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Abstract Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million m3 on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal‐to‐noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

We present a distributed acoustic sensing (DAS) experiment at Grímsvötn, Iceland. This is intended to investigate volcano-microseismicity at Grímsvötn specifically, and to assess the suitability of DAS as a subglacial volcano monitoring tool in general. In spring 2021, we trenched a 12 km long fiber-optic cable into the ice sheet around and within the caldera, followed by nearly one month of continuous recording. An image processing algorithm that exploits spatial coherence in DAS data detects on average ~100 events per day, almost 2 orders of magnitude more than in the regional earthquake catalog. A nonlinear Bayesian inversion reveals the presence of pronounced seismicity clusters, containing events with magnitudes between −3.4 and 1.7. Their close proximity to surface volcanic features suggests a geothermal origin. In addition to painting a fine-scale picture of seismic activity at Grímsvötn, this work confirms the potential of DAS in subglacial volcano monitoring.

Entirely covered by the Vatnajökull ice cap, Grímsvötn is among Iceland’s largest and most hazardous volcanoes. Here we demonstrate that fiber-optic sensing technology deployed on a natural floating ice resonator can detect volcanic tremor at the level of few nanostrain/s, thereby enabling a new mode of subglacial volcano monitoring under harsh conditions. A 12.5 km long fiber-optic cable deployed on Grímsvötn in May 2021 revealed a high level of local earthquake activity, superimposed onto nearly monochromatic oscillations of the caldera. High data quality combined with dense spatial sampling identify these oscillations as flexural gravity wave resonance of the ice sheet that floats atop a subglacial lake. Although being affected by the ambient wavefield, the time–frequency characteristics of observed caldera resonance require the presence of an additional persistent driving force with temporal variations over several days, that is most plausibly explained in terms of low-frequency volcanic tremor. In addition to demonstrating the logistical feasibility of installing a large, high-quality fiber-optic sensing network in a subarctic environment, our experiment shows that ice sheet resonance may act as a natural amplifier of otherwise undetectable (volcanic) signals. This suggests that similar resonators might be used in a targeted fashion to improve monitoring of ice-covered volcanic systems.