Abstract:
Many of the known porphyry copper deposits are situated in the Central Iranian volcanic Belt. The area under study is located in the southern part of this belt and covers an area of about 2600 sq. kms. The climate in the area is a semi arid type. Given the poor soil development but abundant outcrops, the arid/semiarid part of the belt is suitable for remote sensing study. ETM+ images have been used for alteration mapping. Crosta method was found useful for enhancing the areas with hydroxyl and iron oxide minerals. Crosta method has been used on selected 4 and 6 bands. The areas with iron oxide and hydroxyl minerals are enhanced by this method. However the areas with higher grade of alteration are enhan ced by using six bands of ETM+ images.

Introduction
The study area is located in the southern part of Central Iranian Volcanic belt (Figure 1). This belt has a great potential as far as porphyry copper mineralization is concerned. Meiduk and Sar Cheshmeh porphyry copper deposits are presently mined for Cu, and Mo in the area. The area has a semi-arid type of climate and has a mountainous topography. Vegetation cover is substantially poor in the area.


Figure 1: Sketch map showing the position of the Central Iranian Volcanic Belt and porphyry-type Cu deposits sub parallel to the Zagros Thrust Zone(Shahabpour, 1994) 1- Bahreasman, 2- Takht, 3- Kuhe Panj, 4- Darrehzar, 5- Sar Cheshmeh, 6- Meiduk, 7- Gowde kolvary, 8- Darre Zereshg, 9- South of Ardestan, 10- Sharif Abad, 11- Songun (inset). RGB color composite of bands 741. The vegetation is shown in green color.

Most of the known porphyry deposits exhibit a well-developed zonal pattern of mineralization and wallrock alteration that can be defined by broad variations in major oxides and trace element concentrations. These elemental compositions in turn reflect variations in mineralogical composition of the altered zones. Most of the hydrothermal alteration processes produce clay and other silicate minerals (e.g. argillic and phyllic zones). Supergene alteration results in the formation of extensive iron oxide minerals, giving characteristic yellowish or reddish color to the altered rocks. These alteration minerals can be detected by remote sensing techniques (Abrams et al., 1977; Abrams et al., 1984; Buckingham and Sommer, 1983; Elvidge and Lyon, 1984; Amos and Greenbaum, 1989; Drury and Hunt, 1989).

Landsat data has been used for number of years in arid and semi-arid environments to locate areas of iron oxides and/or hydrous minerals(Abrams et al., 1983; Kaufman, 1988; Tangestani and Moore, 2001) which might be associated with hydrothermal alteration zones. The host rocks that contain ore deposits of hydrothermal origin always show the result of interaction with the hydrothermal fluids that change the mineral and chemical composition of the rock and cause the deposition of the ore and related hydrothermal minerals(Rutz-Armenta and Prol-Ledesma, 1998).

The principal component transformation is a multivariate statistical technique that selects uncorrelated linear combinations (eigenvector loadings) of variables in such a way that each successively extracted linear combination, or principal component(PC), has a smaller variance(Singh and Harison, 1985). The principal component analysis is widely used for alteration mapping in metallogenic provinces(Abrams, et al., 1983; Kaufman, 1988; Loughlin, 1991; Bennett, et al., 1993; Tangestani and Moore, 2001).

Crosta technique is also known as feature oriented principal components selection. Through the analysis of the eigenvector values it allows identification of the principal components that contain spectra information about specific minerals, as well as the contribution of each of the original bands to the components in relation with spectral response of the materials of interest. This technique indicates whether the materials are represented bright or dark pixels in the principal components according with the magnitude and sign of the eigenvectors loadings. This technique can be applied on four and six selected bands of TM data( Crosta and Moore, 1989; Rutz-Armenta and Prol-Ledesma, 1998).

Geology of the area
The volcanic-sedimentary rocks of Eocene age are the oldest rocks in the Sar Cheshmeh area represented by pyroclastics, pyroxene trachyandesites, pyroxene andesites, trachyandesites, trachybasalts and andesites. The sedimentary rocks in the volcanic-sedimentary complex are mainly sandstone and less frequently limestone that has very subordinate development in the area. The Eocene volcanic sedimentary rocks are intruded by Oligocene-Miocene plutonic rocks that consist of mainly granodiorite, quartz-diorite, diorite, monzonite, tonalite and granite. The volcanic rock in the immediate vicinity of these intrusives are widely metamorphosed and altered. Most of the plutonic and volcanic rocks are hydrothermally altered and at places they are mineralized. Argillization, sericitization and propylitization are the most common types of hydrothermal alteration in the area. The Neogene sediments consist of mainly loosely consolidated, unsorted and poorly stratified conglomerate and sandstone overlying the Eocene volcanic-sedimentary rocks. Calcarious terraces, dacitic rocks and recent alluvium are the main Quaternary features in the area (Dimitrijevic et al. (1971).

Meiduk area has almost a similar geology. The Eocene Volcanic-Sedimentary rocks are subdivided into trachybasalt and trachyandesitic tuff, lava flows and porphyrites; tuffs, trachyandesitic and trachybasaltic rocks, tuffaceous sediments, andesitic and basaltic rocks. The intrusive rocks are granodiorite to tonalite. The upper Cretaceous Flysch is the oldest and the Quaternary alluvial deposits and gravel fans are the youngest exposures. Sedimentary rocks consist of the Pliocene and Eocene sandstones, marls, sandy calcarenites and conglomerates(Dimitrijevic, 1973).

Data analysis and discussion
ETM+ data of Sar Cheshmeh (Acquisition date 23/6/2001) and Meiduk ( Acquisition date 11/4/2001) areas are used for this study. Both images are cloud free. The images are geometrically corrected by using control points from topographic sheets. Both subscenes are joined together to form a single image. The general statistics and principal component eigenvectors and eigenvalues are calculated (Table 1).

Table 1: A) General statistics for Meiduk and Sar Cheshmeh areas

Minimum	41.000	28.000	21.000	11.000	8.000	7.000
Maximum	185.000	182.000	220.000	168.000	248.000	255.000
Mean	77.990	75.411	94.500	74.532	80.403	72.550
Median	77.000	73.000	92.000	73.000	76.000	69.000
Std. Dev.	8.887	11.044	15.905	12.925	19.404	17.150
Std. Dev. (n-1)	8.887	11.044	15.905	12.925	19.404	17.150
Covariance Eigenvalue	1178.654	80.268	20.146	6.316	4.698	1.523
Percentage of variance	91.2	6.2	1.6	0.46	0.36	0.12

B ) Correlation matrix

	Band1	Band2	Band3	Band4	Band5	Band6
Band1	1.000	0.944	0.892	0.732	0.666	0.711
Band2	0.944	1.000	0.973	0.874	0.836	0.858
Band3	0.892	0.973	1.000	0.921	0.874	0.896
Band4	0.732	0.874	0.921	1.000	0.915	0.905
Band5	0.666	0.836	0.874	0.915	1.000	0.980
Band6	0.711	0.858	0.896	0.905	0.980	1.000

C ) Principal components for six bands

	PC1	PC2	PC3	PC4	PC5	PC6
Band1	0.209	-0.540	-0.316	-0.348	-0.430	-0.509
Band2	0.301	-0.408	-0.147	-0.297	0.068	0.793
Band3	0.446	-0.437	0.121	0.508	0.535	-0.227
Band4	0.359	0.002	0.844	-0.102	-0.384	0.017
Band5	0.548	0.498	-0.121	-0.508	0.375	-0.197
Band6	0.488	0.322	-0.370	0.515	-0.485	0.147

The principal component transformation (eigenvectors and eigenvalues) described in Table 1C , using six ETM+ bands as input bands( bands 1, 2, 3, 4, 5 and 6). As it is observed the first principal component does not contain spectral features relevant in this analysis as it is a combination of all bands with a major contribution from band -5. This component contains 91.2 per cent of the variance of six bands. This PC gives information mainly on albedo and topography. Vegetation is enhanced in PC3 as this PC has higher loading of band-4. PC4 enhances the hydroxyl minerals. This PC has higher loadings of bands 5 and 6 but with opposite signs. It has negative contribution of band 5 and positive contribution of band 6. Therefore pixels that map the hydroxyl minerals will be darker in the final hydroxyl image. But in order to show the areas with hydroxyl minerals in bright pixels an inverse of this PC is obtained(Figure 2). A similar analysis of PC5 shows that the most important contributions come from TM1(-0.43) and TM2(0.54). According to spectral characteristics of iron oxide(Hunt, 1978), it follows that iron oxide will be mapped by bright pixels(Figure 3). An average of hydroxyl and iron oxide images is also obtained. A false color composite image is made(hydroxyl image in red, iron oxide in green and average of these two in blue). In the resulted image all intensely hydrothermally altered areas are shown in bright pixels (Figure 4). The same technique is used on 4 bands. The only disadvantage with using this method on 4 bands is that the sedimentary rocks are also enhanced in the resulted image.


Figure 2: The hydroxyl PC4 image shows the altered areas in bright pixels.


Figure 3: This image is obtained by using the eigenvector loadings of PC5 for enhancing iron oxide. The bright pixels are the areas with iron oxide.


Figure 4: This image is obtained by making RGB of hydroxyl image(red), iron oxide image(green) and average of these two images( blue).The highly altered areas are shown with bright pixels.
Summary and conclusions
The use of satellite images during the early stages of mineral exploration has been very successful in pointing the hydrothermally altered rocks. ETM+ data has been used for enhancing the areas with hydroxyl and iron oxide minerals. Principal component analysis was done on the six bands and the relevant principal components are chosen to obtain images that show iron oxide and hydroxyl minerals(Crosta technique). PC 5 shows the contribution of iron oxide and PC4 shows the contribution of hydroxyl minerals. We can conclude here that Crosta technique can be used as a very reliable method for enhancing the areas with hydrothermal alteration as a fast and cheap tool for exploration of porphyry copper mineralization in the Central Iranian Volcanic Belt.

Acknowledgements
The Sar Cheshmeh and Meiduk copper complexes have provided logistic support for this work.

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