EGU22-6948, updated on 08 Jan 2024
EGU General Assembly 2022
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Mapping Glacier Basal Sliding with Beamforming and Artificial Intelligence

Josefine Umlauft1, Philippe Roux2, Albanne Lecointre2, Florent Gimbert2, Ugo Nanni3, Andrea Walpersdorf2, Bertrand Rouet-LeDuc4, Claudia Hulbert4, Daniel Trugman5, and Paul Johnson4
Josefine Umlauft et al.
  • 1ScaDS.AI / Leipzig University, Humboldtstraße 25, 04105 Leipzig (
  • 2ISTerre Grenoble, Université Grenoble Alpes, 1381 Rue de la Piscine, 38610 Gières, France
  • 3University of Oslo, Department of Geosciences, Sem Sælands vei 1, 0371 Oslo, Norway
  • 4Los Alamos National Laboratory, New Mexico 87545, United States of America
  • 5Jackson School of Geosciences, The University of Texas at Austin, United States of America

The cryosphere is a highly active and dynamic environment that rapidly responds to changing climatic conditions. In particular, the physical processes behind glacial dynamics are poorly understood because they remain challenging to observe. Glacial dynamics are strongly intermittent in time and heterogeneous in space. Thus, monitoring with high spatio-temporal resolution is essential.

In course of the RESOLVE (‘High-resolution imaging in subsurface geophysics : development of a multi-instrument platform for interdisciplinary research’) project, continuous seismic observations were obtained using a dense seismic network (100 nodes, Ø 700 m) installed on Glacier d’Argentière (French Alpes) during May in 2018. This unique data set offers the chance to study targeted processes and dynamics within the cryosphere on a local scale in detail.


To identify seismic signatures of ice beds in the presence of melt-induced microseismic noise, we applied the supervised ML technique gradient tree boosting. The approach has been proven suitable to directly observe the physical state of a tectonic fault. Transferred to glacial settings, seismic surface records could therefore reveal frictional properties of the ice bed, offering completely new means to study the subglacial environment and basal sliding, which is difficult to access with conventional approaches.

We built our ML model as follows: Statistical properties of the continuous seismic records (variance, kurtosis and quantile ranges), meteorological data and a seismic source catalogue obtained using beamforming (matched field processing) serve as features which we fit to measures of the GPS displacement rate of Glacier d’Argentière (labels). Our preliminary results suggest that seismic source activity at the bottom of the glacier strongly correlates with surface displacement rates and hence, is directly linked to basal motion. By ranking the importance of our input features, we have learned that other than for reasonably long monitoring time series along tectonic faults, statistical properties of seismic observations only do not suffice in glacial environments to estimate surface displacement. Additional beamforming features however, are a rich archive that enhance the ML model performance considerably and allow to directly observe ice dynamics.

How to cite: Umlauft, J., Roux, P., Lecointre, A., Gimbert, F., Nanni, U., Walpersdorf, A., Rouet-LeDuc, B., Hulbert, C., Trugman, D., and Johnson, P.: Mapping Glacier Basal Sliding with Beamforming and Artificial Intelligence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6948,, 2022.


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