Identification of hydrothermal alteration mapping using spectral analysis of ASTER Data in NE Tohamiyam Area, Red Sea Hills, NE Sudan
Year: 2024
Type: article
Abstract: The Tohamiyam area is located in the Red Sea Hills in East Sudan. Remote Sensing and GIS investigations were carried out in the study area using the ASTER data on VNIR and SWIR in mineral prospecting. It has increased recently because of its relatively low cost, broad coverage, and unique integral bands, susceptible to alteration minerals. The study aimed to delineate the hydrothermal alteration zones related to mineralization. The area is a part of the Haiya terrane (HT) of the late Proterozoic Arabian-Nubian Shield (ANS). The Haiya terrain consists predominantly of arc-back arc low-grade metavolcanic-sedimentary sequences decorated with dismembered ophiolitic rocks and intruded by granitoid intrusions of different ages. The ASTER semi-hyperspectral data have been treated with a Mixture Tuned Matched Filtering (MTMF) classifier. The MTMF classifier matches the spectral signatures of the indicator minerals with similar spectra from the ASTER–USGS spectral library. MTMF classifier images portrayed at least many sites as highly probable alteration mineralization zones in the NE part of the study area, which conforms with the results obtained from Aster data. This study revealed that the results of mineral prospecting investigations obtained from ASTER data show that they can distinguish the spectral signatures of the indicator minerals and delineate the alteration halos related to mineralization zones. The results of remote sensing applications with field geochemical data show that the spectral and spatial analysis of optical semi-hyperspectral data in mineral exploration investigations is strengthened. This study recommends remote sensing and machine learning technology in mineral studies through hydrothermal alteration within the basement complex rocks of the Nubian Shield in Red Sea hills.
Institutions: University of Bahri, University of Kordofan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406925989