ZEISS Microscopy

Webinar: Automated Petrography

High throughput mineral classification using machine learning

In this webinar, we will review recent developments in automated geological microanalysis, coupling automated multi-polarized slide handling and image acquisition with advanced image processing and machine learning-based pixel classification. Allowing for mineral classification to be performed directly from the digital light microscopy data. Which can then be streamed automatically to powerful image analysis tools allowing for grain sizes and shape, mineral associations to be measured, as well as sample wide modal mineralogies. These machine learning models can be either trained manually or correlated with SEM based AQM to allow for automated mineral training. As analyses can be performed much more rapidly using optical petrography than with SEM-based techniques, locally trained models can then be scaled across many samples. Allowing, for the first time, large scale campaign analyses to be automated.

High throughput optical petrography and contextual mineralogy pose many potential opportunities for quantitative mineral analysis:

  • Contextual mineralogy – the ability to perform high-resolution local analysis without losing sight of macroscopic context
  • Digitization and data loss – researcher data and historic collections are persistent and available for new generations of researchers
  • Entire classrooms can interact with digital petrography data from online digital portals
  • Mineral classification and analysis help to transform microscopy into quantitative petrography

Date: Friday, June 12, 2020 2:30 PM - 3:30 PM SGT
Speaker: Matthew Andrew (ZEISS)

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