Glacier structure and icequakes characterization at Argentière using elastic full waveform inversion

Glaciers, which store nearly 70% of the Earth's freshwater, are undergoing significant changes due to accelerating melting caused by climate change. A better understanding of their behavior and mechanisms is therefore crucial for the years to come. To study these processes, a dense seismic array was deployed on the Argentière Glacier in the Mont Blanc massif (French Alps). The sensor array consists in 98 3-component seismic stations which continuously recorded surface displacements over 35 days in early spring 2018. This period coincided with rising temperatures and rapid glacier evolution, so the sensors captured thousands of events, mostly icequakes.

The aim of this study is to reconstruct the glacier's structure and study icequake mechanisms using elastic Full Waveform Inversion (FWI) on the 3-component data. As the data come from a passive seismic experiment, we have no information about the sources. Before reconstructing the structure it is therefore necessary to work on source parameters estimation. These parameters include spatial localization and mechanism. 

First, we detect and localize the icequakes inside the glacier using a beamforming method called Matched Field Processing (MFP). The detected icequakes are observed to be located mostly at the positions of crevasses on the glacier's surface. Then, we decompose the icequake mechanism into a moment tensor and a time signature wavelet. To estimate these two parameters, we develop a joint inversion method based on waveform analysis using an iterative alternating minimization algorithm. The type of mechanism and the source orientation are then interpreted through the eigenvalue and eigenvector decomposition of the estimated moment tensor. The Fundamental Lune representation is employed to statistically study the distribution of more than 14,000 icequake mechanisms within the glacier, revealing a significant proportion of opening and closing mechanisms associated with crevasses. In certain areas, Double-Couple (DC) mechanisms can also be observed, potentially corresponding to crevasse fault slip events.

Using the estimated source parameters, FWI can be applied to reconstruct the glacier structure. A 3D synthetic crevasse model was created to mimic reality, incorporating the three observed crevasse clusters on the glacier, to evaluate the effectiveness of FWI in reconstructing the model in a given frequency-band. The parameters used in the model include S-wave and P-wave velocities, as well as density. The inversion results reveal several key findings: first, multi-parameter inversion with both S-wave velocity and density yields better results. Second, crevasses can be accurately reconstructed within the considered frequency band, provided the source parameters are well-estimated. Finally, the accuracy of source mechanism estimation significantly impacts the quality of crevasse reconstruction. Importantly, iterating between mechanism estimation and structure reconstruction yields improved results, providing promising insights.