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Integration of remote sensing and aeromagnetic data for mapping structural features and hydrothermal alteration zones in Wadi Allaqi area, South Eastern Desert of Egypt
Accepted Manuscript Integration of remote sensing and aeromagnetic data for mapping structural features and hydrothermal alteration zones in Wadi Allaqi area, South Eastern Desert of Egypt Ahmed M. Eldosouky, Mohamed Abdelkareem, Sayed O. Elkhateeb PII:
S1464-343X(17)30110-3
DOI:
10.1016/j.jafrearsci.2017.03.006
Reference:
AES 2842
To appear in:
Journal of African Earth Sciences
Received Date: 21 November 2016 Revised Date:
22 February 2017
Accepted Date: 7 March 2017
Please cite this article as: Eldosouky, A.M., Abdelkareem, M., Elkhateeb, S.O., Integration of remote sensing and aeromagnetic data for mapping structural features and hydrothermal alteration zones in Wadi Allaqi area, South Eastern Desert of Egypt, Journal of African Earth Sciences (2017), doi: 10.1016/ j.jafrearsci.2017.03.006. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Integration of remote sensing and aeromagnetic data for mapping structural features and
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hydrothermal alteration zones in Wadi Allaqi area, South Eastern Desert of Egypt
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Ahmed M. Eldosouky¹, Mohamed Abdelkareem², Sayed O. Elkhateeb² ¹Egyptian Environmental Affairs Agency (E.E.A.A.), Egypt. ² Department of Geology,
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Faculty of science, South Valley University, 83523 Qena, Egypt
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Corresponding Author: ¹Ahmed Mohammed Eldosouky. Researcher at National Conservation
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Sector, Egyptian Environmental Affairs Agency, Egypt.
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Email: [email protected] , [email protected] .
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Abstract
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Remote sensing and aeromagnetic data provided significant information for detecting potential
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areas of mineralization in Wadi Allaqi in the south Eastern Desert of Egypt. Application of band
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ratios and Crosta technique of Principal Component Analysis (PCA) using Landsat-8
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successfully highlighted the hydrothermal alteration zones and the structural elements
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represented by lithologic contacts and faults/fracture zones. Structural lineaments were also
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successfully extracted using remote sensing and aeromagnetic data. Center for Exploration
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Targeting (CET) Grid analysis and CET Porphyry Analysis techniques were applied for
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constructing the structural complexity heat map and probable near circular features of porphyry
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intrusions respectively. Combining data of lineaments, alteration zones and porphyry intrusions
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after obtaining a consequence of each map allowed predicting and mapping areas of probable
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high mineral resources. Overlaying the present sites of mineralization on the final map validated
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the prepared mineral predictive map. Overall results clearly revealed that areas of high structural
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complexity, fractures/faults density are in agreement with the detected areas of hydrothermal
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alterations which also matched with the known mineralization mines in the study area.
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Key words: Aeromagnetic data; Remote Sensing; Predictive map; Wadi Allaqi. 1. Introduction Sensors aboard satellites and aircraft provided the way to conduct surface and subsurface
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measurements of various features for the Earth planet (geologic structures, lithologic and mineral
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resources purposes). Mapping geology, faults and fractures along with identifying the
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hydrothermally altered rocks are important for mineral exploration purposes (Sabin's 1999).
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Remote sensing data allowed extracting lineaments and mapping hydrothermal alteration
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regions. Integration of various remote sensing data (ASTER, PALSAR, OLI, Hyperion imagery
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and Landsat ETM) are used effectively to map mineral characteristics of the alteration zones,
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delineate regional and local structures and to distinguish between different lithologic units (Pour
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and Hashim, 2014; Pour and Hashim, 2015; Pour and Hashim, 2015; Pournamdary et al., 2014;
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Pournamdary et al., 2014).
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Remote sensing data highlight lithologic contacts that are probably located along structural
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features (Ramsay and Huber 1987) where these contacts could have revealed a subsurface
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extension. Several approaches like band ratios and PCA that were widely employed using
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multispectral data (e.g., Landsat) successfully revealed the hydrothermal alteration regions and
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delineated areas of potential mineralization (Sabin's 1997; Goetz et al., 1983; Rowan et al., 1986;
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Abrams and Hook 1995; Okada et al., 1993;Amuda et al., 2014; Poormirzaee et al., 2010;
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Ramadan et al., 2001; Mia and Fujimitsu 2012). Several approaches like band ratios and PCA
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were widely employed using multispectral data to detect hydrothermal alteration zones (Sabin's
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1999; Mia and Fujimitsu 2012; Aydal et al., 2007; Liu et al., 2013; Tangestani and Moore 2000).
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This is because; the hydrothermal processes are often related to mineralization which may
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consequently change the properties of country rocks.
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Aeromagnetic data analysis and enhancement allowed mapping the shallow and deep geologic
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frameworks (Sharma 1997) and revealing porphyry intrusion (Holden et al., 2011). These
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structural features are reflected in trends and intensities apparent on aeromagnetic maps that
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represent magnetic patterns (Gay, 1972). Moreover, the texture analysis allowed enhancing and
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identifying geologic lineaments like lithologic boundaries, joints and faults. Over the past few
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decades, several studies have paid attention for delineating the porphyry intrusions magnetic
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field data (Williams and Shah 1990; Holden et al., 2008; Core et al., 2009; Elkhateeb and
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Eldosouky 2016). This is because several deposits are associated with the igneous intrusion in
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nature which appears as a near circular-feature. The hydrothermal alteration often surrounded
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these intrusions and revealed subsequent/concentric zones. Porphyry deposits are considered the
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world's predominant source of molybdenum, copper, gold, silver and other by-product metals;
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therefore, the investigations of porphyry intrusions are significant.
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Magnetic field data have the ability to delineate the porphyry intrusions and geologic structures
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and which are most reliable for this purpose. Therefore, several studies focused on detecting the
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structural features and porphyry intrusions using aeromagnetic data (Holden et al., 2008; Core et
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al., 2009; Macnae 1995; Williams and Shah 1990; Elkhateeb and Eldosouky 2016).
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Many researchers have studied Wadi Allaqi district like Abdel Salam and Stern (1996) who
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suggested that the Allaqi shear zone was progressed among four Neoproterozoic deformation
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phases. El Shimi (2005) used remote sensing information for mineral prospecting at Wadi Allaqi
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to map and classify zones of alteration. Elkhateeb and Eldosouky (2016) mapped the porphyry
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intrusion and its relation to mineralization at Wadi Allaqi using aeromagnetic data analysis.
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The aim of the present study is to analyze aeromagnetic data for mapping structural complexity
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and porphyry intrusion zones and remote sensing data for mapping hydrothermal alteration
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zones. Integration of aeromagnetic and remote sensing datasets will be applied for producing a
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mineral predictive map of Wadi Allaqi district.
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2. Geologic setting Wadi Allaqi district is situated in the South Eastern Desert of Egypt (Fig. 1a) which extends
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between latitudes 22º 22’50" and 23º 00’00"N, and longitudes 33º 15’ and 34º 15´ E, covering an
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area about 7050km². The western expansion of the Allaqi-Heiani suture (Stern et al., 1989; Stern
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1994; Abdelsalam and Stern 1996) controlled this area The NW, NE, and NNE are the main
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structural trends controlling Wadi Allaqi district (Ramadan and Sultan 2004) and the shear zone
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that dominated Wadi Allaqi was progressed among four Neoproterozoic deformation stages
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(Abdelsalam and Stern 1996). The surface/near-surface geologic structures in Wadi Allaqi were
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investigated by (Elkhateeb and Eldosouky 2016). These structures represent the favorable sites
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of hydrothermal activities; therefore, remote sensing data were applied to Wadi Allaqi for
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classifying the alteration regions and mineral prospecting (El Shimi, 2005).
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Based on the geological map (Geologic map of Wadi Gabjabah Quadrangle, Egypt 1996) of
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Egyptian Geological Survey and Mining Authority (EGSMA), Wadi Allaqi (Fig.1) occupied by
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the basement rocks, sandstone of Cretaceous age, younger Mesozoic volcanic and sub-volcanic
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rocks.
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Figure 1. (a) Regional geology of the Eastern Desert of Egypt that divided into South Eastern Desert
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(SED), Central Eastern Desert (CED), and North Eastern Desert (NED); (b) Geologic map of Wadi Allaqi
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district (after EGSMA, 1996).
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The majority of Wadi Allaqi district is occupied by basement rocks but the small exposure of
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sedimentary sequence occupies the southwestern corner of the study area (Cretaceous
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Sandstones including Abu Sumbul Formation, Al Jilf Formation, Abu Ajjaj Formation, and Al
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Burj Formation whilst Quaternary sediments fill the wadies). The basement of Wadi Allaqi
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includes (a) Ophiolitic assemblage; (b) Island-arc assemblage, and (c) Late to post-tectonic
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granitoids.
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The ophiolitic assemblages occupy the central part of Wadi Allaqi that are composed of
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overlaped talc carbonate schist, serpentinites, and metagabbros slices and thrust sheets. They are
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thrust southwards upon the island-arc rocks. The island-arc rocks are made up of metavolcanic
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and meta-sedimentary layers that are intruded by diorite and gabbro plugs (Fig. 1 b). Tonalite,
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quartz-diorite, diorite, and gabbro are the main components of the intrusions of gabbro-diorite.
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Metavolcanic and meta-sedimentary rocks are more numerous than the island-arc assemblages.
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The geochemical characteristics of the metavolcanic units show that there are transitional
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conditions between continental-arc and continental margin (El-Nisr 1997). The central area of
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Wadi Allaqi has an abundant presence of the late to post-tectonic granitoids, particularly at Wadi
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Um Shelman area (Fig. 1 b). They appear as isolated circular eroded hills that are draped by
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recent sand sediments. The distribution of post-tectonic granitoids becomes more abundant in
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the area under study towards the north direction. They are red in color, medium grained with
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moderate to high relief. They are intruded in the overhead rock units and being cut by mafic and
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felsic intrusions. 3. Data used and processing techniques
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3.1. Aeromagnetic data
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The total magnetic intensity (TMI) Map data (Fig. 2a) utilized in this investigation was acquired
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from the Egyptian Mineral Resources Authority which is a subsidiary of Aero Service Company
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(1984). The Aeromagnetic scanning was flown with a 120 m flight height and an average
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magnetic inclination of 32.8 N and declination of 1.9 E. So as to perform the CET grid and
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porphyry techniques, it requires pole reduction (RTP). Thus, the magnetic anomaly (TMI) map
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was reduced to pole (RTP) after subtracting the International Geomagnetic Reference Field
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(IGRF) (Fig. 2b). There are several approaches can be utilized using the RTP map as follows:
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3.1.1. Center for exploration targeting (CET) grid analysis technique.
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This technique reinforces discontinuity zones within potential data and highlights native
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variations in magnetic intensity. Areas of magnetic discontinuity are represented in the form of
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skeletal structure by applying the texture enhancement. The output data represents each region of
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the discontinuity zones as a skeletal line segments that belong to each of them, visibly showing
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the variations in the directions and offsets within the structural features (Kovesi 1997).
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This technique utilizes the following steps:
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i. Texture enhancement allowed locating the zones of complex textures associated with
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ii. Phase symmetry can be carried out using the texture enhancement results for detecting zones of lateral discontinuity.
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magnetic discontinuities.
iii. Delineation of structures using the results of phase symmetry to minimize the discontinuities containing zones into line-like structures.
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3.1.2. Center for Exploration Targeting (CET) Porphyry intrusion technique
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The CET Porphyry enhancement begins by executing the circular feature transform (CFT) (Loy
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and Zelinsky 2003), which is intended to detect circular shaped features. Then, the centers of
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elevated or depressed circular features are highlighted. Thereafter, using the amplitude contrast
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transform (ACT) a circular feature will appear as a 'halo', which matches with the circular rim of
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the feature. The outputs are a database file and a polygon file. The database file specifies the
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circular feature centers, the radial symmetry strength, and the radius that are produced in the
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highest response (in both cells and meters). The polygon file contains, for each feature location,
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the circle boundary that is generated by the strongest radial symmetry response. This enables
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visualization of the extent of the detected circular features (Holden et al., 2011).
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In this section, we used Landsat-8 Operational Land Imager (OLI) sensor which was launched on
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February 11, 2013 from Vandenberg Air Force Base in California. OLI provides data from nine
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spectral bands (visible, near and shortwave infrared bands and two thermal long-wave bands).
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Seven of them are compatible with the Thematic Mapper and Enhanced Thematic Mapper
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plus (TM and ETM+) sensors of Landsat satellites. Two new spectral bands, a deep blue
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coastal/aerosol band and a shortwave-infrared cirrus band, allowing measuring quality of the
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water and enhance disclosure of high, thin clouds. ENVI and ArcGIS software packages will be
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used to enhance and analyze these data.
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Two scenes of Landsat-8 data covering Wadi Allaqi were acquired on July/12/2016 (p 174 r 044)
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and Jun/3/2016 (p173r044). Six Landsat-8 bands 2, 3, 4, 5, 6 and 7 (30-m resolution) were used.
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Image transformation including band ratios and PCA were conducted in the present
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investigation. These depend on the spectral properties of the material relative to their
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circumferences (Drury and Hunt 1989; El Janati et al., 2014). In band ratios, the digital number
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(DN) in one band was classified by the congruent DN in another band for each pixel, stretching
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the result value and plotting the new values as an image. This method is used by (Cappiccioni et
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al., 2003; Edgardo et al., 1992) to elicit spectral data from multi-spectral imagery. It depends on
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highlighting the spectral variations of the materials being mapped. Bands 7, 5, 3 displayed in R,
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G, and B render the best color contrast that discriminates geologic units. Moreover, the band
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ratios in R, G, and B was applied. Beside band combinations, PCA (Crosta technique) was
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performed. Contrast stretching technique was utilized to enhance the visual interpretation. In a
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PCA, the statistical factors are processed to define which PC image is better for highlighting the
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target area depending on the eigenvectors of the selected bands. The first component contains the
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greatest variances that decrease through the second PC and third....etc.
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4. Results and discussions
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4.1. Aeromagnetic enhancements
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4.1.1. CET grid enhancement
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Following the procedures of the CET grid analysis technique by calculating the standard
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deviation, phase symmetry, and by applying the amplitude thresholding and skeleton to vectors
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(structural trends) conversion generates in the structural map depicted in figure (2c).
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The CET structural map (Fig. 2c) showed a major shear zone trends in the NW-SE direction
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along Wadi Allaqi district. Based on the CET analysis the predominant structural trends are
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WNW, NW, NNE and NE directions. From the inspection of CET structural map, we can notice
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that structural patterns such as faults are very rare in areas covered by Cretaceous Sandstones in
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the south-western corner of Wadi Allaqi. However, most of the directions NW, WNW, and NE
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are common in the central, northern and eastern parts associating with granitoids, metavolcanics,
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and metasediments.
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4.1.2. CET Porphyry enhancement
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The application of CET Porphyry technique steps on the RTP aeromagnetic map (Figure 2b) of
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Wadi Allaqi territory produced the CET porphyry enhancement map (Figure 2d). This map
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showed the dyke-like structures and porphyry intrusions are along the extracted trends such as
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NW, WNW, NE and N-S directions. The Cretaceous sandstone in the southwestern corner of
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Wadi Allaqi territory showed little response to CET porphyry technique; while, the majority of
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resulted porphyry intrusions are related to the basement complexes.
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Figure 2. (a) Total Magnetic Intensity (TMI) Map of Wadi Allaqi area; (b) RTP map; (c) CET structural
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map of Wadi Allaqi area; (d) CET Porphyry enhancement map showed the porphyry features that marked
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in triangle point feature.
4.2. Remote sensing enhancement
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The remote sensing data were applied for delineating areas of hydrothermal alterations and
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extracting the surface lineaments. The results of image enhancements and transformations can be
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summarized as follows:
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4.2.1. Band ratio Applied band composite 7, 5, 3 in R, G, and B discriminates between felsic, mafic and wadi
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deposits. In this combination, the felsic varieties appear reddish brown color, the mafic rocks are
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dark; however, the wadi deposits look bright tone (Fig. 3 a).
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In mineral exploration, band ratio is commonly used to enhance the spectral characteristic of
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the alteration regions depending on the absorption bands of their altered minerals. For
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example using Landsat-7 the iron bearing (ferrous and ferric oxides) minerals are delineated
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using band ratios 3/5 and 5/4 (Sultan et al., 1986 ) and 3/1 (Abrams 1983). Moreover, band ratio
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5/7 was applied to detect high values of the hydroxyl-bearing minerals (kaolinite, alunite,
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muscovite, epidotes and chlorites) (e.g., Gupta 2003). The hydrothermal alteration areas are
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clearly highlighted in bright tone by applying the band ratio 6/7 of Landsat-8OLI that equal to
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5/7 of Landsat-7ETM+ (Fig. 3b).
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Figure 3. (a) Landsat-8 composite image 7, 5, 3 in R, G, and B; (b) Band ratio 6/7 of Landsat-8; (c)
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extraction of the alteration areas correspondent to high alteration in dark tone; (d) 6/7, 6/5, 5 in R, G, and
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B composite image reveals the alteration areas in yellow color.
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Higher values in band ratios that correspondent to higher areas of alteration in white tone (Fig.
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3b) have been deduced in figure (3c). The results revealed several patches that characterized by
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elongated and semi-circular features are commonly occupying the middle parts of the present
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study. Superimpose, the existing sites of mineralization displaying positive correlation with the
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alteration areas. In order to highlight the alteration areas using band ratio composites 6/7, 6/5, 5
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in R, G, and B, (Ramadan et al.,2001; Ramadan and Sultan 2004) the alteration areas clearly
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marked in yellow color (Fig. 3d). Based on the aforementioned results (3c) and (3d), the existing
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mineralization sites and alteration areas are clearly matched.
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4.2.2. Principal component analysis (PCA)
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In addition to band ratios, PCA was applied to get satisfactory results about the alteration zones.
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PCA is an important technique that provided simple information of multispectral datasets. This
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technique was utilized to minimize the redundancy of information that exist between the
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different bands (Loughlin 1991; Gomez et al., 2005). Crosta and Moore (1989) described the
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Feature-oriented Principal Components Selection (FPCS), which depends on the examination of
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PCA eigenvector loadings to decide which of the principle component images allowed
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concentrating data immediately concerning with the theoretical spectral signatures of certain
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objectives. The pertinent principal component images could show targeted areas in bright or dark
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pixels (Loughlin 1991). Based on the experiment results, the iron- bearing minerals and OH-
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bearing minerals showed absorption features in specific bands rather than another.
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technique requires no atmospheric correction or detail information of the spectral characteristics
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of specific targets. Consequently, it is an effective and accurate tool for delineating alteration
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mineral areas from multispectral data.
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Using PCA technique, the OH-bearing minerals and iron-rich mineral can be extracted by
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transforming the selected bands into their principal components. In order to delineate alteration
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areas of OH-bearing minerals (H-image), selected bands 2, 5, 6, and 7 was transformed using
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PCA(Table 1) to delineate zones rich in OH-bearing minerals (H-image).In such technique,
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bands 2 and 3 have been omitted to avoid mapping iron oxides (Mia and Fujimitsu 2012).
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Band2
PC1
0.125304
PC2
0.504045
PC3
0.748336
PC4
-0.41259
Band6
Band7
Eigenvalue
0.53998
0.618944
0.556442
97.14263
0.653746
-0.19683
-0.528974
1.927717
-0.28392
-0.34473
0.490454
0.553942
0.447691
-0.67774
0.412327
0.375716
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Band5
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TABLE. 1. PCA OF THE SELECTED BANDS 2, 5, 6, AND 7 OF LANDSAT-8
Examining the eigenvector values of the PCA loading of the selected four bands 2, 5, 6, and 7,
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demonstrate that PC3 showed a clear contrast between bands 6 (-0.34473) and 7 (0.490454)
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(Table. 1). Thus, PC3 highlighted the alteration areas in dark tone as a perfect contrast between
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eigenvectors of bands 6 and 7. Due to the negative value of band 6 in PC3, the PC3 was negated
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to produce H-image (OH-bearing minerals). Likewise, applying PCA to the selected bands 2, 4,
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5, and 6 to delineate zones of richer iron oxides, revealed that the PC3 (Table 2) has a good
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contrast between the eigenvectors of band 4 (-0.01317) and 2 (0.76942) allowed delineating
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areas of higher iron-oxides content (F-image) after negating PC3. Although, the high-order PCs
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(PC4) revealing high contrast on the aforementioned techniques it represents significantly
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incorporated noise.
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After obtaining H and F images in a grey scale, a false color composite image "Crosta
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alteration image" was generated after utilizing the OH-bearing minerals (H-image) in Red (Fig.
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4a), the iron-bearing oxides (F- image) in Blue (Fig. 4b) and the H+F image in green (Fig. 4c).
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The alteration zones resulting from this combinations is shown in figure 4d. The final image
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defines alteration zones usually in terms of the abundance of iron oxides and OH-bearing
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minerals richer zones that appear in reddish-yellow (orange). Higher contents of the clay
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minerals in sedimentary rocks that occupy the southwestern corner and some alluvial sediments
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show similar results. The results of Crosta alteration image (Fig. 4d) displays alteration zones
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more clear in number, size, extent and resolution than those produced from band ratios (see Figs.
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3d and 4d).
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TABLE. 2. PCA OF THE SELECTED BANDS 2, 4, 5, AND 6 OF LANDSAT-8 Band 2
Band 4
Band 5
Band 6
Eigenvalue
PC1
0.138887
0.474296
0.578112
0.649262
97.20384
PC2 PC3
0.472344
0.503847
0.239529
-0.68239
2.187718
0.76942
-0.01317
-0.54601
0.331203
0.499853
-0.40693
0.721809
-0.55704
0.055749
0.108585
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Figure 4. False color composite image using Crosta technique: (a) H image , (b) F-image, (c) H-image +
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F-image , (d) alteration areas in yellowish orange color after displaying h (R), F (B) and H+F (G).
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4.3. Structural features and lineament analysis
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Lineaments that represent fracture/fault zones of Wadi Allaqi (Fig. 5a) from remote sensing,
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magnetic, and geologic data. The NW, WNW, NE and N-S are the main revealed from
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aeromagnetic data; while the NE, NNE, NW, WNW and NNW are the dominant structural trends
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revealed from remote sensing processing. Density map of lineament of Wadi Allaqi that obtained
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from extracted trends reveals zones of high structural density that trending in the NW, NE,
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WNW and N directions. Superimpose the existing mines (Fig. 5b) on the lineament density map
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revealed a positive correlation between areas of medium to high lineament density and the sites
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mineralization. This reflecting that structures controlling the mineralization in the present study
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(Domzaliski 1964).
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Figure 5. Lineaments extraction and analysis. (a) lineaments of Wadi Allaqi revealed from remote
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sensing, magnetic and geologic data; (b) Density map of lineaments of Wadi Allaqi overlain by known
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sites of mineralization.
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4.4. Data integration
Several GIS layers such as lineament density of fractures and faults, CET porphyry features and
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hydrothermal alteration areas from band ratios and Crosta technique were combined using GIS
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spatial analysis technique (Abdelkareem et al., 2012; Abdelkareem and El-Baz 2015). This is to
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deduce the optimum area of mineral potentiality. Each layer was classified and the classes were
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ranked based on their relative suitability for controlling mineral resources. For instance, the
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density map of lineament was classified into three classes (Fig. 6a) that assigned numeric values
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3, 2, and 1 represent high, intermediate, and low areas of mineral probability. The areas of higher
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lineament density representing feature/fault zones that promote the movements of the minerals in
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the voids during hydrothermal activities. Figure (6b) represents CET porphyry intrusion density
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map which has been classified into two main classes. Numeric number 1 represents the regions
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of low capacity for bearing the intruded plugs while 2 represents areas with high porphyry
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intrusions.
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Likewise, the regions of higher OH-bearing minerals as detected by band ratios 6/7, 6/5, 5 in R,
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G, and B (Ramadan et al., 2001). Each one was categorized into two classes. are given the higher
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numeric values and Crosta technique density maps (Figures, 6c and 6d respectively) were used in
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the predictive map, representing the favorable sites of mineralization as the ore body occupying
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the center of alteration zone. The areas of low-density values refer to poor or unfavorable sites of
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mineralization. Numeric number 1 shows areas of low alteration density zones while numeric
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number 2 shows high density alteration areas in Wadi Allaqi territory.
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The potential mineralization map (Fig. 6e) was produced after combining lineament density,
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circular features , and alteration zones (Fig. 6: a, b, c , and d). The final map was categorized the
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study area into four classes depending on their weight for bearing mineralization. These zones
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include high, moderate, low and very low zones. Overlay the existing sites of mineralization
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validated the mineral predictive map. According to the predictive map, the region that represents
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the highest probabilities represents a promote location for mineral resources and occupying the
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southern, middle and western parts in Wadi Allaqi district. Correlation between the known
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mineralization sites and the highest class of predictive zone shows a very good matching.
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Field observations were conducted to verify the predictive map. At the northwestern part of
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Wadi Allaqi territory, there are numerous evidences of mineralization around the granodiorites.
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Moreover two gold bearing quartz veins (Fig. 7 a, b)
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northeast of Wadi Hadaiyib area that lie between lat. 22º51᾿33.66᾿᾿N, long. 33º32᾿5.38᾿᾿E and
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lat. 22º53᾿8.07᾿᾿N, long.33º32᾿59.05᾿᾿E, respectively. The estimated gold amounts of these two
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areas are 4.7 g/t and 4.37 g/t respectively. These areas represent the high promising areas in
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potential mineralization map (Fig. 6e).
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associated with granodiorite rocks
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Figure 6. (a) lineament density map; (b) CET porphyry intrusion density map; (c) Band ratio 6/7, 6/5, 5
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in R, G, and B density map (d) Crosta technique density map (e) Potential mineralization map of Wadi
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Allaqi territory.
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Figure 7. (a) and (b) Two gold bearing quartz veins.
5. Conclusions
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Aeromagnetic data and remote sensing were used to detect the spatial relationship between
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porphyry intrusions, alteration zones and fault/fracture zones. Faults and geologic lineaments
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revealed from the enhancement of aeromagnetic data indicate that WNW, NW, NNE and NE are
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the main directions in the area, while, remote sensing lineaments revealed that NE, NNE, NW,
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WNW and NNW are the main structural trends.
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Applying band ratios and PCA further allowed revealing areas of alteration zones and areas of
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high lineament (fractures/faults) intensity. The structural zones were clearly detected in surface
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and subsurface patterns using aeromagnetic data.
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Integration
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detecting areas of predicted mineral resources. Moreover, the predictive map have been tested
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by using the known mineralization sites of Wadi Allaqi district, which will be helped for
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identifying favorable sites for mineralization. Finally, we concluded that integration between
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of the alteration zones, lineaments and porphyry intrusions further helped in
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remote sensing, geophysical and geological data is a beneficial way for mapping mineral
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potentiality at Wadi Allaqi region.
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ACCEPTED MANUSCRIPT Highlights • •
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Aeromagnetic data enhancement in terms of structural complexity and porphyry intrusion. Remote sensing data processing for detecting alteration zones and lineaments. Integration between aeromagnetic and remote sensing data to produce mineralization potential map of the study area.
S1464-343X(17)30110-3
DOI:
10.1016/j.jafrearsci.2017.03.006
Reference:
AES 2842
To appear in:
Journal of African Earth Sciences
Received Date: 21 November 2016 Revised Date:
22 February 2017
Accepted Date: 7 March 2017
Please cite this article as: Eldosouky, A.M., Abdelkareem, M., Elkhateeb, S.O., Integration of remote sensing and aeromagnetic data for mapping structural features and hydrothermal alteration zones in Wadi Allaqi area, South Eastern Desert of Egypt, Journal of African Earth Sciences (2017), doi: 10.1016/ j.jafrearsci.2017.03.006. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Integration of remote sensing and aeromagnetic data for mapping structural features and
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hydrothermal alteration zones in Wadi Allaqi area, South Eastern Desert of Egypt
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Ahmed M. Eldosouky¹, Mohamed Abdelkareem², Sayed O. Elkhateeb² ¹Egyptian Environmental Affairs Agency (E.E.A.A.), Egypt. ² Department of Geology,
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Faculty of science, South Valley University, 83523 Qena, Egypt
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Corresponding Author: ¹Ahmed Mohammed Eldosouky. Researcher at National Conservation
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Sector, Egyptian Environmental Affairs Agency, Egypt.
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Email: [email protected] , [email protected] .
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Abstract
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Remote sensing and aeromagnetic data provided significant information for detecting potential
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areas of mineralization in Wadi Allaqi in the south Eastern Desert of Egypt. Application of band
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ratios and Crosta technique of Principal Component Analysis (PCA) using Landsat-8
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successfully highlighted the hydrothermal alteration zones and the structural elements
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represented by lithologic contacts and faults/fracture zones. Structural lineaments were also
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successfully extracted using remote sensing and aeromagnetic data. Center for Exploration
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Targeting (CET) Grid analysis and CET Porphyry Analysis techniques were applied for
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constructing the structural complexity heat map and probable near circular features of porphyry
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intrusions respectively. Combining data of lineaments, alteration zones and porphyry intrusions
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after obtaining a consequence of each map allowed predicting and mapping areas of probable
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high mineral resources. Overlaying the present sites of mineralization on the final map validated
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the prepared mineral predictive map. Overall results clearly revealed that areas of high structural
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complexity, fractures/faults density are in agreement with the detected areas of hydrothermal
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alterations which also matched with the known mineralization mines in the study area.
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Key words: Aeromagnetic data; Remote Sensing; Predictive map; Wadi Allaqi. 1. Introduction Sensors aboard satellites and aircraft provided the way to conduct surface and subsurface
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measurements of various features for the Earth planet (geologic structures, lithologic and mineral
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resources purposes). Mapping geology, faults and fractures along with identifying the
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hydrothermally altered rocks are important for mineral exploration purposes (Sabin's 1999).
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Remote sensing data allowed extracting lineaments and mapping hydrothermal alteration
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regions. Integration of various remote sensing data (ASTER, PALSAR, OLI, Hyperion imagery
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and Landsat ETM) are used effectively to map mineral characteristics of the alteration zones,
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delineate regional and local structures and to distinguish between different lithologic units (Pour
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and Hashim, 2014; Pour and Hashim, 2015; Pour and Hashim, 2015; Pournamdary et al., 2014;
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Pournamdary et al., 2014).
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Remote sensing data highlight lithologic contacts that are probably located along structural
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features (Ramsay and Huber 1987) where these contacts could have revealed a subsurface
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extension. Several approaches like band ratios and PCA that were widely employed using
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multispectral data (e.g., Landsat) successfully revealed the hydrothermal alteration regions and
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delineated areas of potential mineralization (Sabin's 1997; Goetz et al., 1983; Rowan et al., 1986;
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Abrams and Hook 1995; Okada et al., 1993;Amuda et al., 2014; Poormirzaee et al., 2010;
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Ramadan et al., 2001; Mia and Fujimitsu 2012). Several approaches like band ratios and PCA
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were widely employed using multispectral data to detect hydrothermal alteration zones (Sabin's
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1999; Mia and Fujimitsu 2012; Aydal et al., 2007; Liu et al., 2013; Tangestani and Moore 2000).
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This is because; the hydrothermal processes are often related to mineralization which may
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consequently change the properties of country rocks.
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Aeromagnetic data analysis and enhancement allowed mapping the shallow and deep geologic
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frameworks (Sharma 1997) and revealing porphyry intrusion (Holden et al., 2011). These
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structural features are reflected in trends and intensities apparent on aeromagnetic maps that
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represent magnetic patterns (Gay, 1972). Moreover, the texture analysis allowed enhancing and
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identifying geologic lineaments like lithologic boundaries, joints and faults. Over the past few
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decades, several studies have paid attention for delineating the porphyry intrusions magnetic
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field data (Williams and Shah 1990; Holden et al., 2008; Core et al., 2009; Elkhateeb and
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Eldosouky 2016). This is because several deposits are associated with the igneous intrusion in
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nature which appears as a near circular-feature. The hydrothermal alteration often surrounded
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these intrusions and revealed subsequent/concentric zones. Porphyry deposits are considered the
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world's predominant source of molybdenum, copper, gold, silver and other by-product metals;
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therefore, the investigations of porphyry intrusions are significant.
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Magnetic field data have the ability to delineate the porphyry intrusions and geologic structures
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and which are most reliable for this purpose. Therefore, several studies focused on detecting the
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structural features and porphyry intrusions using aeromagnetic data (Holden et al., 2008; Core et
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al., 2009; Macnae 1995; Williams and Shah 1990; Elkhateeb and Eldosouky 2016).
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Many researchers have studied Wadi Allaqi district like Abdel Salam and Stern (1996) who
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suggested that the Allaqi shear zone was progressed among four Neoproterozoic deformation
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phases. El Shimi (2005) used remote sensing information for mineral prospecting at Wadi Allaqi
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to map and classify zones of alteration. Elkhateeb and Eldosouky (2016) mapped the porphyry
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intrusion and its relation to mineralization at Wadi Allaqi using aeromagnetic data analysis.
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The aim of the present study is to analyze aeromagnetic data for mapping structural complexity
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and porphyry intrusion zones and remote sensing data for mapping hydrothermal alteration
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zones. Integration of aeromagnetic and remote sensing datasets will be applied for producing a
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mineral predictive map of Wadi Allaqi district.
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2. Geologic setting Wadi Allaqi district is situated in the South Eastern Desert of Egypt (Fig. 1a) which extends
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between latitudes 22º 22’50" and 23º 00’00"N, and longitudes 33º 15’ and 34º 15´ E, covering an
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area about 7050km². The western expansion of the Allaqi-Heiani suture (Stern et al., 1989; Stern
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1994; Abdelsalam and Stern 1996) controlled this area The NW, NE, and NNE are the main
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structural trends controlling Wadi Allaqi district (Ramadan and Sultan 2004) and the shear zone
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that dominated Wadi Allaqi was progressed among four Neoproterozoic deformation stages
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(Abdelsalam and Stern 1996). The surface/near-surface geologic structures in Wadi Allaqi were
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investigated by (Elkhateeb and Eldosouky 2016). These structures represent the favorable sites
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of hydrothermal activities; therefore, remote sensing data were applied to Wadi Allaqi for
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classifying the alteration regions and mineral prospecting (El Shimi, 2005).
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Based on the geological map (Geologic map of Wadi Gabjabah Quadrangle, Egypt 1996) of
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Egyptian Geological Survey and Mining Authority (EGSMA), Wadi Allaqi (Fig.1) occupied by
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the basement rocks, sandstone of Cretaceous age, younger Mesozoic volcanic and sub-volcanic
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rocks.
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Figure 1. (a) Regional geology of the Eastern Desert of Egypt that divided into South Eastern Desert
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(SED), Central Eastern Desert (CED), and North Eastern Desert (NED); (b) Geologic map of Wadi Allaqi
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district (after EGSMA, 1996).
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The majority of Wadi Allaqi district is occupied by basement rocks but the small exposure of
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sedimentary sequence occupies the southwestern corner of the study area (Cretaceous
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Sandstones including Abu Sumbul Formation, Al Jilf Formation, Abu Ajjaj Formation, and Al
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Burj Formation whilst Quaternary sediments fill the wadies). The basement of Wadi Allaqi
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includes (a) Ophiolitic assemblage; (b) Island-arc assemblage, and (c) Late to post-tectonic
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granitoids.
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The ophiolitic assemblages occupy the central part of Wadi Allaqi that are composed of
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overlaped talc carbonate schist, serpentinites, and metagabbros slices and thrust sheets. They are
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thrust southwards upon the island-arc rocks. The island-arc rocks are made up of metavolcanic
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and meta-sedimentary layers that are intruded by diorite and gabbro plugs (Fig. 1 b). Tonalite,
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quartz-diorite, diorite, and gabbro are the main components of the intrusions of gabbro-diorite.
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Metavolcanic and meta-sedimentary rocks are more numerous than the island-arc assemblages.
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The geochemical characteristics of the metavolcanic units show that there are transitional
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conditions between continental-arc and continental margin (El-Nisr 1997). The central area of
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Wadi Allaqi has an abundant presence of the late to post-tectonic granitoids, particularly at Wadi
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Um Shelman area (Fig. 1 b). They appear as isolated circular eroded hills that are draped by
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recent sand sediments. The distribution of post-tectonic granitoids becomes more abundant in
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the area under study towards the north direction. They are red in color, medium grained with
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moderate to high relief. They are intruded in the overhead rock units and being cut by mafic and
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felsic intrusions. 3. Data used and processing techniques
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3.1. Aeromagnetic data
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The total magnetic intensity (TMI) Map data (Fig. 2a) utilized in this investigation was acquired
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from the Egyptian Mineral Resources Authority which is a subsidiary of Aero Service Company
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(1984). The Aeromagnetic scanning was flown with a 120 m flight height and an average
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magnetic inclination of 32.8 N and declination of 1.9 E. So as to perform the CET grid and
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porphyry techniques, it requires pole reduction (RTP). Thus, the magnetic anomaly (TMI) map
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was reduced to pole (RTP) after subtracting the International Geomagnetic Reference Field
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(IGRF) (Fig. 2b). There are several approaches can be utilized using the RTP map as follows:
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3.1.1. Center for exploration targeting (CET) grid analysis technique.
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This technique reinforces discontinuity zones within potential data and highlights native
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variations in magnetic intensity. Areas of magnetic discontinuity are represented in the form of
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skeletal structure by applying the texture enhancement. The output data represents each region of
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the discontinuity zones as a skeletal line segments that belong to each of them, visibly showing
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the variations in the directions and offsets within the structural features (Kovesi 1997).
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This technique utilizes the following steps:
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i. Texture enhancement allowed locating the zones of complex textures associated with
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ii. Phase symmetry can be carried out using the texture enhancement results for detecting zones of lateral discontinuity.
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magnetic discontinuities.
iii. Delineation of structures using the results of phase symmetry to minimize the discontinuities containing zones into line-like structures.
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3.1.2. Center for Exploration Targeting (CET) Porphyry intrusion technique
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The CET Porphyry enhancement begins by executing the circular feature transform (CFT) (Loy
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and Zelinsky 2003), which is intended to detect circular shaped features. Then, the centers of
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elevated or depressed circular features are highlighted. Thereafter, using the amplitude contrast
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transform (ACT) a circular feature will appear as a 'halo', which matches with the circular rim of
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the feature. The outputs are a database file and a polygon file. The database file specifies the
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circular feature centers, the radial symmetry strength, and the radius that are produced in the
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highest response (in both cells and meters). The polygon file contains, for each feature location,
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the circle boundary that is generated by the strongest radial symmetry response. This enables
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visualization of the extent of the detected circular features (Holden et al., 2011).
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In this section, we used Landsat-8 Operational Land Imager (OLI) sensor which was launched on
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February 11, 2013 from Vandenberg Air Force Base in California. OLI provides data from nine
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spectral bands (visible, near and shortwave infrared bands and two thermal long-wave bands).
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Seven of them are compatible with the Thematic Mapper and Enhanced Thematic Mapper
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plus (TM and ETM+) sensors of Landsat satellites. Two new spectral bands, a deep blue
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coastal/aerosol band and a shortwave-infrared cirrus band, allowing measuring quality of the
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water and enhance disclosure of high, thin clouds. ENVI and ArcGIS software packages will be
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used to enhance and analyze these data.
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Two scenes of Landsat-8 data covering Wadi Allaqi were acquired on July/12/2016 (p 174 r 044)
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and Jun/3/2016 (p173r044). Six Landsat-8 bands 2, 3, 4, 5, 6 and 7 (30-m resolution) were used.
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Image transformation including band ratios and PCA were conducted in the present
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investigation. These depend on the spectral properties of the material relative to their
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circumferences (Drury and Hunt 1989; El Janati et al., 2014). In band ratios, the digital number
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(DN) in one band was classified by the congruent DN in another band for each pixel, stretching
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the result value and plotting the new values as an image. This method is used by (Cappiccioni et
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al., 2003; Edgardo et al., 1992) to elicit spectral data from multi-spectral imagery. It depends on
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highlighting the spectral variations of the materials being mapped. Bands 7, 5, 3 displayed in R,
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G, and B render the best color contrast that discriminates geologic units. Moreover, the band
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ratios in R, G, and B was applied. Beside band combinations, PCA (Crosta technique) was
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performed. Contrast stretching technique was utilized to enhance the visual interpretation. In a
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PCA, the statistical factors are processed to define which PC image is better for highlighting the
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target area depending on the eigenvectors of the selected bands. The first component contains the
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greatest variances that decrease through the second PC and third....etc.
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4. Results and discussions
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4.1. Aeromagnetic enhancements
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4.1.1. CET grid enhancement
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Following the procedures of the CET grid analysis technique by calculating the standard
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deviation, phase symmetry, and by applying the amplitude thresholding and skeleton to vectors
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(structural trends) conversion generates in the structural map depicted in figure (2c).
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The CET structural map (Fig. 2c) showed a major shear zone trends in the NW-SE direction
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along Wadi Allaqi district. Based on the CET analysis the predominant structural trends are
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WNW, NW, NNE and NE directions. From the inspection of CET structural map, we can notice
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that structural patterns such as faults are very rare in areas covered by Cretaceous Sandstones in
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the south-western corner of Wadi Allaqi. However, most of the directions NW, WNW, and NE
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are common in the central, northern and eastern parts associating with granitoids, metavolcanics,
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and metasediments.
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4.1.2. CET Porphyry enhancement
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The application of CET Porphyry technique steps on the RTP aeromagnetic map (Figure 2b) of
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Wadi Allaqi territory produced the CET porphyry enhancement map (Figure 2d). This map
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showed the dyke-like structures and porphyry intrusions are along the extracted trends such as
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NW, WNW, NE and N-S directions. The Cretaceous sandstone in the southwestern corner of
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Wadi Allaqi territory showed little response to CET porphyry technique; while, the majority of
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resulted porphyry intrusions are related to the basement complexes.
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Figure 2. (a) Total Magnetic Intensity (TMI) Map of Wadi Allaqi area; (b) RTP map; (c) CET structural
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map of Wadi Allaqi area; (d) CET Porphyry enhancement map showed the porphyry features that marked
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in triangle point feature.
4.2. Remote sensing enhancement
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The remote sensing data were applied for delineating areas of hydrothermal alterations and
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extracting the surface lineaments. The results of image enhancements and transformations can be
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summarized as follows:
196 197
4.2.1. Band ratio Applied band composite 7, 5, 3 in R, G, and B discriminates between felsic, mafic and wadi
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deposits. In this combination, the felsic varieties appear reddish brown color, the mafic rocks are
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dark; however, the wadi deposits look bright tone (Fig. 3 a).
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In mineral exploration, band ratio is commonly used to enhance the spectral characteristic of
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the alteration regions depending on the absorption bands of their altered minerals. For
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example using Landsat-7 the iron bearing (ferrous and ferric oxides) minerals are delineated
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using band ratios 3/5 and 5/4 (Sultan et al., 1986 ) and 3/1 (Abrams 1983). Moreover, band ratio
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5/7 was applied to detect high values of the hydroxyl-bearing minerals (kaolinite, alunite,
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muscovite, epidotes and chlorites) (e.g., Gupta 2003). The hydrothermal alteration areas are
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clearly highlighted in bright tone by applying the band ratio 6/7 of Landsat-8OLI that equal to
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5/7 of Landsat-7ETM+ (Fig. 3b).
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Figure 3. (a) Landsat-8 composite image 7, 5, 3 in R, G, and B; (b) Band ratio 6/7 of Landsat-8; (c)
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extraction of the alteration areas correspondent to high alteration in dark tone; (d) 6/7, 6/5, 5 in R, G, and
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B composite image reveals the alteration areas in yellow color.
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Higher values in band ratios that correspondent to higher areas of alteration in white tone (Fig.
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3b) have been deduced in figure (3c). The results revealed several patches that characterized by
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elongated and semi-circular features are commonly occupying the middle parts of the present
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study. Superimpose, the existing sites of mineralization displaying positive correlation with the
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alteration areas. In order to highlight the alteration areas using band ratio composites 6/7, 6/5, 5
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in R, G, and B, (Ramadan et al.,2001; Ramadan and Sultan 2004) the alteration areas clearly
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marked in yellow color (Fig. 3d). Based on the aforementioned results (3c) and (3d), the existing
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mineralization sites and alteration areas are clearly matched.
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In addition to band ratios, PCA was applied to get satisfactory results about the alteration zones.
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PCA is an important technique that provided simple information of multispectral datasets. This
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technique was utilized to minimize the redundancy of information that exist between the
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different bands (Loughlin 1991; Gomez et al., 2005). Crosta and Moore (1989) described the
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Feature-oriented Principal Components Selection (FPCS), which depends on the examination of
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PCA eigenvector loadings to decide which of the principle component images allowed
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concentrating data immediately concerning with the theoretical spectral signatures of certain
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objectives. The pertinent principal component images could show targeted areas in bright or dark
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pixels (Loughlin 1991). Based on the experiment results, the iron- bearing minerals and OH-
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bearing minerals showed absorption features in specific bands rather than another.
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technique requires no atmospheric correction or detail information of the spectral characteristics
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of specific targets. Consequently, it is an effective and accurate tool for delineating alteration
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mineral areas from multispectral data.
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Using PCA technique, the OH-bearing minerals and iron-rich mineral can be extracted by
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transforming the selected bands into their principal components. In order to delineate alteration
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areas of OH-bearing minerals (H-image), selected bands 2, 5, 6, and 7 was transformed using
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PCA(Table 1) to delineate zones rich in OH-bearing minerals (H-image).In such technique,
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bands 2 and 3 have been omitted to avoid mapping iron oxides (Mia and Fujimitsu 2012).
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Band2
PC1
0.125304
PC2
0.504045
PC3
0.748336
PC4
-0.41259
Band6
Band7
Eigenvalue
0.53998
0.618944
0.556442
97.14263
0.653746
-0.19683
-0.528974
1.927717
-0.28392
-0.34473
0.490454
0.553942
0.447691
-0.67774
0.412327
0.375716
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TABLE. 1. PCA OF THE SELECTED BANDS 2, 5, 6, AND 7 OF LANDSAT-8
Examining the eigenvector values of the PCA loading of the selected four bands 2, 5, 6, and 7,
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demonstrate that PC3 showed a clear contrast between bands 6 (-0.34473) and 7 (0.490454)
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(Table. 1). Thus, PC3 highlighted the alteration areas in dark tone as a perfect contrast between
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eigenvectors of bands 6 and 7. Due to the negative value of band 6 in PC3, the PC3 was negated
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to produce H-image (OH-bearing minerals). Likewise, applying PCA to the selected bands 2, 4,
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5, and 6 to delineate zones of richer iron oxides, revealed that the PC3 (Table 2) has a good
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contrast between the eigenvectors of band 4 (-0.01317) and 2 (0.76942) allowed delineating
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areas of higher iron-oxides content (F-image) after negating PC3. Although, the high-order PCs
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(PC4) revealing high contrast on the aforementioned techniques it represents significantly
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incorporated noise.
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After obtaining H and F images in a grey scale, a false color composite image "Crosta
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alteration image" was generated after utilizing the OH-bearing minerals (H-image) in Red (Fig.
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4a), the iron-bearing oxides (F- image) in Blue (Fig. 4b) and the H+F image in green (Fig. 4c).
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The alteration zones resulting from this combinations is shown in figure 4d. The final image
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defines alteration zones usually in terms of the abundance of iron oxides and OH-bearing
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minerals richer zones that appear in reddish-yellow (orange). Higher contents of the clay
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minerals in sedimentary rocks that occupy the southwestern corner and some alluvial sediments
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show similar results. The results of Crosta alteration image (Fig. 4d) displays alteration zones
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more clear in number, size, extent and resolution than those produced from band ratios (see Figs.
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3d and 4d).
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TABLE. 2. PCA OF THE SELECTED BANDS 2, 4, 5, AND 6 OF LANDSAT-8 Band 2
Band 4
Band 5
Band 6
Eigenvalue
PC1
0.138887
0.474296
0.578112
0.649262
97.20384
PC2 PC3
0.472344
0.503847
0.239529
-0.68239
2.187718
0.76942
-0.01317
-0.54601
0.331203
0.499853
-0.40693
0.721809
-0.55704
0.055749
0.108585
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Figure 4. False color composite image using Crosta technique: (a) H image , (b) F-image, (c) H-image +
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F-image , (d) alteration areas in yellowish orange color after displaying h (R), F (B) and H+F (G).
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4.3. Structural features and lineament analysis
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Lineaments that represent fracture/fault zones of Wadi Allaqi (Fig. 5a) from remote sensing,
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magnetic, and geologic data. The NW, WNW, NE and N-S are the main revealed from
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aeromagnetic data; while the NE, NNE, NW, WNW and NNW are the dominant structural trends
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revealed from remote sensing processing. Density map of lineament of Wadi Allaqi that obtained
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from extracted trends reveals zones of high structural density that trending in the NW, NE,
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WNW and N directions. Superimpose the existing mines (Fig. 5b) on the lineament density map
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revealed a positive correlation between areas of medium to high lineament density and the sites
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mineralization. This reflecting that structures controlling the mineralization in the present study
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(Domzaliski 1964).
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Figure 5. Lineaments extraction and analysis. (a) lineaments of Wadi Allaqi revealed from remote
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sensing, magnetic and geologic data; (b) Density map of lineaments of Wadi Allaqi overlain by known
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sites of mineralization.
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4.4. Data integration
Several GIS layers such as lineament density of fractures and faults, CET porphyry features and
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hydrothermal alteration areas from band ratios and Crosta technique were combined using GIS
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spatial analysis technique (Abdelkareem et al., 2012; Abdelkareem and El-Baz 2015). This is to
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deduce the optimum area of mineral potentiality. Each layer was classified and the classes were
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ranked based on their relative suitability for controlling mineral resources. For instance, the
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density map of lineament was classified into three classes (Fig. 6a) that assigned numeric values
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3, 2, and 1 represent high, intermediate, and low areas of mineral probability. The areas of higher
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lineament density representing feature/fault zones that promote the movements of the minerals in
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the voids during hydrothermal activities. Figure (6b) represents CET porphyry intrusion density
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map which has been classified into two main classes. Numeric number 1 represents the regions
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of low capacity for bearing the intruded plugs while 2 represents areas with high porphyry
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intrusions.
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Likewise, the regions of higher OH-bearing minerals as detected by band ratios 6/7, 6/5, 5 in R,
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G, and B (Ramadan et al., 2001). Each one was categorized into two classes. are given the higher
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numeric values and Crosta technique density maps (Figures, 6c and 6d respectively) were used in
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the predictive map, representing the favorable sites of mineralization as the ore body occupying
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the center of alteration zone. The areas of low-density values refer to poor or unfavorable sites of
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mineralization. Numeric number 1 shows areas of low alteration density zones while numeric
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number 2 shows high density alteration areas in Wadi Allaqi territory.
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The potential mineralization map (Fig. 6e) was produced after combining lineament density,
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circular features , and alteration zones (Fig. 6: a, b, c , and d). The final map was categorized the
300
study area into four classes depending on their weight for bearing mineralization. These zones
301
include high, moderate, low and very low zones. Overlay the existing sites of mineralization
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validated the mineral predictive map. According to the predictive map, the region that represents
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the highest probabilities represents a promote location for mineral resources and occupying the
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southern, middle and western parts in Wadi Allaqi district. Correlation between the known
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mineralization sites and the highest class of predictive zone shows a very good matching.
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Field observations were conducted to verify the predictive map. At the northwestern part of
307
Wadi Allaqi territory, there are numerous evidences of mineralization around the granodiorites.
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Moreover two gold bearing quartz veins (Fig. 7 a, b)
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northeast of Wadi Hadaiyib area that lie between lat. 22º51᾿33.66᾿᾿N, long. 33º32᾿5.38᾿᾿E and
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lat. 22º53᾿8.07᾿᾿N, long.33º32᾿59.05᾿᾿E, respectively. The estimated gold amounts of these two
311
areas are 4.7 g/t and 4.37 g/t respectively. These areas represent the high promising areas in
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potential mineralization map (Fig. 6e).
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associated with granodiorite rocks
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Figure 6. (a) lineament density map; (b) CET porphyry intrusion density map; (c) Band ratio 6/7, 6/5, 5
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in R, G, and B density map (d) Crosta technique density map (e) Potential mineralization map of Wadi
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Allaqi territory.
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Figure 7. (a) and (b) Two gold bearing quartz veins.
5. Conclusions
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Aeromagnetic data and remote sensing were used to detect the spatial relationship between
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porphyry intrusions, alteration zones and fault/fracture zones. Faults and geologic lineaments
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revealed from the enhancement of aeromagnetic data indicate that WNW, NW, NNE and NE are
323
the main directions in the area, while, remote sensing lineaments revealed that NE, NNE, NW,
324
WNW and NNW are the main structural trends.
325
Applying band ratios and PCA further allowed revealing areas of alteration zones and areas of
326
high lineament (fractures/faults) intensity. The structural zones were clearly detected in surface
327
and subsurface patterns using aeromagnetic data.
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Integration
329
detecting areas of predicted mineral resources. Moreover, the predictive map have been tested
330
by using the known mineralization sites of Wadi Allaqi district, which will be helped for
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identifying favorable sites for mineralization. Finally, we concluded that integration between
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of the alteration zones, lineaments and porphyry intrusions further helped in
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remote sensing, geophysical and geological data is a beneficial way for mapping mineral
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potentiality at Wadi Allaqi region.
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ACCEPTED MANUSCRIPT Highlights • •
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Aeromagnetic data enhancement in terms of structural complexity and porphyry intrusion. Remote sensing data processing for detecting alteration zones and lineaments. Integration between aeromagnetic and remote sensing data to produce mineralization potential map of the study area.