Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning
Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice shee...
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ftdoajarticles:oai:doaj.org/article:fc13781de5424136992e4f356f725311 2023-05-15T14:03:58+02:00 Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning Xueyuan Tang Kun Luo Sheng Dong Zidong Zhang Bo Sun 2022-09-01T00:00:00Z https://doi.org/10.3390/rs14184507 https://doaj.org/article/fc13781de5424136992e4f356f725311 EN eng MDPI AG https://www.mdpi.com/2072-4292/14/18/4507 https://doaj.org/toc/2072-4292 doi:10.3390/rs14184507 2072-4292 https://doaj.org/article/fc13781de5424136992e4f356f725311 Remote Sensing, Vol 14, Iss 4507, p 4507 (2022) ice-penetrating radar (IPR) internal layer continuity index (ILCI) roughness deep learning Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14184507 2022-12-31T00:32:50Z Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet’s internal structure or bottom and ice flow. Several methods have been proposed in the past two decades to quantitatively calculate the continuity index of ice layer geometry and the roughness of the ice–bedrock interface based on radar echo signals. These methods are mainly based on the average of the absolute value of the vertical gradient of the echo signal amplitude and the standard deviation of the horizontal fluctuation of the bedrock interface. However, these methods are limited by the amount and quality of unprocessed radar datasets and have not been widely used, which also hinders further research, such as the analysis of the englacial reflectivity, the subglacial conditions, and the history of the ice sheets. In this paper, based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction, we propose a new method for calculating the layer continuity index and basal roughness. Using this method, we demonstrate the ice-penetrating radar data processing and compare the imaging and calculation of the radar profiles from Dome A to Zhongshan Station, East Antarctica. We removed the noise from the processed radar data, extracted ice layer continuity features, and used other techniques to verify the calculation. The potential application of this method in the future is illustrated by several examples. We believe that this method can become an effective approach for future Antarctic geophysical and glaciological research and for obtaining more information about the history and dynamics of ice sheets from their radar-extracted internal structure. Article in Journal/Newspaper Antarc* Antarctic Antarctica East Antarctica Directory of Open Access Journals: DOAJ Articles Antarctic East Antarctica Zhongshan ENVELOPE(76.371,76.371,-69.373,-69.373) Zhongshan Station ENVELOPE(76.371,76.371,-69.373,-69.373) Remote Sensing 14 18 4507 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
ice-penetrating radar (IPR) internal layer continuity index (ILCI) roughness deep learning Science Q |
spellingShingle |
ice-penetrating radar (IPR) internal layer continuity index (ILCI) roughness deep learning Science Q Xueyuan Tang Kun Luo Sheng Dong Zidong Zhang Bo Sun Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning |
topic_facet |
ice-penetrating radar (IPR) internal layer continuity index (ILCI) roughness deep learning Science Q |
description |
Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet’s internal structure or bottom and ice flow. Several methods have been proposed in the past two decades to quantitatively calculate the continuity index of ice layer geometry and the roughness of the ice–bedrock interface based on radar echo signals. These methods are mainly based on the average of the absolute value of the vertical gradient of the echo signal amplitude and the standard deviation of the horizontal fluctuation of the bedrock interface. However, these methods are limited by the amount and quality of unprocessed radar datasets and have not been widely used, which also hinders further research, such as the analysis of the englacial reflectivity, the subglacial conditions, and the history of the ice sheets. In this paper, based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction, we propose a new method for calculating the layer continuity index and basal roughness. Using this method, we demonstrate the ice-penetrating radar data processing and compare the imaging and calculation of the radar profiles from Dome A to Zhongshan Station, East Antarctica. We removed the noise from the processed radar data, extracted ice layer continuity features, and used other techniques to verify the calculation. The potential application of this method in the future is illustrated by several examples. We believe that this method can become an effective approach for future Antarctic geophysical and glaciological research and for obtaining more information about the history and dynamics of ice sheets from their radar-extracted internal structure. |
format |
Article in Journal/Newspaper |
author |
Xueyuan Tang Kun Luo Sheng Dong Zidong Zhang Bo Sun |
author_facet |
Xueyuan Tang Kun Luo Sheng Dong Zidong Zhang Bo Sun |
author_sort |
Xueyuan Tang |
title |
Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning |
title_short |
Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning |
title_full |
Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning |
title_fullStr |
Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning |
title_full_unstemmed |
Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning |
title_sort |
quantifying basal roughness and internal layer continuity index of ice sheets by an integrated means with radar data and deep learning |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14184507 https://doaj.org/article/fc13781de5424136992e4f356f725311 |
long_lat |
ENVELOPE(76.371,76.371,-69.373,-69.373) ENVELOPE(76.371,76.371,-69.373,-69.373) |
geographic |
Antarctic East Antarctica Zhongshan Zhongshan Station |
geographic_facet |
Antarctic East Antarctica Zhongshan Zhongshan Station |
genre |
Antarc* Antarctic Antarctica East Antarctica |
genre_facet |
Antarc* Antarctic Antarctica East Antarctica |
op_source |
Remote Sensing, Vol 14, Iss 4507, p 4507 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/18/4507 https://doaj.org/toc/2072-4292 doi:10.3390/rs14184507 2072-4292 https://doaj.org/article/fc13781de5424136992e4f356f725311 |
op_doi |
https://doi.org/10.3390/rs14184507 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
18 |
container_start_page |
4507 |
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1766274885177311232 |