Articles | Volume 19, issue 6
https://doi.org/10.5194/tc-19-2159-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-2159-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
23 Jun 2025
Review article |
| 23 Jun 2025
| 23 Jun 2025
Review article: Feature tracing in radio-echo sounding products of terrestrial ice sheets and planetary bodies
Department of Glaciology, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Constructor University, Bremen, Germany
Olaf Eisen
Department of Glaciology, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Faculty of Geosciences, University of Bremen, Bremen, Germany
Related authors
No articles found.
Ailsa Chung, Frédéric Parrenin, Robert Mulvaney, Luca Vittuari, Massimo Frezzotti, Antonio Zanutta, David A. Lilien, Marie G. P. Cavitte, and Olaf Eisen
The Cryosphere, 19, 4125–4140, https://doi.org/10.5194/tc-19-4125-2025, https://doi.org/10.5194/tc-19-4125-2025, 2025
Short summary
Short summary
We applied an ice flow model to a flow line from the summit of Dome C to the Beyond EPICA ice core drill site on Little Dome C in Antarctica. Results show that the oldest ice at the drill site may be 1.12 Ma (at an age density of 20 kyr m-1) and originate from around 15 km upstream. We also discuss the nature of the 200–250 m thick basal layer which could be composed of stagnant ice, disturbed ice or even accreted ice (possibly containing debris).
Neil Ross, Rebecca J. Sanderson, Bernd Kulessa, Martin Siegert, Guy J. G. Paxman, Keir A. Nichols, Matthew R. Siegfried, Stewart S. R. Jamieson, Michael J. Bentley, Tom A. Jordan, Christine L. Batchelor, David Small, Olaf Eisen, Kate Winter, Robert G. Bingham, S. Louise Callard, Rachel Carr, Christine F. Dow, Helen A. Fricker, Emily Hill, Benjamin H. Hills, Coen Hofstede, Hafeez Jeofry, Felipe Napoleoni, and Wilson Sauthoff
EGUsphere, https://doi.org/10.5194/egusphere-2025-3625, https://doi.org/10.5194/egusphere-2025-3625, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We review previous research into a group of fast-flowing Antarctic ice streams, the Foundation-Patuxent-Academy System. Previously, we knew relatively little how these ice streams flow, how they interact with the ocean, what their geological history was, and how they might evolve in a warming world. By reviewing existing information on these ice streams, we identify the future research needed to determine how they function, and their potential contribution to global sea level rise.
Ole Zeising, Tore Hattermann, Lars Kaleschke, Sophie Berger, Olaf Boebel, Reinhard Drews, M. Reza Ershadi, Tanja Fromm, Frank Pattyn, Daniel Steinhage, and Olaf Eisen
The Cryosphere, 19, 2837–2854, https://doi.org/10.5194/tc-19-2837-2025, https://doi.org/10.5194/tc-19-2837-2025, 2025
Short summary
Short summary
Basal melting of ice shelves impacts the mass loss of the Antarctic Ice Sheet. This study focuses on the Ekström Ice Shelf in East Antarctica, using multiyear data from an autonomous radar system. Results show a surprising seasonal pattern of high melt rates in winter and spring. The seasonalities of sea-ice growth and ocean density indicate that, in winter, dense water enhances plume activity and melt rates. Understanding these dynamics is crucial for improving future mass balance projections.
Tamara Annina Gerber, David A. Lilien, Niels F. Nymand, Daniel Steinhage, Olaf Eisen, and Dorthe Dahl-Jensen
The Cryosphere, 19, 1955–1971, https://doi.org/10.5194/tc-19-1955-2025, https://doi.org/10.5194/tc-19-1955-2025, 2025
Short summary
Short summary
This study examines how anisotropic scattering and birefringence affect radar signals in ice sheets. Using data from northeast Greenland, we show that anisotropic scattering – driven by subtle ice crystal orientation changes – dominates the azimuthal power response. We find a strong link between scattering strength, orientation, and stratigraphy. This suggests anisotropic scattering can reveal crystal fabric orientation and differentiate ice units from distinct climatic periods.
Charlotte M. Carter, Steven Franke, Daniela Jansen, Chris R. Stokes, Veit Helm, John Paden, and Olaf Eisen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1743, https://doi.org/10.5194/egusphere-2025-1743, 2025
Short summary
Short summary
The landscapes beneath actively fast-flowing ice in Greenland have not been explored in detail, as digital elevation models do not have a high enough resolution to see these subglacial features. We use swath radar imaging to visualise these landforms at a high resolution, revealing a landscape that would usually be assumed to be indicative of faster ice flow than the current velocities. Interpretation of the landscape also gives an indication of the properties of the bed beneath the ice stream.
Steven Franke, Daniel Steinhage, Veit Helm, Alexandra M. Zuhr, Julien A. Bodart, Olaf Eisen, and Paul Bons
The Cryosphere, 19, 1153–1180, https://doi.org/10.5194/tc-19-1153-2025, https://doi.org/10.5194/tc-19-1153-2025, 2025
Short summary
Short summary
The study presents internal reflection horizons (IRHs) over an area of 450 000 km² from western Dronning Maud Land, Antarctica, spanning 4.8–91 ka. Using radar and ice core data, nine IRHs were dated and correlated with volcanic events. The data enhance our understanding of the ice sheet's age–depth architecture, accumulation, and dynamics. The findings inform ice flow models and contribute to Antarctic-wide comparisons of IRHs, supporting efforts toward a 3D age–depth ice sheet model.
Emma Pearce, Dimitri Zigone, Coen Hofstede, Andreas Fichtner, Joachim Rimpot, Sune Olander Rasmussen, Johannes Freitag, and Olaf Eisen
The Cryosphere, 18, 4917–4932, https://doi.org/10.5194/tc-18-4917-2024, https://doi.org/10.5194/tc-18-4917-2024, 2024
Short summary
Short summary
Our study near EastGRIP camp in Greenland shows varying firn properties by direction (crucial for studying ice stream stability, structure, surface mass balance, and past climate conditions). We used dispersion curve analysis of Love and Rayleigh waves to show firn is nonuniform along and across the flow of an ice stream due to wind patterns, seasonal variability, and the proximity to the edge of the ice stream. This method better informs firn structure, advancing ice stream understanding.
Robert G. Bingham, Julien A. Bodart, Marie G. P. Cavitte, Ailsa Chung, Rebecca J. Sanderson, Johannes C. R. Sutter, Olaf Eisen, Nanna B. Karlsson, Joseph A. MacGregor, Neil Ross, Duncan A. Young, David W. Ashmore, Andreas Born, Winnie Chu, Xiangbin Cui, Reinhard Drews, Steven Franke, Vikram Goel, John W. Goodge, A. Clara J. Henry, Antoine Hermant, Benjamin H. Hills, Nicholas Holschuh, Michelle R. Koutnik, Gwendolyn J.-M. C. Leysinger Vieli, Emma J. Mackie, Elisa Mantelli, Carlos Martín, 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, Verjan Višnjević, Rodrigo Zamora, and Alexandra Zuhr
EGUsphere, https://doi.org/10.5194/egusphere-2024-2593, https://doi.org/10.5194/egusphere-2024-2593, 2024
Short summary
Short summary
The ice sheets covering Antarctica have built up over millenia through successive snowfall events which become buried and preserved as internal surfaces of equal age detectable with ice-penetrating radar. This paper describes an international initiative to work together on this archival data to build a comprehensive 3-D picture of how old the ice is everywhere across Antarctica, and how this will be used to reconstruct past and predict future ice and climate behaviour.
Falk M. Oraschewski, Inka Koch, M. Reza Ershadi, Jonathan D. Hawkins, Olaf Eisen, and Reinhard Drews
The Cryosphere, 18, 3875–3889, https://doi.org/10.5194/tc-18-3875-2024, https://doi.org/10.5194/tc-18-3875-2024, 2024
Short summary
Short summary
Mountain glaciers have a layered structure which contains information about past snow accumulation and ice flow. Using ground-penetrating radar instruments, the internal structure can be observed. The detection of layers in the deeper parts of a glacier is often difficult. Here, we present a new approach for imaging the englacial structure of an Alpine glacier (Colle Gnifetti, Switzerland and Italy) using a phase-sensitive radar that can detect reflection depth changes at sub-wavelength scales.
Ladina Steiner, Holger Schmithüsen, Jens Wickert, and Olaf Eisen
The Cryosphere, 17, 4903–4916, https://doi.org/10.5194/tc-17-4903-2023, https://doi.org/10.5194/tc-17-4903-2023, 2023
Short summary
Short summary
The present study illustrates the potential of a combined Global Navigation Satellite System reflectometry and refractometry (GNSS-RR) method for accurate, simultaneous, and continuous estimation of in situ snow accumulation, snow water equivalent, and snow density time series. The combined GNSS-RR method was successfully applied on a fast-moving, polar ice shelf. The combined GNSS-RR approach could be highly advantageous for a continuous quantification of ice sheet surface mass balances.
Zhuo Wang, Ailsa Chung, Daniel Steinhage, Frédéric Parrenin, Johannes Freitag, and Olaf Eisen
The Cryosphere, 17, 4297–4314, https://doi.org/10.5194/tc-17-4297-2023, https://doi.org/10.5194/tc-17-4297-2023, 2023
Short summary
Short summary
We combine radar-based observed internal layer stratigraphy of the ice sheet with a 1-D ice flow model in the Dome Fuji region. This results in maps of age and age density of the basal ice, the basal thermal conditions, and reconstructed accumulation rates. Based on modeled age we then identify four potential candidates for ice which is potentially 1.5 Myr old. Our map of basal thermal conditions indicates that melting prevails over the presence of stagnant ice in the study area.
Ailsa Chung, Frédéric Parrenin, Daniel Steinhage, Robert Mulvaney, Carlos Martín, Marie G. P. Cavitte, David A. Lilien, Veit Helm, Drew Taylor, Prasad Gogineni, Catherine Ritz, Massimo Frezzotti, Charles O'Neill, Heinrich Miller, Dorthe Dahl-Jensen, and Olaf Eisen
The Cryosphere, 17, 3461–3483, https://doi.org/10.5194/tc-17-3461-2023, https://doi.org/10.5194/tc-17-3461-2023, 2023
Short summary
Short summary
We combined a numerical model with radar measurements in order to determine the age of ice in the Dome C region of Antarctica. Our results show that at the current ice core drilling sites on Little Dome C, the maximum age of the ice is almost 1.5 Ma. We also highlight a new potential drill site called North Patch with ice up to 2 Ma. Finally, we explore the nature of a stagnant ice layer at the base of the ice sheet which has been independently observed and modelled but is not well understood.
Alice C. Frémand, Peter Fretwell, Julien A. Bodart, Hamish D. Pritchard, Alan Aitken, Jonathan L. Bamber, Robin Bell, Cesidio Bianchi, Robert G. Bingham, Donald D. Blankenship, Gino Casassa, Ginny Catania, Knut Christianson, Howard Conway, Hugh F. J. Corr, Xiangbin Cui, Detlef Damaske, Volkmar Damm, Reinhard Drews, Graeme Eagles, Olaf Eisen, Hannes Eisermann, Fausto Ferraccioli, Elena Field, René Forsberg, Steven Franke, Shuji Fujita, Yonggyu Gim, Vikram Goel, Siva Prasad Gogineni, Jamin Greenbaum, Benjamin Hills, Richard C. A. Hindmarsh, Andrew O. Hoffman, Per Holmlund, Nicholas Holschuh, John W. Holt, Annika N. Horlings, Angelika Humbert, Robert W. Jacobel, Daniela Jansen, Adrian Jenkins, Wilfried Jokat, Tom Jordan, Edward King, Jack Kohler, William Krabill, Mette Kusk Gillespie, Kirsty Langley, Joohan Lee, German Leitchenkov, Carlton Leuschen, Bruce Luyendyk, Joseph MacGregor, Emma MacKie, Kenichi Matsuoka, Mathieu Morlighem, Jérémie Mouginot, Frank O. Nitsche, Yoshifumi Nogi, Ole A. Nost, John Paden, Frank Pattyn, Sergey V. Popov, Eric Rignot, David M. Rippin, Andrés Rivera, Jason Roberts, Neil Ross, Anotonia Ruppel, Dustin M. Schroeder, Martin J. Siegert, Andrew M. Smith, Daniel Steinhage, Michael Studinger, Bo Sun, Ignazio Tabacco, Kirsty Tinto, Stefano Urbini, David Vaughan, Brian C. Welch, Douglas S. Wilson, Duncan A. Young, and Achille Zirizzotti
Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
Short summary
Short summary
This paper presents the release of over 60 years of ice thickness, bed elevation, and surface elevation data acquired over Antarctica by the international community. These data are a crucial component of the Antarctic Bedmap initiative which aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international glaciology and geophysical community.
Ole Zeising, Tamara Annina Gerber, Olaf Eisen, M. Reza Ershadi, Nicolas Stoll, Ilka Weikusat, and Angelika Humbert
The Cryosphere, 17, 1097–1105, https://doi.org/10.5194/tc-17-1097-2023, https://doi.org/10.5194/tc-17-1097-2023, 2023
Short summary
Short summary
The flow of glaciers and ice streams is influenced by crystal fabric orientation. Besides sparse ice cores, these can be investigated by radar measurements. Here, we present an improved method which allows us to infer the horizontal fabric asymmetry using polarimetric phase-sensitive radar data. A validation of the method on a deep ice core from the Greenland Ice Sheet shows an excellent agreement, which is a large improvement over previously used methods.
Vjeran Višnjević, Reinhard Drews, Clemens Schannwell, Inka Koch, Steven Franke, Daniela Jansen, and Olaf Eisen
The Cryosphere, 16, 4763–4777, https://doi.org/10.5194/tc-16-4763-2022, https://doi.org/10.5194/tc-16-4763-2022, 2022
Short summary
Short summary
We present a simple way to model the internal layers of an ice shelf and apply the method to the Roi Baudouin Ice Shelf in East Antarctica. Modeled results are compared to measurements obtained by radar. We distinguish between ice directly formed on the shelf and ice transported from the ice sheet, and we map the spatial changes in the volume of the locally accumulated ice. In this context, we discuss the sensitivity of the ice shelf to future changes in surface accumulation and basal melt.
Julian Gutt, Stefanie Arndt, David Keith Alan Barnes, Horst Bornemann, Thomas Brey, Olaf Eisen, Hauke Flores, Huw Griffiths, Christian Haas, Stefan Hain, Tore Hattermann, Christoph Held, Mario Hoppema, Enrique Isla, Markus Janout, Céline Le Bohec, Heike Link, Felix Christopher Mark, Sebastien Moreau, Scarlett Trimborn, Ilse van Opzeeland, Hans-Otto Pörtner, Fokje Schaafsma, Katharina Teschke, Sandra Tippenhauer, Anton Van de Putte, Mia Wege, Daniel Zitterbart, and Dieter Piepenburg
Biogeosciences, 19, 5313–5342, https://doi.org/10.5194/bg-19-5313-2022, https://doi.org/10.5194/bg-19-5313-2022, 2022
Short summary
Short summary
Long-term ecological observations are key to assess, understand and predict impacts of environmental change on biotas. We present a multidisciplinary framework for such largely lacking investigations in the East Antarctic Southern Ocean, combined with case studies, experimental and modelling work. As climate change is still minor here but is projected to start soon, the timely implementation of this framework provides the unique opportunity to document its ecological impacts from the very onset.
Astrid Oetting, Emma C. Smith, Jan Erik Arndt, Boris Dorschel, Reinhard Drews, Todd A. Ehlers, Christoph Gaedicke, Coen Hofstede, Johann P. Klages, Gerhard Kuhn, Astrid Lambrecht, Andreas Läufer, Christoph Mayer, Ralf Tiedemann, Frank Wilhelms, and Olaf Eisen
The Cryosphere, 16, 2051–2066, https://doi.org/10.5194/tc-16-2051-2022, https://doi.org/10.5194/tc-16-2051-2022, 2022
Short summary
Short summary
This study combines a variety of geophysical measurements in front of and beneath the Ekström Ice Shelf in order to identify and interpret geomorphological evidences of past ice sheet flow, extent and retreat.
The maximal extent of grounded ice in this region was 11 km away from the continental shelf break.
The thickness of palaeo-ice on the calving front around the LGM was estimated to be at least 305 to 320 m.
We provide essential boundary conditions for palaeo-ice-sheet models.
M. Reza Ershadi, Reinhard Drews, Carlos Martín, Olaf Eisen, Catherine Ritz, Hugh Corr, Julia Christmann, Ole Zeising, Angelika Humbert, and Robert Mulvaney
The Cryosphere, 16, 1719–1739, https://doi.org/10.5194/tc-16-1719-2022, https://doi.org/10.5194/tc-16-1719-2022, 2022
Short summary
Short summary
Radio waves transmitted through ice split up and inform us about the ice sheet interior and orientation of single ice crystals. This can be used to infer how ice flows and improve projections on how it will evolve in the future. Here we used an inverse approach and developed a new algorithm to infer ice properties from observed radar data. We applied this technique to the radar data obtained at two EPICA drilling sites, where ice cores were used to validate our results.
Steven Franke, Daniela Jansen, Tobias Binder, John D. Paden, Nils Dörr, Tamara A. Gerber, Heinrich Miller, Dorthe Dahl-Jensen, Veit Helm, Daniel Steinhage, Ilka Weikusat, Frank Wilhelms, and Olaf Eisen
Earth Syst. Sci. Data, 14, 763–779, https://doi.org/10.5194/essd-14-763-2022, https://doi.org/10.5194/essd-14-763-2022, 2022
Short summary
Short summary
The Northeast Greenland Ice Stream (NEGIS) is the largest ice stream in Greenland. In order to better understand the past and future dynamics of the NEGIS, we present a high-resolution airborne radar data set (EGRIP-NOR-2018) for the onset region of the NEGIS. The survey area is centered at the location of the drill site of the East Greenland Ice-Core Project (EastGRIP), and radar profiles cover both shear margins and are aligned parallel to several flow lines.
Johannes Sutter, Hubertus Fischer, and Olaf Eisen
The Cryosphere, 15, 3839–3860, https://doi.org/10.5194/tc-15-3839-2021, https://doi.org/10.5194/tc-15-3839-2021, 2021
Short summary
Short summary
Projections of global sea-level changes in a warming world require ice-sheet models. We expand the calibration of these models by making use of the internal architecture of the Antarctic ice sheet, which is formed by its evolution over many millennia. We propose that using our novel approach to constrain ice sheet models, we will be able to both sharpen our understanding of past and future sea-level changes and identify weaknesses in the parameterisation of current continental-scale models.
David A. Lilien, Daniel Steinhage, Drew Taylor, Frédéric Parrenin, Catherine Ritz, Robert Mulvaney, Carlos Martín, Jie-Bang Yan, Charles O'Neill, Massimo Frezzotti, Heinrich Miller, Prasad Gogineni, Dorthe Dahl-Jensen, and Olaf Eisen
The Cryosphere, 15, 1881–1888, https://doi.org/10.5194/tc-15-1881-2021, https://doi.org/10.5194/tc-15-1881-2021, 2021
Short summary
Short summary
We collected radar data between EDC, an ice core spanning ~800 000 years, and BELDC, the site chosen for a new
oldest icecore at nearby Little Dome C. These data allow us to identify 50 % older internal horizons than previously traced in the area. We fit a model to the ages of those horizons at BELDC to determine the age of deep ice there. We find that there is likely to be 1.5 Myr old ice ~265 m above the bed, with sufficient resolution to preserve desired climatic information.
Coen Hofstede, Sebastian Beyer, Hugh Corr, Olaf Eisen, Tore Hattermann, Veit Helm, Niklas Neckel, Emma C. Smith, Daniel Steinhage, Ole Zeising, and Angelika Humbert
The Cryosphere, 15, 1517–1535, https://doi.org/10.5194/tc-15-1517-2021, https://doi.org/10.5194/tc-15-1517-2021, 2021
Short summary
Short summary
Support Force Glacier rapidly flows into Filcher Ice Shelf of Antarctica. As we know little about this glacier and its subglacial drainage, we used seismic energy to map the transition area from grounded to floating ice where a drainage channel enters the ocean cavity. Soft sediments close to the grounding line are probably transported by this drainage channel. The constant ice thickness over the steeply dipping seabed of the ocean cavity suggests a stable transition and little basal melting.
Stefan Kowalewski, Veit Helm, Elizabeth Mary Morris, and Olaf Eisen
The Cryosphere, 15, 1285–1305, https://doi.org/10.5194/tc-15-1285-2021, https://doi.org/10.5194/tc-15-1285-2021, 2021
Short summary
Short summary
This study presents estimates of total mass input for the Pine Island Glacier (PIG) over the period 2005–2014 from airborne radar measurements. Our analysis reveals a total mass input similar to an earlier estimate for the period 1985–2009 and same area. This suggests a stationary total mass input contrary to the accelerated mass loss of PIG over the past decades. However, we also find that its uncertainty is highly sensitive to the geostatistical assumptions required for its calculation.
Clemens Schannwell, Reinhard Drews, Todd A. Ehlers, Olaf Eisen, Christoph Mayer, Mika Malinen, Emma C. Smith, and Hannes Eisermann
The Cryosphere, 14, 3917–3934, https://doi.org/10.5194/tc-14-3917-2020, https://doi.org/10.5194/tc-14-3917-2020, 2020
Short summary
Short summary
To reduce uncertainties associated with sea level rise projections, an accurate representation of ice flow is paramount. Most ice sheet models rely on simplified versions of the underlying ice flow equations. Due to the high computational costs, ice sheet models based on the complete ice flow equations have been restricted to < 1000 years. Here, we present a new model setup that extends the applicability of such models by an order of magnitude, permitting simulations of 40 000 years.
Alexander H. Weinhart, Johannes Freitag, Maria Hörhold, Sepp Kipfstuhl, and Olaf Eisen
The Cryosphere, 14, 3663–3685, https://doi.org/10.5194/tc-14-3663-2020, https://doi.org/10.5194/tc-14-3663-2020, 2020
Short summary
Short summary
From 1 m snow profiles along a traverse on the East Antarctic Plateau, we calculated a representative surface snow density of 355 kg m−3 for this region with an error less than 1.5 %.
This density is 10 % higher and density fluctuations seem to happen on smaller scales than climate model outputs suggest. Our study can help improve the parameterization of surface snow density in climate models to reduce the error in future sea level predictions.
Cited articles
Aðalgeirsdóttir, G., Guðmundsson, S., Björnsson, H., Pálsson, F., Jóhannesson, T., Hannesdóttir, H., Sigurðsson, S. T., and Berthier, E.: Modelling the 20th and 21st century evolution of Hoffellsjökull glacier, SE-Vatnajökull, Iceland, The Cryosphere, 5, 961–975, https://doi.org/10.5194/tc-5-961-2011, 2011. a
Aggarwal, C. C.: Neural Networks and Deep Learning: A Textbook, Springer International Publishing, Cham, ISBN 9783319944623, 9783319944630, https://doi.org/10.1007/978-3-319-94463-0, 2018. a
Antoine, J.-P., Carrette, P., Murenzi, R., and Piette, B.: Image analysis with two-dimensional continuous wavelet transform, Signal Process., 31, 241–272, https://doi.org/10.1016/0165-1684(93)90085-O, 1993. a
Arbeláez, P., Maire, M., Fowlkes, C., and Malik, J.: Contour Detection and Hierarchical Image Segmentation, IEEE T. Pattern Anal., 33, 898–916, https://doi.org/10.1109/TPAMI.2010.161, 2011. a
Arcone, S. A., Spikes, V. B., and Hamilton, G. S.: Stratigraphic variation within polar firn caused by differential accumulation and ice flow: interpretation of a 400 MHz short-pulse radar profile from West Antarctica, J. Glaciol., 51, 407–422, https://doi.org/10.3189/172756505781829151, 2005. a
Arkin, E., Yadikar, N., Xu, X., Aysa, A., and Ubul, K.: A survey: object detection methods from CNN to transformer, Multimed. Tools Appl., 82, 21353–21383, https://doi.org/10.1007/s11042-022-13801-3, 2023. a
Atefeh Jebeli, Bayu Adhi Tama, Janeja, V. P., Holschuh, N., Jensen, C., Morlighem, M., Macgregor, J. A., and Fahnestock, M. A.: TSSA: Two-step Semi-Supervised Annotation for radargrams on the Greenland ice sheet, IGARSS 2023–2023 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 56–59, https://doi.org/10.13140/RG.2.2.23219.20007, 2023. a, b
Bailey, D., Chang, Y., and Le Moan, S.: Analysing Arbitrary Curves from the Line Hough Transform, J. Imaging Sci., 6, 26, https://doi.org/10.3390/jimaging6040026, 2020. a
Bailey, J. T., Evans, S., and Robin, G. D. Q.: Radio Echo Sounding of Polar Ice Sheets, Nature, 204, 420–421, https://doi.org/10.1038/204420a0, 1964. a
Ballard, D.: Generalizing the Hough transform to detect arbitrary shapes, Comm. Com. Inf. Sc., 13, 111–122, https://doi.org/10.1016/0031-3203(81)90009-1, 1981. a
Bente, K., Marchant, R., and Ramos, F.: Transfer learning for Antarctic bed topography super-resolution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21497, https://doi.org/10.5194/egusphere-egu24-21497, 2024. a
Berger, V., Xu, M., Chu, S., Crandall, D., Paden, J., and Fox, G. C.: Automated Tracking of 2D and 3D Ice Radar Imagery Using Viterbi and TRW-S, in: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, 22–27 July, Valencia, Spain 4162–4165, IEEE, Valencia, ISBN 9781538671504, https://doi.org/10.1109/IGARSS.2018.8519411, 2018. a, b
Beucher, S. and Lantuejoul, C.: Use of Watersheds in Contour Detection, International Workshop on Image Processing: Real-Time Edge and Motion Detection/Estimation, https://people.cmm.minesparis.psl.eu/users/beucher/publi/watershed.pdf (last access: 23 July 2023), 1979. a
Bingham, R. G. and Siegert, M. J.: Radar-derived bed roughness characterization of Institute and Möller ice streams, West Antarctica, and comparison with Siple Coast ice streams, Geophys. Res. Lett., 34, 2007GL031483, https://doi.org/10.1029/2007GL031483, 2007. a
Bingham, R. G., Bodart, J. A., Cavitte, M. G. P., Chung, A., Sanderson, R. J., Sutter, J. C. R., Eisen, O., Karlsson, N. B., MacGregor, J. A., Ross, N., Young, D. A., Ashmore, D. W., Born, A., Chu, W., Cui, X., Drews, R., Franke, S., Goel, V., Goodge, J. W., Henry, A. C. J., Hermant, A., Hills, B. H., Holschuh, N., Koutnik, M. R., Leysinger Vieli, G. J.-M. C., Mackie, E. J., Mantelli, E., Martín, C., Ng, F. S. L., Oraschewski, F. M., Napoleoni, F., Parrenin, F., Popov, S. V., Rieckh, T., Schlegel, R., Schroeder, D. M., Siegert, M. J., Tang, X., Teisberg, T. O., Winter, K., Yan, S., Davis, H., Dow, C. F., Fudge, T. J., Jordan, T. A., Kulessa, B., Matsuoka, K., Nyqvist, C. J., Rahnemoonfar, M., Siegfried, M. R., Singh, S., Višnjević, V., Zamora, R., and Zuhr, A.: Review Article: Antarctica’s internal architecture: Towards a radiostratigraphically-informed age–depth model of the Antarctic ice sheets, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-2593, 2024. a, b
Björnsson, H. and Einarsson, P.: Volcanoes beneath Vatnajökull, Iceland: Evidence from radio echo-sounding, earthquakes and jökulhlaup, Jökull, 40, 147–168, https://doi.org/10.33799/jokull1990.40.147, 1990. a
Björnsson, H. and Pálsson, F.: Radio-echo soundings on Icelandic temperate glaciers: history of techniques and findings, Ann. Glaciol., 61, 25–34, https://doi.org/10.1017/aog.2020.10, 2020. a
Blankenship, D., Kempf, S., Young, D., Richter, T., Schroeder, D., Greenbaum, J., van Ommen, T., Warner, R., Roberts, J., Young, N., Lemeur, E., Siegert, M., and Holt, J.: IceBridge HiCARS 1 L1B Time-Tagged Echo Strength Profiles, Version 1, https://doi.org/10.5067/W2KXX0MYNJ9G, 2017. a
Bogorodsky, V. V., Bentley, C. R., and Gudmandsen, P. E.: Radioglaciology, Springer Netherlands, Dordrecht, oCLC: 840305866, ISBN 9789400952751, https://doi.org/10.1007/978-94-009-5275-1, 1985. a, b, c
Bons, P. D., Jansen, D., Mundel, F., Bauer, C. C., Binder, T., Eisen, O., Jessell, M. W., Llorens, M.-G., Steinbach, F., Steinhage, D., and Weikusat, I.: Converging flow and anisotropy cause large-scale folding in Greenland's ice sheet, Nat. Commun., 7, 11427, https://doi.org/10.1038/ncomms11427, 2016. a
Born, A. and Robinson, A.: Modeling the Greenland englacial stratigraphy, The Cryosphere, 15, 4539–4556, https://doi.org/10.5194/tc-15-4539-2021, 2021. a
Bouguila, N., Fan, W., and Amayri, M. (Eds.): Hidden Markov Models and Applications, Unsupervised and Semi-Supervised Learning, Springer International Publishing, Cham, ISBN 9783030991418, 9783030991425, https://doi.org/10.1007/978-3-030-99142-5, 2022. a, b
Bowling, J. S., Livingstone, S. J., Sole, A. J., and Chu, W.: Distribution and dynamics of Greenland subglacial lakes, Nat. Commun., 10, 2810, https://doi.org/10.1038/s41467-019-10821-w, 2019. a
Brandt, O., Björnsson, H., and Gjessing, Y.: Mass-balance rates derived by mapping internal tephra layers in Mýrdalsjökull and Vatnajökull ice caps, Iceland, Ann. Glaciol., 42, 284–290, https://doi.org/10.3189/172756405781813078, 2005a. a
Brandt, O., Björnsson, H., and Gjessing, Y.: Mass-balance rates derived by mapping internal tephra layers in Mýrdalsjökull and Vatnajökull ice caps, Iceland, Ann. Glaciol., 42, 284–290, https://doi.org/10.3189/172756405781813078, 2005b. a
Burges, C. J.: A Tutorial on Support Vector Machines for Pattern Recognition, Data Min. Knowl. Disc., 2, 121–167, https://doi.org/10.1023/A:1009715923555, 1998. a
Cai, Y., Hu, S., Lang, S., Guo, Y., and Liu, J.: End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery, Appl. Sci., 10, 2501, https://doi.org/10.3390/app10072501, 2020. a, b
Canny, J.: A Computational Approach to Edge Detection, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8, 679–698, https://doi.org/10.1109/TPAMI.1986.4767851, 1986. a, b
Carter, C. M., Bentley, M. J., Jamieson, S. S. R., Paxman, G. J. G., Jordan, T. A., Bodart, J. A., Ross, N., and Napoleoni, F.: Extensive palaeo-surfaces beneath the Evans-Rutford region of the West Antarctic Ice Sheet control modern and past ice flow, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2433, 2023. a
Carter, S. P., Fricker, H. A., and Siegfried, M. R.: Antarctic subglacial lakes drain through sediment-floored canals: theory and model testing on real and idealized domains, The Cryosphere, 11, 381–405, https://doi.org/10.5194/tc-11-381-2017, 2017. a
Casella, G. and George, E. I.: Explaining the Gibbs Sampler, Am. Stat., 46, 167–174, https://doi.org/10.1080/00031305.1992.10475878, 1992. a, b
Catania, G. A. and Neumann, T. A.: Persistent englacial drainage features in the Greenland Ice Sheet: Persistent moulins in Greenland, Geophys. Res. Lett., 37, L02501, https://doi.org/10.1029/2009GL041108, 2010. a
Cavitte, M. G. P., Blankenship, D. D., Young, D. A., Schroeder, D. M., Parrenin, F., Lemeur, E., Macgregor, J. A., and Siegert, M. J.: Deep radiostratigraphy of the East Antarctic plateau: connecting the Dome C and Vostok ice core sites, J. Glaciol., 62, 323–334, https://doi.org/10.1017/jog.2016.11, 2016. a, b, c, d
Chan, T. and Vese, L.: Active contours without edges, IEEE T. Image Process., 10, 266–277, https://doi.org/10.1109/83.902291, 2001. a
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., and Zhou, Y.: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation, arXiv:2102.04306, https://doi.org/10.48550/arXiv.2102.04306, 2021. a
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, arXiv, https://doi.org/10.48550/ARXIV.1802.02611, 2018. a
Chen, Y., Mancini, M., Zhu, X., and Akata, Z.: Semi-Supervised and Unsupervised Deep Visual Learning: A Survey, arXiv:2208.11296, https://doi.org/10.48550/arXiv.2208.11296, 2022. a
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, arXiv:1406.1078, https://doi.org/10.48550/arXiv.1406.1078, 2014. a
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., Holm, E., Ong, S. P., and Wolverton, C.: Recent advances and applications of deep learning methods in materials science, npj Comput. Mat., 8, 59, https://doi.org/10.1038/s41524-022-00734-6, 2022. a
Chunming Li, Rui Huang, Zhaohua Ding, Gatenby, J. C., Metaxas, D. N., and Gore, J. C.: A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI, IEEE T. Image Process., 20, 2007–2016, https://doi.org/10.1109/TIP.2011.2146190, 2011. a, b
Clough, J. W.: Radio-Echo Sounding: Reflections From Internal Layers In Ice Sheets, J. Glaciol., 18, 3–14, https://doi.org/10.3189/S002214300002147X, 1977. a
Conway, H., Hall, B. L., Denton, G. H., Gades, A. M., and Waddington, E. D.: Past and Future Grounding-Line Retreat of the West Antarctic Ice Sheet, Science, 286, 280–283, https://doi.org/10.1126/science.286.5438.280, 1999. a
Conway, H., Catania, G., Raymond, C. F., Gades, A. M., Scambos, T. A., and Engelhardt, H.: Switch of flow direction in an Antarctic ice stream, Nature, 419, 465–467, https://doi.org/10.1038/nature01081, 2002. a
Cortes, C. and Vapnik, V.: Support-vector networks, Mach. Learn., 20, 273–297, https://doi.org/10.1007/BF00994018, 1995. a
Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K.: Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering, IEEE T. Image Process., 16, 2080–2095, https://doi.org/10.1109/TIP.2007.901238, 2007. a
Daniels, D. J. (Ed.): Ground Penetrating Radar, Institution of Engineering and Technology, ISBN 9780863413605, 9780863419928, https://doi.org/10.1049/PBRA015E, 2004. a
Daubechies, I.: Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, ISBN 9780898712742, 9781611970104, https://doi.org/10.1137/1.9781611970104, 1992. a
Delf, R., Schroeder, D. M., Curtis, A., Giannopoulos, A., and Bingham, R. G.: A comparison of automated approaches to extracting englacial-layer geometry from radar data across ice sheets, Ann. Glaciol., 61, 234–241, https://doi.org/10.1017/aog.2020.42, 2020. a, b, c, d
Delibasoglu, I. and Cetin, M.: Improved U-Nets with inception blocks for building detection, J. Appl. Remote Sens., 14, 044512, https://doi.org/10.1117/1.JRS.14.044512, 2020. a
Dong, S., Tang, X., Guo, J., Fu, L., Chen, X., and Sun, B.: EisNet: Extracting Bedrock and Internal Layers From Radiostratigraphy of Ice Sheets With Machine Learning, IEEE T. Geosci. Remote, 60, 1–12, https://doi.org/10.1109/TGRS.2021.3136648, 2022. a, b, c, d
Donini, E., Bovolo, F., Gerekos, C., Carrer, L., and Bruzzone, L.: An Approach to Lava Tube Detection in Radar Sounder Data of the Moon, in: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, 8424–8427, IEEE, Valencia, ISBN 9781538671504, https://doi.org/10.1109/IGARSS.2018.8519146, 2018. a, b, c
Donini, E., Thakur, S., Bovolo, F., and Bruzzone, L.: An automatic approach to map refreezing ice in radar sounder data, in: Image and Signal Processing for Remote Sensing XXV, edited by Bruzzone, L., Bovolo, F., and Benediktsson, J. A., p. 45, SPIE, Strasbourg, France, ISBN 9781510630130, 9781510630147, https://doi.org/10.1117/12.2533169, 2019. a, b, c
Donini, E., Bovolo, F., and Bruzzone, L.: An Unsupervised Deep Learning Method for Subsurface Target Detection in Radar Sounder Data, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2955–2958, IEEE, Brussels, Belgium, ISBN 9781665403696, https://doi.org/10.1109/IGARSS47720.2021.9554785, 2021. a, b, c
Donini, E., Bovolo, F., and Bruzzone, L.: A Deep Learning Architecture for Semantic Segmentation of Radar Sounder Data, IEEE T. Geosci. Remote, 60, 1–14, https://doi.org/10.1109/TGRS.2021.3125773, 2022a. a, b
Donini, E., Carrer, L., Gerekos, C., Bruzzone, L., and Bovolo, F.: An Unsupervised Fuzzy System for the Automatic Detection of Candidate Lava Tubes in Radar Sounder Data, IEEE T. Geosci. Remote, 60, 1–19, https://doi.org/10.1109/TGRS.2021.3062753, 2022b. a, b, c
Donini, E., Kasibovic, A., Garcia, M. H., Bruzzone, L., and Bovolo, F.: An Unsupervised Deep Learning Method for the Super-Resolution of Radar Sounder Data, in: IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 1696–1699, IEEE, Kuala Lumpur, Malaysia, ISBN 9781665427920, https://doi.org/10.1109/IGARSS46834.2022.9884343, 2022c. a, b, c, d, e
Dossi, M., Forte, E., and Pipan, M.: Automated reflection picking and polarity assessment through attribute analysis: Theory and application to synthetic and real ground-penetrating radar data, Geophysics, 80, H23–H35, https://doi.org/10.1190/geo2015-0098.1, 2015. a, b, c, d
Dowdeswell, J., Ottesen, D., Evans, J., Cofaigh, C. Ã., and Anderson, J.: Submarine glacial landforms and rates of ice-stream collapse, Geology, 36, 819–822, https://doi.org/10.1130/G24808A.1, 2008. a
Drews, R., Eisen, O., Weikusat, I., Kipfstuhl, S., Lambrecht, A., Steinhage, D., Wilhelms, F., and Miller, H.: Layer disturbances and the radio-echo free zone in ice sheets, The Cryosphere, 3, 195–203, https://doi.org/10.5194/tc-3-195-2009, 2009. a
Duda, R. O. and Hart, P. E.: Use of the Hough transformation to detect lines and curves in pictures, Commun. ACM, 15, 11–15, https://doi.org/10.1145/361237.361242, 1972. a
Eisen, O., Nixdorf, U., Wilhelms, F., and Miller, H.: Age estimates of isochronous reflection horizons by combining ice core, survey, and synthetic radar data, J. Geophys. Res.-Sol. Ea., 109, 2003JB002858, https://doi.org/10.1029/2003JB002858, 2004. a, b
Eisen, O., Wilhelms, F., Steinhage, D., and Schwander, J.: Improved method to determine radio-echo sounding reflector depths from ice-core profiles of permittivity and conductivity, J. Glaciol., 52, 299–310, https://doi.org/10.3189/172756506781828674, 2006. a, b
Eisen, O., Hamann, I., Kipfstuhl, S., Steinhage, D., and Wilhelms, F.: Direct evidence for continuous radar reflector originating from changes in crystal-orientation fabric, The Cryosphere, 1, 1–10, https://doi.org/10.5194/tc-1-1-2007, 2007. a, b
Eisen, O., Winter, A., Steinhage, D., Kleiner, T., and Humbert, A.: Basal roughness of the East Antarctic Ice Sheet in relation to flow speed and basal thermal state, Ann. Glaciol., 61, 162–175, https://doi.org/10.1017/aog.2020.47, 2020. a
Ekisheva, S. and Borodovsky, M.: Probabilistic models for biological sequences: selection and Maximum Likelihood estimation, Int. J. Bioinformat. Res. Appl., 2, 305, https://doi.org/10.1504/IJBRA.2006.010607, 2006. a, b
Elizar, E., Zulkifley, M. A., Muharar, R., Zaman, M. H. M., and Mustaza, S. M.: A Review on Multiscale-Deep-Learning Applications, Sensors, 22, 7384, https://doi.org/10.3390/s22197384, 2022. a, b
Emek Soylu, B., Guzel, M. S., Bostanci, G. E., Ekinci, F., Asuroglu, T., and Acici, K.: Deep-Learning-Based Approaches for Semantic Segmentation of Natural Scene Images: A Review, Electronics, 12, 2730, https://doi.org/10.3390/electronics12122730, 2023. a
Epstein, C. L. (Ed.): Introduction to the Mathematics of Medical Imaging: Second Edition, Society for Industrial and Applied Mathematics, Philadelphia, PA, 2nd Edn., ISBN 9780898716429, 9780898717792, https://doi.org/10.1137/9780898717792, 2007. a
Felzenszwalb, P. F. and Veksler, O.: Tiered scene labeling with dynamic programming, in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3097–3104, https://doi.org/10.1109/CVPR.2010.5540067, 2010. a
Ferro, A. and Bruzzone, L.: A novel approach to the automatic detection of subsurface features in planetary radar sounder signals, 2011 IEEE International Geoscience and Remote Sensing Symposium, 24–29 July 2011, Vancouver, BC, Canada, ISBN: 978-1-4577-1005-6, https://doi.org/10.1109/IGARSS.2011.6049381, 2011. a, b, c
Fettweis, X.: Reconstruction of the 1979–2006 Greenland ice sheet surface mass balance using the regional climate model MAR, The Cryosphere, 1, 21–40, https://doi.org/10.5194/tc-1-21-2007, 2007. a, b
Freeman, G. J., Alan, C. B., and Holt, J. W.: Automated detection of near surface Martian ice layers in orbital radar data, 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI), 23–25 May 2010, Austin, TX, USA, ISBN: 978-1-4244-7802-6, https://doi.org/10.1109/SSIAI.2010.5483905, 2010. a, b, c
Frigui, H., Ho, K. C., and Gader, P.: Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models, EURASIP J. Adv. Sig. Pr., 2005, 419248, https://doi.org/10.1155/ASP.2005.1867, 2005. a
Frémand, A. C., Fretwell, P., Bodart, J. A., Pritchard, H. D., Aitken, A., Bamber, J. L., Bell, R., Bianchi, C., Bingham, R. G., Blankenship, D. D., Casassa, G., Catania, G., Christianson, K., Conway, H., Corr, H. F. J., Cui, X., Damaske, D., Damm, V., Drews, R., Eagles, G., Eisen, O., Eisermann, H., Ferraccioli, F., Field, E., Forsberg, R., Franke, S., Fujita, S., Gim, Y., Goel, V., Gogineni, S. P., Greenbaum, J., Hills, B., Hindmarsh, R. C. A., Hoffman, A. O., Holmlund, P., Holschuh, N., Holt, J. W., Horlings, A. N., Humbert, A., Jacobel, R. W., Jansen, D., Jenkins, A., Jokat, W., Jordan, T., King, E., Kohler, J., Krabill, W., Kusk Gillespie, M., Langley, K., Lee, J., Leitchenkov, G., Leuschen, C., Luyendyk, B., MacGregor, J., MacKie, E., Matsuoka, K., Morlighem, M., Mouginot, J., Nitsche, F. O., Nogi, Y., Nost, O. A., Paden, J., Pattyn, F., Popov, S. V., Rignot, E., Rippin, D. M., Rivera, A., Roberts, J., Ross, N., Ruppel, A., Schroeder, D. M., Siegert, M. J., Smith, A. M., Steinhage, D., Studinger, M., Sun, B., Tabacco, I., Tinto, K., Urbini, S., Vaughan, D., Welch, B. C., Wilson, D. S., Young, D. A., and Zirizzotti, A.: Antarctic Bedmap data: Findable, Accessible, Interoperable, and Reusable (FAIR) sharing of 60 years of ice bed, surface, and thickness data, Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, 2023. a, b
Fujita, S., Maeno, H., Uratsuka, S., Furukawa, T., Mae, S., Fujii, Y., and Watanabe, O.: Nature of radio echo layering in the Antarctic Ice Sheet detected by a two-frequency experiment, J. Geophys. Res.-Sol. Ea., 104, 13013–13024, https://doi.org/10.1029/1999JB900034, 1999. a, b
Gades, A. M., Raymond, C. F., Conway, H., and Jagobel, R. W.: Bed properties of Siple Dome and adjacent ice streams, West Antarctica, inferred from radio-echo sounding measurements, J. Glaciol., 46, 88–94, https://doi.org/10.3189/172756500781833467, 2000. a, b
Gamba, P. and Lossani, S.: Neural detection of pipe signatures in ground penetrating radar images, IEEE T. Geosci. Remote, 38, 790–797, https://doi.org/10.1109/36.842008, 2000. a
Garcia, M. H., Donini, E., and Bovolo, F.: Automatic Segmentation of Ice Shelves with Deep Learning, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4833–4836, IEEE, Brussels, Belgium, ISBN 9781665403696, https://doi.org/10.1109/IGARSS47720.2021.9553610, 2021. a, b, c, d, e, f, g
Ghahramani, Z.: Probabilistic machine learning and artificial intelligence, Nature, 521, 452–459, https://doi.org/10.1038/nature14541, 2015. a
Ghosh, R. and Bovolo, F.: A Hybrid CNN-Transformer Architecture for Semantic Segmentation of Radar Sounder data, in: IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 1320–1323, IEEE, Kuala Lumpur, Malaysia, ISBN 9781665427920, https://doi.org/10.1109/IGARSS46834.2022.9883124, 2022a. a, b, c
Ghosh, R. and Bovolo, F.: TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data, IEEE T. Geosci. Remote, 60, 1–13, https://doi.org/10.1109/TGRS.2022.3180761, 2022b. a, b, c
Ghosh, R. and Bovolo, F.: An Enhanced Unsupervised Feature Learning Framework For Radar Sounder Signal Segmentation, in: IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, 6920–6923, IEEE, Pasadena, CA, USA, ISBN 9798350320107, https://doi.org/10.1109/IGARSS52108.2023.10281869, 2023a. a
Ghosh, R. and Bovolo, F.: An Unsupervised Framework for Radar Sounder Signal Segmentation Based on Enhanced Self-supervised Transformers, preprint, https://doi.org/10.36227/techrxiv.23987301.v1, 2023b. a, b, c
Ghosh, R. and Bovolo, F.: URS: An Unsupervised Radargram Segmentation Network Based on Self-Supervised ViT With Contrastive Feature Learning Framework, IEEE J. Sel. Top. Appl., 17, 15512–15524, https://doi.org/10.1109/JSTARS.2024.3447879, 2024. a, b
Giannopoulos, A.: Modelling ground penetrating radar by GprMax, Constr. Build. Mater., 19, 755–762, https://doi.org/10.1016/j.conbuildmat.2005.06.007, 2005. a
Goldberg, M. L., Schroeder, D. M., Castelletti, D., Mantelli, E., Ross, N., and Siegert, M. J.: Automated detection and characterization of Antarctic basal units using radar sounding data: demonstration in Institute Ice Stream, West Antarctica, Ann. Glaciol., 61, 242–248, https://doi.org/10.1017/aog.2020.27, 2020. a, b, c
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y.: Generative Adversarial Networks, arXiv, https://doi.org/10.48550/ARXIV.1406.2661, 2014. a
GSSI: https://www.geophysical.com/, 2024. a
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, L., Wang, G., Cai, J., and Chen, T.: Recent Advances in Convolutional Neural Networks, arXiv:1512.07108, https://doi.org/10.48550/arXiv.1512.07108, 2017. a
Gudmandsen, P.: Layer echoes in polar ice sheets, J. Glaciol., 15, 95–101, https://doi.org/10.3189/S0022143000034304, 1975. a, b
Guo, P., Su, X., Zhang, H., Wang, M., and Bao, F.: A Multi-Scaled Receptive Field Learning Approach for Medical Image Segmentation, in: ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1414–1418, IEEE, Barcelona, Spain, ISBN 9781509066315, https://doi.org/10.1109/ICASSP40776.2020.9054030, 2020. a
Guðmundsson, S., Björnsson, H., Jóhannesson, T., Aðalgeirsdóttir, G., Pálsson, F., and Sigurðsson, O.: Similarities and differences in the response to climate warming of two ice caps in Iceland, Hydrol. Res., 40, 495–502, https://doi.org/10.2166/nh.2009.210, 2009. a
Hall, M., Bougher, B., and Bianco, E.: Pick This! Social image interpretation, in: SEG Technical Program Expanded Abstracts 2015, Society of Exploration Geophysicists, New Orleans, Louisiana, 1772–1775, https://doi.org/10.1190/segam2015-5920795.1, 2015. a
Hamilton, M., Zhang, Z., Hariharan, B., Snavely, N., and Freeman, W. T.: Unsupervised Semantic Segmentation by Distilling Feature Correspondences, arXiv:2203.08414, https://doi.org/10.48550/arXiv.2203.08414, 2022. a, b
Hamilton, W. L., Ying, R., and Leskovec, J.: Inductive Representation Learning on Large Graphs, arXiv:1706.02216, https://doi.org/10.48550/arXiv.1706.02216, 2018. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a, b, c
He, K., Gkioxari, G., Dollar, P., and Girshick, R.: Mask R-CNN, in: 2017 IEEE International Conference on Computer Vision (ICCV), 2980–2988, IEEE, Venice, ISBN 9781538610329, https://doi.org/10.1109/ICCV.2017.322, 2017. a
Heaven, D.: Why deep-learning AIs are so easy to fool, Nature, 574, 163–166, https://doi.org/10.1038/d41586-019-03013-5, 2019. a
Hendrycks, D., Lee, K., and Mazeika, M.: Using Pre-Training Can Improve Model Robustness and Uncertainty, arXiv:1901.09960, https://doi.org/10.48550/arXiv.1901.09960, 2019. a
Heric, D. and Zazula, D.: Combined edge detection using wavelet transform and signal registration, Image Vision Comput., 25, 652–662, https://doi.org/10.1016/j.imavis.2006.05.008, 2007. a
Herron, M. M. and Langway, C. C.: Firn Densification: An Empirical Model, J. Glaciol., 25, 373–385, https://doi.org/10.3189/S0022143000015239, 1980. a
Hewamalage, H., Bergmeir, C., and Bandara, K.: Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions, Int. J. Forecast., 37, 388–427, https://doi.org/10.1016/j.ijforecast.2020.06.008, 2021. a
Hindmarsh, R. C., Leysinger Vieli, G. J.-M., and Parrenin, F.: A large-scale numerical model for computing isochrone geometry, Ann. Glaciol., 50, 130–140, https://doi.org/10.3189/172756409789097450, 2009. a
Hinton, G. E., Osindero, S., and Teh, Y.-W.: A Fast Learning Algorithm for Deep Belief Nets, Neural Comput., 18, 1527–1554, https://doi.org/10.1162/neco.2006.18.7.1527, 2006. a
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Comput., 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997. a
Holschuh, N., Parizek, B. R., Alley, R. B., and Anandakrishnan, S.: Decoding ice sheet behavior using englacial layer slopes, Geophys. Res. Lett., 44, 5561–5570, https://doi.org/10.1002/2017GL073417, 2017. a, b, c
Huber, E. and Hans, G.: RGPR – An open-source package to process and visualize GPR data, in: 2018 17th International Conference on Ground Penetrating Radar (GPR), 1–4, IEEE, Rapperswil, ISBN 9781538657775, https://doi.org/10.1109/ICGPR.2018.8441658, 2018. a
Hyun, M., Jeong, J., and Kwak, N.: Class-Imbalanced Semi-Supervised Learning, arXiv:2002.06815, https://doi.org/10.48550/arXiv.2002.06815, 2020. a
Ibikunle, O., Paden, J., Rahnemoonfar, M., Crandall, D., and Yari, M.: Snow Radar Layer Tracking Using Iterative Neural Network Approach, in: IGARSS 2020 – 2020 IEEE International Geoscience and Remote Sensing Symposium, 2960–2963, IEEE, Waikoloa, HI, USA, ISBN 9781728163741, https://doi.org/10.1109/IGARSS39084.2020.9323957, 2020. a, b
Ibikunle, O., Talasila, H. M., Varshney, D., Paden, J. D., Li, J., and Rahnemoonfar, M.: Snow Radar Echogram Layer Tracker: Deep Neural Networks for radar data from NASA Operation IceBridge, in: 2023 IEEE Radar Conference (RadarConf23), 1–6, IEEE, San Antonio, TX, USA, ISBN 9781665436694, https://doi.org/10.1109/RadarConf2351548.2023.10149734, 2023. a, b
Ilisei, A.-M., Ferro, A., and Bruzzone, L.: A technique for the automatic estimation of ice thickness and bedrock properties from radar sounder data acquired at Antarctica, 2012 IEEE International Geoscience and Remote Sensing Symposium, 22–27 July 2012, Munich, Germany, https://doi.org/10.1109/IGARSS.2012.6350482, 2012. a, b
Inc., G. T.: Geolitix, https://www.geolitix.com (last access: 17 March 2024), saaS platform for GPR processing, 2025. a
Jansen, D., Llorens, M.-G., Westhoff, J., Steinbach, F., Kipfstuhl, S., Bons, P. D., Griera, A., and Weikusat, I.: Small-scale disturbances in the stratigraphy of the NEEM ice core: observations and numerical model simulations, The Cryosphere, 10, 359–370, https://doi.org/10.5194/tc-10-359-2016, 2016. a
Jansen, D., Franke, S., Bauer, C. C., Binder, T., Dahl-Jensen, D., Eichler, J., Eisen, O., Hu, Y., Kerch, J., Llorens, M.-G., Miller, H., Neckel, N., Paden, J., De Riese, T., Sachau, T., Stoll, N., Weikusat, I., Wilhelms, F., Zhang, Y., and Bons, P. D.: Shear margins in upper half of Northeast Greenland Ice Stream were established two millennia ago, Nat. Commun., 15, 1193, https://doi.org/10.1038/s41467-024-45021-8, 2024. a, b, c
Jenkins, A., Dutrieux, P., Jacobs, S., Steig, E. J., Gudmundsson, G. H., Smith, J., and Heywood, K. J.: Decadal Ocean Forcing and Antarctic Ice Sheet Response: LESSONS FROM THE AMUNDSEN SEA, Oceanography, 29, 106–117, https://www.jstor.org/stable/24862286 (last access: 16 September 2023), 2016. a
Jha, D., Smedsrud, P. H., Riegler, M. A., Johansen, D., Lange, T. D., Halvorsen, P., and H. D. Johansen: ResUNet
Ji, S., Wei, S., and Lu, M.: Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set, IEEE T. Geosci. Remote, 57, 574–586, https://doi.org/10.1109/TGRS.2018.2858817, 2019. a
Jordan, M. I.: Graphical Models, Stat. Sci., 19, 140–155, https://doi.org/10.1214/088342304000000026, 2004. a
Joshi, J. B., Nandakumar, K., Patwardhan, A. W., Nayak, A. K., Pareek, V., Gumulya, M., Wu, C., Minocha, N., Pal, E., Kumar, M., Bhusare, V., Tiwari, S., Lote, D., Mali, C., Kulkarni, A., and Tamhankar, S.: Computational fluid dynamics, in: Advances of Computational Fluid Dynamics in Nuclear Reactor Design and Safety Assessment, 21–238, Elsevier, ISBN 9780081023372, https://doi.org/10.1016/B978-0-08-102337-2.00002-X, 2019. a
Jouvet, G., Röllin, S., Sahli, H., Corcho, J., Gnägi, L., Compagno, L., Sidler, D., Schwikowski, M., Bauder, A., and Funk, M.: Mapping the age of ice of Gauligletscher combining surface radionuclide contamination and ice flow modeling, The Cryosphere, 14, 4233–4251, https://doi.org/10.5194/tc-14-4233-2020, 2020. a
Kamangir, H., Rahnemoonfar, M., Dobbs, D., Paden, J., and Fox, G.: Deep Hybrid Wavelet Network for Ice Boundary Detection in Radra Imagery, in: IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, 3449–3452, IEEE, Valencia, ISBN 9781538671504, https://doi.org/10.1109/IGARSS.2018.8518617, 2018. a, b, c
Karhunen, K.: Zur Spektraltheorie stochastischer Prozesse, Ann. Acad. Sci. Finn. Ser. A, 1, 34 pp., 1946. a
Karimi, H., Derr, T., and Tang, J.: Characterizing the Decision Boundary of Deep Neural Networks, arXiv:1912.11460, https://doi.org/10.48550/arXiv.1912.11460, 2020. a
Karlsson, N. B., Rippin, D. M., Bingham, R. G., and Vaughan, D. G.: A “continuity-index” for assessing ice-sheet dynamics from radar-sounded internal layers, Earth Planet. Sc. Lett., 335/336, 88–94, https://doi.org/10.1016/j.epsl.2012.04.034, 2012. a
Karlsson, N. B., Dahl-Jensen, D., Prasad Gogineni, S., and Paden, J. D.: Tracing the depth of the Holocene ice in North Greenland from radio-echo sounding data, Ann. Glaciol., 54, 44–50, https://doi.org/10.3189/2013AoG64A057, 2013. a, b
Kaspersen, J. H., Langø, T., and Lindseth, F.: Wavelet-based edge detection in ultrasound images, Ultrasound Med. Biol., 27, 89–99, https://doi.org/10.1016/S0301-5629(00)00321-5, 2001. a, b, c
Kass, M., Witkin, A., and Terzopoulos, D.: Snakes: Active contour models, Int. J. Comput. Vision, 1, 321–331, https://doi.org/10.1007/BF00133570, 1988. a, b
Keeler, D. G., Rupper, S. B., Forster, R., and Miege, C.: A Probabilistic Automated Isochrone Picking Routine to Derive Annual Surface Mass Balance From Radar Echograms, IEEE T. Geosci. Remote, 58, 8598–8611, https://doi.org/10.1109/TGRS.2020.2989102, 2020. a, b
Keisling, B. A., Christianson, K., Alley, R. B., Peters, L. E., Christian, J. E., Anandakrishnan, S., Riverman, K. L., Muto, A., and Jacobel, R. W.: Basal conditions and ice dynamics inferred from radar-derived internal stratigraphy of the northeast Greenland ice stream, Ann. Glaciol., 55, 127–137, https://doi.org/10.3189/2014AoG67A090, 2014. a
Khami, N., Imtiaz, O., Abidi, A., Aedavelli, A., Goff, A., Pisel, J. R., and Pyrcz, M. J.: Automatic Feature Highlighting in Noisy RES Data With CycleGAN, arXiv:2108.11283, https://doi.org/10.48550/arXiv.2108.11283, 2021. a, b
Khodadadzadeh, M., Ilisei, A.-M., and Bruzzone, L.: A technique based on adaptive windows for the classification of radar sounder data, in: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),3739–3742, IEEE, Fort Worth, TX, ISBN 9781509049516, https://doi.org/10.1109/IGARSS.2017.8127812, 2017. a, b
Kipf, T. N. and Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks, arXiv, https://doi.org/10.48550/ARXIV.1609.02907, 2016. a
Koenig, L. S., Ivanoff, A., Alexander, P. M., MacGregor, J. A., Fettweis, X., Panzer, B., Paden, J. D., Forster, R. R., Das, I., McConnell, J. R., Tedesco, M., Leuschen, C., and Gogineni, P.: Annual Greenland accumulation rates (2009–2012) from airborne snow radar, The Cryosphere, 10, 1739–1752, https://doi.org/10.5194/tc-10-1739-2016, 2016. a, b, c, d, e, f, g, h, i
Kowalewski, S., Helm, V., Morris, E. M., and Eisen, O.: The regional-scale surface mass balance of Pine Island Glacier, West Antarctica, over the period 2005–2014, derived from airborne radar soundings and neutron probe measurements, The Cryosphere, 15, 1285–1305, https://doi.org/10.5194/tc-15-1285-2021, 2021. a
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: ImageNet classification with deep convolutional neural networks, Commun. ACM, 60, 84–90, https://doi.org/10.1145/3065386, 2017a. a, b
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: ImageNet classification with deep convolutional neural networks, Commun. ACM, 60, 84–90, https://doi.org/10.1145/3065386, 2017b. a, b
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D.: Backpropagation Applied to Handwritten Zip Code Recognition, Neural Comput., 1, 541–551, https://doi.org/10.1162/neco.1989.1.4.541, 1989. a
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P.: Gradient-based learning applied to document recognition, Proceed. IEEE, 86, 2278–2324, https://doi.org/10.1109/5.726791, 1998. a, b, c, d
LeCun, Y., Kavukcuoglu, K., and Farabet, C.: Convolutional networks and applications in vision, in: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 253–256, https://doi.org/10.1109/ISCAS.2010.5537907, 2010. a
Lee, S., Mitchell, J., Crandall, D. J., and Fox, G. C.: Estimating bedrock and surface layer boundaries and confidence intervals in ice sheet radar imagery using MCMC, in: 2014 IEEE International Conference on Image Processing (ICIP), 111–115, IEEE, Paris, France, ISBN 9781479957514, https://doi.org/10.1109/ICIP.2014.7025021, 2014. a, b, c, d, e
Lenaerts, J. T. M., Smeets, C. J. P. P., Nishimura, K., Eijkelboom, M., Boot, W., Van Den Broeke, M. R., and Van De Berg, W. J.: Drifting snow measurements on the Greenland Ice Sheet and their application for model evaluation, The Cryosphere, 8, 801–814, https://doi.org/10.5194/tc-8-801-2014, 2014. a
Leysinger Vieli, G. J.-M. C., Hindmarsh, R. C. A., Siegert, M. J., and Bo, S.: Time-dependence of the spatial pattern of accumulation rate in East Antarctica deduced from isochronic radar layers using a 3-D numerical ice flow model: EAST ANTARCTICA ISOCHRONIC LAYER MODELING, J. Geophys. Res.-Earth, 116, F02018, https://doi.org/10.1029/2010JF001785, 2011. a, b
Leysinger Vieli, G.-M., Hindmarsh, R., and Siegert, M.: Three-dimensional flow influences on radar layer stratigraphy, Ann. Glaciol., 46, 22–28, https://doi.org/10.3189/172756407782871729, 2007. a
Li, Y., Ding, L., and Gao, X.: On the Decision Boundary of Deep Neural Networks, arXiv:1808.05385, https://doi.org/10.48550/arXiv.1808.05385, 2019. a
Li, Y., Zhao, Z., Luo, Y., and Qiu, Z.: Real-Time Pattern-Recognition of GPR Images with YOLO v3 Implemented by Tensorflow, Sensors, 20, 6476, https://doi.org/10.3390/s20226476, 2020. a
Li, Y., Liu, C., Yue, G., Gao, Q., and Du, Y.: Deep learning-based pavement subsurface distress detection via ground penetrating radar data, Automat. Constr., 142, 104516, https://doi.org/10.1016/j.autcon.2022.104516, 2022. a
Lilien, D. A., Hills, B. H., Driscol, J., Jacobel, R., and Christianson, K.: ImpDAR: an open-source impulse radar processor, Ann. Glaciol., 61, 114–123, https://doi.org/10.1017/aog.2020.44, 2020. a
Lilien, D. A., Steinhage, D., Taylor, D., Parrenin, F., Ritz, C., Mulvaney, R., Martín, C., Yan, J.-B., O'Neill, C., Frezzotti, M., Miller, H., Gogineni, P., Dahl-Jensen, D., and Eisen, O.: Brief communication: New radar constraints support presence of ice older than 1.5 Myr at Little Dome C, The Cryosphere, 15, 1881–1888, https://doi.org/10.5194/tc-15-1881-2021, 2021. a
Lin, J.: Divergence measures based on the Shannon entropy, IEEE T. Inform. Theory, 37, 145–151, https://doi.org/10.1109/18.61115, 1991. a, b
Lin, P., Zheng, C., Yang, Y., and Gu, J.: Medical Image Segmentation by Level Set Method Incorporating Region and Boundary Statistical Information, in: Progress in Pattern Recognition, Image Analysis and Applications, edited by Sanfeliu, A., MartÃnez Trinidad, J. F., and Carrasco Ochoa, J. A., 654–660, Springer, Berlin, Heidelberg, ISBN 9783540304630, https://doi.org/10.1007/978-3-540-30463-0_82, 2004. a, b, c
Lines, A., Elliott, J., Lewis, G., and Ray, L.: Hybrid GPR Layer Picking Method Using Average Square Difference Function, in: IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, 3606–3609, IEEE, Yokohama, Japan, ISBN 9781538691540, https://doi.org/10.1109/IGARSS.2019.8898780, 2019. a, b
Liu, L., Martín-Barragán, B., and Prieto, F. J.: A projection multi-objective SVM method for multi-class classification, Comput. Ind. Eng., 158, 107425, https://doi.org/10.1016/j.cie.2021.107425, 2021. a
Liu, Z. and Rahnemoonfar, M.: Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network, arXiv:2406.15299, https://doi.org/10.48550/arXiv.2406.15299, 2024. a, b
Liu-Schiaffini, M., Ng, G., Grima, C., and Young, D.: Ice Thickness From Deep Learning and Conditional Random Fields: Application to Ice-Penetrating Radar Data With Radiometric Validation, IEEE T. Geosci. Remote, 60, 1–14, https://doi.org/10.1109/TGRS.2022.3214147, 2022. a, b
Long, J., Shelhamer, E., and Darrell, T.: Fully Convolutional Networks for Semantic Segmentation, arXiv, https://doi.org/10.48550/ARXIV.1411.4038, 2014. a
Loève, M.: Probability Theory I, Vol. 45, in: Graduate Texts in Mathematics, Springer New York, New York, NY, ISBN 9781468494662, 9781468494648, https://doi.org/10.1007/978-1-4684-9464-8, 1977. a
Lythe, M. B. and Vaughan, D. G.: BEDMAP: A new ice thickness and subglacial topographic model of Antarctica, J. Geophys. Res.-Sol. Ea., 106, 11335–11351, https://doi.org/10.1029/2000JB900449, 2001. a
MacGregor, J. A., Matsuoka, K., Waddington, E. D., Winebrenner, D. P., and Pattyn, F.: Spatial variation of englacial radar attenuation: Modeling approach and application to the Vostok flowline, J. Geophys. Res.-Earth, 117, 2011JF002327, https://doi.org/10.1029/2011JF002327, 2012. a
MacGregor, J. A., Fahnestock, M. A., Catania, G. A., Paden, J. D., Prasad Gogineni, S., Young, S. K., Rybarski, S. C., Mabrey, A. N., Wagman, B. M., and Morlighem, M.: Radiostratigraphy and age structure of the Greenland Ice Sheet, J. Geophys. Res.-Earth, 120, 212–241, https://doi.org/10.1002/2014JF003215, 2015. a, b, c, d, e, f, g, h, i
Malladi, R., Sethian, J., and Vemuri, B.: Shape modeling with front propagation: a level set approach, IEEE T. Pattern Anal., 17, 158–175, https://doi.org/10.1109/34.368173, 1995. a
Mallat, S. and Hwang, W.: Singularity detection and processing with wavelets, IEEE T. Inform. Theory, 38, 617–643, https://doi.org/10.1109/18.119727, 1992. a, b, c
Martin, D., Fowlkes, C., Tal, D., and Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics, in: Proc. 8th Int'l Conf. Computer Vision, Vol. 2, 416–423, IEEE Comput. Soc., 416–423, https://doi.org/10.1109/ICCV.2001.937655, 2001. a
MathWorks: Optimization Toolbox version: 9.4 (R2022b), https://www.mathworks.com, 2022. a
Medley, B., Joughin, I., Smith, B. E., Das, S. B., Steig, E. J., Conway, H., Gogineni, S., Lewis, C., Criscitiello, A. S., McConnell, J. R., Van Den Broeke, M. R., Lenaerts, J. T. M., Bromwich, D. H., Nicolas, J. P., and Leuschen, C.: Constraining the recent mass balance of Pine Island and Thwaites glaciers, West Antarctica, with airborne observations of snow accumulation, The Cryosphere, 8, 1375–1392, https://doi.org/10.5194/tc-8-1375-2014, 2014. a
Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., and Terzopoulos, D.: Image Segmentation Using Deep Learning: A Survey, arXiv:2001.05566, https://doi.org/10.48550/arXiv.2001.05566, 2020. a, b
Miners, W. D.: Modeling the radio echo reflections inside the ice sheet at Summit, Greenland, J. Geophys. Res., 107, 2172, https://doi.org/10.1029/2001JB000535, 2002. a
Mirowski, P. and Vlachos, A.: Dependency Recurrent Neural Language Models for Sentence Completion, arXiv:1507.01193, https://doi.org/10.48550/arXiv.1507.01193, 2015. a
Mitchell, J. E., Crandall, D. J., Fox, G. C., and Paden, J. D.: A semi-automatic approach for estimating near surface internal layers from snow radar imagery, 2013 IEEE International Geoscience and Remote Sensing Symposium – IGARSS, 21–26 July 2013, Melbourne, VIC, Australia, ISBN: 978-1-4799-1114-1, https://doi.org/10.1109/IGARSS.2013.6723737, 2013a. a, b, c
Mitchell, J. E., Crandall, D. J., Fox, G. C., Rahnemoonfar, M., and Paden, J. D.: A semi-automatic approach for estimating bedrock and surface layers from multichannel coherent radar depth sounder imagery, p. 88921E, Dresden, Germany, https://doi.org/10.1117/12.2028992, 2013b. a, b
Mohajerani, Y., Wood, M., Velicogna, I., and Rignot, E.: Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study, Remote Sens., 11, 74, https://doi.org/10.3390/rs11010074, 2019. a
Mohajerani, Y., Jeong, S., Scheuchl, B., Velicogna, I., Rignot, E., and Milillo, P.: Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning, Sci. Rep., 11, 4992, https://doi.org/10.1038/s41598-021-84309-3, 2021. a, b, c, d
Moore, J. and Paren, J.: A new technique for dielectric logging of antarctic ice cores, J. Phys. Coll., 48, C1-155–C1-160, https://doi.org/10.1051/jphyscol:1987123, 1987. a
Moqadam, H. and Eisen, O.: IRHMapNet Radargram and Mask Patches Dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.13985741, 2024. a, b
Moqadam, H., Steinhage, D., Eisen, O., and Wilhelm, A.: Tracing Extended Internal Stratigraphy in Ice Sheets using Computer Vision Approaches, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9532, https://doi.org/10.5194/egusphere-egu23-9532, 2023.
Moqadam, H., Steinhage, D., Wilhelm, A., and Eisen, O.: Going deeper with deep learning: automatically tracing internal reflection horizons in ice sheets, Methodology and Benchmark Data Set, https://doi.org/10.1029/2024JH000493, 2025. a, b, c
NEEM community members: Eemian interglacial reconstructed from a Greenland folded ice core, Nature, 493, 489–494, https://doi.org/10.1038/nature11789, 2013. a
Nereson, N. A. and Raymond, C. F.: The elevation history of ice streams and the spatial accumulation pattern along the Siple Coast of West Antarctica inferred from ground-based radar data from three inter-ice-stream ridges, J. Glaciol., 47, 303–313, https://doi.org/10.3189/172756501781832197, 2001. a
Nereson, N. A., Raymond, C. F., Jacobel, R., and Waddington, E. D.: The accumulation pattern across Siple Dome, West Antarctica, inferred from radar-detected internal layers, J. Glaciol., 46, 75–87, https://doi.org/10.3189/172756500781833449, 2000. a, b
Nye, J. F.: Correction Factor for Accumulation Measured by the Thickness of the Annual Layers in an Ice Sheet, J. Glaciol., 4, 785–788, https://doi.org/10.3189/S0022143000028367, 1963. a
Oliver, C. and Quegan, S.: Understanding Synthetic Aperture Radar Images, SciTech publishing Inc., ISBN 1891121316, 9781891121319, 2004. a
Osher, S. and Sethian, J. A.: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys., 79, 12–49, https://doi.org/10.1016/0021-9991(88)90002-2, 1988. a
Pal, N. R. and Pal, S. K.: A review on image segmentation techniques, Pattern Recognition, 26, 1277–1294, https://doi.org/10.1016/0031-3203(93)90135-J, 1993. a
Panton, C.: Automated mapping of local layer slope and tracing of internal layers in radio echograms, Ann. Glaciol., 55, 71–77, https://doi.org/10.3189/2014AoG67A048, 2014. a, b, c, d
Paren, J. G. and Robin, G. D. Q.: Internal Reflections in Polar Ice Sheets, J. Glaciol., 14, 251–259, https://doi.org/10.3189/S0022143000021730, 1975. a
Parrenin, F.: New modeling of the Vostok ice flow line and implication for the glaciological chronology of the Vostok ice core, J. Geophys. Res., 109, D20102, https://doi.org/10.1029/2004JD004561, 2004. a
Parrenin, F., Cavitte, M. G. P., Blankenship, D. D., Chappellaz, J., Fischer, H., Gagliardini, O., Masson-Delmotte, V., Passalacqua, O., Ritz, C., Roberts, J., Siegert, M. J., and Young, D. A.: Is there 1.5-million-year-old ice near Dome C, Antarctica?, The Cryosphere, 11, 2427–2437, https://doi.org/10.5194/tc-11-2427-2017, 2017. a
Pasolli, E., Melgani, F., and Donelli, M.: Automatic Analysis of GPR Images: A Pattern-Recognition Approach, IEEE T. Geosci. Remote, 47, 2206–2217, https://doi.org/10.1109/TGRS.2009.2012701, 2009. a
Peng, C., Zheng, L., Liang, Q., Li, T., Wu, J., and Cheng, X.: ST-SOLOv2: Tracing Depth Hoar Layers in Antarctic Ice Sheet From Airborne Radar Echograms With Deep Learning, IEEE T. Geosci. Remote, 62, 1–14, https://doi.org/10.1109/TGRS.2024.3480698, 2024. a, b
Peng, X., Min, D., and Chan, A.: A comparison on texture classification algorithms for remote sensing data, in: IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004, IGARSS'04, Proceedings. 2004, Vol. 2, 1057–1060, IEEE, Anchorage, AK, USA, ISBN 9780780387423, https://doi.org/10.1109/IGARSS.2004.1368593, 2004. a
Pitcher, L. H., Smith, L. C., Gleason, C. J., Miège, C., Ryan, J. C., Hagedorn, B., Van As, D., Chu, W., and Forster, R. R.: Direct Observation of Winter Meltwater Drainage From the Greenland Ice Sheet, Geophys. Res. Lett., 47, e2019GL086521, https://doi.org/10.1029/2019GL086521, 2020. a
Plewes, L. and Hubbard, B.: A review of the use of radio-echo sounding in glaciology, Prog. Phys. Geogr., 25, 203–236, https://doi.org/10.1191/030913301668581943, 2001a. a
Plewes, L. A. and Hubbard, B.: A review of the use of radio-echo sounding in glaciology, Prog. Phys. Geogr., 25, 203–236, https://doi.org/10.1177/030913330102500203, 2001b. a, b
Qin, T., Liu, T.-Y., Zhang, X.-D., Wang, D.-S., and Li, H.: Global ranking using Continuous Conditional Random Fields, in: Proceedings of the 21st International Conference on Neural Information Processing Systems, NIPS'08, 1281–1288, Curran Associates Inc., Red Hook, NY, USA, ISBN 9781605609492, https://doi.org/10.5555/2981780.2981940, 2008. a
Radon, J.: On the determination of functions from their integral values along certain manifolds, IEEE T. Med. Imaging, 5, 170–176, https://doi.org/10.1109/TMI.1986.4307775, 1986. a
Rahnemoonfar, M., Habashi, A. A., Paden, J., and Fox, G. C.: Automatic Ice thickness estimation in radar imagery based on charged particles concept, in: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3743–3746, IEEE, Fort Worth, TX, ISBN 9781509049516, https://doi.org/10.1109/IGARSS.2017.8127813, 2017b. a, b
Rahnemoonfar, M., Johnson, J., and Paden, J.: AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network, Sensors, 19, 5479, https://doi.org/10.3390/s19245479, 2019. a, b, c
Rahnemoonfar, M., Yari, M., Paden, J., Koenig, L., and Ibikunle, O.: Deep multi-scale learning for automatic tracking of internal layers of ice in radar data, J. Glaciol., 67, 39–48, https://doi.org/10.1017/jog.2020.80, 2021. a, b, c, d
Raney, R.: The delay/Doppler radar altimeter, IEEE T. Geosc. Remote, 36, 1578–1588, https://doi.org/10.1109/36.718861, 1998. a
Rawat, W. and Wang, Z.: Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review, Neural Comput., 29, 2352–2449, https://doi.org/10.1162/neco_a_00990, 2017. a
Reichman, D., Collins, L. M., and Malof, J. M.: Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar, in: 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), 1–5, IEEE, Edinburgh, United Kingdom, ISBN 9781509054848, https://doi.org/10.1109/IWAGPR.2017.7996100, 2017. a, b
Reid, M. A., Gifford, C. M., Jefferson, M., Akers, E. L., Finyom, G., and Agah, A.: Automated Polar ice thickness estimation from radar imagery, 2010 IEEE International Geoscience and Remote Sensing Symposium, 25–30 July 2010, Honolulu, HI, USA, ISBN: 978-1-4244-9566-5, https://doi.org/10.1109/IGARSS.2010.5651707, 2010. a, b, c
Rippin, D. M., Siegert, M. J., Bamber, J. L., Vaughan, D. G., and Corr, H. F. J.: Switch-off of a major enhanced ice flow unit in East Antarctica, Geophys. Res. Lett., 33, L15501, https://doi.org/10.1029/2006GL026648, 2006. a
Roberts, L. G.: Machine perception of three-dimensional solids, Ph.D. thesis, Massachusetts Institute of Technology, Dept. of Electrical Engineering, MIT Press, Cambridge, MA, 1963. a
Robin, G. D. Q.: Radio-Echo Sounding: Glaciological Interpretations and Applications, J. Glaciol., 15, 49–64, https://doi.org/10.3189/S0022143000034262, 1975. a
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge, Int. J. Comput. Vision, 115, 211–252, https://doi.org/10.1007/s11263-015-0816-y, 2015. a
Salvador, A., Bellver, M., Campos, V., Baradad, M., Marques, F., Torres, J., and Giro-i Nieto, X.: Recurrent Neural Networks for Semantic Instance Segmentation, arXiv:1712.00617, https://doi.org/10.48550/arXiv.1712.00617, 2019. a
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C.: MobileNetV2: Inverted Residuals and Linear Bottlenecks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4510–4520, IEEE, Salt Lake City, UT, ISBN 9781538664209, https://doi.org/10.1109/CVPR.2018.00474, 2018. a
Sandmeier, K.: Program for Processing of Seismic, Acoustic or Electromagnetic Reflection, Refraction and Transmission Data, 2016. a
Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation, J. Electron. Imaging, 13, 146, https://doi.org/10.1117/1.1631315, 2004. a
Sarker, I. H.: Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions, SN Computer Science, 2, 420, https://doi.org/10.1007/s42979-021-00815-1, 2021. a
Schlegel, R., Kulessa, B., Murray, T., and Eisen, O.: Towards a common terminology in radioglaciology, Annals of Glaciology, 63, 8–12, https://doi.org/10.1017/aog.2023.2, 2022. a, b
Schroeder, D. M., Blankenship, D. D., and Young, D. A.: Evidence for a water system transition beneath Thwaites Glacier, West Antarctica, P. Natl. Acad. Sci. USA, 110, 12225–12228, https://doi.org/10.1073/pnas.1302828110, 2013. a
Schroeder, D. M., Bingham, R. G., Blankenship, D. D., Christianson, K., Eisen, O., Flowers, G. E., Karlsson, N. B., Koutnik, M. R., Paden, J. D., and Siegert, M. J.: Five decades of radioglaciology, Ann. Glaciol., 61, 1–13, https://doi.org/10.1017/aog.2020.11, 2020. a, b
Schroeder, D. M., Broome, A. L., Conger, A., Lynch, A., Mackie, E. J., and Tarzona, A.: Radiometric analysis of digitized Z-scope records in archival radar sounding film, J. Glaciol., 68, 733–740, https://doi.org/10.1017/jog.2021.130, 2022. a
Sensors Software Inc: https://www.sensoft.ca/, 2024. a
Siddique, N., Paheding, S., Elkin, C. P., and Devabhaktuni, V.: U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications, IEEE Access, 9, 82031–82057, https://doi.org/10.1109/ACCESS.2021.3086020, 2021. a, b
Siegert, M. J.: On the origin, nature and uses of Antarctic ice-sheet radio-echo layering, Prog. Phys. Geogr., 23, 159–179, https://doi.org/10.1177/030913339902300201, 1999. a, b
Siegert, M. J., Hodgkinst, R., and Dowdeswell, J. A.: Internal radio-echo layering at Vostok station, Antarctica, as an independent stratigraphie control on the ice-core record, Ann. Glaciol., 27, 360–364, https://doi.org/10.3189/1998AoG27-1-360-364, 1998. a
Siegert, M. J., Welch, B., Morse, D., Vieli, A., Blankenship, D. D., Joughin, I., King, E. C., Vieli, G. J.-M. C. L., Payne, A. J., and Jacobel, R.: Ice Flow Direction Change in Interior West Antarctica, Science, 305, 1948–1951, https://doi.org/10.1126/science.1101072, 2004. a, b, c, d
Simonyan, K. and Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv, https://doi.org/10.48550/ARXIV.1409.1556, 2014. a, b, c, d
Smock, B. and Wilson, J.: Efficient multiple layer boundary detection in ground-penetrating radar data using an extended Viterbi algorithm, p. 83571X, Baltimore, Maryland, USA, https://doi.org/10.1117/12.921236, 2012. a, b
Smock, B., Gader, P., and Wilson, J.: DynaMax+ ground-tracking algorithm, in: Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, Vol. 8017, 537–546, SPIE, https://doi.org/10.1117/12.884229, 2011. a
Sobel, I. and Feldman, G.: An Isotropic 3×3 Image Gradient Operator, https://doi.org/10.13140/RG.2.1.1912.4965, 2015. a, b
Sonka, M., Hlavac, V., and Boyle, R.: Image processing, analysis, and machine vision, Cengage Learning, Stamford, CT, USA, 4th Edn., ISBN 9781133593607, 2015. a
Soria, X., Sappa, A., Humanante, P., and Akbarinia, A.: Dense Extreme Inception Network for Edge Detection, arXiv, https://doi.org/10.48550/ARXIV.2112.02250, 2021. a
Stauffer, C. and Grimson, W.: Adaptive background mixture models for real-time tracking, in: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 246–252, IEEE Comput. Soc, Fort Collins, CO, USA, ISBN 9780769501499, https://doi.org/10.1109/CVPR.1999.784637, 1999. a
Steger, C.: An unbiased detector of curvilinear structures, IEEE T. Pattern Anal., 20, 113–125, https://doi.org/10.1109/34.659930, 1998. a, b
Steinhage, D., Nixdorf, U., Meyer, U., and Miller, H.: Subglacial topography and internal structure of central and western Dronning Maud Land, Antarctica, determined from airborne radio echo sounding, J. Appl. Geophys., 47, 183–189, https://doi.org/10.1016/S0926-9851(01)00063-5, 2001. a
Steinhage, D., Kipfstuhl, S., Nixdorf, U., and Miller, H.: Internal structure of the ice sheet between Kohnen station and Dome Fuji, Antarctica, revealed by airborne radio-echo sounding, Ann. Glaciol., 54, 163–167, https://doi.org/10.3189/2013AoG64A113, 2013. a, b, c
Su, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., Pietikäinen, M., and Liu, L.: Pixel Difference Networks for Efficient Edge Detection, arXiv:2108.07009, https://doi.org/10.48550/arXiv.2108.07009, 2021. a
Suh, S., Lukowicz, P., and Lee, Y. O.: Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks, Knowl.-Based Syst., 237, 107866, https://doi.org/10.1016/j.knosys.2021.107866, 2022. a
Sukhobok, Y. A., Verkhovtsev, L. R., and Ponomarchuk, Y. V.: Automatic Evaluation of Pavement Thickness in GPR Data with Artificial Neural Networks, IOP C. Ser. Earth Environ., 272, 022202, https://doi.org/10.1088/1755-1315/272/2/022202, 2019. a
Sutter, J., Fischer, H., and Eisen, O.: Investigating the internal structure of the Antarctic ice sheet: the utility of isochrones for spatiotemporal ice-sheet model calibration, The Cryosphere, 15, 3839–3860, https://doi.org/10.5194/tc-15-3839-2021, 2021. a, b, c, d
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A.: Going Deeper with Convolutions, arXiv, https://doi.org/10.48550/ARXIV.1409.4842, 2014. a, b
Szymczyk, P. and Szymczyk, M.: Classification of geological structure using ground penetrating radar and Laplace transform artificial neural networks, Neurocomputing, 148, 354–362, https://doi.org/10.1016/j.neucom.2014.06.025, 2015. a
Taner, M. T., Koehler, F., and Sheriff, R. E.: Complex seismic trace analysis, Geophysics, 44, 1041–1063, https://doi.org/10.1190/1.1440994, 1979. a
Tang, X., Dong, S., Luo, K., Guo, J., Li, L., and Sun, B.: Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets, Remote Sens., 14, 399, https://doi.org/10.3390/rs14020399, 2022. a, b
Tang, Y., You, S., Xu, C., Shi, B., and Xu, C.: Bringing Giant Neural Networks Down to Earth with Unlabeled Data, arXiv:1907.06065, https://doi.org/10.48550/arXiv.1907.06065, 2019. a, b
Todkar, S. S., Le Bastard, C., Ihamouten, A., Baltazart, V., Derobert, X., Fauchard, C., Guilbert, D., and Bosc, F.: Detection of debondings with Ground Penetrating Radar using a machine learning method, in: 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), 1–6, IEEE, Edinburgh, United Kingdom, ISBN 9781509054848, https://doi.org/10.1109/IWAGPR.2017.7996056, 2017. a
Tomasini, U. and Wyart, M.: How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model, arXiv:2404.10727, https://doi.org/10.48550/arXiv.2404.10727, 2024. a
Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M.: Learning Spatiotemporal Features with 3D Convolutional Networks, arXiv, https://doi.org/10.48550/ARXIV.1412.0767, 2014. a
Vapnik, V. N., Golowich, S. E., and Smola, A.: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing, in: Neural Information Processing Systems, https://api.semanticscholar.org/CorpusID:19196574 (last access: 2 July 2023), 1996. a
Varshney, D., Rahnemoonfar, M., Yari, M., and Paden, J.: Regression Networks for Calculating Englacial Layer Thickness, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2393–2396, IEEE, Brussels, Belgium, ISBN 9781665403696, https://doi.org/10.1109/IGARSS47720.2021.9553596, 2021a. a, b, c
Varshney, D., Ibikunle, O., Paden, J., and Rahnemoonfar, M.: Learning Snow Layer Thickness Through Physics Defined Labels, in: IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 1233–1236, IEEE, Kuala Lumpur, Malaysia, ISBN 9781665427920, https://doi.org/10.1109/IGARSS46834.2022.9884370, 2022. a, b, c
Varshney, D., Yari, M., Ibikunle, O., Li, J., Paden, J., and Rahnemoonfar, M.: Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Firn Layers in Radar Echograms, arXiv:2310.19574, https://doi.org/10.48550/arXiv.2310.19574, 2023. a, b
Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE T. Inform. Theory, 13, 260–269, https://doi.org/10.1109/TIT.1967.1054010, 1967. a
Waddington, E. D., Neumann, T. A., Koutnik, M. R., Marshall, H.-P., and Morse, D. L.: Inference of accumulation-rate patterns from deep layers in glaciers and ice sheets, J. Glaciol., 53, 694–712, https://doi.org/10.3189/002214307784409351, 2007. a, b, c
Wang, X., Zhang, R., Kong, T., Li, L., and Shen, C.: SOLOv2: Dynamic and Fast Instance Segmentation, arXiv:2003.10152, https://doi.org/10.48550/arXiv.2003.10152, 2020a. a, b
Wang, Y., Xu, M., Paden, J., Koenig, L., Fox, G., and Crandall, D.: Deep Tiered Image Segmentation For Detecting Internal Ice Layers in Radar Imagery, arXiv, https://doi.org/10.48550/ARXIV.2010.03712, 2020b. a, b, c, d
Weiss, K., Khoshgoftaar, T. M., and Wang, D.: A survey of transfer learning, J. Big Data, 3, 9, https://doi.org/10.1186/s40537-016-0043-6, 2016. a
Winter, A., Steinhage, D., Arnold, E. J., Blankenship, D. D., Cavitte, M. G. P., Corr, H. F. J., Paden, J. D., Urbini, S., Young, D. A., and Eisen, O.: Comparison of measurements from different radio-echo sounding systems and synchronization with the ice core at Dome C, Antarctica, The Cryosphere, 11, 653–668, https://doi.org/10.5194/tc-11-653-2017, 2017. a
Winter, A., Steinhage, D., Creyts, T. T., Kleiner, T., and Eisen, O.: Age stratigraphy in the East Antarctic Ice Sheet inferred from radio-echo sounding horizons, Earth Syst. Sci. Data, 11, 1069–1081, https://doi.org/10.5194/essd-11-1069-2019, 2019. a, b, c
Winter, K., Woodward, J., Ross, N., Dunning, S. A., Bingham, R. G., Corr, H. F. J., and Siegert, M. J.: Airborne radar evidence for tributary flow switching in Institute Ice Stream, West Antarctica: Implications for ice sheet configuration and dynamics, J. Geophys. Res.-Earth, 120, 1611–1625, https://doi.org/10.1002/2015JF003518, 2015. a
Woodward, J. and Burke, M. J.: Applications of Ground-Penetrating Radar to Glacial and Frozen Materials, J. Environ. Eng. Geophys., 12, 69–85, https://doi.org/10.2113/JEEG12.1.69, 2007. a
Xia, X. and Kulis, B.: W-Net: A Deep Model for Fully Unsupervised Image Segmentation, arXiv, https://doi.org/10.48550/ARXIV.1711.08506, 2017. a, b
Xiao Wang and Han Wang: Evolutionary gibbs sampler for image segmentation, in: 2004 International Conference on Image Processing, 2004, ICIP '04., Vol. 5, 3479–3482, IEEE, Singapore, ISBN 9780780385542, https://doi.org/10.1109/ICIP.2004.1421864, 2004. a
Xie, S. and Tu, Z.: Holistically-Nested Edge Detection, in: 2015 IEEE International Conference on Computer Vision (ICCV), 1395–1403, IEEE, Santiago, Chile, ISBN 9781467383912, https://doi.org/10.1109/ICCV.2015.164, 2015. a
Xiong, H., Zhang, Z., and Li, J.: GPRlab: A ground penetrating radar data processing and analysis software based on MATLAB, SoftwareX, 26, 101720, https://doi.org/10.1016/j.softx.2024.101720, 2024. a
Xiong, S. and Muller, J.-P.: EXTRACTION OF ICE SHEET LAYERS FROM TWO INTERSECTED RADAR ECHOGRAMS NEAR NEEM ICE CORE IN GREENLAND, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B7, 585–591, https://doi.org/10.5194/isprs-archives-XLI-B7-585-2016, 2016. a, b
Xiong, S., Muller, J.-P., and Carretero, R.: A New Method for Automatically Tracing Englacial Layers from MCoRDS Data in NW Greenland, Remote Sens., 10, 43, https://doi.org/10.3390/rs10010043, 2017. a, b, c, d
Xu, M., Crandall, D. J., Fox, G. C., and Paden, J. D.: Automatic estimation of ice bottom surfaces from radar imagery, in: 2017 IEEE International Conference on Image Processing (ICIP), 340–344, IEEE, Beijing, China, ISBN 9781509021758, https://doi.org/10.1109/ICIP.2017.8296299, 2017. a, b, c, d, e, f, g, h, i
Xu, M., Fan, C., Paden, J. D., Fox, G. C., and Crandall, D. J.: Multi-task Spatiotemporal Neural Networks for Structured Surface Reconstruction, in: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 1273–1282, IEEE, Lake Tahoe, NV, ISBN 9781538648865, https://doi.org/10.1109/WACV.2018.00144, 2018. a, b
Yari, M., Rahnemoonfar, M., Paden, J., Oluwanisola, I., Koenig, L., and Montgomery, L.: Smart Tracking of Internal Layers of Ice in Radar Data via Multi-Scale Learning, in: 2019 IEEE International Conference on Big Data (Big Data), 5462–5468, IEEE, Los Angeles, CA, USA, ISBN 9781728108582, https://doi.org/10.1109/BigData47090.2019.9006083, 2019. a, b, c
Yari, M., Rahnemoonfar, M., and Paden, J.: Multi-Scale and Temporal Transfer Learning for Automatic Tracking of Internal Ice Layers, in: IGARSS 2020 – 2020 IEEE International Geoscience and Remote Sensing Symposium, 6934–6937, IEEE, Waikoloa, HI, USA, ISBN 9781728163741, https://doi.org/10.1109/IGARSS39084.2020.9323758, 2020. a, b, c, d
Zalatan, B. and Rahnemoonfar, M.: Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar, arXiv, https://doi.org/10.48550/ARXIV.2302.00817, 2023. a, b, c
Zeiler, M. D. and Fergus, R.: Visualizing and Understanding Convolutional Networks, arXiv:1311.2901, https://doi.org/10.48550/arXiv.1311.2901, 2013. a
Zeng, L., Zhang, X., Xie, X., Zhou, B., Xu, C., and Lambot, S.: Measuring annular thickness of backfill grouting behind shield tunnel lining based on GPR monitoring and data mining, Automat. Constr., 150, 104811, https://doi.org/10.1016/j.autcon.2023.104811, 2023. a
Zhang, E., Liu, L., and Huang, L.: Automatically delineating the calving front of Jakobshavn Isbræ from multitemporal TerraSAR-X images: a deep learning approach, The Cryosphere, 13, 1729–1741, https://doi.org/10.5194/tc-13-1729-2019, 2019. a
Zhang, J., Lu, Y., Yang, Z., Zhu, X., Zheng, T., Liu, X., Tian, Y., and Li, W.: Recognition of void defects in airport runways using ground-penetrating radar and shallow CNN, Automat. Constr., 138, 104260, https://doi.org/10.1016/j.autcon.2022.104260, 2022. a, b
Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L.: Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising, IEEE Transactions on Image Processing, 26, 3142–3155, arXiv:1608.03981, https://doi.org/10.1109/TIP.2017.2662206, 2017. a
Zhang, R., Ouyang, W., and Cham, W.-K.: Image Edge Detection Using Hidden Markov Chain Model Based on the Non-Decimated Wavelet, in: 2008 Second International Conference on Future Generation Communication and Networking Symposia, Vol. 3, 111–114, https://doi.org/10.1109/FGCNS.2008.20, 2008. a
Zhang, Y., Liu, H., and Hu, Q.: TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation, https://doi.org/10.48550/arXiv.2102.08005, arXiv:2102.08005 [cs], 2021. a
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J.: Pyramid Scene Parsing Network, arXiv, https://doi.org/10.48550/ARXIV.1612.01105, 2016. a
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., and Parmar, M.: A review of convolutional neural networks in computer vision, Artif. Intell. Rev., 57, 99, https://doi.org/10.1007/s10462-024-10721-6, 2024. a
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J.: UNet
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J.: UNet
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A.: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks, in: 2017 IEEE International Conference on Computer Vision (ICCV), 2242–2251, IEEE, Venice, ISBN 9781538610329, https://doi.org/10.1109/ICCV.2017.244, 2017. a
Short summary
This is an overview of methodologies that have been applied to map the internal reflection horizons, or ice-layer boundaries, of ice sheets on Earth and other planets. We briefly explain radar applications in glaciology and the methods which have been used and published. There are summaries of the published work of the last 2 decades. Finally, we conclude by introducing the gaps and opportunities for further advancement in this field, and we present possible future directions.
This is an overview of methodologies that have been applied to map the internal reflection...
