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ICELEARNING SIGNED

Artificial Intelligence techniques for ice core analyses

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 ICELEARNING project word cloud

Explore the words cloud of the ICELEARNING project. It provides you a very rough idea of what is the project "ICELEARNING" about.

methodology    cores    trapped    proposer    breaking    artificial    commercial    dust    barbante    insoluble    particle    trace    date    ca    impurity    foscari    retrieve    prerequisites    assemblages    diluted    diatom    foraminiferal    icelearning    climatic    university    innovative    synergy    flow    cfa    detection    techniques    microscope    imperative    venice    analytical    last    paleoresearch    learning    paleoceanography    basis    bergen    biosphere    grains    samples    microscopy    geology    marine    representing    detections    volcanic    prof    ground    machine    observations    record    ice    pollen    quantification    records    producing    melted    antarctic    carlo    recognition    models    images    routine    atmospheric    paleoclimate    environmental    surpassing    geoscience    algorithms    imaging    icelerning    science    pattern    preconditions    continuous    ultra    sediment    expert    automatic    destructive    volcanism    manual    except    counting    predictive    intelligence    oceanic    realms    core    suitable    myr    classification    particles    missing   

Project "ICELEARNING" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITA CA' FOSCARI VENEZIA 

Organization address
address: DORSODURO 3246
city: VENEZIA
postcode: 30123
website: www.unive.it

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Total cost 171˙473 €
 EC max contribution 171˙473 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2018
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2020
 Duration (year-month-day) from 2020-01-15   to  2022-01-14

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITA CA' FOSCARI VENEZIA IT (VENEZIA) coordinator 171˙473.00

Map

 Project 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.

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The information about "ICELEARNING" are provided by the European Opendata Portal: CORDIS opendata.

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