FagraDASfjall: Toward the integration of DAS into seismic networks for volcano monitoring

Recent volcanic activity in Iceland has attracted significant attention, threatening populations on the Reykjanes Peninsula and necessitating enhanced monitoring efforts. In this context, the use of Distributed Acoustic Sensing (DAS) in Iceland has significantly increased.
In this work, we focus our attention on a DAS dataset collected during the 2021 Geldingadalir eruption, which took place in the Fagradalsfjall volcanic system. Our goal is to explore how DAS can be integrated into a seismic network to detect and locate volcanic tremor. To do so, we analyze the spatial coherence of both DAS and seismic stations records using CovSeisNet (Seydoux et al. 2016, Soubestre et al. 2019).

First, we investigate the integration of DAS data into a seismic network for tremor source location. To this end, we quantify how differences between instruments and their associated measured physical quantities may affect the recorded phase. Specifically, when comparing a broadband seismometer measuring velocity with a DAS system measuring strain rate, the spatial derivative along the fiber axis induces a phase lag of pi/2 between the two sensors. We find that this is not the primary source of uncertainty in our current network configuration. We also observe that combining a dense DAS array (8 channels) located on one side of the volcano with only a few stations surrounding it results in a suboptimal network geometry. To address this, we explore scaling schemes designed to balance the relative contributions of each sensor and/or sensor pair. These approaches prove highly effective in reducing location uncertainty.
Across the experiment, we observe an increase in spatial coherence at low frequency (0.1–1 Hz) that could have been interpreted as a signal. However, we could not find it in the seismic network and after changing the interrogator parameter it disappeared. We show that this band of more coherent energy can be explained by an interplay between array geometry artifacts in the coherence calculation (array aperture vs. wavelength) and the DAS self-noise.

Overall, our work demonstrates that DAS technology can be effectively integrated into seismic networks, and provides approaches to manage the high measurement density inherent to DAS while distinguishing coherency caused by the interrogator.