).

On the classified mineral distribution maps(Fig. 5), we clearly observed mineralogical andtextural differences among the marginal andcentral parts of the pillow basalts and massivebasalts. In the pillow basalts (Fig. 5a,b), plagio-clase phenocrysts occurred singly or in mono- orpolymineralic glomeroporphyritic clots with clino-pyroxene. Most pyroxene phenocrysts (com-monly 0.5–1 mm) had subhedral morphology andoccurred as solitary phenocrysts, intergrown withplagioclase, or within polymineralic glomeropor-phyritic clots with plagioclase (Expedition 301Scientists 2005). Textural differences were ob-served between the central parts of pillow lavas(21R-4W, 25R-1W, 35R-2W) with intersertal tex-tures (Fig. 5b) and the marginal parts of pillowlavas (2R-3W, 4R-3W, 24R-1W, 32R-2W) with hya-loophitic textures with large amounts of glass andclay (Fig. 5a).

In the massive basalts, on the other hand, wewere unable to observe phenocrysts in our thin-sections (Fig. 5c). The groundmass of the massivebasalts is holocrystalline; the mineral maps clearlyshow that the massive basalts included largegrains of plagioclase and clinopyroxene in thegroundmass or fine-grained phase. Texturally, themassive basalts are predominantly intersertal tointergranular with euhedral to subhedral plagio-clase (Fig. 5c).

MODAL PROPORTION

By dividing the number of pixels belonging to eachclass (or mineral) by the total number of pixelsin the mapping area (600 ¥ 600) (Fig. 5), we esti-mated the modal proportion of each sample(Fig. 7). The modal proportion is usually deter-mined under an optical microscope; however,microscope observations consume an enormousamount of time (Maloy & Treiman 2007) and theresults are much dependent on the observer’s skill.

When we performed point-counting methodsto obtain modal proportion, the proportion ofhigh-refractive-index minerals (e.g. clinopyroxene)and opaque minerals (e.g. magnetite) increased(Fig. 7). In Figure 7, we compare the modal pro-portion from SOM classification (closed symbols inFig. 7) and the proportion by point-counting undera microscope (open symbols in Fig. 7). To obtainthe modal proportion under the microscope, wecounted over 2000 points for each mapping area.The high-refractive-index minerals and opaqueminerals are more noticeable compared with thelow-refractive-index minerals under the micro-scope, even though high-refractive-index mineralsare covered with low-refractive-index mineralsespecially when they occur as fine grains. On theother hand, fine-grained low-refractive-index min-erals are sometimes very difficult to classify and inthat case we did not count the mineral but skippedto the next position. Therefore, the modal propor-tion derived from our method is different fromthe proportion estimated in the point-countingmethod. Furthermore, because the clay-filledbubble was counted as a pore in microscope obser-vations, but was treated as clay in the SOM classi-fication, the proportion of pores was differentbetween the two methods (Fig. 7).

The modal proportions of the minerals derivedfrom SOM analysis (Fig. 7) showed that the cen-tral parts of pillow basalt samples (21R-4W, 25R-1W, 35R-2W; Fig. 7b) have similar mineralogical



Fig. 5 Mineral distribution maps classified by the SOM. We divided mapping area of each thin-section into classes (minerals) po, gl + cl, pl, cpx, andmt. Mineral maps with similar lithologies are grouped into (a) marginal parts of pillow basalts, (b) central parts of pillow basalts, and (c) massive basalts.

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characteristics to the massive basalts (11R-1W,15R-3W, 18R-2W; Fig. 7c), in that both containlarge proportions of clinopyroxene and plagioclase.

Although electron backscatter diffraction(EBSD) automatically determines modal propor-tion as well as morphology of minerals (Dingley

2000), EBSD hardly classifies and distinguishesbetween fine glasses, clay minerals, and pores.Furthermore, the results derived from EBSD aremuch influenced by surface conditions of thin-sections; therefore, we needed much time toprepare thin-sections with smooth surfaces.

Fig. 6 Comparison of the classified results (a-1, b-1) and the photomicrographs (a-2, a-3, b-2, b-3) of the yellow rectangular areas in the classified mapof 18R-2W. (a-2) and (b-2) are microphotographs in plane-polarized light. (a-3) and (b-3) are microphotographs with crossed-polars.

Fig. 7 Modal proportions of minerals in (a) marginal parts of pillow basalts (Fig. 5a), (b) central parts of pillow basalts (Fig. 5b), and (c) massive basalts(Fig. 5c). The central parts of pillow samples had similar characteristics to the massive basalts. Open symbols represent the modal proportions derived frompoint-counting using an optical microscope; closed symbols represent modal proportion from SOM classification. The clay-filled bubbles were treated aspores in point-counting and as clay in the SOM classification.



Especially, the altered rock and fault rock, whichinclude much clay and glass, are difficult to polishsmoothly for EBSD measurement because of thefriability of clay and glass.

GRAIN SIZE DISTRIBUTION

By using digitized mineral distribution maps(Fig. 5), we could quantify the grain-size distribu-tion of the minerals and their phenocrysts (Fig. 8).To determine the size and number of each type ofmineral, we counted the number of adjoining

pixels of the same class on the classified mineralmap (Fig. 8). By defining a threshold for the grainsize (number of pixels) of minerals, furthermore,we could extract minerals of a specified size (e.g.phenocrysts). When we thus defined a mineral ofgreater than 10-6 m2 (diameter ~1 mm) as a pheno-cryst, and one greater than 10-7 m2 but less than10-6 m2 as a microphenocryst, we could determinethe number of phenocrysts and microphenocrystsof each mineral in the mapping area (Table 2) onthe basis of the grain-size distributions of the min-erals (Fig. 8).

Fig. 8 Grain-size distributions of plagioclase (pl), clinopyroxene (cpx), and pore space (po). Vertical axis is number of phenocrysts in thin-section(1.2 cm ¥ 1.2 cm). Horizontal axis is size of phenocrysts. The horizontal bar in each symbol represents the range of phenocryst size.

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In this analysis, because the effect of coales-cence of grains of same species can not beneglected, we treated overlapping crystals as asingle crystal. Especially when modal abundancesof the mineral are large (e.g. plagioclase in massivebasalt), the error could be large. To accuratelydetermine grain size distribution of phenocrysts,we should use the EBSD technique, which canidentify grain boundaries between the same min-erals with different crystallographic orientations(e.g. Dingley 2000).

The grain-size distribution of the minerals(Fig. 8 and Table 2) differs between the marginalparts of the pillow basalts, the central parts ofthe pillow basalts, and the massive basalts. Theaverage number of plagioclase phenocrysts in themapping area is 0.50 in the marginal parts of pillowbasalts, 2.67 in the central parts of pillow basalts,and 0.67 in the massive basalt (Table 2). Therefore,in our samples, the central parts of pillow basaltshave dominant phenocrysts.

ADVANTAGES AND DISADVANTAGES OFOUR METHOD

Because we easily define the classificationboundaries on the stabilized neuron map, which

visualizes multi-dimensional topological data in atwo-dimensional way, we can treat lithology whichcontains many minerals. By using the SOM, fur-thermore, we can classify relatively many miner-als by limited input elements. To evaluate theavailability of our method for lithologies whichcontain multiple minerals, we applied our methodto altered basalt within an accretionary complex(Fig. 9). For the classification of multiple mineralsin the altered basalt, we input data of seven ele-ments (Si, Al, Fe, Mg, Ca, Na, K) to the SOM andconstructed a classified neuron map. We thenclassified the mapping area into eight mineralsincluding unclassified minerals and pores: quartz,amphibole, plagioclase, illite, zeolite, chlorite,carbonates, and unclassified minerals (Fig. 9).Although the original minerals in the basalt hadbeen replaced with several new minerals and thetexture had become complex, by our methodwe were able to classify eight minerals inthin-section.

To check the reliability of our method for classi-fication of the altered basalt (Fig. 9), we conducteda numerical experiment (Togami et al. 2000)(Fig. 10). First, synthetic mineral distributionmaps of 50 px ¥ 50 px were prepared (Fig. 10a).In this numerical experiment, we prepared sevenminerals (quartz, amphibole, plagioclase, illite,

Table 2 Number of phenocrysts and microphenocrysts of plagioclase and clinopyroxene in each thin-section

Sample number Plagioclase Clinopyroxene

Pillow margin2R-3W, 142–147 Phenocryst 0 0

361.46 mbsf Microphenocryst 11 24R-3W, 123–128 Phenocryst 1 0



551.88 mbsf Microphenocryst 5 0Pillow center

21R-4W, 62–67 Phenocryst 3 1495.11 mbsf Microphenocryst 15 13



Massive basalt11R-1W, 80–85 Phenocryst 0 0



472.46 mbsf Microphenocryst 98 2

mbsf, meters below seafloor.Here we defined a mineral with >10-6 m2 as a phenocryst, and one >10-7 m2 but <10-6 m2 as a microphenocryst.



zeolite, chlorite, carbonates) and other minerals, asin the case of the altered basalt (Fig. 9). From thesynthetic mineral distribution maps (Fig. 10a), weobtained pseudo X-ray intensity maps by findingthe typical neuron of each mineral on the classifiedneuron map (e.g. black arrows in Fig. 4) andextracting its X-ray intensities. A value randomlysampled from the Poisson distribution, whosemean value was extracted from a typical neuron ofeach mineral (e.g. black arrows in Fig. 4), was usedas a value corresponding to the pseudo X-rayintensity maps (Fig. 10b) (Togami et al. 2000). Forthe mean value of X-ray intensities of the ‘otherminerals’, we used the average value of X-rayintensities of the seven minerals. Then we appliedthe SOM to the pseudo-X-ray intensity maps inorder to classify minerals (Fig. 10c). When wecompared the synthetic mineral distribution map(Fig. 10a) with the classified map (Fig. 10c), weconfirmed that almost all minerals can be esti-mated using our method. However, amphibole wasdifficult to estimate (Fig. 10c) because the amphib-ole does not have distinctive features in the modalproportion and we could not define its clear bound-ary on the neuron map.

Because the definition of classification bound-aries on the neuron map (Fig. 3e; Fig. 4) containsarbitrary assumptions, the maximum likelihood(least-squares) method (e.g. Togami et al. 2000;Michibayashi et al. 2002) has an advantage forlithology that contains a small number of minerals.Furthermore, the method proposed by Togamiet al. (2000) can estimate errors quantitatively inmineral classification.

SUMMARY

We developed a method of mineral classification byEPMA (X-ray intensity) maps using the SOM for awide range of rocks. This method has an advantagein classification of relatively many minerals fromlimited input elemental maps. In this study, weconstructed X-ray intensity maps of basalticsamples, and then applied the method to constructmineral distribution maps from several X-rayintensity maps. Furthermore, we applied thismethod to the altered basalt and successfully clas-sified eight minerals in thin-section. By countingthe pixels for each mineral, we determined the

Fig. 9 Mineral distribution map ofthe altered basalt classified by the SOM.We divided the mapping area into eightclasses: quartz, illite, carbonates, plagio-clase, chlorite, amphibole, zeolite, andunclassified minerals including porespace.

Fig. 10 Procedure of the numerical experiment in order to check the reliability of our method for the classification of altered basalt (Togami et al. 2000).(a) Synthetic mineral distribution maps. These minerals correspond to the classified minerals in Figure 9. Color represents their modal proportions. Themodal proportion of almost all parts of the mineral is 1 (brown). However, the modal proportion of the mineral boundary is assumed to be 0.5 (green), andthe proportion of mineral corner is assumed to be 0.25 (light blue). (b) Pseudo X-ray intensity maps constructed from the synthetic mineral distributionmaps. (c) Mineral distribution maps classified from the pseudo X-ray intensity maps by the SOM.

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modal proportion of the samples. Furthermore, weused the digitized data of the mineral map to quan-tify the grain-size distribution of minerals.

ACKNOWLEDGEMENTS

This research used samples provided by the Inte-grated Ocean Drilling Program (IODP). We aregrateful to IODP Expedition 301 scientists andcrews, especially to A. Fisher, University of Cali-fornia, Santa Cruz, T. Urabe, The University ofTokyo, A. Klaus, Texas A&M University, and G.Iturrino, Columbia University. We also thankIsland Arc Editor H. Maekawa, Osaka PrefecturalUniversity and Associate Editor K. Michibayashi,Shizuoka University for helpful reviews of themanuscript. The base of the program code for theself-organizing map was provided by T. Kohonen,Helsinki University of Technology (Kohonen et al.1996). This work was partly supported by a Grant-in-Aid for Young Scientists (B) from the JapaneseSociety for the Phomotion of Science: 20760568.

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