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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ftdoajarticles:oai:doaj.org/article:26973bf1a54e40ab8f5af7156ff70189 2023-05-15T16:38:33+02:00 Detection of ice core particles via deep neural networks N. Maffezzoli E. Cook W. G. M. van der Bilt E. N. Støren D. Festi F. Muthreich A. W. R. Seddon F. Burgay G. Baccolo A. R. F. Mygind T. Petersen A. Spolaor S. Vascon M. Pelillo P. Ferretti R. S. dos Reis J. C. Simões Y. Ronen B. Delmonte M. Viccaro J. P. Steffensen D. Dahl-Jensen K. H. Nisancioglu C. Barbante 2023-02-01T00:00:00Z https://doi.org/10.5194/tc-17-539-2023 https://doaj.org/article/26973bf1a54e40ab8f5af7156ff70189 EN eng Copernicus Publications https://tc.copernicus.org/articles/17/539/2023/tc-17-539-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-539-2023 1994-0416 1994-0424 https://doaj.org/article/26973bf1a54e40ab8f5af7156ff70189 The Cryosphere, Vol 17, Pp 539-565 (2023) Environmental sciences GE1-350 Geology QE1-996.5 article 2023 ftdoajarticles https://doi.org/10.5194/tc-17-539-2023 2023-02-12T01:31:09Z 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. Article in Journal/Newspaper ice core The Cryosphere Directory of Open Access Journals: DOAJ Articles The Cryosphere 17 2 539 565 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 N. Maffezzoli E. Cook W. G. M. van der Bilt E. N. Støren D. Festi F. Muthreich A. W. R. Seddon F. Burgay G. Baccolo A. R. F. Mygind T. Petersen A. Spolaor S. Vascon M. Pelillo P. Ferretti R. S. dos Reis J. C. Simões Y. Ronen B. Delmonte M. Viccaro J. P. Steffensen D. Dahl-Jensen K. H. Nisancioglu C. Barbante Detection of ice core particles via deep neural networks |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
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 |
Article in Journal/Newspaper |
author |
N. Maffezzoli E. Cook W. G. M. van der Bilt E. N. Støren D. Festi F. Muthreich A. W. R. Seddon F. Burgay G. Baccolo A. R. F. Mygind T. Petersen A. Spolaor S. Vascon M. Pelillo P. Ferretti R. S. dos Reis J. C. Simões Y. Ronen B. Delmonte M. Viccaro J. P. Steffensen D. Dahl-Jensen K. H. Nisancioglu C. Barbante |
author_facet |
N. Maffezzoli E. Cook W. G. M. van der Bilt E. N. Støren D. Festi F. Muthreich A. W. R. Seddon F. Burgay G. Baccolo A. R. F. Mygind T. Petersen A. Spolaor S. Vascon M. Pelillo P. Ferretti R. S. dos Reis J. C. Simões Y. Ronen B. Delmonte M. Viccaro J. P. Steffensen D. Dahl-Jensen K. H. Nisancioglu C. Barbante |
author_sort |
N. Maffezzoli |
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 |
publisher |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-539-2023 https://doaj.org/article/26973bf1a54e40ab8f5af7156ff70189 |
genre |
ice core The Cryosphere |
genre_facet |
ice core The Cryosphere |
op_source |
The Cryosphere, Vol 17, Pp 539-565 (2023) |
op_relation |
https://tc.copernicus.org/articles/17/539/2023/tc-17-539-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-539-2023 1994-0416 1994-0424 https://doaj.org/article/26973bf1a54e40ab8f5af7156ff70189 |
op_doi |
https://doi.org/10.5194/tc-17-539-2023 |
container_title |
The Cryosphere |
container_volume |
17 |
container_issue |
2 |
container_start_page |
539 |
op_container_end_page |
565 |
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1766028846005485568 |