Geology and Hydrothermal Alteration of the Chapi Chiara Prospect and Nearby Targets, Southern Peru, Using ASTER Data and Reflectance Spectroscopy

Abstract

Southern Peru contains several small- and medium-sized epithermal Au-Ag (+/- base metals) deposits related to Miocene-Pliocene metallogenetic belts. Specifically, the characterization of the geology and mapping of hydrothermal alteration zones of the Chapi Chiara prospect, Canahuire deposit, and Cerro Millo and San Antonio de Esquilache targets were done with data acquired by Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spaceborne sensor. ASTER mapping techniques included (1) band ratio and principal component analysis (Crosta technique) applied to the visible and near-infrared and short-wave infrared bands to produce a regional hydrothermal alteration map (alunite and/or kaolinite, illite-muscovite and/or smectite, iron-bearing minerals) and (2) spectral indices and selective principal component analysis applied to the thermal infrared bands to detect quartz- and carbonates-bearing targets, respectively. These methods were used to establish a hydrothermal zoning pattern in paleostratovolcanoes, where the Chapi Chiara, Cerro Millo, and San Antonio de Esquilache targets are located. This zoning pattern was used to infer erosion conditions and the potential for metal deposits based on the mineralogy, which was also analyzed using reflectance spectroscopy and petrography. In addition, ASTER data were used to characterize the carbonate host rocks, the quartz-bearing units of the Yura Group, and the quartz-poor unit associated with the phreatic and phreatomagmatic breccias in the Canahuire deposit region. This characterization led to the development of a favorability model for the occurrence of "Canahuire-type" deposits based on spatial analysis using the fuzzy logic technique.

Full text

©2015 Society of Economic Geologists, Inc.
Economic Geology, v. 110, pp. 73–90
Geology and Hydrothermal Alteration of the Chapi Chiara Prospect and Nearby Targets,
Southern Peru, Using ASTER Data and Reectance Spectroscopy
T A C,1 A P C,1,† C L B T,2
A M S,2  J L S1
1 Institute of Geosciences/University of Campinas, R. João Pandiá Calógeras, 51, 13083-870 Campinas-São Paulo, Brazil
2 Institute of Geosciences/University of Brasília, Campus Universitário Darcy Ribeiro S/n, Asa Norte, 70910-900 Brasília – DF, Brazil
Abstract
Southern Peru contains several small- and medium-sized epithermal Au-Ag (± base metals) deposits related
to Miocene-Pliocene metallogenetic belts. Specically, the characterization of the geology and mapping of
hydrothermal alteration zones of the Chapi Chiara prospect, Canahuire deposit, and Cerro Millo and San
Antonio de Esquilache targets were done with data acquired by Advanced Spaceborne Thermal Emission and
Reection Radiometer (ASTER) spaceborne sensor. ASTER mapping techniques included (1) band ratio and
principal component analysis (Crósta technique) applied to the visible and near-infrared and short-wave infra-
red bands to produce a regional hydrothermal alteration map (alunite and/or kaolinite, illite-muscovite and/
or smectite, iron-bearing minerals) and (2) spectral indices and selective principal component analysis applied
to the thermal infrared bands to detect quartz- and carbonates-bearing targets, respectively. These methods
were used to establish a hydrothermal zoning pattern in paleostratovolcanoes, where the Chapi Chiara, Cerro
Millo, and San Antonio de Esquilache targets are located. This zoning pattern was used to infer erosion condi-
tions and the potential for metal deposits based on the mineralogy, which was also analyzed using reectance
spectroscopy and petrography. In addition, ASTER data were used to characterize the carbonate host rocks, the
quartz-bearing units of the Yura Group, and the quartz-poor unit associated with the phreatic and phreatomag-
matic breccias in the Canahuire deposit region. This characterization led to the development of a favorability
model for the occurrence of “Canahuire-type” deposits based on spatial analysis using the fuzzy logic technique.
Introduction
P is the main gold-producing country in Latin America,
providing for 6% of the global gold exploitation budget (U.S.
Geological Survey, 2013). The importance of Peruvian gold
mainly results from its world-class epithermal Au-Ag depos-
its, which are found in the northern region, including Yanaco-
cha (~12 Ma), Lagunas Norte, La Virgen, Quiruvilca, Sipán,
and Pierina (~14.5 Ma). These deposits are characterized by
sedimentary and volcanic host rocks that are located in Mio-
cene-Pliocene metallogenetic belts in the Andean Cordillera
(Quispe et al., 2008; Carlotto et al., 2009; Fig. 1A).
Conversely, the southern region of Peru has been known for
its Au-Ag (± base metals) epithermal deposits, which are of
small and medium size, with a predominance of low to inter-
mediate suldation deposits. Examples include the Caylloma
(~18 Ma, Echavarria et al., 2006), Orcopampa (~18 Ma, Gib-
son et al., 1990, 1995), Selene (~14 Ma, Acosta et al., 2008),
Shila, Paula (~11–10 Ma, Chauvet et al., 2006), and Arcata
(~5.4 Ma, Candiotti et al., 1990; Carlotto et al., 2009) depos-
its (Fig. 1B). In addition, high suldation deposits are found
in this region, including the Poracota, Baños del Índio, Santa
Rosa (~7 Ma), and Tucari (~4 Ma, Aruntani district). In these
deposits, mineralization is usually related to volcanic rocks
from the Barroso Group (Miocene-Pliocene), with minor
occurrences associated with intrusive felsic rocks (Loayza et
al., 2004; Fig. 1B). The epithermal deposits in southern Peru
were regionally controlled by the Condoroma-Caylloma fault
system, which separates the Abancay-Condoroma (northeast)
and Puquio-Caylloma-Incapuquio (southwest) geotectonic
domains (Acosta et al., 2008; Figs. 1B, 2).
One of the main current mineral exploration targets in
southern Peru is the Canahuire epithermal Au-Cu-Ag deposit,
the largest discovery in the Peruvian region in the last decade.
This deposit is characterized by mineral exploration vectors
that differ from other deposits in the region, such as carbonate
and phreatic and phreatomagmatic breccia host rocks, which
are controlled by fault systems mainly oriented in the NW-SE
and WNW-ESE directions. In addition, the hydrothermal
alteration mineral assemblage at Canahuire differs from the
assemblages associated with other epithermal deposits in the
region, which are hosted by intermediary and acidic volcanic
rocks (Santos et al., 2011). The Canahuire deposit and the
Caspiche porphyry Cu-Au (Chile), La Colosa porphyry gold
(Colombia), Lagunas Norte high suldation epithermal gold
(Peru), and Fruta del Norte intermediate suldation epither-
mal gold (Ecuador) deposits constitute the main mineral dis-
coveries in the early 21st century in South America. Each one
has in excess of 5 Moz of gold (Vidal, 2012).
This paper presents a regional investigation of the strategic
area characterized by the Chapi Chiara prospect, Canahuire
deposit, and Cerro Millo and San Antonio de Esquilache
targets, which are located near the border of the Puno and
Moquegua provinces (Fig. 1B). The investigation is based on
data acquired by Advanced Spaceborne Thermal Emission
and Reection Radiometer (ASTER) multispectral sensor,
combined with reectance spectroscopy data. The main goals
of this study are to characterize rock types and the proximal
hydrothermal alteration zones by the use of spectral responses
related to the ASTER bands in the visible and near-infrared
(VNIR), short-wave infrared (SWIR), and thermal infrared
(TIR) ranges. The potential of ASTER for mineral exploration
has already been demonstrated by work such as that of Crósta et
0361-0128/15/4276/73-18 73 Submitted: September 30, 2013
Accepted: May 16, 2014
† Corresponding author: e-mail, alvaro@ige.unicamp.br

74 CARRINO ET AL.
Canahuire Tucari
Santa Rosa
Baños del Índio
Pacific
Ocean
PERU
BRAZIL
BOLIVIA
ECUADOR
COLOMBIA
Arasi
Selene
Poracota
Caylloma
Arcata
Pierina
La Virgen
Lagunas Norte
Yanacocha
Sipán
Quiruvilca
Ares
Paula
Shila
Legend
Au (Ag±basemetals) deposit
Metallogenetic belts
EAu-Ag (± base
metals)Beltof
pithermal
Miocene-Pliocene
EpithermalAu-Ag (± basemetals)
Belt of Miocene
Arequipa
Tacna
Moquegua
Puno
Cusco
CHILE
BOLIVIA
Titicaca
Lake
Arasi
Caylloma
Paula
Ares
Arcata
Shila
Pacific
Ocean Moquegua
N
PERU
City
Legend
Road
Deposit
Province boundary
Prospect
Baños
delIndio
B
72°0’0’’W 70°0’0’’W
16°0’0’’S
18°0’0’’S
060km
A
CerroMillo
Study
area
CCFS
SanAntonio de
Esquilache
Canahuire
Chapi
Chiara Tucari
Santa
Rosa
Cerro Millo Legend
Cerro
Chucapaca
Canahuire
Jacumarine
Lake
Tambo River
Chapi
Chiara
San Antonio
de Esquilache
6km
Fault
Hydrography
Indication of
prospect or
deposit regions
Moraine, alluvium
Tacaza Group
Maure Group
Barroso Group
Igneous
intrusion
Yura Group
Puno Group
Geologic units
San
Antonio
River
C
F. 1. (A) Location of the main Peruvian Au-Ag epithermal metallogenetic belts (Carlotto et al., 2009), highlighting the
southern region, where the area of study is inserted at the borders of the Puno and Moquegua provinces (B). (C) Simplied
geologic map of the area under study (adapted from Marocco and Del Pino, 1966; Hawkins et al., 1984; INGEMMET, 2003),
showing the oldest unit, represented by metasedimentary and sedimentary rocks from Yura Group (Jurassic-Cretaceous),
sedimentary and volcanic rocks from Puno Group (Upper Cretaceous-end of the Paleocene), volcanic rocks from Tacaza
Group (Oligocene-lower Miocene), lacustrine sedimentary and volcanic rocks of Maure Group (lower Miocene), volcanic
intermediate rocks from Barroso Group (lower Miocene-Pliocene), moraine (Pleistocene), and alluvium. CCFS = Condo-
roma-Caylloma fault system, extracted from Acosta et al. (2008).

GEOLOGY AND HYDROTHERMAL ALTERATION OF THE CHAPI CHIARA PROSPECT AND NEARBY TARGETS, PERU 75
al. (2003), Ducart et al. (2006), Mars and Rowan (2006), Rowan
et al. (2006), Crósta et al. (2009), and Mars and Rowan (2011).
Geologic Setting
The study area is located in the Andean Cordillera at alti-
tudes in excess of 5,000 m.a.s.l. The oldest geologic unit is
the Jurassic-Cretaceous Yura Group (Fig. 1C), which consists
of quartzite, limestone, sandstone, and shale. Sediment inux
occurred over large areas in the southern Peru during peri-
ods of marine transgression. Following the uplift of the Andes
(~90 Ma), the sedimentary rocks were subjected to at least
three phases of compression: Peruvian (84–79 Ma), Incaic
(59–22 Ma), and Quechua (~17–1.6 Ma, Benavides-Cáceres,
1999), resulting in metamorphism and structural control of
the units following an NW-SE regional trend (Palácios et al.,
1993; León et al., 2000).
The Yura Group is subdivided into several formations,
including the Labra (gray and black shale alternating with
grayish sandstone), Gramadal (gray limestone with fossils and
sandstone and dolomite strata), and Hualhuani (white sand-
stone alternating with carbonaceous shale layers; Palácios et
al., 1993; Lipa and Valdivia, 2004).
Conglomerate and sandstone are the main rock types of the
Puno Group, in addition to some limestone and tuff layers that
originated between approximately the Upper Cretaceous and
the end of the Paleocene (Fig. 1C; Palácios et al., 1993; Palácios,
1995; León et al., 2000). The rocks lie in an angular unconfor-
mity over the Yura Group and were deposited in a continental
environment during the uplift phases (Palácios et al., 1993).
The Tacaza Group consists of andesite, dacite, rhyolite,
basalt, tuff, volcanic breccia, and volcaniclastic sandstone
(Fig. 1C). This group was formed during the Oligocene and
the lower Miocene, following the formation of sedimentary
(e.g., limestone, sandstone, mudstone, siltstone) and volca-
nic (e.g., lava and tuff of trachyandesitic, dacitic, and rhyo-
litic composition) rocks of the Maure Group. These rocks are
associated with a lacustrine environment that was originated
between approximately 11 and 8 Ma (Palácios, 1995; Sánchez
and León, 1995; Benavides-Cáceres, 1999).
The most recent volcanic unit is the Barroso Group
(~7Ma–present), which is composed of andesite, trachy-
andesite, volcanic breccia, and tuff banks of andesitic to
trachyandesitic composition, with minor amounts of dacite
and rhyolite (Fig. 1C; Palácios, 1995; Sánchez and León,
1995; Benavides-Cáceres, 1999). In the study area, well-
preserved paleostratovolcanoes are part of the Barroso
Group (Fig. 2).
Moraines (representing the Pleistocene glaciation record)
and alluvial deposits constitute the most recent units in the
area (Fig. 1C).
Prospecting targets
The Canahuire intermediate suldation epithermal deposit
is the main mineral exploration target in the region. Cana-
huire is operated by Gold Fields Inc. and Compañia de Minas
Buenaventura for the exploitation of gold, copper, and silver.
This deposit is located northwest of Cerro Chucapaca (Fig.
2), which comprises a paleostratovolcano marked by wide
ChapiChiara
Canahuire
SanAntoniode
Esquilache
CerroMillo
5km
N
Domain of
well-preserved
paleostratovolcanoes
relatedtorocks of
Maureand Barroso
groups
Domainofrocks
of Puno andTacaza groups,
and sedimentary and
volcanic rocks most recent
(Maure and Barroso groups)
Band 7
Band 3Band1
I
II
DomainofYura
Grouprocks
(Jurassic-Cretaceous)
Domain of
volcanic
rocksof
Tacaza
Group
IV
III
Igneous intrusions
Igneous
intrusion
CCFS approximate boundary
NW-SEfaults
Cerro
Chucapaca
F. 2. Perspective view of the study area compiled from an ASTER band 7, 3, 1 (RGB) color composite overlaid on an
ASTER Global Digital Elevation Model (GDEM) at 30-m spatial resolution. The markers indicate the main targets for min-
eral exploitation and the lithological and structural domains. CCFS = Condoroma-Caylloma fault system.

76 CARRINO ET AL.
hydrothermal zoning with advanced argillic alteration miner-
als in the core of the epithermal system.
In Canahuire, the alteration is subdivided into sideritiza-
tion (siderite-quartz), argillization (smectite ± illite ± kaolin-
ite ± quartz), and silicication facies with two mineralization
stages. The rst stage is characterized by pyrite, marcasite,
pyrrhotite, arsenopyrite, melnikovite, magnetite, and wol-
framite; the second is the main economic mineralization
stage, composed of gold, pyrite, chalcopyrite, bismuthinite,
arsenopyrite, tennantite-tetrahedrite, sphalerite, and galena.
In general, the alteration and mineralization have lithological
and structural controls. The lithological controls are marked
by limestone from the Gramadal Formation (Yura Group),
and the structural controls are characterized by a system of
faults and folds that are mainly orientated in the WNW-ESE
and NW-SE directions. These structures favored the forma-
tion of a dilational jog, which is characterized by phreatic and
phreatomagmatic breccias (Santos et al., 2011).
The Cerro Millo high suldation epithermal prospect is
located in a well-preserved paleostratovolcano (Fig. 2) and
is characterized by the occurrence of gold, copper, and sil-
ver hosted by andesite from the lower Miocene age (Hennig
et al., 2008). The alteration pattern is marked by a pervasive
advanced argillic zone (predominance of alunite) that grades
into an argillic zone predominantly composed of quartz,
kaolinite, and smectite. The hypogene alunite was dated at
10.8 ± 0.9 Ma (Ar-Ar method), synchronous with the andesite
volcanism dated by Hennig et al. (2008) (biotite Ar-Ar: 11.0 ±
0.5 Ma). Hennig et al. (2008) associated these volcanic rocks
with the Maure Group, as opposed to the Barroso Group,
with which they were previously associated. The most distal
part of this system is marked by propylitic alteration, com-
posed of chlorite, calcite, epidote, and quartz. Gold, Ag, Ba,
Hg, As, and Sb concentrations were observed at lower topo-
graphic areas and are associated with possible porphyry-type
mineralization at depth (Hennig et al., 2008).
Similar to Cerro Millo, San Antonio de Esquilache is located
in another paleostratovolcano (Fig. 2) and has been exploited
since colonial times for Cu, Ag, Au, Pb, and Zn related mainly
to diorite (Palácios et al., 1993). In addition, Chapi Chiara
prospect, located in a Miocene-Pliocene paleostratovolcano,
was recently investigated by a joint venture between Vena
Resources Inc. and Gold Fields Inc. for gold and other met-
als. However, little geologic information has been published
regarding the Chapi Chiara prospect.
Materials and Methods
ASTER data used for digital processing in this study were
acquired on April 19, 2005, and consist of AST_7XT VNIR-
SWIR crosstalk-corrected, reectance data and level 1B radi-
ance TIR data. The main characteristics of the 14 spectral
bands of the ASTER sensor that were used in this study are
shown in Table 1 (Abrams et al., 2002). ASTER GDEM data,
with 30-m spatial resolution, were also employed for produc-
ing perspective views and to analyze the relationship between
alteration and topography.
In addition, 37 petrographic thin sections of rock samples
collected during eldwork in the Chapi Chiara prospect were
analyzed to conrm the existence of the minerals mapped.
Moreover, 671 reectance spectroscopy data points were
acquired with the TerraSpec™ spectroradiometer, from Ana-
lytical Spectral Devices. These data were available for the
Chapi Chiara prospect area. The features of the TerraSpec™
spectroradiometer are described in TerraSpec Explorer
(2011). A eld of view of 2 cm in diameter was used.
ASTER VNIR and SWIR bands processing
The ASTER VNIR and SWIR bands were combined into a
single image le with 30-m spatial resolution. The band 3/band
2 ratio was applied for enhancing the photosynthetic vegetation
targets and then used as a mask in the subsequent mineral map-
ping, similar to the method used by Mars and Rowan (2006).
The principal component analysis (PCA) technique known
as Crósta technique (Loughlin, 1991) was employed to iden-
tify areas characterized by illite-muscovite and/or smectite
and iron-bearing minerals. Based on a variation of the PCA
technique developed by Crósta et al. (2003) specically for
ASTER bands, subsets of four spectral bands were selected
for each mineral type under investigation. This selection
was based on the existence of spectral gradients marked by
absorption and high-reectance spectral features. The PCA
technique determines decorrelated linear combinations of
the variables (multispectral bands) based on the calculation
of the eigenvectors that comprise the weighted and linear
combinations of the input image in the principal components
(PC) produced. This is done by algebraic methods, used to
determine the variance-covariance matrix of multispectral
bands (Mather, 2004). The variance information of the PCs is
referred to as eigenvalue and decreases successively for each
PC that is generated.
Based on Crósta et al. (2003), the following bands were
selected: (1) Bands 1, 3, 5, and 6 were used to identify illite-
muscovite and/or smectite. These minerals are marked by a
typical absorption gradient between bands 5 (~2.165 μm) and
6 (~2.205 μm) of ASTER (Fig. 3A). The intense absorption
feature of band 6 is due to the vibrational process of the Al-OH
bond (Hunt, 1977, 1979). (2) Bands 1, 2, 3, and 4 were used
to identify the iron-bearing minerals (e.g., hematite, goethite,
and jarosite). These minerals are marked by absorption fea-
tures in bands 1 (~0.560 μm) and 3 (~0.820 μm), derived from
the electronic transition process of ferric iron ions (Fig. 3A).
T 1. Characteristics of the VNIR, SWIR, and TIR Subsystems of
the ASTER Sensor (Abrams et al., 2002)
Subsystem Spectral band Spectral range (μm) Spectral resolution
VNIR 1 0.520–0.600 15 m
2 0.630–0.690
3N 0.780–0.860
3B 0.780–0.860
SWIR 4 1.600–1.700 30 m
5 2.145–2.185
6 2.185–2.225
7 2.235–2.285
8 2.295–2.365
9 2.360–2.430
TIR 10 8.125–8.475 90 m
11 8.475–8.825
12 8.925–9.275
13 10.250–10.950
14 10.950–11.650

GEOLOGY AND HYDROTHERMAL ALTERATION OF THE CHAPI CHIARA PROSPECT AND NEARBY TARGETS, PERU 77
In the both cases, PC4 was the image that concentrated the
spectral information of interest due to the high weight of the
eigenvectors (with contrasting signals) related to the spectral
bands that characterize the mineral targets investigated. Dark
pixels in the PC4 images represent the targets of interest,
according to the analysis of the eigenvector signals and their
relationship with the spectral gradient that characterizes each
target; in these cases, an eigenvector negative signal is associ-
ated with the contribution of the original spectral band infor-
mation marked by high reectance, whereas an eigenvector
positive signal is related to the contribution of the original
spectral band information characterized by low reectance.
In both cases, the histograms of the respective images were
inverted to convert these pixels into bright tones, thus facili-
tating visual perception.
For mapping alunite- and kaolinite-bearing targets, with
special attention to the discrimination of advanced argillic
alteration, the band ratio shown in equation 1, extracted from
Mars and Rowan (2006), was employed:
(band 4 + band 7)
———–———— . (1)
(band 5 + band 6)
This ratio enhances minerals such as kaolinite group miner-
als and/or alunite, with the possibility of mapping both min-
erals together. Based on the spectral responses of these two
mineral classes, the numerator of equation 1 accounts for the
high reectance values for alunite and kaolinite (bands 4 and
7), whereas the denominator accounts for low values, due to
the diagnostic absorptions features of alunite and kaolinite
group minerals (bands 5 and 6), all derived from vibrational
processes of Al-OH bonds (Fig. 3A; Hunt 1977, 1979; Cloutis
et al., 2006). Thus, the result comprises an image of favor-
ability for alunite and/or kaolinite group minerals, shown in
bright pixels.
Next, the three images generated had their histograms man-
ually adjusted to highlight the brightest pixels, thus enhancing
the most favorable mineral targets. This procedure was done
using the results of the interpretation of the reectance spec-
troscopy data as a guide for the adjustment of the histograms,
choosing DN thresholds also by coherence of the spatial dis-
tribution of pixels, similar to what was done by Rowan et al.
(2006). The images were submitted to median ltering using
a 3 × 3 window and then vectorized to produce single classes
for alunite and/or kaolinite group minerals, illite-muscovite
and/or smectite, and iron-bearing minerals. They were then
converted into a GIS dataset and the mineral mixture classes
were dened where two or three ASTER-mapped mineral
classes overlapped each other. This resulted in a thematic
map of the regional hydrothermal alteration, characterized by
seven classes of pure minerals or mineral mixtures (Fig. 3B).
Cerro Millo
SanAntonio
de Esquilache
Chapi
Chiara
Cerro
Chucapaca
N
N
N
B
A
Reflectance (offset for clarity)
ASTER spectral bands
21 34567
Jarosite
1.0 1.5 2.0
Goethite
Muscovite
Montmorillonite
Illite
Kaolinite
Alunite
89
Wavelength (µm)
Legend
N
Alteration
Alteration cores inferred and
shown in the Figure 9
M+I
AK+M
AK+I
M
AK
I
AK+M+I
Canahuire
N
N
N
N
F. 3. Spectral curves for alunite, kaolinite, illite, montmorillonite, muscovite, jarosite, and goethite from the USGS
spectral library (Clark et al., 2007), resampled to the ASTER spectral resolution (A). Thematic map produced through the
processing of ASTER VNIR and SWIR bands for alunite and/or kaolinite group minerals, illite-muscovite and/or smectite,
and iron-bearing minerals is shown in (B). AK = alunite and/or kaolinite group minerals, I = iron-bearing minerals, M = illite-
muscovite and/or smectite, N = new potential targets for mineral exploration.

78 CARRINO ET AL.
According to Rockwell et al. (2006), the spectral resolu-
tion of ASTER in the visible to short-wave infrared range
is not adequate for separating the kaolinite group minerals
(e.g., kaolinite, nacrite, dickite) or the illite-muscovite and the
smectite group minerals (e.g., nontronite, montmorillonite).
Therefore, only the kaolinite group minerals and illite-musco-
vite and/or smectite minerals are mentioned in this study. The
members of these mineral groups were not separated in this
regional analysis.
Additionally, the accuracy of the regional hydrothermal
alteration map (Fig. 3B) was assessed by using 10% of the
total sampling points that are concentrated in the area of the
Chapi Chiara gold prospect. Overall, 68 points were randomly
selected from the 671 data points. The random method was
used because it avoids tendencies and allows the subset to
represent the entire data population.
The 68 spectral curves were interpreted visually and also
with The Spectral Geologist™ software. This interpreted
mineralogy works as a reference for comparison with the min-
eral labeling of the corresponding pixels in the map shown in
Figure 3B (Table 2). The 68 sampling points were selected
to ensure that only one measurement was considered within
the coverage of a sampling unit (corresponding to the area of
one pixel, i.e., 900 m²) when more than one measurement was
performed within the perimeter of one sampling unit.
The overall accuracy was computed from the data in Table
2 by nding the ratio of the sum of the correctly classied
sampling units to the total number of samples that were used
T 2. Comparison of the Mineralogy Interpreted from Spectral Curves with the Respective Pixels (900 m²) in
the Hydrothermal Alteration Map (Fig. 3B)
Mineralogy interpreted using Mineralogy representing pixels in the regional alteration map
Sample no. X (m) Y (m) reectance spectroscopy (reference) shown in Figure 3B
106860 359,481 8,209,669 Montmorillonite No minerals of interest
106935 359,001 8,205,282 Kaolinite + goethite Alunite and/or kaolinite group minerals
106954 357,963 8,204,896 Montmorillonite + kaolinite Illite-muscovite and/or smectite + iron-bearing minerals
106957 357,912 8,204,825 Montmorillonite + jarosite Illite-muscovite and/or smectite + iron-bearing minerals
106979 357,866 8,206,004 Alunite Alunite and/or kaolinite group minerals + iron-bearing minerals
107626 357,007 8,206,567 Natroalunite Alunite and/or kaolinite group minerals
107627 357,038 8,206,602 Dickite Alunite and/or kaolinite group minerals
107630 357,027 8,206,624 Alunite + goethite Alunite and/or kaolinite group minerals + iron-bearing minerals + illite-
muscovite and/or smectite
107631 357,060 8,206,655 Kaolinite + dickite Alunite and/or kaolinite group minerals
107634 357,128 8,206,742 Dickite Illite-muscovite and/or smectite
107654 357,273 8,206,408 Alunite + goethite Alunite and/or kaolinite group minerals + iron-bearing minerals + illite-
muscovite and/or smectite
107656 356,970 8,206,148 Alunite Alunite and/or kaolinite group minerals + iron-bearing minerals
107664 357,088 8,206,307 Natroalunite Alunite and/or kaolinite group minerals + illite-muscovite and/or smectite
107666 357,094 8,206,346 Alunite Alunite and/or kaolinite group minerals
107687 358,072 8,205,638 Alunite Alunite and/or kaolinite group minerals
108328 359,567 8,207,736 Kaolinite No minerals of interest
108351 358,394 8,207,411 Kaolinite No minerals of interest
108365 358,026 8,207,890 Montmorillonite Alunite and/or kaolinite group minerals
108370 357,927 8,207,709 Montmorillonite + kaolinite No minerals of interest
108391 359,069 8,206,918 Montmorillonite Iron-bearing minerals
108550 359,862 8,208,211 Kaolinite Alunite and/or kaolinite group minerals
108560 359,588 8,207,991 Kaolinite Alunite and/or kaolinite group minerals + iron-bearing minerals
108565 359,597 8,207,943 Kaolinite + goethite Alunite and/or kaolinite group minerals + iron-bearing minerals
108594 358,474 8,209,136 Montmorillonite + goethite No minerals of interest
108605 359,556 8,209,530 Montmorillonite No minerals of interest
108614 359,806 8,209,680 Montmorillonite No minerals of interest
108621 359,424 8,209,542 Montmorillonite No minerals of interest
108630 360,204 8,208,022 Kaolinite Alunite and/or kaolinite group minerals
108632 360,159 8,207,995 Kaolinite + alunite Alunite and/or kaolinite group minerals
108638 359,889 8,207,863 Kaolinite Alunite and/or kaolinite group minerals + iron-bearing minerals + illite-
muscovite and/or smectite
108642 359,861 8,208,059 Montmorillonite Alunite and/or kaolinite group minerals + iron-bearing minerals
108648 359,445 8,207,927 Kaolinite Alunite and/or kaolinite group minerals + iron-bearing minerals + illite-
muscovite and/or smectite
108658 358,839 8,207,871 Kaolinite Alunite and/or kaolinite group minerals
108683 358,929 8,208,516 Montmorillonite + goethite No minerals of interest
108695 357,525 8,206,908 Nacrite + jarosite Alunite and/or kaolinite group minerals + iron-bearing minerals
108696 357,534 8,205,278 Montmorillonite + goethite No minerals of interest
108703 357,435 8,206,806 Dickite + natroalunite Illite-muscovite and/or smectite + iron-bearing minerals
108708 357,475 8,205,532 Smectite and illite Alunite and/or kaolinite group minerals + iron-bearing minerals
108718 357,779 8,205,366 Alunite Alunite and/or kaolinite group minerals
108724 357,711 8,205,335 Kaolinite + dickite Alunite and/or kaolinite group minerals
108725 357,763 8,205,558 Alunite + goethite Alunite and/or kaolinite group minerals
108729 357,727 8,205,705 Alunite Alunite and/or kaolinite group minerals
108734 357,014 8,205,747 Alunite + dickite Alunite and/or kaolinite group minerals + iron-bearing minerals
108743 357,424 8,205,810 Dickite + goethite Alunite and/or kaolinite group minerals + iron-bearing minerals

GEOLOGY AND HYDROTHERMAL ALTERATION OF THE CHAPI CHIARA PROSPECT AND NEARBY TARGETS, PERU 79
as reference data (Congalton and Green, 2009). For assess-
ing the overall accuracy, we considered the inuence of (1)
the spectral mixture, (2) the amount of each mineral in each
sampling unit, (3) the spectral contrast of the minerals, and
(4) the ASTER spectral resolution. We assumed a validation
condition that at least one mineral recognized by reectance
spectroscopy was correctly mapped in its respective pixel area
in the map of Figure 3B. Based on this criterion, an overall
accuracy of 60.29% was achieved (Table 2).
ASTER TIR bands processing
The ASTER TIR bands were used to identify the carbon-
ate- and quartz-bearing targets. First, data were corrected for
atmospheric effects using an algorithm similar to the In-Scene
Atmospheric Compensation (ISAC) proposed by Young et
al. (2002). These data were corrected for emittance values,
applying the Emissivity Normalization algorithm present in
ENVI.
The selective principal component analysis technique
(Chavez and Kwarteng, 1989) was used to map potential areas
for the occurrence of carbonates. In this case, bands 13 and 14
were used to depict the spectral gradient marked by high and
low emittance values between these bands, which are typical
of carbonates (e.g., calcite, dolomite; Fig. 4). The resulting
PC2 image is characterized by eigenvalues with high modular
values. In addition, the negative value resulting from the con-
tribution of band 13 (high emittance) and the positive value
resulting from the contributions of band 14 (low emittance)
imply that the carbonate-bearing areas are shown in the PC2
image as dark pixels. To understand the results better, the his-
togram of PC2 was inverted to highlight the potentially car-
bonate bearing areas as bright pixels (Fig. 4A).
The quartz-rich targets were mapped based on the quartz
index (QI) proposed by Rockwell and Hofstra (2008) and the
mac index proposed by Ninomiya et al. (2005). The mac
index is referred to in this study as the silica-poor index
(SI). The QI and SI indices are shown in equations 2 and 3,
respectively:
band 11 band 13
QI = ——————–—— ⋅ ———– ; (2)
(band 10 + band 12) band 12
band 12 ⋅ (band 14)3
SI = ————–————– . (3)
(band 13)4
Both equations make use of the spectral contrast that char-
acterizes quartz, including the high emittance values of bands
11, 13, and 14, and low emittance values in bands 10 and 12
(Fig. 4). Regarding equation 1, areas potentially rich in quartz
are shown as bright pixels (Fig. 4B). In relation to the second
equation, dark pixels indicate quartz-rich targets (Fig. 4C).
Integration of the results was achieved by combining the
quartz index, the silica-poor index, and the carbonates-PC2
(after histogram inversion) into the RGB color system (Fig.
4D). Due to the small size of the areas of exposed carbonate
rocks, which are associated with shades of blue in Figure 4D,
they are difcult to be seen at this scale. However, they can
be easily seen in the enlargements shown in the Results and
Discussion section.
108750 357,206 8,205,476 Alunite + dickite Alunite and/or kaolinite group minerals + iron-bearing minerals
108753 357,154 8,205,383 Montmorillonite Alunite and/or kaolinite group minerals + iron-bearing minerals + illite-musco-
vite and/or smectite
108757 357,232 8,205,235 Alunite + kaolinite + goethite Alunite and/or kaolinite group minerals + iron-bearing minerals
108758 357,581 8,205,432 Montmorillonite Alunite and/or kaolinite group minerals
108759 357,796 8,205,132 Montmorillonite No minerals of interest
108762 357,437 8,205,156 Kaolinite + alunite + jarosite Alunite and/or kaolinite group minerals
108764 357,735 8,204,968 Montmorillonite Illite-muscovite and/or smectite
108772 357,946 8,206,490 Alunite + kaolinite Alunite and/or kaolinite group minerals + iron-bearing minerals + illite-
muscovite and/or smectite
108784 357,597 8,206,815 Montmorillonite Alunite and/or kaolinite group minerals + iron-bearing minerals + illite-musco-
vite and/or smectite
108789 357,388 8,204,608 Alunite + goethite Alunite and/or kaolinite group minerals + iron-bearing minerals
108791 357,310 8,204,662 Alunite Alunite and/or kaolinite group minerals + iron-bearing minerals
108797 357,545 8,204,190 Montmorillonite + goethite Alunite and/or kaolinite group minerals + iron-bearing minerals
108799 357,610 8,204,022 Kaolinite + alunite Alunite and/or kaolinite group minerals + iron-bearing minerals
108805 357,254 8,204,004 Kaolinite + goethite Alunite and/or kaolinite group minerals + iron-bearing minerals
110001 358,778 8,205,925 Kaolinite + goethite No minerals of interest
110002 358,834 8,205,902 Montmorillonite + goethite Illite-muscovite and/or smectite
110006 359,279 8,205,633 Montmorillonite No minerals of interest
110008 359,277 8,205,537 Montmorillonite No minerals of interest
110011 359,094 8,205,686 Montmorillonite + jarosite No minerals of interest
110013 358,860 8,205,829 Montmorillonite + goethite No minerals of interest
110118 358,843 8,206,742 Montmorillonite Illite-muscovite and/or smectite
110205 360,925 8,207,195 Montmorillonite + jarosite Alunite and/or kaolinite group minerals
110207 360,897 8,207,126 Montmorillonite + kaolinite No minerals of interest
110208 361,013 8,207,387 Montmorillonite Alunite and/or kaolinite group minerals
Overall accuracy = 60.29%
Notes: The minerals that are in bold agree with the reectance spectroscopy recognition (reference)
T 2. (Cont.)
Mineralogy interpreted using Mineralogy representing pixels in the regional alteration map
Sample no. X (m) Y (m) reectance spectroscopy (reference) shown in Figure 3B

80 CARRINO ET AL.
Spatial data modeling
Aside from the regional characterization of the hydrother-
mal alteration zones and rock types, this study proposes the
generation of a favorability model for the potential analogous
occurrences of Canahuire-type deposits by using the fuzzy
logic technique (An et al., 1991; Bonham-Carter, 1994).
Fuzzy logic is a spatial data modeling technique in which
the input data (in raster format) are submitted to a fuzzi-
cation process by using fuzzy membership functions. These
fuzzy membership functions are used to scale the original data
into degrees of variability between 0 (no membership) and
1 (full membership). The small, large, and categorical fuzzy
membership functions are among the most widely adopted.
The small and large functions are used to indicate, respec-
tively, which small and large values within the original numer-
ical data distribution make up the interval of the highest
fuzzy membership. To accomplish this task, the midpoint and
spread parameters are subjectively determined. The rst one
corresponds with a 0.5 fuzzy membership value, and the sec-
ond one represents how fast the fuzzy membership decreases
from 1 to 0. Greater spread values correspond to faster fuzzy
membership changes from 1 (present) to 0 (absent). If the
original data are categorical (e.g., geologic units), the categori-
cal fuzzy membership function should be used to determine
which category should be attached to high or low fuzzy mem-
berships (An et al., 1991; Bonham-Carter, 1994).
Following this process, the fuzzy membership values that
refer to each evidence map should be combined using fuzzy
operators, such as Fuzzy And, Or, Product, Sum and/or Gamma,
to generate a nal favorability model (Bonham-Carter, 1994).
Results and Discussion
The study area was affected by the Condoroma-Caylloma
fault system, which is marked by an NW-SE regional trend.
Scores of the
quartz index
(QI) (Rockwell
and Hofstra,
2008)
3.23
-6.89
Quartz
B
C
Scores of the
silica-poor
index (SI)
(Ninomiya
et al., 2005)
1.128
-0.305
Silica
-0.004
0.007
Carbonates
QI
SI Cb
Cerro Millo
Canahuire
San Antonio
de Esquilache
Group
Yura
Chapi
Chiara
CCFS
A
Scores
of the PC2
(after histogram
inversion)
for enhancing
carbonates (Cb)
(Selective
Principal
Component
Analysis)
Alluvium
315000 360000
315000 360000
315000 360000
822500081950008225000
8195000
82250008195000
5km
5km
5km
D
Emittance (offset for clarity)
Quartz
Calcite
8.5
9.5
10.0
9.0
10.5
11.0
Wavelength (µm)
10
11
14
13
12
ASTER spectral bands
Canahuire
F. 4. Images generated from the ASTER TIR bands processing to identify carbonate-rich (A), quartz-rich (B), and
silica-poor areas (C). These images were produced by using selective principal components analysis (carbonates—Cb) and
the spectral indexes proposed by Rockwell and Hofstra (2008) (quartz index—QI) and Ninomiya et al. (2005) (silica-poor
index—SI). The color composition of these images is shown in (D). Examples of emittance curves for calcite and quartz from
the Johns Hopkins University spectral library, resampled to the ASTER TIR spectral resolution, help to clarify the emittance
gradients that discriminate these target minerals. CCFS = Condoroma-Caylloma fault system.

GEOLOGY AND HYDROTHERMAL ALTERATION OF THE CHAPI CHIARA PROSPECT AND NEARBY TARGETS, PERU 81
This trend is compatible with the tectonic contact between
the older terrains that are associated with the Yura Group
(in the southwest sector of the region) and the younger ter-
rains that are represented by sedimentary, volcanic, and
volcanosedimentary units from the Cretaceous to Miocene-
Pliocene period and characterized by well-preserved paleo-
stratovolcanoes (Fig. 2). As observed by Acosta et al. (2008),
the Condoroma-Caylloma fault system plays an important role
in the regional structural control of epithermal mineraliza-
tion in southern Peru. In addition, the Condoroma-Caylloma
fault system potentially plays a role in the mineral exploration
targets that are highlighted in Figures 5 through 11. These
results are discussed below.
Chapi Chiara prospect region
The Chapi Chiara prospect is characterized by extensive
hydrothermal alteration cores that are composed of alunite-
kaolinite group minerals (advanced argillic alteration) and
hydrothermal quartz. These cores are located in the southwest
sector of the prospect and are shown in the regional alteration
maps of the paleostratovolcano (Fig. 5A, B). A domain with no
pervasive alteration and with porphyritic andesite (protolith)
that was characterized by plagioclase and clinopyroxene (e.g.,
points CC-08 and CC-13) is located in the northeast sector of
the area (Fig. 5C).
The central sector of the prospect is characterized by
hydrothermal breccias that follow an N65E trend and occur at
altitudes greater than 5,000 m.a.s.l. (Fig. 5C). Hydrothermal
breccias are composed of quartz and clay minerals (kaolinite
and smectite), which are related to the total replacement of
plagioclase and vug lling processes (photomicrograph of the
CC-18 sample in Fig. 5).
The main mineral assemblage identied by reectance
spectroscopy of eld samples includes alunite supergroup
minerals, such as alunite (K end-member) and natroalunite
(Na end-member; Bayliss et al., 2010), observed as thin tabu-
lar crystals (photomicrograph of sample CC-56 in Fig. 6) and
discriminated by their absorption features at wavelengths
of ~1.430 and 1.479 μm (K end-member) and ~1.430 and
1.494 μm (Na end-member), derived from the vibrational
process of the O-H bonds. Common absorption features of
different end-members of the alunite supergroup miner-
als comprise those centered at 1.766 (vibrational process of
the O-H bonds), 2.172, 2.210, 2.325 (vibrational process of
the Al-OH bonds), and ~2.400 μm (vibrational process of
the S-O bonds). Furthermore, kaolinite group minerals were
identied by their absorption features located at wavelengths
of 1.395 and 1.414 μm (kaolinite) and 1.383 and 1.414 μm
(dickite), including the doublets in the ~2.200-μm spec-
tral region resulting from the vibrational process of Al-OH
bonds (features centered at 2.160 and 2.206 μm for kaolin-
ite and at 2.180 and 2.206 μm for dickite). Topaz mixed with
alunite and natroalunite was identied based on absorption
features at wavelengths of 1.403 and 2.085 μm, derived from
the vibrational process of the O-H bonds. Diaspore was also
identied by the broad absorption feature between ~1.600
and 2.250 μm, with more intense absorption centered at
~1.800μm arising from vibrational process of O-H bonds, as
well as the pyrophyllite mixed with alunite with intermediate
composition (absorption feature at ~1.486 μm) and dickite,
identied based on the intense and persistent absorption fea-
tures located at ~1.394 (O-H) and 2.168 (Al-OH) μm (Hunt,
1977, 1979; Pontual et al., 2008; Fig. 6A).
This mineral assemblage is mainly concentrated in the
advanced argillic alteration cores at higher altitudes in the
southwest sector of the Chapi Chiara prospect, as shown in
Figure 6B. In this gure, rock sampling points represent the
advanced argillic alteration characterized by quartz, alunite,
natroalunite, dickite, kaolinite and/or topaz, pyrophyllite, dia-
spore (e.g., samples CC-56, CC-55, CC-37, CC-34, 108707),
the argillic alteration marked by smectite, illite, and quartz
(e.g., CC-52 sample), and the propylitic alteration, composed
of quartz, plagioclase, epidote, chlorite, calcite, and clay min-
erals (e.g., CC-31A sample). We can observe that some argil-
lic and propylitic samples were not well mapped due to the
limited size of the outcrops, with alunite and/or kaolinite pre-
dominating in this sector. This is also shown in Table 2 by the
high correspondence of the advanced argillic alteration refer-
ence points (alunite and/or kaolinite assemblage) in relation
to the respective mapped pixel, and the low correspondence
observed in the case of the argillic and/or propylitic alteration
reference points (e.g., smectite, illite, kaolinite), thus possibly
accounting for the overall accuracy of 60.29%.
By integrating the mineralogical data mapped from ASTER
with the petrographic and reectance spectroscopy data, it
was possible to distinguish (1) a high suldation epithermal
system in the Chapi Chiara prospect, characterized by an
advanced argillic alteration zone (alunite supergroup miner-
als-kaolinite group minerals ± topaz ± diaspore ± pyrophyl-
lite), which changes into argillic and propylitic alteration in
the more distal parts; (2) the existence of meter-size transi-
tions among the different alteration zones in the southwest
sector of the prospect, a possibility previously mentioned by
Hedenquist et al. (2000); and (3) a deep erosion level in the
epithermal system highlighted in the southwest sector of the
prospect, indicated by the current exposed hypogene miner-
als originated from hydrothermal uids marked by conditions
of high temperature (above ~200°–250°C) and acidic pH (for
example, diaspore, pyrophyllite, topaz, and natroalunite).
Additionally, the occurrence of epidote in the propylitic zone
(e.g., sample CC-31A, Fig. 6) indicates conditions of high
temperature (≥200°–230°C) and neutral pH of the hydro-
thermal uid (White and Hedenquist, 1995; Thompson and
Thompson, 1996; Hedenquist et al., 2000). The intense ero-
sion of shallow hydrothermal systems decreases their mineral
potential, because eventual mineralized levels, which tend to
be located in the upper levels of the system (cf. White and
Hedenquist, 1995), might have been removed by erosion.
This seems to be the case here, as the high-temperature min-
eralogy indicates that the current erosion level has reached
the deeper parts of the system.
Canahuire deposit region
The Canahuire deposit is located northwest of Cerro Chu-
capaca, which is associated with a paleostratovolcano that was
affected by an intense hydrothermal process (Fig. 3B). The
spectral-mineralogical map obtained from the ASTER TIR
bands (Fig. 4D) shows the extensive distribution of the Yura
Group quartzite and sandstone. This group is affected by
NW-SE and WNW-ESE structures, which provide a record

82 CARRINO ET AL.
Hydrothermal alteration
minerals
Approximate
boundary of
Chapi Chiara
Crest
Lineament
Potential area for quartz
C
~500 m
E
SW sector
Centraland NE sectors
Legend Legend
CC-13 CC-08
Andesite Andesite
0.25mm
CC-13 0.25 mm
Cpx
Pl
Cpx
CC-18
Hydrothermal
breccia
Qtz+
clay
Qtz+
clay
SW sector
Centralsector
NE sector
SW sector
Centralsector
NE sector
B
A
CC-18
M+I
AK+M
AK+I
M
AK
I
AK+M+I
F. 5. Detail of the hydrothermal alteration map of the paleostratovolcano that contains the Chapi Chiara prospect (A).
The same area is shown in (B), where potential areas for quartz were highlighted by applying the quartz index of Rockwell
and Hofstra (2008). In (C), the location of some rock samples is shown in a perspective view (magenta lines), associated with
petrographic thin sections and/or reectance spectroscopy analysis. The perspective image consists of ASTER band 3 and
the mineral map draped over an ASTER GDEM. Examples of porphyritic andesite (photomicrograph of CC-13 sample) and
hydrothermal breccia (photomicrograph of CC-18 sample) are also presented. AK = alunite and/or kaolinite group minerals,
Cpx = clinopyroxene, I = iron-bearing minerals, M = illite-muscovite and/or smectite, Pl = plagioclase, Qtz = quartz.

GEOLOGY AND HYDROTHERMAL ALTERATION OF THE CHAPI CHIARA PROSPECT AND NEARBY TARGETS, PERU 83
SW sector of Chapi Chiara
B~500 m
Advanced argillic alteration
(quartz-kaolinite group minerals
and/or alunite±topaz ±
diaspore±pyrophyllite)
Quartz-
illite-smectite
Propylitic alteration
(quartz-plagioclase-
chlorite-calcite-epidote-
clay minerals)
Core of
quartz-alunite-
kaolinite group
minerals
Reflectance factor (offset for clarity)
1.4 1.6
1.5
Wavelength (µm)
1.394
1.479
2.0
CC-55
CC-56
CC-34
1.403
1.430
1.414
O-H
O-H
O-H
2.4
1.494
1.434
CC-37
1.383
O-H
O-H
CC-55
CC-56
CC-34
CC-37
108707
2.085
1.766 1.766
2.172
2.210
2.325
1.800
~2.206
2.325
O-H
2.400
S-O
Al-OH
O-H
2.160-2.180
O-H
Absorption feature of topaz
Main absorption features:
Other absorption feature of
alunite supergroup minerals
Absorption feature of
µ
dickite
(1.383, 1.414, 2.180, 2.206 µm)
and kaolinite (1.395, 1.414,
2.160, 2.206 m)
Absorption feature of alunite
(intermediate composition)
Absorption feature of diaspore
A
CC-34
CC-37
CC-52
CC-56
CC-55
108707
CC-52
Mica
0.20 mm
0.08 mm
CC-56
Qtz
Alu
Alu
CC-31A
1.479
1.486
Absorption feature of natroalunite
(Na end-member)
Absorption feature of alunite
(K end-member)
0.25 mm
Ep
Ep
Pl
CC-31A
~2.168
Absorption feature of pyrophyllite
1.395
Broad absorption
feature (diaspore)
F. 6. Examples of spectral curves from rock samples obtained with the TerraSpec™ spectroradiometer (A). Details
from the southwest sector of the Chapi Chiara prospect with indicators for advanced argillic, argillic, and propylitic alteration
samples are shown in (B). The perspective image consists of ASTER band 3 and the mineral map draped over an ASTER
GDEM. Photomicrographs (transmitted light/crossed nicols) of samples from the propylitic (CC-31A), argillic (CC-52), and
advanced argillic (CC-56) alteration zones are also shown. The legend for the mineral classes is the same as the legend pre-
sented in Figure 5. Alu = alunite, Ep = epidote, Pl = plagioclase, Qtz = quartz.

84 CARRINO ET AL.
Colluvium
Phreatic and phreato-
magmatic breccias
Rhyolite
Quartzite (Hualhuani
Formation)
Sandstone and shale
(Hualhuani Formation)
Sandstone and black shale
(Labra Formation)
Structure
Geologic units
Legend
Quartz Index (QI)
Carbonates
(PC2-after
histogram
inversion)
lica-
poor
index
(SI)
Si
Legend
Legend
Alteration
Limestone (Gramadal Fm.)
B
C
A
0 500m
Gramadal
Formation
Hualhuani or
Labra formations
Phreatic and
phreatomagmatic
breccias
Alteration
of Cerro
Chucapaca
M+I
AK+M
AK+I
M
AK
I
AK+M+I
F. 7. Canahuire deposit region, depicted as
follows: (A) color composition to highlight the
occurrence of quartz and carbonate through the
processing of ASTER TIR bands; (B) in the geo-
logic map by Santos et al. (2011), integrated with
ASTER band 3; and (C) in the alteration map pro-
duced by processing of ASTER VNIR and SWIR
spectral bands. AK = alunite and/or kaolinite group
minerals, I = iron-bearing minerals, and M = illite-
muscovite and/or smectite.

GEOLOGY AND HYDROTHERMAL ALTERATION OF THE CHAPI CHIARA PROSPECT AND NEARBY TARGETS, PERU 85
of three main deformation phases (Peruvian, Incaic, and Que-
chua) that occurred since the beginning of the Andean Cycle
(Benavides-Cáceres, 1999).
In the Canahuire deposit, carbonate rocks of the Gramadal
Formation that host the Au-Cu-Ag mineralization, together
with the phreatic and phreatomagmatic breccias marked by
low silica content, as well as the quartz-rich units (Labra and
Hualhuani formations; Fig. 7A), were all very well character-
ized in comparison with the detailed geologic map by Santos
et al. (2011) (Fig. 7B). These results indicated that ASTER
TIR bands can be applied to prospecting for “Canahuire-
type” deposits in this geologic setting.
Additionally, the low quartz content in the area of the
deposit is also accompanied by moderate argillic alteration
(illite-smectite ± iron-bearing minerals) and the absence of
pervasive or structurally controlled alunite (Fig. 7C).
The area where the Yura Group occurs is shown in detail in
the Figure 8, depicting the occurrence of quartz-bearing units
(Labra and Hualhuani formations) and carbonates (Grama-
dal Formation). The limited extension of the carbonate rocks
and the stripe noise of the thermal infrared images render
a detailed analysis of this unit difcult. However, enlarge-
ments of this regional view, shown in the Figure 8A, B, and C
subareas, allow the visualization of potential carbonate targets
(shades of blue), as indicated by the arrows. Special attention
is given to the carbonate-bearing targets near Cerro Chuca-
paca that are depicted in Figure 8B. This emphasis is due to
the fact that the source of the hydrothermal uids associated
with the magmatic-hydrothermal activity that affected Cerro
Chucapaca might have also contributed to (1) the mineral-
ization in Canahuire and (2) possible hidden mineralization,
related to carbonate rocks near Cerro Chucapaca and char-
acterized by a similar lithological (carbonate rocks of Yura
Group), hydrothermal (proximity of alteration cores of Mio-
cene-Pliocene age), and structural (NW-SE and WNW-ESE
fault control) context in relation to Canahuire.
Based on the ASTER products, the main interpreted explor-
atory vectors were integrated using the fuzzy logic technique
to produce a favorability model for the Canahuire-type depos-
its. The input data comprise (Fig. 9) the following: (1) zones
of inuence from the structures in the NW-SE and WNW-
ESE directions (e.g., Figs. 8, 9): generation of buffers of 0 to
1,000m using the Euclidean distance and applying the small
fuzzy membership function (midpoint = 250 m and spread
= 4); (2) areas of low quartz content based on the QI image
(Figs. 4B, 9): application of the categorical fuzzy membership
Quartz Index (QI)
(Labraor Hualhuani formations)
Carbonates
(Gramadal
Formation)
Silica-poor
index (SI)
B
B
A
C
C
Canahuire
Alteration
core
(Cerro
Chucapaca)
Interpreted lineaments
Alteration core related to Cerro
Chucapaca
Legend
A
Example of potential area forc
arbonates
F. 8. Rocks of the Yura Group (sandstone, quartzite, and carbonate rocks) highlighted in the ASTER TIR bands (cf. Fig.
4D). The three regions labeled A, B, and C correspond to the potential carbonate-bearing areas.

86 CARRINO ET AL.
function (high quartz content = 0.05 and low quartz content
= 0.50); (3) possible carbonate targets (Gramadal Formation/
Yura Group), depicted in the PC2 image (Figs. 4A, 9): genera-
tion of buffers of 0 to 500 m by using the Euclidean distance
and applying the small fuzzy membership function (midpoint
= 250 m and spread = 5); (4) the proximity of the alteration
core of the Cerro Chucapaca (e.g., Fig. 8), which is associated
with Miocene volcanism and has been interpreted as the possi-
ble source of the hydrothermal uid that affected the metased-
imentary and sedimentary rocks (Yura Group), generating the
mineralization in Canahuire. Complementarily, other altera-
tion cores inferred from the regional alteration map (red lines
in Fig. 3B) were also considered in the fuzzication process,
characterized by buffers with 0 to 1,000 m by using the Euclid-
ean distance, following the application of the small fuzzy mem-
bership function (midpoint = 400 m and spread = 4).
The fuzzy operators used were Fuzzy Product and Fuzzy
And. The Fuzzy Product operator is based on the multiplica-
tion of the fuzzy memberships in the evidence maps. Thus,
the result is equal to or less than the smallest contribution
from the fuzzy membership values in the input maps. The
Fuzzy And operator provides results for each location (pixel)
that is equivalent to the smallest fuzzy membership values in
the evidence maps (Bonham-Carter, 1994).
The Fuzzy Product operator was used to combine the
fuzzied images of low quartz contents (QI values) and high
carbonate contents to produce an image that was labeled “lith-
ological control” (Fig. 9). This image was combined with the
NW-SE and WNW-ESE structure image by using the Fuzzy
And operator to generate the “lithological-structural con-
trol” output. Next, the “lithological-structural control” image
was combined with the image that depicts the proximity of
Quartz
image
(QI)
Lithological control
Main alteration
cores relatedto
Mio-Pliocene
volcanism
Carbonate-bearing
target image
(P 2)C
Lithological-
s controltructural
Structures
(NW-SE and
WNW-ESE)
Favorability
model
Categorical fuzzy
membership
0.05
Low
High
Quartz
content:
Carbonate
Buffersof0-1,000 mand use
of small fuzzy membership
midpoint:250 m
spread: 4
Buffersof0-1,000mand use
of small fuzzy membership
midpoint:400 m
spread: 4
Buffersof 0-500 mand use
of small fuzzy membership
midpoint:250 m
spread:5
Fuzzy
Product
Fuzzy
And
Fuzzy
And
Fuzzification
Membership Membership Membership
1
0
1
0
1
0
Inputdata
0.50
Membership
F. 9. Proposed predictive model for Canahuire-type deposits through the use of remote sensing products integrated by
fuzzy logic technique.

GEOLOGY AND HYDROTHERMAL ALTERATION OF THE CHAPI CHIARA PROSPECT AND NEARBY TARGETS, PERU 87
the alteration cores related to Miocene-Pliocene volcanism,
applying the Fuzzy And operator (Fig. 9), producing the
favorability model for the Canahuire-type deposits (Fig. 10).
Priority was given to the analysis of potential targets nearby
Cerro Chucapaca, as shown in Figure 10, to reassert the
likely relationship between the hydrothermal uid contem-
poraneity that affected the Cerro Chucapaca area and the
hydrothermal uid that reacted with carbonate rocks to gen-
erate ore deposits in the Canahuire and other possible still
unveiled and similar targets in the area. The result obtained
specically for the Canahuire deposit area is validated by
the descriptions and geologic map presented by Santos et
al. (2011) (cf. Fig. 7B). This map indicates that areas more
favorable for mineralization are associated with carbonate
rocks from the Yura Group (Gramadal Formation) and with
phreatic and phreatomagmatic breccias aligned in the WNW-
ESE direction (Fig. 10).
San Antonio de Esquilache, Cerro Millo, and other targets
Based on the regional hydrothermal alteration map of Figure
3B, the following targets were identied in the study region:
1. San Antonio de Esquilache, mainly characterized by
advanced argillic and argillic alteration zones (alunite, kaolin-
ite group minerals, and minerals such as illite and/or smec-
tite), where a dioritic intrusion is located in the central sector
of the intensely eroded paleostratovolcano, marked by the
potential for Cu-Ag-Au and other base metals (Fig. 11A).
2. The high suldation epithermal gold prospect of Cerro
Millo, similar to the Chapi Chiara prospect. For example,
the central and southwest sector of the system is marked by
an advanced argillic alteration zone (alunite and/or kaolinite
group minerals) that transitions outward to an argillic altera-
tion zone (kaolinite group minerals and illite and/or smectite;
Fig. 11A, B). The alunite-kaolinite assemblage mapped has a
Cerro
Chucapaca
Canahuire
Potential
targets
Canahuire
Potential
targets nearby
Cerro Chucapaca
Legend
Favorability
Very high
High
Intermediate
3km
650m
750m
F. 10. Favorability model for Canahuire-type deposits. The result is draped on ASTER band 3.

88 CARRINO ET AL.
different pattern in comparison with an alteration map pro-
duced by Hennig et al. (2008) (Fig. 11C), probably due to the
low number of analyses (approximately 50 rock samples) with
X-ray diffractometry and analytic geochemical methods used
by these authors, and the fact that they did not use a reec-
tance and/or imaging spectroscopy technique, which would
favor fast mineralogical recognition in the region.
3. Cerro Chucapaca, a paleostratovolcano that is marked
by a single core consisting of an advanced argillic alteration
zone (alunite and kaolinite group minerals ± iron-bearing
minerals) transitioning into mineralogy that is typical of argil-
lic alteration (kaolinite group minerals and illite and/or smec-
tite ± iron-bearing minerals). This target comprises one of the
largest alteration centers in the study area (Fig. 3B).
4. New potential targets that are marked by advanced argil-
lic and argillic alteration zones occur in the southwest and
west sectors of the regional hydrothermal alteration map (Fig.
3B: labeled N).
Conclusions
ASTER is an important and strategic tool for character-
izing geologic units and hydrothermal targets in areas of
well-exposed bedrock, such as the Andean Cordillera. In this
context, ASTER thermal infrared bands were decisive for
mapping the rock types of the Yura Group, the host rocks and
structures of the Canahuire deposit, and possible hydrother-
mal quartz-rich targets in the Chapi Chiara prospect. New
potential targets for investigating Canahuire-type epithermal
systems were identied in the region, including the produc-
tion of a favorability model using the fuzzy logic technique
in the ASTER products. Regarding the Canahuire deposit
region, the mineral favorability results were compatible with
the geologic-metallogenic descriptions that were given by
Santos et al. (2011).
Regarding the Chapi Chiara prospect, a high suldation
epithermal system was identied, marked by a large proximal
alteration core composed mainly of alunite-kaolinite group
minerals. A deeply eroded system can also be inferred by
the presence of hypogene minerals that were formed at high
temperature and low pH (diaspore, pyrophyllite, topaz, and
natroalunite) or at high temperature and neutral pH (epi-
dote). This mineralogy, discriminated by the use of reec-
tance spectroscopy and petrographic analysis, might suggest
a decrease of the potential for gold.
M+I
AK+M
Alteration
Legend
AK+I
M
AK
I
500 m
B
500 m
C
Alteration (Cerro Millo)
Advanced argillic alteration
(alunite-kaolinite-quartz)
Argillicalteration
(kaolinite-smectite-quartz)
Propylitic alteration
(chlorite-calcite-
epidote-quartz)
Fresh andesite-dacite
Moderate silicification Strong silicification
Vuggy silica
Fault
367500 369000
82268008225400
82268008225400
367500 369000
AK+M+I
Dioritic
intrusion
Cerro Millo
A
360000 366000
822500082200008215000
F. 11. Detail of the Cerro Millo and San Antonio de Esquilache areas in the regional hydrothermal map (A). San Anto-
nio de Esquilache target is associated with the occurrence of dioritic intrusion. In (B), the enlargement for the Cerro Millo
prospect is presented. This result is compared with the hydrothermal alteration map produced by Hennig et al. (2008) (C).
AK = alunite and/or kaolinite group minerals, I = iron-bearing minerals, and M = illite-muscovite and/or smectite.

GEOLOGY AND HYDROTHERMAL ALTERATION OF THE CHAPI CHIARA PROSPECT AND NEARBY TARGETS, PERU 89
Additionally, as proposed for the Cerro Millo high sul-
dation epithermal gold prospect (Hennig et al., 2008), the
potential of porphyry-type mineralization in deep levels is
suggested for Chapi Chiara, possibly related to dioritic intru-
sion, similar to San Antonio de Esquilache.
Acknowledgments
We thank Gold Fields Inc., especially geologist Francisco
Azevedo, for supporting the development of this project. T.A.
Carrino acknowledges the State of São Paulo Research Foun-
dation (FAPESP) for providing a doctoral scholarship (grant
2011/00106-8), the University of Campinas and the University
of Brasília for providing technical support and the use of their
laboratories, and geologists Teresa Guevara, Shirley Custodio,
Auri Morro Muñoz, and Mariela Reyes. A.P. Crósta and A.M.
Silva thank the National Council for Scientic and Techno-
logical Development for their research grants. The authors
also thank the reviewers (J. Mars, M. Abrams, and S. Perry)
for their signicant contributions.
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