This study investigates the lithological and mineralogical heterogeneity of the Inguijjem-Aït Ahmane serpentinite massif (Bou Azzer ophiolite, Morocco) using an integrated approach combining field-based petrology and advanced remote sensing. Previously regarded as a homogeneous unit, the massif exhibits significant lithological complexity. Initially, a detailed mineralogical characterization was performed using optical microscopy, X-ray diffraction (XRPD), and micro-Raman spectroscopy to identify and validate the specific serpentinite facies (meta-dunites, meta-harzburgites, meta-wehrlites, and meta-pyroxenites). These ground-truth data were subsequently used to train a supervised classification model based on the Support Vector Machine (SVM) algorithm, applied to Landsat OLI-8 imagery enhanced by Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). The SVM classification successfully discriminated the lithological units, revealing that meta-dunite is the dominant facies, while meta-harzburgites are concentrated in the western sector. The reliability of the mapping was confirmed by a high Overall Accuracy of 97.54 % and a Kappa coefficient of 0.96, demonstrating the robustness of the model. The study highlights that despite intense serpentinization, the observed spectral heterogeneity reflects primary lithological variations rather than secondary alteration patterns. These findings underscore the effectiveness of coupling detailed mineralogical analysis with machinelearning techniques for mapping complex ophiolitic terrains.
UNRAVELLING THE HETEROGENEITY OF THE SERPENTINIZED OPHIOLITIC MANTLE (ANTI-ATLAS, MOROCCO): MINERALOGICAL IMPLICATIONS AND LANDSAT OLI-8 MAPPING
Rosalda PunturoWriting – Review & Editing
2026-01-01
Abstract
This study investigates the lithological and mineralogical heterogeneity of the Inguijjem-Aït Ahmane serpentinite massif (Bou Azzer ophiolite, Morocco) using an integrated approach combining field-based petrology and advanced remote sensing. Previously regarded as a homogeneous unit, the massif exhibits significant lithological complexity. Initially, a detailed mineralogical characterization was performed using optical microscopy, X-ray diffraction (XRPD), and micro-Raman spectroscopy to identify and validate the specific serpentinite facies (meta-dunites, meta-harzburgites, meta-wehrlites, and meta-pyroxenites). These ground-truth data were subsequently used to train a supervised classification model based on the Support Vector Machine (SVM) algorithm, applied to Landsat OLI-8 imagery enhanced by Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). The SVM classification successfully discriminated the lithological units, revealing that meta-dunite is the dominant facies, while meta-harzburgites are concentrated in the western sector. The reliability of the mapping was confirmed by a high Overall Accuracy of 97.54 % and a Kappa coefficient of 0.96, demonstrating the robustness of the model. The study highlights that despite intense serpentinization, the observed spectral heterogeneity reflects primary lithological variations rather than secondary alteration patterns. These findings underscore the effectiveness of coupling detailed mineralogical analysis with machinelearning techniques for mapping complex ophiolitic terrains.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


