<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">OJG</journal-id><journal-title-group><journal-title>Open Journal of Geology</journal-title></journal-title-group><issn pub-type="epub">2161-7570</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/ojg.2017.78081</article-id><article-id pub-id-type="publisher-id">OJG-78706</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Earth&amp;Environmental Sciences</subject></subj-group></article-categories><title-group><article-title>
 
 
  Alteration Mapping in Central Urumieh-Dokhtar Magmatic Assemblage of Iran Using ASTER and OLI Data
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Farshad</surname><given-names>Khodadadi</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Ramin</surname><given-names>Arfania</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib></contrib-group><aff id="aff1"><addr-line>Department of Geology, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran</addr-line></aff><author-notes><corresp id="cor1">* E-mail:<email>1khodadadi.farshad@gmail.com(FK)</email>;</corresp></author-notes><pub-date pub-type="epub"><day>07</day><month>08</month><year>2017</year></pub-date><volume>07</volume><issue>08</issue><fpage>1215</fpage><lpage>1226</lpage><history><date date-type="received"><day>July</day>	<month>26,</month>	<year>2017</year></date><date date-type="rev-recd"><day>Accepted:</day>	<month>August</month>	<year>21,</year>	</date><date date-type="accepted"><day>August</day>	<month>24,</month>	<year>2017</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  The study area is located on the Urumieh-Dokhtar Magmatic Assemblage of Iran, in the west of Ardestan between the East longitude 52
  &amp;#176;1' to 52
  &amp;#176;18' and the North latitude 33
  &amp;#176;17' to 33
  &amp;#176;27'. Remote sensing techniques are suitable for studying the alterations occurring in the igneous terranes. The alteration zones are well illustrated by implementation of the principal component analysis and the Crosta methods and Spectral Feature Fitting on ASTER data. In order to identify the lineaments, both Landsat-8 satellite imagery and GDEM-ASTER data are used in spatial processing. Using directional filtering and automatic extraction of lineaments, a tectonic lineaments map is prepared. Then alteration maps, tectonic lineaments map and 1:100,000 geology map are used to identify areas with high potential of Cu mineralization.
 
</p></abstract><kwd-group><kwd>Principal Component Analysis</kwd><kwd> Crosta Method</kwd><kwd> Spectral Feature Fitting</kwd><kwd> Alteration</kwd><kwd> Lineament</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>The study area is located in Ardestan region. This region, due to the presence of a large part of the Urumieh-Dokhtar volcano belt (Iran copper belt and gold metallogeny zone) as well as the Sanandaj-Sirjan zone (iron, gold, lead and zinc metallogeny zone of Iran), has a lot metallic and non-metallic mines. Due to the lack of systematic discovery and compliance with global standards, the province has high potential for unknown metal deposits even at ground level [<xref ref-type="bibr" rid="scirp.78706-ref1">1</xref>] . It uses ASTER-OLI satellite data and spectral methods for detecting alterations in this area for the first time. Due to the fact that the Urumieh-Dokhtar belt is a metallogenic belt, study on alterations can lead to the exploration of new metal deposits.</p><p>The best guideline for detecting the copper mineralization is high density of fractures inside or around the intrusions [<xref ref-type="bibr" rid="scirp.78706-ref2">2</xref>] . Faults and fractures can affect mineralization by leading magma and hydrothermal fluids to shallower levels, and accumulate or concentrate metal elements [<xref ref-type="bibr" rid="scirp.78706-ref3">3</xref>] . Lineaments play an important role in the initial exploration of geological structures for the identification of minerals [<xref ref-type="bibr" rid="scirp.78706-ref4">4</xref>] . Therefore, areas with good mineral potential are associated with high density of faults and fractures. The study and processing of satellite data in identifying complications such as rock type, contacts, linear and circular structures, alteration and iron oxide zones can be used directly for the identification of mineral deposits [<xref ref-type="bibr" rid="scirp.78706-ref5">5</xref>] . One of the advantages of remote sensing technique is to identify a wide range with high precision and low costs [<xref ref-type="bibr" rid="scirp.78706-ref6">6</xref>] . Therefore, remote sensing and satellite image processing techniques play an important role in the exploration of mineral deposits. In this study, we identify the alteration zones with high potential for Cu mineralization using remotely sensed image processing methods.</p><p>Porphyry copper deposits typically occur in conjunction with hydrothermal alteration zones. In fact, a quartz nucleus is surrounded with potassium minerals, such as Orthoclase with multiple zones including clay and other hydroxyl group minerals with spectral absorption properties detected in the short-wave infrared spectrum of the electromagnetic spectrum [<xref ref-type="bibr" rid="scirp.78706-ref7">7</xref>] [<xref ref-type="bibr" rid="scirp.78706-ref8">8</xref>] [<xref ref-type="bibr" rid="scirp.78706-ref9">9</xref>] . In some cases, an oxide zone has been expanded with supergene alteration processes with iron oxide minerals, which is located above the porphyry system. Iron oxides are one of the most important mineral groups that have changed hydrothermally and are accumulated and are particularly converted to altered (yellow or red) stones which are called the Gossan [<xref ref-type="bibr" rid="scirp.78706-ref10">10</xref>] . Hydrothermal alteration minerals with detectable absorption spectral properties, especially at infrared wavelengths near infrared wavelengths, can be used in the early stages to identify porphyry copper deposits as a guide.</p><p>In order to prepare a map of alterations and find points with Cu mineral potential, the area of Orumieh-Dokhtar in the west of Ardestan city is selected. The study area is located in the quadrangle of 1:100,000 Ardestan geological map and between 52˚1' and 52˚18' Eastern latitude and northern latitude 33˚17' to 33˚27' (<xref ref-type="fig" rid="fig1">Figure 1</xref>), in the vicinity of the strike-slip Qom-Zefreh fault. Preliminary library studies show that this region has a high Cu mineral potential in relation to alteration. The rock masses in the study area are located in west of Ardestan (extracted from the geological map of Ardestan 1:100,000) and are mainly as follows: andesite, andesitic basalt, tuff breccia, ignimbrite with some shale and pyro-clastic stone (this rock mass occupies a large part of the studied area), dacite, rhyodacite and locally rhyolitic tuff, gray to light graytuff breccia and ignimbrite. Other lithologies in this area contain Porphyritie andesite, alteration zones,</p><fig id="fig1"  position="float"><label><xref ref-type="fig" rid="fig1">Figure 1</xref></label><caption><title> Location of study area in the Qom-Zefreh fault zone</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/11-1210953x2.png"/></fig><p>sandstone, nummulitic limestone, siltstone, marl, andesitic lava, granite and grano- diorites (in small quantities), most of which are related to the period Eocene and, to a lesser extent, Cretaceous, Eocene-Oligocene, Oligocene-Miocene and Miocene.</p></sec><sec id="s2"><title>2. Materials and Methods</title><p>Considering the proper spectral resolution of the ASTER images, especially in the short-wave Infrared spectral, and the suitability of this spectral range for the identification of alteration minerals and the separation of hydrothermal alteration zones, we can use these data to draw up a map of alterations. Landsat-8 satellite images can also be used due to an acceptable spectral resolution for detecting locations of iron oxide minerals, Gossan Zones and hydroxyl-containing minerals (<xref ref-type="fig" rid="fig2">Figure 2</xref>). Also Landsat-8 satellite images are also be used to identify the lineament, with the appropriate ASTER resolution (16-bit). In this study, the L1T ASTER images March 11, 2008 and Landsat-8 images, July 26, 2014 are</p><fig id="fig2"  position="float"><label><xref ref-type="fig" rid="fig2">Figure 2</xref></label><caption><title> ASTER and OLI processing steps and mapping techniques flowchart</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/11-1210953x3.png"/></fig><p>used, both of which are covered with clouds less than 10% and the given DEM images in 2011 is used. The geological map of Ardestan that prepared with 1:100,000 scale under the supervision of the Geological Survey of Iran by Emami et al. in 2000, is used.</p><sec id="s2_1"><title>2.1. Alteration Zone Detection</title><p>Principal Components Analysis is a technique that converts remote sensing data (which is highly correlated to each other) to more independent, smaller, more compact and more interpretable data [<xref ref-type="bibr" rid="scirp.78706-ref11">11</xref>] . Crosta’s method is also a kind of principal components analysis in which bands that are sensitive to target pixels (typically 4 bands) are selected, and then by observing the statistical file obtained by this method and observing the difference between the bands, the best band of the PC is chosen to display the phenomenon (Tables 1-5). The purpose of this method on OLI data is to detect iron oxide and Gossan. For this purpose bands 2, 4, 5, 6 are used to generate a PC image that features sharp absorption at band 2 and reflection at band 4 for indicating Iron oxide, and bands 2, 3, 4, 6 are used to generate a PC image that features sharp absorption at band 3 and reflection at band 6 for indicating Gossan [<xref ref-type="bibr" rid="scirp.78706-ref12">12</xref>] [<xref ref-type="bibr" rid="scirp.78706-ref13">13</xref>] . The aim of this method on ASTER images is to detect Propylithic, Argillic and Phyllic alterations zones. For this purpose, bands 1, 3, 5, 8bands are used to generate a PC image that features absorption at band 8 and reflection at band 5 for indicating Propylitic alteration zone (Chlorite, Epidote), bands 1, 4, 6, 7 are used to generate a PC image that features absorption at band 6 and reflection at band 4 for indicating Argillic alteration</p><table-wrap id="table1" ><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> Correlation between ASTER and PC bands for Epidote representation</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Axis</th><th align="center" valign="middle" >PC1</th><th align="center" valign="middle" >PC2</th><th align="center" valign="middle" >PC3</th><th align="center" valign="middle" >PC4</th></tr></thead><tr><td align="center" valign="middle" >ASTER B1</td><td align="center" valign="middle" >0.947296</td><td align="center" valign="middle" >0.298160</td><td align="center" valign="middle" >0.020182</td><td align="center" valign="middle" >0.115426</td></tr><tr><td align="center" valign="middle" >ASTER B3</td><td align="center" valign="middle" >0.978254</td><td align="center" valign="middle" >0.177071</td><td align="center" valign="middle" >−0.036598</td><td align="center" valign="middle" >−0.101616</td></tr><tr><td align="center" valign="middle" >ASTER B5</td><td align="center" valign="middle" >0.973816</td><td align="center" valign="middle" >−0.203029</td><td align="center" valign="middle" >−0.094829</td><td align="center" valign="middle" >0.038324</td></tr><tr><td align="center" valign="middle" >ASTER B8</td><td align="center" valign="middle" >0.983543</td><td align="center" valign="middle" >−0.146489</td><td align="center" valign="middle" >0.104930</td><td align="center" valign="middle" >−0.013176</td></tr></tbody></table></table-wrap><table-wrap id="table2" ><label><xref ref-type="table" rid="table2">Table 2</xref></label><caption><title> Correlation between ASTER and PC bands for Kaolinite representation</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Axis</th><th align="center" valign="middle" >PC1</th><th align="center" valign="middle" >PC2</th><th align="center" valign="middle" >PC3</th><th align="center" valign="middle" >PC4</th></tr></thead><tr><td align="center" valign="middle" >ASTER B1</td><td align="center" valign="middle" >0.903518</td><td align="center" valign="middle" >0.428100</td><td align="center" valign="middle" >−0.018877</td><td align="center" valign="middle" >−0.005377</td></tr><tr><td align="center" valign="middle" >ASTER B4</td><td align="center" valign="middle" >0.991147</td><td align="center" valign="middle" >−0.098893</td><td align="center" valign="middle" >−0.085405</td><td align="center" valign="middle" >0.023515</td></tr><tr><td align="center" valign="middle" >ASTER B6</td><td align="center" valign="middle" >0.992776</td><td align="center" valign="middle" >−0.072644</td><td align="center" valign="middle" >0.031129</td><td align="center" valign="middle" >−0.090272</td></tr><tr><td align="center" valign="middle" >ASTER B7</td><td align="center" valign="middle" >0.994117</td><td align="center" valign="middle" >−0.037719</td><td align="center" valign="middle" >0.078522</td><td align="center" valign="middle" >0.064361</td></tr></tbody></table></table-wrap><table-wrap id="table3" ><label><xref ref-type="table" rid="table3">Table 3</xref></label><caption><title> Correlation between ASTER and PC bands for Muscovite representation</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Axis</th><th align="center" valign="middle" >PC1</th><th align="center" valign="middle" >PC2</th><th align="center" valign="middle" >PC3</th><th align="center" valign="middle" >PC4</th></tr></thead><tr><td align="center" valign="middle" >ASTER B1</td><td align="center" valign="middle" >−0.940572</td><td align="center" valign="middle" >−0.318448</td><td align="center" valign="middle" >−0.117780</td><td align="center" valign="middle" >0.006622</td></tr><tr><td align="center" valign="middle" >ASTER B3</td><td align="center" valign="middle" >−0.974500</td><td align="center" valign="middle" >−0.197699</td><td align="center" valign="middle" >0.105922</td><td align="center" valign="middle" >−0.006730</td></tr><tr><td align="center" valign="middle" >ASTER B5</td><td align="center" valign="middle" >−0.979850</td><td align="center" valign="middle" >0.186021</td><td align="center" valign="middle" >−0.021618</td><td align="center" valign="middle" >−0.069449</td></tr><tr><td align="center" valign="middle" >ASTER B6</td><td align="center" valign="middle" >−0.981260</td><td align="center" valign="middle" >0.182265</td><td align="center" valign="middle" >−0.004023</td><td align="center" valign="middle" >0.062389</td></tr></tbody></table></table-wrap><table-wrap id="table4" ><label><xref ref-type="table" rid="table4">Table 4</xref></label><caption><title> Correlation between OLI and PC bands for Iron oxide representation</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >PC4</th><th align="center" valign="middle" >PC3</th><th align="center" valign="middle" >PC2</th><th align="center" valign="middle" >PC1</th><th align="center" valign="middle" >Axis</th></tr></thead><tr><td align="center" valign="middle" >−0.022447</td><td align="center" valign="middle" >0.126848</td><td align="center" valign="middle" >0.463749</td><td align="center" valign="middle" >0.876551</td><td align="center" valign="middle" >OLI B2</td></tr><tr><td align="center" valign="middle" >0.060618</td><td align="center" valign="middle" >−0.056984</td><td align="center" valign="middle" >0.128862</td><td align="center" valign="middle" >0.988166</td><td align="center" valign="middle" >OLI B4</td></tr><tr><td align="center" valign="middle" >−0.043713</td><td align="center" valign="middle" >−0.092055</td><td align="center" valign="middle" >0.024391</td><td align="center" valign="middle" >0.994495</td><td align="center" valign="middle" >OLI B5</td></tr><tr><td align="center" valign="middle" >0.001860</td><td align="center" valign="middle" >0.057885</td><td align="center" valign="middle" >−0.212103</td><td align="center" valign="middle" >0.975530</td><td align="center" valign="middle" >OLI B6</td></tr></tbody></table></table-wrap><table-wrap id="table5" ><label><xref ref-type="table" rid="table5">Table 5</xref></label><caption><title> Correlation between OLI and PC bands for Gossan representation</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >PC4</th><th align="center" valign="middle" >PC3</th><th align="center" valign="middle" >PC2</th><th align="center" valign="middle" >PC1</th><th align="center" valign="middle" >Axis</th></tr></thead><tr><td align="center" valign="middle" >0.021803</td><td align="center" valign="middle" >−0.107072</td><td align="center" valign="middle" >0.426595</td><td align="center" valign="middle" >0.897818</td><td align="center" valign="middle" >OLI B2</td></tr><tr><td align="center" valign="middle" >−0.041286</td><td align="center" valign="middle" >0.015888</td><td align="center" valign="middle" >0.195226</td><td align="center" valign="middle" >0.979760</td><td align="center" valign="middle" >OLI B3</td></tr><tr><td align="center" valign="middle" >0.019304</td><td align="center" valign="middle" >0.097432</td><td align="center" valign="middle" >0.087835</td><td align="center" valign="middle" >0.991171</td><td align="center" valign="middle" >OLI B4</td></tr><tr><td align="center" valign="middle" >0.001082</td><td align="center" valign="middle" >−0.030910</td><td align="center" valign="middle" >−0.252288</td><td align="center" valign="middle" >0.967158</td><td align="center" valign="middle" >OLI B6</td></tr></tbody></table></table-wrap><p>zone (Kaolinite, Montmorillonite), and 1, 3, 5, 6 bands are used to generate a PC image that features sharp absorption at band 6 and reflection at band 5 for indicating Phyllic alteration (Sericite) [<xref ref-type="bibr" rid="scirp.78706-ref14">14</xref>] [<xref ref-type="bibr" rid="scirp.78706-ref15">15</xref>] .</p><p>Spectral Feature Fitting (SFF) is a technique that uses peak or peaks of absorption in the entire spectral pixel curve and by comparing it (its location and geometry) with the reference spectrum (mineral spectrum library) it uses mineral or the target phenomenon to identify given phenomenon [<xref ref-type="bibr" rid="scirp.78706-ref16">16</xref>] [<xref ref-type="bibr" rid="scirp.78706-ref17">17</xref>] . The SFF method is only performed on ASTER images. This method is performed on the basis of USGS spectrum library to display the index minerals including Epidote, Chlorite, Kaolinite, Montmorillonite, and Muscovite, to identify Propylithic, Argillic and Phyllic alteration zones respectively. The mapping of the alterations obtained in this study is presented in <xref ref-type="fig" rid="fig3">Figure 3</xref>.</p></sec><sec id="s2_2"><title>2.2. Lineament Mapping</title><p>A directional filter with a kernel size = 3 &#215; 3 and angles of 0, 45, 90, 135, 180, 225, 270, and 315 is performed on OLI images. Then, to reduce the dimensions of the data, the PCA conversion on the images obtained from each angle is performed in order to minimize the dimensions of the data and to collect the most information in a band.</p><fig id="fig3"  position="float"><label><xref ref-type="fig" rid="fig3">Figure 3</xref></label><caption><title> Hydrothermal alteration zones on OLI image, RGB: 432</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/11-1210953x4.png"/></fig><p>Two groups of hill shade images are also obtained from DEM images with a 45-degree angle and Azimuth’s angles of 0, 45, 90, 135 (as the first group) and 180, 225, 270, and 315 (as the second group). Then, by the difference between the average of the first group and the average of the second group, an image is obtained. This image is used to extract the lineament and the lineament is extracted well. The map of the lineament and the density of the lineament are shown in (<xref ref-type="fig" rid="fig4">Figure 4</xref>).</p></sec></sec><sec id="s3"><title>3. Results</title><p>As it is shown in the mapping the alterations (<xref ref-type="fig" rid="fig3">Figure 3</xref>), two Argillic and Phyllic alteration zones are highly interrelated and the Propylitic zone is present in a small amount around them, which is indicative of Propylitic system in the region. Also, the high density of the lineament interrelating with Argillic and Phyllic alterations that represent a circular structure in this region, increases the probability of mineral potential. By integrating the mapping of the alterations and mapping the density of the lineament with the geological map of the region, a compilation map is created to identify the mineral potential areas. Subsequently, mineral potential areas are determined by implementing multi-criteria decision making, Analytical Hierarchy Process (AHP) method on three criteria: lithology, alteration and lineament density. In decision making AHP method for this study, three main criteria that are directly related to mineralization are considered. Each of the main criteria is effective by several sub-criteria. For example alteration has two sub-criteria: 1) High accumulation (full zonation of alterations) 2) Low accumulation (incomplete zonation of alterations), (<xref ref-type="table" rid="table6">Table 6</xref>,</p><fig-group id="fig4"><label><xref ref-type="fig" rid="fig4">Figure 4</xref></label><caption><title> (a) Lineament map of study area; (b) Lineament density map.</title></caption><fig id ="fig4_1"><label> (b)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/11-1210953x5.png"/></fig><fig id ="fig4_2"><label></label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/11-1210953x6.png"/></fig></fig-group><table-wrap id="table6" ><label><xref ref-type="table" rid="table6">Table 6</xref></label><caption><title> The weight value for criterion and sub-criterion</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >AHP value</th><th align="center" valign="middle" >Criterion</th><th align="center" valign="middle" >Weight</th></tr></thead><tr><td align="center" valign="middle" ></td><td align="center" valign="middle" >Lithology</td><td align="center" valign="middle" >0.625</td></tr><tr><td align="center" valign="middle" ></td><td align="center" valign="middle" >Alteration</td><td align="center" valign="middle" >0.238</td></tr><tr><td align="center" valign="middle" ></td><td align="center" valign="middle" >Lineament</td><td align="center" valign="middle" >0.136</td></tr><tr><td align="center" valign="middle" >AHP value</td><td align="center" valign="middle" >Sub-criterion: Lithology types</td><td align="center" valign="middle" >Weight</td></tr><tr><td align="center" valign="middle" >0.14125</td><td align="center" valign="middle" >Rhyodacite, Dacite, Rhyolitic tuff-Andesitic basalt, Andesite, Acidic volcanics (rhyolite-dacite)-Porphyritie andesite and Dacitic andesite</td><td align="center" valign="middle" >0.226</td></tr><tr><td align="center" valign="middle" >0.12125</td><td align="center" valign="middle" >Hydrothermally altered zone</td><td align="center" valign="middle" >0.194</td></tr><tr><td align="center" valign="middle" >0.100625</td><td align="center" valign="middle" >Altered andesitic lava-Rhyolite-Rhyodacite tuff and ignimbrite</td><td align="center" valign="middle" >0.161</td></tr><tr><td align="center" valign="middle" >0.080625</td><td align="center" valign="middle" >Granite, Granodiorite</td><td align="center" valign="middle" >0.129</td></tr><tr><td align="center" valign="middle" >0.060625</td><td align="center" valign="middle" >Alternation limestone and Andesitic lava-Young and Low level traces Old and High level traces-Recent alluvium-Old and Young fans</td><td align="center" valign="middle" >0.097</td></tr><tr><td align="center" valign="middle" >AHP value</td><td align="center" valign="middle" >Sub-criterion: Lineament density</td><td align="center" valign="middle" >Weight</td></tr><tr><td align="center" valign="middle" >0.062594</td><td align="center" valign="middle" >Very high density</td><td align="center" valign="middle" >0.263</td></tr><tr><td align="center" valign="middle" >0.048552</td><td align="center" valign="middle" >High density</td><td align="center" valign="middle" >0.204</td></tr><tr><td align="center" valign="middle" >0.042364</td><td align="center" valign="middle" >Middle density</td><td align="center" valign="middle" >0.178</td></tr><tr><td align="center" valign="middle" >0.033796</td><td align="center" valign="middle" >Low density</td><td align="center" valign="middle" >0.142</td></tr><tr><td align="center" valign="middle" >0.025466</td><td align="center" valign="middle" >Very low density</td><td align="center" valign="middle" >0.107</td></tr><tr><td align="center" valign="middle" >0.016898</td><td align="center" valign="middle" >No density</td><td align="center" valign="middle" >0.071</td></tr><tr><td align="center" valign="middle" >AHP value</td><td align="center" valign="middle" >Sub-criterion: Alteration</td><td align="center" valign="middle" >Weight</td></tr><tr><td align="center" valign="middle" >0.071808</td><td align="center" valign="middle" >High accumulation</td><td align="center" valign="middle" >0.528</td></tr><tr><td align="center" valign="middle" >0.010472</td><td align="center" valign="middle" >Low accumulation</td><td align="center" valign="middle" >0.077</td></tr></tbody></table></table-wrap><p><xref ref-type="table" rid="table7">Table 7</xref> and <xref ref-type="fig" rid="fig5">Figure 5</xref>). In field surveys, the points identified by spectral pro- cessing are sampled (<xref ref-type="fig" rid="fig6">Figure 6</xref> and <xref ref-type="fig" rid="fig7">Figure 7</xref>), and from these samples, thin sections are prepared. In the study of thin sections, Feldspar (with alteration effects) can be seen in the background of clay (<xref ref-type="fig" rid="fig8">Figure 8</xref>(a)), in these sections, Sericite and Epidote also are well visible (Figures 8(b)-(d)). Therefore, the study of thin sections confirms the accuracy of the alteration spectral processing performed in this study.</p></sec><sec id="s4"><title>4. Conclusion</title><p>・ <xref ref-type="table" rid="table7">Table 7</xref> shows the highest AHP values for different areas. These values are the sum of the multiplication of the criteria with sub-criteria of each area. Areas with the highest AHP values show high potential for Cu mineralization.</p><fig id="fig5"  position="float"><label><xref ref-type="fig" rid="fig5">Figure 5</xref></label><caption><title> Mineral promising areas derived from multi-criteria decision making by AHP method with lithology, lineament density and alteration criteria; red ranges represent the highest potential</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/11-1210953x7.png"/></fig><fig id="fig6"  position="float"><label><xref ref-type="fig" rid="fig6">Figure 6</xref></label><caption><title> Map of sampling points in the area</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/11-1210953x8.png"/></fig><fig id="fig7"  position="float"><label><xref ref-type="fig" rid="fig7">Figure 7</xref></label><caption><title> Images of rock units from two sampling points: (a) point C (altered volcanic rock), (b) point D (the feldspar, altered to sericite)</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/11-1210953x9.png"/></fig><fig id="fig8"  position="float"><label><xref ref-type="fig" rid="fig8">Figure 8</xref></label><caption><title> Fourth in-section images of the study: (a) XPL 100&#215;; Feldspar with alteration effects in the context of clay related to the sampling point C; (b) XPL 100&#215;; Epidote in altered context from the sampling point A; (c) PPL 100&#215;; Plagioclase altered to sericite in the fine grains context related to the sampling point D; (d) PPL 40&#215;; The fine grain texture is altered, sampling point H</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/11-1210953x10.png"/></fig><table-wrap id="table7" ><label><xref ref-type="table" rid="table7">Table 7</xref></label><caption><title> Highest AHP values for different areas</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >AHP-value</th><th align="center" valign="middle" >0.275652</th><th align="center" valign="middle" >0.26161</th><th align="center" valign="middle" >0.255652</th><th align="center" valign="middle" >0.255422</th><th align="center" valign="middle" >0.246854</th><th align="center" valign="middle" >0.24161</th></tr></thead></tbody></table></table-wrap><p>・ The use of ASTER images due to the proper spectral resolution, especially in the spectral range of SWIR, for the presentation of clay minerals and alternation zones and OLI data, also has spectral resolution suitable in the VNIR spectral range that is very useful for the appearance of iron oxide minerals and Gossan zone. Therefore, it is better to use both types of data to explore mineral resources (especially metal deposits).</p><p>・ In order to visualize the lineament, the hill shade images and directional filtering are used and at the end of the lineaments that are common in both methods, are used to produce a map of the lineament. In this study, these two methods are very useful due to viewing the image from different angles.</p><p>・ In this study, due to the identification of the circular structure in the region and the concentration of points with mineral potential on it, the importance of these types of structures in identifying areas with mineral potential areas was emphasized.</p></sec><sec id="s5"><title>Cite this paper</title><p>Khodadadi, F. and Arfania, R. (2017) Alteration Mapping in Central Urumieh-Dokhtar Magmatic Assemblage of Iran Using ASTER and OLI Data. 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