Geological Mapping And Mineral Prospectivity Using Remote Sensing And

Geological Mapping And Mineral Prospectivity Using Remote Sensing And This study is therefore aimed at testing the viability of using remote sensing and geographic information system (gis) techniques for geological mapping and prospecting for gold mineralization in the area. The research focuses on the duobaoshan ore district in northeastern china, employing gis based machine learning methods to generate a mineral prospectivity mapping and enhance the accuracy of target area delineation for potential mineral deposits.

Pdf Geological Mapping And Mineral Prospectivity Using Remote Sensing Due to the swift development of remote sensing based technologies and artificial intelligence (ai), in particular, machine learning (ml) and deep learning (dl), the study of the mineral rich. Remote sensing and gis effectively enhance geological mapping and mineral prospectivity assessment in northeast sudan. the study area spans approximately 1379 km², located in the red sea hills region. Keywords— mineral prospectivity mapping, artificial intelligence, remote sensing, machine learning, geological exploration, supervised classification, data integration. This study is therefore aimed at testing the viability of using remote sensing and geographic information system (gis) techniques for geological mapping and prospecting for gold mineralization in the area. the study area is located in northeast sudan and covers an area of about 1379 km2.

Revolutionizing Mineral Exploration Through Remote Sensing And Gis The Keywords— mineral prospectivity mapping, artificial intelligence, remote sensing, machine learning, geological exploration, supervised classification, data integration. This study is therefore aimed at testing the viability of using remote sensing and geographic information system (gis) techniques for geological mapping and prospecting for gold mineralization in the area. the study area is located in northeast sudan and covers an area of about 1379 km2. Machine learning (ml) algorithms have promoted the development of predictive modeling of mineral prospectivity, enabling data driven decision making processes by integrating multi source geological information, leading to efficient and accurate prediction of mineral exploration targets. This paper presents a comprehensive overview of how ml techniques are revolutionizing mineral prospectivity mapping (mpm) by effectively processing and analyzing diverse geological, geophysical, geochemical, and remote sensing data. Together, these developments underscore a trend towards intelligent, data driven remote sensing methodologies that improve both regional mapping and targeted mineral exploration. The advanced spaceborne thermal emission and reflection radiometer (aster) remote sensing data was used for mapping zones of hydrothermal alteration, while assessment of geologic structures is based on automated extraction of lineaments from a digital elevation model.