identification of mineral intensity along drifts in the dayingezhuang deposit, jiaodong gold...

Original Articlerge_117 98..108

Identification of Mineral Intensity along Drifts in theDayingezhuang Deposit, Jiaodong Gold Province, China

Li Wan,1,2 Qingfei Wang,2,3 Jun Deng,2,3 Qingjie Gong,2,3 Liqiang Yang2,3 and Huan Liu

2,3

1School of Mathematics and Information Science, Guangzhou University, Guangzhou, Guangdong, China, 2Key Laboratory ofLithosphere Tectonics and Lithoprobing Technology of Ministry of Education, China University of Geosciences, Beijing, Chinaand 3State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing, China

Abstract

Great mineral intensity in drifts or drills is characterized by both the high proportion and spatial cluster of thehigh grades. Great mineral intensity indicates the high ore quality and low dilution in the exploitation. Thefractal dimension, the fluctuation exponent proposed in this paper and the lacunarity are utilized to analyze themineral intensity along drifts in the Dayingezhuang gold deposit in Jiaodong gold province, China. It is proventhat the combination of the parameters can identify the mineral intensity. The fractal dimension and fluctuationexponent are negatively correlated. In the places where the fractal dimension is small and fluctuation exponentis great, the mineralization is more pronounced. While in the case that the fractal dimensions are similar,smaller fluctuation exponent means more clustered structure of high grades and greater mineral intensity. Inthe diagram of fractal dimension versus lacunarity, the drifts with great mineral intensity can also be identified.The methods used in this paper provide a relatively comprehensive description for local mineral intensity,providing information for both the ore-forming process and the exploitation.

Keywords: fluctuation exponent, fractal dimension, Jiaodong gold province, lacunarity, mineral intensity.

1. Introduction

Mineral intensity is a basic concept for the appraisalof enrichment strength of ore-forming elements, whichcan give information for both the ore-forming processand the exploitation. Since the drift is a necessary andessential item during the exploration and exploitationin a deposit, identifying mineral intensity in the drift isselected in this study. In an explored deposit, continu-ous channel sampling across the drifts has been com-pleted and the equidistant grades along each drift areobtained, which provide the data basis for the analysisof local mineral intensity. There are two aspects con-trolling the mineral intensity in the drifts in a deposit.

The more important is the proportion of the high grademineralization. The greater proportion of high grademineralization means the greater mineral intensity. Theless important is the spatial distribution of the highgrade mineralization. Under the condition that thehigh grades are densely distributed or clustered, thethickness of the orebody is relatively smaller and itsgrade is greater, it is considered to have greater mineralintensity. Moreover, the more clustered distributionof over-cut-off grades is likely to reduce the ore lossand dilution rates, which can give helpful informationfor development of the orebody around the drifts. Thelocations with great mineral intensity means develop-ment of favourable geological conditions for ore

Received 26 November 2008. Accepted for publication 17 February 2009.Corresponding author: Q. WANG, State Key Laboratory of Geological Processes and Mineral Resources, China University ofGeosciences, No. 29, Xueyuan Road, Beijing 100083, China. Email: [email protected]

doi: 10.1111/j.1751-3928.2010.00117.x Resource Geology Vol. 60, No. 1: 98–108

98© 2010 The Authors

Journal compilation © 2010 The Society of Resource Geology

formation, such as a high density of cracks for elementdeposition, and this kind of location can provide theores with great quantity and high quality and reducethe ore loss effectively.

Natural objects often have similar features at variousscales. Measures of these features, e.g. total number,total length, abundance, average roughness, etc. aredependent on the scales on which the features areobserved. Fractal models assume that this dependenceis the same across the range of scale, i.e. scale invariantin this range, and this range is called the non-scalerange. The distribution of ore deposits shows scaleinvariant or statistical self-similar. Moreover, it espe-cially presents cluster structure, meaning the oredeposits are closely spaced in several small areas, justlike some other geological objects, e.g. metamorphicveins, structures. The metal distribution along drifts inmost deposits is not even, showing heterogeneousstructure. For example, in structure-controlled golddeposits, the orebodies have poorly defined andirregular geometries. Continuity of individual veins isnot great due to their podiform to en-echelon nature.High grade zones are narrow and associated with thinstinger veins and their immediate host rocks. Gradedistribution is strongly skewed and outliers exist(anomalously high values outside the main popula-tion) which result in grade bias. There are internal com-plexities and zones of low grade or barren host rockresulting in heavy dilution (Annels, 1991). In the tradi-tional explorations, the total thickness and averagegrade of the orebody on the drift are calculated as thebasic parameters. Yet the cluster characteristic is notinvolved in these parameters, quantitative descriptionof the mineral intensity on drifts needs more detailedresearch via proper tools.

Fractal theory is used to study the characteristics ofscale invariance and heterogeneity (Burrough, 1993;Turcotte, 1997). Many fractal models are applied in thestudy of ore deposits and geochemical data, includingthe number-size model (Carlson, 1991; Monecke et al.,2005; Wang et al., 2007a), perimeter-area model (Chenget al., 1994; Cheng, 1995, 2003; Zhang et al. 2001), grade-tonnage model (Turcotte, 1997, 2002), and multifractalmodel (Agterberg et al., 1996; Cheng, 1999, 2000, 2004;Wang et al., 2008). The most popular model is thenumber-size model, which is used to characterize thedistribution of deposits based on the number of depos-its versus their sizes (Carlson, 1991; Agterberg, 1995;Turcotte, 2002; Wan & Wang, 2007). The number-sizemodel is recently applied in dealing with the gradedistribution in the deposit, where the corresponding

fractal dimension obtained from the model can revealthe scatter pattern and size distribution of the data, butcan not reflect the spatial distribution. Several param-eters calculated by the scale invariance principle, suchas the Hurst exponent in the self-affine fractal domain,box dimension in self similar domain, are applied toanalyze the characteristics of grade spatial distribution(Malamud & Turcotte, 1999; Wan & Wang, 2007; Wanget al., 2007b; Deng et al., 2008a); yet these parameterscan not discover the cluster structure of the grades.Mandelbrot (1995) also reported that fractal dimen-sions are very far from providing a complete charac-terization of a set’s texture. Different fractal sets mayshare the same fractal dimension values, but have dif-ferent textures (Mandelbrot, 1983; Dong, 2000). Via thescale invariance principle, this paper proposes a fluc-tuation exponent via length variation method to studythe cluster structure. Mandelbrot (1983) introduced theterm lacunarity to characterize the different clusterstructures, which may have the same fractal dimensionvalues. Lacunarity is a scale-dependent measure ofspatial complexity of a dataset (Plotnick et al., 1993;Dong, 2000). In data analysis, lacunarity represents thedistribution of high values: low lacunarity means thehigh values are homogeneously distributed, whereas,high lacunarity represents data that is heterogeneous,i.e. with cluster structure.

In this paper, the Dayingezhuang structure-controlled alteration-rock type gold ore deposit in Jia-odong gold province, China is selected for case study.Based on the grades along drifts, the fractal dimensionin combination with the fluctuation exponent and thelacunarity are applied to describe the local mineralintensity.

2. Geological settings anddata preparation

The Dayingezhuang ore deposit is located in themiddle segment of the Zhaoping fault zone in Jiaodonggold province, China (Fig. 1). The Jiaodong gold prov-ince is famous for its gold production, and structure-controlled alteration rock gold deposits formed in theMesozoic dominate the province (Deng et al., 2006,2008b; Yang et al., 2006, 2007). The reserves of the Day-ingezhuang are more than 100 t, with an estimatedannual production greater than 2.6 t.

Wallrocks comprise both metamorphosed Precam-brian sequences and Mesozoic intrusions. A cataclasticaltered zone in the footwall of the Zhaoping fault con-trols the occurrences of gold mineralization. Wallrock

Identification of Mineral Intensity

© 2010 The AuthorsJournal compilation © 2010 The Society of Resource Geology 99

alteration related to gold mineralization consists ofK-feldspar alteration, silicification, sericitization, chlo-ritization, phyllic alteration, sericite-quartz alterationand carbonatization. The degree of fracturing and alter-ation gradually weakens away from the Zhaoping frac-ture plane.

In the deposit, Nos. I and II altered zones are thebiggest. The No. II altered zone located between drift64 and drift 89 in the northern part of the deposit,includes 73 orebodies, in which the No. II-1 orebody isthe greatest. The No. II-1 orebody extends from -26 mto -492 m within drift 66 and drift 88, with a cut-offgrade of 2 g/t. The main exploration levels include-140 m, -175 m, -210 m and -290 m, etc. (Fig. 2). Thispaper focuses on the analysis of the mineral intensity inthe No. II-1 orebody and surrounding alteration zone.

The gold grade data are obtained from the continu-ous channel samples with 1 m in length along differentdrifts on several levels. The non-mineralized drift isnamed in this paper when most grades are lower than

the cut-off grade and the orebody can not be demar-cated clearly, and the mineralized drift is defined whenpart of the grades are greater than the cut-off and theorebody develops (Fig. 2).

3. Mathematical methods3.1 Fractal dimension

The number-size model is expressed by the formula:

N r Cr C r DD( ) = > > >− , , ,0 0 0 (1)

here r represents the scale, it represents the grade inthis paper; C is a constant; D is the fractal dimensionand N(r) is number of grades counted when they areequal to or greater than grade value r. Plotting a groupof scales r1, r2, . . . ,rn and the corresponding N(r1),N(r2), . . . , N(rn) into a ln-ln diagram, we can get D fromthe slope of a least-square regression line for the datapoints. The smaller dimension indicates the greaterproportion of the relatively higher grades in thedataset, that is, the greater mineral intensity.

By means of fractal calculations, we find that thefractal model has multi-scale fractal characteristics. Theplots are fitted by two straight lines divided by anintersection grade in ln-ln coordinates, indicating thereexist two non-scale ranges. The intersection gradevaries within a small range around 0.5 g/t. The inter-section grades in the intensively mineralized drifts aregreater than those in the barely mineralized drifts. Thefractal dimension in the first non-scale range is muchsmaller than that in the second non-scale range. Thispaper mainly concerns the distribution of highergrades, so the fractal dimension of the second non-scale range is studied. For obtaining a relatively highgoodness of fit of the plots, and effectively comparingthe fractal dimensions between the drifts, we set0.5 g/t as the beginning of the second non-scale rangefor the calculation of all the drifts involved in this paper(Fig. 3).

3.2 Fluctuation exponent

As illustrated in Figure 4, a grade curve is drawn, withthe equidistant sample location on the horizontal axisand the grade value on the vertical axis. Picking thegrades with horizontal interval e to constitute a newcurve g(e), then the total length L(e) of the curve g(e) canbe calculated. Assuming the grade curve possessesscale invariance characteristics, the length L(e) showpower-law relation with the horizontal interval e, i.e.

Fig. 1 Generalized geological map of Dayingezhuanggold deposit.

L. Wan et al.



L e e F( ) ∝ − (2)

where F is named as the fluctuation exponent. We canchange the interval e and acquire a series of ei (I = 1, 2,3, . . . , n), and hence get the corresponding L(ei). If theL(ei) are linearly related to the corresponding ei underln-ln coordinates, the fluctuation exponent is obtainedfrom the slope of the fitting line by the least squaremethod.

The fluctuation exponent is constrained both by theproportion of high grades and their spatial distribu-tion. Considering the case where the grades are alllower than the cut-off, with the increase of interval ei,the L(ei) decreases slower resulting in a small slope inthe lnL(ei)-lnei coordinates and a low F (Fig. 4a); whilewhen a few grades are beyond the cut-off, with theincrease of ei, the L(ei) decreases faster relatively dueto the fact that part of the high grades are skippedduring calculation (Fig. 4b); so the greater number of

the high grades, the greater probability of skippinghigh grades and the faster decrease of the L(ei) result-ing in a high F. So the fluctuation exponent can reflectthe proportion of the high grades in the data andshould show a negative relationship to the fractaldimension. For the same reason, the L(ei) tends todecrease slower in the case where the high grades areclustered than that where they are evenly distributed.When the proportions of high grades on two driftsare similar, a lower fluctuation exponent suggests amore intensely clustered location of the high grades;and in contrast, a greater fluctuation exponent indi-cates the high grade distribution is more dispersive(as show in Fig 4a, c).

Changing the interval ei = 1, 2, 3, 4, 5 in turn and getthe corresponding length L(ei), we can acquire thefluctuation exponent of the drifts on several levelsin the Dayingezhuang deposit by formula (Eqn 2)(Fig. 5).

Fig. 2 Plane geological map on the-210 m level in Dayingezhuanggold deposit. 1.orebody; 2. cata-clastic altered zone; 3. granodior-ite; 4. diorite porphyrite; 5. fault;6. geological boundary; 7. samplerange.

Fig. 3 Fractal distribution of gold grade distribution on the -210 m level in Dayingezhuang gold deposit. (a) drift 68.5; (b)drift 70.



3.3 Lacunarity

The method for calculating lacunarity was first intro-duced by Mandelbrot (1983) and several other algo-rithms for computing lacunarity have been developed(Allain & Cloitre, 1991; Dong, 2000). Lacunarity is notconfined to binary data, but can also be used withquantitative data (Plotnick et al., 1993). Allain andCloitre (1991) initiated a conceptually straightforwardand computationally simple “gliding box” algorithmfor calculating lacunarity.

For a grade sequence with overall number Nt, linkthe end and start points to constitute a circle. Consider

a segment of size re (re = 1, 2 . . . , Nt/2) which “glides”forward to cover the circle of data without repetition ofthe segments. M is the sum of grade in the segment. Letn(M, re) be the number of gliding boxes with size re andgrade sum M. Dividing n(M, re) by the total number ofsegments N(re) = Nt - re + 1, we obtain the probabilityfunction P(M, re). The statistical moments of this dis-tribution now can be determined as:

Z re M P M re

N reM n M re

N reM re

Qq q

M

q

M

iq

i

( ) ( ) = ( )

=( )

( )

=( )

( )

∑

∑

∑

,

,1

1

(3)

where Z reQq( ) ( ) is the qth order moment of P(M, re), and

the sum is for all gliding boxes with M > 0.Lacunarity at scale re can be defined as (Allain &

Cloitre, 1991):

Λ reZ re

Z reQ

Q

( ) =( )( )[ ]

( )

( )

2

1 2 (4)

The lacunarity decreases with the increase of re. Thelacunarity and the fluctuation exponent can both reflectthe evenness of the spatial distribution of high grademineralization. The correlation coefficient betweenlacunarity and the fluctuation exponent is positive.

4. Result analysis

The fractal dimension, the fluctuation exponent and thelacunarity with 1 � re � 10 for grade distribution of thedrifts in the Dayingezhuang gold deposit are listed inTable 1.

It is obvious that the fractal dimension and fluctua-tion exponent in the non-mineralized drifts are distin-guished from those in the mineralized drifts. As shownin Figure 6, the plots of the fractal dimension and fluc-tuation exponent can be classified into three parts con-fined by the dashed lines. From part 1 to part 3, thefractal dimension decreases and fluctuation exponentincreases. The drifts in the first part are non-mineralized, those in the second part have lowermineral intensity, and those in the third part havegreater mineral intensity. The fractal dimensions of thenon-mineralized drifts located in part 1 in Figure 6 aregreater than 2.00. The fluctuation exponents in non-mineralized drifts vary from 0.08 to 0.12, while, in themineralized drifts located in parts 2 and 3 in Figure 6,

Fig. 4 Explanation of the meaning of fluctuation expo-nent of gold grade distribution. (a) grade curve in non-mineralized area; (b) grade curve in mineralized areawhere high grades are sparsely distributed; (c) gradecurve in mineralized area where high grades aredensely distributed. The real line is the grade curveg(e = 1) and the dash line represents curve g(e = 2).

L. Wan et al.



the fluctuation exponents range from 0.13 to 0.67.Moreover, the parameters in the intensely mineralizedarea are also smaller than those in the weakly miner-alized area where the orebody is less developed. Forinstance, on -210 m level, the fractal dimensions andfluctuation exponents of the drifts 75 and 78.5 withinpart 3 in Figure 6, where the orebody is more devel-oped, are much different from those of the drifts 73 and73.5 within part 2 (Figs 2, 6). The correlation coefficientof fluctuation exponent and fractal dimension amongall the calculated drifts is - 0.85, which indicates a nega-tive relevance of the two indexes. In Figure 6, the trendof the plots can be represented by a broad grey line. Inpart 1, the range of the fractal dimension is greater andthat of the fluctuation exponent is smaller, thus, theslope of the trend line is greater, compared with theother two parts. While, in part 3, the range of the fluc-tuation exponent is greater and the slope of the trendline is smaller comparatively. It indicates the intenselymineralized drifts show more difference in clusterstructure. Besides, when the fractal dimensions D aresimilar, a greater fluctuation exponent suggests a morecluster structure of the metal distribution. In the case ofthat the fractal dimensions are similar, we can identifythe mineral intensity of the drifts according to the fluc-tuation exponent. For instance, on -175 m level, thedimensions of the drifts 73 and 75 are only with thedifference of 0.08, but the fluctuation exponents showobvious distinctness with the difference of 0.27 (Fig. 6).The distribution of high grades of drift 75 illustrated inFigure 7 shows that it is more intensely clustered thanthat of drift 73 shown in Figure 8. In the drift 73, theredevelops several very high grades nearby the position55 on the horizontal axis, which can not be totally

detected by the fluctuation exponent, but can bereflected by the lacunarity, since it is more sensitive tothe development of the anomalously high grade distri-bution, especially when re is small.

The lacunarities in the mineralized area are mostlymuch greater than those in the non-mineralized drifts,showing that the grade distribution along the non-mineralized drifts is more homogeneous in general(Fig. 9). Plots of fractal dimension and lacunarity withre = 1 is shown in Figure 10. Generally, with thedecrease of fractal dimension, the lacunarity, i.e. thecluster degree increases, as illustrated by the broadgrey line. Most plots in Figure 10 can be classified intothree regions. The grades in the drift in region 3 onthe left are more homogeneously distributed and themineral intensity is relatively smaller. For instance, thedrift 73 on -210 m level, shows similar lacunarity asthose in the non-mineralized drifts. The low lacunari-ties in these drifts indicate the equidistant distributionof high grades (Figs 2, 11). The drifts in region 1 on theright show more cluster structure in the grade distri-bution and often include outliers.

The fluctuation exponent and lacunarity of the driftsbasically show positive relation in the drifts when thedrifts show similar fractal dimension. Occasionallywhen the fluctuation exponent is small, the lacunarityis great, such as the drifts 75, 75.5 and 77 on the -175 mlevel, drift 70 on the -210 m level and the drift 75 on the-290 m level. Omitting these drifts, the correlationcoefficient of the fluctuation exponent and the lacunar-ity with re = 1 reaches to 0.78. Checking grade curveof the omitted drifts, such as 70 drift on the -210 mlevel shown in Figure 12, it reveals that few veryhigh grades or even outliers develop. Therefore, the

Fig. 5 Calculation of fluctuation exponent of gold grade distribution on the -210 m level in Dayingezhuang gold deposit. (a)drift 68.5; (b) drift 70.



Tab

le1

Frac

tald

imen

sion

,fluc

tuat

ion

expo

nent

and

lacu

nari

tyof

gold

grad

ed

istr

ibut

ion

alon

gd

rift

sin

Day

inge

zhua

nggo

ldor

ed

epos

it,C

hina

Loc

atio

nTy

peFr

acta

ldim

ensi

onFl

uctu

atio

nex

pone

ntL

acun

arit

y

Lev

el(m

)D

rift

DR

2S.

EF

R2

S.E

re=

1re

=2

re=

3re

=4

re=

5re

=6

re=

7re

=8

re=

9re

=10

-175

73M

iner

aliz

ed1.

090.

960.

250.

470.

910.

082.

742.

452.

322.

182.

061.

951.

851.

761.

681.

6275

Min

eral

ized

1.01

0.90

0.29

0.2

0.97

0.03

2.45

2.27

2.11

1.98

1.88

1.80

1.73

1.66

1.60

1.55

75.5

Min

eral

ized

1.13

0.98

0.12

0.28

0.98

0.03

3.15

2.79

2.50

2.28

2.11

1.99

1.90

1.83

1.77

1.81

77M

iner

aliz

ed0.

620.

990.

070.

560.

970.

073.

453.

172.

962.

812.

702.

612.

512.

422.

332.

2578

.5M

iner

aliz

ed0.

430.

950.

050.

670.

90.

172.

221.

941.

781.

661.

561.

491.

431.

381.

341.

3182

.5M

iner

aliz

ed1.

870.

970.

210.

130.

90.

042.

602.

081.

811.

661.

551.

471.

401.

351.

311.

28

-210

68.5

Non

-min

eral

ized

2.04

0.93

0.28

0.1

0.99

0.03

1.44

1.29

1.20

1.17

1.15

1.13

1.12

1.11

1.11

1.10

69.5

Min

eral

ized

1.34

0.95

0.20

0.29

0.95

0.05

2.53

2.01

1.74

1.55

1.43

1.34

1.28

1.25

1.22

1.19

70M

iner

aliz

ed1.

330.

980.

150.

210.

990.

024.

363.

412.

872.

582.

452.

342.

242.

132.

041.

9570

.5N

on-m

iner

aliz

ed2.

090.

980.

180.

090.

920.

021.

891.

661.

551.

501.

451.

411.

381.

361.

341.

3372

.5N

on-m

iner

aliz

ed2.

250.

900.

480.

120.

980.

061.

361.

311.

281.

261.

241.

231.

221.

211.

201.

2073

Min

eral

ized

1.45

0.94

0.28

0.31

0.94

0.05

1.68

1.50

1.40

1.34

1.29

1.26

1.23

1.21

1.19

1.17

73–7

4.5

Min

eral

ized

1.54

0.96

0.25

0.23

0.93

0.05

2.17

1.97

1.84

1.76

1.69

1.63

1.58

1.55

1.53

1.51

75M

iner

aliz

ed0.

980.

960.

250.

710.

930.

152.

622.

292.

051.

881.

771.

661.

581.

511.

451.

3978

.5M

iner

aliz

ed0.

980.

960.

240.

630.

940.

112.

672.

121.

821.

651.

521.

431.

351.

291.

251.

22

-282

76M

iner

aliz

ed1.

080.

900.

390.

430.

970.

052.

081.

971.

911.

861.

831.

811.

781.

761.

731.

7078

Non

-min

eral

ized

2.10

0.98

0.18

0.11

0.95

0.02

1.41

1.25

1.19

1.15

1.13

1.11

1.10

1.09

1.08

1.07

-290

72.5

Min

eral

ized

1.52

0.91

0.29

0.14

0.96

0.02

1.49

1.42

1.39

1.37

1.36

1.35

1.34

1.33

1.33

1.32

75M

iner

aliz

ed1.

270.

960.

240.

30.

930.

083.

913.

293.

062.

852.

652.

502.

372.

262.

172.

1078

Non

-min

eral

ized

2.09

0.98

0.16

0.08

0.99

0.02

1.48

1.37

1.33

1.30

1.26

1.24

1.22

1.20

1.18

1.17

Cor

rela

tion

coef

ficie

ntbe

twee

nF

and

D-0

.85

Cor

rela

tion

coef

ficie

ntbe

twee

nF

and

lacu

nari

ty(r

e=

1)af

ter

omit

ting

five

dri

fts

0.78

0.75

0.7

0.66

0.62

0.58

0.54

0.51

0.48

0.45

L. Wan et al.



lacunarity is sensitive to the existence of few very highgrades and complements the information obtained byfluctuation exponent.

5. Conclusions

Based on the fractal dimension calculated by thenumber-size model, the scatter pattern of the dataset,i.e. proportion of the higher grade mineralization, inthe different drifts in the Dayingezhuang deposit isdetermined. The fluctuation exponent proposed in thispaper and lacunarity are used to describe the spatialdistribution of the high grade mineralization. The com-bination of the parameters is applied for identificationof local mineral intensity.

The results show that the fractal dimensions in thenon-mineralized drifts are greater than those in theweakly mineralized zone, and those in the intenselymineralized zone. The fluctuation exponents andaverage lacunarity in the non-mineralized zone aresmaller than those in the mineralized zone. The fractaldimension shows negative relationship with the fluc-tuation exponent and the lacunarity, indicating themetal distribution shows more heterogeneous andcluster structure with the increase of the number ofhigher grades in the drift.

Both the diagram of fractal dimension versusfluctuation exponent, and that of fractal dimensionversus lacunarity, can identify the mineral intensity.When the fractal dimension is great and fluctuation

Fig. 6 Plots of Hurst exponent andfractal dimension of gold gradedistribution along drifts in Day-ingezhuang gold ore deposit.

Fig. 7 Diagram of the grade curve along drift 75 on the -175 m level in Dayingezhuang gold deposit.



exponent is relatively small, the mineral intensity issmall. Under similar fractal dimensions, the smallerfluctuation exponents indicate that the high gradesare more intensely clustered and the mineral intensityis greater (Fig. 6). The combination of the fractaldimension and lacunarity can obtain a similar result,except that the lacunarity is greatly affected by theexistence of very high grade mineralization, showinga relatively high value since it is calculated by thebox-sliding approach. The plots of the drifts involved

in the two diagrams in this paper set up a frameworkfor the identification of mineral intensity of the driftsin the Dayingezhuang deposit or in other structure-controlled gold deposits. The analysis for the drifts inthis paper is also suitable for the other explorationengineering, e.g. drills, trenches. The methods used inthis paper provide more comprehensive descriptionand comparison for local mineral intensity and infor-mation for the orebody development around theexploration engineering.

Fig. 8 Diagram of the grade curve along drift 73 on the -175 m level in Dayingezhuang gold deposit.

Fig. 9 Lacunarity in different drifton the -210 m level in Day-ingezhuang gold ore deposit.

L. Wan et al.



Acknowledgments

We thank Academician Pengda Zhao, ProfessorQiuming Cheng for advice on fractals. We appreciatethe valuable and careful comments from the editors ofResource Geology and the two anonymous reviewers.This research is supported by National Natural ScienceFoundation of China (Grant No.40572063, 40672064,

40872194, 40872068) and Program for Changjiangscholars and Innovative Research Team in University(PCSIRT).

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Fig. 10 Plots of Lacunarity andfractal dimensions of gold gradedistribution along drifts in Day-ingezhuang gold deposit.

Fig. 11 Diagram of the grade curvealong drift 73 on the -210 m levelin Dayingezhuang gold deposit.

Fig. 12 Diagram of the grade curvealong drift 70 on the -210 m levelin Dayingezhuang gold deposit.



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identification of mineral intensity along drifts in the dayingezhuang deposit, jiaodong gold...

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