Integrating Automated Lineament Extraction, Magnetic Data, and Machine Learning-Based Lithological Mapping in the Anti Atlas, Morocco

   Abstract

This study explores advanced remote sensing, geophysical, and geospatial methodologies applied to the geologically diverse Aït Semgane region in Morocco. A multi-disciplinary approach was adopted, combining (1) automated lineament extraction using Digital Elevation Models (DEMs) and various topographic indices, (2) lithological classification leveraging machine learning algorithms on multispectral data, and (3) the integration of magnetic data to enhance geological interpretation.

For lineament analysis, approaches such as the Topographic Position Index (TPI), Hillshade, and shading models were applied to datasets including SRTM, ALOS PALSAR, and Sentinel-1 InSAR. Results highlighted the TPI method’s high sensitivity in detecting tectonic features, especially in NE-SW and E-W orientations, aligning with established geological knowledge. Cartographic analysis revealed fault density concentrations in the NW and southern sectors, confirming the tectonic complexity of the region.

Lithological classification was conducted using Support Vector Machines (SVM), Random Trees (RT), and Artificial Neural Networks (ANN) applied to Landsat 9 and Sentinel-2 data. SVM, particularly with Minimum Noise Fraction (MNF) transformation, consistently outperformed other algorithms, achieving high classification accuracies and well-defined lithological boundaries. The integration of dimensionality reduction techniques like MNF proved crucial for enhancing classification quality, while PCA showed limited efficacy.

Magnetic data were incorporated to validate and refine the tectonic and lithological interpretations, offering additional insights into subsurface structures and enhancing the understanding of fault systems and mineralized zones.

This research demonstrates the synergy between automated lineament extraction, machine learning-based lithological mapping, and magnetic data for improving geological analysis. The methodologies applied here have practical implications for mineral exploration and tectonic studies, offering robust tools for mapping complex terrains. Future research will aim to refine dimensionality reduction techniques, explore hyperspectral datasets, and further integrate geophysical data to enhance geological mapping accuracy.