Application of ASTER and Landsat 8 imagery data and mathematical evaluation method in detecting iron minerals contamination in the Chadormalu iron mine area, central Iran

Moghtaderi, Arsia; Moore, Farid; Ranjbar, Hojjatollah

doi:10.1117/1.jrs.11.016027

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…Remote sensing data have a great capability for mapping hydrothermal alteration zones and are extensively and successfully used for distinguishing hydrothermal alteration minerals and zones in metallogenic provinces around the world [8,9,[72][73][74]. Several image processing techniques are broadly applied to remote sensing imagery for classifying, identifying, and distinguishing spatial distribution of alteration minerals and zones [61,62]. Band ratios, Principal Component Analysis (PCA), Independent Component Analysis (ICA), Matched-Filtering (MF), Mixture-Tuned Matched-Filtering (MTMF), Linear Spectral Mixing (LUS), and Constrained Energy Minimization (CEM) methods have been extensively implemented on ASTER data for mapping alteration zones associated with porphyry copper deposits [75][76][77].…”

Section: Discussionmentioning

confidence: 99%

“…Figure 3a shows an RGB color composite (R:3, G:2, B:1) of the ASTER full scene covering the study area. In the RBD method, considering the points of absorption and reflectance of mineral spectra, to determine the alterations, the band ratios (B7 + B5)/B6 for phyllic, the ratio (B7 + B4) / B5 for the argillic, and the ratio (B7 + B9)/B8 for propylitic alterations were used [61,62] (Figure 3b-d). To identify the alterations using LSU or SFF methods, the reference spectra related to the indicator minerals of each alteration zones were extracted from the USGS spectral library [63].…”

Section: Detection Of the Alteration Zonesmentioning

confidence: 99%

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Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

et al. 2021

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The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world.

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Section: Discussionmentioning

confidence: 99%

Section: Detection Of the Alteration Zonesmentioning

confidence: 99%

Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

et al. 2021

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“…Using ASTER data alone, or in combination with data from other satellites (Landsat and Hyperion) or airborne geophysics, methods were developed over targets in India, Iran, Brazil, and Australia by Rajendran et al [148], Huang et al [149], Duuring et al [150], Mansouri et al [151], and Mazhari et al [152]. One report by Moghtaderi et al [153] used ASTER and Landsat data to determine iron mineral contamination in an iron mine area in Iran. These studies mainly relied on the VNIR bands, as this spectral region covers the diagnostic spectral absorption features associated with ferric and ferrous iron minerals.…”

Section: Aster Data Applied To Exploration For Other Mineralsmentioning

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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“…Satellite images record vast geological and environmental features from various map scales. In examining regoliths, the visible to short wave infrared (SWIR) can map iron-rich minerals, clay, and associated residual bauxite occurrences (Moghtaderi et al, 2017). Minerals and rocks exhibit different spectral patterns due to distinct spectral absorption features (Asadzadeh and de Souza Filho, 2016).…”

Section: Introductionmentioning

confidence: 99%

Multispectral Remote Sensing to Delineate the Distribution Area of Scandium-Bearing Minerals in Bauxite Mining Sites, West Kalimantan Province, Indonesia

Nugraheni

Anggraini²,

Setiawan

et al. 2023

MGPB

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The rising demand for scandium led to massive exploration activities for its discovery from mining by-products. Therefore, this study attempts to delineate the distribution of scandium-bearing minerals in the surrounding bauxite mining area, Tayan District, West Kalimantan Province, Indonesia. Preliminary studies were conducted by applying optical sensors to discriminate the types of minerals, such as kaolinite, gibbsite, goethite, and quartz. The spectral information aids the reconnaissance study by providing data on specific rocks and minerals using the short-wave infrared (SWIR), processed into a series of bands with spectral ranges from 0.35 to 2.5 μm. The data was then compared with the structural lineament from the ALOS PALSAR imagery to infer the prospective area with the structural pattern. The integrated band math minerals and geochemical data taken from X-ray fluorescence and Inductively Coupled Plasma-Mass Spectrometry suggest that the Sc-bearing minerals were disseminated predominantly on the bauxite laterite profile from pyroxene diorite and diorite parent rock weathering. The spectral range for goethite as the Sc-bearing minerals is from 0.43 to 1.03, with the main absorption features from 2.0 to. 2.4. Furthermore, goethite is mainly concentrated at the top bauxite horizon associated with the structurally related hill. The ore-bearing minerals also occupied the tailing pond and some beneficiation areas in relatively minor proportion. This study is undoubtedly valuable for the practical need to support mineral exploration through remote predictive mapping.

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Application of ASTER and Landsat 8 imagery data and mathematical evaluation method in detecting iron minerals contamination in the Chadormalu iron mine area, central Iran

Cited by 10 publications

References 21 publications

Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration

Multispectral Remote Sensing to Delineate the Distribution Area of Scandium-Bearing Minerals in Bauxite Mining Sites, West Kalimantan Province, Indonesia

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