Automated Taxonomic Identification of Calcareous Nannofossils from Microscopic Imagery Using Convolutional Neural Networks

Calcareous nannofossils represent a key proxy for biostratigraphy and paleoenvironmental reconstructions, due to their high abundance, widespread distribution and rapid evolutionary turnover. However, conventional taxonomic identification under optical or electron microscopy remains time-consuming and strongly dependent on expert interpretation, especially when working with large datasets and heterogeneous assemblages. This limitation is critical for high-resolution stratigraphic studies in complex sedimentary settings where reworking, redeposition and tectonic transport may generate mixed-age associations.

This poster focuses on qualitative and quantitative investigations of Quaternary calcareous nannofossils based on microscopic analyses and the development of an automated taxonomic identification workflow. We propose a deep learning approach using a convolutional neural network (CNN) trained on curated image catalogues of nannofossil taxa, aiming to achieve end-to-end classification of microfossil imagery. The targeted temporal interval spans approximately on the last 25,000 years (since the LGM – Last Glacial Maximum), focused on samples from the NW Black Sea cores.

Beyond accelerating routine identifications, automated classification has the potential to provide more objective and reproducible taxonomic assignments, enabling consistent quantitative counting and supporting multidisciplinary analyses linking nannofossil variability to paleoenvironmental controls such as salinity, nutrient input and temperature. The proposed workflow represents a step toward scalable microfossil taxonomy, supporting robust stratigraphic correlations and palaeoceanographic interpretations in Quaternary successions.

Keywords: nannofossils, neural networks, image recognition