- The FlowCam instrument: the characterization of the instrument was carried out at the University of Bergen. The characterization included studies on the blank background level, optimization of the settings for ice core measurements and selection of the necessary materials for trace level analyses. Two 3-month secondment periods at the University of Bergen were carried out in 2020 and 2021.
- Training dataset acquisiton: we create a training dataset of the following types of particles found in ice core records: mineral dust, basaltic tephra, felsic tephra and three types of pollen grains: Corylus Avellana, Quercus Robur, Quercus Suber. The overall dataset consisted of more than 100,000 images.
- Model development: the training dataset was used to train a supervised-learning classification model. Research on the model was carried out throughout the project timeline and included investigations on the model architecture, preprocessing and data augmentation routines, training, validation and hyperparameter optimization.
- Investigation of ice core samples using the trained model. The trained model was deployed to investigate the content of particles inside GRIP (Greenland Ice Core Project) ice core samples, dating 13,000 to 17,000 years ago. The samples were selected as corresponding to known volcanic horizons during the Bølling–Allerød (GI-1) and glacial (GS-2) periods. The samples have been analyzed and the acquired images were classified by the model to infer the composition of particles contained within. The model found many volcanic shards. We have also investigated ice samples from the Quelccaya ice core (Peru).
- Dissemination and result exploitation: the project activities were disseminated at 2 conferences: the European Geophysical Union 2020 (virtual), American Geophysical Union 2021 (13-17 December 2021, New Orleans, USA). A website was created and updates were released during the project: www.icelearning.net. We have been in close collaboration with the instrument manufacturing company (Fluid Imaging Technologies Corporation). The project datasets were used as an Assignment for a MSc course “Applied Machine Learning” at the University of Copenhagen in 2021 and is currently being the topic of a 1 year Msc thesis in physics. A peer-reviewed publication is scheduled to be submitted within the first half of 2022 to The Cryosphere journal discussing all results of the project research and the developed methodology. All datasets and the model will be released publicly to foster further research.
- Results: we have successfully collected a training dataset for each class under investigation (mineral dust, Felsic tephra, Basaltic tephra, Corylus Avellana pollen, Quercus Robur pollen, Quercus Suber pollen) and developed a model capable of classifying the particles. With respect to dust, we have also developed a methodology that can quantify dust mass concentrations with similar performance and several advantages compared to state-of-the-art methods.
