Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning
TL;DR: In this paper , the authors proposed a new method for calculating the layer continuity index and basal roughness based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction.
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Abstract: 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.
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Citations
Basal Melt Patterns around the Deep Ice Core Drilling Site in the Dome A Region from Ice-Penetrating Radar Measurements
Hao Wang,Xueyuan Tang,Enzhao Xiao,Kun Luo,Sheng Dong,Bo Sun +5 more
TL;DR: In this paper , a linear absorption model was used to determine wet and dry ice-bedrock interfaces around the Kunlun drilling site, and an artificial intelligence model was applied to extract the ice−bedrock interface for inferring the ice thickness.
Airborne Radio-Echo Sounding Data Denoising Using Particle Swarm Optimization and Multivariate Variational Mode Decomposition
Yuhan Chen,Kun Luo,Lijuan Wang,Xueyuan Tang +3 more
TL;DR: The results confirmed the quality of the proposed PSO–MVMD method in attenuating the RES signal noise, enhancing the weak signal of the ice base, and improving the SNR.
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Deep Clustering in Radar Subglacial Reflector Reveals New Subglacial Lakes
07 Jun 2023
TL;DR: In this article , the authors collected and generated a dataset of one-dimensional reflector waveform features from IPR images of the Gamburtsev Subglacial Mountains region in the CReSIS database, to investigate these features.
Review article: AntArchitecture – building an age–depth model from Antarctica's radiostratigraphy to explore ice-sheet evolution
Robert G. Bingham,Julien A. Bodart,Marie G. P. Cavitte,Dr. Fadi Javid,Rebecca J. Sanderson,Olaf Eisen,Nanna B. Karlsson,Joseph A. MacGregor,Neil Ross,Duncan A. Young,David W. Ashmore,Andreas Born,Winnie Chu,Xiang-Bin Cui,Reinhard Drews,Steven Franke,Vikram Goel,John W. Goodge,A. Clara J. Henry,Antoine Hermant,AMNH Mammalogy,Nicholas Holschuh,Michelle R. Koutnik,Gwendolyn J.-M. C. Leysinger Vieli,Emma J. MacKie,Elisa Mantelli,Carlos Martin,Felix S.L. Ng,Falk M. Oraschewski,Felipe Napoleoni,Frédéric Parrenin,Sergey V. Popov,Therese Rieckh,Rebecca Schlegel,Dustin M. Schroeder,Martin J. Siegert,Xueyuan Tang,Thomas O. Teisberg,Kate Winter,Shuai Yan,Harry Davis,Christine F. Dow,Tyler J. Fudge,Tom A. Jordan,Bernd Kulessa,Kenichi Matsuoka,Clara J. Nyqvist,Maryam Rahnemoonfar,Matthew R. Siegfried,Shivangini Singh,Vjeran Višnjević,Rodrigo Zamora,Alexandra Zuhr +52 more
TL;DR: This review article explores the use of radiostratigraphy to build an age-depth model of Antarctica's ice sheets, enabling the reconstruction of past climate and ice dynamics on large scales, and outlines its applications in paleoclimate research and ice sheet modeling.
Deep Clustering in Radar Subglacial Reflector Reveals New Subglacial Lakes
TL;DR: In this paper , the authors collected and generated a dataset of one-dimensional reflector waveform features from IPR images of the Gamburtsev Subglacial Mountains region in the CReSIS database, to investigate these features.
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