Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-539-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-539-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Feb 2023
Research article |
| 07 Feb 2023
| 07 Feb 2023
Detection of ice core particles via deep neural networks
Niccolò Maffezzoli
CORRESPONDING AUTHOR
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30172 Venice, Italy
Institute of Polar Sciences, ISP-CNR, Via Torino 155, 30172 Venice, Italy
Eliza Cook
Physics of Ice, Climate and Earth, Niels Bohr Institute, University of Copenhagen, 2200 Copenhagen, Denmark
Willem G. M. van der Bilt
Department of Earth Science, University of Bergen, Allégaten 41, 5020 Bergen, Norway
Bjerknes Centre for Climate Research, Jahnebakken 5, 5020 Bergen, Norway
Eivind N. Støren
Department of Earth Science, University of Bergen, Allégaten 41, 5020 Bergen, Norway
Daniela Festi
GeoSphere Austria, Neulinggasse 38, 1030 Vienna, Austria
Florian Muthreich
Bjerknes Centre for Climate Research, Jahnebakken 5, 5020 Bergen, Norway
Department of Biological Sciences, University of Bergen, Thormøhlensgate 53A, 5006 Bergen, Norway
Alistair W. R. Seddon
Bjerknes Centre for Climate Research, Jahnebakken 5, 5020 Bergen, Norway
Department of Biological Sciences, University of Bergen, Thormøhlensgate 53A, 5006 Bergen, Norway
François Burgay
Laboratory of Environmental Chemistry (LUC), Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen, Switzerland
Giovanni Baccolo
Laboratory of Environmental Chemistry (LUC), Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen, Switzerland
Department of Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
Amalie R. F. Mygind
Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
Troels Petersen
Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
Andrea Spolaor
Institute of Polar Sciences, ISP-CNR, Via Torino 155, 30172 Venice, Italy
Sebastiano Vascon
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30172 Venice, Italy
Marcello Pelillo
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30172 Venice, Italy
Patrizia Ferretti
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30172 Venice, Italy
Rafael S. dos Reis
Centro Polar e Climático, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil
Jefferson C. Simões
Centro Polar e Climático, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil
Climate Change Institute, University of Maine, Orono, ME 04469, USA
Yuval Ronen
Department of Earth Science, University of Bergen, Allégaten 41, 5020 Bergen, Norway
Barbara Delmonte
Department of Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
Marco Viccaro
Dipartimento di Scienze Biologiche Geologiche e Ambientali, Università degli Studi di Catania, Corso Italia 57, 95129 Catania, Italy
Istituto Nazionale di Geofisica e Vulcanologia – Sezione di Catania, Osservatorio Etneo, Piazza Roma 2, 95125 Catania, Italy
Jørgen Peder Steffensen
Physics of Ice, Climate and Earth, Niels Bohr Institute, University of Copenhagen, 2200 Copenhagen, Denmark
Dorthe Dahl-Jensen
Physics of Ice, Climate and Earth, Niels Bohr Institute, University of Copenhagen, 2200 Copenhagen, Denmark
Kerim H. Nisancioglu
Department of Earth Science, University of Bergen, Allégaten 41, 5020 Bergen, Norway
Bjerknes Centre for Climate Research, Jahnebakken 5, 5020 Bergen, Norway
Carlo Barbante
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30172 Venice, Italy
Institute of Polar Sciences, ISP-CNR, Via Torino 155, 30172 Venice, Italy
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Cited
4 citations as recorded by crossref.
- Method development and application of object detection and classification to Quaternary fossil pollen sequences R. von Allmen et al. 10.1016/j.quascirev.2024.108521
- An evaluation of two cryptotephra quantification methods applied on lacustrine sediments with distant Laacher See tephra fallout V. Meier et al. 10.1016/j.quageo.2025.101670
- Aeolian dust and diatoms at Roosevelt Island (Ross Sea, Antarctica) over the last 2 millennia reveal the local expression of climate changes and the history of the Ross Sea polynya S. Lagorio et al. 10.5194/cp-21-1323-2025
- Ice-Core Micro-CT Image Segmentation With Deep Learning and Gaussian Mixture Model F. Bagherzadeh et al. 10.1109/TGRS.2023.3334867
4 citations as recorded by crossref.
- Method development and application of object detection and classification to Quaternary fossil pollen sequences R. von Allmen et al. 10.1016/j.quascirev.2024.108521
- An evaluation of two cryptotephra quantification methods applied on lacustrine sediments with distant Laacher See tephra fallout V. Meier et al. 10.1016/j.quageo.2025.101670
- Aeolian dust and diatoms at Roosevelt Island (Ross Sea, Antarctica) over the last 2 millennia reveal the local expression of climate changes and the history of the Ross Sea polynya S. Lagorio et al. 10.5194/cp-21-1323-2025
- Ice-Core Micro-CT Image Segmentation With Deep Learning and Gaussian Mixture Model F. Bagherzadeh et al. 10.1109/TGRS.2023.3334867
Latest update: 31 Oct 2025
Short summary
Multiple lines of research in ice core science are limited by manually intensive and time-consuming optical microscopy investigations for the detection of insoluble particles, from pollen grains to volcanic shards. To help overcome these limitations and support researchers, we present a novel methodology for the identification and autonomous classification of ice core insoluble particles based on flow image microscopy and neural networks.
Multiple lines of research in ice core science are limited by manually intensive and...
