Detection of ice core particles via deep neural networks
Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, r...
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ftcopernicus:oai:publications.copernicus.org:tc105130 2023-05-15T16:38:33+02:00 Detection of ice core particles via deep neural networks Maffezzoli, Niccolò Cook, Eliza Bilt, Willem G. M. Støren, Eivind N. Festi, Daniela Muthreich, Florian Seddon, Alistair W. R. Burgay, François Baccolo, Giovanni Mygind, Amalie R. F. Petersen, Troels Spolaor, Andrea Vascon, Sebastiano Pelillo, Marcello Ferretti, Patrizia Reis, Rafael S. Simões, Jefferson C. Ronen, Yuval Delmonte, Barbara Viccaro, Marco Steffensen, Jørgen Peder Dahl-Jensen, Dorthe Nisancioglu, Kerim H. Barbante, Carlo 2023-02-07 application/pdf https://doi.org/10.5194/tc-17-539-2023 https://tc.copernicus.org/articles/17/539/2023/ eng eng doi:10.5194/tc-17-539-2023 https://tc.copernicus.org/articles/17/539/2023/ eISSN: 1994-0424 Text 2023 ftcopernicus https://doi.org/10.5194/tc-17-539-2023 2023-02-13T17:22:57Z Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen ( Corylus avellana , Quercus robur , Quercus suber ), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented. Text ice core Copernicus Publications: E-Journals The Cryosphere 17 2 539 565 |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen ( Corylus avellana , Quercus robur , Quercus suber ), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented. |
format |
Text |
author |
Maffezzoli, Niccolò Cook, Eliza Bilt, Willem G. M. Støren, Eivind N. Festi, Daniela Muthreich, Florian Seddon, Alistair W. R. Burgay, François Baccolo, Giovanni Mygind, Amalie R. F. Petersen, Troels Spolaor, Andrea Vascon, Sebastiano Pelillo, Marcello Ferretti, Patrizia Reis, Rafael S. Simões, Jefferson C. Ronen, Yuval Delmonte, Barbara Viccaro, Marco Steffensen, Jørgen Peder Dahl-Jensen, Dorthe Nisancioglu, Kerim H. Barbante, Carlo |
spellingShingle |
Maffezzoli, Niccolò Cook, Eliza Bilt, Willem G. M. Støren, Eivind N. Festi, Daniela Muthreich, Florian Seddon, Alistair W. R. Burgay, François Baccolo, Giovanni Mygind, Amalie R. F. Petersen, Troels Spolaor, Andrea Vascon, Sebastiano Pelillo, Marcello Ferretti, Patrizia Reis, Rafael S. Simões, Jefferson C. Ronen, Yuval Delmonte, Barbara Viccaro, Marco Steffensen, Jørgen Peder Dahl-Jensen, Dorthe Nisancioglu, Kerim H. Barbante, Carlo Detection of ice core particles via deep neural networks |
author_facet |
Maffezzoli, Niccolò Cook, Eliza Bilt, Willem G. M. Støren, Eivind N. Festi, Daniela Muthreich, Florian Seddon, Alistair W. R. Burgay, François Baccolo, Giovanni Mygind, Amalie R. F. Petersen, Troels Spolaor, Andrea Vascon, Sebastiano Pelillo, Marcello Ferretti, Patrizia Reis, Rafael S. Simões, Jefferson C. Ronen, Yuval Delmonte, Barbara Viccaro, Marco Steffensen, Jørgen Peder Dahl-Jensen, Dorthe Nisancioglu, Kerim H. Barbante, Carlo |
author_sort |
Maffezzoli, Niccolò |
title |
Detection of ice core particles via deep neural networks |
title_short |
Detection of ice core particles via deep neural networks |
title_full |
Detection of ice core particles via deep neural networks |
title_fullStr |
Detection of ice core particles via deep neural networks |
title_full_unstemmed |
Detection of ice core particles via deep neural networks |
title_sort |
detection of ice core particles via deep neural networks |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-539-2023 https://tc.copernicus.org/articles/17/539/2023/ |
genre |
ice core |
genre_facet |
ice core |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-17-539-2023 https://tc.copernicus.org/articles/17/539/2023/ |
op_doi |
https://doi.org/10.5194/tc-17-539-2023 |
container_title |
The Cryosphere |
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17 |
container_issue |
2 |
container_start_page |
539 |
op_container_end_page |
565 |
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1766028845612269568 |