Mineral Prospectivity Mapping Using Integrated Remote Sensing and Gis in Kerkasha - Southwest Eritrea

Download or read book Mineral Prospectivity Mapping Using Integrated Remote Sensing and Gis in Kerkasha - Southwest Eritrea written by Finhas Tsehaye Zerai and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study evaluates the potential for mineral prospectivity mapping (MPM) within the Kerkesha area, southwestern Eritrea using remote sensing and geochemical data analysis. 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. Integration of these alteration and structural dataset with surface geochemical data were used in identifying pathfinder elements associated with Au-Cu-Zn mineralization as well as evaluating and delineating anomalous mineralization regions in this relatively underexplored region of Arabia Nubia Shield (ANS). Specifically, the modeling approach for the extraction and the interpretation of mineralization-related spectral footprints uses selective principal component analysis (SPCA), while the lineament features, which were extracted from different digital terrain models, were integrated with the soil geochemical data and modeled by principal component analysis (PCA). The results reveal a northeast-southwest trend of lineaments, delineate zones of hydrothermal alteration which indicate presence of multi-deposit type mineralization, and identify pathfinder elements. In addition, Au-Cu-Zn anomalous zones are extracted by one class support vector machine (OCSVM) and performances of such classification is validated by Kruskal-Wallis and Pearson's Chi-square tests. The results show significance in differences between the anomalous and non-anomalous zones and existence of a relationship between known mineral deposits and predicted anomalies. The proposed MPM shows promising results for robust automated delineation and understanding of mineralization processes.