Ice core microstructure segmentation with deep neural networks ...
<!--!introduction!--> Ice cores are among the most important natural archives that can provide valuable information from the past environment of our planet. The signals related to the history of the earth are preserved in structure, bubbles, water isotopes, and impurities, which can be retrace...
| Main Authors: | , , |
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| Format: | Conference Object |
| Language: | unknown |
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GFZ German Research Centre for Geosciences
2023
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| Online Access: | https://dx.doi.org/10.57757/iugg23-2605 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019306 |
| Summary: | <!--!introduction!--> Ice cores are among the most important natural archives that can provide valuable information from the past environment of our planet. The signals related to the history of the earth are preserved in structure, bubbles, water isotopes, and impurities, which can be retraced by studying polar ice cores. Although recent technological advancements have made it possible to perform non-destructive tests such as micro-CT scans to study structure and bubbles, performing a high-quality automated segmentation of ice cores from different regions and depths is still a challenge. The CT images of various depths have different pixel intensities, and they might appear with a range of noise, artifacts, and beam hardening issues. The traditional segmentation methods, such as thresholding and edge finding is tedious to be applied on all sort of different ice core CT images, thus, we took advantage of deep learning algorithms to facilitate this task. Besides the image noise diversities, scanning ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
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