Flank instability and sector collapses, which pose major threats, are common on volcanic islands. On 22 Dec 2018, a sector collapse event occurred at Anak Krakatau volcano in the Sunda Strait, triggering a deadly tsunami. Here we use multiparametric ground-based and space-borne data to show that prior to its collapse, the volcano exhibited an elevated state of activity, including precursory thermal anomalies, an increase in the island’s surface area, and a gradual seaward motion of its southwestern flank on a dipping décollement. Two minutes after a small earthquake, seismic signals characterize the collapse of the volcano’s flank at 13:55 UTC. This sector collapse decapitated the cone-shaped edifice and triggered a tsunami that caused 430 fatalities. We discuss the nature of the precursor processes underpinning the collapse that culminated in a complex hazard cascade with important implications for the early detection of potential flank instability at other volcanoes.
Open-conduit volcanic systems are typically characterized by unsealed volcanic conduits feeding permanent or quasi-permanent volcanic activity. This persistent activity limits our ability to read changes in the monitored parameters, making the assessment of possible eruptive crises more difficult. We show how an integrated approach to monitoring can solve this problem, opening a new way to data interpretation. The increasing rate of explosive transients, tremor amplitude, thermal emissions of ejected tephra, and rise of the very-long- period (VLP) seismic source towards the surface are interpreted as indicating an upward migration of the magma column in response to an increased magma input rate. During the 2014 flank eruption of Stromboli, this magma input pre- ceded the effusive eruption by several months. When the new lateral effusive vent opened on the Sciara del Fuoco slope, the effusion was accompanied by a large ground deflation, a deepening of the VLP seismic source, and the cessation of summit explosive activity. Such observations suggest the drainage of a superficial magma reservoir confined between the crater terrace and the effusive vent. We show how this model successfully reproduces the measured rate of effusion, the observed rate of ground deflation, and the deepening of the VLP seismic source. This study also demonstrates the ability of the geophysical network to detect superficial magma recharge within an open-conduit system and to track magma drainage during the effusive crisis, with a great impact on hazard assessment
Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO2 gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards.
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