Distributed acoustic sensing for volcano monitoring
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Date
2025
Publication Type
Doctoral Thesis
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Abstract
This thesis explores the potential of distributed acoustic sensing (DAS) for volcano monitoring. The last decade has seen a rapid rise in the use of fibre-optic technologies for seismic sensing, of which DAS has been one of the most well-established. DAS interrogates an optical fibre, resulting in a seismic network with a high spatiotemporal resolution. Dense sampling and flexible deployments make DAS particularly well suited to the study of volcanoes. Volcanoes pose hazards from local to global scales, and flexible deployments facilitate real-time monitoring to identify eruptive precursors, while dense sampling captures details in the seismic signature of a volcano.
This thesis investigates the research question "What are the advantages and challenges to using DAS in volcanic settings?" through a series of case studies at different volcanoes, from submarine volcanic cones to glacially covered calderas. In each experiment, I highlight different aspects concerning the potential of DAS on volcanoes, such as logistical constraints, the range of signals that DAS detects, and how to optimise algorithms for DAS. Specifically, I focus on the detection of events through image-processing techniques, their location through probabilistic sampling and grid search approaches, and their focal mechanism with full-waveform inversion (FWI).
In general, I find that the spatial density of DAS leads to a higher sensitivity to detect seismicity, and that DAS has a consistent detection threshold given the magnitude and distance of events. The data also support the discovery of previously-unknown environmental signals over a broad range of frequencies, such as geothermal tremor and ice sheet resonance. Through simulating DAS data in complex media, we can identify and physically interpret the origin of local seismic events. Yet, I discuss in several experiments that the design of the fibre layout and coupling of the cable remain essential to facilitate the analysis.
Through the combination of DAS with a range of data processing algorithms of varying complexity, as appropriate to the data quality and desired outcomes, I can maximise the extraction of information from volcanoes. From initial estimates that indicate clusters of seismicity, to high-resolution modelling to constrain the focal mechanism of a source within a complex landscape: DAS has the potential to transform volcano monitoring.
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Examiner : Fichtner, Andreas
Examiner : Bowden, Daniel
Examiner : Caudron, Corentin
Examiner: Bowden, Daniel
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ETH Zurich
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Subject
Distributed acoustic sensing (DAS); Volcano seismology; Volcano monitoring
Organisational unit
03971 - Fichtner, Andreas / Fichtner, Andreas