Integration of remote sensing, gravity and geochemical data for exploration of Cu-mineralization in Alwar basin, Rajasthan, India

Chattoraj, Shovan Lal; Prasad, Gokul; Sharma, Ram P.; Ray, P. K. Champati; Meer, F.D. van der; Guha, Arindam; Pour, Amin Beiranvand

doi:10.1016/j.jag.2020.102162

Cited by 29 publications

(11 citation statements)

References 41 publications

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“…Geological mapping and quantification of hydrothermal alteration in volcanic systems have traditionally been carried out using ground and field geological mapping, combined with scanning electron microscopy (SEM), X‐ray diffraction (XRD), and X‐ray fluorescence (XRF), and isotope and fluid inclusions studies, among others, to constrain the paragenesis of the alteration mineral suites (Ball et al, 2013; Christenson & Wood, 1993; John et al, 2008; Nuñez‐Hernández et al, 2020; Piochi et al, 2019; Rye, 2005; Zimbelman et al, 2005). Multispectral satellite remote sensing in the visible and near infrared (VNIR; 300–1,000 nm) and shortwave infrared (SWIR; 1,000–2,500 nm) is often used to upscale mapping efforts, using Landsat series (Mia & Fujimitsu, 2012; Wright et al, 2001), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER; Chattoraj et al, 2020; Galvão et al, 2005), and WorldView (Kruse et al, 2015). Hyperspectral remote sensing is becoming a common tool for mineral alteration mapping, improving the differentiation among key indicator minerals such as alunite, jarosite, kaolinite, montmorillonite and illite, and mica, offering a cost‐effective but highly sophisticated technological solution (Carrino et al, 2018; Crosta et al, 1998; Crowley et al, 2003; Hellman & Ramsey, 2004; Kereszturi et al, 2018; Swayze et al, 2014; van der Meer, 2004).…”

Section: Introductionmentioning

confidence: 99%

Hydrothermal Alteration on Composite Volcanoes: Mineralogy, Hyperspectral Imaging, and Aeromagnetic Study of Mt Ruapehu, New Zealand

Keresztúri

Schaefer

Miller

et al. 2020

Geochem Geophys Geosyst

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Prolonged volcanic activity can induce surface weathering and hydrothermal alteration that is a primary control on edifice instability, posing a complex hazard with its challenges to accurately forecast and mitigate. This study uses a frequently active composite volcano, Mt Ruapehu, New Zealand, to develop a conceptual model of surface weathering and hydrothermal alteration applicable to long‐lived composite volcanoes. The alteration on Mt Ruapehu was classified using ground samples as non‐altered, supergene argillic, intermediate argillic, and advanced argillic. The first two classes have a paragenesis that is consistent with surficial infiltration and circulation of low‐temperature (<40°C) neutral to mildly acidic fluids, inducing chemical weathering and formation of weathering rims on rock surfaces. The intermediate and advanced argillic alteration formed from hotter (≥100°C) hydrothermal fluids with lower pH, interacting with the andesitic to dacitic host rocks. The distribution of weathering and hydrothermal alteration has been mapped with airborne hyperspectral imaging through image classification, while aeromagnetic data inversion was used to map alteration to up to 500‐m depth. The joint use of hyperspectral imaging complements the geophysical methods since it can spectrally identify hydrothermal alteration mineralogy. This study established a conceptual model of hydrothermal alteration history of Mt Ruapehu, exemplifying a long‐lived and nested active and ancient hydrothermal system. This study's combination approach can be used to indicate the most likely sources of future debris avalanches, which are a significant hazard on Ruapehu.

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Section: Introductionmentioning

confidence: 99%

Hydrothermal Alteration on Composite Volcanoes: Mineralogy, Hyperspectral Imaging, and Aeromagnetic Study of Mt Ruapehu, New Zealand

Keresztúri

Schaefer

Miller

et al. 2020

Geochem Geophys Geosyst

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“…Lithological mapping of the exposed outcrops and their associated features are demarcated with the help of PCA, ICA, and MNF analyses to govern the mineral potentiality of the outcrops [9,17,40,42,44,62,63]. Different supervised, unsupervised, machine learning and prospectivity mapping algorithms were applied to the optical datasets to prepare the mineralogical and prospective zone maps of the region and these maps contain the information about the mineral potential zones which were utilized for the perspective of mineral explorations [17,20,21,31,42,47,48,[64][65][66][67][68][69][70][71].…”

Section: à2mentioning

confidence: 99%

“…The ASTER SWIR dataset has the spillover of the energy from band 4 to band 5 and band 9 which is known as crosstalk effects [17,41,45,47,93,94]. Crosstalk correction was applied for the removal of effects from the dataset and to enhances the spectral signatures of the minerals/rocks.…”

Section: Preprocessing Of Swir and Tir Datasetsmentioning

confidence: 99%

“…Multispectral Remote Sensing is utilized in the domain of geosciences for lithological mapping [9][10][11][12][13][14][15][16], mineral mapping [17][18][19][20][21][22][23][24], identification of the alteration zones related to the base metal mineralization [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], structural features as a controlling factor for mineralization [26,28,[42][43][44][45][46] and mapping for demarcating favorable zones of mineralization [21,47,48]. Spectral characteristic absorption features of the rocks and minerals are utilized for the identification and mapping of lithologies and minerals like calcite, dolomite, clay, mica, and ultramafics, etc.…”

Section: Introductionmentioning

confidence: 99%

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Feature-Oriented Principal Component Selection (FPCS) for Delineation of the Geological Units Using the Integration of SWIR and TIR ASTER Data

Jain¹

2022

Recent Remote Sensing Sensor Applications - Satellites and Unmanned Aerial Vehicles (UAVs)

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Geological studies have been performed using the Band Ratios (BR), Relative Band Depth (RBD), Mineral Indices (MI), Principal Component Analysis (PCA), Independent Component Analysis (ICA), lithological and mineral classification techniques from Short-Wave Infrared (SWIR) and Thermal Infrared (TIR) data. The chapter aims to delineate various geological units present in the area using the combination of SWIR and TIR ASTER bands through the Feature-Oriented Principal Component Selection (FPCS) technique. Different BRs and RBDs were applied to map the minerals having Al-OH and Mg-OH compounds with the chemical composition of clay (kaolinite, smectite), mica (sericite, muscovite, illite), ultramafic (lizardite, antigorite, chrysotile), talc, and carbonate (dolomite) from SWIR bands. The MI was used to map quartz-rich, mafic/ultramafic, and carbonate rocks using TIR bands. The BRs, RBDs, and MIs mapped the geological units but every single greyscale image showed a variety of features. To compile these features False Color Composite (FCC) was prepared by the combination of RBDs and MIs in the R:G:B channels which demarked various geological units to a larger extent present in the region. To overcome the limitation, the FPCS technique was applied with the integration of all BRs, RBDs, and MIs. The FPCS technique extracts valuable information from different input bands and shifts the information in the first few bands. The generated eigenvalues and eigenvectors represented the retrieved information in the specific band. The loadings of the eigenvector were used for the selection of the different brands to create the FCC for the delineation of geological strata. The best discrimination was made by the selection of FPCS1, FPCS3, and FPCS6 which differentiated all the geological units like ultramafics, dolomites, thin bands of talc, and muscovite and illite (as phyllite and mica-schist), silica-rich rocks (as quartzite), and granite outcrops.

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“…The resulting MNF images have been shown to play an important role in rock recognition and boundary delineation, especially for the main local minerals. The second group includes mineral pixel analysis and classification based on spectral libraries such as spectral angle mapping (SAM 14 ), spectral feature fitting, spectral separation (UNMIX 31 , 33 ), and mixture tuned matched filtering (MTMF). Of these methods, SAM and MTMF were the most practical in the study.…”

Section: Introductionmentioning

confidence: 99%

Application of hyperspectral remote sensing for supplementary investigation of polymetallic deposits in Huaniushan ore region, northwestern China

Wan¹,

Fan

Moushun

2021

Sci Rep

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A gold–silver–lead–zinc polymetallic ore was selected in Huaniushan, Gansu Province as the study area. Hyperspectral aerial images as the primary information source, ground spectrum tests, and sampling analysis were used as auxiliary techniques. They were combined with large-scale mineral and geological maps and other high-resolution satellite remote sensing images. Hyperspectral remote sensing classification identification and quantitative analysis methods were used to study the main mineral resources and rock mass occurrence. Finally, deposit distribution information was extracted and validated. The results showed that the effective classification methods by hyperspectral images were spectral angle mapping, minimum noise fraction transform, and mixed tuned matched filtering. Based on the ground survey, combined with sampling analysis, the accuracy of classification was 80%. The recognition rate of the main ore body—the iron-manganese cap lead–zinc oxide ore—was as high as 81%. This research showed that hyperspectral remote sensing in this mining area has excellent demonstration effects and is worth completing and supplementing original mineral and geological maps. The targets are important areas for detailed follow-up on mineral resource exploration.

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Integration of remote sensing, gravity and geochemical data for exploration of Cu-mineralization in Alwar basin, Rajasthan, India

Cited by 29 publications

References 41 publications

Hydrothermal Alteration on Composite Volcanoes: Mineralogy, Hyperspectral Imaging, and Aeromagnetic Study of Mt Ruapehu, New Zealand

Hydrothermal Alteration on Composite Volcanoes: Mineralogy, Hyperspectral Imaging, and Aeromagnetic Study of Mt Ruapehu, New Zealand

Feature-Oriented Principal Component Selection (FPCS) for Delineation of the Geological Units Using the Integration of SWIR and TIR ASTER Data

Application of hyperspectral remote sensing for supplementary investigation of polymetallic deposits in Huaniushan ore region, northwestern China

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