| Project Detail |
Unlocking the mysteries of our prehistoric past with artificial intelligence
The climate history of Earth can be studied by analysing ice cores – ice cylinders drilled out of ice sheets from Greenland and Antarctica or alpine glaciers. Amongst the impurities found in ice cores are insoluble particles, just like volcanic glass particles or particles of biological origin such as pollen and algae. Detecting these particles is crucial to understanding the past conditions and interactions between the components of the climate system. The EU-funded ICELEARNING project will develop a technique for automatically detecting insoluble particles in ice cores using artificial intelligence pattern recognition techniques. These ground-breaking automatic and non-destructive methods can reveal further information about the climatic and environmental changes in Antarctica over the last 1.5 million years.
Objective
The detection of insoluble particles trapped in ice or sediment cores, like pollen grains, foraminiferal and diatom assemblages, volcanic and dust particles represents the basis for paleoresearch on the biosphere, volcanism and oceanic and atmospheric realms. To date, except for ice core dust, this analytical goal is achieved during years of particle observations by manual microscopy. Artificial Intelligence predictive models are already applied to several research fields within geoscience, but up to date its implementation to paleoclimate is missing. With ICELEARNING, I aim to develop a two-phase routine for the automatic quantification of insoluble particles trapped in ice cores. The routine is based on a commercial Flow Imaging Microscope producing particle images from within melted ice samples. The images are then analyzed by Pattern Recognition algorithms which will be developed for automatic particle classification and counting. The routine will be specifically developed in order to be implemented in Continuous Flow Analysis (CFA) systems, therefore surpassing the traditional methods by providing continuous particle records from ice cores. ICELEARNING methodology is suitable to any diluted sample, thus representing a ground-breaking analytical advancement from ice core science to marine geology. This innovative routine is automatic and non-destructive, imperative prerequisites for the future Antarctic ice core project analytical measurements, aiming to retrieve a continuous climatic and environmental record covering the last 1.5 Myr. ICELERNING will be developed at Ca’ Foscari University of Venice with Prof. Carlo Barbante, leading expert in trace and ultra-trace level impurity detections in ice cores and with the University of Bergen, a top institution in marine geology and paleoceanography. This unique synergy, in addition to the proposer’s knowledge of CFA systems and machine learning techniques will provide the best preconditions for the project success. |
