Integration and analysis of airborne geophysics and remote sensing data for exploration of porphyry copper deposits in the Central Iranian Volcanic Belt
Sar Cheshmeh area is situated within the southern part of the Central Iranian Volcanic-Sedimentary complex, southeast of Kerman City. Its geological evolution can be simplified as (a) formation and folding of Early Tertiary Volcanic-Sedimentary rocks, (b) emplacement of Late Tertiary granodiorite, diorite, quartz diorite, monzonite, and tonalite in the Volcanic-Sedimentary complex. Their subsequent faulting, fracturing, alteration and mineralization, both within the porphyry rocks and the associated volcanic rocks, followed by (c) formation of supergene environment and oxidation zone in some of the deposits(Shahabpour, 1982). Hydrothermal alteration involving chlorite, sericite, epidote, carbonate, silica, tourmaline and clay minerals are common. However phyllic, argillic and propylitic alteration are more common in the area (Dimitrijevic, 1973). The Eocene volcanic-sedimentary rocks consist of andesite, trachyandesite, trachybasalt, agglomerate and tuffs, lava flows and sedimentary rocks. The intrusive rocks are granodiorite, diorite and monzonite. The oldest and youngest exposed rocks are the upper Lower Eocene volcanic rocks and the Quaternary alluvial deposits and gravel fans, respectively. Some well known copper deposits are situated in this area (Figure 1). Darrehzar area is located southeast of the Sar Cheshmeh porphyry Cu deposit. The topography around the deposit is mountainous. Data from detailed geophysical, geochemical and geological survey carried out in 1969 are given by GSI(1973). The deposit was later studied by Maanijou (1994) , Ranjbar(1996) and Ranjbar et al. (2001). The Darrehzar porphyry is situated in a diorite-quartz diorite pluton of Oligocene-Miocene age that intrudes an Eocene Volcanic-Sedimentary complex comprised mainly of volcaniclastics, andesite, trachyandesite and sedimentary rocks. The porphyry locally grades into granodiorite. The hydrothermally altered rocks are highly fractured, and supergene alteration has produced extensive limonite and leaching of sulfide, given a characteristic reddish or yellowish color to the altered rocks. A weathered zone is developed a few meters to 80 meters below the surface(GSI. 1973). Propylitic and phyllic alteration are pervasive in the surface rocks with sporadic small areas of argillic alteration(Maanijou, 1993). Potassic alteration is not seen at surface, possibly as a result of an intense phyllic overprint or surface related weathering. Lithogeochemical data has shown that Cu concentration is restricted to the quartz-sericite zone in the area(Ranjbar, 1996).  Figure 2: Iron oxide image which is prepared by using Crosta method. The altered areas are shown as bright pixels. Vegetation is depicted in dark pixels. 3. Data analysis and discussion Principal component analysis determines the eigenvectors of a variance-covariance or a correlation matrix. The analysis consists of a linear transformation of m original variables to m new variables, where each new variable is a linear combination of the old. The process is performed in a fashion that requires that each new variable account for, successively, as much of the total variance as possible. The use of principal components in exploration has been to separate variable associations into a number of groups of variables that together account for the greater part of the observed variability in the original data (Davis, 1986). This type of analysis is useful when there are number of data layers which can be overlain one over another. With the advent of geographic information system, integrated analysis of spatially distributed data can be done easier. This type of analysis can either be done on satellite images or other geo-data sets.  Figure 3: RGB image of K, Th and U counts in red, green and blue respectively. The altered areas are shown by bright pixels. Analysis of remote sensing data The principal component analysis is widely used for alteration mapping in metallogenic provinces(Kaufman, 1988; Crosta and Moore, 1989; Loughlin, 1991; Benett, et al, 1993 and Rutz-Armenta and Prol-Ledesma, 1998). Three techniques have been suggested for analyzing satellite images. These three techniques are the standard PCA on six bands of Landsat, selective PCA on two bands(e g. band7 and 5 for detection of hydrous minerals) and developed selective PCA or crosta technique on four or six bands. The ETM+ data is analysed and we found that crosta method on six bands is useful for enhancing the hydrothermally altered areas. Figure 2 shows the areas with iron oxide bearing minerals(Ranjbar et al., 2002). |