Mapping Glacier-Wide Basal Sliding with Artificial Intelligence and Distributed Acoustic Sensing

• Applicant: Dr. Josefine Umlauft
• Subject Area: Geophysics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 509104471

A profound understanding and the formulation of sliding laws for glacier basal motion are still a big challenge for the scientific community but essentially needed for hazard assessment and the generation of prediction models. “Cryoseismology” is a new and emerging field that addresses processes within the glacial environment via the analysis of continuous seismic records. It was commonly believed that glacier sliding is a slow and smooth process, but recent cryoseismological studies show accumulating evidence for sudden microseismic stick-slip events that can even coalesce into sustained tremor. Those were observed to cause ice-stream wide sliding episodes with surface displacements of tens of centimeters per day. Due to glacial noise from other cryoseismic sources, signals related to glacier basal motion are often masked and difficult to observe by the human eye. New approaches are needed which involve on-ice seismological measurements densely sampled in space and time, as well as modern tools that efficiently analyze such large datasets and reveal previously hidden signals. The main goal of this project is to monitor and map glacier basal motion along the entire length of an Alpine glacier. This will elucidate the role of frictional sliding in different surface melt and ice-thicknesses regimes and allow addressing the following key questions: 1. Is subglacial stick-slip sliding a local phenomenon or does it affect the entire extent of a glacier with different surface melt and ice thickness regimes? 2. How do subglacial events respond to changing meteorological conditions, in particular melt-induced surges? 3. Does the distribution of stick-slip activity and changes thereof under different hydraulic conditions allow to predict the stability and failure of steep ice tongues? To address those research questions, we propose to analyze a newly acquired DAS (distributed acoustic sensing) dataset from Rhônegletscher (Swiss Alps). During one-month seismic data were recorded with a fiber-optic cable over an extent of 9 km covering Rhônegletscher from its accumulation to its ablation zone. The time series is provided with a high sampling in space and time. Hence, it is well suited to monitor spatio-temporal dynamics of a temperate Alpine glacier. In the tectonic environment, direct observation and quantification of fault friction has recently been accomplished using supervised Machine Learning (gradient tree boosting) based on seismic data. In this project, the approach will be transferred to the glacial environment and applied to the DAS dataset from Rhônegletscher. We aim to set up a Machine Learning model that picks up previously hidden signals related to glacier basal motion on the DAS records and uncovers the underlying function between surface displacement and seismic data. This would allow us to measure glacier sliding behavior directly from the surface and thus open completely new perspectives for ice dynamic monitoring.

• DFG Programme: Research Grants
• International Connection: Switzerland
• Cooperation Partner: Professor Dr. Fabian Thomas Walter