This study used the ability of remote sensing technology to identify alteration zones in porphyry copper mining and Iron oxides of area in south Nain district in Iran by using Lands at-8 data source. The band ratio of 3/2 derived from image spectra was used to indicate the distribution of iron oxides and 6/3 for identifying gossan. Hydrothermal alteration mineral zones associated with porphyry copper mineralization identified and discriminated based on two algorithms of target detection, MTTCIMF and OSP. Those techniques identified porphyry copper mineralization in study area and six points were diagnosed as the best location for ore exploration. For more accurate study and recognition between mineralization and tectonic structure of district, the lineament map of area was produced by applying Gaussian high-pass filter on IRS data. The Spatial distribution of hydrothermal alteration zones has been verified by inspection in field works and Fuzzy logic. Results showed that image processing techniques have a great ability to obtain comprehensive information for reconnaissance stage of porphyry copper exploration in the case study and assist researcher to explore porphyry copper and iron oxides regions before time-consuming and costly ground investigation.
Satellite Remote sensing is a useful tool for mapping lithology and surface mineralogy and exploring ore deposits [
spectral absorption features in the visible to middle infrared from 0.4 to 1.1 µm of the electromagnetic spectrum shows in
Iran is a semi-arid country. It has been divided into several units depends on a relatively unique record of stratigraphy, magmatic activities, metamorphism orogenic event, tectonics and overall geological style [
Two main faults, Dehsheer-Baft from East and Qum-Zefreh from west have confined the district. The function of them produced transvers faults in area. Qum-Zefreh fault system reflects a dextral strike―slip displacement that have had an important role in generating igneous rocks in area. Deh-sheer fault is lateral strike-slip and colored melange complex can be seen in length of it in Naein area... Being ensnared between two faults allows lava to stay and alter the surrounding rocks. The result of this operation is alteration of dacite rocks which have lost their chemical structure and have changed to chlorite and Sericite. During this process minerals such as magnetite, Chalcopyrite, Chalcocite, Bornite were formed (GIS, 2004). The altered rocks in this area confirm the presence of porphyry copper deposit.
Lands at 8 is a North American Earth observation satellite launched on February 11, 2013. Lands at8 data can be downloaded at (http://earthexplorer.usgs.gov) and has been used in wide range of studies [
Lands at 8 carries two instruments: The Operational Land Imager (OLI)1 sensor and Thermal Infrared Sensor (TIRS)2 This satellite image has 11 bands: 5 in the visible and Near-infrared (VNIR), 2 in the thermal infrared (TIR) region of the electromagnetic spectrum, 2 in the SWIR region, new band (ultrablue) for coastal and aerosol studies and another new band for cirrus cloud detection and 1 panchromatic channel (band 8). Spatial resolution is 15 meters for the panchromatic band, 30 meters for VNIR and SWIR bands and 100 meters for the TIR bands. The images which has been used in this study was downloaded from US Geological Survey (USGS) website on July 25, 2013 consisted of cloud free level 1 Terrain corrected (L1T) scene of the study area in central Iran. The level 1T data product provides systematic radiometric accuracy, geometric accuracy by incorporating ground control points, while also employing a digital elevation model DEM for topographic accuracy (https://landsat.usgs.gov/landsat-8-l8-data-users-handbook-section-4).
The images were pre-georeferenced to UTM zone 39 North projection with using the WGS-84datum. In addition Lands at8 L1T data converted to reflectance using the Internal Average Relative Reflection IAAR method. This dialog is used to convert raw DN values to relative reflectance and that is more useful for mineralogical mapping for this study. This algorithm is recommended for mineralogical mapping as a preferred calibration technique, which it dose not necessitate to have the prior knowledge of samples that collected from the field. This is particularly effective for reducing hyperspectral data to relative reflectance in an area where no ground measurements exist and little is known about the scene. For this case study just bands of OLI sensor consist of 2, 3, 4 in visible region, 5 in near infrared and 6, 7 that are posited in SWIR region, were processed and analyzed by ENVI (Environment For Visualizing Image) version 5.1.
The Indian remote sensing satellite (IRS)3 was launched on December 17, 1988. In this study IRS satellite data obtained from geological survey of Iran on may20, 2015. In this case study just panchromatic band of IRS data were utilized because of its high spatial resolution. The image converted to universal Transverse Mercator zone 39 N from the WGS-84 datum.
Band ratio Images improve the contrast between features by dividing Digital number value of one band to the Digital number value of another band. Band ratios are very useful for highlighting certain feature or minerals that cannot be seen in the raw bands [
This study describes the utility of Lands at-8 OLI data for sub-pixel mineral investigation using target detection algorithms for identification hydrothermal alteration zones. Satellites acquire images of earth surface in many electromagnetic spectrums in such a way that a complete spectral pattern of each pixel can be derived for target detection, discrimination and classification. Most of the surface minerals show diagnostic spectral signature in VNIR and SWIR of electromagnetic spectrum which enables their detection base on characteristics spectral signature. Minerals detected according to the similarity of the Image pixel with the reference spectra based on the threshold value derived from standard spectrum database [
1) OSP
Orthogonal Subspace Projection (OSP)6 first designs to eliminate the response of non-targets, then applies matched filter (MF) to match the desired target from the data. The matched filter is the optimal linear filter for maximizing the signal to noise ratio (SNR) in the presence of additive stochastic noise [
2) MTTCIMF
MTTCIMF developed by [
Lineaments are natural and man-made geomorphic features that have a surface expression, which could be fault, fracture, dykes, geological sharp boundaries or artificial road and canals Gaussian high-pass filter used to enhance the lineaments from IRS satellite image. The experience has shown that the best interpretation is achieved with the use of the panchromatic band, which has high spatial resolution (5 m) that will increase the accuracy and precision of detecting lineaments. In order to obtain a better image for interpretation, processing with Gaussian high-pass filtering technique take place. The function of this technique is to enhance the high frequency components. The linear and edge in the original image become more obvious and sharper in the filtered image. The aim of detecting lineaments in this research is to clear relationship between lineament and mineralization in the case study. Lineaments have important role in initial mineral exploration. Mineralization is associated with Lineaments, vein and shear zone systems when those are active. The aim of detecting lineaments of this study is to clear relationship between lineaments of study area with porphyry copper mineralization that will be find through image processing. The relationship between lineaments, structure, and mineralization was emphasized by [
Lands at-8 consisted of 11 bands. The first spectral band of (0.433 - 0.453 µm) is a deep-blue band designed for studies of coastal water and aerosols and cannot be used to detect geological features. So it was therefore excluded from research. Band 2 is positioned in the blue (0.450 - 0.515 µm), band 3 in the green (0.525 - 0.600 µm) and band 4 in the red (0.630 - 0.680 µm). Band 5 is located in (0.845 - 0.88 µm) near-infrared region of electromagnetic spectrum. Short wave infrared (band 6: 1.560 - 1.660 µm, band 7: 2.100 - 2.300 µm) is used for imaging soil types, geological features and minerals such as copper and sulfates. Panchromatic, cirrus cloud (band 9) and TIR bands (band 10: 10.6 - 11.1 µm, band 11:
11.5 - 12.5 µm) were not used in this study. Several color combination of Lands at-8 were created. Vegetation shows absorption at 0.45-0.68 µm and high reflectance in the near infrared from 0.7 to 1.2 µm and hence become more clear in color composite that contain near infrared region that is positioned in band 5 of Lands at-8.
is a numerical indicator that use the visible and near-infrared bands of the electromagnetic spectrum defined as (NIR − red)/(NIR + red), where NIR stand for the spectral reflectance measurement in near infrared, corresponds to Lands at-8 band 5, and red corresponds to Lands at-8 band 4.
Hydrothermally altered rocks are identified by iron oxide, clay, carbonate, and sulfate minerals, that have diagnostic absorption signatures. Electronic processes produce absorption features in the visible and near infrared radiation (0.4 to 1.1 μm) due to the presence of transition elements such as Fe2+, Fe3+ and often changed by Mn, Cr, and Ni in the crystal structure of the minerals. Supergne alteration processes, interaction with air and surface water, over porphyry copper bodies generate Fe-rich crust with abroad iron oxide/hydroxide minerals (yellowish to reddish color altered rocks), that are collectively termed gossan [
MTTCIMF and OSP algorithm detect Argillic, Phyllic, Propylitic alteration based on key minerals spectrum. Two algorithms were performed to identify alteration zone by considering the reference spectral acquired from standard spectrum data base of USGS. The output of MTTCIMF is set of images that give TCIMF and infeasibility scores with target images of each minerals and the output of OSP algorithm is target images and OSP images of selected minerals for each alteration. In this research alteration mineral assemblages are demonstrated with different colors, narrow argillic areas as blue color for kaolinite and yellow color as alunite, broad phyllic as green color for muscovite. propylitic zone as cyne color for epidote, red color for chlorite and pink color for calcite that has the most expanse in this area.
The results of all image processing of band ratio and target detection were perused and six points were selected as the best location for investigation porphyry copper deposit.
comparison between image processing results and fieldwork. The latitude and longitude of six points were obtained and the equivalent of them on the ground by GPS survey were found. Rock sampling have been done to obtain comprehensive information of the study area.
The relationship between lineaments of area and mineralization was evaluated by fuzzy logical model. This method is a partly knowledge-driven and partly data-driven approach [
Distance of lineaments | Weighting | Selected mineralization points |
---|---|---|
0 - 100 | 3 | 6 |
100 - 200 | 5 | 5 |
200 - 300 | 2 | 4 |
300 - 400 | 6 | 3 |
400 - 500 | 4 | 2 |
500 - 600 | 11 | 6 |
This study is the first use of remote sensing techniques in exploring minerals in this area. In this research applicability of Lands at-8OLI data for obtaining information on hydrothermal alteration associated with porphyry copper deposits iron oxide and gossan evaluated. Band ratio and target detection carried out for detailed hydrothermal alteration mapping, resulting in the identification of copper mineralization. Analysis of Lands at8 OLI level 1 T data performed after applying atmospheric correction using Internal Average Relative Reflection (IARR) method .Results of band ratio indicated the iron oxide and gossan of area can be utilized as a useful tool for mapping porphyry copper deposits. The result of MTTCIMF and OSP showed their capability in distinguishing of the argillic and phyllic and propylitic mineral assemblages based on their spectral properties. Lineaments of area were detected base on High Gussion filtering to make clear the relationship of them with mineralization of area. Results are proven to be effective with results of field work and Fuzzy logic. The results were validated using fuzzy logic and comparison between the image-based results and field surveying. To do this, we randomly selected six points over the study area for the field surveying process. In all six points we observed the correlation between Iron and Goassan and the existing faults of the area. This investigation shows that the integration of the image processing techniques and Lands at8 data have great ability to assist economic geologist for initial stages of mineral exploration, and can be extrapolated to intact area for exploring high potential copper mineralization zones.
This study was carried out as part of the first author’s Master of Science thesis at Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
Mahan, A. and Arfania, R. (2018) Exploring Porphyry Copper Deposits in the Central Iran Using Remote Sensing Techniques. Open Journal of Geology, 8, 606-622. https://doi.org/10.4236/ojg.2018.86035