Detection of ice core particles via deep neural networks

Research output: Contribution to journalJournal articlepeer-review

Documents

  • Niccolo Maffezzoli
  • Willem G. M. van der Bilt
  • Eivind N. Storen
  • Daniela Festi
  • Florian Muthreich
  • Alistair W. R. Seddon
  • Francois Burgay
  • Giovanni Baccolo
  • Amalie R. F. Mygind
  • Andrea Spolaor
  • Sebastiano Vascon
  • Marcello Pelillo
  • Patrizia Ferretti
  • Rafael S. dos Reis
  • Jefferson C. Simoes
  • Yuval Ronen
  • Barbara Delmonte
  • Marco Viccaro
  • Kerim H. Nisancioglu
  • Carlo Barbante

Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented.

Original languageEnglish
JournalCryosphere
Volume17
Issue number2
Pages (from-to)539-565
Number of pages27
ISSN1994-0416
DOIs
Publication statusPublished - 7 Feb 2023

    Research areas

  • MINERAL DUST, DOME, GREENLAND, FLOWCAM, GLACIER, SIZE, VARIABILITY, ANTARCTICA, MARKER, RECORD

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