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Submarine volcano monitoring with Distributed Acoustic Sensing at Kolumbo, Greece.

Sara Klaasen 1, Thomas Hudson1, Paraskevi Nomikou2, Andreas Fichtner1

  • Affiliations: 1 Department of Earth and Planetary Sciences, ETH Zürich, Zürich, Switzerland 2 Faculty of Geology and Geoenvironment, National and Kapodistrian University of Athens, Athens, Greece

  • Presentation type: Poster

  • Presentation time: Thursday 16:30 - 18:30, Room Poster Hall

  • Poster Board Number: 122

  • Programme No: 2.1.32

  • Theme 2 > Session 1

Abstract

We present the results of an experiment with Distributed Acoustic Sensing (DAS) at Santorini and Kolumbo in Greece and the potential of DAS to augment existing seismic monitoring networks. Kolumbo is an active submarine volcano in the Aegean Sea, and its vicinity to the densely populated island of Santorini emphasises the need for continuous monitoring.   A 45 km long fibre-optic cable connects the islands of Santorini and Ios, where we acquired data for two months (October -- December 2021). Here, we discovered around one thousand events, using an automated earthquake detection algorithm that is based on image processing techniques. DAS reveals an approximate doubling in the number of events detected. Surprisingly, DAS detects many distant events of low magnitudes, while it also misses several larger and nearby events that the existing network does record. Manually picked first arrivals are combined with a travel-time look-up table based on a 1D velocity model to conduct a grid search to locate the events. The ability to locate events depends heavily on the fibre layout and source location, which we verify with synthetic tests. Finally, we compare the performance of DAS to the existing seismic network to locate events, showing that the addition of DAS can refine the event localisation.   The results of our experiment highlight the potential of DAS to complement existing monitoring networks to study active submarine volcanoes by locally lowering the detection threshold of a network and decreasing the uncertainty in the event locations.