Canadian Mineralogist Yol.24, pp. 695-708(1986)
MNUIoeTT: A DATA BASE FOR MINERALSAND A COMPUTERPROGRAMFOR THEIR IDENTIFICATION DORIAN G.W. SMITH ANDDAVID P. LEIBOVITZ Department of Geology, University of Alberta, Edmonton, Alberta T6G 283
ABSTRACT MINIDENTis a program for interactive mineral identification and mineral data base management, rewritten in FORTRAN 77, Data have been stored for about 4000 minersl g'egp5, speciesand varieties.Thesedata comprise: composition, optical properties,symmery, cell dimensions, density, Vickers and Mohs hardness,d-values and relative intensities for the strongestfive X-ray powder-diffraction Iines, the PDF number, polymorphs if any, occunences, localities, year frst describedand sourcesof the data. As yet, however, not all minerals have data stored for all properties. The program will generate a list of minerals whose propertiss,lig 'i'i1hin input ranges for an unidentified mineral or display and rank twenty possibleidentities for an unknown. It can also be used to tabulate chosen properties of matched minerals or to tabulate minerals (in the data base)that have certain specified properties. Tests using data for known species to simulate unidentified minerals show high reliability, given accurateinput information, and surprising successevenwith qualitative inputdata. The MTNIDENTsoftware cunently uses about 400 kbytes of memory, and the data baseusedin mineral idOntification usesa futher l0 Mbytes. Running time for a typical identification procedure rangesfrom about 0.05 to 3.0 secondsof CPU time on tle AMDAHL 580/FF mainframe computer, on which the program has beendeveloped.The cycle time of this computer is about 23 ns. Keywords: Fortran 77, computer program, data base, mineral composition, mineral identification,
SoMnrarns MTNIDENT d6signeun progr4mme d'ordinateur pour I'identification desmin6raux et Ie maniement desdorm€es min6ralesservant e ce$e fin. Ce programme a 6t6 r€-6crit en FORTRAN-:71, Lq donn6es rassembl6esconcernent environ 4,000esp&esmin€rales,y comprisvaridtdset familles de min6raux. Ce sont: composition chimique, optique, sym€trie,maille, poids spdcifique,duretd(Vickerset Mohs), distancesinter-rdticulaires(d) et intensit6srelativesdescinq raies les plus intensesdu clich6 X de poudre, numdro de la fiche PDF, formespolymorphes,modesde gisementlocalitds, ann€ede la premidre description et sourcesdes donn6es.A pr€sentlespropri6t6sne sont pasencoretoutesmis€s en placepour tous les mindraux inclus. Ce programmepeut fournir une liste de min6raux dont toute propri6t6 num6rique a une valeur comprise entre les limites d€termindes sur l'dchantillon 6tudi6 ou bien citer et ranger, en ordre de probabilitd, vingt identifications possiblespour tel 6chantillon. Il est dgalementcapablede construireun tableau soit de certainespropri6t6s choisiessur deux 6chantillonsassortis, soit desmin6raux (d6ji dans le systOme)qui possddent
certainespropri6t6s.Lesessaisfond€ssur lesdonndesde min&6u( connusen we de reproduireles propri&dsde I'dchantillond idenlifiersesontaver€sdiguesdeconfiance, dansla mesuredel'exactitudedeI'input; maism0meavec un input qualitatif, le taux de reussiteestsurprenant.Le Mmlowr utilised prdsentenviron400kbytesdem€moire (software)et 10Mbytesde plus pour I'identificationdes min€raux.Le tempsrequispour uned6terminationd'un mindralvarie,typiquement, (temps de0.05i 3.0secondes CPID sur ordinateurAMDAHL 580,/FF,instrumentqui le programme.La dur6edu cycleest a servid ddvelopper d'environ23 ns. Mots-clds:Fortran-77,ordinateur(progtammed'), banquede donn6es,compositiondesmindraux,determination. INTRoDUCTION The identification of mineralshasalwaysbeenone of the recurring tasks of qarth scientistsinvolved in such diverse specializations a$ mineralogy, crystallography, geochemistry, experimental mineralogy, petrology, ore mineralogy, mineral exploration, mineral beneficiation, clay minsvalsgy and even meteoritics. Traditionally, the approachesto identification have been principally through the determination of optical properties and characterization on the basisof X-ray powder-diffraction patterns. Although optical techniques have the advantage of being nondestructive,generallyX-ray-diffraction methods have proved the more powerful and unequivocal. In receutyearsthe power of the computer hasbeenaddedto the X-ray-diffraction techniques, and sophhticatedprogramshavebeendevelopedthat permit identifications on the basisof input information on d-values and line intensities. However, although expert practitioners of the technique can produce satisfactory powder photo$aphs from extraordinarily small quantities of material, sampling selectivity is oflen inadequate to deal with intergrowths on a scaleof a few pm or less,such as are commonlyencounteredin natural rocks. In the optical area, the NISOMI sy$tem (Atkin & Harvey 1979, 1982) is an example of the application of a microcomputer system to the identification of opaque minerals on the basis of a careful measurement of their reflectanceat 16different wavelengths. Once again, however,it is seldompossibleto resolve satisfactorily the finer-grained intergrowths in terms of the reflectances of the component minerals.
695
696
THE CANADIAN MINERALOGIST
Although suchtechniqueshavebeenappliedwidely in many of the fields mentioned above, over the last decadeor two optical and X-ray information has been augmentedmore and more frequently by data on chemicalcomposition.Thesedata havebeenobtained principally using the electronmicroprobe and other microbeam techniques, which have the virtue of being virtually nondestructive and spatially highly selective.Such information, as well as becoming more abundant, has becomemore accurateas the proceduresofmicroanalysis havebeen further and further perfected. The developmentof sophisticated computer software and hardware has contributed greatly to the refinement of microanalytical techniques. However, the next logical step, that of using the power of the modern computer to assist in identifying a mineral tlat hasbeenanalyzed,has not beentaken. MnInBNr was originally conceivedas a compositional data basethat would fulfill this role. However, the advantageof integrating the compositional approachwith othersbasedon optical, X-ray and additional properties,was quickly realized.An on-goingproject involving constructionof a substantial data baseand creation of programs required for the identification of minerals was undertaken. The first working version was describedby Smith & Leibovitz (1984).That FORTRAN IV versionof MINIISNT used a data basethat contained only very limited data for some2000 minerals. The entry of real analytical data for so many minerals represents a major undertakingin itself. It was found, however, that remarkably goodresults could often be obtained using chemical cornpositions calculated by the program itself from formulae that had beeninput; most of the examplesof the applicationsreportedby Smith & Leibovitz (1984)used such calculatedcompositional data. Notwithstanding these successes,it becameapparent that in some casesthe calculated compositionaldata did not produce unambiguous mineral identifications, and frequently the correct mineral was not at the top of the list of probable identities. The task of entering real compositional data for osmany minslals aspossiblewastherefore undertaken as a matter of urgency. Concurrently, basic data were enteredfor as many of the missing mineral speciesas possible,and the entire program was rewritten in FORTRAN 77. This version of FORTRAN is now more widespreadand is considered to be more portable, i.e., less $ystemdependent.It provides a number of important facilities not availablein FORTRAN IV. The modified data base and program form the subject of the presentcommunication. THE DATA.BASE
PARAII4STERS
The problem of mineral classificationhad to be facedin the constructionof the database.Although
the division into classes,groups, speciesand varieties is widely used,the situation is not alwaysso simple and straightforward, Terms such as far'.rily, have al.lbeen supergroup,subgroup'andsubspecies used. To take an elample, cleavelanditeis a variety of albite that might be consideredto be a member of either the plagioclaseor the alkali feldspargroups, both ofwhich belongto the feldsparsupergroup,or perhaps feldspar family, which falls in the tectosilicate classof the silicate minerals. Although possible levelsof nomenclatureare lesscomplex for some other minerals,the basisfor the useof a particular term of classificationis not alwayswell defined. Thus in certain cases,all minerals within a group havethe samestructure (e.9., the garnets).In other groups the structures are similar (e.9., the plagioclasefeldspars),whereasin yet other groups,suchasthe zeolites (which are sometimesreferred to as a family), the structures may be very different although they retain certain features in common. It was eventually decidedfor the purposesof this data baseto adopt a primary division into "types" and species.The term l/pe is used here to refer to a fundamentaldivision on the basisof chemicalcomposition, e.g., silicates,carbonates,oxides, efc. It has been used to avoid the implications of other terms such as class, which has been used in a more restrictedsense,for example,the nesosilicate,inosilicate and cyclosilicateclassesof the silicate minerals. The works of Hey (1962,1963)and Fenaiolo (1982) have been used extensively in allotting minerals to thesetypes. If a mineral has obviously mixed character, it can be assignedto two different types so that a searchbasedon either would bring the mineral to light. Thus, for example, the mineral heidornite, (SO)2CI(OH)2, which has the formula Na2Ca3B5O6 would be listed under the boratesand sulfates,rather than chloridesand hydroxides' since Cl and (OH) clearly play a subordinaterole to the borate and sulfate radicals. A hierarchical schemeof classification has beendevelopedusing other division suchas varieties, polytypes, subspecies,series, subgtoups, groups, families, subclassesand classes.So far, however, it has been implemented only to a very limited extent (e.9., for the feldspars).When it is more complete,it should be of value in permitting rangesof propertieswithin the various divisionsto be examined. At the time of writing, the MINIDENTdata base includesinformation for approximately4000mineral groups, speciesand varieties.Of these,about 3250 are representedby real analytical data. The following parametershavebeenenteredfor as many ofthe minerals as possible with the time and resources available: l. Name 2. Mineral type 3. Formula
Mntloerr: DATA BASE FOR MINERALS
4. Composition (concentrationsof all elements present in weight 9o) Co-ordination number of the cations 6 . Indices of refraction 1 Optic axial angle (2V7) 8 . Orientation of the optic axial plane (for monoclinic minerals) 9. Color (pleochroicscheme) 10. Dispersion 1 1 . System of symmetry t 2 . Spacegroup 1 3 . Unit-cell dimensions and angles 14. Strongestfive X-ray powder-diffractiea linss, in order of decreasingrelative intensity 1 5 . Number of powder-diffraction file @DF) in the JCPDS system 16. Density 17. Mohs hardness 18. Vickers hardnessnumber 19. Reflectanceat wavelengthsof470, 546,589and 650 nm 20. Typical occlurences 2 t . Geographicallocations of samplesfor which data are stored' 22. Literature sourcesfrom which sampledata were obtained 23. Polymorphs 24. Year mineral was flrst described 25. Remarks
43.261 sl Fe Ca AI
F
t{n H Ba NI P
t7.903 6.753 7 .493 5.62t 6.@7 2 . 71 0 1.29t o.490
o. ooo o. 062 o .o r 3 o.ooo o.ooo o.ooo
14.579 t9.380 4.977 1o.992 8 . '175 I .205 7.062 9 . O €I 6.972 8. 250 4.O57 6. 193 2. toa 2.434 1. 1 4 4 2 . 3 2 4 o.185 o.420 o.125 o,263 o. ro5 o.24C o.027 o.o54 o.o24 o. o47 o.ot3 o. 026
Occuraence: C o m m o n l y o c c u r s It appears l n t r a c h y b a s a l t a , ocat Dogo,
Okl
o.281 25. o. 443 5.904 4 1.425 I .436 6 o.620 1.422 a o.946 2.338 4 o.656 2.561 6 r.04t o.747 6 o.454 o.at9 12 o.499 1 2 o.26t o. ta9 o.o87* o.o50 6 o. o20 o.o72 o.934
o. oo2 o.oo4 o. oo4
ln carBptonlt€s. basaltlc dyk€s
: Bould€r Dam. Arizona tslahds, dapan. Uest of t Greenl
Kakujo-san,
697
Certain of the aboveparameters(for examplethe spacegroup, the PDF file number, locations of samples, elc.) are of no use in identification of unknowns. However, it was felt that this information would be of value. In future other data fields may be added. AIso, it will be clear that data will remain absent in certain fields for many minerals. For example,the reflectancevaluesand Vickershardnessnumbers will be available, in general, only for ore minerals. Similarly, the indicesof refraction will not be available for many of the ore minerals. TYPESoF Dane rNrHn Dara Bass Four different typesofinformation havebeenused to constructthe data base.Theseare: mineral data, sample data, general data and compiled data. Mineral data are tlose that are constant and thus common to all samples.Someexamplesare: name, formula, symmetry, space group, PDF number, polymorphs and the year first described.On the other hand, sample data pertain to particular samplesof a mineral, and many such samplesmay be included in the data base. Someexamplesare congposition, indicesof refraction, 2V, color, unit-celldimensions u16 angles,density,reflectancesand Vickers hardnessnumbers.In many instancesliterature sources usedin constructingthe data basepresentonly minimum and maximum values for mineral parameters,
a n(alph
6
5.O 5.5
//@tol
12 6
Also ln monzonlt€ and monchlqulte. I & scorla. Found as cognat€ x€nollths
vo I can ln trachyte.
Copinshay, Orkneys. Tak€notsojl, t apan. Tlkalsl, Gonoura-machi, Japan. Ounedln, New Z€aland.
Frc. l. Example of the listing of compiled data for the mineral kaersutite.
698
THE CANADIAN MINERALOGIST
thesehaving beendetermined from observationson many different samples. Wherever such data are used, they are referred to as generaldata. Note in this context that the data base for a given mineral may contain general data from more than one source.Generaldata arenot necessarilythe most useful but are often the only kind presented.With the exceptionof composition, the examplesof general data are exactly the same as those given above for sampledata. The last category of data is that used by MrNIDEl.rr for identification purposes. These are compiled data, calculatedfrom the data that have been entered into the MrNIneNr data baseas sample or generaldata. Maxima, minima, meansand, in the caseof compositions,standarddeviationsare determined. Where insufficient real compositional information has been entered, theoretical maxima and minima are calculated by the program from the formula that has beenentered. The mean valuesare then replaced by values midway between the minimum and maximum, and standard deviations are then omitted. Figure 1 is an example of the listing of compiled data for the mineral kaersutite. MINIDENTwill generateon requestsuch a tabulation for any of the minerals in the compiled data base. A hard copy of the compileddata baseis generated after everymajor update and kept on hand for referencepurposes.However, sincethe data baseoccupies 10 binders (more than 4000 pages), unnecessary regenerationin this form is avoided as much as possible. To the abovekinds of data might be addeda fifth, data for an unidentified mineral (UM). This is the information about a samplethat is being investigated for identificathat a user will enter into MTNIDENTT tion purposes.Throughoutthe rest of the paper,the term UM data will be used with this meaning.
Souncss or IxronuanloN FoR THEDATA BAsE Many different source$have been used to obtain the information that has been incorporated into the data base. In particular, Deer et al. (1962, 1963, ln8,1982), Dietrich(1959),Embrey& Fuller (1980), Fleischer(1983),Fleischeret ol. (1984),Henry (197), Hey (1962, 1963), Pienot (1979), Roberts e/ a/. (1974)and Uytenbogaardt& Burke (1971)havebeen particularly important soluces. All new minslalg reported n The American Mineralogist since 1966 havebeenincorporated, andMinerologicolAbstracts has been used extensivelyto track down additional information for many minerals. Certain minerals or mineral groups (e.g., the clay minslals) have been coveredin greater detail than othersbecausespecialized data basesintended originally for other specific useshave been incorporated into MTNIDENT. The powder-diffraction data that have been included are based on those in the mineral section of the JCPDSmanual for 1984.Where data are not given there, wherever possible, data have been obtained from elsewherein the literature. Optical properties have beenbasedlargely on those given by Trdger (1979),Winchell (1950, Winchell & Winchell (1951),Roberts et al. (1974),Dietrich (1969),with values for reflectance coming mainly from Henry (1977). THE MINIDENT
PROGRAM
In addition to the data basediscussedabove,the MTNIDENTpackage includes programs designedto enableusersto identify mineralsand to tabulatetheir properties. Two alternative approachesare embodied in the mineral identification software. Theseare Selectedby issuing one or other of the commands MATCH and IDENTIFY.
Al "rlt4
Frc. 2. Energy-dispersionspectrumfor the mineral tugtupite.
699
MtNlnrr+r: DATA BASE FOR MINERALS
different users' interpretations of the data and of thesequalitative terms. The alternativecommandIDENTIFI resultsin a quite different approach, in which the most likely matchesare selectedon the basisof all input data. Whereasthe MATCH procedure will categorically reject any mineral for which any tnput parameterlies outsidethe known ranges,IDENTIFY simply assigns demeritpoints for the mismatches.Up to 20 of the most Iikely matchesmay eventuallybe listed, and the matching index for eachof theseshown. This Total Matching (TM) index is complex and is based on both the degreeof match or mismatchand the importance of the parameters being matched. In its original conception, MINIoENT was intended to identify minerals on the basis of composition. Although, ashas beenexplained,this basis
The MATCH commandsearchesthe data basefor everymineral that exactlymatchesthe input data and permits the user to list these minerals in alphabetical order. To allow some latitude in the matching of an unidentified mineral with minerals in the data base,UM data are normally enteredas a range. For sxampl€'the input Si = 18-22would indicatethat, to be consideredas a match, a candidatemineral would have to contain between 18 and 22 wt.oloof the elementSi. Any minerals lying evenslightly outside this range will be rejected. As many additional stipulations as are wished may be addedto minimize the number of matching minerals that will be listed. If the accuracyof UM compositionaldata is dubious, then the qualitative terms major (3-10090), minor (0.1-590)and trace (< 190)may be entered. The overlap in theselanges is intended to allow for
TABLE Cl.majonleavelnatch Af.majorlw Sl.majorlu ) Ident c o n m a n d o ! ? u n k n o w n l w o ' m a j o r l w N a . n a j o r l w 3727 not match€d, 15 match€d. ) 3 7 4 2 mfnerafa examlned, O lgnorsd, connandro? tabulat€ unknom nm6 fornula E O u Na u Al r Sl v Cl
MiUar[rot
)1,
A l,
12U41\V
l t )U4
r gudrd
:
DAVYNE Na,ca, k)8A l65 .|6024(cl, So4, co3)2-3 DELHAYELITE Na,K) lOCa5Al55l32O8O(Cl 2, F2,So4)3 : l8H2o DIPYRE ( N a , C a ) 7 ( S l , A l ) 1 2 0 2 4 ( S o 4 , O H , c o 3 , C l) g : H 2 o FRANZINITE GIUSEPPETTITE( N a , K , C a ) 7 - 8 ( S r , A r ) r 2 0 2 4 ( S O 4 , c r ) l - 2 HACKiiANITE (Na,ca)7-8( AI, s r ) r 2(o, S)24( so4, c I 2, IoHl 2 ) LAZUPITE ( N a , c a , K ) 8 ( S I , A l ) t 2 0 2 4 ( S o 4 , C o 3 , CI , o H ) 4 : H 2 o LIOTTITE MARIALITE 3tlaAlSl3(,6:NaCl MICFOSOI{MITE ( N a , c a , K ) 7 - 8 ( S l , A l ) 1 2 0 2 4 ( c l , S o 4 , c o 3 ) 2 - 3 ( N a . C a , K ) 9 ( s I , A I ) I 2 0 2 4 ( [ o H ] 2 , S o 4 ,c o s , c I 2 ) 3 : SACROFANITE ( N a , c a , K ) 4 A I 3 ( A t , S I ) 3 s I 6 0 2 4 ( c I , F , o H , c o 3 , s O 4) SCAPOLITE SODALITE
TABLE
5.293 3.91 2. 197 o. 36 o. 7a 6,333 o.47 2.57 3.O95 4.5 o. 69 2. to6 6.021
12.241 5 .544 17.507
2
)Id€nt command'o? unknownlu O'majorlw Na.majorlr Al.majorlw Sl.maJorly Cl.@Jorlsav€lldentlfy )3742 nlneratB exanlned, O lgnor€d, top 20 ld€ntlfied. connandoo? tabulat€ uhknown len 13 name tm l€n 44 fornula tl O u Na v Al v Sl y Cl )ld€nt
8A | 65 I 6024 (C I , SO4, CO3) 2-3
AFGHANITE CROSOMMITE SODALITE TUGTUPITE LIOTTITE SCAPOLITE ZEOLITE FERSUITE TUNISITE ZUNYITE ZZONITE dUSITE LEIFITE * FoFnula truncated.
( N a , C a , K ) 8 ( S r , A r ) r 2 0 2 4 ( C l , S O 4 ,C o 3 ) 3 : H 2 O SNaAlSlSOS:NaCl (Na,Cs,K)7-8(Sl,Al
) 12024(cl,So4,
co3)2-3
Na4B€AlSl40l2Cl (Na, Ca,K)8( Sl . AI ) I 2024( So4, Cos,c l, oH)4 :H2o ( N a , K ) l o c a s a 1 6 5I 3 2 O s O ( Cl 2 , F 2 , S O 4) 3 : l 8 H 2 o | 3S I 9024C I ( N a , C a , K ) 4 A l 3 ( A I , S r ) 3 5 1 6 0 2 4( C l , F , o H , c o 3 , S o 4 ) ( N a 2 . K 2 ,C a , B a ) ( A l , S l , 0 2 ) ? : ? H 2 O ( c a , c e , N a ) ( N b , T a , T I ) 2 ( O , O H ,F ) 6
| 4 [ c o s] 4 [ O H I 8 C r A r r 3 s r 5 0 2 0 ( o H , Fi s) c r l N a I O . s - 2 [ c a J 2 - 3 . 2 t A l] 4 . 5 - s . 4 [ S l ] 6 . 6 - 7 . 5 . . . * ( c a , N a , K ) 5 ( s l , a I ) 6 0 1 5: 5 H 2 o Na2(Sl,al,Be)7(O,OH,F) 14
41.302 42,743 4t.555 45.622 46, 172 40.638 43. O€4 44.O75 47.534 45.44r 45 .949 44.434 14.726 45. 455 60. I r6 51.381 46.7 49.252 51 .424
I .4aa 1 4 . 4 6 9 9 .485 ! 3.499 t8.46 t6,832 7 . 4 4 5 'to.979 4 . 2 4 9 u,425 17.507 r 7.03 l8 .o94 6.274 5 , 9 1 3 13. lBtt 4.ga 2.374 5 . 7 5 9 12.824 5.544 13,986 so.o 43.533 3.aal 16.742 14.9 3. 539 1 6 . 8 3 1 . 0 7 6 30.oaa 2.944 | 3.893 't3.337 9,47 5.Oat
. t4 . 6 8 6 17.34 21.46 1 4. 2 1 1 7. 2 7 1 23.446 14.262 24.547 25.O21 23,968 43.746 16. so7 o,5 I | .265 22.216 t3,733 33.936
.293 4.428 6.33S 3. OS5 4.5 6,02t 6 ,66 2.47 3.91 2. 197 2. to6 o.997 5.303 4.4 o,62
700 TABLE
THE CANADIAN MINERALOGIST 3 T U G T U P I T E C O M P O S I T I O N( W T . % )
(1) H Be
o
Na Mg AI
5r
s
cl Ca K F€ Ga TOTAL (l)
4 0. 7 1 $ t8.06 6.32 27.6 7.85
iOO.OO
(2) n.a. 1.92 41.43 t8.43 n.d. 5.78 24.61 o. 02 7.4't n.d. n.d. n.d. n.E.
'too.oo
(3) o. 17 1,92 42.76 1 7. 9 7 o.06 6.23 2 3. 6 9 o. 17 6.62 o.18 o.22 o.ot roo.oo
probe analysl6 Senl-quantltatlve electnon IEDAROI', proceBsed program by ln-houa€ (2) Quantltatlv€ p r o c o sa€d analyal6 thnough EDATA2 (5mlth & Gold, 1979); recalculat€d to loo% from analytlcal totat of 99.62%, (Be flx€d at kno{n value). (3) lnformatlon avaltabl6 on actual compoaltlon of tugtuplte average of I lt€natune analysea recalculat€d to tOO%. n.d. not det€ct€d. n.a. not aought. * - calculated by dlfference from IOO%.
dure can also be called upon to rank the minerals that havebeenoutput by MATCH, in order of probabiliry. This is a relatively simple and comparatively inexpensivetask oncethe'list of possibilitieshasbeen gxeatlyreducedby use of MATCH. Electron-microprobedataare now very coulmon and often of high quality. Moreover, the advent and perfectionof the energy-dispersion spectrometerhave further increasedthe amount of information that is obtained, not only quantitative but also semiquantitative and qualitative data. Becauseof the importance placed on compositionalmatching, a special facility has been included in the program whereby the listing of analytical data output by the dataprocessingand matrix-correction software of an electron microprobe can be entered directly for use by MATCH or IDENTIFY procedures the MTNIDENT without the need for intervention by the user. At present, compatibility with the output of EDATA2 (smith & Gold 1979)hasbeenesrablishedand resred becausethat program is capable of the simultaneous integratedprocessingofenergy- and wavelengthdispersion electron-microprobe analytical data. However,with very minor modification, the output from most such programs should be suitable for entry into theseMNlosttr procedures. For earth scientists(and others) using an energydispersion spectrometer in a qualitative role, offers the possibility of determining conMTNIDENT veniently and rapidly what are either the possibleor the most likely identities of the mineral leing examined. This can be done by entering MAJOR, MINOR or TRACE for the concentrationsof each of the elementsobservedin an energy-dispersion spectrum,or alternatively enteringapproximateconcentrations with due allowance being made for anaIytical uncertainties. The MNInsNr data base,in addition to the fields previously described, also contains a synonymy. Entry of a synonym will result in the generally acceptedname being supplied. Entry of a discredited mineral name will result in a mes$ageto this effect being printed, usually together with a very brief explanation or reference. If information about a mineral is requestedby useof the synonym(e.9., disthene for kyanite), a messagewill be printed giving the acceptedname, and then the data requestedwill be listed.
hasbeenbroadenedto embracemany other properties, a bias toward compositionhas been retained. Thus in calculating the matching indices, gireater weight is placedon the correctnessof a compositidnal mdshing index (CM) than on the other properties match (OPM). A third index, the information matching index (IM), is basedupon the proportion of"the properties entered for an unknown that were matchedwith the information in the data base.As the data basebecomesmore and more complete, the importanceof the IM index will decline.The total matchingindex (TM) is a weightedcombination of the precedingthree indices.It should be noted that only thoseparametersof an unidentified mineral for which data are entered will affect the matching indices. Thus if no compositional data at all were available and perhapsonly optical or X-ray data (or both) were entered,an excellentTM index could still !s eltainsd for a particular mineral in the data base. Of thesetwo approachesto mineral identification, the MATCH procedure will generally lead most quickly to a positive identification or to the largest reduction in the number of possibilities,provided that ampledata of good quality are input. It should be noted again, however,that the input of erroneEXAMPLES oF APPLICATIONS oF MINIDENT ous data or of data with an inadequate margin of uncertainty $pecifiedwill causeMATCH to fail. For As an illustration of the different possible exampleif the Mohs hardnesswere enteredas 4-5, and the value storedin the compiled data baseis 5.5, approachesto mineral identification by MINIDENT, data for tugtupite NaaBeAlSiaOl2Clhave been then that mineral will be excludedfrom the list of possibilitiesprinted. entered as if it were an unidentified mineral. This Where few data are available or where the data mineral was chosenbecauseit is unusualand thereare qualitative in nature, the IDENTIFY procedure fore unlikely to be recognized by the averageuser will commonly be found the most useful. This proce- on the basis of its hand-specimenpropertiesor its
Mu{IoeNr: DATA BASE FOR MINERALS
701
TABLE )Ident commando'? unknownlv 0=35-45lNa=16-20lw Al=5-8 w Si=24:gOlw Ct=e.e-g.alsavelmatcn )unknown input"? 7 match )3742 minenals examlned, O ilnored, 3735 not nitcheo, cornrnandlr? tab unknolrn nane tm u O I Na rJ Al e Sl r,, Cl )Ident TT unidentlfied FRANZINITE GIUSEPPETTITE LAZURITE LIOTTITE ITIICROSOtTtMITE SACROFANITE
o
Ne
35 .9
67.4 71.O 60.4 6t.2 70.7 73.8 100.o
45.O 45.52I 43.56 43,50't 44.O75 46.172 44.734 43, 064
optical properties,or, for that matter, on the basis of its energy-dispersionspectrum(wheretle presence of Be will not be apparent, exceptin the unlikely circumstancethat a high-quality 'windowless' detector is used).Figure 2 showsthe spectrumrecordedon an Ortec EEDS II energy-dispersionsystemattached to an ARL SEMQ microprobe operatedwith a 15 kV elestron-acceleratingpotential. It is a simplematter for an analyst to identify the peakspresent, and evenvery limited experiencewould be sufficient for an analystto recognizethat all the labeledelements are presentin major proportions. Theseelements were entered into the MATCH procedure, and the resultsobtained are shownin Table l. Note that trace and evenminor elementsshould not be usedin the MATCH procedure to avoid the possibility of a mineral being rejectedsimply becausenone of the samplesforming the data basehappenedto contain such relatively unimportant constituents. When the same data are put through the IDENTIFY procedure, trace and minor element concentrations can be included; their presenceor absencein data-base sampleswill not have a disproportionate effect on the calculated matching indices becauseweightings are reduced as concentrations decrease. Table 2 showsthe results of processingthe qualitative data for tugtupite through IDENTIFI. Table 3 shows the results obtained for tugtupite when the acquired spectrum has been processed through an in-house program for semiquantitative analysis. This program takes a very simplified approach to background fitting anddverlap correction and makes no attempt to calcqlate matrix corrections. It is found that for most silibatesthe results obtained are accurate to about I l59o relative, sometimesbeing worse, but often appreciablybetter. Note also that Be will not appear in the results becausethe BeKradiation is too soft (i.e., of far too low an energy)to passthrough the detectorwindow, the Au contact-surface film and the Si dead layer. The resultsof MATCH and IDENTIFY, with the datain Table3 asinput, areshownin Tables4 and 5.
15.O 20.o 8,531 1o.66 14,429 5.913 8.249 12.241 IA
6CI'
5.O nal 13.342 15.I t5 t4.6 t3 13.189 14. O25 13.2 e
,t'fa
24.O 30. o 15.164 15.542 15.201 14.262 14.21 15.454 '?
6.3
o.36 o.7a o.47 2.57 4.5 o.59
AAE
TABLE Ident command" n? unknown Unknovrn lnputu"? w O=4O.71 Unknown Inputn"? w Na=18.06 Unknown input'r"? w Al=6,32 Unknown lnputou? w 51.27.06 Unknown lnputro? w CI.7.85 Unknown Inputn"? Save Ident commandon? ldentlfy 3742 mlnerals examlned, O 'lgnored, top 20 ldentlf led. Ident command!n? tabulate -on? unknown name tm lfled
sample
TUGTUPITE \,ADEITE EL I SAVETI NSKI TE SAPPHIRINE-.ITC A L K A L I- F E L D S P A R SCHULENBERGITE PLAGIOCLASE T E T R A K A L S I LI T E E F E R R O - A N T H O P H YI T LL I TE I O-ANTHOPHYLL MAGNES SPODIOPHYLLITE MOUNTAINITE C LI N O F E R R O S I L I T E CL INOHYPERSTHENE GIUSEPPETTITE KARPINSKITE SACROFANITE M C G I L LI T E S O D A LI T E
77.9 70.8 68. 1 67 .6 66. 't 65.6 64.s 64.3 62.A 62.4 6t.4 61.2 61.1 6t. l 60.4 58 .8 54.4 56.7 56 .2
It should be noted that in Table 4, for example, the elementconcentrationsaxethe averagesor medns for the mineralslisted. In the caseof minerals for which there are no real analytical data stored, these values will simply be the meansof the maxima and minima calculated from the formula. In the caseof minerals where there are only a few real data, the
702
THE CANADIAN MINERALOGIST
calculatedmeansare replacedby the actual averages, but the calculatedminima and maxima are retained. In Table 4 it will be observed that tugtupite is the only mineral for which the mean concentrations are consistentlycloseto the input values. The TABULATE facility also permits minimum andmaximum valuesto be displayed. In the interest of conserving space,this hasnot beendonein the tablesreproduced here. Resultsof a fully quantitative analysisof the s4me material processedthroueh EDATA2 are also shown in Table 3, Here the Be content was fixed at a known value. However, the common procedureof assuming that the difference betweenthe analytical oxide total and 1@90correspondsto water could havebeen taken equally well. Sucha false assumptionwill have only very minor effectson the matrix correctionsand the accuracy of the analysis for other elements becausethe BeO actually making up the missing material and the H2O assumedare of similar averageatomic number; neither would be involved significantly in either absorption or fluorescencecorrections. In addition to entering the quantitative analyticat data here, the strongestX-ray-diffraction lines measured from a powder photograph by an undergraduate student have been added, and the combined input has been processed through MATCH and IDENTIFY to give the resultsin Tables 6 ard7.
The presenceof a doublet, not recognizedby the student, resultedin the omission of one of the strongestfive lines, a sigrrificantly larger error than would otherwisehavebeenmade in the dvalues for the one component of the doublet that was measured,and an error in the sequenceof the strongest lines. This can be consideredin a senseto be a 'worst case'in that normally a much more experiencedmineralogist would be making tlre observations.Furthermore, all of the d valueswereobtained from a powder photograph using a simple plastic overlay scale,and the relative intensities were estimated visually. Subsequent tests using the X-ray data alone showed that tugtupite was, nevertheless,one of the most likely of a handful of possibilitieslisted by IDENTIFY. Finally, optical data obtained by the same student (indices of refraction, symmetry and color) were added to the data, which were then reprocessed10 give the results shown in Tables 8 and 9. As can be seen from this seriesof illustrations, MNInsNr always included tugtupite as one of the possibilities and IDENTIFY quiclly homes in on it as a highly probable candidate as the quality and amount of data entered are increased. As mentionedearlier, MINIDENToffers the possibility of tabulating information from the data base which, for some reason,is of interest to the user. As an exampleof this application, Table 4 showsthe name plus O, Na, Al, Si and Cl concentrations as
TABLE ) I d e n t c o m m a n d " " ? u n k n o w n l w o = 4 1 . 4 4 1 w - N a = 1 8 . 8 31 w A l = 5 . 5 - 6 . O )Unknown inputn"? w S1=24:251w S=O.02lw Cl=7. 2-7 .Glsavelmatch ) E l e r n e n t S w l t h w e l g h t o f O . O 2 O %h a s b e e n l g n o r e d ( C U T O F F = 1 - O % ) . exanlned, O ignored, 3736 not matched, 6 matched. 13742 minerals )Ident command[n? tab unknown name tm w O w Na w Al w S'l w S w Cl
s AI si 7.2 o. 02 24.O 5.5 14.642 or)9 41 FF'A 2A.A 7.6 19.O18 6.O 3 .973 o.78 1 5 . 11 5 1 5 . 5 4 2 10.66 4 3 56 GIUSEPPETTITE 62.7 6.899 o.47 14.429 14.613 15.201 49. I 4S sol LAZURITE 2.57 3.468 13. 189 14.262 5.913 5 1 . 7 44 07s LIOTTITE 4.5 2.331 14.21 r4.o25 a.249 MICROSOMMITE 4 7 . O 46 172 3.112 o.59 15.454 62.5 44 7 5 4 12.241 13.2 SACROFANITE o _3 3 4 3 ()64 1 A- O 9 4 6 . 2 7 4 23.446 TUGTUPITE 99.2 I d e n t c o m m a n d ini ? u n k n o w n I d - v a I u e s = 3 . 5 5 , 6 . 15 , 2 . 5 1 , 2 . O 3 | s a v e I m a t c h and 6 minerals examlned, O lgnored, 5 not matched, I matched. unknown len 13 name opm d-values Ident commandon? tabulate Name unldentifled
Iunldentlfied le I
I
TM
o
Na
41.026
6. O88
2.4'35
2.O1 2. 2.5
2.42
703
MndosNr: DATA BASF FOR MINERALS
TABLE
1 5l =24.61 ) I d e n t c o m m a n d r " ' ?u n k n o l r n l w O = 4 1 . 4 41 w N a = . | 8 . 8 3 1 wA l = 5 . 7 S . w ) U n k n o w nl n p u t n " ? t , S = 9 . 0 2l w C l = 7 . 4 1 l d - v a l u e s = 3 . 5 5 , 6 . 1 5 , 2 . 5 1 , 2 . 0 3 , 2 . 3 2 5 )UnknownInput"u? savel identlfy )3742 minenals examlned, O lgnored" top 20 ldentlfled. ) I d e n t c o m m a n d o " ?t a b u l a t e u n k n o w n n a m e t m c m o p m l m d - v a l u e s
unldentlfled
sample
TUGTUPITE S A P P H I R I N E -I T C F E R R O _ A N T H O P H YILTLE PLAGIOCLASE CL I NOHYPERSTHENE SCHULENBERGITE SPODIOPHYLLITE ALKALI -FELDSPAR SODALITE SERPENTINE FOI{LERITE MCGILLITE UM.7 GIUSEPPETTITE UM-9 JUSITE ISTISUITE NOSEAN P S E U D O T H U RNIG I T E
3.514 6.O88 2.445 2.O t
97.O 74. 1 64.8 63.9 63.4 62.8 62.6 60.5 56.9 55 .9 55.3 54.3 53.7 52 .6 52.2 52- 1 52.O 5t.4 51.2
99. O 94.1 71 .4
[email protected] 65 .9 100.o 6s.3 roo.o 6 4 . 6 'too.o 81.6 30.9 63.7
[email protected] 6t.8
[email protected] 59 .4 47.5 56.7 100.o 56, 1 100.o 67.8 40.4 54.4 too.o 6t.6 55.3 53.O
[email protected] 63.4 46.4
r o o . o3 . 5 2
6.13
3.57
2.5
2-O2
t86 3.54'l 3.209 2.7
2.527
88.9
90.o 87.5 88.9
roo.o 7.
90.o 87.5
[email protected] 9t .7 9t .7 '
[email protected] 9r .7 100.o 9t.7
[email protected] s 2 . 8 100.o 9 1. 7 58. I 65.2
[email protected] 52.1 100.o 90.o 68.4 23.9
3.62
6 .27
2.O9
2.37
2.56
2.56
7.16
2.89
3.57
2.11
9.712 3.446 3.126 2.141 6.42 2.8t
2.3
3.64
2.55
.t.62
3 .71
2 .63
6.45
9.09
2.87
4.75
2.35
1.87
1.44
9.58
NOTE: mlnerals for whlch there are no d-values tend to be favoned b€cause there cannot be a mlsmatch r{lth the unknown d-values.
TABLE ) I O e n t c o m m a n d n " ?u n k n o w n l w H 2 O = 1. 9 1 l w O = { 1 . 4 4 1 w N ? = 1 8 . 8 3 )Unknown lnputn"? w Al=5.5-6.Olw S1=ia-2Slw S=O.02lw Cl=7.2-7.6 )Unknown lnputn"? d-values=3.55, 6.15, 2.51, 2.03 )Unknown lnputo u? n(onega)=1.5 ln(epsl lon)=1 .5 hexagonal )Unknown lnputn.? symmetnyTtetnagonal, tnlgonal, )Unknown lnput" r? colour(orhega)=coloun less )Unknown inputn n? coloun(epsi lon)=661ounless I savelmatcn )Element H wlth welght of O.216% has been lgnored (CUTOFF=l.O%). ) E l e m e n t S w t t h w e t g h t o f O . O 2 O %h a s b e e n l g n o r e d ( c u t o f f = 1 . O % ) . )3742 minerals examined, O lgnoned, 3741 not matched, I matched. )Ident command't'r? tabu'l ate unknown name tm cm opm lm )-nu? n(omega) n(epsl lon) symmetry
un ldent i f
Multiple
I
I
Tetn
704
THE CANADIAN MINERALOGIST
TABLE
) I d e n t c o m m a n d " " ?u n k n o w n l wH z O = I . 9 1| w Q = 4 1 . 4 4 ) U n k n o w nl n p u t " " ? w N a = 1 8 . 8 3 1 wA l = 5 . 7 8 ) U n k n o w nl n p u t " r ' ? w S 1 = 2 4 . 6l l w S = O . O l2w C l = 7 . 4 1 ) U n k n o w nl n p u t n u ? d - v = 3 . 5 5 , 6 . 1 5 , 2 . 5 1 , 2 . O 3 , 2 . 3 2 5 ) U n k n o w nl n p u t " " ? n ( o m e g a ) = 1 . 5l n ( e p s l l o n ) = 1 . 5 ) U n k n o w n l n p u t " " ? s y m m e t r y = t e t r a g o n a l, t r l g , h e x )UnknownInput" "? coloun(omega)=colourl€as )Unknowninput" "? coloun(epsl lon)=qslourless )Unknownlnput" n? savel ldentlfy )9742 mlnenals examlned, O lgnoned, )top 20 ldenttfied. )lAent commandtrtt?tab unknown nane tm cm opm im Name unldentified
TM
CM
94.5 64.4 50.2 49.5 42.4 40. 5 39.7 38.3 97 .7 36.8 36.8 33.9 32.6 ?2 .4 32.2 32.O 31.8 31 .8 31.O 30. 1
94.1 98.2 64.2 100.o 5 9 . 4 7 0 .o 58.2 7 0 .o 45. I 100.o 47 .A 70.o 42.4
[email protected] 59.4 o.o 58.2 o.o 57.O o.o
OPM
sample
FOWLERITE F E R R O - A N T H O P H YILTLE FERRO-HORNBLENDE GARNET S P O D I O P H Y LI L TE A L K A LI - F E L D S P A R M A G N E ISO- A N T H O P H Y LILT E M A G N E ISO _ H O R N B L E N D E CLINOFERROSILITE C LI N O H Y P E R S T H E N E uM-256 T H O R T VIET I T E PHOLIDOL ITE GIUSEPPETTITE SACROFANITE POLYLITHIONITE FRANZINITE W A D S L EIYT E
well as the TM index for each mineral listed. Such a tabulation could also be useful, for example,in suggestingto a user other propertiesthat might be measuredas a meansof distinguishingamong the various possibilities. Another possibleapplication of this tabulating facility is illustrated in Table 10,which lists the name, formula, density and PDF numbers of all known hydrated Be minerals. These are here arbitrarily definedascontainingmore than 0.590Be and 0.190 H. Note that a few mineralsthat may containminor Be and H, but that are not truly hydrated Beminerals,appearin this list. It would be a simplenext step to get the complete composition of any or all of the minerals shown in Table 10. Becauseofthe wide range ofparameters that have beenincluded, the data baseoffers the possibility of
s7.o
o.o
88.9 66.7 66.7 70.6 70. o 68.7 57. 1 86.7 88.2 46.7 86.7 71.4
49.7 18.7 48.9 o . o ' too.o 34.5
[email protected] 64.7 56.3 87.5 38.6 4.7 aa.2 47 .3 45.9 12.3 90.9 88 .9 37.O 63. I 47 .7 o.o 90.o 35.4 60. 5
examiningthe patternsof variation of any parameter with any other. A good exampleis provided by calculation of the Gladstone-Dalerelationship (Mandarino 1979).In Figure 3, this relationshipbetween mean index of refraction with density and composition hasbeenplotted for all mineralsin the data base that contain more than 10 wt.9o oxygen. Table 11 showsthe "compatibility index" for this large group of minerals,in terms of the percentageof the group showing superior, excellent, good' fair and poor compatibility, as defined by Mandarino (L979). However, no attempt has been made to apply the special constants recommendedby Mandarino for 'general' consinosilicatesand nesosilicates:the same tants have been used throughout. Table 11 and Figure 3 are useful not only for demonstrating the wide applicability of this Gladstone-Dale relation-
MINIDEN"I:DATA BASE FOR MINERALS
TABLE
705
1O
) Ident conmandon? unknownI w Be=O.5- lOO.Ol w H=O.I -
[email protected] I save I match )3742 mlnerats examlned, O tgnored, 3693 ;ot matched. 49 matched. )Ident commandd!? tabulate unknown name w Bs w H forrnula unldentlfled
sanple
ALLANITE A M I N O FIFT E 2.234 BAVENITE 2.202 BAZZT'|E 4.81 BEARSITE 6. O35 BEHOITE 20.94s BERBORITE r4. 161 BERGSLAGITE 4.644 't4.92 BERTRANDITE BERYL 4.513 B E R Y L ILT E 14.413 B E R Y L ILU M - M A R G A R I T E o . 4 2 2 BITYITE 1.792 BOIdLEYITE 2.765 CHIAVENNITE 3.351 EPIDIDYUITE 3.995 EUCLASE 6.781 EUOIDYMITE s.fJ74 FAHEYITE 2.753 FRANSOLETITE 3.351 GLUCINE 10.497 GUGIAITE 3 . 3 1I HAMBERGITE 19.25 HARSTIGITE 7.11 HELBERTRANDITE 12 . 3 0 9 HERDERITE 5.652 HINGGANITE 3.783 HINGGANITE-(YB) 3.879 -HERDER HYDROXYL I TE 5.7A2 IDOCRASE o.262 dEFFREYITE 2.919 dOESMITHITE 1.71fJ KARPINSKYITE o.93 LEIFITE L 369 LEUCOPHANE 3.956 LOVDARITE 2.4A6 MELI PHANITE s.764 MILARITE 1. 8 9 1 MORAESITE 9. tog R H O DZI I T E 4.373 ROSCHERITE 4.634 SEMENOVITE 2.964 SORENSENITE 2.743 SPHEROBERTRANDITE 1 6 . 2 8 6 TI PTOPITE 5 .441 TUGTUPITE 1.928 U R A L O LI T E 6.7 17 VAYRYNENITE 4.542 * Formula truncated.
o . 2 3 7 c e , c a , Y ) 2 ( A l . F e ) 3 [ S l 0 4 30H o , 7 2 2 C a 2 ( B e , A I ) S I 2 O 7 O H: H 2 O r2si9026[oH]2 o.307 ( s c , A l) 2 s r 6 0 1 8 o.291 3 . 2 4 5 Be2ASO4OH:4H2O 4 .685 BeloHI2 2.297 Be2BO3(oH,F):H2O o . 6 7 1 CaBeASO4OH o.867 Be4Sl207[OH]2 o . t 8 9 Be3AI 25 i 60 l8 2 . 4 8 4 B e 3 S i 0 4 [ o H ] 2 :H 2 o ( c a , M g ,F e 2 + , B a () L i , N a , K ) O . 2 5 ( A,rF e 3 + ) 2 (. . . * o.5 o . 7 2 7 c a l l A I 2 A l B e SI 2 0 l O l o H l 2 o . 6 4 5 C a LI A I 2 Al B e Si 2 0 l O l O H l2 1 . 2 6 4 CaMnB€2SI 50 I 3 [ 0H ] 2 : 2H2O o . 4 4 3 NaBeSi 3O7OH o . 6 7 3 B€A'lS I O4OH o . 4 3 9 NaBeSI 307oH 1 . 7 5 4 (Mn, Mg) Fe3+2Be2lPQ474 r6H2o 1.6 H2Ca3Be2[ PO414:4H2O ' t . 5 3 3 caBe4lPo4J2[ oH 4 : O. 5H2o ] o . o 9 4 Ca2B€SI 207 1 . 1 5 3 Be2BO3oH o . 2 4 7 c a 6 ( M n ,M g ) B e 4 Si 6 ( o , o H )2 4 2 . 6 6 2 ( B e , C a ) 4 ( S i , A r ) 2 0 7[ o H l 2 : 3 H 2 Q o . 5 3 1 CaBePO4F o.439 (Y,Yb)Beslo4oH o.425 (Yb,Y)BeSlo4oH o . 6 5 4 caBePo4OH o . 2 0 9 ca loMs2A I 4 [ S i 04 I 5 [ S I 2o7 l2loHl 4 o . 2 0 1 ( C a ,N a ) 2 ( B e A , I ) SI 2 ( o , o H ) 7 o . 7 2 7 ( ca, Pb) 3 ( Mg, Fe2+,Fe3+ ) 5S i 6Be2O22IOH12 o . 8 3 9 N a 2 ( B e ,Z n , M g ) AI 2 5 i 6 0 1 6[ o H ] 2 o . 3 3 6 N a 2 ( S l , A l , B e ) 7 ( o , o HF, ) 1 4 o . t 0 7 ( N a , c a ) 2 B a s i2 ( o , o H , F ) z I . 3 7 5 ( N a ,K , c a ) 4 ( B e , A I ) 2 5 l 6 0 1 6: 4 H 2 O o . o 3 4 ( c a , N a ) 2 B e ( S r , A )l 2 ( o , o H ,F ) 7 o . 1 7 9 K2Ca4Al 2Be4SI 24O60: H20 4 . 4 5 4 Be2PO4OH:4H2O o . o 5 9 CsAI 4Be4B'l1o2sI oH] 4 1 . 3 4 1 C a ( M n ,F e 2 + )2 B e 3[ P o 4 ] 3 [ o H ] 3 o . 2 0 r ( C a ,C e ,L a , N a ) l O - 12 ( F e , M n) ( S i . B e )2 0 ( o , o H ,F ) 4 8 o. 573 Na4SnBe2Si60l6[OH]4 I . 3 0 9 B e 5 ( S t , A I , F e 3 + )2 0 7 l O H 1 4 o . 3 8 ( L l , K , N a ,C a ) 8 8 e 6 [ P o 4 ] 6 [ o H ] 4 o. 337 Na4BeAlsl40l2cl 2 . 4 5 5 CaBe3[ Po4I 2 [OH] 2 : 4H2o o . 5 8 r BeldnPO4(OH,F)
NOTE: Concentratlons
ship but also for highlighting inconsistencies.Thus a fust version of Figure 3 revealed several gossly discrepant points that were quickly traced to entry errors in the data base.Lessextremediscrepancies may also arise from errors in one or more of the parametersusedin calculating the relationship, Le.,
ahovrn for
Be and H are average values.
indicesof refraction, densityand composition. Other errors may remain in the Gladstone-Dale constants used,and it may be necessaryto useconstants'tuned' to particular typesor groups of mineralsif a tighter fit is to be achieved. Several systemsnow exist for automated modal
706
THE CANADIAN MINERALOGIST
L I
z o
=
"o lol
0.24 (N - l)
/
O.32 D
0.36
0.,{0
Frc. 3. Gladstone-Dale relationship between molar refractivity calculated from physical measurements * tN - l)/Dl and from chemicaldata [Sum (KI PI / 100)]'
TABLE
11
GLADSTONE DALE RELATIONSHIP: C O M P A T I B I L I T YI N D I C E S SUPERIOR EXCELLENT GOOD FAIR POOR
5O5 ANALYSES 3ss ANALYSES '176 ANALYSES .I28 ANALYSES 457 ANALYSES
31% 22% 11% A% 28%
all of the minerals present or, alternatively, simply identifying those not previouslynoted by the user. Once the identity of a mineral with a given set of X-ray-emissioncharacteristicshas beenrecognizsd by MrNInnNr, all points having thesecharacteristics might be automatically allocated to that mineral without further intervention by MnqIoeNT. Here we seethe possibility of developingone of the so-called "expert systems". Such an application has been neither tried nor testedto date. CoNCLUSIONS
analysisby electronmicroprobe,somebeingavailaspecble commerciallyfor usewith energy-dispersion trometers.At presentthe successfulappliiation of such software is dependentupon a proper recognition of the minerals present in a rock and, with the aif of the analytical system, definition of these in terms of their X-ray-emission characteristics. Such systemswork well for common mineralspresentin major amounts. They do not function so well, however, for minor modal constituents, opaque phases,or uncommon minerals, all of which tend to be overlooked and henceend up in an undifferentiated "other mineralsand mixtures" group. One can envisageMTNIonNTbeing of considerableuse in such applications, either as a meansof recognizing
MINIDENTis now a working and useful package, although it is certainly far from fully developedor complete.Significantgapsremain in the data for particular parameters(e.9., co-ordination number of cations), and fields for many other properties might be added. Thus no information has been included so far on thermodynamic constants, electrical and magneticproperties, nor have any D.T.A. data been included. Data missing from existing fields will be added as soon as practicable, and the data basewill be updatedperiodically asnew mineralsare described and when previously described but unnarired minerals are given an IMA-approved name. With so many data entered into the existing data base, it is inevitable that errors will have been inadvertently
MINIDENT:DATA BASE FOR MINERALS
introduced. Thesewill be corrected as soon as thev come to light. Future developmentsmay include the use of new fields if it appears that this would signifigantly improve the usefulnessof the program. However, ths minslal, sampleand generaldata on which the compileddata are basedalreadyoccupy 12Mbytes. It is, therefore, stored on tape between use and updating. The compileddata basepresentlyoccupies 4 Mbytes. Obviously there are practical limits to the extent to which thesedata basescan be expanded, particularly the compiled data base, which must be accessiblefrom disk before any identification can be performed. At the time of writing MINIDENT is beingrun on anAMDAHL 580/FFmainfrarnecomputer, and typically eachMATCH procedure takes about 0.05 secondsCPU time to run. The IDENTIFY procedure is somewhat more expensive,running in about 3 secondsif the entire data baseis being considered.Efforts will be madeto reducerunning times (and hencecosts)in future versions.Furthermore, attempts may be made to produce a microcomputer version. This would certainly require the availability of a high-capacity rapid-access storage facility such as a hard disk. The MrnIoBNr data baseis not for sale.However, it can be accessed at the University of Alberfa Computer Centre through DATAPAC or other similar datacommunicationsnetworks. Potential users should contact the first author for further information concerningproceduresfor registeringwith the Computer Centre as a user. Actoqowl,gncetvrENTs
707
Nisomi-8l: an automatedsys& -(1982): tem for opaquemineral identification in polished section.In ProcessMineralogyII: Applicationsin Metallurgy, Ceramicsand Geology(R.D. Hagni, ed.). Proc. Symp.Metall. Soc.AIME, NewYork. Deen,W.A., Howre,R.A. & Zussrrtar, J. (1962,1963): Rock-Forming Minerals. Yols. I-5. Longman, London. (1978): Rock-Forming & Minerals. 24. Single-Chain Silicates. Longman, London. -,
&-(1982):Rock-Forming Minerals. 1A. Orthosilicales.Longman, London.
Drsr*rcu, R.V. (1969): Mineral Tobles. HandSpecimenProperties of 1500Minerals. McGrawHill, New York. Ervrnnpv, P.G. & Furrrn, J.P. (1980):A Manual oJNew MineralNames1892-1978.BritishMuseum(Natural History),/OxfordUniversityPress,London. Frnnanolo,J.A. (1982):A systematic classificationof non-silicateminerals.Bull. Amer. Mus. Nat. Hist. 172,l-237. Fruscunn, M- (1983): Glossary of Mineral Species. MineralogicalRecordInc., Tucson,Arizona. -,
Wttcox, R.E. & Matzro, J.J. (1984): Microscopic determination of the nonopaque minerals.U.S, Geol. Sum, Bull. 1627.
HeNny,N.F.M., ed. (1977):IMA/COM quantitative data file. International MineralogicalAssociation. CommercialAgents:Mccrone ResearchAssociates Ltd., 2 Mccrone Mews,BelsizeLane,LondonNW 5BG.
At various times over the last severalyearsthe following people have contributed substantially to this project by enteringinformation into the data base. Their meticulouscarewhile carrying out a task that Hnv, M.H. (1962):An Index of Mineral Speciesond must often have been wearisomehas been essential VarietiesAnanged Chemically (2nd ed.). British to the successof the project: Louise de St. Jorre, Museum(Natural History), London. Jay Guidos, Chuck Mah, Heideh Omoumi, Morris Maccagno, Edith Hutcheson, Robert Pinckston, (1963):Appendix to the SecondEdition of an Jan Schutz and Keith Tymofichuk. Mr. Desmond Index of Mineral Speciesand VarietiesArranged Wynne has provided invaluable advice on computChemically. British Museum (Natural History), ing matters throughout this project. We are also London, grateful to Dr. J.A. Mandarino for helpful discusJ.A. (1979):The Gladstone-Dalerelasionsand to Dr. J.A. Jambor for critically reading MANDARTNo, tionship. III. Some general applications.Caz. the manuscript. Part ofthe work hasbeensupported Mineral.17,7l-76. financially by NSERC grant number A4254 to the first author. Pmnnor, R.M. (1979):Chemicalond Determinative Tablesof Mineralogy (lVithout the Silicates).MasREFERENCES son PublishingU.S.A., Inc., New York. Arnn,B,P. & Henvev,P.K. (1979):Theuseof quan- RonnRrs, W.L., Rarr, G.R. & Wessn, J. (1974):Encytitative color valuesfor opaquemineral identificaclopediaof Minerals.Van NostrandReinhold,New tion. Can. Mineral. 17,639-U7, York.
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Surrn, D.G.W. & Gor.n,C.M. (1979):EDATA2: A UvrBNsocAARDr, W. & Bunrn, E.A.J. (1971)lTables FORTRAN IV computer program for processing for Microscopic ldentification of Ore Minerals, wavelength atd/or energy dispersive electron Elsevier,Amsterdam. microprobe analyses. Proc. 14th Ann. Mtg. Microbeam Anal, Soc. (San Antonio, Texas), Wnscunn, A.N. (1950: Elementsof Optical Mineraln3-n8. og:t. IIL Deteminative Tables.J, Wiley & Sons, New York. & Lrrnovrrz, D.P. (1984):A computer-based systemfor identification of mineralson the basisof & Wu*cunrr,,H. (1951):Elementsof Optical compositionand other properties.Int. Geol. Congress27th (Moscow)5 (lG'll), 169(abstr.) Mineralogy. II. Descriptionsof Minerals (4th ed.). J. Wiley & Sons,New York. Tn0crn,W.E. (1979):OpticalDeterminationof RockFormingMinerals.1. DeterminativeTables(4thGerman ed.: H.U. Bambauer,F. Taborsky& H.D. Trochim, editors; English translation by C. Hoffman). SchweizerbarttscheYerlagsbuchhandlung, ReceivedOctober18, 1985,rcvisedmanuscriptaccepted March 8, 1986. Stuttgart.
