Automated heavy mineral analysis of silt-sized sediment by artificial-intelligence guided Raman Spectroscopy

Nils Keno Lünsdorf , Jan Ontje Lünsdorf , Gábor Újvári & Hilmar von Eynatten
Compositional data on heavy minerals is fundamental in sedimentary provenance analysis. Typically, this data is gathered by optical microscopy and more recently, by mineral chemical analysis (MLA, QEMSCAN) or Raman micro-spectroscopy. In silt-sized sediments optical microscopy is unfeasible. We introduce a systematic and highly efficient approach to assess the heavy mineral composition in fine grain-size fractions (10-30 µm and 30-62 µm) by Raman micro-spectroscopy. The approach starts with a web-application that creates and visualizes large...
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