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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
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6742
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
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 43
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
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6744
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.
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 45
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
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6746
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)
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 47
Table 1 (continued)
Deposit name Country Stratigraphic age Gold production as Tonnes gold Deposit type
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)
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6748
Table 1 (continued)
Deposit name Country Stratigraphic age Gold production as Tonnes gold Deposit type
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.
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 49
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.
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6750
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.
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 51
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
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6752
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.
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 53
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
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6754
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.
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 55
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.
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6756
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.
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 57
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
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6758
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;
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 59
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
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6760
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.
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 61
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–6762
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.
M. Billa et al. / Ore Geology Reviews 25 (2004) 39–67 63
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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