An evaluation of the quartz crystal microbalance as a mercury vapour sensor for soil gases

Journal of Geochemical Exploration Elsevier Publishing Company, Amsterdam - Printed in The Netherlands

INTERPRETATION OF A ROCK GEOCHEMICAL EXPLORATION SURVEY IN CYPRUS - STATISTICAL AND GRAPHICAL TECHNIQUES

G.J.S. GOVETT

Department of Geology, University of New Brunswick, Fredericton, N.B. (Canada) (Received November 12, 1971) (Resubmitted January 12, 1972)

ABSTRACT Govett, G.J.S., 1972. Interpretation of a rock geochemical exploration survey in Cyprus - statistical and graphical techniques. J. Geochem. Explor., 1: 77-102. A rock geochemistry orientation survey in Cyprus showed that anomalous dispersion patterns for Cu, Zn, Ni, and Co could be reliably measured in pillow lavas for a maximum distance of only 20 m from a sulphide deposit. Frequency distributions of the elements showed systematic (though small) differences between background areas and groups of samples from traverses extending more than 2 km from mineralization. It is concluded that the variation in concentration of individual elements alone is inadequate as a basis for an exploration procedure. A very successful exploration technique using the combined inter-variation of all the elements measured was developed for these unpromising data. Two interpretative methods are described - a computer calculation of characteristic discriminant functions for each of several pre-determined groups (e.g., anomalous and background) and a graphical derivation of what is termed "determinative functions" which allows the classification of individual samples as background or anomalous. Both of these techniques allow the recognition of an anomalous zone more than 2 km from the mineralization; either technique may be useful for exploration geochemistry in regions of weak anomalies. INTRODUCTION

General statement A computer application of discriminant analysis and a graphical procedure to derive what the writer has termed "determinative functions" were developed at the University of New Brunswick in 1 9 6 8 - 1 9 6 9 to interpret the results of a rock geochemical survey conducted in Cyprus during 1 9 6 7 - 1 9 6 8 under the auspices of the United Nations Special Fund and the Cyprus Geological Survey. These procedures were first described in an unpublished United Nations report and were later published in summary form by the United Nations (Govett, 1969, pp.23, 4 8 - 5 4 ) ; since then they have been considerably refined and are currently being used successfully on a number of rock geochemistry and stream survey projects at the University of New Brunswick.

78

G.J.S. G O V E T T

Both of the procedures seek to express multiple trace element concentrations in a sample or group of samples as a single number and, on the basis of this number, to assign a particular sample to one of several pre-defined groups- in exploration, to identify a sample as background or anomalous. The purpose of this paper is to show the utility of discriminant analysis in geochemical exploration and to compare a computer-aided interpretive technique with a simple graphical procedure using the results of the Cyprus survey as an illustration.

Summary of geology The sulphide deposits in Cyprus occur in a pillow lava and dyke sequence (the Troodos Volcanic Complex) peripheral to the Troodos Massif which forms a mountainous range trending northwest-southeast in central-southern Cyprus (Fig. 1). The Troodos Massif ,,Kombio

o Aptiki \A oNicosio

Sutphide mines sompted Other sutphide mines Geochemicot troverses Cites ond towns

1

~-'~/~j-

~_ ~ . ~ . . ~

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\\

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o

4

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and younger) -

8

I I ~ I / : : :~ SCALEIN KILOMETERS

~

contact

Upper Pillow Lovos LowerPittowLovos ~Troodos votconic complex Group

~l

contoct

Dio~se ond Troodos

rgneous complex

Fig.1. L o c a t i o n o f sulphide m i n e s and g e o c h e m i c a l traverses. T y = TyUiria; G = Galini; N = Nikitari; Ad = Arhediou; Kb = Kambia; S = Sha; A = Alambra; T = TrouUi; K = K o p h i n o u ; Lf = Lefka; Ka = Kalavasos; V = Vavla; E = Eptagonia; M = Moni; P = Paraklisha; St = Saittas; Kp = Kato Platres; Py = Pyrrinia; A y = Ayia; Ag = Argaka.

INTERPRETATIONOF A GEOCHEMICALSURVEY

79

consists of ultrabasic plutonic rocks (the Troodos Plutonic Complex) fringed mostly by gabbros with multiple diabase dykes (the Diabase). The pillow lava sequence is JurassicCretaceous in age and is overlain unconformably by sedimentary rocks of Upper Cretaceous age. The lava sequence comprises basalts and olivine basalts. The latter (called Upper Pillow Lavas) are restricted mainly to the top of the series and are essentially post-mineralization in age. The pre-sulphide lavas (Lower Pillow Lavas) are mainly basaltic in composition and are also distinguished from the Upper Pillow Lavas by many more intrusive dykes which range in composition from andesite to basalt. The number of dykes increases stratigraphically downwards until the rock is essentially all dyke material with some remnant pillow lavas (the Basal Group). The sulphide bodies occur as fiat-topped, basin-shaped bodies within the lava series. Where recent erosion has not stripped the overlying cover, the sulphides are overlain by thin ochreous sedimentary beds, which are in turn succeeded by olivine basalts. There is normally a stockwork zone with disseminated pyrite and fracture-filling pyrite and chalcopyrite beneath the orebodies. The orebodies are dominantly massive pyrite with minor chalcopyrite and sphalerite in their lower levels and dominantly conglomeratic, porous pyrite towards their top; the conglomeratic pyrite normally has concentrations of chalcopyrite and other copper sulphides and sphalerite (see Constantinou and Govett, 1972a and b).

Summary of geochemical survey The main results and procedural details of the investigation have been given elsewhere (Govett and Pantazis, 1971); material pertinent to the present paper is only briefly summarized here. A series of traverses across the whole volcanic belt and at right angles to the strike of lavas (Fig.l) were sampled at 180 m intervals for determination of background. Samples of all different rock types (lavas, centre of pillows, chilled margins, interstitial material between pillows, and dykes) were taken at each sample station; multiple samples (normally two to ten) were taken at many sample stations to determine sample variability. Similar samples were taken in the vicinity of the mineralization along traverses extending up to about 2.5 km from the orebody; detailed samples were also collected within the open pits of the orebody. All samples were crushed to pass through an 80-mesh screen, digested with hot concentrated nitric acid, and the elements determined on a Perkin Elmer 290-B Atomic Absorption Spectrophotometer. The precision was monitored by the inclusion of two or three standard samples in each batch of 50 samples; the precision for all elements of all concentrations was maintained within the limits of +- 2 to -+ 20%. Thirteen trace elements were determined on a selected series of samples but, within the analytical limitations of the method, only Cu, Zn, Ni and Co showed significant variations; accordingly, analysis on the remainder of the samples was restricted to these four elements.

80

G.J.S. GOVETT

The concentration of trace elements in pillow lavas varies according to petrological rock type, geographical location, secondary processes, and proximity to mineralization. Nickel is the only element to show a significant variation associated with petrological variation. Accordingly, the background lavas were divided on the basis of their Ni concentration into four classes (regardless of their supposed stratigraphic positions). Background Classes. Class IV: /> 160 p.p.m. Ni, average 248 p.p.m. Ni; olivine basalts (Upper Pillow Lavas); 15.5% of sample stations. Class III: 80-159 p.p.m. Ni, average 107 p.p.m. Ni; very basic basalts, probably mostly Upper Pillow Lavas; 14.5% of sample stations. Class 1I: < 80 p.p.m. Ni, average 52 p.p.m. Ni and modal value 5 0 - 6 0 p.p.m. Ni; basalts, Lower Pillow Lavas; 36% of sample stations. Altered Class: Class 1: < 80 p.p.m. Ni, average 27 p.p.m. Ni, modal value 20 30 p.p.m. Ni; mostly altered basalts, probably chiefly Lower Pillow Lavas, but may include some Upper Pillow Lavas; 34% of sample stations. Classes 11, III, and IV are collectively designated as "Background Classes". Class I is distinct from the other three classes and was designated as the "Altered Class" since the samples, all from four traverses, are characterized by alteration and silification. The mean values for Cu, Zn, Ni, and Co for each of the classes are included in Table I. Apart from the defined difference in concentration of Ni, there is little to distinguish the three Background Classes from each other, while the Altered Class is distinguished from the Background Classes by higher Cu and Zn and lower Ni concentrations. The frequency distribution of Cu in all four classes is distinctly bimodal, with the break at 60 80 p.p.m. Classes II, III and IV were accordingly divided geographically into a high-copper and a low-copper zone depending upon whether the mean Cu for a complete traverse was greater or less than 65 p.p.m., respectively (Table I). VARIATION IN TRACE ELEMENT DISTRIBUTIONIN THE VICINITY OF MINERALIZATION Discernable anomalous concentrations of individual elements rarely extend more than 20 m from the ore contact. Clearly, this is inadequate for exploration purposes; the problem posed was to determine whether anomalous haloes do extend further than this and, if so, how to recognize them. The general character of the frequency distribution of Cu, Zn, Ni, and Co in pillow lavas around mines is illustrated by histograms in Fig.2 for Skouriotissa and Mathiati mines; these two mines were selected as being typical of the two main types of dispersion associated with mineralization. For comparison, the frequency distributions for Background Class II, the combined Background Classes II, III, and IV, the low-copper zone, the high-copper zone and the Altered Class are also shown in Fig.2. At Skouriotissa the distribution of Cu conforms to the low-copper background zone, Co and Zn are higher than background, while Ni is similar to background. The distribution of Cu and Zn at

81

INTERPRETATION OF A GEOCHEMICAL SURVEY TABLE I Mean values of Cu, Zn, Ni, and Co for sample groups Designation on

Description of sample group

Background B I 1I III IV

Number of

Mean values (p.p.m.)

samples

Fig.4, 5, 6, 7

Classes I, II, lII and IV combined Altered Class Background II Background III Background IV High-copper Low-copper

Cu

Zn

Ni

Co

168 57 61 24 26 48 63

72 84 65 69 66 89 49

65 79 56 65 50 62 54

84 34 52 107 248 105 113

33 35 30 33 37 33 32

Around ore pits 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Skouriotissa SW Skouriotissa NE Mathiati SW Mathiati NE Memi N Memi S Agrokipia A Agrokipia AO Agrokipia B Agrokipia BO Sha-Kambia Mavri Kambia Troulli Kalavasos

37 22 31 25 26 43 97 28 26 19 27 22 7 15

56 65 54 76 38 166 48 47 54 65 52 68 65 43

93 67 86 73 63 368 106 58 69 68 63 236 84 49

71 59 28 22 95 19 60 52 65 73 35 9 44 88

44 43 37 29 39 41 55 42 48 49 34 26 62 39

Within ore pits 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Skouriotissa, N face, E pit Skouriotissa, E face, W pit, top Skouriotissa, E face, W pit, bottom Skouriotissa, S face, E pit Agrokipia A, S face Agrokipia A, W face Agrokipia A, N face Agrokipia A, N face Mathiati I, SW face Mathiati II, NE face Mathiati II1, E face Memi S Memi N Kambia

8

18 8 8 15 13 30 11 29 50 24 20 15 12

67 111 149 113 615 315 71 1,144 39 96 64 52 34 210

180 2,280 4,943 7,920 1,320 510 91 1,936 102 173 126 86 86 430

89 107 118 248 71 28 58 61 23 28 28 55 5 26

57 59 77 86 55 68 46 75 40 44 43 42 20 37

Gossanized pillows 29 30 31

Mawi Kambia Mathiati Agrokipia

33 31 7

201 365 413

149 190 228

10 9 17

17 17 32

82

G.J.S. GOVETT :",,

(C) "- - high-Cu zone - - low-Cu zone

(b)

(c)

2O

--- BockQround II,III,IV - - BockQround II

II Mathiotis SW

IO

{0) [~]Skouriotisse 0

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---Beckground I (Altered)

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140

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I00 120 140 160 Cu

,

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.

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120 140 160

Fig.2. Frequency distributions of Cu, Zn, Ni and Co in various groups of Background, Altered and Mines samples. Mathiati is similar to that at Skouriotissa, but Ni is abnormally low compared with background. It will be noted that the Altered Class shows characteristics similar to Mathiati and Skouriotissa, but differs in having higher concentrations o f Cu. As a generalization, there is an increase in mean Zn and Co and, in many cases, a decrease in mean Cu and Ni in the vicinity o f the sulphide bodies (see Table I for mean values in lavas in the vicinity of ore bodies). These trends become more pronounced closer to mineralization, while at the ore-wallrock contact, the concentrations of Cu increase

INTERPRETATIONOF A GEOCHEMICALSURVEY

83

abruptly (in places to ore grade) over 5 cm; Zn, on the other hand, commonly decreases to comparatively low levels; Co shows an increase; Ni remains low (Constantinou and Govett, 1972b). There is significant regional variation in the distribution of trace elements; Cu decreases away from the Troodos Massif, and most of the mines lie in a low-copper zone with less than 65 p.p.m. Cu (this regional variation is distinct from the common decrease in Cu concentrations close to the mines). The distribution of Zn is essentially the reverse of that of Cu; most of the mines lie in a high-zinc zone with more than 65 p.p.m. Zn. Nickel and Co are less distinctly zoned; there is a tendency for the mines to correspond with a highcobalt zone. It is clear from the above that while there are distinct variations in the frequency distributions of trace elements in pillow lavas attributable to the presence of mineralization, the absolute differences in concentration are very small. It is equally clear from the frequency distribution for any particular element that there is considerable overlap in the concentration of Background Class samples with those from the vicinity of mineralization. Furthermore, although most mine areas are distinctly different from background, the character of the difference varies (e.g., compare Skouriotissa SW with Mathiati SW, Fig.2). Insofar as the objective of this particular study was to empirically classify routine samples into "anomalous" or "background" groups, these differences had to be ignored for purposes of data treatment. Thus the presumed anomalous samples were divided into groups based simply upon their geographical location relative to various mines; no account was taken of the fact that some of the samples from, say, the Skouriotissa SW group, were taken 100 m from the orebody, while others were taken more than 2 km from the orebody. The only exception to this is that some sample groups were divided into two where there were obvious differences in metal content either side of a major structural zone (e.g., Skouriotissa SW and NE; Memi S and N; Mathiati SW and NE). A complete list of the sample groups is given in Table I. The two interpretative techniques described below - discriminant and determinative functions - were developed specifically to allow the recognition of mineralized zones in a situation where no single element gives a significant anomaly and where even the combined variation of several elements is very small. DISCRIMINANTANALYSIS

Theoretical The illustrative frequency distributions of elements in rocks from background and mineralized areas shown in Fig.2 amply demonstrate the conclusion that no single measurable attribute could be found to uniquely discriminate between a rock 100 m away from mineralization and a rock 10 km from mineralization. It seemed possible, however, that some mathematical combination of the concentrations of the four elements which were each found to vary as a function of proximity to mineralization might be used

84

G.J.S. GOVETT

to discriminate between anomalous and background conditions. The discriminant function was an obvious statistical procedure to classify individuals into population groups on the basis of a number of measured variables (Hoel, 1947; Kendall, 1948; Rao, 1952; Miller and Kahn, 1962; Krumbein and Graybill, 1965). This technique, while commonly used in medical and biological research, has only recently been used in geological work (Klovan and Billings, 1967; Cameron, 1969; Cameron et al., 1971 and Howarth, 1971a and b). The writer first applied the technique to a preliminary interpretation of the Cyprus data in 1967 (Govett, 1969; Govett and Pantazis, 1971). The discriminant function is a multivariate classifying function that may be used to assign an unknown individual with several measured variable attributes, xl, x 2 . . . Xk, to one of several groups on the basis of a linear function representing all k variables and weighting the variables according to their contribution to the discrimination. The discriminant function takes into consideration the scatter, or variance, of each of the k variables in each group and is chosen to maximize the ratio of the difference between sample means and the standard deviation within the group. The technique was applied to the Cyprus data using an IBM System 360/50 GH computer to process the data. An IBM Scientific Subroutine Package FORTRAN program for Discriminant Analysis was modified to determine sets of linear discriminant functions, and a simple computation and classification program, called SCORTEST, was developed to calculate discriminant scores (see below) for individual samples. The functions derived from the discriminant analysis program are of the form: aCu+b Zn+cNi+dCo+C where a, b, c, and d are coefficients and C is a constant. A different linear function yielding the best separation of the individuals belonging to the groups is derived for each specified sample group. To determine whether a given individual belongs to one group or another (e.g., Background 11 versus Skouriotissa SW), the program SCORTEST was used to compute the algebraic values of each function; these are called the "discriminant score". The program then classified each sample according to which function gave the greatest discriminant score.

Application To test the applicability of the technique as an exploration tool in Cyprus, two sets of mine samples, Skouriotissa SW and Mathiati SW, were selected as representative anomalous groups (see above), and functions were derived by setting them against various combinations of Background Classes. Six different possible combinations of background and anomalous groups were used; in addition, functions were also derived to discriminate the Altered Class from Background Class II. These seven tests and the resulting functions are listed on Table II. The results of the tests are shown on Table III.

Variables

Cu, Zn, Ni, Co

Cu, Zn, Co

Cu, Zn, Co

Cu, Zn, Co

Cu, Zn, Ni, Co

Cu, Zn, Co

Cu, Zn, Ni, Co

Test number

1

2

3

4

5

6

7

Background II Skouriotissa SW Background II, III, IV Skouriotissa SW and Mathiati SW High-copper zone Skouriotissa SW and Mathiati SW Low-copper zone Skouriotissa SW and Mathiati SW Altered Class Background II Skouriotissa SW Mathiati SW Altered Class Background II Skouriotissa SW Mathiati SW Altered Class Background II

Groups

Description of discriminant functions for groups tested

TABLE II

Co

0.02366 0.01220 0.02928 0.01971 0.04070 0.02072 0.04054 0.04424 0.02873 0.02266 0.02045 0.01885 0.02877 0.02276 0.02059 0.01887 0.02457 0.02163

0.05970 0.10198 0.05711 0.10121 0.05190 0.08103 0.06630 0.12020 0.07853 0.05783 0.09680 0.08758 0.07554 0.05071 0.08713 0.08585 0.07609 0.04867

0.03450 0.04744 0.02281 0.05421 0.07369 0.01319 0.07133 0.16828

0.22144 0.33473 0.28510 0.34892 0.32152 0.39863 0.26938 0.33229 0.35256 0.27827 0.40427 0.36503 0.37184 0.32409 0.46656 0.37618 0.29589 0.20292

- 6.69982 -14.16885 - 7.20993 -12.20309 - 8.73714 -12.33556 - 7.04756 -13.40454 -10.92360 - 7.97265 -16.63506 -11.14426 -10.76199 - 7.06015 -14.94877 -11.09025 -10.47276 - 9.48702

C

Ni

Cu

Zn

Constant

Coefficients

< r~

Z O

,..]

,..]

Number of variables

4

3

3

3

4

3

4

Test

1

2

3

4

5

6

7

Background II Skouriotissa SW

High-copper zone Skouriotissa SW and Mathiati SW

-

Altered Class Background I1 Skouriotissa SW Mathiati SW

1 - Altered Class 2 Background I1

1 - Altered Class 2 - Background II 3 Skouriotissa SW 4 Mathiati SW

1 2 3 4

1 - Low-copper zone 2 - Skouriotissa SW and Mathiati SW

1 2

1 - Background II, III, IV 2 Skouriotissa SW and Mathiati SW

1 2

Sample groups

57 61

57 61 37 31

57 61 37 31

63 68

48 68

111 68

61 37

Number of samples

85 84

23 64 73 71

33 79 76 77

84 81

77 87

82 81

87 84

~ Correctly classified

Efficiency of discrimination between background and anomalous samples in standard type groups ~various combinations)

TABLE llI

© <

b~

INTERPRETATIONOF A GEOCHEMICALSURVEY

87

From the data in Table III alone it could be concluded that any of the two-group functions would give adequate discrimination for exploration work. To further evaluate the results of these seven tests, all other samples (both background and mines) not used in deriving the functions were tested. The relative proportions of all known anomalous area samples correctly classified were compared with all known background, samples correctly classified. These data are given in Table IV and V.

Discussion o f results Considering the data in Tables IV and V, the tests using all four variables (tests 1 and 5) give a consistently high degree of correct classification of anomalous samples; on the other hand, they fail badly on Background III and IV samples, although they do give an acceptable level of correct classification for Background I1 samples. In the present investigation, since only about 4% of the samples from mine areas have Ni concentrations greater than 80 p.p.m. (and therefore could be confused with Background III and IV), such samples could have been disregarded in all classifications without serious loss. However, it must be realized that the orebodies used for testing lie at or close to the surface with much of the high-nickel lavas (Background Ili and IV type) eroded away. New deposits must be sought beneath a cover of Background III and IV type lavas. Therefore, it is important that the latter type lavas are clearly discriminated from anomalous lavas. The obvious procedure would be to omit Ni as a variable; where this is done (tests 2, 3, 4, and 6) the misclassification of Background IV samples (and, to a lesser extent, Background III) is significantly less, although the misclassification of Background 1I is marginally worse; furthermore, the mines groups generally show a slightly better proportion of correctly classified samples. The classification can obviously be improved by combinding two or more tests. There are several possibilities for the data given; one is illustrated in Fig.3. This combines test 6, which has good classification for anomalous areas and Background II but is poor for Background III and IV, with test 2, which is only moderately good for anomalous areas but is very good for Background II, III, and IV. All samples were tested on test 6 initially; those samples classified as anomalous and which had Ni concentrations ~> 80 p.p.m, were then tested with test 2. This procedure made little difference to anomalous area samples but, of course, dramatically improved the classification of Background III and IV samples; as a result, all background samples and most of the sample groups nearest to mines were 70-85% correctly classified. As a further refinement, all those samples which were classified as Background II-type as a result of the combination of tests 6 and 2 were then reclassified with test 7 into either Altered Class or Background II Class; the former were added to those originally classified as Altered Class by test 6 and are shown as Altered on Fig.3. It should be borne in mind that the sample group Mines (a) on Fig.3 is closer to sulphide orebodies than the sample group Mines (b). The former group has a significantly higher proportion of samples classified as anomalous compared to the latter group.

Number of samples

57 61 24 26 37 22 31 25 97 28 26 19 26 43

Sample groups

Altered Class Background II Background III Background IV Skouriotissa SW Skouriotissa NE Mathiati SW Mathiati NE Agrokipia A Agrokipia AO Agrokipia B Agrokipia BO Memi N Memi S

13 21 73 84 59 64 25 71 43 85 47 50 72

29

Testl 42 15 17 23 84 59 77 28 64 50 73 42 42 79

Test2 45 21 21 31 100 82 71 36 92 68 92 84 46 65

Test 3 50 25 25 15 94 55 77 36 53 32 73 42 42 84

Test4

Per cent of samples classified as mines

Comparative classification of sample groups with all tests

TABLE IV

42 13 42 92 87 59 84 48 73 50 88 53 59 74

Test5 33 16 17 38 89 65 84 32 76 50 89 74 42 70

Test6 85 16 0 0 57 45 90 100 62 46 46 5 25 95

Test7

Per cent as Altered

0 <

b~

57 61 24 26 37 22 31 25 97 28 26 19 26 43

Altered Class Background II Background III Background IV Skouriotissa SW Skouriotissa NE Mathiati SW Mathiati NE Agrokipia A Agrokipia AO Agrokipia B Agrokipia BO Memi N Memi S

32 8 0 0 0 14 10 20 11 18 4 5 8 25

AL-CL

26 79 58 8 13 27 6 32 16 32 8 42 33 3

BK-II

Test 5. % classified as:

* AL-CL = Altered Class BK-II = Background Class II SK = Skouriotissa SW MATH = Mathiati SW SK and MATH = Skouriotissa SW and Mathiati SW combined

Number of samples

Sample group

Classification of samples by tests 5 and 6 into four groups*

TABLE V

BK-II 37 64 62 62 11 25 13 60 19 39 7 21 58 7

AL-CL 29 20 21 0 0 10 3 8 5 11 4 5 0 23

SK and MATH 42 13 42 92 87 59 84 48 73 50 88 53 59 74

MATH 40 5 0 0 11 14 78 48 20 10 19 0 17 44

SK 2 8 42 92 76 45 6 0 53 40 69 53 42 30

Test 6. % classified as:

16 6 8.5 38 73 55 13 12 72 46 69 63 42 44

SK 18 10 8.5 0 16 10 71 20 4 4 20 11 0 26

MATH

34 16 17 38 89 65 84 32 76 50 89 74 42 70

OO ',O

SK and MATH

90

G.J.S. GOVETT

Mines (Anomalous) type

I

Background i (Altered) type

MINES (a) Skouriotissa SW

~---. . . . . . . . . . . . . . . .

~1

Mathiotis

[

•

SW

Agrokipia B

[

Agroklpio A

[

Merni S

E

•

MINES (b) Skour,ohssa NE

E.-

Mothiotis NE Agrok=pio Bo

~

Agrok~pia Ao

L__

M,,~i N

E_

m

_ _ ~ 1

ALTERED BACKGROUND Background I

[__

BACKGROUND Background I I Background III

~__~

Background IV

~'--~ (~)

1-210

,~per 'cento;

' go ' , ; o

Fig.3. Classifications of samples by combined tests 6, 2 and 7. The combined test 6, test 2, and test 7 classification of samples for each individual traverse for Altered and Background Classes (disregarding the division into Background 1I, Ill, and IV) is given in Table VI; in Table IV the mine data illustrated in Fig.3 is also shown for comparison. Although the weighted averages show that there is a clear difference between Background and all mines samples, some of the individual background (and altered areas) are less certainly distinct, especially when compared with the more distant mines samples Mines (b). For example, Ayia appears to be distinctly anomalous and should be investigated in detail, while additional samples should be taken at Nikitari and Lefkara; samples from these areas should be eliminated from a background class if they are shown to have mineralization. Some of the Altered Class areas also clearly need further investigation: Arhediou lies in a mineralized belt and is readily explainable; by analogy with Arhediou and the mines groups, Pyrrinia is quite possibly near an area of mineralization (its proximity to Ayia should be noted), while Kato Platres and Galini could be interpreted as lying further away from mineralization. In using discriminant analysis, some limit must be set to the proportion of misclassifications that are to be admitted in judging the relative effectiveness of the tests as an exploration tool; this should be realistic in terms of practical application and adequate to provide a good separation of background and anomalous samples. It is obviously not possible to effect a total separation since, even in a continuously variable system between one extreme and the other, there must be a transitional zone which, for practical purposes,

INTERPRETATION OF A GEOCHEMICAL SURVEY

91

TABLE VI Classification of samples by area using the three-test screening (tests 6, 2, and 7) Sample

% classified as: Mines type

Altered type

Background II, III, and IV Paraklisha Moni Nikitari TyUiria Vavla Eptagonia Kophinou Ayia Lysso Argaka Lefka Saittas

0 20 30 11 20 0 14 63 13 0 25 20

0 0 0 11 0 0 17 13 13 14 25 0

Altered class Galini Kato Platres Arhediou Pyrrinia

0 13 58 50

88 75 21 50

Mines (a) Skoudotissa SW Mathiati SW Agrokipia A Agrokipia B Memi S Kalavasos TrouUi

86 81 76 89 70 71 88

0 3 9 4 27 0 0

Mines (b) Skouriotissa NE Mathiati NE Agrokipia AO Agrokipia BO Memi N

65 32 50 74 42

24 68 25 5 4

Weighted average Background II, III, and IV Altered class Mines (a) Mines (b) All mines

16 33 82 53 71

3 46 10 26 15

may be considered as either background or anomalous. In the present case, there are obviously background-type samples included in the mines samples. These (generally the most distant or those belonging to different stratigraphic levels due to faulting) could have been removed to make the discrimination more effective. It was concluded, however, that

92

G.J.S. GOVETT

a more realistic assessment of the technique could be made by using the complete sample groups as collected. These qualifications similarly apply to the background data. Testing Background II against the Altered Class, 84% and 86%, respectively, were correctly classified; although it was obvious that some Background I1 samples were more akin to the Altered Class (and, in thin section, obviously physically altered), they were allowed to remain in the Background II Class to achieve realism since there are many small areas of minor alteration in Cyprus which are encountered in routine sampling. Similarly, although there were obviously a few Background II-type samples in the Altered Class, they were also allowed to remain. Since both the Background II Class and the Altered Class consciously included samples that belong to the other class, as an experiment misclassified samples of" Background ll or the Altered Class were re-assigned to their respective classes. New functions were derived which gave 100% correct classification and these new groups were then used with the mines samples to derive new functions. This procedure, when tested, resulted in only a few per cent better classification of anomalous and background samples. A serious complicating factor in very refined interpretation of the data presented is that there are good grounds for supposing that anomalous trace element concentrations arise from two distinctly different processes. In rocks which essentially pre-date mineralization and, especially those low in the stratigraphic sequence, there seems to be widespread introduction of metals and even very low-grade mineralization. In rocks which post-date mineralization there are haloes of abnormal metal concentrations directly related to large sulphide masses which necessarily must have arisen by secondary processes (Govett and Pantazis, 1971 ). There are inadequate data to characterize these different effects by discriminant analysis, especially since it is probable that the pre-mineralization lavas are also affected by the secondary processes. It seems possible, however, that the former effect gives rise to predominantly Altered Class-type anomalies and the latter effect to the Mines-type anomalies. If this can be shown to be true, then high proportions of Altered Class samples are of relatively little importance as an indication of the presence of mineralization unless accompanied by Mines-type anomalous samples. Thus, Altered Class-type anomalies should be regarded as being related to mineralization stratigraphically higher and not to underlying mineralization; unless there are high-nickel lavas of the postmineralization type in the vicinity beneath which significant mineralization may be protected, it must be presumed to have been eroded away. DETERMINATIVEFUNCTION

Development of the method A technique to derive a graphical representation of the data to classify groups of samples as background or anomalous was also developed. This technique has the advantage that it can be applied with access to only a desk calculator or even a slide-rule; it suffers

INTERPRETATIONOF A GEOCHEMICALSURVEY

93

from the disadvantage that only population means are used and no consideration is given to population variance (this could have been done, but- it would have made the procedure so unwieldly that its advantages would have been nullified). Consideration of the frequency distributions of elements (see above) shows that Zn and Co tend to increase, and Cu and Ni tend to decrease in lavas near to mineralization compared with lavas in non-mineralized areas. This suggests that the difference between lavas from mineralized and non-mineralized areas may be enhanced by plotting Cu/Zn ratios versus Ni/Co ratios. This is done in Fig.4, where it is seen that most of the lavas from mineralized areas fall in a quite different zone from the background lavas. A line (labelled D1 in Fig.4) is drawn to give maximum separation between Background II (II on Fig.4) and lavas from Skouriotissa SW (1, Fig.4) and Mathiati SW (3, Fig.4). When plotted, lavas from Skouriotissa NE (2, Fig.4), Mathiati NE (4, Fig.4), Agrokipia B (10, Fig.4), and Kalavasos (14, Fig.4) fall on the background side of line D,. As a generalization, the further away a sample plot lies from the line DI on the anomalous side, the more anomalous is the sample.

->1.6

30,3i

0

Pillow lavae~background

•

Pillowlavas, outside pits or above mineralization

0

Pillow laves,within open pits Gossanlzed pillow loyal Line of best fit,background

---

1.4 A29

~

Line of maximum separation between background and anomalous

0

1.2

T

6

oe .

.

.

.

.

- - ~

1.0

"~0.8

,.

•

bQ 0 9 ~ , . ~ C o

J

0.6

~o~ 0.4

o, 2

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- " ~ , _

0,9

o,~

o23

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0

i 0.4

I o.e

I 12.

17 Ol

IG o 1.6

I 2.0

I 2.4

I~ 2B

I :32.

I ~.6

I 4.0

• ->4.4

N VCo

Fig.4. Variation of Cu/Zn ratio as a function of Ni/Co ratio for pillow lavas. (Numbers identify samples listed in Table I.) Mathematically, the equation for line D, can be expressed in terms of Cu/Zn and Ni/ Co ratios and the actual values substituted in the equation; negative values will be anomalous and positive values will be background (points on the line D , , of course, equal zero). Moreover, the greater the negative value, the more anomalous is the sample. This expression:

94

G.J.S. GOVETT D1 = Cu/Zn + (0.175 Ni/Co) - 1.l

is designated as a "determinative function" in analogy with discriminant functions; similarly, the values for D~ obtained by substituting for Cu/Zn and Ni/Co are designated as "determinative scores"+ Since the Ni/Co ratio varies due to wide variation in Ni with petrological changes, the Ni/Co ratio is plotted against p.p.m. Co in Fig.5 to assist in distinguishing petrological variations from those due to mineralization. A line, D2, is drawn to give maximum separation between Background II (//, Fig.5) and samples from Skouriotissa SW (1, Fig.5) and Mathiati SW (3, Fig.5). In this plot, Kalavasos (14, Fig.5), Memi N (5, Fig.5), and Memi N Pit (27, Fig.5) lie on the background side of the line; the Altered Class (i, Fig.5) lies on the anomalous side of the line. A determinative function: D2 = Co - (l 0.5 Ni/Co) - 20.0 is calculated from the graph; background lavas give negative scores for D2 and anomalous hvas give positive scores. As a generalization, the greater the positive score, the more anomalous is the sample. OrB

~'80 022 70

017

02O

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o

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E

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.

-.

.

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Pillow Iovas, bockground

e=2 /

•

Pillowlavas, outside pits or above mineralization

027

E~ Pillow Iovus, within open pits

I

O

O B

,_7

~ ,~

43 Gossanized pillow Iovos --

I

1o

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/

~

20

•

~

6.

~+

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_,Ni/~

I

Line of best fit,background Line of maximum separation between background and anomalous

I

I

0.4

0.8

a.. 1.2

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2.0

2.4

2.6

3+2

,

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I,

3,6

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I

i+4.4

Ni/Co

Fig+5. Variation of Ni/Co ratio as a function of Co concentration for pillow lavas. (Numbers identify sample groups in Table I), It may reasonably be expected that a combination of Dr and 02 determinative functions would give a better separation of the various situations, Actual values for D1 and D~ are calculated and plotted on Fig.6. Generally there are four possibilities:

INTERPRETATION OF A GEOCHEMICAL SURVEY

"45

017

0

Pillow lavos,bockgrotmd

•

Pillow ktvae,outtide pltsor above mineralization

0 2 ~ 22

O Pillow k~von,wlthln opeit pits

40

G Gonsonlzed pillow Iovas Boo, ground zone r~ Altered zone

018 013

3O

20

95

='~1 (324 ~3o

%'

o,~

I0

cP' ,i ~0,

2ln 09 ee

/

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/' n27

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• 14

-

-IO

,¢. -20 /

/

/

/

-30

/

-40 /t

/

/

_& _&

, -0.4

! -0.2

0.4

0.2

0.6

08

1.0

12

1.4

1.6

Oi Fig.6. D 1 and D 2 determinative scores for pillow lavas. (Numbers identify sample groups listed in Table I).

DI D1 D1 D]

negative,D2 positive, D2 negative,D2 positive,D2

positive-anomalous negative-background negative-uncertain positive-uncertain

Reference to Fig.6 shows that, in fact, most of the sample groups (both background and anomalous) fall along a single trend and are separated into well-defined zones. A line, D3, is drawn through the Altered Class and at right angles to the trend of the background samples. Another determinative function: D3 =D2 - 62.5 D1 i.e., D3 = Co - 21.4 Ni/Co - 62.5 Cu/Zn + 48.8 is calculated; negative scores are background and positive scores are anomalous. The greater the positive values, the more anomalous is the sample. A background zone and an altered zone is separately designated on Fig.6. On this basis, only Kalavasos (14, Fig.6) falls in the background zone, while Skouriotissa NE (2, Fig.6), Mathiati NE (4, Fig.6), and Agrokipia BO (19, Fig.6) fall in the altered zone. The rocks from Memi N, both within the pit (27, Fig.6) and outside the pit (5, Fig.6), seem to be a special case since they are the only two examples that have negative values for both Dt and D2.

96

G.J.S. GOVETT

Discussion of results The values of D3 are calculated and plotted on Fig.7 where the separation between different sample groups is more clearly seen. Moreover, there is a definite trend of increase in D3 scores towards mineralization. For example, compare the pairs 7 and 8 at Agrokipia A and the pairs 9 and 10 at Agrokipia B where the odd number in both cases is nearest to mineralization. The same is found within the pits: compare the series 15, 16, and 17 and the series 15 and 18 at Skouriotissa or the series 21 and 22 at Agrokipia A where the higher numbers are closest to the ore. •

Pillow

lava= Outside pits or above mineralization

O PIHow lavas within pits Kolavosos

- - 4 Troulli

I

Mavri Kambio

I

• 12 28 0

Sha-Kambia Agrokipia B

9

~

I

Agrokipia A

8•

Memi

5e J I 4e 1

Mothiotis Skourtotissa

2e I

I

0

Bockground

-12110

-,~o -~

-~o

26 O

28 O

e6 24 23 o~o 15 C~

tO

17 O

r6 r8 DO

0

Background -140

~o e7

: :

-Jo

Altered ~

-~o

~

Anomalous •

~o ",b

~o

~

~o

D3 Fig.7. D 3 determinative scores for pillow "lavas.(Numbers identify sample groups listed in Table 1).

The Kalavasos samples are scarcely anomalous (14, Fig.7) but it must be noted that they are taken from a sequence of high-nickel (average 88 p.p.m.) post-mineralization lavas more than 100 m above the orebody, and their D 3 determinative score should be compared with the determinative score for Background 1II; on this basis, Kalavasos is just as anomalous as is Skouriotissa NE (21, Fig.7) compared with Background II (11, Fig.7). On the other hand, Mavri Kambia samples are clearly anomalous (12, Fig.7), despite the fact that extensive drilling there has failed to reveal significant mineralization (however, note the classification of the gossan at Marvi Kambia, discussed below). There are large numbers of gossanized pillow lavas in Cyprus, and it would be useful to be able to identify those which may overlie economic mineralization. Accordingly, results from these gossanized areas are plotted in Fig.4, 5 and 6. In Fig.4 the three gossans lie in the background zone, although somewhat apart from the background trend. In Fig.5 the

INTERPRETATIONOF A GEOCHEMICALSURVEY

97

Agrokipia gossan lies in the anomalous zone, while both the Mavri Kambia and Mathiati gossan lie close together in the background zone. In Fig.6 the Mavri Kambia gossan lies distinctly in the background zone, while the Mathiati and Agrokipia gossans lie outside, towards the zone where most of the values for the actual ore lie (see below). This is consistent with the fact that Mavri Kambia is a non-productive mineralized zone and probably represents the root of an orebody now removed by erosion; both Mathiati and Agrokipia are productive mines. The D~ and D2 scores for various analyses of ores are plotted on Fig.8 (description of samples and analyses are given in Table VII). The individual scores are generally far greater than the corresponding scores for lavas and, more importantly, they show a quite different pattern. Thus, with a few exceptions, most of the ores fall in the +D] +D2 quadrant (compared with most of the pillow lavas adjacent to ore which fall in the -D~ +D2 quadrant). This may be interpreted as strong supporting evidence that the anomalous metal distributions in the pillow lavas are not due to the same processes which gave rise to the ore deposits; it has been suggested that the anomalous haloes are due essentially to secondary processes (Govett and Pantazis, 1971 ; Govett, 1972). • Agrokipio A 0 Skourlotlssa x Mothlotls O Agrokipla B A Kokklnoyla ...~ Zone o f b a c k g r o u n d [-~ A r e a o f pillow lava

~140,"

120 " I00

0

•6

diagram

-

616 80 14 6O

19

~o

D2 40

x4 i

20

•

n8 17

cjO i if ~

-' •

-I

QIO I

|N 0

|

I

I

I

2

I

I

I

I

I

I

I

I

4

6

8

I0

12 DI

14

16

18

20

22

~'24

Fig.8. D 1 and D2 determinative scoresfor ore samples. (Numbers identify sample groups listed in Table VII). There is little definite trend of D~ and D2 determinative scores related to the ore zone in individual deposits. There is, however, an ill-defined tendency for each deposit to have characteristic mean scores: thus the average 0 3 scores for Skouriotissa, Agrokipia A,

Skouriotissa Mathiati, massive zone Mathiati, zone B Mathiati, stockwork Mathiati, stoekwork Agrokipia A Algokipia B, massive zone Agrokipia B, massive zone Agrokipia B, massive zone Agrokipia B, massive zone Agrokipia B, massive zone Agrokipia B, stockwork Kokkinoyia, massive zone Kokkinoyia, massive zone Kokkinoyia, massive zone Kokkinoyia, zone B Kokkinoyia, zone B Kokkinoyia, stockwork Kokkinoyia, stockwork Kokkinoyia, stockwork

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

* Data given in downward succession through the orebody

Description of sample groups

Designation on Fig.8

10 12 8 25 7 19 1 1 1 1 1 22 7 9 7 2 2 5 4 9

Number of samples

Average concentration of Cu, Zn, Ni and Co in ore from various orebodies*

TABLE VII

21,527 2,850 893 1,480 430 1,600 3,400 122,000 20,700 9,400 270 362 60,386 19,211 22,500 8,650 35,850 19,180 8,525 3,928

Cu 747 1,050 10,450 770 324 83 78,000 42,000 10,100 6,000 415 520 5,374 808 3,183 550 1,250 1,880 338 322

Zn

Means (p.p.m.)

24 44 36 27 27 61 30 45 30 15 45 36 72 56 64 45 60 116 38 32

Ni 330 53 33 62 42 133 25 20 35 35 50 81 38 96 35 115 49 42 94 79

Co

0 0 < ~n