Artificial Intelligence techniques for ice core analyses

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Why - Scientific rationale

The climate history of the Earth can be investigated by analysing ice cores – ice cylinders extracted from glaciers or ice sheets like Greenland and Antarctica. The ice stratification along the core depth provides a continuous record of the past climate: the deeper the ice is, the older. In ice cores it is possible to find particles that were once deposited and became trapped in the ice matrix. Amongst those are insoluble particles, just like volcanic glass particles injected into the atmosphere during volcanic explosions, mineral dust from deserts or particles of biological origin such as pollen and algae sourced from forests or from the surface of the oceans. Detecting these particles is crucial to understand the past conditions of atmosphere, the biosphere and the oceans in their past interactions. At present, revealing such particles is carried out during time-intensive manual microscopy sessions. The Marie-Curie EU-funded ICELEARNING project aims to develop an innovative technique for automatically revealing insoluble particles in ice cores, by combining automatic image analysis with Artificial Intelligence pattern recognition techniques. This powerful synergy can provide knowledge about the past climate over the last 1.5 million years.

The project



Niccolò Maffezzoli

Ca' Foscari University of Venice (IT)

Department of Environmental Science, Informatics and Statistics

+39 041 234 8504


Project Advisor

Carlo Barbante

+39 041 234 8549


Ca Foscari University of Venice


Project co-advisor

Kerim Nisancioglu

University of Bergen (NO)

Bjerknes Centre for Climate Research (NO)

+47 55 58 98 66


Laboratory Manager

Eivind Wilhelm Nagel Støren

University of Bergen (NO)


University of Bergen Bjerknes Centre


The ICELEARNING project has received funding from the European Union's Horizon H2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 845115. See EU page.

European Union