I’m advancing a set of hyperspectral remote-sensing studies aimed at outlining copper, silver and gold mineralisation. Your task is propose the methodology for these commodities and case studies, map the minerals using high resolution satellite data. Your core task is to apply— and clearly document— unmixing techniques that isolate and quantify the diagnostic mineral signatures in the imagery, then translate those results into easily consumable mineral-deposit maps. If you prefer ERDAS, ENVI, Python libraries such as Spectral Python or scikit-learn, or another well-established workflow, that’s fine; what matters is that the methodology is transparent and reproducible. I’ll provide area of interest. In return I expect: • A short technical note outlining the methodolog/ unmixing approach and assumptions • Geo-referenced raster or vector outputs highlighting probable copper, silver and gold zones • A concise interpretation report that explains confidence levels and suggests next exploration steps If issues crop up that would benefit from additional algorithms (classification or change detection), I’m open to your recommendations, but the immediate deliverable is an interpretable mineral map produced through robust spectral unmixing.