Descripción del proyecto
Desvelando los misterios de nuestro pasado histórico con la inteligencia artificial
La historia climática de la Tierra se puede estudiar analizando testigos de hielo, unos cilindros de agua congelada extraídos mediante la perforación de placas de hielo de Groenlandia, la Antártida o los glaciares alpinos. Entre las impurezas encontradas en los testigos de hielo hay partículas insolubles, tales como partículas de vidrio volcánico u otras de origen biológico como polen y algas. La detección de estas partículas es crucial para comprender cómo eran en el pasado las condiciones y las interacciones entre los componentes del sistema climático. El proyecto ICELEARNING, financiado con fondos europeos, desarrollará una técnica para detectar automáticamente partículas insolubles en testigos de hielo mediante técnicas de reconocimiento de patrones de inteligencia artificial. Estos novedosos métodos, automáticos y no destructivos, podrían desvelar información nueva sobre los cambios climáticos y medioambientales en la Antártida durante los últimos 1,5 millones de años.
Objetivo
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.
Ámbito científico
- 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
Programa(s)
Régimen de financiación
MSCA-IF-EF-ST - Standard EFCoordinador
30123 Venezia
Italia