10.1007 s12517-013-0969-3

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Arab J Geosci
DOI 10.1007/s12517-013-0969-3
ORIGINAL PAPER
Exploration of gold mineralization in a tropical region using
Earth Observing-1 (EO1) and JERS-1 SAR data: a case study
from Bau gold field, Sarawak, Malaysia
Amin Beiranvand Pour & Mazlan Hashim &
Maged Marghany
Received: 20 September 2012 / Accepted: 3 May 2013
# Saudi Society for Geosciences 2013
Abstract Bau gold mining district, located near Kuching,
Sarawak, Malaysia, is a Carlin style gold deposits. Geological
analyses coupled with remote sensing data were used to detect
hydrothermal alteration rocks and structure elements associated with this type of gold mineralization. Image processing
techniques, including principal components analysis, linear
spectral unmixing, and Laplacian algorithms, were employed
to carry out spectrolithological–structural mapping of mineralized zones, using Advanced Land Imager, Hyperion, and
JERS-1 synthetic aperture radar scenes covering the study
area and surrounding terrain. Hydrothermally alteration mineral zones were detected along the SSW to NNE structural
trend of the Tai Parit fault that corresponds to the areas of
occurrence of the gold mineralization in the Bau limestone.
The results show that potentially interesting areas are observable by the methods used, despite limited bedrock exposure in
this region and the constraints imposed by the tropical
environment.
Keywords Gold mineralization . Gold exploration .
Tropical regions . EO1 data . JERS-1 SAR data .
Bau gold field
A. B. Pour : M. Hashim (*) : M. Marghany
Institute of Geospatial Science & Technology (INSTeG),
Universiti Teknologi Malaysia, 81310 UTM Skudai,
Johor Bahru, Malaysia
e-mail: [email protected]
A. B. Pour
e-mail: [email protected]
M. Marghany
e-mail: [email protected]
Introduction
Remote sensing images are used for mineral exploration by
association mapping as indicated by geology and the faults
and fractures that localize ore deposits (Spatz 1997) and
indentifying hydrothermally altered rocks by their spectral
signatures (Sabins 1999; Pour and Hashim 2012a).
Multispectral and hyperspectral remote sensing sensors were
used for geological applications, ranging from a few spectral
bands to more than 100 contiguous bands, covering the visible
to the shortwave infrared regions of the electromagnetic spectrum (Abrams et al. 1983; Rowan and Wetlaufer 1981; Spatz
and Wilson 1995; Cocks et al. 1998; Kruse et al. 1999).
Landsat Multi-Spectral Scanner, Landsat Thematic Mapper,
and Syste`m Pour l’Observation de la Terre with four to seven
spectral bands have been used for regional scales of geological
mapping (Goetz et al. 1983; Sultan et al. 1987; Kavak 2005;
Kargi 2007). HyMap and the Airborne Visible/IR Image
Spectrometer hyperspectral sensors with 126 to 224 contiguous bands were used to provide information about hydrothermal alteration minerals on the Earth’s surface (Clark et al.
1991; Perry 2004; Hellman and Ramsey 2004; Bedini et al.
2009; Bedini 2011).
Recognizing hydrothermally altered rocks through remote
sensing instruments have been widely and successfully used
for the exploration of epithermal gold and porphyry copper
deposits (Sabins 1999; Zhang et al. 2007; Di Tommaso and
Rubinstein 2007; Gabr et al. 2010; Bedini 2011; Madani and
Emam 2011; Amri et al. 2011; Salem et al. 2011; Abbaszadeh
and Hezarkhani 2011; Pour et al. 2011; Pour and Hashim
2011a, b, c, d, Pour and Hashim 2012a, b; Pour and Hashim
2013). Most of these investigations have been conducted in
Arab J Geosci
arid and semi-arid terrains, with large exposures of geologic
materials, allowing the acquisition of spectral information
directly from rock–soil assemblages. However, in the tropical
environments, this condition is rarely observed (Carranza and
Hall 2002; Magalhaes and Souza Filho 2012; Hashim et al.
2013; Pour et al. 2013) because vegetation is a prime component of the landscape. Moreover, the persistent cloud cover
and limited bedrock exposures are other obstacles imposed by
tropical environments.
In the tropics, synthetic aperture radar (SAR) data are
particularly appropriate because microwave signals can penetrate the persistent cloud coverage. Structural geology is enhanced by the side looking aspect of the active SAR sensor,
which produce radar “shadows,” highlighting micro-relief.
Radar transmits and detects radiation between 2.0 to 100 cm,
typically at 2.5–3.8 cm (X band), 4.0–7.5 cm (C band), and
15.0–30.0 cm (L band). Coarse textural variation of outcrop
and surface roughness, including faults, folds, topographic
breaks, bedding, depressions, lithologies, and intrusive contacts, can be detected using L-band radar images (Spatz 1997).
In this study, the possibility of identifying hydrothermally
altered rocks and faults and fractures associated with ore
mineralization in the Bau gold mining district, Sarawak,
Malaysia, is examined using the Earth Observing-1 (EO-1)
and the Japanese Earth Resource Satellite-1 (JERS-1) SAR
remote sensing data. Bau gold field is located 25 km southwest of Kuching city (1°25′50″N, 110°10′48″E), Sarawak
Fig. 1 Location of the study area in Southeast Asia. The Bau gold
mining district is on westernmost Borneo. It is located on the state of
Sarawak which is part of Malaysia (modified from Eric 2001)
province, and is one of the largest gold ore deposit in eastern
Malaysia, on the Borneo island in Southeast Asia (Figs. 1 and 2).
The climate of Bau is characterized by heavy but seasonal
rainfall, uniform temperature (20–35 °C), and high humidity.
The annual average rainfall is 4,232.6 mm, or 167 in.
(Andriesse 1972). Some 40 % of the land is primary rain forest,
mostly of the Dipterocarpaceae type, restricted to the infertile
limestone hills and the higher mountains (Schuh 1993).
Bau is gold field with Carlin style gold deposits. Carlin-type
gold deposits are sediment-hosted disseminated gold deposits,
which are hosted by a variety of permeable sedimentary rocks,
especially thinly bedded, silty dolomites or limestones, cut by
high-angle faults (Bagby and Berger 1985; Percival et al. 1990;
Sillitoe and Bonham 1990). Nearly all the deposits contain
felsic intrusive rocks, commonly in the form of dikes or sills.
Orebodies may be confined to fault zones or may be irregularly
shaped replacements in the adjoining rocks. Mineralization is
characterized by an association of micron- to submicron-sized
Au with As, Sb, Hg, Tl, Ba, F, and in places W, Mo, and Sn,
but is deficient in base metals. Gold-bearing rocks underwent
decalcification, silicification, and argillic alteration and are
associated closely with structurally localized replacement of
carbonate rock by jasperoid (Sillitoe and Bonham 1990;
Arehart 1996; Rockwell and Hofstra 2008; Zhu et al. 2011).
These characteristics can be used as an indicator for the initial
stages of Carlin-type gold exploration using remote sensing
data.
Bau gold field is located on a mineralized trend belt, and a
variety of styles of mineralization was recognized, ranging
from mesothermal to low-temperature deposits. Six distinct
types of ore deposits were found to occur at Bau mining
district, including porphyry copper deposits (Gunong Ropih,
Gunong Juala), contact metasomatic Cu–Au skarn deposits
(Arong Bakit), cordilleran vein-type mesothennal precious
metal and Pb–Zn–Ag sulfide deposits (Sarabau Mine,
Bekajang, Bukit Young, Tai Parit), disseminated and shalehosted gold deposits (Jugan), and distal and low-temperature
Hg–Ba and As deposits (Tegora, Gading) (Schuh 1993).
Previous geological studies indicated that gold, copper, and
Pb–Zn–Ag sulfide mineralization exhibits significant structural (faults) and stratigraphic (composition/permeability) controls, which are associated with hydrothermal alteration zones,
consist of decarbonatization, silicification (jasperiod formation), and argilization (Percival et al. 1990; Sillitoe and
Bonham 1990; Schuh 1993; Kim 1994; Dill and Horn 1996).
To date, remote sensing study is not carried out in the study
area to identify hydrothermally altered rocks and structure
elements associated with ore mineralization for exploring
potentially interesting areas of gold mineralization in the
Bau mining district. Therefore, this investigation attempted
to acquire comprehensive and accurate information for exploring high economic-potential ore mineralization zones
using the integration of the EO-1 and the JERS-1 SAR remote
Arab J Geosci
Fig. 2 Geologic map of the
Bau mining district (modified
from Percival et al. 1990)
sensing data. The objectives of this research are: (1) to identify
the potentially interesting areas for exploration of new prospects of ore mineralization using EO-1 and the JERS-1 SAR
remote sensing data and (2) to introduce new approach for
discriminating hydrothermal alteration zones and identifying
the fault-controlled and stratiform gold orebodies in the tropical
regions.
Geology of the Bau District
Borneo is the product of Mesozoic accretion of ophiolitic,
island arc, and micro-continental fragments, together with
marginal basin fill, onto a Paleozoic-continental core in the
southwest of the island (Hutchison 1989; Metcalfe 1996;
Wilson 2002). West Sarawak is underlain by the Northwest
Kalimantan Block, which contains the oldest rocks on
Borneo, with a minimum age of pre-Moscovian (308 m.a.)
(Williams et al. 1988; Meyerhoff 1995). The pre-Moscovian
Tuang Formation and the Late Carboniferous to Early
Permian Terbat Limestone crop out some 40 km southeastern
and east of Bau (Wilson 2002). This older basement consists
of regionally metamorphosed greenstones. It was deformed by
Moscovian and Early Triassic events and possibly underlies
the Bau area. The oldest units known in Bau are the Late
Triassic andesitic Serian Volcanics. Bau is their westernmost
occurrence. Their discontinuous, patchy outcrop pattern in the
Penrissen area some 30 km southeastern of Bau suggests
buildup of individual volcanic edifices. The Early Jurassic
orogeny brought the emplacement of the Jagoi–Kisam granodiorite complex (Kirk 1968; Schuh 1993).
Early Jurassic sediments are absent, and extensive erosion presumably took place. The Late Jurassic commenced
with a transgression of argillaceous sandstones of the Krian
Member in Bau and the correlative Kedadom Formation in
the Penrissen area. Deposition of the thick shallow marine
limestones of the Bau Limestone Formation followed, and
massive rudist patch reefs developed that rim paleo-highs of
Arab J Geosci
Early Jurassic granodiorite and Triassic Serian andesite
(Schuh 1993).
The Cretaceous Pedawan Formation consists of extensive
Bouma-bedded turbidites; it was obducted in a Late
Cretaceous accretion event. Geographically, the Pedawan is
in an intermediate position between the former Early Jurassic
to Early Cretaceous Serabang subduction melange complex to
the west and the later Eocene Lubok Antu melange complex to
the east. Post-accretion uplift during the Early Tertiary triggered the development of intermontane molasse basins. The
resulting Kayan Formation in Bau and Silantek Formation in
the Klingkang Rang, 60 km to the southeast, are characterized
by terrestrial sandstones (Hamilton 1979).
Three trends of petrographically similar, intermediate to
felsic porphyritic stocks of early Late Miocene age occur in
the Bau District. The stocks in all three trends are small, ranging
from 0.3 to 3 km in diameter. Numerous sills and dikes accompany the stocks. Gunong Orat and Gunong Murong are major
representatives of at least six east–west trending stocks located
some 3 km north of Bau. The second trend of some 14 intrusions runs ENE through the Pangkalan Tebang area in the
south of the Bau District. Its structural grain was pre-set by the
older Jagoi–Kisam trend. Mineralization is principally associated with the third trend of at least 12 stocks that run from SSW
to NNE for some 30 km, from the Indonesian border through
Bau town. This trend is named henceforth the Bau Trend
(Schuh and Guilbert 1990; Schuh 1993).
The intrusions and gold deposits define a linear northeastto north-northeast-striking belt or trend at least 30 km long and
approximately 8 km wide. The trend encompasses northeastto north-northeast-striking high-angle normal faults and part
of a regional east-northeast-striking anticline, as well as an
orthogonal set of subsidiary faults and fractures (Fig. 2). The
trend is believed to reflect a zone of deep crustal weakness.
Diverse styles of gold mineralization are present in the Bau
trend (Sillitoe and Bonham 1990). Porphyry-type quartz
stockworks carrying low-grade Cu–Mo–Au mineralization
are associated with K-silicate and sericitic alteration in at least
four of the porphyry stocks (Metal Mining Agency of Japan
1985). Stocks also contain arsenopyrite-, sphalerite-, galena-,
and stibnite-bearing hydrothermal breccias, veins, and pervasive disseminations, all of which are auriferous and accompanied by intense sericitic alteration (Sillitoe and Bonham
1990).
Carbonate-replacement gold mineralization occurs along
the contact of the Bau Limestone with the Pedawan
Formation in close proximity to steeply dipping faults.
These deposits also occur within massive Bau Limestone
and shale along brecciated fault zones. All vein gold–arsenic–antimony mineralization occur in high-angle faults, fractures, joints, and bedding planes within massive units of the
Bau limestone (Fig. 2). Ore mineralization at Bau is much
more structurally controlled (Percival et al. 1990; Schuh
1993; Dill and Horn 1996). However, ubiquitous jasperiod,
silicification, carbonatization, sericitization, and argillic alteration can be seen associated with ore mineralization in the
study area (Kim 1994).
Igneous activity continued throughout the Tertiary,
evidenced by the older 48 m.a. Sirenggok granodiorite near
Bau (Kirk 1968). Post-mineralization igneous activity resulted
in a string of east–west trending stocks, the larger ones being
Gunong Orat and Gunong Murong. Regional uplift continued
through the late Tertiary and Quaternary, resulting in widespread erosion to expose the Miocene stocks. Pleistocene sea
level fluctuations caused a peneplanation surface at a level of
60–90 m.
Materials and methods
Remote sensing data and characteristics
EO1 data
The EO-1 satellite was launched on November 21, 2000 as
part of NASA’s New Millennium Program technology pathfinding activities to enable more effective (and less costly)
hardware and strategies for meeting earth science mission
needs in the twenty-first century. The EO-1 platform includes
three of the most advanced remote sensing instruments (a) The
Advanced Land Imager (ALI), (b) Hyperion, and (c) The
Linear Etalon Imaging Spectral Array Atmospheric
Corrector. These sensors can be used in a variety of scientific
disciplines (Beck 2003; Ungar et al. 2003).
The ALI sensor was built as archetype for the next production Landsat satellites. The multispectral characteristics maintain to Enhanced Thematic Mapper Plus sensor on Landsat-7
with a spatial resolution of 30 m, but the swath width is 37 km
(Hearn et al. 2001; National Aeronautics and Space
Administration 2002, 2004; Wulder et al. 2008). ALI has ten
channels spanning the visible and near infrared (VNIR) to
shortwave infrared (SWIR) (0.4 to 2.35 μm) (one panchromatic band, six bands in VNIR, and three bands in SWIR).
VNIR bands (0.4 to 1.0 μm) are especially useful for discriminating among ferric-iron-bearing minerals. SWIR bands
(1.20 to 2.35 μm) are sensitive to hydroxyl (OH) minerals
that can be found in the alteration zones associated with
hydrothermal ore deposits (Hubbard et al. 2003; Hubbard
and Crowley 2005).
Hyperion is the first spaceborn hyperspectral sensor in
commission across the spectral coverage from 0.4 to 2.5
micrometer and 10-nm spectral resolution. It is a push broom
instrument with 242 spectral channels over a 7.6-km swath
width and 30 m spatial resolution. The first 70 bands are in the
VNIR (0.4 to 1.0 μm) and the second 172 bands in the SWIR
(0.9 to 2.5 μm) (Barry and Pearlman 2001; Folkman et al.
Arab J Geosci
2001; Pearlman et al. 2003). Hyperion SWIR bands (2.0 to
2.5 μm) can uniquely identify and map hydroxyl-bearing
minerals, sulfates, and carbonates in the hydrothermal alteration assemblages (Bishop et al. 2011; Gersman et al. 2008;
Kruse et al. 2003). First subset of VNIR bands between 0.4
and 1.3 μm can also be used to highlight iron oxide minerals
(Bishop et al. 2011).
A cloud-free level 1B ALI and Hyperion image was
obtained through the US Geological Survey Earth Resources
Observation System Data Center. ALI and Hyperion scenes
were acquired during dry season on August 28, 2004 for the
Bau mining district. The images were pre-georeferenced to
UTM zone 40 North projection using the WGS-84 datum.
JERS-1 SAR data
The JERS-1 was launched into a solar-synchronous orbit at an
altitude of 568 km by the National Space Development
Agency of Japan (NASDA) on February 11, 1992. It is stayed
in operation for 6.5 years, until contact was lost in October
1998. The JERS-1 carried an L-band (1,275 MHz/23.5 cm)
SAR which operated with HH polarization and a fixed 35°
off-nadir angle. Satellite recurrence cycle was 44 days, with an
image swath width of 75 km (Rosenqvist et al. 2000, 2004).
The JERS-1 in commission to observation around the
world aimed at resource exploitation, geological structural
mapping, national land survey, agriculture, forestry, fishery,
environmental protection, disaster protection, coastal monitoring, etc. (Rosenqvist 1996; Metternicht and Zinck 1998;
Miranda et al. 1998; Hashim et al. 1999; Rosenqvist et al.
2000). In the tropical regions, SAR data are particularly
appropriate, as microwave signals penetrate the persistent
cloud coverage. Structural geology investigations that are
searching for mineral deposits and hydrocarbon traps can
be developed using SAR data in arid/semi-arid and
tropical/sub-tropical terrains (Rhealut et al. 1991; Singhroy
1992; Kusky and Ramadan 2002).
The JERS-1 SAR data were acquired from the NASDA. In
the present study, the data were selected from 100 m L-band
SAR image mosaics of Borneo/Kalimantan, which are available on request as a part of JERS-1 SAR Global Rain Forest
Mapping Project (GRFM) for Insular Southeast Asia and
Papua New Guinea (Rosenqvist et al. 2000). GRFM was an
international project led by NASDA during 1995–1999 aimed
at producing spatially and temporally contiguous SAR data on
the tropical belt on the Earth (Shimada and Isoguchi 2002;
Rosenqvist et al. 2004). The Borneo/Kalimantan SAR image
mosaics were provided in October–November 1996. The
radiometric and geometric corrections of each mosaic were
applied by NASDA (Shimada and Isoguchi 2002). A spatial
subset scene covering Bau gold mining district and surrounding areas was extracted from the Borneo/Kalimantan image
mosaics.
Image processing methods
Preprocessing of EO1 data
Spectral bands covering the VNIR (0.4 and 1.3 μm) and
SWIR (2.0–2.4 μm) spectral ranges of Hyperion data were
selected in this study. Preprocessing was involved removal of
overlapping and inactive bands (1–7, 58–81, 164–185, and
225–242), destriping and atmospheric correction (Gersman et
al. 2008). After radiometric corrections, there is still a pronounced vertical striping pattern in the Hyperion data. Such
striping is often seen in data acquired using push broom
technology (Kruse et al. 2003; Hashim et al. 2004).
Destriping of the Hyperion Level 1B data was accomplished
before atmospheric correction. To correct the atmospheric
effects, Atmospheric Correction Now (ACORN) software
was used to retrieve the surface reflectance (Kruse et al.
2003; ACORNTM 5.0 2004). During the atmospheric correction, raw radiance data from imaging spectrometer are rescaled to reflectance data. Therefore, all spectra are shifted to
nearly the same albedo. The resultant spectra can be compared
with the reflectance spectra of the laboratory or filed spectra,
directly (San and Suzen 2010). ALI Level 1B data were also
converted to surface reflectance using ACORN software. The
ALI panchromatic band has not been used in this study.
Image processing methods
ALI, Hyperion, and JERS-1 data of the target site were
processed using the Environment for Visualizing Images version 5.0 software package. To evaluate the ALI data principal
components analysis (PCA) method was employed for enhancing the hydrothermally altered rocks associated with gold
mineralization, lithological units, and vegetation at regional
scale. Linear spectral unmixing (LSU) was applied on VNIR
and SWIR bands of Hyperion for mapping iron oxide/
hydroxide minerals and clay mineral assemblages associated
with gold mineralization at district scale. The Laplacian algorithm was applied for automatic detection of linear and curvature features using JERS-1 data at regional scale.
The PCA is a multivariate statistical technique that selects
uncorrelated linear combinations (Eigenvector loadings) of
variables in such a way that each component successively
extracted linear combination and has a smaller variance
(Singh and Harrison 1985; Jensen 2005; Chang et al. 2006).
The eigenvalues also contain important information.
According to Crosta and Moore (1989) and Loughlin
(1991), a PC image with moderate to high eigenvector loading
for diagnostic reflective and absorptive bands of mineral or
mineral group with opposite signs enhances that mineral. If
the loading are positive in reflective band of a mineral, the
image tone will be bright, and if they are negative, the image
tone will be dark for the enhanced target mineral. Thus,
Arab J Geosci
The LSU method is used to determine the relative abundance of materials that are depicted in multispectral or
hyperspectral imagery based on the materials' spectral characteristics (Shimabukuro and Smith 1991). The reflectance at
each pixel of the image is assumed to be a linear combination
of the reflectance of each material (or end-member) present
within the pixel. This technique also known as sub-pixel
sampling, or spectral mixture analysis, is a widely used procedure to determine the proportion of constituent materials within a pixel based on the materials’ spectral characteristics
(Boardman 1989, 1992).
The Laplacian algorithm is commonly used for edge detection in digital image processing (Van Dokkum 2001). The
Laplacian algorithm was implemented on JERS-1 SAR data
by convolving the image by using a spatial matrix. The
Laplacian mask is rotationally symmetric, or isotropic, which
means that edges at all orientations contribute to the result.
Laplacian algorithm is applied by selection of one mask and
convolving it with the image (Gonzalez and Woods 2006).
The above convolution mask returns a value of zero, and by
increasing the center coefficients by one, each mask returns to
original gray level. Therefore, we put the sum of coefficients
by zero to acquire more information regarding the edge of
geological features that are existed in JERS-1 SAR data.
Results and discussion
Fig. 3 Regional view of the study area using bands 7, 8, and 9 of ALI
as RGB color composite
eigenvector loading in each PCA would identify the PC
image in which the spectral information of mineral being
examined is loaded. This information usually represents, in
quantitative terms, a very small fraction of the total information content of the original bands, but it is expected that the
loaded information indicates the spectral signature of the
desired mineral.
Table 1 Principal components
analysis on VNIR+SWIR bands
of ALI indicates eigenvector for
selected spatial subset scene
covering the Bau gold mining
district
Figure 3 shows a regional view of the ALI full scene using
bands 7, 8, and 9 (SWIR bands) as RGB color composite.
Geological structures, urban area, and rivers can be easily
distinguished; Bau anticline and selected spatial subset scene
covering Bau gold mining district and surrounding terrains are
specially marked in the scene.
Standard PCA transformation was applied to selected spatial subset scene of ALI covering the Bau gold mining district.
New image components were generated from the nine
VNIR+SWIR bands of ALI data. The image eigenvectors
and eigenvalues were obtained from PCA using covariance
Input bands Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 Band 8 Band 9 Eigenvalues
PC1
PC2
PC3
PC4
PC5
PC6
PC7
PC8
PC9
0.318
−0.303
0.247
−0.193
0.294
−0.430
−0.149
0.274
−0.002
0.324
−0.359
0.344
−0.292
−0.225
0.360
0.323
−0.603
−0.009
0.330
−0.360
0.066
0.211
−0.370
0.433
−0.304
0.542
−0.005
0.339
−0.440
−0.740
0.128
0.142
−0.470
0.109
−0.180
−0.016
0.331
0.379
0.175
0.217
0.501
0.265
−0.399
−0.237
0.335
0.331
0.347
0.312
0.145
0.134
0.056
0.398
0.198
−0.657
0.334
0.273
0.139
0.015
−0.428
−0.279
0.334
0.149
0.574
0.343
0.321
−0.291
0.608
−0.376
−0.216
0.536
−0.299
−0.334
0.348
0.110
−0.740
−0.614
0.332
0.279
0.227
0.161
0.118
7.442
0.544
0.302
0.064
0.020
0.017
0.011
0.010
0.006
Arab J Geosci
matrix on all nine VNIR+SWIR bands. PCA outputs are
presented as tables of statistic factors, and selected PCA
images from these transformations are reproduced in figures
to support the discussion. Statistic results are shown in
Table 1.
After analyzing the magnitude and sign of the eigenvector
loadings and eigenvalues for the Bau gold mining district, we
realized that the first principal component (PC1) accounts
74.42 % of total eigenvalue, which is higher value among
PCA images in the scene. PCA image with higher eigenvalue
contains most of the spectral information in the scene (Jensen
2005). Eigenvector loadings for PC2 indicate that PC2 image
can manifest vegetation as bright pixels due to the negative
contribution from bands 1 to 4 and positive contribution from
5 to 6 in VNIR bands region (Table 1; Fig. 4). Figure 5 shows
a panoramic view of vegetation covers in the Bau area.
Spectral signatures observed in remote sensing imagery are
related almost entirely to vegetation because it is dominated
material on earths’ surface in tropical/sub-tropical environment. So, the identification and separation of vegetation effects is significant to test the application of optical remote
sensing data for detecting mineralogy of soils and rocks in
areas of tropical/sub-tropical climates.
The existence of vegetation within a pixel can reduce the
2.20-μm absorption depth related to AlOH/MgOH content
(Bierwirth et al. 2002; Galvao et al. 2005; Rodger and
Fig. 4 PC2 image indicates vegetation as bright pixels in the scene for
the Bau gold mining district
Fig. 5 A panoramic view of vegetation covers in the Bau area
Cudahy 2009). Vegetation has also spectral characteristics
similar to iron oxide minerals in the VNIR from 0.7 to
1.2 μm (Ruiz-Armenta and Prol-Ledesma 1998). Vegetation
shows absorption features from 0.45 to 0.68 μm (a reflectance
peak at ~0.55 μm) and high reflectance in near infrared. It is
observed that iron oxide/hydroxide minerals have high reflectance in the range of 0.63 to 0.69 μm, while in the range of
0.76 to 0.90 μm covers higher range of the vegetation rededge reflectance feature in near infrared. This characteristic
Fig. 6 PC3 image illustrates iron oxide minerals as yellow pixels in
the scene for the Bau gold mining district
Arab J Geosci
Fig. 7 Association of iron rich rocks (gossan) with superficial exposure of the Jugan prospect
Fig. 9 Spectral mineral signatures extracted from VNIR bands of
Hyperion
can be used to differentiate iron oxide/hydroxide minerals
from vegetation (Crosta and Moore 1989; Ruiz-Armenta and
Prol-Ledesma 1998). It seems that bands 4 (0.633–0.69 μm)
and 5 (0.775–0.805 μm) of ALI have distinctive characteristics that can be utilized to discriminate iron oxide/hydroxide
minerals from vegetation.
Iron oxides can be distinguished in PC3 image as bright
pixels in PC3 because of the negative contribution from band
4 (−0.740) and positive weighting of band 5 (0.344) (Table 1;
Fig. 6). Figure 7 shows the association of iron oxide rich rocks
with superficial exposure of the Jugan prospect. Clay minerals
have absorption features from 2.1 to 2.4 μm (the equivalent to
band 9 of ALI) and reflectance from 1.55 to 1.75 μm (the
equivalent to band 8 of ALI) (Hunt 1977). PC4 image manifests desired information related to Al (OH)-bearing (clay)
minerals as bright pixels. Eigenvector loadings of band 8 in
PC4 are (0.608) and band 9 (−0.614). Thus, clay minerals
(hydrothermally altered rocks) appear as bright pixels in PC4
image that are associated with fault and facture elements
(Table 1; Fig. 8). PC5 to PC9 images have low eigenvalue
contents (0.020 to 0.006) (Table 1); hence, they do not have
informative spectral information for geological mapping applications in the scene.
Accordingly, PC2 detects the vegetation effects, and PC3
and PC4 images contain spectral information for identifying
particular materials such as iron oxide and clay minerals
Fig. 8 PC4 image shows hydrothermally altered rocks as magenta
pixels in the scene for the Bau gold mining district
Fig. 10 Spectral mineral signatures extracted from SWIR bands of
Hyperion
Arab J Geosci
Fig. 13 Association of advanced argillic alteration with ore mineralization (gold-bearing arsenopyrite) in the Jugan prospect
associated with geological structures of the study area at a
regional scale. This method distinguished the high economic
potential areas for gold exploration purposes minimizing the
erroneous effects of vegetation in the tropical environment.
The results are supported by previous geology studies and
field investigations in the Bau gold mining district (Sillitoe
and Bonham 1990; Percival et al. 1990; Dill and Horn 1996).
Linear spectral unmixing (LSU) was applied to a selected
spatial subset scene covering the Bau gold mining district for
district scale mineral mapping purposes. Two spectral subsets
of Hyperion data were analyzed separately to detect iron
oxide/hydroxide minerals and hydroxyl-bearing (clay) alteration mineral assemblages. The first subset (VNIR) covering
90 bands (excluded overlapping and inactive bands) between
0.4 and 1.3 μm were used for highlighting iron oxide/hydroxide
minerals, and second subset (SWIR) of 40 bands between 2.00
Fig. 12 Image map of SWIR bands of Hyperion shows the abundance
of clay minerals in Bau gold mining district
Fig. 14 JERS-1SAR processed image shows the geological structures
in the Bau and surrounding areas at regional scale
Fig. 11 Image map of VNIR bands of Hyperion shows the abundance
of iron oxide minerals in Bau gold mining district
Arab J Geosci
Fig. 15 Detailed local structure elements in the Bau mining district. Black line boundary delimited the known deposits
and 2.40 μm (185 to 225) were processed for detecting
hydroxyl-bearing (clay) alteration mineral. Erroneous vegetation effects were excluded using forced invariance approach
that has been proposed by Crippen and Blom (2001) to subdue
the expression of vegetation and enhance the expression of the
under-lying lithology in remotely sensed imagery. AIGFig. 16 Schematic cross
section showing structurally
controlled gold mineralization
in the Bau area
developed hyperspectral analysis processing methods (Kruse
and Boardman 2000; Kruse et al. 2003) were used to extract
end-member spectra from Hyperion subsets for applying linear
spectral unmixing technique.
The methods were applied to the selected spatial subset
scene covering the Bau gold mining district. The extracted
Arab J Geosci
end-member spectra were identified using USGS spectral
library as reference spectra. Figures 9 and 10 illustrate the
extracted end-member spectra for two spectral subsets.
Considering the shape and position of absorption feature, the
minerals are characterized as hematite–limonite–goethite for
first subset (Fig. 9). Goethite, hematite, and limonite have
strong Fe3+ absorption features at 0.48 and 0.83–0.97 μm
(Hunt et al. 1971; Fig. 9). The extracted signatures for second
subset suggest the existence of hydroxyl minerals such as
sericite–kaolinite (Fig. 10). Sericitically altered rocks typically
contain sericite, a fine-grained form of muscovite that has a
distinct Al–OH absorption feature at 2.2 μm and a less intense
absorption feature at 2.35 μm (Abrams and Brown 1984;
Spatz and Wilson 1995; Fig. 10). Kaolinite is typical constituents of advanced argillic alteration that exhibit Al–OH 2.165
and 2.2 μm absorption features (Hunt 1977; Hunt and Ashley
1979; Rowan et al. 2003; Fig. 10).
Figure 11 shows image map of the selected spatial subset
scene for first subset (VNIR), showing spectrally predominant
minerals as colored pixels that are overlaid on the gray-scale
image background of Hyperion band 36. The abundance of
iron oxide minerals is represented as purple (high abundance)
and green pixels (low abundance) in the Bau subset scene
(Fig. 11). Iron oxide concentrations are obviously associated
with lineament structures and as well as open pit quarries of
gold mining activities in the Bau area. Iron oxide anomalies
generally range from high to moderate, but quite pronounced
locally along the structures. Prior to the opening of the pit of
the gold deposits in the Bau area, the mining areas were
covered by iron rich rocks (gossan), which presented the
superficial expression (Schuh 1993). Iron oxide concentrations are obviously associated with lineament structures and
as well as known prospects in the Bau area. Most of the gold
prospects such as Boring, Kapor, Umbut, Sirenggok, Jugan,
and Bukit Sarin are associated with high abundance iron
oxides areas, although some of the prospects can be seen with
low abundance iron oxide zones (Taiton, Bukit Young, Tai
Parit, Bekajang). Some potentially interesting areas with high
abundance of iron oxide minerals are detected in center and
western parts of the scene (Fig. 11).
Therefore, spectral identification of these iron-rich rocks is
significant to detect this type of deposits in tropical/sub-tropical regions. Iron oxide/hydroxide minerals produce during
supergene alteration and render characteristic yellowish or
reddish color to the altered rocks, which are collectively
termed gossan (Abdelsalam and Stern 2000). Iron oxides are
one of the important mineral groups that are associated with
hydrothermally altered rocks over porphyry copper–gold ore
bodies (Sabins 1999).
Figure 12 demonstrates image map of the selected spatial
subset scenes for second subset (SWIR). Sericite and kaolinite
abundance is illustrated as red (high abundance) and yellow
(low abundance) pixels that are overlaid on the gray-scale
image background of Hyperion band 205. Detected pixels
are associated with fault and fracture structures in the Bau
scene. Most of the gold prospects, including Kapor, Taiton,
Umbut, Bukit Young, Tai Parit, Bukit Sarin, Jugan, Bekajang,
and Jambusan, are located in high abundance clay minerals
area that pointed by arrows in the scene (Fig. 12). Field and
geological investigations indicated that they are associated
with advanced argillic alteration, decalcification, brecciation,
and silicification of the sedimentary host rocks (Schuh 1993).
Figure 13 shows the association of advanced argillic alteration
with ore mineralization (gold-bearing arsenopyrite) in the
Jugan prospect.
Thus, the LSU method detected alteration minerals using
extracted end-member spectra directly from the Hyperion
image at a district scale. The results derived from VNIR and
SWIR bands of Hyperion almost compared well by one
another, and most of the detected minerals occupy fault and
fracture structures in the Bau gold mining district. However,
iron oxides in tropical soils may have large specific surface
areas (Osei and Singh 1999), and hence, their presence, even
in small amounts, could have a great influence on remote
sensing results.
The Laplacian algorithm identified the edges of the surrounding pixels in the JERS-1SAR image. Hence, geological
structures were obviously detected in the study area at both
regional and district scales. Figure 14 shows the geological
structures, including Bau anticline, Bau anticline axis, Tai
Parit fault, and several linear and curvature features in the
Bau and surrounding areas at a regional scale. Bau anticline
(NE–SW trending) and its axis are located in south and
southwestern part of the JERS-1SAR image (Fig. 14). Tai
Parit fault (NNE-trending) and several linear and curvature
features (WNW to NW trending) dissected Bau limestone,
which are identifiable in western part of the scene (Fig. 14).
These regional and local structures influenced the location of
the gold mineralization because they acted as major conduits
for hydrothermal fluids. Therefore, faults and fractures served
as primary structural control on gold mineralization processes
in the study area.
Previous geological studies verified that the gold mineralization areas are cut by a network of NW and NE trending
faults and joints, along which the mineralizing fluids have
passed upward into the calcareous host rocks (Percival et al.
1990; Schuh 1993; Dill and Horn 1996). Figure 15 shows
detailed local structure elements in the Bau mining district.
Most of the known gold deposits are located on the western
side, and few of them can be seen in the eastern part of the
image, which are delimited by black line boundary. Figure 16
shows schematic cross section of structurally controlled gold
mineralization in the Bau area.
Accordingly, the clear appearances of the lineament features
in the Bau gold mining district were produced by applying the
Laplacian algorithm on the JERS-1SAR data. In fact, the
Arab J Geosci
Laplacian algorithm avoids a decreasing resolution by making
a weighted combination of running average with the neighbor
surrounding pixels. The method can reduced the noise in the
features’ edge areas without sacrificing edge sharpness
(Marghany and Hashim 2010). Alteration signatures identified
by ALI and Hyperion data are well-matched with detected
faults and fractures using JERS-1SAR image. Gold mineralization at Bau is structurally controlled (Schuh 1993); for this
reason, a detailed detection of geological structures can be very
useful in identifying potentially interesting areas in this region
and surrounding terrain.
Conclusions
The results presented in this study demonstrate the importance
and advantages of the combined use of EO1 and JERS-1 SAR
remote sensing data in detecting potentially interesting areas
of gold mineralization in tropical/sub-tropical regions.
Structurally controlled gold mineralization indicators, including iron oxides, clay minerals, and faults and fractures in Bau
gold mining district, have been detected using PCA, LSU, and
Laplacian algorithms. Additional research is required to extend and explore the relationships of carbonate-replacement
gold mineralization occurs along the contact of the Bau
Limestone with the Pedawan Formation, quartz–gold-bearing
veins and silicification associated with faults, joints, and fractures, and detailed mineral mapping of identified altered
zones. These geological features can be detected and more
broadly applicable to provide an opportunity for detecting
potentially interesting areas of gold mineralization using the
Advanced Spaceborne Thermal Emission and Reflection
Radiometer and Phased Array L-band Synthetic Aperture
Radar data in the study area.
Acknowledgments This study is conducted as a part of post-doctoral
fellowship scheme granted by Universiti Teknologi Malaysia. We
acknowledge the assistance of the Olympus Pacific Minerals INC.
Company (North Borneo Gold SDN BHD) for their logistic support
during the field investigations and ground truth data collection, as well
as appreciate their assistance in various other ways during this research. We also would like to express our great appreciation to the
anonymous reviewers for their very useful and constructive comments
and suggestions for improvement of this manuscript.
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