EGU2020-3493, updated on 19 Jan 2021
https://doi.org/10.5194/egusphere-egu2020-3493
EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Characterizing glacial processes applying classical beamforming and machine learning

Josefine Umlauft1, Philippe Roux2, Florent Gimbert2, Albanne Lecointre2, Bertrand Rouet-LeDuc3, Daniel Taylor Trugman3, and Paul Allan Johnson3
Josefine Umlauft et al.
  • 1Leipzig University, Institute of Geophysics and Geology, Leipzig, Germany (josefine.umlauft@uni-leipzig.de)
  • 2ISTerre Grenoble, Université Grenoble Alpes, Grenoble, France
  • 3Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America

The cryosphere is a highly active and dynamic environment that rapidly responds to changing climatic conditions. processes behind are poorly understood 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 project, continuous seismic observations were obtained using a dense seismic network (100 nodes, Ø 700 m) installed on the Argentière Glacier (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.

We classical beamforming within the of the array (matched field processing) and unsupervised machine learning techniques to identify, cluster and locate seismic sources in 5D (x, y, z, velocity, time). Sources located with high resolution and accuracy related to processes and activity within the ice body, e.g. the geometry and dynamics of crevasses or the interaction at the glacier/bedrock interface, depending on the meteorological conditions such as daily temperature fluctuations or snow fall. Our preliminary results indicate strong potential in poorly resolved sources, which can be observed with statistical consistency reveal new insights into structural features/ physical properties of the glacier (e.g. analysis of scatterers).

How to cite: Umlauft, J., Roux, P., Gimbert, F., Lecointre, A., Rouet-LeDuc, B., Trugman, D. T., and Johnson, P. A.: Characterizing glacial processes applying classical beamforming and machine learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3493, https://doi.org/10.5194/egusphere-egu2020-3493, 2020

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