Description du projet
Percer les mystères de notre passé préhistorique grâce à l’intelligence artificielle
L’histoire climatique de la Terre peut être étudiée en analysant des carottes de glace, des cylindres de glace forés dans les calottes glaciaires du Groenland et de l’Antarctique ou dans les glaciers alpins. Parmi les impuretés présentes dans ces carottes de glace figurent des particules insolubles, notamment des particules de verre volcanique ou des particules d’origine biologique (par exemple du pollen et des algues). L’identification de ces particules est cruciale pour comprendre les conditions passées et les interactions entre les différentes composantes du système climatique. Le projet ICELEARNING, financé par l’UE, mettra au point une technique d’identification automatique des particules insolubles dans les carottes de glace qui recourra à des techniques de reconnaissance de formes via l’intelligence artificielle. Ces méthodes révolutionnaires, automatiques et non destructives, pourraient révéler d’autres informations sur les changements climatiques et environnementaux en Antarctique au cours des dernières 1,5 million d’années écoulées.
Objectif
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.
Champ scientifique
- natural sciencesearth and related environmental sciencespalaeontologypaleoclimatology
- natural sciencesearth and related environmental sciencesgeologyvolcanology
- natural sciencesphysical sciencesopticsmicroscopy
- natural sciencesearth and related environmental sciencesatmospheric sciencesmeteorologybiosphera
- natural sciencescomputer and information sciencesartificial intelligencepattern recognition
Programme(s)
Régime de financement
MSCA-IF-EF-ST - Standard EFCoordinateur
30123 Venezia
Italie