article · Scientific Reports
Mapping rock types and hydrothermal alteration zones in complex geological regions like the East African Rift is technically challenging. Research in the Kafta Humera district of Ethiopia evaluated satellite data from Landsat-8 and ASTER to identify basaltic rocks, Quaternary sediments, and mineralised zones. Data preprocessing techniques, including atmospheric correction, Optimum Index Factor analysis, and Selective Principal Component Analysis, helped reduce redundancy and clarify spectral signatures. Comparing two supervised classification algorithms revealed that Maximum Likelihood Classification achieved an overall accuracy of 80 percent, outperforming the Support Vector Machine method, which attained 73.5 percent. The resulting maps accurately highlighted granite, sandstone, basalt, and diagnostic minerals such as clay and iron oxides. Shortwave infrared bands from ASTER proved particularly effective for identifying alteration zones, while lineament mapping delineated structural pathways for hydrothermal fluids. This combined approach offers an accessible, cost-effective framework for early exploration in rift terrains.
Identifying mineral deposits traditionally requires extensive and costly ground surveys across rugged landscapes. By demonstrating that satellite imagery combined with standard statistical classification algorithms can reliably differentiate rock types and alteration signatures, this work shows how remote sensing can accelerate preliminary mineral exploration. This approach lowers initial survey costs and directs field teams to the most promising sites in complex geological settings.
This methodology offers an applied workflow that mineral exploration companies and geological survey agencies can adopt to cut prospecting costs. Because it relies on readily accessible multispectral satellite datasets and standard classification tools, the approach is tested and ready for deployment in early-stage regional target generation. Moving to operational commercial software or higher accuracy services would require testing with hyperspectral data and advanced machine learning models as suggested.
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Remote sensing is an important technique for lithological mapping and hydrothermal alteration detection; yet, achieving high classification accuracy remains difficult in complex geological environments. This study addresses the difficulty of delineating lithological units and alteration zones in the Kafta Humera district of Ethiopia, a region within the East African Rift characterized by intricate volcanic and sedimentary formations. The primary objective was to evaluate the integration of Landsat-8 OLI and ASTER multispectral datasets for mapping basaltic rocks, Quaternary deposits, and mineralized zones while comparing the performance of supervised classification algorithms. The methodology involved atmospheric correction (FLAASH), Optimum Index Factor (OIF) analysis, and Selective Principal Component Analysis (SPCA) to reduce data redundancy and enhance spectral signatures. To classify these features, the study compared Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM) methods. Results indicated that MLC outperformed SVM, achieving an overall accuracy of 80% compared to 73.5%. The MLC-derived maps accurately identified significant rock units—including granite, sandstone, and basalt—alongside diagnostic clay, iron oxide, and ferrous oxide minerals. ASTER’s shortwave infrared (SWIR) bands were particularly instrumental in distinguishing argillic, phyllic, and propylitic alteration halos. Furthermore, lineament analysis revealed critical structural conduits for hydrothermal fluids. In conclusion, the integration of PCA and MLC with multi-sensor datasets provides a robust, cost-effective framework for mineral exploration in rift-influenced terrains. To further enhance classification precision in geologically "noisy" environments, the study recommends the future adoption of hyperspectral imagery and advanced machine learning architectures.
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DOI: 10.1038/s41598-026-66950-y
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