Removal of ‘strip noise’ in radio-echo sounding data using combined wavelet and 2-D DFT filtering
ABSTRACT Radio-echo sounding (RES) can be used to understand ice-sheet processes, englacial flow structures and bed properties, making it one of the most popular tools in glaciological exploration. However, RES data are often subject to ‘strip noise’, caused by internal instrument noise and interfer...
Published in: | Annals of Glaciology |
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Language: | English |
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Online Access: | http://dx.doi.org/10.1017/aog.2019.4 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305519000041 |
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crcambridgeupr:10.1017/aog.2019.4 2024-06-23T07:45:35+00:00 Removal of ‘strip noise’ in radio-echo sounding data using combined wavelet and 2-D DFT filtering Wang, Bangbing Sun, Bo Wang, Jiaxin Greenbaum, Jamin Guo, Jingxue Lindzey, Laura Cui, Xiangbin Young, Duncan A. Blankenship, Donald D. Siegert, Martin J. 2019 http://dx.doi.org/10.1017/aog.2019.4 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305519000041 en eng Cambridge University Press (CUP) http://creativecommons.org/licenses/by/4.0/ Annals of Glaciology volume 61, issue 81, page 124-134 ISSN 0260-3055 1727-5644 journal-article 2019 crcambridgeupr https://doi.org/10.1017/aog.2019.4 2024-06-12T04:04:14Z ABSTRACT Radio-echo sounding (RES) can be used to understand ice-sheet processes, englacial flow structures and bed properties, making it one of the most popular tools in glaciological exploration. However, RES data are often subject to ‘strip noise’, caused by internal instrument noise and interference, and/or external environmental interference, which can hamper measurement and interpretation. For example, strip noise can result in reduced power from the bed, affecting the quality of ice thickness measurements and the characterization of subglacial conditions. Here, we present a method for removing strip noise based on combined wavelet and two-dimensional (2-D) Fourier filtering. First, we implement discrete wavelet decomposition on RES data to obtain multi-scale wavelet components. Then, 2-D discrete Fourier transform (DFT) spectral analysis is performed on components containing the noise. In the Fourier domain, the 2-D DFT spectrum of strip noise keeps its linear features and can be removed with a ‘targeted masking’ operation. Finally, inverse wavelet transforms are performed on all wavelet components, including strip-removed components, to restore the data with enhanced fidelity. Model tests and field-data processing demonstrate the method removes strip noise well and, incidentally, can remove the strong first reflector from the ice surface, thus improving the general quality of radar data. Article in Journal/Newspaper Annals of Glaciology Ice Sheet Cambridge University Press Annals of Glaciology 61 81 124 134 |
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Open Polar |
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Cambridge University Press |
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crcambridgeupr |
language |
English |
description |
ABSTRACT Radio-echo sounding (RES) can be used to understand ice-sheet processes, englacial flow structures and bed properties, making it one of the most popular tools in glaciological exploration. However, RES data are often subject to ‘strip noise’, caused by internal instrument noise and interference, and/or external environmental interference, which can hamper measurement and interpretation. For example, strip noise can result in reduced power from the bed, affecting the quality of ice thickness measurements and the characterization of subglacial conditions. Here, we present a method for removing strip noise based on combined wavelet and two-dimensional (2-D) Fourier filtering. First, we implement discrete wavelet decomposition on RES data to obtain multi-scale wavelet components. Then, 2-D discrete Fourier transform (DFT) spectral analysis is performed on components containing the noise. In the Fourier domain, the 2-D DFT spectrum of strip noise keeps its linear features and can be removed with a ‘targeted masking’ operation. Finally, inverse wavelet transforms are performed on all wavelet components, including strip-removed components, to restore the data with enhanced fidelity. Model tests and field-data processing demonstrate the method removes strip noise well and, incidentally, can remove the strong first reflector from the ice surface, thus improving the general quality of radar data. |
format |
Article in Journal/Newspaper |
author |
Wang, Bangbing Sun, Bo Wang, Jiaxin Greenbaum, Jamin Guo, Jingxue Lindzey, Laura Cui, Xiangbin Young, Duncan A. Blankenship, Donald D. Siegert, Martin J. |
spellingShingle |
Wang, Bangbing Sun, Bo Wang, Jiaxin Greenbaum, Jamin Guo, Jingxue Lindzey, Laura Cui, Xiangbin Young, Duncan A. Blankenship, Donald D. Siegert, Martin J. Removal of ‘strip noise’ in radio-echo sounding data using combined wavelet and 2-D DFT filtering |
author_facet |
Wang, Bangbing Sun, Bo Wang, Jiaxin Greenbaum, Jamin Guo, Jingxue Lindzey, Laura Cui, Xiangbin Young, Duncan A. Blankenship, Donald D. Siegert, Martin J. |
author_sort |
Wang, Bangbing |
title |
Removal of ‘strip noise’ in radio-echo sounding data using combined wavelet and 2-D DFT filtering |
title_short |
Removal of ‘strip noise’ in radio-echo sounding data using combined wavelet and 2-D DFT filtering |
title_full |
Removal of ‘strip noise’ in radio-echo sounding data using combined wavelet and 2-D DFT filtering |
title_fullStr |
Removal of ‘strip noise’ in radio-echo sounding data using combined wavelet and 2-D DFT filtering |
title_full_unstemmed |
Removal of ‘strip noise’ in radio-echo sounding data using combined wavelet and 2-D DFT filtering |
title_sort |
removal of ‘strip noise’ in radio-echo sounding data using combined wavelet and 2-d dft filtering |
publisher |
Cambridge University Press (CUP) |
publishDate |
2019 |
url |
http://dx.doi.org/10.1017/aog.2019.4 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305519000041 |
genre |
Annals of Glaciology Ice Sheet |
genre_facet |
Annals of Glaciology Ice Sheet |
op_source |
Annals of Glaciology volume 61, issue 81, page 124-134 ISSN 0260-3055 1727-5644 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1017/aog.2019.4 |
container_title |
Annals of Glaciology |
container_volume |
61 |
container_issue |
81 |
container_start_page |
124 |
op_container_end_page |
134 |
_version_ |
1802641388077056000 |