Abstract

Ophiolites represents a distinctive variety of igneous rock that comprises fragments of oceanic crust with the underlying mantle portion. Their mineral assemblages and distribution relation, both spatial and temporal are in close association with the complex mantle dynamics of the Earth. Nidar ophiolite complex located towards SE of Ladakh is one of such ophiolite assemblages exposed well in the Indus Suture Zone of Himalayas. It represents parts of Neo-Tethys ocean that existed during Mesozoic-Cenozoic period between continental plates of India and Eurasia. The earlier studies for Indian ophiolites are mostly field and analytical based with a limited work carried out using remote sensing techniques and that adds scope to apply this approach for further exploration of ophiolites. Advanced tools and techniques especially, hyperspectral remote sensing and machine learning based approach can be effective operational tools for exploration of ophiolites that provide assessable terrains for characterization of Earth’s mantle, serve analogs for planetary crustal rocks and also have economic significance. This study has utilized high-resolution multispectral (Landsat OLI and ASTER) and hyperspectral data from various missions including the recent PRISMA mission which has provided the first hyperspectral datasets for lithological mapping of Nidar ophiolite section. The multiple high-resolution datasets were used for identification and mapping of the various lithounits of the Nidar ophiolites together with the underlying and overlying associated rocks based on band ratioing, derived spectral compositional indices and spectral characterization. The differentiated lithounits of the Nidar ophiolites are interpreted based on spectral response of lithological units in different bands. In addition, classification based on machine learning techniques have been applied to the PRISMA data to generate a detailed classification map of the study area where the various lithological units of the ophiolites and associated rock exposures belonging to various geological formations are clearly discernible. The results have been validated from geological field visit to the study area and spectral analysis of the field samples.

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