Automated Mineral Cluster Detection in ASTER Data Using Topological Machine Learning: A Novel Data-Driven Approach for Geological Exploration in Ait Saoun, Anti Atlas, Morocco

Geological mapping in complex metallogenic provinces often relies on band ratios and thresholding techniques. While effective for simple targets, these traditional methods struggle to capture non-linear spectral associations inherent in natural mineral mixtures and require significant prior knowledge of the target mineralogy. This study introduces a novel, data-driven unsupervised pipeline for mineral target generation, applied to the Aït Saoun region in the Moroccan Anti-Atlas, a strategic zone characterized by polymetallic occurrences (Cu, Co, Fe, Mn).

We leverage the full spectral topology of ASTER satellite imagery (VNIR-SWIR bands) rather than reduced indices. Our approach integrates topological manifold learning to reduce the high-dimensional spectral space, followed by density-based spatial clustering to delineate mineral clusters. This combination allows for the preservation of local data structure and the automated rejection of noise without human supervision.

The pipeline successfully identified spatially coherent clusters corresponding to specific hydrothermal alteration zones. It autonomously distinguished between structural iron-manganese anomalies and lithology-controlled copper mineralization a nuance often missed by standard linear ratios. The metallogenic relevance of these spectral clusters was rigorously validated through field mapping and geochemical analysis using Atomic Absorption Spectroscopy (AAS). Results confirmed economic grades in the predicted zones, yielding Copper concentrations up to 2.60% in propylitic alteration zones and Iron-Manganese oxide grades (21.94% Fe, 1.80% Mn) in tectonic corridors. Furthermore, the detection of distal barite anomalies highlights the method’s capability to map complete hydrothermal zonations.

These findings demonstrate that topological machine learning offers a robust, superior alternative to conventional remote sensing techniques for vectoring exploration targets in arid environments. By converting raw spectral data into validated metallogenic maps, this pipeline provides a scalable tool for de-risking early-stage mineral exploration in the Anti-Atlas.