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Ore Geology Reviews 25 (2004) 39–67

Predicting gold-rich epithermal and porphyry systems in the central

Andes with a continental-scale metallogenic GIS

M. Billa*, D. Cassard, A.L.W. Lips, V. Bouchot, B. Tourliere,G. Stein, L. Guillou-Frottier

Mineral Resources Division, BRGM, 3 avenue Claude Guillemin-BP 6009, 45060 Orleans cedex 2, France

Accepted 4 January 2004

Available online

Abstract

BRGM’s GIS Andes, a comprehensive continental-scale metallogenic information system for the entire Andes Cordillera, is

based on original syntheses structured into thematic layers. The aim of developing the GIS was to produce an integrated tool to

understand ore deposit localization in the Andes. A fundamental question arising at the outset was whether the tool would be

suitable for producing predictive mineral-resource maps at the continental scale, considering that previous predictive studies

focus only on the regional scale. The continental-scale synthesis implied working with heterogeneous data in terms of

distribution, quantity, quality, and in particular, accuracy. The benefit is the ability to include uncommon parameters linked to the

geodynamic evolution of the active margin and significant only at the continental scale. In view of the particularities of the GIS

dataset, an ‘‘expert-guided data-driven’’ approach was adopted for the multicriteria processing; an approach that combined expert

knowledge and the use of elementary statistics, allowing to provide a link between the tectonic development of the whole Andean

margin and the spatial and temporal distribution of individual mining districts.

This study was purposely restricted to assessing the distribution of Neogene gold in the central Andes between lat. 3j and

33jS, thus (a) incorporating well constrained data on the present morphology of the convergent margin, and (b) avoiding

ambiguities in the less well constrained older history of the complex evolution of the Andean margin. Five regional parameters

were selected and were considered to have a significant influence on the Neogene magmatic-hydrothermal ore formation at

continental scale. The five parameters: (i) host-rock lithostratigraphy, (ii) lithostratigraphic contacts, (iii) structural discontinuities,

and (iv) depth and (v) dip of the Wadati-Benioff zone modeled from seismic data, had assigned favorability scores, from 0 to 2,

based on their associated metal content with respect to a set of identified Neogene gold deposits. The next step was to calculate

favorability maps for each criterion that were combined to create an overall (cumulative) favorability map or predictive gold map.

Verifying the predictive map against known gold deposits, it was found that the cumulative favorability score of z 4 (out of 10

maximum) located about two-thirds of the known gold-bearing epithermal and porphyry deposits and 95% of the metal content; a

cumulative favorability score of z 5 reduced these figures to 50% of the deposits and 71% of the metal content, and that of z 6

relocated 24% of the deposits and 51% of the metal content. In addition to verifying the method, the predictive map outlines new

potentially favorable gold areas and even indicates that some known districts could well host yet undiscovered mineralization.

D 2004 Published by Elsevier B.V.

Keywords: Gold; Metallogeny; Andes; Cenozoic; GIS; Multi-criteria processing

* Corresponding author. Tel.: +33-2-38-64-33-35; fax: +33-2-38-64-47-29.

0169-1368/$ - see front matter D 2004 Published by Elsevier B.V.

doi:10.1016/j.oregeorev.2004.01.002

E-mail address: [email protected] (M. Billa).

M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6740

1. Introduction

When BRGM (Bureau de Recherches Geologiques

et Minieres) undertook the development of GIS

Andes, the underlying aim of the project was to

develop a tool that would facilitate metallogenic

understanding at the scale of the entire orogen and

provide a mineral-resources assessment of any zone

within the Cordillera (Cassard, 1999a,b). The system

had therefore to incorporate a variety of information

that would need to be organized into thematic layers,

controlled, homogenized, at times synthesized and

always georeferenced with the greatest possible pre-

cision. The objective was to develop a user-friendly

and reliable tool that would eliminate the need of

having to struggle ceaselessly with problems of scale,

projection system, multiple lithostratigraphic scales of

only local value, etc. The main drawback of such a

continental-scale system is that the information it

contains will never be of the same quality in terms

of homogeneity, precision, and spatial distribution, as

that of a system developed at a regional scale for a

single mining district. Moreover, the data are com-

monly not of the same type: e.g., exploration geo-

chemical or geophysical surveys are only available at

the district scale. Conversely, other parameters, such

as those relative to the geodynamic evolution of the

Andean margin, are only relevant at the continental

scale. The fundamental question that arises is whether

such a system can be used to establish predictive

mineral-resource maps, i.e., reveal zones of high metal

potential that, if not unknown, have been at least

underestimated.

The use of geodynamic parameters in an optic of

mineral exploration is easily justifiable at the scale of

the Andes Cordillera where the spatial and temporal

distribution of the metalliferous deposits is correlated

to subduction-related magmatic activity. In terms of

spatial distribution, a zoning of the deposits parallel to

the mountain belt is classically noted and interpreted

as linked to subduction of the Nazca oceanic plate

below the South American continent, as well as to a

segmentation in the distribution of the mineralized

districts due to transverse discontinuities of various

origins (e.g., Sillitoe, 1974; De Silva and Francis,

1991; Oyarzun, 2000). From the temporal standpoint,

many authors have recognized paroxysmal periods of

mineralization (or ‘‘metalliferous peaks’’) and have

globally correlated these with the late-magmatic

stages of the orogenic phases, i.e., specific periods

in the geodynamic evolution of the Andean active

margin (Sillitoe, 1988, 1991; Gibson et al., 1995;

Marcoux et al., 1998; Noble and McKee, 1999;

Oyarzun, 2000; Cassard et al., 2000). The influence

of the subduction dynamics and geometry on both

magmatism and volcanism (cf. Marsh and Carmi-

chael, 1974; Bremond d’Ars et al., 1995) and a fortiori

on emplacement of the mineralization in and outside

the Andes (cf. Haeussler et al., 1995; Kesler, 1997;

Sillitoe, 1997; Goldfarb et al., 2001; Lips, 2002) has

thus been the subject of many discussions and pro-

posed models. For the central Andes, Kay et al. (1999)

and Kay and Mpodozis (2001) hypothesize, in partic-

ular, on the effect of temporal changes in subducting

slab geometry on mineralization processes during the

Neogene.

In order to (a) incorporate well-constrained data on

the present morphology of the convergent margin, and

(b) avoid ambiguities in the less well constrained

older history of the complex evolution of the Andean

margin, our study has as yet only considered the

distribution of gold in Neogene epithermal and por-

phyry deposits in the central Andes between 3j and

33jS, i.e., along the segment of the cordillera enclos-

ing the Bolivian Orocline (Fig. 1). This segment,

about 3000 km long, constitutes the main gold-bear-

ing province of the Andes with a total potential,

estimated from the data within GIS Andes, of 8360

tons of gold, representing about 65% of that in the

entire Andes belt, and with the main period of

mineralization being contemporaneous with the major

geodynamic events succeeding the rupture of the

Farallon plate at about 26 Ma (Pilger, 1984; Pardo-

Casas and Molnar, 1987).

Processing the GIS Andes data has made it possi-

ble to (i) comprehensively display the distribution of

the deposits within the study area, (ii) determine,

quantify and rank the criteria controlling this distri-

bution, so as to establish a relational model, and (iii)

synthesize the criteria in order to produce a predictive

map of the Neogene epithermal-porphyry gold sys-

tems, i.e., a map that indicates the areas with a strong

resemblance to those containing the known deposits.

Following a brief outline of the metallogenic

evolution of the Andes, the data-processing procedure

used for establishing the predictive map will be

Fig. 1. Location of all gold districts with respect to the dip of the Wadati-Benioff zone. The deposits selected for favorability studies (Table 1)

are represented by a symbol indicating their individual metal content. All gold districts (Neogene and pre-Neogene) are represented by 25 km

buffer zones for which the cumulative gold content is calculated.

M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 41


presented. The study will explain the criteria that were

combined in order to provide a favorability score that

enabled not only to relocate the known major gold

districts, but also delineate new potential areas.

2. Recapitulation of the orogenic, geodynamic and

metallogenic evolution of the Andes

The Andes cordillera is one of the most important

mineralized belts of the world as far as production is

concerned (cf. Cabello, 2000), notably for copper,

molybdenum, gold, silver, tin and zinc and, to a lesser

extent, antimony, lead, bismuth, cadmium and tung-

sten. It contains giant deposits (cf. Zentilli and Mak-

saev, 1995; Sillitoe, 1997; Laznicka, 1999) such as the

porphyry copper deposits of Chile (Chuquicamata, El

Teniente), the tin and silver deposits of Bolivia (Cerro

Rico de Potosı), the epithermal gold deposits of

Yanacocha (Peru) and El Indio district (Chile), the

gold-rich porphyry deposits of Bajo de la Alumbrera

(Argentina), the porphyry gold deposits of the Mar-

icunga belt (Chile), and the polymetallic zinc deposits

of Cerro de Pasco (Peru).

2.1. The Andes before 100 Ma

The South American craton and its Andean margin

were formed through tectonic accretion of various

geological units during the Proterozoic and Paleozoic,

followed at the end of the Paleozoic and beginning of

the Mesozoic by generalized extension related to the

fragmentation of Pangea (Ramos, 1988, 1994). The

Jurassic and Early Cretaceous were a period of dom-

inantly zinc and barite polymetallic mineralization on

the Peruvian carbonate shelf (Soler, 1986; Fontbote,

1990a,b) and of the first significant Cu mineralization

in Chile (Camus, 1980; Sillitoe, 1992). The foreland

area (Bolivian province) was already a site of Sn/W

mineralization at this time (Oyarzun, 1990).

2.2. The Andes after 100 Ma

Since the Late Cretaceous, the definitive opening

of the South Atlantic Ocean (Scotese et al., 1988;

Jaillard et al., 2000) has profoundly modified the

geodynamic context of the Andes. The established

continent-ocean convergence became accommodated

by subduction of the oceanic plate and phenomena

such as tectonic erosion of the margin, retreat of the

trench, and intracontinental deformation. Different

tectonic and magmatic episodes can be correlated

with major changes in the convergence pattern, such

as orientation and rate of convergence, dip of subduc-

tion, and displacement of the trench (Soler and Bon-

homme, 1990; Scheuber et al., 1994).

The earlier extensional regime was followed by a

period marked by several compressive episodes and

several metalliferous peaks associated with changes in

the deformation and magmatic patterns. In Chile, the

first Cu(Au) porphyry mineralization of the Andean

cycle took place between 104 and 98 Ma at Andacollo

(Oyarzun, 1990; Sillitoe, 1991, 1992; Oyarzun et al.,

1996), where it was locally associated with a younger

‘low sulfidation’-type epithermal gold mineralization

(91 Ma). During the Paleocene (52–57 Ma), a group

of large porphyry Cu(Mo,Ag) deposits was emplaced

in southern Peru (Cardozo and Cedillo, 1990; Clark et

al., 1990). The extensions of this province in Chile

correspond to (a) smaller porphyry Cu deposits, (b)

tourmalinized Cu (Au,W) breccia pipes, and (c) small

Au and Au–Ag epithermal deposits (Oyarzun, 1990).

Plate convergence became less oblique during the

Eocene–Oligocene transition, causing oceanic arcs to

collide with the continent in Colombia (Ramos and

Aleman, 2000). This period corresponds in Peru to a

compressive phase with basin inversion (Jaillard et al.,

2000), and in Chile to a modification in the strike-slip

movement of the major structures and to a tectonic

inversion of older basins (Scheuber et al., 1994,

1995). For northern Chile, this interval was the major

metallogenic period for copper (cf. Oyarzun and

Frutos, 1980; Oyarzun, 1990; Sillitoe, 1991, 1992)

with the emplacement of giant porphyry Cu and Mo

deposits (cf. Sillitoe, 1997; Ossandon et al., 2001;

Oyarzun et al., 2001). Simultaneously, more modest

mineralization took place in Peru (Petersen, 1965;

Cardozo and Cedillo, 1990), including Cu(Au) skarns,

Cu(Ag) stockworks in volcanic rocks, and various

polymetallic vein deposits.

2.3. The neogene Au, Cu, Zn, Sn, Ag metallogenic

peak

The break-up of the Farallon plate into the Cocos

and Nazca plates at about 26 Ma marks the beginning


of a period of more rapid and more orthogonal

convergence along most of the central and southern

Andes (Pilger, 1984; Pardo-Casas and Molnar, 1987).

The Miocene –Pliocene deformation (Quechua

phases—Sebrier and Soler, 1991) accentuated the

orogenic shortening that, with a value of about 320

km, was maximal at the latitude of the Bolivian

Orocline (Schmitz, 1994; Whitman et al., 1996; Kley

et al., 1999) where it is associated with major crustal

thickening, locally reaching 70–74 km (Beck et al.,

1996). Lesser shortening associated with a strike-slip

component occurred to the north and south of the

orocline. This difference in the amount of shortening

resulted, mainly during the last 12 my, in accentuation

of the curvature of the Arica Elbow (Wortel, 1984;

Isacks, 1988).

The post-early Miocene shallowing of the subduc-

tion zone at either end of the Bolivian Orocline could

account for (a) decreasing amounts of volcanic activ-

ity in the Main/Western Cordillera, (b) eastward

broadening of the volcanic arc, and (c) migration of

both the compressional deformation front and the

foreland-basin system into the Precordillera and Sub-

andean belt (Kay and Mpodozis, 2001).

The Neogene represents a major metallogenic

epoch with a wide variety of mineralization being

developed during the various magmatic phases. This

includes, in particular, the giant epithermal, gold-rich

porphyry and porphyry gold deposits (Yanacocha,

Maricunga belt, El Indio belt, Bajo de la Alumbrera,

Portovelo), Sn–Ag (Cerro Potosı), Zn (Cerro de

Pasco), and Cu (El Teniente). The gold mineralization

was emplaced throughout the Miocene, from 25 to 7

Ma (Sillitoe, 1991; Noble and McKee, 1999), with

periods of maximum development during the Early

Miocene (Maricunga belt in part; Chile) and Late

Miocene (Portovelo, Ecuador; Yanacocha, northern

Peru; Orcopampa, southern Peru; El Indio belt, Chile;

and Bajo de la Alumbrera, Argentina).

3. Construction of a regional ‘‘epithermal-

porphyry system’’ deposit model

3.1. General approach

The first step is to determine what parameter

combinations controlled the spatial and temporal dis-

tributions of the ore deposits at the continental scale.

The resultant ‘‘data association model’’ can (a) be

based on the geologists’ experience and expertise, (b)

integrate existing metallogenic models from model

bases, or (c) result from the search for relevant

relational criteria within existing databases through

statistical analysis. The different approaches are not

contradictory and were combined during the present

study.

Favorability maps were constructed for a range

of parameters through multicriteria processing of

the GIS Andes data using suitable software such as

BRGM’s SynArcR and ESRI’s Spatial AnalystR.The favorability maps were then combined to obtain

a cumulative favorability, or predictive map.

The two approaches normally used to establish

predictive metallogenic potential (Bonham-Carter,

1994; Braux, 1996; Bonham-Carter and Raines,

2002) are as follows.

(1) The ‘‘knowledge-driven’’ approach based on the

expert’s exploration knowledge. It uses methods

such as fuzzy logic or the Dempster-Shafer belief

functions. This determinist approach can also

incorporate existing deposit models (cf. Cox and

Singer, 1987; Bouchot et al., 2001) or conceptual

models of mineralized systems (Wyborn et al.,

1994).

(2) The ‘‘data-driven’’ statistical approach, also called

the ‘‘stochastic’’ (Bouchot et al., 2000) or

‘‘empirical’’ approach (Knox-Robinson and

Groves, 1997), based on the quantification of

relationships (associations) between the criteria

(evidential themes) and the known deposits. This

uses techniques such as regression, weight of

evidence, neural networks (cf. Brown et al., 2000;

Bougrain et al., 2003) and data mining (cf. Salleb

and Vrain, 2000).

Weight of evidence (WoE) modeling is a proba-

bility-based approach (Bonham-Carter, 1994) that

uses Bayes Rule to combine evidence with a condi-

tional independence assumption. It can be applied

where sufficient data is available to estimate the

relative importance of evidence by statistical means.

This method has been applied many times for min-

eral-exploration mapping (e.g., Bonham-Carter et al.,

1989; Agterberg and Bonham-Carter, 1990; Carranza


and Hale, 1999), the goal being to predict the

presence of a set of point objects (here the mineral

deposits), which are treated as binary—i.e., present

or absent. The calculated parameters obtained at the

end of the processing enable the decision as to

whether or not the studied associations are discrim-

inant and to compare the responses of the different

evidence themes so as to retain only the most

significant for multicriteria combination.

For the present study we decided to use an ‘‘expert-

guided data-driven’’ approach, i.e. combining the two

classic approaches. As GIS Andes is a continental-

scale metallogenic GIS based on a 1:2,000,000-scale

geological synthesis, this scale, although advanta-

geous in terms of understanding global phenomena,

is disadvantageous in that (a) the resolution is some-

times insufficient to characterize certain phenomena,

and (b) it is difficult to obtain homogeneous informa-

tion on the entire area in terms of quality, quantity and

spatial distribution. These were also decisive factors

for restricting the study area to the central Andes and

selecting only certain deposit types.

Two examples give a better understanding of the

rationale for using a combined approach:

(1) The 1:2,000,000 scale inevitably means that some

information is degraded. In the present case, some

intrusions controlling porphyry deposits are com-

monly too small to be represented in the

geological data layer of the GIS. Consequently,

during blind statistical treatment, the deposit is

attributed to the hosting polygon, which can thus

acquire a fictive favorability with no geological

basis.

(2) The GIS Andes ‘‘Deposits’’ database at present

contains about 3300 records concerning deposits

mined in the past, deposits currently being mined

or under development, and projects under evalu-

ation. It was not really feasible to integrate the

smaller mineral occurrences for the reasons stated

earlier—i.e., information that is very inhomoge-

neous in terms of quality and quantity from one

country to another, that is difficult to check, and

that commonly presents major problems regarding

precise location. A ‘‘data-driven’’ approach based

solely on WoE modeling would have been

confronted with a limited number of data; in

particular, it would not have been possible to

exploit the advantages of the database and, more

especially, the metal content of the deposits.

3.2. The metallogenic model

One of the striking features of the current sub-

duction of the Nazca plate is the along-strike varia-

tion in the dip of the Wadati-Benioff zone, from

subhorizontal flat-slab segments to normal subduc-

tion angles, which correlates with a similar segmen-

tation of the active volcanism (e.g., Ramos, 1999).

Thus, the central segment of the Andes between 14jand 27jS, which is characterized by normal subduc-

tion and active volcanism, is flanked to the north and

south by flat-slab segments with no active volca-

nism; these are known respectively as the ‘‘Peruvian

flat-slab segment’’ between 5j and 14jS and the

‘‘Pampean flat-slab segment’’ between 27j and 33jS(Cahill and Isacks, 1992; Ramos and Aleman, 2000).

The northern Andes, north of 5jN, corresponds to

the Bucaramanga segment where flat-slab subduction

has been recognized along the Colombian margin

(Pennington, 1981).

Plotting the large-scale distribution of gold miner-

alization and the tonnages of contained metal reveals

an along-strike variation similar to that of the subduc-

tion zone, particularly for the Neogene epithermal and

porphyry mineralization (Fig. 1). The fertile segments

containing most of the gold correspond to the shal-

low-dipping zones (Cassard et al., 2001), whereas the

‘‘Bolivian’’ central segment appears to be much less

mineralized. According to Kay and Mpodozis (2001),

this preferential association between the Neogene

epithermal-porphyry type mineralization and the flat

segments is due to release of fluids linked to dehy-

dration of the mantle or lower crust above a progres-

sively shallowing and cooler subducting oceanic slab.

Such a geodynamic context and geometric pattern of

the subducted plate could favor the genesis of partic-

ular adakitic-type magmas (Defant and Drummond,

1990; Gutsher et al., 2000; Beate et al., 2001; Gutsher,

2001), thus explaining the ‘‘Au-adakite connection’’

revealed by Thieblemont et al. (1997).

3.3. Three-step processing

The applied procedure (Fig. 2) combines the two

approaches described earlier, with the expert’s choices

Fig. 2. Flow chart to generate final predictive map.


being based on a statistical study of the object

families.

3.3.1. Step 1: Selecting the geographic area and

deposit population for the study; identifying the

criteria for processing

The construction of a predictive map requires a

homogeneous population of deposits that can be

controlled by a set of identical factors: e.g. same

commodity, same ore deposit type, and belonging to

the same metallogenic epoch. To respond to these

constraints, our study limited itself to the single

element—gold—in the most recent mineralized peri-

od—the Neogene—within the central Andes between

latitudes 3j and 33jS, which is the segment where the

main known epithermal and porphyry gold minerali-

zation occurs.

The selection, made from GIS Andes data, groups

the deposits in which gold is either the main com-

modity or a by-product, and that belong strictly to the

epithermal or porphyry types. The age of mineraliza-

tion is a difficult parameter to use because direct

absolute age data of individual mineral deposits are

relatively rare. The regional syntheses (Oyarzun,

1990, 2000; Cardozo and Cedillo, 1990; Clark et al.,

1990; Sillitoe et al., 1991; Vila and Sillitoe, 1991;

Ericksen and Cunningham, 1993; McKee et al., 1994;

Noble and McKee, 1997, 1999; Petersen, 1999)

nevertheless made it possible to confirm the chrono-

logic homogeneity of the studied population. The

mineralization studied is Neogene and was emplaced

between 7 Ma (e.g., Choquelimpie, Arcata) and 23

Ma (e.g., Refugio, Maricunga belt), with a maximum

frequency between 7 and 15 Ma. The deposits

emplaced prior to the Neogene, such as Quicay and

Andacollo, were omitted. Taking into account (a) the

defined problem, (b) the information available in GIS

Andes, and (c) the constraints outlined earlier, the

population studied was restricted to 113 deposits (i.e.,

30% of all deposits listed in the area). The 113


selected deposits contain more than 6350 tons of gold,

or about 75% of the known metal in this segment of

the Andes (Table 1).

Once the study area had been defined, it was

necessary to examine the geographic distribution of

the selected deposits and identify what spatial rela-

tionships with the surrounding geology and what

information from the other thematic layers of the

GIS could be used to establish favorability criteria.

The important point was (a) to determine whether a

spatial relationship actually exists between the select-

ed deposits and a specific phenomenon, and whether

such is relevant from a metallogenic standpoint, and

(b) to ensure that inappropriate relationships were

excluded. This first analysis was realized by simply

superimposing the information present in different

layers of the GIS in order to establish thematic maps

combining the position of the deposits with other

types of data (compiled or calculated).

Five types of spatial relationships (or criteria) were

retained from this first analytical step. Three of these

correspond to classically used geological criteria, i.e.,

(i) the lithostratigraphy of the host rock (synthesized

into 69 units), (ii) the contacts between the different

lithostratigraphic units, and (iii) the structural discon-

tinuities (faults and photosatellite-derived lineaments).

The other two correspond to geodynamic criteria, i.e.,

(iv) the depth and (v) dip of the Wadati-Benioff zone.

Other potentially interesting criteria that were test-

ed, but not retained for various reasons, were: (i)

Phenomena that were not independent (cf. above)

and/or had already been taken into account by one

of the adopted criteria (e.g., distance from the trench,

Holocene volcanism); (ii) Data which were insuffi-

cient in number (e.g., heat-flow and crustal-thickness

measurements) or that were too diverse from a geo-

graphic point of view (e.g., detailed geology synthe-

sized into 308 units, whole-rock geochemical data);

(iii) Spatial relationships that were difficult to interpret

at the scale of the study (e.g., number of earthquakes

per unit area, slope of the topography, change of dip

angle of the Wadati-Benioff zone, etc.); (iv) Lack of

resolution for certain interpolated data (gravity data).

3.3.2. Step 2: Quantifying the criteria

The second step consisted in attributing a quanti-

fied value, the favorability, to each of the selected

criteria, taking care to avoid over- or underestimating

certain associations, then mapping these favorabilities

over the entire study area. Several methods can be

used to attribute a favorability score to a criterion (cf.

review by Knox-Robinson and Groves, 1997): (i) The

Boolean method in which, for a given criterion, each

element of the map is either favorable or unfavorable

for the presence of deposits. A major drawback of this

methodology is that all identified relationships are

treated equally upon integration; (ii) The WoE mod-

eling methodology (described earlier), which corre-

sponds to the probability of the presence of a deposit,

independently of its size; the method requires fairly

well developed populations and the use of criteria that

are conditionally independent; (iii) The algebraic

methodology (Knox-Robinson, 1994), which aims to

determine a density of occurrences or of contained

metal per unit area, and in this way by-passes certain

restrictions of the WoE modeling.

In the case of the central Andes, this study was

based on deposits only (excluding smaller occurren-

ces) to take into account the metal content by alge-

braic methodology and thus include the deposit size,

which is considered as a fundamental parameter

(Routhier, 1963, 1980; Laznicka, 1999). Two factors

were calculated for each criterion object family con-

sidered in order to rank the favorabilities:

(1) The relative metal content (ratio of the tonnage

associated with the criterion object family against

the total tonnage in the study zone) weighted by

the relative area/length (i.e., ratio of the area/

length of the criterion object family against the

total area/length in the study zone):

ðAufam=AutotÞ=ðAreafam=AreatotÞ orðAufam=AutotÞ=ðLengthfam=LengthtotÞ

The ratio is >1 for the criterion families with a

higher than average favorability, and < 1 for the

criterion families with a lower than average

favorability.

(2) The value of the metal content per unit area or

length, expressed respectively in kg/km2 and kg/

km:

Aufam=Areafam or Aufam=Lengthfam

These results were then synthesized very simply by

a score representing the degree of favorability for each

Table 1

List of Neogene epithermal and porphyry gold deposits which served as input for the favorability calculations (tonnage of 0.03 tons contained

gold has been taken as statistical minimum tonnage for the smaller sized deposits)

Deposit name Country Stratigraphic age Gold production as Tonnes gold Deposit type

Agua Rica ARG U Miocene Main commodity 304 Porphyry

Angascola ECU Neogene By-product 0.03 Porphyry

Antofalla Este ARG Pliocene By-product 0.03 Epithermal (LS)

Arcata PER U Miocene By-product 6.3 Epithermal (LS)

Ares PER Mio/Pliocene Main commodity 30 Epithermal (LS)

Augusta ECU Neogene Main commodity 0.03 Porphyry

Aurora y Patricia PER Neogene By-product 0.03 Epithermal

Bajo de la Alumbrera ARG U Miocene Main commodity 491 Porphyry

Bajo de San Lucas ARG U Miocene By-product 0.03 Porphyry

Bolivar (Ag, COMIBOL) BOL M Miocene By-product 0.03 Epithermal (LS)

Canariaco PER Neogene By-product 0.03 Porphyry

Capillitas (Restauradora, Carmelitas) ARG Mio/Pliocene Main commodity 1.2 Epithermal (LS)

Caracoles BOL L Miocene By-product 0.03 Epithermal

Carrizalillo de las Bombas CHL Neogene Main commodity 0.03 Epithermal

Casapalca PER Neogene By-product 0.03 Epithermal (LS)

Caudalosa PER Neogene By-product 0.03 Epithermal (LS)

Caylloma PER L Miocene By-product 13.5 Epithermal (LS)

Cerro Atajo ARG Miocene By-product 0.03 Porphyry

Cerro Casale CHL M Miocene Main commodity 723 Porphyry

Cerro Corona PER M Miocene Main commodity 317 Porphyry

Cerro de Pasco PER M Miocene By-product 0.03 Epithermal

Cerro Jesus PER Mio/Pliocene By-product 0.03 Epithermal (LS)

Cerro Lina ARG Neogene By-product 0.03 Epithermal

Cerro Oros Mayo ARG Miocene By-product 0.03 Epithermal (LS)

Chaucha ECU U Miocene By-product 0.03 Porphyry

Chinchilla (San Domingo) ARG Neogene By-product 0.03 Epithermal

Chocaya (Animas) BOL M Miocene By-product 50 Sn-Porphyry

Choquelimpie CHL U Miocene By-product 26 Epithermal (HS)

Chorolque BOL L Miocene By-product 0.03 Porphyry

Cocanes PER M Miocene Main commodity 285 Porphyry

Colpayoc PER Neogene Main commodity 0.03 Porphyry

Diablillos ARG Neogene By-product 20.8 Porphyry

El Capote CHL Neogene Main commodity 4 Epithermal (LS)

El Indio CHL U Miocene Main commodity 195 Epithermal (HS)

El Pachon ARG U Miocene By-product 17.6 Porphyry

El Palomo PER Neogene Main commodity 0.03 Epithermal (LS)

El Tambo (Wendy, Kimberly, Canto Sur) CHL U Miocene Main commodity 66.9 Epithermal (HS)

Famatina district ARG Pliocene Main commodity 190 Porphyry

Farallon grupo PER Neogene By-product 0.03 Epithermal (LS)

Farallon Negro ARG U Miocene Main commodity 19 Epithermal (LS)

Fierro Urcu ECU Mio/Pliocene By-product 25 Porphyry

Guadalupe BOL Neogene By-product 1 Epithermal (HS)

Huancapeti (Collaracra) PER Neogene By-product 0.5 Epithermal (LS)

Huaquillas PER Neogene Main commodity 13.7 Porphyry

Incognita PER M Miocene Main commodity 0.5 Epithermal (HS)

Julcani PER U Miocene By-product 1.8 Epithermal (LS)

Kharma BOL Miocene Main commodity 0.03 Epithermal

Kori Kollo BOL M Miocene Main commodity 161 Epithermal (LS)

La Carolina ARG Neogene Main commodity 0.03 Epithermal (HS)

La Coipa CHL L Miocene Main commodity 93.7 Epithermal (HS)

La Granja PER M Miocene By-product 80 Porphyry

La Joya BOL M Miocene Main commodity 0.03 Epithermal (LS)

(continued on next page)


Table 1 (continued)


La Mejicana (Upulungos, San Pedro) ARG Pliocene Main commodity 2 Epithermal (HS)

La Pepa (Vizcachas) CHL L Miocene Main commodity 14.6 Porphyry

La Poposa ARG Miocene Main commodity 0.03 Epithermal (HS)

La Tigrera (La Playa) ECU Neogene Main commodity 1.8 Porphyry

Laguar ECU Neogene By-product 0.03 Porphyry

Lama project (Morro Oeste, Penelope) ARG Neogene Main commodity 150 Epithermal (HS)

Lampa-Cacachara PER Neogene By-product 0.03 Epithermal (LS)

Laurani BOL U Miocene By-product 5 Epithermal (HS)

Llipa PER Neogene By-product 0.1 Epithermal

Lobo CHL M Miocene Main commodity 128 Porphyry

Los Pelambres CHL U Miocene By-product 0.03 Porphyry

Machu Socavon BOL U Miocene By-product 0.03 Epithermal (HS)

Magistral PER Neogene By-product 0.03 Porphyry

Marte (Puntiagudo Hill) CHL M Miocene Main commodity 65.8 Porphyry

Mesa de Plata (San Antonio, Blanca) BOL Miocene By-product 0.03 Epithermal (LS)

Michiquillay PER L Miocene Main commodity 163 Porphyry

Minas Conga-Chailhuagon PER M Miocene Main commodity 69.8 Porphyry

Orcopampa PER U Miocene By-product 12.5 Epithermal (LS)

Organullo (Jules Verne) ARG Neogene By-product 0.03 Epithermal (LS)

Pachamamita ARG Neogene By-product 0.03 Porphyry

Pampa Blanca ECU Neogene Main commodity 0.03 Epithermal

Pascua (Esperanza orebody) CHL Neogene Main commodity 622 Epithermal (HS)

Pierina PER M Miocene Main commodity 222 Epithermal (HS)

Piuntza ECU Neogene By-product 0.03 Porphyry

Poracota PER Neogene Main commodity 0.03 Epithermal (HS)

Portovelo ECU M Miocene Main commodity 242 Epithermal (LS)

Pueblo Viejo BOL M Miocene By-product 0.03 Epithermal

Puntillas CHL Neogene By-product 0.03 Porphyry

Quiruvilca PER M Miocene By-product 0.03 Epithermal (HS)

Refugio (Verde, Pancho-Guanaco) CHL L Miocene Main commodity 422 Porphyry

Rio Frio ARG Miocene Main commodity 0.03 Epithermal (HS)

Sabiango ECU Neogene Main commodity 0.03 Porphyry

Salpo – Milluachaqui PER Neogene By-product 7.5 Epithermal (LS)

San Andres (Cerro Llallagua) BOL Neogene Main commodity 0.3 Epithermal (LS)

San Antonio de Esquilache PER Mio/Pliocene By-product 0.3 Epithermal (LS)

San Bartolome ECU Miocene By-product 0.1 Epithermal (LS)

San Fernando ECU Pliocene By-product 0.03 Epithermal (HS)

San Francisco de Los Andes ARG Neogene By-product 0.03 Epithermal

San Genaro PER Neogene By-product 0.8 Epithermal (LS)

San Jorge ARG Miocene By-product 29.2 Porphyry

San Jose de Oruro BOL M Miocene By-product 0.03 Porphyry

San Juan de Lucanas PER Neogene By-product 4.7 Epithermal (LS)

San Miguel no. 8 PER Neogene Main commodity 0.03 Epithermal (LS)

Sancarron CHL M Miocene Main commodity 0.03 Epithermal (HS)

Santa Cecilia CHL L Miocene Main commodity 0.03 Epithermal (HS)

Santa Isabel (Candelaria) CHL Miocene By-product 0.5 Porphyry

Santa Rosa PER Neogene Main commodity 14 Epithermal (LS)

Sayapullo PER M Miocene By-product 0.03 Epithermal (LS)

Shila PER U Miocene Main commodity 4.5 Epithermal (LS)

Sipan PER Neogene Main commodity 28.1 Epithermal (HS)

Sonia-Susana BOL U Miocene Main commodity 0.03 Epithermal (LS)

Tantahuatay PER M Miocene Main commodity 14 Porphyry

Tasna BOL L Miocene By-product 36 Sn-Porphyry

Todos Santos BOL U Miocene Main commodity 0.1 Epithermal (LS)


Table 1 (continued)


Toro Mocho PER U Miocene By-product 0.03 Epithermal

Ubina (distr.) BOL L Miocene By-product 0.03 Porphyry

Veladero ARG Miocene Main commodity 181 Epithermal (HS)

Yalguaraz ARG Neogene By-product 0.03 Porphyry

Yanacocha PER M Miocene Main commodity 760 Epithermal (HS)

Yanacocha-La Zanja PER Mio/Pliocene Main commodity 0.03 Epithermal (HS)

Zancarron (Chezanco) ARG Neogene Main commodity 11.4 Epithermal (HS)

L, Lower; M, Middle, U, Upper; LS, Low Sulfidation; HS, High Sulfidation.


criterion object family: not favorable = 0, favor-

able = 1, very favorable = 2. The cuts were established

from statistical studies of the favorability distribution

on histograms; a choice that was justified by the fact

that, at the continental scale being used, the apparent

precision provided by the use of a continuous favor-

ability value is not necessarily relevant. Lacking any

argument to favor any particular criterion, this method

enables a simpler analysis of the validity of the final

result, which is the predictive map.

A favorability map was established for each crite-

rion with the relevant score (0, 1 or 2) being allocated

to each pixel (pixel size = 10� 10 km) of the area

polygons for the criterion ‘‘Lithostratigraphy’’, and

the grid-calculated favorabilities for the criteria

‘‘Depth of the Wadati-Benioff zone’’ and ‘‘Dip of

the Wadati-Benioff zone’’, and to each pixel of the

linear criteria ‘‘Lithostratigraphic contacts’’ and

‘‘Structural lineaments’’. In this way it was possible

to rapidly locate areas with similar characteristics.

3.3.3. Step 3: combining the criteria

Prospecting for mineral deposits requires the ap-

plication of many approaches. Research into deposit

models has shown that the combination of several

objects (lithologic context, structure, geochemical or

geophysical anomaly, alteration zone, etc.) is often a

powerful prospecting tool. This applies also to the

approach adopted in this study: the combination of

several favorability criteria provides a predictive map

with well-targeted areas related to the model under

investigation. This map should allow relocation of a

high percentage of known deposits, which itself

provides a type of quality control for the methodology

adopted.

The predictive map synthesizing the favorabilities

is established by combining the favorability scores for

each criterion within each 10� 10 km pixel. Because

it was not really possible to characterize the relative

influence of the different criteria, no weighting was

added: each criterion was considered, at this scale,

as playing an equal role in the distribution of the

mineralization.

4. The favorability criteria

The favorability criteria retained can be divided into

two main themes; the one ‘‘geological’’ in the broad

sense, i.e., host-rock lithostratigraphy, lithostrati-

graphic contacts, and structural discontinuities, and

the other ‘‘geodynamic’’, using the 3D geometry of the

subduction zone determined from a seismic data com-

pilation (Cassard, 1999a) based on the USGS earth-

quake database (National Earthquake Information

Center, World Data Center for Seismology, Denver).

4.1. Criterion 1: ‘‘Host-rock lithostratigraphy’’

Epithermal and porphyry deposits are genetically

linked to magmatism and its associated hydrothermal

activity. In the GIS, this genetic relationship is

revealed by the fact that 68% of the deposits used

for the study, and 71% of the gold tonnage, are hosted

by plutonic, volcanic, and volcano-sedimentary rocks

that occupy only 30% of the total area. The retained

deposits, all being of Neogene age, have a preferential

association with magmatic formations of Tertiary age

(Fig. 3). Other lithostratigraphic relationships however

exist, because the epithermal mineralization may ap-

pear in an older host rock, and because the commonly

small porphyry intrusions cannot all be represented at

the 1:2,000,000 scale of the GIS (as discussed before).

In this last case, it is the lithology of the host polygon

of the porphyry deposit, which can be of varied nature

and age, that is taken into account.

Fig. 3. Distribution of gold deposits and tonnage per favorable host-rock lithostratigraphy.


For reasons of homogeneity, all the calculations

were based on the digital geological synthesis of the

Andes that BRGM constructed for GIS Andes (Cas-

sard, 1999a). Also the lithostratigraphic codes of this

synthesis were used (Appendix A) rather than adapt-

ing those of the national geological maps, which come

from five different countries and are thus rather

diverse.

Indexing the number of deposits, the cumulative

tonnage of gold, and the surface area of each lithos-

tratigraphic unit (Table 2) ranks a unit’s favorability

on the basis of:

(1) The ratio of the percentage of gold contained in

each lithostratigraphic unit (tonnage associated

with the lithostratigraphic unit compared to the

total tonnage) by the percentage of its surface area

(area of the lithostratigraphic unit compared to the

total area), which expresses the relative impor-

tance of the mineralized phenomenon:

ðAufam=AutotÞ=ðAreafam=AreatotÞ;

(2) The average quantity of gold per unit area:

Aufam=Areafam ðin kg=km2Þ:

The obtained results were coded into three clas-

ses—not very favorable = 0, favorable = 1, and very

favorable = 2.

The scoring indicates that Tertiary volcanic (Tv)

and Tertiary to Quaternary volcanic (QTv) formations,

as well as the Tertiary plutons (Tp), are the most

favorable polygons, which is not surprising consider-

ing the age and the type of the gold deposits under

investigation. However, perhaps more surprising, is

that the scoring also indicates some areas of the pre-

Mesozoic basement as being highly favorable. This

results from deposits such as Lama and Pascua that, at

the 1:2,000,000 scale, are associated with basement

rock polygons. In reality, however, we know that the

pre-Mesozoic basement is cut by small Tertiary intru-

sions, showing that old rocks were exploited during

younger periods.

4.2. Criterion 2: ‘‘Lithostratigraphic contacts’’

The proximity of some lithostratigraphic contacts

is considered, in an empirical way, as favorable for the

presence of the various types of mineralization. For

example, Sillitoe (1997) noted that about half of the

largest epithermal deposits in the circum-Pacific re-

gion are located at contacts between formations of

contrasted lithology, and considers these relationships

as an important prospecting criterion. Contrasts in

rheology and porosity favor the channeling and trap-

ping of mineralized fluids, thus explaining the poten-

tial favorability of lithostratigraphic contacts.

Table 2

Favorability scores assigned to the lithostratigraphic units

Litho-stratigraphic

unit code

Number

of deposits

Au

(tons)

Area (km2) %Au %Area %Au/%Area kg/km2 Assigned

favorability

score

Q 9 161 537,143 2.54 23.03 0.11 0.300 0

Qv 3 29 55,303 0.46 2.37 0.19 0.529 0

QTv 8 943 89,052 14.85 3.82 3.89 10.590 2

Ts 3 144 300,074 2.26 12.87 0.18 0.479 0

Tv 39 1712 189,635 26.95 8.13 3.31 9.029 2

Tp 6 514 24,124 8.09 1.03 7.82 21.296 2

Tvb 1 1 15,536 0.01 0.67 0.02 0.045 0

KTs 4 279 37,384 4.40 1.60 2.74 7.475 1

KTp 4 226 44,417 3.56 1.90 1.87 5.097 1

Ks 9 556 170,011 8.75 7.29 1.20 3.268 1

Kvs 2 15 18,603 0.23 0.80 0.29 0.786 0

Kv 2 0 22,867 0.00 0.98 0.00 0.003 0

Kp 1 0 28,869 0.00 1.24 0.00 0.001 0

JKs 1 0 11,527 0.00 0.49 0.00 0.003 0

Jvs 1 13 15,691 0.20 0.67 0.29 0.797 0

Jv 2 14 22,313 0.22 0.96 0.23 0.615 0

Jp 1 0 8764 0.00 0.38 0.00 0.003 0

TrJs 2 0 28,473 0.00 1.22 0.00 0.002 0

Trvs 2 0 4557 0.00 0.20 0.00 0.013 0

PzMvs 1 422 22,656 6.64 0.97 6.84 18.618 1a

PzMv 1 150 10,497 2.36 0.45 5.25 14.290 1a

Pzs 4 36 287,270 0.57 12.32 0.05 0.126 0

Pzp 3 644 49,164 10.14 2.11 4.81 13.101 2

PePzm 4 494 48,500 7.77 2.08 3.74 10.176 1

As for subsequent tables: Number of deposits: Total number of deposits included in individual family; Au (ton): Cumulative tonnage of gold

represented by all the deposits in each family; Area (km2): Total area represented by each family; %Au: Relative amount of gold represented by

each family as a percentage of the total gold content; %Area: Area represented by each class as a percentage of the total area; %Au/%Area:

Relative density of the gold mineralization per family; kg/km2: Metal content per unit area; Assigned favorability score: not very favorable = 0,

favorable = 1, very favorable = 2.a The favorability score of units PzMvs and PzMv was reduced to 1 because of the very low number of deposits.


The Andes GIS determined that the reference

deposits are generally not isolated in the middle of

a lithostratigraphic unit, but are located close to its

contacts, commonly within 5 km from the contact; at

the 1:2,000,000 scale, this practically superimposes

them on the contact. Considering that contrasts

in petrophysical properties (rheology, porosity, reac-

tivity, etc.) of neighboring lithologies play a role in

the development of mineralization, it becomes inter-

esting to locate areas presenting the same contrasting

characteristics.

Each lithostratigraphic contact is characterized by

the two units in contact, giving a total of 660 different

types of contact, of which 213 are spatially associated

with a gold deposit. The metal content of each contact

was calculated according to the known deposits in its

vicinity, with (i) 100% of the total metal content being

assigned to deposits located within 5 km of the

contact, (2) linearly decreasing to 0% of the total

metal content for deposits located 10 km or more from

the contact. The favorability of the 213 lithostrati-

graphic contacts lying near the selected deposits was

then ranked according to the assigned gold content

using the same type of procedure as for the lithostrati-

graphic units.

Twenty families of contacts are considered as

very favorable and 25 as favorable (Table 3 and

Fig. 4); these account for 80% of the gold and 12%

of the total length. All other lithostratigraphic con-

tacts are considered as unfavorable. Most of the

favorable and very favorable contacts are between

(a) the Tertiary volcanic and plutonic units and the

Table 3

Favorability scores related to the lithostratigraphic contacts (see Table 2 for explanation of columns)

Litho-stratigraphic

contact code

Mineralized

length (km)

Au

(ton)

Length

(km)

%Au %Length %Au/%Length kg/km Assigned

favorability

score

Q-Pzp 40 862 40 1.51 0.01 139.52 21.638 2

Pzp-Tv 18 1021 60 1.79 0.02 110.32 17.109 2

PzMp-PzMv 90 3242 199 5.68 0.05 105.25 16.324 2

PzMv-Tv 16 994 69 1.74 0.02 92.34 14.322 2

PzMv-PzMv 17 455 34 0.8 0.01 86.03 13.342 2

Kvs-Trvs 24 390 37 0.68 0.01 68.65 10.646 2

QTv-Pzp 11 154 17 0.27 0 57.75 8.957 2

Ks-KTs 49 1050 121 1.84 0.03 56.09 8.699 2

Pzm-Pzp 110 1517 191 2.66 0.05 51.35 7.963 2

Tp-Tv 46 1429 184 2.5 0.05 50.08 7.768 2

Js-Kvs 35 484 67 0.85 0.02 46.28 7.178 2

QTv-Tv 43 1002 171 1.75 0.05 37.85 5.87 2

QTv-PzMvs 38 1610 277 2.82 0.08 37.5 5.815 2

KTp-Tv 26 824 153 1.44 0.04 34.61 5.367 2

KTs-Trvs 87 808 159 1.41 0.04 32.74 5.078 2

KTp-Kv 124 973 202 1.7 0.05 31.08 4.82 2

Tp-Tvb 15 192 42 0.34 0.01 29.68 4.603 2

PzMvs-PzMvs 81 1502 390 2.63 0.11 24.82 3.849 2

Pzp-Ts 72 1049 284 1.84 0.08 23.83 3.695 2

Pzp-PzMvs 69 1575 440 2.76 0.12 23.07 3.578 2

Qe-QTv 64 562 266 0.98 0.07 13.63 2.114 1

PePzs-Pzp 13 240 128 0.42 0.03 12.07 1.872 1

Js-Trvs 103 885 528 1.55 0.14 10.8 1.675 1

Pzm-Tv 1348 6284 3753 11.01 1.02 10.8 1.675 1

QTs-KTs 199 1491 955 2.61 0.26 10.06 1.561 1

Pzm-QTv 55 696 505 1.22 0.14 8.9 1.38 1

Pzm-Js 88 531 411 0.93 0.11 8.34 1.293 1

PePzs-Tvb 46 383 344 0.67 0.09 7.19 1.115 1

Pzp-KTs 40 143 142 0.25 0.04 6.47 1.003 1

QTv-KTs 14 177 179 0.31 0.05 6.4 0.992 1

Tp-Pzp 101 443 472 0.78 0.13 6.05 0.938 1

PePzm-Tp 51 138 151 0.24 0.04 5.89 0.913 1

Jv-Tv 12 136 156 0.24 0.04 5.65 0.876 1

Pzs-Ts 118 451 554 0.79 0.15 5.26 0.815 1

QTs-Trvs 83 195 262 0.34 0.07 4.8 0.745 1

Pzp-Tvb 26 260 376 0.45 0.1 4.46 0.691 1

Pzm-Trp 46 361 634 0.63 0.17 3.67 0.569 1

Pzs-PzMv 288 732 1354 1.28 0.37 3.48 0.54 1

PePzm-Q 34 145 284 0.25 0.08 3.3 0.512 1

Q-PzMvs 1939 3169 7441 5.55 2.02 2.75 0.426 1

PzMvs-Ts 243 815 1954 1.43 0.53 2.69 0.417 1

QTv-Js 149 661 1677 1.16 0.46 2.54 0.394 1

PePzm-Pzvs 482 661 1915 1.16 0.52 2.23 0.345 1

Pzvs-Tv 1591 2816 8217 4.93 2.23 2.21 0.343 1

Jm-Tv 287 571 1696 1 0.46 2.17 0.337 1


entire Tertiary to Quaternary sequence, and (b) the

other lithostratigraphic units. However, some of the

contacts between (a) the Cretaceous volcanic rocks

and the entire Paleozoic/Proterozoic sequence,

and (b) other lithostratigraphic units are also con-

sidered as favorable because of their geographic

proximity to certain large deposits (e.g., Agua

Rica). The cumulative length of the favorable and

Fig. 4. Distribution of gold content per total length (logarithmic) and per length unit of lithostratigraphic contacts.


very favorable contacts is only 12% of the total

length of lithostratigraphic contacts. It is thus se-

lective, making it possible to focus attention on the

zones where the nature of the lithostratigraphic

contacts is similar to that of the areas hosting the

deposits.

4.3. Criterion 3: ‘‘Structural discontinuities’’

The presence of structural discontinuities is gen-

erally considered in metallogeny as an important

criterion for the presence of deposits. In conceptual

models (Wyborn et al., 1994), these are the ‘‘trans-

port pathways’’ that guide the fertile magmatism and

favor the circulation of hydrothermal fluids and the

emplacement of the metal concentrations. This pa-

rameter has been used in several favorability maps

compiled through the ‘‘data-driven’’ approach (cf.

Prevot et al., 1995; Asadi et al., 1998; Yun et al.,

1998) and is a determining parameter in ‘‘Stress

Mapping’’ methods aimed at characterizing low-

stress areas favorable for deposit emplacement (cf.

Groves et al., 2000).

A close association of epithermal and porphyry

deposits with fractures of various magnitudes has

often been observed, both at the regional scale (cf.

Oyarzun, 2000; Hanus et al., 2000) and at more local

scales. A study of the largest epithermal and porphyry

gold deposits in the circum-Pacific area shows that

more than half of the porphyry deposits are associated

with major faults and lineaments that are commonly

transverse to the arc, with the epithermal deposits

appearing to be more directly controlled by normal

faults or strike-slip faults with a normal component

(Sillitoe, 1997).

The structural discontinuities contained in GIS

Andes are structures shown on the geological synthe-

sis maps of the different countries, lineaments derived

by satellite imagery from Spot 4 Vegetation images,

with a 1�1 km resolution, and discontinuities deter-

mined from interpretation of the geophysical data

(BRGM, 2001). The reference deposits in GIS Andes

are mostly < 10 km from these structures. Because the

information relative to the kinematics of these struc-

tural discontinuities is too fragmentary to be used, it

was decided to use their trends as the criterion

families.

The favorability of each structural element (Table

4), classified by its trend, was calculated from the

existing deposits by assigning (i) the total metal

content (100%) of deposits located within 10 km of

the discontinuity, and (ii) linearly decreasing the per-

centage of the metal content to 0 for deposits located at

20 km or more from the discontinuity. Taking such a

zone of influence into consideration can be justified by

the fact that a major structure is commonly accompa-

nied by smaller satellite structures. The same type of

calculation as for lithostratigraphic contacts was used

Table 4

Favorability scores related to the structural discontinuity trends (see Table 2 for explanation of columns)

Structural

discontinuity trend

Mineralized

length (km)

Au

(ton)

Length

(km)

%Au %Length %Au/%Length kg/km Assigned

favorability

score

N0j to 10jE 2038 3813 13,792 7.80 10 0.79 276.46 0

N10j to 20jE 2061 3673 12,765 7.51 9 0.83 287.73 0

N20j to 30jE 1382 2657 9367 5.43 7 0.81 283.66 0

N30j to 40jE 1582 1423 7038 2.91 5 0.58 202.26 0

N40j to 50jE 1562 886 5521 1.81 4 0.46 160.40 0

N50j to 60jE 2029 2669 6552 5.46 5 1.17 407.33 1

N60j to 70jE 924 1830 7295 3.74 5 0.72 250.89 0

N70j to 80jE 1062 2440 3880 4.99 3 1.81 629.02 1

N80j to 90jE 76 1058 1415 2.16 1 2.15 747.60 2

N90j to 100jE 136 2656 2387 5.43 2 3.19 1112.98 2

N100j to 110jE 1158 554 5199 1.13 4 0.31 106.56 0

N110j to 120jE 1055 5206 6098 10.64 4 2.45 853.79 2

N120j to 130jE 627 1220 6494 2.49 5 0.54 187.89 0

N130j to 140jE 815 3187 8941 6.52 6 1.02 356.42 0

N140j to 150jE 623 2469 9685 5.05 7 0.73 254.91 0

N150j to 160jE 1353 2780 11,267 5.68 8 0.71 246.73 0

N160j to 170jE 1477 6260 10,526 12.80 7 1.71 594.76 1

N170j to 180jE 2269 4126 12,165 8.44 9 0.97 339.16 0


for ranking the favorability of the various category

families. The results were subsequently coded into

three classes: absent and not (very) favorable = 0,

moderately favorable = 1, favorable = 2.

Note that a structural discontinuity that does not a

priori control a deposit is attributed a score of zero, the

same as for no discontinuity at all. This policy was

adopted to remain consistent with the procedure for

assessing the other criteria. It should also be borne in

mind that, during this type of calculation, a single

deposit can contribute to the favorability of several

discontinuity trends.

The results in Fig. 5 show that transverse structures

trending N050j–060jE, N070j–100jE and N110j–120jE are dominant as regards the gold distribution,

thus confirming the results of previous studies at both

the Andes scale (Sillitoe, 1974; Hanus et al., 1999,

2000) and the more restricted province scale (e.g.,

Torres and Enrıquez, 1996; Quiroz, 1997; Rivera,

1997; Richards and Villeneuve, 2002), underlining

the importance of the transverse structures. One must

not, however, underestimate the northerly trending

(N160j–170jE) structures, parallel to the Chilean

strike-slip system, which also play an important

metallogenic role (cf. Lindsay et al., 1995; Ossandon

et al., 2001).

4.4. Criteria 4: ‘‘Depth of the Wadati-Benioff zone’’

Several information layers of GIS Andes have a

direct link with the geodynamic context of the Andes,

with the most significant criteria, as regards the

observed relationships between the distribution of

the mineralization and the geodynamics, being the

depth and geometry of the subducted plate. The use of

these GIS thematic layers, however, raises the funda-

mental question of how the geometry of the subducted

plate evolved during the Neogene. In other words, it is

a question of verifying if the currently observed

spatial relationship between gold mineralization and

today’s flat segments of the Wadati-Benioff zone is

not a coincidence, i.e., that the flattening phenomenon

is compatible with the period of mineralization?

It is generally accepted that the flattening in the

Peruvian and Pampean segments occurred after frag-

mentation of the Farallon plate at f 25–26 Ma, and

the onset of fast (f 100 mm/year) and nearly orthog-

onal convergence. In Chile (f 30jS), the beginning

of the eastward migration of magmatism at around 18

Ma (Kay and Mpodozis, 2001) tends to show that the

flattening was already active, following a period of

quiescence at 20–18 Ma. This timing is consistent

with the spatial and temporal variations of Andean

Fig. 5. Distribution of gold content per unit area relative to discontinuity trends.


subduction based on the age of the subducted litho-

sphere and its implication for the geometry of the

Wadati-Benioff zone as proposed by Wortel (1984). In

the Peruvian segment (central and northern Peru,

north of 14jS), however, it seems that the flattening

is more recent and occurred only during the last 5 Ma

(James, 1978; Sebrier and Soler, 1991). The amount

Fig. 6. Distribution of the epithermal and porphyry gold deposits relative to

of flattening in the Chilean segment reduced magmat-

ic activity since 9 Ma and caused its cessation at f 6

Ma (Jordan et al., 1983; Jordan and Gardeweg, 1989).

The deposits located over the flat segments are

therefore considered as those that were emplaced

during the flattening, in connection with the magma-

tism associated with this evolution (Kay et al., 1999).

depth of the Wadati-Benioff zone and the distance from the trench.

Fig. 7. Distribution of the number of deposits and metal content per unit area relative to the depth range of the Wadati-Benioff zone.


The spatial links observed today would thus indeed be

a reflection of an older association between deposits,

magmatism and changes in slab geometry.

The distribution of the deposits (Figs. 6 and 7) in

relation to the current depths of the Wadati-Benioff

zone modeled from seismic data indicates a maximum

frequency of deposits and metal content for the 75–

125 km depth range; a second favorable zone appears

at depths of 150 to 200 km. The deeper zones, from

225 to 250 km, are marked by the presence of deposits

that generally have low metal contents, the exceptions

Table 5

Favorability scores related to different depths of the Wadati-Benioff zone

Depth range (km) Number

of deposits

Au

(tonne)

Area

(km2)

%

< 75 5 4.12 201,400 0

z 75 to < 100 25 1766.83 366,100 27

z 100 to < 125 39 2246.18 477,300 35

z 125 to < 150 10 291.45 276,000 4

z 150 to < 175 4 953.3 171,600 15

z 175 to < 200 9 816.99 100,900 12

z 200 to < 225 4 20.89 99,200 0

z 225 to < 250 9 252.89 88,000 3

z 250 to < 300 6 0.18 129,300 0

z 300 to < 350 1 0.03 86,100 0

z 350 1 0.03 334,300 0

a Favorability reduced to 1 in view to the low number of deposits).

being Kori Kollo (161t Au), Chocaya (50t Au; tin-

porphyry, Table 1), and Tasna (36t Au; tin-porphyry),

deposits located in the central Bolivian segment (see

Fig. 1).

The first preferred zone corresponds to depths for

which the dip of the oceanic plate classically induces

partial melting, which gives rise to the calc-alkaline

magmatism of the main arc (Kay et al., 1999). The

other zones, not so well delimited, could result from

the eastward migration of the magmatism during the

Miocene (cf. above) or correspond to deeper magma-

(see Table 2 for explanation of columns)

Au %Area %Au/%Area kg/km2 Assigned

favorability

score

.06 8.64 0.01 0.020 0

.81 15.70 1.77 4.826 2

.36 20.47 1.73 4.706 2

.59 11.83 0.39 1.056 0

.01 7.36 2.04 5.555 1a

.86 4.33 2.97 8.097 2

.33 4.25 0.08 0.211 0

.98 3.77 1.05 2.874 1

.00 5.54 0.00 0.001 0

.00 3.69 0.00 0.000 0

.00 14.33 0.00 0.000 0

Fig. 8. Distribution of the epithermal and porphyry gold deposits relative to the dip of the Wadati-Benioff zone and the distance from the trench.


tism in the inner arc (Petersen, 1999), possibly asso-

ciated with a steepening of subduction at the eastern

edge of a flat segment (James and Sacks, 1999).

For each depth range (Table 5), the number of

deposits, the cumulative quantity of gold and the

corresponding area were indexed on a horizontal grid.

The favorability of the different depth ranges was

established in the same way as for the other criteria

Fig. 9. Distribution of the number of deposits and metal content per

and coded into three classes: not very favorable = 0,

favorable = 1, and very favorable = 2.

4.5. Criterion 5: ‘‘Dip of the Wadati-Benioff zone’’

The morphology of the subducted plate in the

central Andes (Fig. 1) is characterized by the presence

of gently dipping segments in both the north and the

unit area relative to the dip ranges of the Wadati-Benioff zone.

Table 6

Favorability scores related to different dips of the Wadati-Benioff zone (see Table 2 for explanation of columns)

Dip range Number

of deposits

Au (ton) Area (km2) %Au %Area %Au/%Area kg/km2 Assigned

favorability

score

< 6j 5 0.22 146,400 0.00 6.28 0.00 0.002 0

z 6j to < 8j 7 3.22 121,700 0.05 5.22 0.01 0.026 0

z 8j to < 10j 10 1076.35 104,800 16.94 4.49 3.77 10.271 2

z 10j to < 12j 11 1224.72 141,700 19.28 6.08 3.17 8.643 2

z 12j to < 14j 14 951.12 133,200 14.97 5.71 2.62 7.141 2

z 14j to < 16j 10 441.25 166,100 6.95 7.12 0.97 2.657 1

z 16j to < 18j 7 53.32 152,700 0.84 6.55 0.13 0.349 0

z 18j to < 20j 3 753 145,300 11.85 6.23 1.90 5.182 1

z 20j to < 22j 11 743.12 69,600 11.70 2.98 3.92 10.677 2

z 22j to < 26j 1 14.6 132,000 0.23 5.56 0.04 0.111 0

z 26j to < 30j 5 0.69 108,200 0.01 4.78 0.00 0.006 0

z 30j to < 40j 24 1090.36 304,800 17.16 13.07 1.31 3.577 1

z 40j to < 50j 4 0.39 316,800 0.01 13.58 0.00 0.001 0

z 50j to < 60j 1 0.03 242,700 0.00 12.39 0.00 0.000 0

z 60j 0 0 44,200 0.00 1.9 0 0 0


south (Ramos and Aleman, 2000) bordering the

central Bolivian area where the dip is of the order

of 30j.The distribution of the gold mineralization (Figs.

8 and 9) indicates a maximum number of deposits and

metal content in the areas immediately above the

shallowly dipping segments: the main deposits are

associated with dips of between 8j and 22j, althoughwith a deficit of mineralization (not yet explained) in

the 16j to 18j dip range. This distribution has two

notable exceptions: (i) The Farellon Negro district that

lies within a generally shallow dipping segment, but

close to a zone where subduction is steepening; and

(ii) the steeper central Bolivian segment, which is not

highly mineralized, apart from the aforementioned

Kori Kollo, Tasna and Chocaya deposits that lie

farther to the east.

For each dip range (Table 6), we indexed the

number of deposits, the cumulative quantity of gold

and the corresponding area, ranked them in the same

way as for the other categories and coded them into

three classes: not very favorable = 0, favorable = 1,

Fig. 10. Predictive map of the Central Andes for Neogene epithermal and

mineral districts: Inset 1: Yanacocha (Peru)-Portovelo (Equador) (abbrevia

La Granja; C. Cor.: Cerro Corona; Yan.: Yanacocha; Min. C.: Minas Cong

Argentina) (abbreviations: Coipa: La Coipa; Lob.: Lobo-Marte; Ref.: Refug

Ind.: El Indio; Tamb.: Tambo); Inset 3: Bajo de la Alumbrera (Argentina

Fam.: Famatina district).

and very favorable = 2. The observed bimodal dis-

tribution of the mineralization within the 8j to 22jrange, although not satisfactorily explained, was

respected during the allocation of favorability

scores; it is reflected by a lower score for the 14–

20j range. The 30–40j range was attributed a

favorable score, which is essentially justified by

the evolution of the central ‘Bolivian’ segment (as

discussed later).

5. Compilation of the predictive map and

discussion

An overall favorability map (Fig. 10), also called a

predictive map, was compiled for the epithermal and

porphyry gold mineralization by simply adding, with-

out any ranking or weighting, the scores of the

different criteria outlined above. Superposing the

favorability criteria scores of the different criteria base

maps provides a predictive map on which the overall

favorability is represented on a scale of 0–9 (with 10

porphyry gold deposits. Insets show details of the more important

tions: Port.: Portovelo; Fier.: Fierro Urcu; Huaq.: Huaquillas; Granj.:

a; Mich.: Michiquillay); Inset 2: Maricunga belt-El Indio belt (Chile,

io; C. Cas: Cerro Casale; Pasc.: Pascua; Lam.: Lama; Vel.: Veladero;

) (abbreviations: B. Al.: Bajo de la Alumbrera; Ag. R.: Agua Rica;


Table 8

Favorability scores associated with the deposits (bearer pixels) and

their respective 10 km (average value calculated over 4 pixels) and

20 km (average value calculated over 16 pixels) buffer zones

Area (Average)

Favorability

Cumulated

Au (tons)

%

Au

Number

of

deposits

%

Deposits

Deposit z 6 3108.21 49 27 24

bearer pixel 5 1425.77 22 22 19.5

(10� 10 km) 4 1505.20 24 24 21

< 4 313.71 5 40 35.5

10 km buffer z 6 303.65 5 10 9

zone around z 5 to < 6 2838.74 45 26 23

deposit z 4 to < 5 2803.73 44 32 28

z 3 to < 4 149.78 2 16 14

< 3 256.99 4 29 26

20 km buffer z 3 3419.30 54 36 32

zone around z 2 to < 3 2655.36 42 48 42

deposit z 1 to < 2 241.99 3.5 20 18

< 1 36.24 0.5 9 8


being theoretically the maximum score but not

achieved in the present study). Table 7 summarizes

the retained criteria with their calculated favorabilities

and adopted thresholds.

The geodynamic criteria (depth and dip of the

Wadati-Benioff zone) are large-scale favorability cri-

teria which, combined with the lithostratigraphy, en-

able broad delimitation of the favorable areas to which

arbitrarily scores of between 4 and 6 (maximum value

for the three criteria under consideration) may be

assigned. Adding the structural discontinuities and

lithostratigraphic contacts makes it possible to focus

on targets that are smaller, but obviously with high

potential interest. Their cumulated score, integrating

the regional favorability, can arbitrarily be estimated

at z 7 out of a possible total of 10. Study of the map

(Fig. 10) shows that the choice of this cutoff (7 and

above) to identify the areas of high gold potential is

realistic. The size of these areas is not excessive and

their distribution, which locally presents some degree

of organization reflecting that of the known mineral-

ized districts, is not too erratic.

Despite the limitations imposed by the continental

scale, the results presented in the synthesis (i.e.,

the predictive map) appear to be satisfactory because

the processing, which does not directly integrate the

location of the deposits, returns about 64% of the

epithermal and porphyry gold deposits and 95% of

Table 7

Summary of the favorability scores assigned to the different criteria

Criterion Very favorable Score: 2

Lithostratigraphic unit Mainly plutonic and volcanic

rocks related to Tertiary arcs

and Paleozoic plutonic and

volcanosedimentary rocks

Lithostratigraphic contact Mainly boundaries of Tertiary

plutonic and volcanic rocks,

and Paleozoic plutonic and

volcano-sedimentary rocks

Structural trend Mainly transverse discontinuities

N80j to 100jE and N110j to

120jE

Depth of Wadati-Benioff zone Shallow to moderately deep

From 75 to 125 km and from

175 to 200 km

Dip of Wadati-Benioff zone Flat to moderately dipping

From 8j to 14j and from

20j to 22j

the metal content using a favorability score of z 4;

with higher favorability scores, and thus greater

selectivity, these percentages decrease to 43% of

the deposits and 71% of the metal content at a

favorability score of z 5, and to 24% of the deposits

and 49% of the metal content at a favorability score

of z 6 (Table 8).

To test the robustness of the predictive map, it is

useful to verify what happens to this favorability

Favorable Score: 1 Not very favorable Score: 0

Sedimentary and Mesozoic

rocks

Contacts between sedimentary

rocks

Longitudinal discontinuities:

N50j to 60jE, N70j to

80jE and N160j to 170jEN000j to 050jE, N060j to

070jE, N100j to 110jE, N120jto 160jE, and N170j to 180jEDeep

From 150 to 175 km and

from 225 to 250 km

Steeply dipping

From 14j to 16j, from 18jto 20j and from 30j to 40j

Fig. 11. Average favorabilities of the deposit buffer zones calculated for the 10 and 20 km buffer zones around the 113 selected deposits.



within a radius of 10 km of a known deposit. It allows

determination of the integrator effect of the multi-

criteria model used. Each deposit therefore has been

assigned the arithmetic mean of the favorabilities of

pixels contained wholly or partly within a 10-km-

radius buffer zone. The results, summarized in Table

8, indicate that an average favorability score of z 6

corresponds to a relatively restricted area and is

mainly indicative of the proximity of a deposit,

whereas average scores of between 4 and 5 determine

‘‘district type’’ mineralized envelopes. The plot of

these average favorability scores around each of the

reference deposits make it possible to compare the

response of the different districts (Fig. 11). To estab-

lish a comparison with the regional response, the

scores were also calculated for buffers of 20-km

radius.

The deposits of the major Portovelo, Yanacocha,

Orcopampa and Maricunga-belt gold districts are par-

ticularly well marked by the presence of highly favor-

able zones, both at the local scale and on a more

regional level. Other major districts return a less clear

response, albeit still at a good level, as is the case of the

El Indio belt and the Farellon Negro district that are

marked by a high or medium favorability in an envi-

ronment where the background favorability is < 2.

However, our relational model is not applicable (or

only in a very limited way) to the Bolivian deposits and,

only in a more general way, to the deposits of the

eastern edge of the central Andes. Kori Kollo is marked

by a medium to low favorability in an environment of

very low to zero favorability, and Tasna would not have

been found by this study.

It thus appears that the applied model has a

relatively high sensitivity: it is sufficiently integrated

to define districts, without erasing the contrasts with

the more strongly mineralized zones. However, it also

appears to be very ‘‘geodynamically dependent’’, with

the retained options having strong implications: up to

4 points of the favorability score are related to the

geometry of the subduction zone. This is a choice that

is easily justified when assessing the Au metal content

between the various segments of Wadati-Benioff zone

(Fig. 1).

Relocating the known deposits used for establish-

ing the favorability criteria—a sort of ‘‘training set’’—

is not the final goal of this study. It is only a means to

check the validity and quality of the approach. The

interest is obviously in identifying new mineral-ex-

ploration targets. Such new targets are represented by

pixels with a favorability score of z 7 on the predic-

tive map (Fig. 10), which are not associated with the

‘‘input deposits’’, and are located near (50–100 km)

or as continuations of known districts. Some corre-

spond to totally new areas, in places characterized by

the presence of scattered known gold occurrences that

were not considered in the calculations.

It would thus appear that the use of a continental-

scale GIS, in spite of the constraints that it imposes, is

valid for establishing predictive maps. In spite of the

major effort involved in research, control, synthesis

and preliminary formatting of the data prior to pro-

cessing, two main problems generally arise: (i) one

concerns the data, i.e., their heterogeneity as regards

both quality and coverage, and (ii) the other concerns

the precision of the result; it is illusory, considering

the precision of the original data, i.e., uncertainties

concerning certain deposit locations, errors of the

order of 2 km in plotting geological boundaries at

1:2,000,000 scale, etc.—to use a pixel size of less than

10� 10 km for the restitution. In the present study, the

uncertainties and the lack of reliable age data on the

mineralization make it necessary to drastically reduce

the number of input deposits. On the other hand, the

‘‘continental’’ approach favors the use of new criteria

that take on full significance at this scale. Here

we adopted geodynamic criteria, which nevertheless

required ensuring a spatial and temporal validity

bracket.

Acknowledgements

GIS Andes was developed within the context of

two BRGM R&D projects: ‘‘Andes metallogeny’’ and

‘‘Global Environmental and Metallogenic Syntheses’’

(GEMS). The development of the GIS layers involved

many BRGM specialists, all gratefully acknowledged

but too numerous to be cited individually. Detailed

reviews by R.H. Sillitoe and V. Maksaev are

acknowledged and have substantially contributed to

improve the manuscript. We would also like to thank

J.-L. Lescuyer for his critical review an earlier

manuscript and P. Skipwith for his translation and

initial editing. This work is BRGM Contribution

No. 2319.


Appendix A. Abbreviations of lithostratigraphic

units

Q: Undifferentiated Quaternary; Qe: Quaternary

salar evaporite deposits; Qv: Undifferentiated Quater-

nary arc volcanites; Qvo: Quaternary ophiolitic com-

plex; QTs: Undifferentiated Tertiary and Quaternary

marine and continental deposits; QTv: Undifferenti-

ated Tertiary and Quaternary volcanites; Ts: Undiffer-

entiated Tertiary marine and continental deposits; Tv:

Undifferentiated Tertiary volcanites; Tp: Undifferen-

tiated Tertiary plutonites; Tvs: Undifferentiated Ter-

tiary volcano-sedimentary deposits; Tvb: Tertiary

basaltic volcanites; Tpb: Tertiary gabbro-type intru-

sives; Tm: Tertiary metamorphic rocks; KTs: Undif-

ferentiated Cretaceous and Tertiary marine and

continental deposits; KTv: Undifferentiated Creta-

ceous and Tertiary volcanites; KTp: Undifferentiated

Cretaceous and Tertiary plutonites; KTpo: Cretaceous

and Tertiary andesite and diorite porphyry; KTpu:

Cretaceous and Tertiary ophiolites, ultramafic rocks;

K: Undifferentiated Cretaceous; Ks: Undifferentiated

Cretaceous marine and continental deposits; Kvs:

Cretaceous continental volcano-sedimentary deposits;

Kv: Undifferentiated Cretaceous volcanites; Kp: Un-

differentiated Cretaceous plutonites; Kpb: Cretaceous

gabbro-type intrusives; Kpu: Cretaceous ultramafic

rocks; Kpo: Cretaceous ophiolites; Km: Cretaceous

metamorphic rocks; JTp: Undifferentiated Jurassic,

Cretaceous and Tertiary plutonites; JK: Undifferenti-

ated Jurassic–Cretaceous; JKs: Undifferentiated Ju-

rassic and Cretaceous marine and continental

deposits; JKvs: Jurassic and Cretaceous volcano-

sedimentary deposits; JKv: Undifferentiated Jurassic

and Cretaceous volcanites; JKp: Undifferentiated

Jurassic and Cretaceous plutonites; JKpu: Jurassic

and Cretaceous ultramafic rocks; JKm: Jurassic and

Cretaceous metamorphic rocks; Js: Undifferentiated

Jurassic marine and continental deposits; Jvs: Undif-

ferentiated Jurassic volcano-sedimentary deposits; Jv:

Undifferentiated Jurassic volcanites; Jvb: Jurassic

basaltic volcanites; Jp: Undifferentiated Jurassic plu-

tonites; Jpb: Jurassic gabbro-type intrusives; Jvo:

Jurassic ophiolitic complex; Jm: Undifferentiated

Jurassic metamorphic rocks; TrKvs: Undifferentiated

Triassic and Jurassic volcano-sedimentary deposits;

TrJs: Undifferentiated Triassic and Jurassic marine

and continental deposits; TrJvs: Triassic and Jurassic

continental volcano-sedimentary deposits; TrJp: Un-

differentiated Triassic and Jurassic plutonites; Trs:

Undifferentiated Triassic marine and continental

deposits; Trvs: Undifferentiated Triassic volcano-sed-

imentary deposits; Trv: Undifferentiated Triassic vol-

canites; Trp: Undifferentiated Triassic plutonites;

PzMz: Undifferentiated Paleozoic–Mesozoic; PzMs:

Undifferentiated Paleozoic and Mesozoic marine and

continental deposits; PzMvs: Undifferentiated Paleo-

zoic and Mesozoic volcano-sedimentary deposits;

PzMv: Undifferentiated Paleozoic and Mesozoic vol-

canites; PzMp: Undifferentiated Paleozoic and Me-

sozoic plutonites; Pzs: Undifferentiated Paleozoic

marine and continental deposits; Pzvs: Paleozoic

volcano-sedimentary deposits; Pzv: Undifferentiated

Paleozoic volcanites; Pzp: Undifferentiated Paleozoic

plutonites; Pzvo: Paleozoic ophiolitic complex; Pzm:

Undifferentiated Paleozoic metamorphic rocks;

PePzs: Undifferentiated Proterozoic and Paleozoic

marine and continental deposits; PePzvo: Undifferen-

tiated Proterozic and Paleozoic mafic and ultramafic

rocks; PePzp: Undifferentiated Proterozic and Paleo-

zoic plutonites; PePzm: Undifferentiated Proterozic

and Paleozoic metamorphic rocks; Pe: Undifferenti-

ated Proterozoic; Pep: Undifferentiated Proterozoic

plutonites; Pem: Undifferentiated Proterozoic meta-

morphic rocks.

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