coal resource classification and geostatistics

11
Coal resource classification and geostatistics By KEITH 0 WHITCHURCH 1 , Graduate Member, AD STEWART GILLlES 2 , Member and GEOFFREY 0 JUSr, Fellow for uranium and thorium where Nuclear Energy Authority/Intemational Atomic Energy Authority (NEW/IAEA) guidelines have been adopted. For all other commodities the approach taken varies from country to country and often from state to state with different methods used for different commodities. Most resources are however, classified according to two sets of criteria, geological assurance and economic facility. The relationship between them has been summarised in the well known McKelvy Box, Figure 1 (McKelvy, 1972). The principal FIG 1 - McKelvy box scheme (as recommended by the USBM/USGS) the hatched area represents that portion to which geostatistical methods may be applicable. differences between codes lies in the defmition of these criteria. This paper principally addresses resource classification on the basis of geological assurance. The degree of certainty with which an estimate of material in the ground can be made is dependent on the amount of exploration undertaken and the nature of the deposit. The different codes recognise a range of geological assurance from certainty to extreme speculation. Consequently a diverse body of opinion exists on how geological assurance should be assigned to an estimate or on how a resource classification scheme should be subdivided on the basis of that assurance. Table 1 has been prepared to summarise the categories of geological assurance for a number of codes. It should be noted that owing to the diversity of class defmitions a strict comparison of these groupings is not possible. Table 1 should therefore be used as a guide only. This does, however, highlight the difficulty in comparing resource estimates based on different classification codes. Categories of assurance are usually defined in general terms only. For example an extract from the Bureau of Mineral Resources classification system (BMR-1984) defines: ABSTRACT An internationally recognised and unifonn method for classification, categorisation and designation of mineral and energy resources is not yet available. With the increasing need for reliable and comparable coal resource data it is necessary to standardise the traditional classification procedures by quantifying the three basic evaluation criteria of economic feasibility, geologic assurance and recovery. For well documented deposits, geostatistical methods can considerably improve classification quality. A review is undertaken of major classification with particular emphasis on those that incorporate limits on estimation error. A geostatistically based algorithm for classifying coal resources within the current Queensland and New South Wales codes has been developed. Application of the classification algorithm for resources from a number of seams exhibiting different structural characteristics is assessed. The method is found to give classification results that closely reflect the error associated with an estimate of resource quantities based on current sampling densities. lbis requires a careful geostatistical analysis with an emphasis on geological awareness. Resource category restrictions although albitrarily assigned should remain constant for all deposits being compared. This requires some engineering judgement with additional interpretation also required for isolated and peripheral block. Keywords: coal, geostatistics, resource classification. INTRODUCTION The economic incentive to assess and classify mineable coal resources so as to enable the calculation of reserve tonnage and grade has increased significantly with present international commodity marketing conditions. Improvements to classification concepts and defmitions are needed as it is recognised that no current practice produces results that are free of some degree of subjectivity and therefore readily reproducable. Quantification of geological assurance is a major difficulty in most classification costs. The magnitude of the error associated with an estimate of the quantity and quality of a resource needs to be understood. Geostatistics allows calculation of the variance of errors associated with an estimate and is a potentially valuable tool for classifying resources on the basis of geological assurance. In a review of major classification codes particular emphasis was placed on those addressing geological assurance. Codes which incorporate limits on estimation error were analysed in conjunction with a study of the impact that geostatistics may have in developing an understanding in this area. A geostatistically based algorithm for classifying coal resources within the framework of the current Queensland and New South Wales codes was specifically developed to compare results with current practice. Seams exhibiting different structural characteristics were used as case studies to assess the sensitivity of the algorithm in classifying resources. RESOURCE CLASSIFICATION PRINCIPLES There is no internationally recognised and uniform method for classification, categorisation an designation of mineral and energy resources. The one exception to this is the classification systems u i o z o u .., ... :> '" u j o z o IDENTifiED UNDISCOVERED , ... on ... '" ... u :i o z o u '" ... o "' "' .. C> "' o C> z on <t "' .. u z 1. Tutorial Fellow in Mining Engineering 2. Senior Lecturer in Mining Engineering 3. Reader in Mining Engineering, University of Queensland, St Lucia Qld 4067. 4. Original manuscript received on 11 May 1987 Measured Resources: Those resources for which the sites for inspection, sampling and measurement are spaced so closely that their shape and mineral content are well The AusIMM Proceedings No 2 t990 7

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  • Coal resource classification and geostatistics

    By KEITH 0 WHITCHURCH1, Graduate Member, AD STEWART GILLlES2, Member andGEOFFREY 0 JUSr, Fellow

    for uranium and thorium where Nuclear EnergyAuthority/Intemational Atomic Energy Authority (NEW/IAEA)guidelines have been adopted. For all other commodities theapproach taken varies from country to country and often from stateto state with different methods used for different commodities.

    Most resources are however, classified according to two sets ofcriteria, geological assurance and economic facility. Therelationship between them has been summarised in the well knownMcKelvy Box, Figure 1 (McKelvy, 1972). The principal

    FIG 1 - McKelvy box scheme (as recommended by theUSBM/USGS) the hatched area represents that portion to which

    geostatistical methods may be applicable.

    differences between codes lies in the defmition of these criteria.This paper principally addresses resource classification on the basisof geological assurance.

    The degree of certainty with which an estimate of material in theground can be made is dependent on the amount of explorationundertaken and the nature of the deposit. The different codesrecognise a range of geological assurance from certainty to extremespeculation. Consequently a diverse body of opinion exists on howgeological assurance should be assigned to an estimate or on how aresource classification scheme should be subdivided on the basis ofthat assurance. Table 1 has been prepared to summarise thecategories of geological assurance for a number of codes. It shouldbe noted that owing to the diversity of class defmitions a strictcomparison of these groupings is not possible. Table 1 shouldtherefore be used as a guide only. This does, however, highlight thedifficulty in comparing resource estimates based on differentclassification codes.

    Categories of assurance are usually defined in general terms only.For example an extract from the Bureau of Mineral Resourcesclassification system (BMR-1984) defines:

    ABSTRACT

    An internationally recognised and unifonn method for classification,categorisation and designation of mineral and energy resources is not yetavailable. With the increasing need for reliable and comparable coalresource data it is necessary to standardise the traditional classificationprocedures by quantifying the three basic evaluation criteria of economicfeasibility, geologic assurance and recovery. For well documented deposits,geostatistical methods can considerably improve classification quality. Areview is undertaken of major classification with particular emphasis onthose that incorporate limits on estimation error.A geostatistically based algorithm for classifying coal resources within thecurrent Queensland and New South Wales codes has been developed.Application of the classification algorithm for resources from a number ofseams exhibiting different structural characteristics is assessed. The methodis found to give classification results that closely reflect the error associatedwith an estimate of resource quantities based on current sampling densities.lbis requires a careful geostatistical analysis with an emphasis ongeological awareness. Resource category restrictions although albitrarilyassigned should remain constant for all deposits being compared. Thisrequires some engineering judgement with additional interpretation alsorequired for isolated and peripheral block.Keywords: coal, geostatistics, resource classification.

    INTRODUCTION

    The economic incentive to assess and classify mineable coalresources so as to enable the calculation of reserve tonnage andgrade has increased significantly with present internationalcommodity marketing conditions. Improvements to classificationconcepts and defmitions are needed as it is recognised that nocurrent practice produces results that are free of some degree ofsubjectivity and therefore readily reproducable. Quantification ofgeological assurance is a major difficulty in most classificationcosts. The magnitude of the error associated with an estimate of thequantity and quality of a resource needs to be understood.Geostatistics allows calculation of the variance of errors associatedwith an estimate and is a potentially valuable tool for classifyingresources on the basis of geological assurance.

    In a review of major classification codes particular emphasis wasplaced on those addressing geological assurance. Codes whichincorporate limits on estimation error were analysed in conjunctionwith a study of the impact that geostatistics may have in developingan understanding in this area.

    A geostatistically based algorithm for classifying coal resourceswithin the framework of the current Queensland and New SouthWales codes was specifically developed to compare results withcurrent practice. Seams exhibiting different structuralcharacteristics were used as case studies to assess the sensitivity ofthe algorithm in classifying resources.

    RESOURCE CLASSIFICATION PRINCIPLES

    There is no internationally recognised and uniform method forclassification, categorisation an designation of mineral and energyresources. The one exception to this is the classification systems

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  • K D WHITCHURCH, ADS GILLIES AND G D JUST

    TABLE 1Comparison ofcategories ofgeological assurance.

    USBMDE~O STRATED I

    SGS M~5VoRED INDICATED INFERRED .ro>OT1'1ETlCAl. I SPEc...U r EAUSIMM MEASURED INDICA TED INFERRED PRE ORE RESER/ESAMIC PROVED PROBABL.E POSSIBLE SAGE1....1~~ERALlSAT CNI

    A B Cl C2 0GDMB IN SIGHT PROBABLE INOICA Er> INFERRED PROGNOSTIC1959 ERR 101 ERR lOI r"~'~~~O' ERR 301 AS' 10-)0'"s, gal .. .ss 10-g01 .. ss )O-~O'SSR A B Cl I C2

    OLD MEASLRED INDICATED INFE'lRED1985 013T < I~ DIU < 2JO,oI IOI'~ f-''' Z

    'ww

    OLDMEASURED INDICATED INFERRED

    1978 OI.sT ( II(M Ol.sr < ZJQ.4 OI$T < 41(),4~ ZOI

    SOUTH MEASURED INDICA TEDINFERRED

    AUSTRALIA 015T < I~ 013T < ZJO,(.

    ~ le,

    ESC R-l R-2 R-3

    BMR MEASURED INDICATEDINFERRED HYPOTHETICAL sPEC ...'l..A j .1=

    STANDARD IN SIGHT PROBABLEI POSSIB E t,; 'CERTAIN U~ r

  • 01

    I

    However, this does not give information on the form of thedistribution. Comparisons between estimated grades and actualgrades (after mining) have shown the distribution of errors (Ugarte,1972) to closely approximate a Gaussian distribution with a meanzso equal to zero and a variance f:1, equal to the kriging variance 0-,

    k(Figure 2). This approximation has been shown to be closest for a9v

    *~ 005-

    O/oCu

    0' 095

    FIG 2 - Histogram of experimental kriging errors of blocks from thecopper deposit of Chiquicamata (Chile). The Gaussian distribution

    with the respective mean and variance (dotted line) and the 0.95per cent confidence interval are shown. Overlapping areas of the

    two curves are hatched.

    CO FIDENCE LIMITS AND RESOURCECLASSIFICATIO CODES

    COAL RESOURCE CLASSIFICATION AND GEOSTATISTICS

    although this code is, at first glance very convincing, it does in factcontain some ambiguities that can be explained by the followingexample.

    A block in a deposit with an error of nine per cent at a level ofconfidence of 90 per cent qualifies for the category of provenresources. A neighbouring block with an error of 12 per cent at thesame confidence level does not qualify for the category of provenresources. However, at a confidence limit of 80 per cent, (that forprobable resources) an error of only nine per cent is obtained(Figure 3).

    Wellmer (1983) recommended that the level of confidence bemaintained at a constant level for all classes of assurance with onlythe error limits varying. Similar approaches have been advocatedby Deihl and David (1982), Froidevaux (1983) and the GDMB(Wellmer, 1983). Table 3 shows the new GDMB system thatfollows this recommendation.

    TABLE 3ClassifICation system ofthe GDMB/1983 (after We//mer, 1983)

    Category Upper limit of error Level of confidence

    Proven 10% 90%Probable 20% 90%Possible 1 30% 90%Possible 11 50% 90%Unclassified >50% 90

    SCOPE AND APPLICABILITY OF GEOSTATISTICS

    The use of geostatistics requires quantitative data and certainminimum restrictions with respect to the number and positions ofsample information. Theoretical reasoning and practical experiencehave shown that a minimum of 30 to 50 sample points evenlydistributed over any field of interest are necessary to obtain areliable variogram (Diehl and David, 1982). Hence a resourceclassification system based on geostatistics must be restricted tofairly well documented deposits. As a rule of thumb Deihl andDavid (1982) suggest that geostatistical methods are onlyapplicable to those resource classification categories covered in thehatched area of the McKelvey Box (Figure 1). This roughlyequates to the measured, indicated and inferred class one categoriesof the new Queensland Code.

    The Influence of block size on classification

    The earliest resources classification code to make use of confidenceintervals and error limits to define categories of geologicalassurance was the 1959 (GDMB Code, Table 2). Under this Codewith decreasing assurance classes, the limits of error become widerand the level of confidence lower. Wellmer (1983) points out that,

    TABLE 2Classification system ofthe GBMB (1959)

    Category

    ProvenProbableIndicatedInferredPrognostic

    Upper limit of error

    10%20%30%30

    Level of confidence

    90%79 -90%50 -70%30 - 60%10 - 30%

    The estimation variance 0i of any deposit parameter is a functionof the size of the block being estimated as discussed by Deihl andDavid (1982). Any block classification that is dependent inestimation variance is therefore a function of block size. As anexample, using a set of borehole data for an irregularly drilledbrown coal deposit, the relative error of estimation has beencalculated at, with a 95 per cent confidence limit (Equation 1) forsquare blocks of varying sizes. The results of these calculations areshown in Figure 4 and indicate the influence of block size onkriging variance and hence on the outcome of resourceclassification. The percentage error calculated by equation 1 hasbeen plotted against the length of the block sides expressed as apercentage of the seam thickness variogram range.

    From a statistical point of view it is clear that classification ofresources without considering the relation between the respectivedeposit quantity and the estimation variance is meaningless. Thesolution to this problem is neither simple nor straightforward and infact Sabourin (1983) noted that there are almost as many proposedsolutions as there are authors who have considered the problem. It

    The AusIMM Proceedings N021990 9

  • K D WillTCHURCH, ADS Gll..LIES AND G D JUST

    OF RELATIVEERRORS

    LIMIT0.3

    0.2

    0.1

    PROVEN PROBABLEDEGREE OFASSURANCE

    POSSIBLE

    FIG 3 - Undefined areas (AB) in old GDMB-classification of 1959.C L = Confidence level

    A = at 90 per cent level of confidence (probability) above 10 per cent, at 80 per cent level of confidence (probability) below 10 per cent relativeerror.

    B = at 80 per cent level of confidence (probability) above 20 per cent, at 70 per cent level of confidence (probability) below 20 per cent relativeerror.

    100

    10

    is beyond the scope of this paper to review all proposed solutions,although it is worth noting that in many cases interim solutions onlyare suggested such as in the case of the GDMB (Wellmer, 1983).

    A definition of maximum block size based on drill hole spacing(Figure 5) combines some of the better aspects of a number of thesolutions proposed by different authors, while observing therestrictions inherent in the current Queensland and New SouthWales codes. In this case the maximum block size allowable foreach class of resource is therefore dependent on the maximum drillhole spacing for that class. This limit on block size is thereforesuggested for use in the proposed geostatistical solution.

    -0 00 00PROPOSED CLASSIFICATION CLASS CRITERIA

    PERCfNTAC, 0" RANC,

    FIG 4 - Relative kriging error (at 95 per cent confidence level)versus block side dimension as a percentage ofvariogram range.

    FIG 5 - Polygonal block showing the relationship between drillholespacing and block dimensions

    The proposed resource class system for use in this study is defmedby drill hole spacings and error limits. Each class is based on themaximum drill hole spacing specified in the current Queenslandcode. For class A resources the maximum allowable error is basedon the estimation error specified for the most restrictive resourceclass (measured) under the Queensland Coal Reserve code in use upto 1985. Error limits for the less restrictive classes B and C have

    TABLE 4Proposed resource class limits. Drill hole spacing is based on

    Queensland and New South Wales codes.

    Resource Maximum Maximum Maximum BlockClass Illowable drill hole block iuntion

    ~ spacing IreI area(95" c.1.) km tm2 tm2

    A 20 1 1(_12) O.2SB 40 2 ..(-~) 1.00C 60 .. 16(_42

  • COAL RESOURCE CLASSIFICATIO AND GEOSTATISTICS

    APPLICATION OF THE GEOSTATISTICALLY BASEDCLASSIFICATIO METHOD

    o

    l-----J'--_-'2000M

    oo

    Case Study 1 - Classification of a laterally persistentdeposit

    An undeveloped brown coal deposit was analysed according to thepreviously mentioned procedures using the limits for variousclasses of resource detailed in Table 4. The deposit was sampled onan irregular grid by 91 drill holes extending over a region 7.5 km

    o

    constraints, the block is classified to this restrictive category, classA. Should the area of the block under study surpass the upper limitof class A without obtaining the necessary precision, the procedurecontinues with the less restrictive requirements of class B and so nountil the block is fmally classified. In order to maximise theresource quantities in the upper classes, the i~on procedurebegins at the location with the lowest value for ~ determined bypoint kriging the deposit on a regular grid. The stepwise extensionsof any block are constrained by the principle that an increase isalways performed iIlihe direction where the gradient of precision orrate of change of~ is least. After fmal classification, the blockarea is recorded on file, the results of the estimation printed and theprocedure re-starts at another point with local minimum krigingvariance.

    A FORTRAN program has been written to perform theclassification automatically for one selected parameter. A series ofchecks within this program ensure that the shape of the developingblock is controlled to avoid intricate block contours.

    been assigned arbitrarily although they follow recommendations putforward by Fettweis (1979), Diehl and David (1982) and Wellmer(1983). This information is summarised in Table 4.

    Calculations for this geostatistical classification of resourcesfollow a series of steps as defined by Diehl and David (1982).1. Determination of economically relevant parameters such as

    coal thickness or ash content, upon which the classificationwill be based.

    2. Review of raw data and preparation of basic data flies3. Classical statistical analysis of parameter data and variogram

    calculation.4. Determination of the outline of presently feasible resources

    by geological and technical criteria, such as depth oroxidation limits.

    5. Further sub-division of the areas defmed in point four, intoblocks that satisfy the predefined constraints of a specificcategory of geological assurance with respect to:(a) dimensions, and(b) parameter confidence levels.

    6. Calculation of in-situ and recoverable tonnages of eachblock defmed in point five and compilation of totalquantities for each class of resource.

    The above steps are standard well-defmed procedures in practicalgeostatistics with the exception of point five which required thedevelopment of a special algorithm. This presents fundamentalproblems in the division and definition of blocks to satisfy resourceclass constraints in respect of both area and the precision of thegrade estimates. The algorithm needs to ensure that a maximumresource quantity is assigned to the category with the highest degreeof geological assurance.

    The proposed algorithm uses an iterative method starting with asmall block that is enlarged step by step. After each incrementalincrease, the area of the enlarged block and associated krigingvariance are calculated and compared with constraints for the firstand most restrictive resource class. If both area:fld confidenceinterval calculated from the kriging variance (Ok ) satisfy the

    FIG 6(a) - Exploratory drillhole locations for Case Study 1 showing500 m ranges of influence

    FIG 6(b) - Exploratory drillhole locations for Case Study 1 showing250 m ranges of influence.

    The AusIMM Proceedings No21990 11

  • K D WIDTCHURCH, ADS GILUES AND G D JUST

    north-south by 3.5 km east-west. Three major coal plies wereidentified. Classification results for the lowest ply only arediscussed.

    Figures 6(a) and 6(b) show the locations of exploratory drillholes; the circles represent arbitrary ranges of influence for eachhole. Using this type of arbitrary assignment it can be seen thatareas of a deposit can be placed in a number of different classes ofresource depending on the range of influence selected. Thishighlights the need for an objective method for classifyingresources.

    TABLESPrelimifUlry classifICation results for the laterally persistent deposit

    ----,---Ac1w B c1UI C elw U ellS.!

    Bit TX. Ma Vel. Bit TK Ana Vel. Bit TK Ala Vel. Bit TK NU Vel.1 1.316 0.25 0.329 2 0.67 1.25 0.14 10 0.6.5 2..5 1.61 19 0.~7 1.25 0.5917 0.111 1.00 0.111 3 0.65 1.25 0.11 11 0.59 0-' 0.30 21 0.61 0.75 0.'131 2.371 0.25 0-'93 ~ 0.66 1.25 0.16 '20 0.62 0.75 0.~6 29 0.~3 0.25 0.107

    11 2.960 0.25 0.7~0 , 0.99 1.2.5 l.27 21 0.65 0.25 0.16 30 0.'9 2.00 1. I1412 1.527 O.~ 0.7~ 6 O.~ 2.2.5 1.0 2.5 0.75 0-'0 0.31 32 0.~4 0.25 0.109

    1~ 2.~16 0.2.5 0.622 9 0.69 1.2.5 0.17 26 0." 0-'0 o.~~ 33 0.57 0.25 0.1~213 0.71 1.2.5 0." 27 0.92 0.25 0.2315 0.6' 3.00 1.9516 0.61 1.25 0.7717 1.09 1.25 1.3722 I.U 1.75 3.1723 1.14 O.SO 0.57

    2~ 1.67 0.2.5 0.~231 2.69 0.2.5 0.67

    TocaI 3.16 15.9 3.6 2.6

    Preliminary statistical analysis of the seam thickness datasuggested a bimodal distribution, and several apparenlly ano~alousdata points were also indicated. Analysis of the seam thicknesscontours (Figure 7), indicated that no valid reason existed, based oncurrent information, for excluding any of the data points and allfurther analysis was therefore based on the complete data set.

    A large number of attempts were made to calculate anexperimental variogram model that accurately described the ply'sthickness. The final theoretical variogram which has been usedfollows an isotropic spherical model with a range of 2300 m, a sillof 0.29 m2 and no nugget effect (Figure 8). Although the geologyof the deposit indicated possible anisotropy, insufficient dataexisted for this step to be undertaken.

    Validation of the variogram model by a "jack-knifing" methodindicated that it provides an accurate description of the ply

    2000'"CONTOUR ~-ERVA 0 5'"

    FIG 7 - Contours of raw seam thickness data - laterally persistentdeposit.

    thickness over the field of interest.The deposit was kriged with 500 m by 500 m blocks and the

    resulting thickness and estimation error (at a 95 per cent confidencelevel) contoured (Figures 9 and 10). At first examination, thecontours of estimation errors indicated that there was litllepossibility of any significant resources falling in the measuredcategory.

    Classification of the deposit was carried out using thegeostatistically based computer program for square block iterationswith a side dimension of 500 m. The results have been plotted andare presented in Figure 11 and Table 5. The majority of the regionsassigned to the various classes closely follows the distribution thatwould be expected from contours of relative error (Figure 10). Asexpected only a very small region has been classified as A class; atotal of 2.5 square kilometres from a deposit covering some 31.5square kilometres. What is at first glance surprising, is that the Aclass region does not correspond to the area of higher drilling

    TABLE 6Final classification results for the laterally persistent deposit

    A class B class CclassBit TK Area Vol. Bit TK Area Vol. Bit TK Area Vol.

    1 1.316 0.15 0.329 2 0.67 1.25 0.84 10 0.65 2.5 1.617 0.811 1.00 0.811 3 0.65 1.25 0.81 18 0.59 0..5 0.308 2.371 0.25 0.593 04 0.66 1.25 0.86 20 0.62 0.75 0...6

    11 2.960 0.15 0.7040 S 0.99 1.25 1.27 21 0.65 0.25 0.1612 1.521 O.SO 0.764 6 0.64 2.25 1.043 15 0.7S 0.50 0.3814 2.0486 0.15 0.622 9 0.69 1.25 0.87 26 0.88 0.50 0.44

    13 0.71 1.25 0.88 27 0.92 0.25 0.23IS 0.65 3.00 1.95 19 0.47 1.25 0.59116 0.61 1.25 0.77 28 0.68 0.75 0.51317 1.09 1.25 1.37 29 0.43 0.25 0.10722 1.15 2.75 3.17 30 0.59 2.00 1.18423 1.14 O.SO 0.57 32 0.44 0.15 0.10924 1.67 0.25 0.42 33 0.57 0.25 0.14231 2.69 0.25 0.67

    Total 3.859 15.88 6.23

    12 021990 The AusIMM Proceedings

  • COAL RESOURCE CLASSIFlCATION AND GEOSTATISTICS

    VARIOGRAM

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    0.150480.142260.245860.328110.32177O. 44273o 22594 O. 33O. 28557O. 45391O. 553290.43029O. 303280.36030.27/140.265560.24764O. 37446 o. 220.22827

    o - 350350 - 700700 - 10501050 - 14001400 - 17501750 - 210021 00 - 24502450 - 28002800 - 31 5031 50 - 35003500 - 38503850 - 42004200 - 45504550 - 49004900 - 52505250 - 56005600 - 59505950 - 63006300 - 66506650 - 7000

    011

    700-+----..--- ---t--- -1--- ---1---- +

    I 400 21 00 2800 3500 4200 4900- I - - - I - -----j5600 6300 7000

    FIG 8 - Average seam thickness variogram -laterally persistent deposit.

    2000MCONTOUR INTERVAL = 025M

    FIG 9 - Kriged seam thickness contours based on 500 m x 500 mblocks - laterally persistent deposit.

    FIG 10 - Relative kriging error (95 per cent confidence level)contours based on 500 m x 500 m blocks -laterally persistent

    deposit.The AuslMM Proceodings No21990 13

  • K D WHITCHURCH, ADS GILLIES AND G D JUST

    FIG 11 - Resource classification results -laterally persistent deposit

    2000M

    _ MEASUREJ

    FIG 12 - Traditionally derived resource classification results.

    density. This may be easily explained by the fact that theclassification is based on relative precision (error/estimate) ratherthan error alone. For this reason the class of resource to which aparticular region is assigned, is a function of both the drillingdensity and the seam thickness in that region.

    A number of regions near the deposit boundary remainunclassified. This situation occurs due to the constraints placed onthe growth of these regions by previously classified regions and thedeposit boundary. It should be noted, however that the depositboundary tends to be a region of high uncertainty. This is reflected

    FIG 13 - Final classification results for the laterally persistentdeposit.

    1000M

    FIG 14 - Major structural features - structurally disturbed deposit.

    by the fact that, with few exceptions, regions near the boundary areclassified into the least restrictive classes of resource. For thisdeposit, and with knowledge of the existence of coal seamcontinuity, it is recommended that unclassified areas that fall withinthe deposit boundary are considered as falling within the lowestrestrictive class of resource.

    Another apparent anomaly is the existence of two isolated C classregions (blocks 18 and 21, Figure 11), surrounded by B classregions. The presence of these regions may be attributed to therestrictions placed on their growth by previously classified regions

    14 No 21990 The AusIMM Proceedings

  • SUMMARY STATISTICStlEAIlI,'AF: I HNCESTD. ['E\I.

    SI'E:,J~1E5:3I:UF:TOS I 5

    1

    COAL RESOURCE CLASSIFICATION AND GEOSTATISTICS

    FOR UrlTRANSFORMED DATAo. 4 (1'~ '" 0 24 '3E + 0 10.2990468'?E+'Jl0.17292971E+Ol0.25723405E+01\C' .13504897E+02

    , HIS T Cr'~ P F1 11 > ..SOUTHERN AREA COMPLETE DATA SET

    CE: S/ F:ELA CUtlL UPPERF F.E(~ FRECI FRE(~ CELL L!I'lIT (.. 20 4

    + + +6 0.018 0.018 o .12'O6E+('l .....':' 0,027 O. 045 0.22::2E+(11 +>

    4:, 0.136 0.181 0.3178E+01 + t .. ~:+-~ ... '+1'" -. (1.548 O. 72'3 e. 4134E+('1 +~t ~~*~~.~~~~~... ~ .. +~. ~

    4~ 8 f~-:O 0.855 0.509(IE+1

  • K D WHITCHURCH. ADS GILUES AND G D JUST

    III 1I/~40M OJB L1N~\

    60M O/B LINE

    ---FAULT A

    40M O/B LINE

    , ....u: MU

    100..

    FIG 17 (a) - Seam thickness contours for the southern zone. FIG 18 (a) - Structural features of the southern region.

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    LOXLIN[~

    FIG 17(b) - Expanded view of the sample area shownin Figure 17(a).

    and the fact that they represent isolated zones of high estimationerror. Such locations exist due to isolated thinning of the deposit ora local decrease in drilling density. Whatever the reason for theexistence of a particular isolated region it is clear that each shouldbe given close individual attention at the fmal stage in theclassification process. Some subjective judgement is at presentconsidered unavoidable in the treatment of such regions leading tothe conclusion that further work is needed to address this problem.For the purpose of this case study these isolated regions wereconsidered to be of sufficient size to be classed as independent ofthe surrounding blocks and to therefore accurately represent thepresence of locations of low relative precision. The two regionsunder question, blocks 18 and 21. were consequently leftunchanged from the original computer base classifications. Finalresults for this deposit are presented in Table 6.

    A comparison of the regions assigned to the previous resourceclasses by traditional methods (Figure 12) and by the geostatistical

    FIG 18(b) - Expanded view at the sample area shown in Figure 18(a).

    algorithm (Figure 13) reveals little correlation. This may beexplained by the fact that the traditional results were based on drillhole spacing only. Use of this estimation procedure tends to distortand increase the resource quantity in categories of greatestgeological assurance in this case. However, this approach isallowable under the present Queensland and New South Walescodes although it is no true indication of the level of confidenceassociated with the estimate of the resource quantity. In contrast,the results generated by the geostatistically based algorithm closelyreflect the level of confidence and as such may be considered moresoundly based.

    Case Study 2 - Classification of a structurally disturbeddeposit

    An anthracitic deposit was chosen as the second case study because

    16 021990 The AusIMM Proceedings

  • of the highly structurally-disturbed state of the seams. Complexfaulting in this area gives rise to several fundamental problems notevident in the analysis of brown coal deposit. Major structuralfeatures of this anthracite deposit are shown in Figure 14.

    Preliminary investigations of the sample information indicatedthe need to sub-divide the deposit into regions that were accepted asgeologically continuous for the purpose of geostatistical analysis.Two major regions were identified, the area north of fault A (Figure14) and the larger area to the south. The southern zone is of greaterinterest and will be discussed. Classical statistical analysis of theraw sample data revealed a standard deviation of almost 50 per centof the sample mean of about 4.1 m thickness (Figure 15). The largevalue of the standard deviation can be attributed to the presence of anumber of apparently anomalous high values.

    In particular it is noted that three samples recorded thicknessesgreater than 13.0 m. Removal of all data lying in oxidised regionsdid little to improve the results (Figure 16).

    Contouring of raw thickness values, however, revealed thepresence of a large number of isolated high and low values (Figure17a). This situation is clearly illustrated in Figure 17(b) in whichadjacent sample values range from two m to seven m. Furtherdrilling showed that these were a reflection of the high degree ofstructural disturbances present in the region. Systems of normaland reverse faults effectively isolate small blocks of the regions intoareas that should be analysed separately (Figures 18(a) and (b)).These geological characteristics make it extremely difficult to applygeostatistics, or indeed any estimation method to this region.Consequently this is considered to be an area where geostatisticalclassification is difficult to apply.

    Various attempts were made to construct a variogram model forthis region. The experimental variogram exhibited a sphericalmodel structure with a short range hole effect. The hole effect is areflection of the closely spaced seam structural changes and sodistorts any analysis based on a examination of seam thickness.This emphasises the fact that any geostatistical analysis undertakenwithout due emphasis on deposit geology may be misleading andresult in highly erroneous conclusions. In this case since no furtheranalysis was undertaken, no comparison with traditionally derivedresults was possible. It may however, be concluded that in depositswhere geostatistics is not readily applicable for structural reasonsthe possibility of A class resources is remote.

    CONCLUSIONS

    Coal resource classification concepts and definitions have beenstudied and a geostatistically based algorithm derived for assigningmine blocks to classes of varying geological assurance. In use, thealgorithm gives classification results that closely reflect the errorassociated with the kriged estimate of resource quantities. Themethod provides a consistent basis for comparison of differentdeposits. It requires a careful geostatistical analysis with particularemphasis on details of geological variations. From its use, it ispossible to predict the increase in sampling density required toattain a higher classification category for a particular area. Resultsare readily approachable and require a minimum of subjectivejudgement. Resource category restrictions must be arbitrarilyassigned and remain constant for all deposits being compared.

    COAL RESOURCE CLASSIFICATION AND GEOSTATISTICS

    Finally, it must be emphasised that, as with all classificationapproaches, . engineering judgement is needed. In particular,interpretation may be required with isolated and peripheral blocks.Further research is warranted on these aspects.

    ACKNOWLEDGEMENTS

    The support of CSR Ltd during the study is acknowledged. Thispaper was prepared within the Department of Mining andMetallurgical Engineering, University of Queensland. Discussionswith various staff and students assisted in the formulation of theconcepts presented.

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    Diehl, P. and David, M., 1982b. Geostatistical concepts for ore reserveclassification, in Froc. 17th Int. APCOM Symp. pp.413-424 (ColoradoSchool of Mines).

    Fettweis, G., 1979. Developments in Economic Geology, Volwne 10: WorldCoal Resources, Methods of Assessment and Results, (Elsevier:Amsterdam)

    Froidevaux, R, 1982. Geostatistics and ore reserve classification, CIM Bull,75:843 pp.77-83

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    McKelvy, V.E. Mineral resource estimates and public policy, AmericanScientist, 60, (January-February): 32-40.

    Sabourin, RL., 1983a. Application of a geostatistical method toquantitatively define various categories of resources, in Proc NATOAdvanced Study Institute on Geostatistics for Natural ResourceCharacterisation, Lake Tahoe, pp201-205.

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    United States Bureau of Mines and US Geological Survey 1976. Principlesof the mineral resource classification system, USBM/USGS, GeologicalSurvey Bulletin 1450-A.

    United States Bureau of Mines and US Geological Survey, 1980. Principlesof a resource/reserve classification system: Geol. Survey Circ.831.

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    Wigglesworth, K.E, 1981. Code for assessment and reporting of coalresources and reserves of South Australian coal deposits, Dept. of Minesand Energy, South Australia, Report. BK. No 81/86.

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