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The ICELEARNING project - Artificial Intelligence techniques for ice core analyses

The idea of this project came up when I visited a marine lab at Bergen University and I saw an instrument that produces photos of visible particles dispersed in liquid samples. The instrument is called "FlowCam". I immediately thought that we could use this technique to analyse the insoluble particulate found inside ice core samples, which is routinely analyzed after melting, aka liquid. The resulting image dataset could be then crunched by machine learning techniques to try to classify the classes of insoluble particles that are found in ice records, which in general are: mineral dust (by far the most abundant), volcanic glass (also known as tephra), but also, depending on the coring site, terrestrial pollen, as well as marine particles which are at times found in coastal ice cores such as the Ross Ice Core in Antarctica. The crucial advantage is that the classification and thus the calculation of the impurity concentration (which is the main variable with a climate significance) would be automatic (with some accuracy), thus supporting and potentially surpassing manual microscopy, which is often the only viable solution to identify some of these particles. The purpose of this document is to provide some information on the project concept and activities, currently stopped due to the covid-19 pandemic.