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Abstract/Summary
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<jats:p>Abstract. An innovative multi-analytical approach comprising mineralogical, minero-chemical, and microstructural analyses as well as an indirect machine learning-based statistical method was applied to investigate the mineralogy and the mineral chemistry of spinel subgroup minerals (SSMs) of different ultramafic rocks from the high-pressure metaophiolites of the Voltri Massif (Central Liguria, NW Italy). The study was focused on the correlation between the compositional variations of SSMs and their texture, microstructure, and the degree of serpentinization of the host rock. The SSM occurs with three main textures and microstructures linked to the progressive serpentinization and deformation of ultramafic rocks during the Alpine orogenic events: (i) Cr-spinel porphyroclasts with various degrees of recrystallization (up to magnetite porphyroblasts) within partially serpentinized peridotite and massive serpentinite; (ii) magnetite crystals associated with pseudomorphic and non-pseudomorphic serpentine textures (e.g., mesh, hourglass, ribbon, and interpenetrating textures) in partially serpentinized peridotite and massive serpentinites; and (iii) magnetite crystals re-oriented along the foliations developed in serpentine schist. The chemical composition of SSMs varies systematically within the textures and microstructures. These processes also affected the chemical composition of SSMs, the availability of Mn, Zn, Ni, and Co in solution, and their consequent incorporation in the lattice of chromian spinel due to olivine breakdown, the major repository of these elements in ultramafic rocks. At a general scale, the trace and ultratrace variability is primarily related to the petrologic and tectonic evolution but, at a local scale, also the mineralogical, lithological, structural, and textural features correlated to the degree of serpentinization and/or deformation. These significantly influence the distribution and concentration of trace and ultratrace elements in SSMs. The results of the present work were also confirmed by an innovative indirect statistical method performed through the Weka Machine Learning Workbench.
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