Regional scale demarcation of alteration zone using ASTER imageries in South Purulia Shear Zone, East India: Implication for mineral exploration in vegetated regions

Mahanta, Pankajini; Maiti, Sabyasachi

doi:10.1016/j.oregeorev.2018.07.028

Cited by 29 publications

(9 citation statements)

References 47 publications

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“…Satellite remote-sensing data and advances in digital image processing (DIP) techniques provided a new impulse to the development of lithological mapping. Spectral data from space and airborne sensors were widely applied to geological mapping, including lithological discrimination [1][2][3][4][5][6][7][8], structural mapping [9], hydrothermal alteration [10][11][12], and economic mineral deposits [13][14][15][16][17]. Because of their cost effectiveness, especially in mapping inaccessible areas [4,[18][19][20] and in the production of small-scale maps, remote-sensing methods provide a good alternative to traditional field work [21].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

Bachri

Hakdaoui

Raji

et al. 2019

IJGI

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Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

Bachri

Hakdaoui

Raji

et al. 2019

IJGI

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“…They used the lab measurements to help interpret ASTER ratio and principal components images, and found that the combination of lab and remote sensing data were effective in mapping argillic alteration zones. In a highly vegetated area in India, Mahanta and Maiti [96] used band ratios and principal components analysis of ASTER data to map alteration assemblages: iron-rich gossans, sericitization, ferruginization, and chloritization. They identified two potential mineral prospects, one for hydrothermal polymetallic sulfides, and secondary iron and manganese mineralization.…”

Section: General Alteration Mappingmentioning

confidence: 99%

Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration

Abrams

Yamaguchi

2019

Remote Sensing

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The Advanced Spaceborne Thermal Emission and Reflection Radiometer is one of five instruments operating on the National Aeronautics and Space Administration (NASA) Terra platform. Launched in 1999, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been acquiring optical data for 20 years. ASTER is a joint project between Japan’s Ministry of Economy, Trade and Industry; and U.S. National Aeronautics and Space Administration. Numerous reports of geologic mapping and mineral exploration applications of ASTER data attest to the unique capabilities of the instrument. Until 2000, Landsat was the instrument of choice to provide surface composition information. Its scanners had two broadband short wave infrared (SWIR) bands and a single thermal infrared band. A single SWIR band amalgamated all diagnostic absorption features in the 2–2.5 micron wavelength region into a single band, providing no information on mineral composition. Clays, carbonates, and sulfates could only be detected as a single group. The single thermal infrared (TIR) band provided no information on silicate composition (felsic vs. mafic igneous rocks; quartz content of sedimentary rocks). Since 2000, all of these mineralogical distinctions, and more, could be accomplished due to ASTER’s unique, high spatial resolution multispectral bands: six in the SWIR and five in the TIR. The data have sufficient information to provide good results using the simplest techniques, like band ratios, or more sophisticated analyses, like machine learning. A robust archive of images facilitated use of the data for global exploration and mapping.

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“…Moreover, during the ore mineral precipitation processes, hydrothermal fluids react with the mineral constituents of lithological units they are passing and produce hydrothermal alteration zones with distinctive mineral assemblages [7]. The presence of intrusive rocks and hydrothermal alteration zones associated with polymetallic vein-type deposits provide an important guide for exploring this type of ore mineralization especially by the application of advanced satellite remote sensing data [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) contains appropriate spectral and spatial resolution to detect spectral absorption features of hydrothermal alteration minerals and lithological units [10][11][12][13][14][15][16]. ASTER datasets can be used to identify and remotely map hydrothermal alteration zones associated with polymetallic vein-type ore deposits in vegetated regions and well-exposed terrain, especially in a semi-arid environment [8,9,17]. ASTER measures reflected radiation in three bands in the 0.52-to 0.86 µm (the visible and near-infrared (VNIR)), six bands in the 1.6-to 2.43 µm (the shortwave infrared (SWIR)), and five bands of emitted radiation in the 8.125-to 11.65 µm (the thermal infrared (TIR)) with 15, 30, and 90 meter resolution, respectively [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, some studies used ASTER data for the exploration of polymetallic vein-type ore deposits. Mahanta and Maiti [8] used ASTER VNIR+SWIR spectral data for mapping alteration zones such as kaolinization, ferruginization, silicification, phosphatization, and sulphidation associated with the polymetallic vein-type mineralization in the South Purulia Shear Zone (SPSZ), East India. In a recent work, Ahmadirouhani et al [9] identified and mapped hydrothermal alteration zones, including propylitic, phyllic, argillic, and gossan with Cu-Fe-Au vein-type mineralization using ASTER VNIR+SWIR spectral bands in the Bajestan region, northern sector of the Lut Block, East Iran.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Different Algorithms to Map Hydrothermal Alteration Zones Using ASTER Remote Sensing Data for Polymetallic Vein-Type Ore Exploration: Toroud–Chahshirin Magmatic Belt (TCMB), North Iran

et al. 2019

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Polymetallic vein-type ores are important sources of precious metal and a principal type of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud–Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units. The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The approach used in this research has great implications for the exploration of epithermal polymetallic vein-type mineralization in other base metals provinces in Iran and semi-arid regions around the world.

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Regional scale demarcation of alteration zone using ASTER imageries in South Purulia Shear Zone, East India: Implication for mineral exploration in vegetated regions

Cited by 29 publications

References 47 publications

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration

Comparison of Different Algorithms to Map Hydrothermal Alteration Zones Using ASTER Remote Sensing Data for Polymetallic Vein-Type Ore Exploration: Toroud–Chahshirin Magmatic Belt (TCMB), North Iran

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