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The potential of fibre-optic sensing for volcano monitoring and imaging.

Sara Klaasen 1, Sebastian Noe 1, Solvi Thrastarson 1, Lars Gebraad 1, Thomas Hudson 1, Yesim Cubuk-Sabuncu 2, Kristín Jónsdóttir 2, Jan Dettmer 3, Paraskevi Nomikou 4, Andreas Fichtner 1

  • Affiliations: 1 Department of Earth and Planetary Sciences, ETH Zürich, Zürich, Switzerland; 2 Icelandic Meteorological Office, Reykjavik, Iceland; 3 Department of Earth, Energy and Environment, University of Calgary, Calgary, Canada; 4 Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, Athens, Greece

  • Presentation type: ECR Invited talk

  • Presentation time: Friday – 04.07.25, 11:40 - 12:05, Room R380

  • Theme 8

Abstract

Emerging Distributed Acoustic Sensing (DAS) technologies are starting to transform the local-scale monitoring of earthquakes and volcanoes. DAS interrogates a fibre-optic to produce a spatially dense network of seismic sensors that produces dynamic strain measurements in the sub-micrometre range. In addition to seismic measurements on the metre-scale, DAS benefits from relatively low labour and financial costs, as well as negligible maintenance requirements. Especially in remote and inaccessible areas, such as volcanoes, this can lead to new insights into the dynamics and structure of the system. In this presentation, we explore the strengths and challenges of DAS on volcanoes through a series of case studies. Most fundamentally, DAS facilitates the discovery of a wide range of volcanism-related phenomena that standard seismometer networks may miss. These include high levels of volcanism-related seismicity, low amplitude tremor, and resonance effects. With spectral-element simulations, we can model DAS data in complex environments with topography, a 3D heterogeneous subsurface structure and water layers. Combining these simulations with densely sampled DAS data, increases the resolution of both tomographic models and earthquake source inversions. At the same time, the potentially lower data quality and one-component measurements of DAS can make automated analyses, such as travel-time picking and phase identification, more challenging. Current research is focused on the development of novel analysis techniques that overcome these challenges and enable the incorporation of DAS into real-time volcano monitoring networks.