application of geostatistics to identify gold-rich areas ...hera.ugr.es/doi/15006657.pdfapplication...

Application of geostatistics to identify gold-rich areas inthe Finisterre-Fervenza region, NW Spain

Rosario JimeÂ nez-Espinosa a, *, Mario Chica-Olmob

aDepartamento de GeologõÂa, Escuela PoliteÂcnica Superior, Universidad de JaeÂn, Virgen de la Cabeza, 2, 23071 JaeÂn, SpainbDepartamento de GeodinaÂmica, Facultad de Ciencias, Universidad de Granada/IACT, Fuentenueva s/n, 18071 Granada Spain

Received 5 August 1997; accepted 25 February 1998

Editorial handling by N. Gustasson

Abstract

Three univariate geostatistical methods of estimation are applied to a geochemical data set. The studied methods

are: ordinary kriging (cross-validation), factorial kriging, and indicator kriging. These techniques use theprobabilistic and spatial behaviour of geochemical variables, giving a tool for identifying potential anomalous areasto locate mineralization. Ordinary kriging is easy to apply and to interpret the results. It has the advantage of usingthe same experimental grid points for its estimates, and no additional grid points are needed. Factorial kriging

decomposes the raw variable into as many components as there are identi®ed structures in the variogram. This,however, is a complex method and its application is more di�cult than that of ordinary or indicator kriging. Themain advantages of indicator kriging are that data are used by their rank order, being more robust about outlier

values, and that the presentation of results is simple. Nevertheless, indicator kriging is incapable of separatinganomalous values and the high values from the background, which have a behaviour di�erent to the anomaly. Inthis work, the results of the application of these 3 kriging methods to a set of mineral exploration data obtained

from a geochemical survey carried out in NW Spain are presented. This area is characterised by the presence of Aumineral occurrences. The kriging methods were applied to As, considered as a path®nder of Au in this area.Numerical treatment of Au is not applicable, because it presents most values equal to the detection limit, and a

series of extreme values. The results of the application of ordinary kriging, factorial kriging and indicator kriging toAs make possible the location of a series of rich values, sited along a N±S shear zone, considered a structure relatedto the presence of Au. # 1998 Elsevier Science Ltd. All rights reserved.

1. Introduction

A geochemical study in mining exploration consists

of two particular stages. The ®rst one corresponds to

experimental data acquisition, by collection of samples

and their analysis. The second step, and on which we

focus our attention in this work, concerns the treat-

ment and interpretation of available numerical infor-

mation. The speci®c features of geochemical

exploration studies are the treatment of a huge amount

of data, the imprecision of this data, the multivariate

character, and especially, the spatial dependence of

variables. This latter characteristic gives these variables

their regionalized behaviour (Journel and Huijbregts,

1978), as the basis of geostatistical methods. This

study attempts to achieve the numerical characteris-

ation of the anomaly, and its di�erentiation from the

background.

In this work, three univariate geostatistical methods

of estimation are presented. These are based on the

spatial and probabilistic behaviour of geochemical

variables, and are of great interest for mineral prospec-

Applied Geochemistry 14 (1999) 133±145

0883-2927/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved.

PII: S0883-2927(98 )00035-3

PERGAMON

* Corresponding author. E-mail:[email protected].

tion, because they consider the geochemical elementconsidering its content and its location. The proposed

techniques are: ordinary kriging (cross-validation), fac-torial kriging and indicator kriging. Each of these hasits individual features, but used together they are use-

ful tools for recognising geochemical anomalies.Data from a mining exploration survey carried out

in Galicia (NW Spain) are used to apply these method-

ologies. This area is of mining interest because of thepresence of Au mineralization. Nevertheless, Au pre-sents signi®cant problems for numerical treatment, As

has been used to evaluate the metallogenetic import-ance of this zone. Arsenic can be considered a path®n-der of Au in this zone, as the two are geneticallyrelated. The three proposed methods are applied to

As, showing the advantages and disadvantages ofeach.

2. Geological setting

The study area is in La CorunÄ a province, in theregion of Galicia (NW Spain), a zone of mining inter-

est due to the Au mineralization discovered there.

Geologically, this area is located in the Iberian Massif,and speci®cally in the Galicia-Tras os Montes Zone. In

this Zone two domains are clearly di�erentiated:

Complexes of Ma®c Rock Domain (CMRD), andGalicia-Tras os Montes Schist Domain (GTMSD), sep-

arated by an important thrust (Fig. 1(a)). The ®rstDomain is made up of ma®c, ultrama®c and gneissic

rocks, over which appears a succession of metasedi-ments. There are 5 Complexes: Cabo Ortegal, Ordenes,

Malpica-Tuy, Braganc° a and Morais. The Galicia-Tras

os Montes Schist Domain is the relative autochtho-nous, consisting of a potent sequence of clastic and

deformed rocks, and was metamorphosed during theHercynian orogeny (Julivert, 1971; Ponce de LeoÂ n and

Chourkroune, 1980; Farias, 1992).

Concerning the local geology, the study area appearsin the contact between the two Domains mentioned

above. Speci®cally, it is located between the Malpica-

Tuy Unit (MTU) and the Central Galicia Schist Area(CGSA), which belong to CMRD and GTMSD, re-

spectively. Fig. 1(b) presents a geological scheme ofthe di�erent outcropping lithologies (ENADIMSA,

Fig. 1. Geology of the study area: (a) map of the regional geology (modi®ed from Farias, 1992); (b) schematic map of the local ge-

ology, with the spatial locations of the sample data (modi®ed from Agudo-FernaÂ ndez et al., 1989).

R. JimeÂnez-Espinosa, M. Chica-Olmo / Applied Geochemistry 14 (1999) 133±145134

1987). The most signi®cant structural feature of thiszone is the presence of a mylonitic band, with an ap-

proximate N±S orientation, which shows the tectoniccontact between MTU and CGSA. The deformationmainly a�ects rocks from CGSA, generating levels of

mylonites. In the Malpica-Tuy Unit mylonitic bandsalso appear, but with less super®cial development.Posterior to the mylonitization event, a cataclastic pro-

cess with hydrothermal contribution occurred (Jahoda,1987; Castroviejo, 1990; Porter and Alvarez-MoraÂ n,1992).

3. Mineralization

Mineralizations are essentially Au and related met-allic sulphides. Bonnemaison and Marcoux (1987) pro-posed a model for this mineralization called ``post-metamorphic type, Au±As subtype'', with a polyphasic

genesis in the Au accumulation. The mineralised belt,with an approximate NNE±SSW direction, is in themost eastern and mylonitized part of the MTU, just at

the contact with CGSA. Most of the ores ®ll openspaces or veins or cement breccias, located in severallithologies, but always in the most deformed ones. A

direct relation exists between mineralization and laterbrittle deformation with hydrothermal ®lling. This hy-drothermal contribution produces di�erent types of

alterations, mainly silici®cation, potassic alteration andsericitization. Moreover, Au crystallisation is linked tothat of arsenopyrite, with the Au appearing by destabi-lisation of arsenopyrite (Agudo-FernaÂ ndez et al., 1989;

GarcõÂ a del Amo et al., 1992). According to Porter andAlvarez-MoraÂ n (1992), in this kind of deposit the sys-tematic association between Au and As suggests that

As is an important factor in the mobilisation, transportand deposition of Au.

4. Data set

The data set used for this study comes from a geo-

chemical prospecting survey carried out byENADIMSA in the ``Reserva Nacional Finisterre B2-Fervenza'', in Galicia, NW Spain. This survey com-prised 602 soil samples, which were collected at

50 � 100 m. grid intervals (Fig. 1(b)). In each sample32 chemical elements were analysed. The samples weretaken, in a greater part, at the horizon C of an Acrisol

(FAO-UNESCO, 1988).The analytical equipment was: ICP for As with a

JOBIN YVON JY-707, and for Au, Atomic

Absorption was used with a PERKIN ELMER 5000.These analyses were made at the Laboratories of theENADIMSA in Madrid (Spain).

Because of the need for data con®dentiality (prop-erty of ENADIMSA) additional information cannotbe given.

The aim of this survey was the location of Au min-eralization: this element, however, presents problemsfor numerical treatment. Most of the values lie close tothe detection limit and a few extreme data are pre-

sented (Fig. 2). This results in di�culties in the appli-cation of geostatistical methods to study Au. Giventhese characteristics of Au, the use of other related el-

ements is required. In this sense, As is the most appro-priate because of their common origin, and so it canbe considered as a path®nder of Au. Principal com-

ponent analysis shows this relation between As andAu, with a single component for these elements. No re-lation between Au and other variables is evident(Table 1). Furthermore, the correlation between Au

and As can be veri®ed by a scattergram of these el-ements, with a correlation coe�cient of 0.76 (Fig. 3).Statistically, As presents a lognormal trend with a

positive skew distribution, as can be seen in the quan-tile plot (Fig. 8). The plot of raw values of As isshown in Fig. 4.

5. Spatial structure of As

Geostatistical analysis is based on variograms. Thisis extensively explained in many books, e.g. Journeland Huijbregts (1978) or Isaaks and Srivastava (1989).

Fig. 2. Plot of the raw data of Au.

R. JimeÂnez-Espinosa, M. Chica-Olmo / Applied Geochemistry 14 (1999) 133±145 135

Applications of geostatistics to geochemical explora-

tion have been described by many authors (Sandjivy,

1984; Sousa, 1989; JimeÂ nez-Espinosa et al., 1993a;

Bellehumeur et al., 1994, among others).

Experimental and ®tted variograms of As were com-

puted following the main directions of the sampling

grid (Fig. 5). The directional variograms show an im-

portant nugget e�ect, indicating that more than 40%

of the spatial variation is due to small scale random

variations. The longest range is displayed in the N±S

direction, this direction being parallel to the main

mineralised belt in the area with the lower spatial

variability. The smaller range, obviously, corresponds

to the orthogonal direction in the area where As pre-

sents the greatest spatial variability. Diagonal direc-

tions have an intermediate feature, with ranges

between those of the two main directions, there

appearing a marked N±S geometric anisotropy for this

element in the study area. The shape of the experimen-

tal variograms reveals the geological and geochemical

characteristics of the area, with a concentration of

high values coincident with the structural N±S direc-

tion (Fig. 4). The model used for As (g(h) = C0 + C1-

sph (h,A anis)) is made up of an important nuggete�ect, (C0 = 3000 ppm2), which exceeds 40% of theexperimental variance, and a spherical structure C1-

sph (h,A anis) (Fig. 5). This spherical spatial modelhas the following features: a sill (C1) of 4000 ppm2

(around 60% of variance), and ranges (A) with a geo-metric anisotropy:

AEÿW � 250 m; AN45E � 500 m;

ANÿS � 1800 m; AN135E � 500 m:

6. Geostatistical estimation: comparison of the results

In this section, the results obtained from the appli-

cation of three geostatistical methods of estimation toAs are shown. These techniques are ordinary kriging(cross-validation), factorial kriging and indicator kri-

ging; all of them considered as appropriate to identifymineralised zones, and showing the advantages andlimitations described in this work.

Table 1

Varimax rotated component weigths of di�erent variables in the study area

Pc1 Pc2 Pc3 Pc4 Pc5 Pc6 PC7 PC8

Al 0.14 0.42 ÿ0.60 ÿ0.14 ÿ0.06 0.12 0.24 0.08

As 0.11 ÿ0.09 ÿ0.06 ÿ0.04 0.77 0.10 0.24 ÿ0.01Au ÿ0.06 0.03 0.01 0.05 0.81 ÿ0.05 ÿ0.06 0.02

B ÿ0.01 ÿ0.21 0.04 0.03 0.03 ÿ0.12 0.77 ÿ0.18Ba 0.16 0.67 ÿ0.20 0.07 0.07 ÿ0.19 ÿ0.10 0.11

Be 0.22 ÿ0.10 0.07 ÿ0.17 0.11 0.59 ÿ0.12 0.28

Ca 0.06 0.15 0.78 0.10 ÿ0.03 0.04 0.18 0.03

Cd 0.78 0.22 ÿ0.11 0.09 ÿ0.01 0.06 0.06 0.03

Co 0.58 0.04 ÿ0.16 0.14 0.01 0.03 ÿ0.08 0.59

Cr 0.88 ÿ0.13 0.05 ÿ0.06 ÿ0.04 0.08 ÿ0.16 0.01

Cu 0.61 ÿ0.11 ÿ0.14 0.12 0.32 0.07 0.12 0.07

Fe 0.89 ÿ0.08 0.08 0.19 ÿ0.02 ÿ0.01 ÿ0.06 0.06

K 0.04 0.66 0.06 0.06 0.01 0.11 ÿ0.13 ÿ0.35Li 0.22 0.17 ÿ0.20 0.48 ÿ0.14 0.37 0.24 0.24

Mg 0.63 0.20 0.06 0.41 ÿ0.05 0.20 ÿ0.05 0.21

Mn 0.41 ÿ0.16 0.11 0.44 0.08 0.12 0.10 0.53

Na ÿ0.29 0.11 0.56 ÿ0.22 ÿ0.07 0.30 ÿ0.26 0.26

Nb ÿ0.10 ÿ0.01 0.04 0.81 0.08 ÿ0.05 0.04 0.04

Ni 0.74 0.06 ÿ0.13 ÿ0.09 ÿ0.09 0.14 ÿ0.17 0.28

P ÿ0.20 0.17 ÿ0.05 0.08 0.10 ÿ0.06 0.77 0.08

Pb ÿ0.13 0.33 ÿ0.55 ÿ0.06 0.17 ÿ0.05 0.52 0.00

Sn 0.09 ÿ0.08 0.04 0.11 ÿ0.01 0.80 ÿ0.09 ÿ0.15Sr ÿ0.22 0.74 0.17 ÿ0.05 ÿ0.13 ÿ0.12 0.27 0.13

Ti 0.41 0.03 0.11 0.78 ÿ0.03 ÿ0.02 ÿ0.01 ÿ0.07V 0.91 ÿ0.12 0.12 0.01 0.01 0.04 ÿ0.09 ÿ0.01Y 0.37 0.05 0.11 ÿ0.07 ÿ0.01 ÿ0.06 ÿ0.35 0.46

Zn 0.57 0.22 ÿ0.24 0.10 0.08 0.06 ÿ0.05 0.21

Percent of variance 21.7 7.79 7.02 7.78 5.50 5.33 7.74 5.41


6.1. Ordinary kriging: cross-validation

Ordinary kriging (OK) is a well-known method of

geostatistical estimation. The particularity of this tech-nique consists of the use of second order stationaryrandom functions (Matheron, 1970). In the ®eld of

ordinary kriging, an interesting procedure in geochem-ical exploration is that of cross-validation (Isaaks andSrivastava, 1989; JimeÂ nez-Espinosa, 1993). In a cross-

validation exercise, the estimation method (OK) istested at the locations of existing samples. The value ata particular sample location is temporarily discarded

from the sample data set; the value at the same lo-cation is then estimated using the remaining samples.The estimate can be compared to the true samplevalue that was initially removed from the sample data

set. This procedure is repeated for all availablesamples. An estimator, Z *(xa), [a= 1 to n samples] ofeach experimental datum, z(xa), [a = 1 to n samples],

is built, temporarily removing the sample value to beestimated. Repeating this procedure for all n exper-imental data, we can obtain the experimental distri-

bution of the error, e = [Z *(x)ÿZ(xa)], is easily ®ttedto a normal distribution, with E[e]10 and E[e]21s 2

K.If assuming that, the procedure for anomaly selectionconsists of working point by point and considering as

anomalous the group of samples of n 0 values whosereal estimation errors are higher than k times the stan-dard deviation s, the Zscore.

zscore � jZ�x a� ÿ Z *�x a�js

> k

or

jZ�x a� ÿ Z *�x a�j > ks

k can be chosen from the normal distribution andgiven a con®dence level depending on each study.This method has been used in several geochemical

and hydrogeochemical surveys, see for example,Suslick (1981), Chica-Olmo (1989), JimeÂ nez-Espinosaand Chica-Olmo (1992).

6.1.1. Results of OK for AsOK was applied to As, as a path®nder of Au, with

the objective of identifying potential anomalous points

Fig. 3. Scattergram of Au vs As, with a correlation coe�cient of 0.76.

Fig. 4. Plot of the raw data of As.


of Au. As estimation errors are approximately nor-mally distributed (see JimeÂ nez-Espinosa, 1993), with anaverage near to zero (0.29). The determination of the

anomalous values for As has been made usingk= 1.96, which represents probability of 5% forextreme high and low values in a normal distribution.The anomalous samples identi®ed by cross-validation

are plotted in a map (Fig. 6). These values arerestricted to the contact area between metasedimentrocks (MT) and the biotite-bearing gneisses (ONB), in

the southern part of the study area. They also appearat the boundary between these two lithologies and cat-aclastic rocks (CR), in the central part. These locations

are coincident with areas where mobilisation of hydro-thermal ¯uids appears during the brittle deformationstage (Porter and Alvarez-MoraÂ n, 1992).

6.2. Factorial kriging

Analysis of variogram leads to modelling the varia-

bility of the variable as the sum of several basic struc-tures of covariance or basic variograms. Geochemicallyeach of the basic structures is directly related to spatial

scales (or ``frequency bands'') of the geochemical vari-able. Thus, short-range structures, with random beha-viour, are associated with the anomalous component.

Fig. 5. Directional variograms of As.

Fig. 6. Results of the application of ordinary kriging (cross-

validation) to As (k = 1.96) showing values of raw data for

As.


On the other hand, long-range structures are continu-ous in their distribution as well in their spatial variabil-

ity, being associated with the geochemical background.Factorial kriging (FK) allows us to estimate, at everypoint of the study area several new variables, whose

variograms correspond to the frequency levels of thevariable under study. The application is made in theunivariate and stationary frame (Matheron, 1982,

Sandjivy, 1984, 1985). Let Z(x) be the regionalizedvariable concentration of a geochemical element in adomain V. Let one further consider this variable to be

a realisation of a second-order stationary randomfunction. Given these hypotheses, a linear decompo-sition of Z(x) into as many basic components, Yu(x),as there are identi®ed structures, is made:

Z�x� � auYu�x� u � 0 to n

where the Yu(x) are jointly orthogonal random func-tions.After a series of numerical transformations, the esti-

mation of the basic components, Y *u �x� is obtained

(see Sandjivy, 1984). Then the spatial components canbe mapped, this being a more useful tool than factorsobtained from any non-spatial factor analysis.

Therefore, it is important to consider that the Y0 com-ponent, corresponding to the nugget e�ect, holds onlyat the experimental points; thus, Y0 should not be pre-

sented on contour maps but in punctual presentationsonly.

Factorial kriging has been extensively used in thestudy of geological and geochemical data (see forexample, Sandjivy, 1983, 1984, 1985; Galli et al., 1984;

Muge et al., 1987; Sousa, 1988, 1989; Jaquet, 1989;JimeÂ nez-Espinosa, 1993; JimeÂ nez-Espinosa et al.,1993a,b; Goovaerts and Sonnet, 1993).

6.2.1. Application of factorial kriging to AsThe model used to ®t the As variograms

(g(h) = C0+ C1-sph (h,A anis)) is composed of a

nugget e�ect plus an anisotropic spherical structure,with the anisotropy spreaded out along the N±S direc-tion (Fig. 5 and Section 5 on spacial structure of As).

Estimates are made of the two components corre-sponding to each structure identi®ed in the variogramat the nodes of a regular grid of 50 � 100 m.

Analysing the As dot map (Fig. 4), a high value zonecan be detected in the southern part of this area, in thebiotite gneiss rocks (ONB) to the S of the cataclastic

rocks. Factorial kriging allows the identi®cation ofanomalous samples, since not all high value raw dataareas are truly anomalous; many of them belong tobackground. The map of the Y1 component (Fig. 7(b))

shows the background of As content in this area. Thehigher values of Y1 are at the outcrops of the metase-diments containing arsenopyrite as a stable mineral.

Fig. 7. Results of the application of factorial kriging to As: (a) map of the Y0 component; (b) map of the Y1 component.


This map establishes a continuous structure, easy tocompare to the geological mapping. On the other

hand, areas can be detected where mobilisation of hy-drothermal ¯uids destabilised the arsenopyrite, liberat-ing the Au and As, in relation to the brittle

deformation phase. The Y0 component (Fig. 7(a)) rep-resents these areas, the anomaly with the short-rangestructure being associated with random behaviour.

6.3. Indicator kriging

An important problem associated with the analysisof the geochemical information is the presence ofskewed distributions with a high coe�cient of vari-

ation. Another problem is that values below detectionlimit are grouped at detection limit. Experimental var-iograms become extremely sensitive to high and low

values, and are practically useless. In these situations,two traditional solutions are proposed: (i) trim o� theextreme values, based on geological or probabilistic

critera; or, (ii) transform the data by means of asmoothing function or the natural logarithms. The ®rstapproach is very simplistic and not acceptable whenthese data carry the most valuable structural infor-

mation, not to mention their economic weight. Log-transformations are non-linear, and that calls for non-linear estimation techniques (i.e., disjunctive kriging),

which require a hypothesis about the distribution.An interesting solution to avoid these problems

might be the application of nonparametric, distri-

bution-free estimation methods, like indicator kriging(Journel, 1983). This technique is based on the trans-

formation of raw data as a function of an indicator.Thus, we consider a new data set made up of only``zeros'' and ``ones'', namely indicator variables, prior

to the establishment of a series of cut-o�s, z. The indi-cator function is de®ned as a step function of z:

i�x; z� � � 0, if z�x�Rz1, if z�x� > z

The proportion of contents z(x) below the cut-o� z,

within an area A, is written as:

f�A; z��A

i�x; z�dx 2 �0,1�

As functions of z, both i(x; z) and f(A; z) can be con-sidered as cumulative distribution functions, with f(A;z) being the average of all i(x; z) for any x $ A. Theestimation of the density function of Z(x) is based onthe repetition of the estimation process for di�erent

cut-o�s, becoming the ®nal estimator by the leastsquares method of the conditional density function.From a geochemical point of view, the estimation ofthe density function is not as important as the estab-

lishment of some speci®c indicator variables that corre-spond to speci®c geochemical cut-o�s. Thus, makingthe estimation based on these new variables, we can

obtain a series of estimates belonging to [0,1].For a given z, the random function I(x; z) has a

binomial distribution with a semivariogram de®ned as:

Fig. 8. Quantile plot of As, showing the way the indicators of this element were selected.


gI(h; z)=12E {[I(x + h; z)ÿ I(x; z)]2}. The indicator

variograms are robust against extreme values, sincetheir estimation does not call for the data values them-selves but rather for their rank order concerning a

given cut-o� z. Obtaining estimates with indicator vari-ables consists of the application of a kriging algorithmto these new variables, with OK being the most used

(see for example Journel and Huijbregts, 1978).

6.3.1. Application of indicator kriging to AsIndicator kriging was applied to As to locate high

content zones for this element, but with the ®nal goal

of identifying Au areas, using a 25 � 50 m estimationgrid (JimeÂ nez-Espinosa and Chica-Olmo, 1995). TheAs indicators were established by the quantile plot (q-plot), where one can detect the two main ruptures in

the plot (Fig. 8). The initial rupture identi®es thebranch of As distribution corresponding to the ®rstanomaly population; this might correspond to the

well-known ``beginning anomalous content'' of Royer(1988). The authors considered this initial break pointin the distribution to select the ®rst cut-o� value and

its consequent indicator variable: approximately the75th percentile corresponding to a z value of z0.75=55ppm. The following cut-o� value was obtained in the

same way. Using the q-plot, a second break point can

be detected around the 90th percentile, corresponding

to a branch of the distribution that has a second

anomalous population for the extreme values. This

point can be considered similar to the ``signi®cant

anomalous content'' of Royer (1988). The cut-o� value

(z0.9=108 ppm) gives the next indicator variable.

Thus, two indicators have been created: IAs75 and

IAs90, corresponding to the two main anomalous

populations for As in this area.

Variograms of IAs75 show a good structure, with

anisotropic directional variograms and the N±S direc-

tion appearing as the most elongated of the anisotropy

(Fig. 9). Therefore, the anisotropy is smoothed in com-

parison with the raw data variograms. The model

®tted to IAs75 is similar to that of raw As, a nugget

e�ect (C0 = 0.08), with an anisotropic spherical model

made up of C1=0.11 and AE±W=250 m; AN45E=500

m; AN±S=900 m; AN135E=500 m. The results are pre-

sented as two types of grey scale maps: (i) a map

showing estimates of the indicator variable, with values

ranging from 0 to 1 and (ii) a binary map (Solow,

1986) where only two values are presented, ``zeros''

and ``ones''. This is obtained from the previous map,

where original values are transformed to 0 if these fall

Fig. 9. Directional variograms of the indicator variable IAs75


below the indicator average, or to 1 if these are above

this value. The maps of IAs75 are shown in Fig. 10(a)

and (b). The grey scale map (Fig. 10(a)) allows one to

distinguish high value zones for As and for this cut-

o�, mainly in the western part of working area and of

less importance, in the central part. These zones are

shown more clearly in the binary map (Fig. 10(b)).

The granitic rocks (GH and GRH) appear completely

free of important contents for As, as do the cataclastic

rocks (CR) themselves. These latter rocks act as a min-

eralization channel, but do not carry the mineraliz-

ation, with the greater As contents remaining in

metasediments (MT) and in the contact between these

and biotite gneisses (ONB).

Doing the same procedure for IAs90, corresponding

to a cut-o� of 108 ppm, variograms have been

Fig. 10. Results of the application of indicator kriging to IAs75 (z = 55 ppm): (a) map of the IAs75; (b) corresponding binary map

for this variable, where all black points are above the cut-o� value.


obtained similar to IAs75. The estimates of IAs90 are

shown on maps that appear in Fig. 11(a) and (b). The

map for the indicator variable restricts even more the

location of high value zones for As, due to the more

selective cut-o� used, with the rich zone at the contact

of metasediments with adjacent rocks, cataclastic rocks

and biotite gneisses. These areas are clearer in the re-

spective binary map (Fig. 11(b)).

7. Discussion and conclusions

The single element maps of Au and As give the

starting point for further studies. These maps allow the

location of the richest regions for these elements, and

it is a very useful tool. However, the use of several nu-

merical methods can help to identify speci®c areas of

interest. In this way, three di�erent geostatistical tech-

Fig. 11. Results of the application of indicator kriging to IAs90 (z= 108 ppm): (a) map of the IAs75; (b) corresponding binary map

for this variable, where all black points are above the cut-o� value.


niques have been selected to show their bene®ts in

interpreting geochemical data. In some cases thesemethods are very useful in regional or reconnaissancescale mapping and also in detailed scale exploration.

Ordinary kriging is an interesting technique in the®eld of geochemical prospecting. First, it is easy to

apply and to interpret the results. Moreover, it pre-sents the advantage of using the same experimentalgrid points for its estimates. Since, in this geochemical

prospecting survey the grids are quite narrow, ad-ditional grid points are not needed. It is a goodmethod to distinguish anomalous values from back-

ground values, but it needs some statistical conditionsabout residual distribution.

Factorial kriging represents a more advanced treat-ment of geochemical data, since it allows raw variablesto be decomposed into as many basic components as

there are identi®ed structures in the variogram and tobe anomalies separated from the background.However, it is a somewhat more complex method and

is more di�cult to apply than ordinary or indicatorkriging. Nevertheless, in practice, some kriging soft-

ware can be adapted to it and applied, e.g. GSLIBsoftware (Deutsch and Journel, 1992).The great advantage of indicator kriging is that data

are used by their rank order in relation to any cut-o�,allowing for a more comprehensive structural analysis,

while it is more robust concerning outlier values.Indicator kriging does not require any new software,so it can be carried out by known linear geostatistics.

Moreover, the clearness in the presentation of resultsshould be emphasised, evident in the use of explicitmaps such as the indicator and the binary charts.

Nevertheless, indicator kriging has the disadvantage ofbeing incapable of separating anomalous values from

background values. Therefore, this method must beconsidered as a guide technique in the location of po-tentially rich areas. Considering the three methods

applied, factorial kriging can be considered as themost appropriate technique to identify and separategeochemical anomalies from background and it needs

no conditions on distribution of basic variable to esti-mate. The other two methods are a good prior guide

to locate highly concentrated areas.Concerning the results of this work, the location of

anomalous values clearly appears along the N±S struc-

ture corresponding to the shear zone and particularlyaround cataclastic rocks (CR), gneissic rocks (ONB),or metasediments (MT). These are the lithologies

where Au mineralization appears due to the circulationof rich ¯uids through cataclastic rocks, during the

stage of brittle deformation. The three methods repro-duce in di�erent ways, through cartographic maps, theabove general conclusion, about the importance of the

structural control of the spatial distribution of As andAu in the study area.

Acknowledgements

First, the authors are very grateful to the referees,Dr Gustavsson and Dr Salminen (Geological Surveyof Finland), for their very useful suggestions, which

helped to improve the original manuscript. Also, wewould like to thank Dr Sousa for his valuable com-ments. Thankful acknowledgement is made to

ENADIMSA for providing data. The study was sup-ported by ``ConsejerõÂ a de EducacioÂ n y Ciencia'' of the``Junta de AndalucõÂ a'' (Research Group RNN 0122).

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