Duke Hanna 2003 Geological Interpretation for Resource Modelling and Estimation

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Dute. JHnnd Hamn. PJ.2001. w a:Mcxlcllingmd : 9 i n t a ~ i c n inMinualRcmm.:ea ntl Ore sen-e EstinliiQ - eAu rJMM Q;de 10 Good Pra i


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of the initial drilling campaign. Early interpretations will betes ted by subsequent drill ing which wi l l result in mod if ica tionsand improvem ent to the geological interpretation. The geologist w ill beco me familiar with the re ource database and startdiscarding o r qua li fying the use of faulty or incomplete data.lndi rect data, uch a downho le geophy ic , wi ll be used tosupplement data from di rec t sampling so that the interpre tationis based on all rel evant and eros -va lidated data. This effi cientuse of ava i lab e data is a function of familiarity and understanding of both . oft ware and geology by the competent resourcegeo logist.

Losing valuable knowledgeWhen chan ge . in project sta take place there is a rea l dan ge rof Jos ing thi s va luable knowledge of the data. Problems mayalso arise when new staff adopt dif ferent geologica l desc riplions and lithology codes inconsislent wilh those used in theoriginal resource databa e.

Computer based methodsResource geo logi l these day have acces to computerso ftw are which allows them 10 establ ish databases and toanalyse and display exploration dam. Apan from collec lingdata, the geologi t can use software to chec k and interpret lhedata in plan, sect ion and with 3D images. The use of colourgraphic displays and image process ing techniques help thegeo logist displ ay all relevant data when interpre ting. Howeverthe real benefil of compuler-based melhod is the abi li ty toe m bine tringent tati lica l and geostati tical analy i of lhedata with sound geo logica l interpretation to create a validmodel of the deposit. This model is both graphical and numerica l , and can be used to communicate man y of the elements ofthe geolog ist 's undersmnding of the struclure and compositiono ft he deposit.

BLOCK MODEUING FOR MINER L RESOURCEESTIM TION

A resource block model is a computer based representationof adepos it in whi ch geo logical zones are defined and fi l led w ithblock , which areassigned estim ated va lue. of gradesand olheratlributes. A resource block model i only ever a representalionof lhe conceptual geo logica l mode l of a deposit. However,since tonn age and grade resource estimales are derived fromlhe block model, i l i importan lhat lhe key element of lhegeo logica l model are accuralely re flecled in the block model.Block mode l are relative ly quick and easy to genera le andhen ce should always be checked aga inst the relevant data v iaplotting on plan , sec tion. and in 30

Geological controlson block modellingIt is mportant lo consl ra in the blockm ode l by the rnain comrolling eleme nt o f geol gical in lerpre tati n, uch a Jode boundarie . zones ofmineralisation. faultsand lhe spatial di stributionof g rad es and Oher auribules .

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Software techniques, whi ch allow the geo logist to imposegeological controls on the block modell ing process, fall into1wo categories.l . methods of de fining geological boundaries (domains

such as Jode zones, zones of weathered ore and zones ofin lemal was le; and

2. modelling algorilhm for in lerpo lating lhe palia distribution of grades and other auribute w ilhin the domains.

The two are related as lhe domain boundariCJ are u. ed 10prevent unreali stic smoolhing and spread ing ofes1ima1ed blockgrad es across di fferent zones of minera lisation.

INTERPRET TION OF GEOLOGIC L DOM INSIn resource e timalion the eleclion of geological domainimportant. Domains are defined as zones which are geologicall y and tati ticall y homoge neou . Guibal ( 1997 and thisvolume) po inted oul lhal the tali lics and variography shouldback up the choice of domains. Creating geo ogical domainboundaries, which de fine lhe geomel ry of the Jode and otherzo nes, is the basic meUtod of providi ng geo logical control incomputer-based resource modelling. However not all depositscontain mineralisalion which has clearly de fined domainboundaries (Slegman, 1999 and this volume).

Grade domai nsDefining domains on the bas is of grade is often an i lerativepreces and usually tart with se lec lin g minera li ed interseclions (in lercep l ) in each borehole. omel imes a very low assayva lue (the mineralisation indica tor grade) is u ed to define theboundary between mineralised and unminera lised samples.l ntercept elec tion, anolher mean of defini ng thi boundary,invol ves co mposiling assaysover a mnimum imerva l length 10achieve amnimum grade value. ThCi e meU10ds beco me a Jiu lemore complicated i f lhe depos it ha more than one metal orproduct (such as Cu-Au or Pb -Zn -A g).

eolog ical domainsIt is more usual. howe ver, that assays are only part of lhe story.Parameters including lithology, formations, ve ining, faul ts,alteralion and length/grade intercept selection ma y all play apart in defini ng geologica l domain boundaries .

Hard vs soft domain boundariesDomain boundaries are o flen re ferred 10 a ei ther 'hard ' or'so ft ' Figure 1). For dep o its w ith hard domain boundaries.such coal and sedime ntar y zinc depo. iLs, U e definition of domain bounda ries is relati ve ly traightforward. Seamroof and fl oor boundaries are usually sha rp and lhe focus of thegeologist is on how to co mbine pli es and corre a e seamsacros.sthedepo it T hu Figure 2a), a coal . eam could begeologicallybounded as two seams or as one lh cker seam wi th animerburden included. Coal, phosphates and other layeredfonnations are also sometmes defined by a sofl boundary. Forexample, ca lcium conlent may determine the aye r or bedse lec lion in a phosphate. So a phosphate horizon could bebounded by ' best ' grade (Figure 2b). Ea ch interpretation w ill


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Data sets often ha ve two or more attributes (eg go ld andcopper), or have two or more data types (eg grade va lues fromboth diamond core holes and reverse circulation dri l l holes).

Scatterp lots of the two va riables provide informationaboutthe degree of co rrelation be tween them . A strong correlationbetween two variables such as grade and densi ty ( G), ove r arangeof values hould be prope rl y evaluated. I ft he SG ha astrong positi ve co rrelation wi th the grade (such as in so rne isulph ide ores) then the g rade could be underestimated icorrelation witl1 SG is not take n in to con ideration.

VariographyVariography i the ca lculation o f experimenta l variogram andsubseq uent fitting o f app ropriate variogram models. Geo log ical ideas about grade distribution can often be conlirrned byvariograp hy. Since it renec ts the actual spatial distri bution ofg rades (Figure 5 , i t can be a powerful ex ploratory too forgeologists. Variogram mode l form the basis of grade e timation using kriging.

Some of the commonly used variogram modell in gpractices include: define the nuggetusing close lys paced data - usually via the

downhole variogram ; the fi rst part of the variograrn (near lhe origin) is the most

i mportant to mode l accurately; the most commonly used variogram model is the spherica l

mode l ; look for nested structures; and plot indicator variogrruns for higher, middle and lower endsof lhe grade range to see i the distribution varies substan

tiall y.

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Ranoe 7om

r....._ Aocural o in firsl par ot varic am

~ ~ . o o S t ~ - c - . ' ' o o - ~ ~ ~ ~ - ~

Figure 5 - V riogr mof assay values long strike in ab se metal deposlt showing a range of pproxim tely270 metres.

Grade continuity within domainsVariograms provide the geo logist wi th a useful measure ofcont.inuity of mineralisation in dif ferent directions. However

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CH PTER o l o g i c a l n t e r p r e t a ~ o n for Resource odelling ond Estimat ion

interpretation of variogram structu res can be hindered by datawhich i not representativeof lhe doma n. This ' noisy data' cancome fro m a number of sources , including ampling error clu tered data, i .olated ex treme va lu es or data incorrec tlyincluded in the do main.

patial continuity is a use ful concep t when mode ll ingdifferent types of depos it (Figure 6 . It hould be notedhoweve r, that whi lst so rne dep os its such as coa , show hi ghcontinui ty in one or two direction for sorne attributes (eg seamthickness), they may show much e s . ~ continui ty for o thers (egsulphur) .

GRADE CONTINUITY WITHIN DOMAINS

Variog ramRanges

DIFFUS E ~

eg.supergeneAu

~ i c a le.g. PorphyryCu-Au

+200m

DI SC ONTIN UOUSSpatial Continu ity CONTINUOUS

Figure 6 The continuity of gr des nd other ttributeswithin dom ins for different deposits varies from discon-

t inuous to continuous.

FILLING DOMAINS WITH LOCKSWhen the domain in terpre tation is complete the geo logist isable to con. truct a block mode l l i l l ing the three-dimen ionalgeometry of each domain with blocks of a unique domainnumber. Chec k pi tting hould now be carried out on thi blockmodel to see that it forms an acceptable vo lume model of thedomains.

Blocks for tonnag emodelsTo mode l domain geometry it i importan that block . izes besufticiently small (pa rticular y at 'hard' domain boundaries) 10provide adequate vo lumetric accuracy Figure 7) - noting thebore hole spac ing on whi ch the domain bou ndarie were inter preted . Seam models are built wi th roof, floor and thicknessgrid and a uch can be con idered a block model one blockthick (variab le lh ickness) for each eam .

Blocks for grade modelsTo model the grade distribution within a domain it is oftenimportan that blocks be regularised and no smaller than a


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With Domain controlt01mage is lower but gradehigher

tonnage is higher ut gradelower

igure 9 Grade estmatlon wlth and wlthout domalncontrol.

This rnay mean using different search param eters fordifferent grade ran ge in the same domain ifthere is significanloca l variation.

eighting t hniqu sBl ock grades are computed using a local averaging algorithm,se lecting sample va lues within a search radius and weightingthern somehow. W eighting techniques include inversedistanceweighting (lsaak and Srivas tava, 19 9 pp 257- 259 andkriging. Kr iging is a geostatist ica l method ofallocating opt imalweights for loca l estimation based on the variograrn model seeIsaaks and Sri vas tava, 19 9 pp 278- 322 for a discussion ofOrdinary Kr iging). Types of kr iging include block, point,lognormal and indicator.

One ofthe importan concepts in block grade estim ation ithat of pos itive and nega tive weights. Witll inverse distancemethods where al l weights are pos itive, the es timated grademust fall between the maximum and mnimum grade of thesamples elected. Kr iging u es botl1 posit ive and nega tiveweights, which alth ugh inruitive ly acceptable, need to beu.ed with care so that estmate. rnake . ense (beware ofnega tive grade e ti mates for exa rnple).

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HPTE R Geological lnterprelation lor Resource Modelling and Estimation

Local varia tion within domainsI f large di f ferences in Lhe rnag nitude of grade ex isL within asingledornain, say du e to small areas of extreme values, th ere isa dange r thatt here will be larger errors of gradeestimation insomeblocks compared to ot 1ers depending on the grade. Strat eg ies to recti fy or minimi e thi s 'condi tional bias' includemultiple estimation (' nes ted ' in terpolations as desc ribed i nBischoff and M orley, 1993 and indicator kriging. Theseex tremeva lues areo ften due to srnaller sca le structures suchmineralised stockwork ve ins, which are discontinuous anddifficult to dornain separately.

Checking the block grade modelOnce the block grade rnode l has been crea ted , sys ternaticplotting o f plans, sections and cut-away 3D views should becarr ied out to check the rnodel against relevant borehole andother data.

Defining resource model confidenceIt is important for the geo logist to de termi ne and communicatet e confidence levels acros. the block rnodel. This confidencei a function of a numbe r of fac tors including data re liabil ity,data den sity, grade continuity and grade estirnation. Calculation of the nurnbe r o f sarnples used 10 es timate grades in eachblock and their average di tance, provide the geologi t withsimple mea ures of block estimation confidence. These andother rneasure can be u.ed to generate reliability rnap andas i t the geologist in claSl i fyi ng part. of the ourceIn ferred, Ind ica ted or M ea ured (JORC, 1999 and republi hedin this volume, see Appendix 1 in line with the JORC CodeJoint Ore Reserves Comrnittee, formed by The AusiMM, AIGand Minerals Council of Austra l ia) reporting criteria.However, it is mportan thatthe geologist (Competen Person)makes a professional, experience-based judgernent on deter mining the Mineral Re ource and Ore Re. erve e as. i fication.Thi mean thatthedeterrnination hould not be made olelyonthe bas is of statistica l parameters, however those paran1etersare deri ved.

Spatial distribution of other attributesMo tdepo i tscontain a nurnberof at tributes tllatare ofinteres to er than the main economic grade cornponenL The mai nauri bute, which w ill a ffect tonna ge es ti rnations of the resource,is the bulk dens ty oft he Jode materia l. Th is can vary markedlyfrom dorna in to dorna in and even within a doma in. Prov idedLhere are sufficient samples availab le, bulk density can bemodelled in much the same way as grade.

Other auributes of in teresL include Lhose that will af fecLfuture mining, such as ge techn ical parameters, traceelementsthat will affect milling recoverie grain size (metallurgica lrecovery), gas and groundwater.

It is of increas ing imponance to incl ude in the resourcernodelt ho e attributes that wi l l affect e envi ronment uch asulphur in coal and radioacti ve con tituent (eg rno nazite in

mineral sands) .


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The resource block databaseThe block model created from the doma n boundaries prov idesthe framework for the compl ete reso urce model. Th is resourcemodel fonns a Lhree-d imensional database with a number ofva lues be ing able to be ass igned to each block location.Es timated values for grades and a ll the other attri butesmodelled are entered in to the databa e.

SOME OF THE TR PS NO UMITATIONSComputer based me thodsof reso uroe mode lling andesti mationhave been around for many years and ane widely used i n themining indu try. Howeverthey are not without their limi tationand traps.l . The trap i. not to check - it is so quick and y to

create computer models. M istakes are eas ily made andmodel do not match the da ta. The model should alwayrnake sense Check it again t the data and o ther esti ma tion methods.

2. Zero Lhicknes intercept at the edge of deposits - gradesshould be carried to thee dge rather than the lower valuesbeing interpolated.

3. Limitation include not catering in the one resourcernodel for arcas of detailed infomtation and aneas ofsparse data. Exoept for sub-blocking, rn ost block modelshave a uni fonn block size. Separate models of diffenentresolution can be generated , i f nequired.

4. Too much smoothing dueto inappropriate search strate-gies.

5. Blocks too small or too large for th e density of data.6. ot u ing all nelevant data to adva ntage.7. Reso uroe modelling with out using the dinect invo lve

ment of the geologi. t who know the data and understan ds the geology of the deposit.

8. Insu ff icient or inappropriate data to model the true natureof the minerali ation, ie low confidence ' inferredresource'.

MODELliNG REQUIREMENTS FOR DIFFERENTDEPOSIT lYPES

Using the experience from modelling a great variety of deposits. it ispossible to make a generali sed classif ication of depositson the basi. of domain geometry and grade continui ty (Figure10 . This approac h establ ishes some guidelines to build va l dmodels foreach type of deposit. Th is has implicat ions both forthe densi ty of data requined and the modelling methods likely tobeemployed.

The fo ll ow ing comments compare di ffenent interpnetationand modelling methods for thnee types of deposi ts familiar tothe authors. The summary of modelling nequinements for eachof Ute depos it type: . hown in Table 1 is ~ e d on th e authors'experience and hence can only be negarded as pneliminary, notdefiniti ve .

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GEOMETRV vs SP TIAL CONTINUITYSHARP

g. coaJ (th lckness)ulphur)Dom ainBoundary eg . Auveils

_ e.g Porphyry

\ } ~ p e r g e n e / ~ ~ \ Cu-Au/ l Au V

DIFFUSE -10m +200m.ISCONTINUOUSSpatial Continuity CONTINUOUSFigure 10 Domain boundary vs spatial continuit yDiagram for group ing mineral depositsSeam deposits highly o n t i ~ u o u s with sharpboundoriesF r tra tified minerali. ation such as coal, trat i fonn Pb-Zn andsedimentary iron ore deposits, defining the lode or seam i s ofkey imponanoe.

Precious metal ve indeposits moderate continuityand sharpdiffuse boundariesFor deposit of high va riability uch a rnany prec iou metalan d uran ium deposits . structune is less well defined an d grade isoften more difficul t to model accura tely. Many pnecious metalvein rype deposit how complex trucrure which i ften di ff icult to model accuratel y u ing the density of su rface drillingnormally ava ilable at the tim e of exp loration.

Porphyry Cu u Mo deposits good verticalcontinuity and diffuse boundariesLarge low grade deposi ts sueh as the porphyry Cu-A u deposiLSof ten show gradalional one zonal grade distributions. Accurategrade estimation in U1ese deposits i of utmos t importancebecause of the high vu lnerability in economic viability toe venminor nuctuation. in metal price.

CONCLUSIONSI n an age of ncreasing computer usage. it is importan not tolose sight of the geo logy, and to exercise disciplined dataanalysis and sound geological in terpnetation when creatingresource block models.Theconceptual geological model o f thedepos i t must honour the geo logy. Essential elemen ts of goodprac tice in resource modell i ng i nclude:


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CHAPTER G e o l o g n t e r p r e t a ~ o n lor Resource Modelling ond Estimation

T BLEreliminary comparison of esource modelling requirements for three types ofmineral deposits

rteria ool and Other Seom OepositsDoto


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REFEREN ESBischof f, K and Morley, , 1993. Geology, Re un::e Definition and

Reserve E imation at Mount harlotte, IG'llgoorlie Wcs temAustralia, in Proceedings o[ lile Imemalional Mining CeolagyCon[erence, pp l - 17 (1lte Au rala ian In titule of Mining andMetallurgy: Melbourne).

Cra m. A A and Duke. J H. 1992. Geo log ica l Control inComputer-Ba scd Rc source E imation. SMEDG/A IG Rcsoun::eEvaluation Symposium. Sydncy.

Dukc. JH 1987. Goo logical Modelling Tcchniqu es in Mine Pianning.in Proceedings Pacijic Rim Conference 1987, Pl>85 1 854 (1lte

i a n Instituteof Mining and Mctallu rgy: Melbourne).Gu ibal. D. 1997. Variography. A Tool for thc Resourcc Goolog ist. in

Proceedings The Resource Database Toward s 2000. pp85- 91(The Australasian I nstitute o f Mining and Mctallurgy, Il lawarraBraneh: Wollongong). Reviscd rutel oepublished in this vo lumeas:Guiba l, D ,2001. Variography, a Too l for thc Re urce Goo logist,in Mineral Resource and Ore Reserl e Esrimalion - Th e Aus/MMCuide 10 Cood Praclice (Ed: A Edwards). pp85- 90 (TheAustralasian Insti tute of Mining and Mctallurgy: Melbou rne).

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Hantllt, PJaot amcron, J L, 1997. Comput.cr Databasc rutel Goologi ca l Modclling of Huntcr Valley Geology. Pmceedings o[ lileThirry Firsl Newcaslle Symposium on Advances in lile Sludy o[ ileSydney Das n April. Newcastlc . SV , Australia, pp53- 55.

I saak s. E H and Srivastava. R M , 1989 . n Imroduclion To ApplietlCeostalislics (Oxford Universiry Press: New York) .

JOR . 1999. Australasian Code for Repotting of M ineral Resourcesand Ore Reserves te JOR C Code). The Joint Ore ResetvesCo mmittcc of 1lte Au tralru ian Institute of Mining and Mctallu rgy, Au ralian Inti ute of Goosc ienti s attdMineral Co unci 1of AUSI.ralia (repub lishcd in this volume, . e Appettdix 1,pp683-698.)

tegman , C L 1999. How Goological Envelopcs Impact on thcReso urcc Es timate - A Case History of Ad vrutccd Proj ccts in thcCobar Field. in Proceedings Resource/Resetve EsrimationPmclice in lile Cemml Wesr New Soulh \Vales Mining Indus/1)(hcld 23 July 1998) (Ed: C L tegman). pp97 - 120 (TheAustralasian I ns titute of Mining and Metallurgy : Melbourne).Revised and n::published in th is volu me Stegman. C L. 200 .How Doman Envelopes lmpact on the ResourceEstimatc - CaseStudies from tltc obar Gold Field. NSW. Austral ia. in MineralResource and Ore Resen lt Es1ima1ion - The Aus/MM Cuide 10Cood Prac1ice (Ed : A Edwards), pp221- 236 (The AustmlasirutInstituteof Mining attd Metallurgy: o u r n c

Duke Hanna 2003 Geological Interpretation for Resource Modelling and Estimation

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