ASTER detection of chromite bearing mineralized zones in Semail Ophiolite Massifs of the northern Oman Mountains: Exploration strategy

Rajendran, Sankaran; Al-Khirbash, Salah; Pracejus, Bernhard; Nasir, Sobhi; Al-Abri, Amani Humaid; Kusky, Timothy; Ghulam, Abduwasit

doi:10.1016/j.oregeorev.2011.09.010

Cited by 102 publications

(66 citation statements)

References 43 publications

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“…Each scene of ASTER covers 60 × 60 km 2 on land [43]. ASTER provides more spectral bands in the SWIR than say Landsat, which therefor increases its ability to spectrally discriminate minerals and rocks on the Earth's surface [2,13,[44][45][46]. We processed a cloud-free ASTER level 1A scene acquired on 24 August 2003 and provided by the Iraq Geological Survey (GEOSURV-Iraq).…”

Section: Data Characteristics and Softwarementioning

confidence: 99%

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

2014

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Abstract:The mineral ore potential of many mountainous regions of the world, like the Kurdistan region of Iraq, remains unexplored. For logistical and sometimes political reasons, these areas are difficult to map using traditional methods. We highlight the improvement in remote sensing geological mapping that arises from the integration of geomorphic features in classifications. The Mawat Ophiolite Complex (MOC) is located in the NE of Iraq and is known for its mineral deposits. The aims of this study are: (I) to refine the existing lithological map of the MOC; (II) to identify the best discriminatory datasets for lithological classification, including geomorphic features and textures; and (III) to identify potential locations with high concentrations of chromite. We performed a Support Vector Machine (SVM) classification method to allow the joint use of geomorphic features, textures and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The updated map allowed the identification of a new mafic body and a substantial improvement of the geometry of the known lithological units. The use of geomorphic features allowed for the increase of the overall accuracy from 73% to 79.3%. In addition, we detected chromite occurrences within the ophiolite by applying Spectral Angle Mapping (SAM) technique. We identified two new locations having high concentrations of chromite and verified one of these promising areas and has coarsely crystalline chromite within dunite host rock. The chromium (Cr 2 O 3 ) concentration is ~8.46%. The SAM and SVM methods applied on ASTER satellite data show that these can be used as a powerful tool to explore ore deposits and to further improve lithological mapping in mountainous semi-arid regions.

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Section: Data Characteristics and Softwarementioning

confidence: 99%

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

2014

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“…Landsat TM images have been used with mixed success to map igneous and metamorphic units (e.g., [11,[13][14][15][16][17]), as well as clastic, carbonate, and evaporitic lithologies (e.g., [18][19][20][21][22]). TM data have been applied with some success in the mapping of prominent zones of mineral alteration such as those marked by the presence of abundant oxides and hydroxides (e.g., [23,24]).…”

Section: Discrimination Of Rock Types Using Multispectral Remote Sensmentioning

confidence: 99%

“…TM data have been applied with some success in the mapping of prominent zones of mineral alteration such as those marked by the presence of abundant oxides and hydroxides (e.g., [23,24]). In general, because the potential for discrimination of lithologies is greatest in the near-and mid-infrared ranges of the spectrum, TM bands 3 (0.63-0.69 μm), 4 (0.76-0.90 μm), 5 (1.55-1.75 μm), and 7 (2.080-2.35 μm) have proven to be especially useful in past geological work (e.g., [17,25,26]). …”

Section: Discrimination Of Rock Types Using Multispectral Remote Sensmentioning

confidence: 99%

“…Several groups [17,27,28] have used TM bands 4, 5, and 7 to successfully discriminate between major ultramafic (serpentinized harzburguite with dunite) and mafic classes (including deeper gabbroic intrusives, shallower diabase intrusives, and basaltic extrusives) at the Semail ophiolite in Oman, though important ambiguities were associated with some hydrothermally altered zones. TM data were successfully used to discriminate lithological classes of the Betsimisaraka suture of Madagascar, including granite, gabbro, and gneiss [29].…”

Section: Discrimination Of Rock Types Using Multispectral Remote Sensmentioning

confidence: 99%

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Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada

Leverington

Moon

2012

Remote Sensing

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Abstract:In order to better constrain the utility of multispectral datasets in the characterization of surface materials, Landsat Thematic Mapper (TM) data were evaluated in the discrimination of geological classes in the Cape Smith Belt of Quebec, a greenstone belt that hosts Early Proterozoic units including those of the Purtuniq ophiolite. Ground-based measurements collected for the study area highlight the importance of chemical alteration in controlling the reflectance properties of key geological classes. The spatial distribution of exposed lithologies in the study area was determined through (1) image classification using a feedforward backpropagation neural network classifier; and (2) generation of fraction images for spectral end members using a linear unmixing algorithm and ground reflectance data. Despite some shortcomings, the database of surface cover generated by the neural network classifier is a useful representation of the spatial distribution of exposed geological materials in the study area, with an overall agreement with ground truth of 87.7%. In contrast, the fraction images generated through unmixing are poor representations of ground truth for several key lithological classes. These results underscore both the considerable utility and marked limitations of Landsat TM data in the mapping of igneous and metamorphic lithologies.

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“…BRs can be useful for highlighting certain features or materials that cannot be seen in the raw bands. Similarly, the choice of bands depends on their spectral reflectance and positions of the absorption bands of the mineral being mapped (Inzana et al, 2003;Kujjo, 2010;Rajendran et al, 2012). For instance, to enhance a specific alteration mineral that hosts a distinct absorption feature, the most unique spectral ratio for that mineral is employed.…”

Section: Band Ratiomentioning

confidence: 99%

Introducing Au Potential Areas, Using Remote Sensing and Geochemical Data Processing Using Fractal Methods in Chartagh, Western Azerbijan – Iran

Mansouri¹,

Feizi²

2016

Archives of Mining Sciences

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The studied area -Chartagh -is located in the East of Azerbaijan gharbi Province, Iran. In this paper, geology map, ASTER satellite images were used and after processing these images with ENVI softwares, geochemical data analysis consisting of lithogeochemical samples, within geological field observations. On ASTER data; using a number of selected methods including band ratio, Minimum Noise Fraction (MNF) and Spectral Angle Maper (SAM) distinguished alternation zones. Geochemical anomalies were separated by number -size (N-S) fractal method. (N-S) fractal method was utilized for High intensive Au, As and Ag anomalies.

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ASTER detection of chromite bearing mineralized zones in Semail Ophiolite Massifs of the northern Oman Mountains: Exploration strategy

Cited by 102 publications

References 43 publications

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada

Introducing Au Potential Areas, Using Remote Sensing and Geochemical Data Processing Using Fractal Methods in Chartagh, Western Azerbijan – Iran

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