Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework.

The present study is based on heavy mineral assemblages (HM) of top 104-cm thick section of gravity core SK 221 (Lat. 8 • 7.12 N; Long. 73 • 16.38 E and water depth -2188 m) located near the ChagosLaccadive Ridge in the southeastern Arabian Sea to evaluate the provenance and paleoenvironmental changes during the last 32 kyr. The biogenic carbonate, acid insoluble residue, magnetic susceptibility, total organic carbon (TOC) and clay based humidity index, i.e., kaolinite/illite ratio are also utilized to correlate with the above paleoenvironmental changes. Ilmenite, garnet, staurolite, pyroxenes, andalusite and zircon are the dominant HM with moderate to low ZTR (zircon-tourmaline-rutile) index indicating instability of the sediments and rapid erosion in the source region. The characteristic HM suggest their mixed sources from the basic igneous, gneisses/granites, high grade metamorphic rocks and sandstones occurring mainly in the western and southwestern India. The temporal variations of HM, AIR (acid insoluble residue), MS (magnetic susceptibility), biogenic carbonate and C org (TOC) during preglacial and early Holocene suggest intensive weathering, erosion, and transportation of terrigenous detritus from continental region by fluvial processes and summer monsoon led high biogenic productivity, respectively. The convective mixing of waters due to intense winter monsoon resulted in very high biogenic carbonate content during the early stages of glacial period. The HM and associated proxies indicated that the winter monsoons of Heinrich (H3, H2, and H1) and Younger Dryas (YD) events and summer monsoons of Bølling/Allerød (BA) event were not strong enough to bring drastic changes in the above parameters.

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A close scrutiny of core data from a producing carbonate field resolved an apparent difference between permeability evident from core plug analysis and from testing of wells in the field. This was attributed to a bias inherent in the data collection, particularly as the samping techniques avoided the most permeable features. Instead of invoking a pervasive fracture network in the static geological model, which might compromise a planned water injection campaign, it is now evident that the permeability mismatch can be satisfactorily modeled with a horizontal multiplier, and that water injection should result in a significant increase in ultimate oil recovery.

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