Snapshot and time-dependent inversions of basal sliding using automatic generation of adjoint code on graphics processing units

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Pubblicato in:Journal of Glaciology vol. 71 (2025)
Autore principale: Utkin, Ivan
Altri autori: Chen, Yilu, Räss, Ludovic, Werder, Mauro A
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Cambridge University Press
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022 |a 0022-1430 
022 |a 1727-5652 
024 7 |a 10.1017/jog.2025.40  |2 doi 
035 |a 3213019401 
045 2 |b d20250101  |b d20251231 
100 1 |a Utkin, Ivan  |u Laboratory of Hydraulics, Hydrology and Glaciology, EXCLAIM, ETH Zurich, Zurich, Switzerland; Swiss Federal Institute for Forest, Snow and Landscape Research, EXCLAIM, bâtiment ALPOLE, Sion, ETH Zurich, Zurich, Switzerland 
245 1 |a Snapshot and time-dependent inversions of basal sliding using automatic generation of adjoint code on graphics processing units 
260 |b Cambridge University Press  |c 2025 
513 |a Journal Article 
520 3 |a Basal sliding and other processes affecting ice flow are challenging to constrain due to limited direct observations. Inversion methods, which typically fit an ice-flow model to observed surface velocities, enable the reconstruction of basal properties from readily available data. We present a numerical inversion framework for reconstructing the glacier basal sliding coefficient, applied to both synthetic and real-world alpine glacier scenarios. The framework employs automatic differentiation (AD) to generate adjoint code and runs in parallel on graphics processing units. We explore two inversion workflows using the shallow ice approximation as the forward model: a time-independent approach fitting to a single snapshot of annual ice velocity and a time-dependent inversion accounting for both ice velocity and changes in geometry. We find that the time-dependent inversion yields more robust and accurate velocity fields than the snapshot inversion. However, it does not significantly improve the problematic initial transients often encountered in forward model runs that employ sliding fields from snapshot inversions. This is likely due to the limitations of the forward model. This methodology is transferable to more complex forward models and can be readily implemented in languages supporting AD. 
651 4 |a Greenland 
651 4 |a Antarctica 
653 |a Glaciers 
653 |a Time dependence 
653 |a Deep learning 
653 |a Flow velocity 
653 |a Inversions 
653 |a Approximation 
653 |a Velocity distribution 
653 |a Boundary conditions 
653 |a Surface velocity 
653 |a Mountain glaciers 
653 |a Programming languages 
653 |a Physics 
653 |a Ice 
653 |a Graphics 
653 |a Artificial intelligence 
653 |a Graphics processing units 
653 |a Velocity 
653 |a Ice sheets 
653 |a Slumping 
653 |a Methods 
653 |a Geometry 
653 |a Sliding 
653 |a Enzymes 
653 |a Environmental 
700 1 |a Chen, Yilu  |u Laboratory of Hydraulics, Hydrology and Glaciology, EXCLAIM, ETH Zurich, Zurich, Switzerland; Swiss Federal Institute for Forest, Snow and Landscape Research, EXCLAIM, bâtiment ALPOLE, Sion, ETH Zurich, Zurich, Switzerland; EXCLAIM, ETH Zurich, Zurich, Switzerland 
700 1 |a Räss, Ludovic  |u Swiss Geocomputing Centre, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland 
700 1 |a Werder, Mauro A  |u Laboratory of Hydraulics, Hydrology and Glaciology, EXCLAIM, ETH Zurich, Zurich, Switzerland; Swiss Federal Institute for Forest, Snow and Landscape Research, EXCLAIM, bâtiment ALPOLE, Sion, ETH Zurich, Zurich, Switzerland 
773 0 |t Journal of Glaciology  |g vol. 71 (2025) 
786 0 |d ProQuest  |t Research Library 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3213019401/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3213019401/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch 
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