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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.

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

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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