Driven to collapse: Volcanic eruptions occur frequently, but only rarely are they large enough to cause the top of the mountain to collapse and form a caldera. Gudmundsson et al. used a variety of geophysical tools to monitor the caldera formation that accompanied the 2014 Bárdarbunga volcanic eruption in Iceland. The volcanic edifice became unstable as magma from beneath Bárdarbunga spilled out into the nearby Holuhraun lava field. The timing of the gradual collapse revealed that it is the eruption that drives caldera formation and not the other way around. Science , this issue p. 262

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.

Near‐field ground‐motion data are available in semi‐real time either from modern strong‐motion or continuous Global Positioning System (GPS) networks, allowing robust solutions for earthquake source parameters, which are useful for rapid disaster assessment and early warning. These wide applications require the ground‐motion data to cover a very broad frequency band that, however, is usually not available. This paper presents a case study on the 2011 M_w 9.0 Tohoku earthquake, showing how the ground‐motion information from geodetic and seismic instrumentations is complementary, and suggesting the joint use of both types of data, particularly when the network coverage is sparse. First the strong‐motion records from the two Japanese networks, K‐NET and KiK‐Net, are analyzed using an automatic empirical baseline correction tool. The static coseismic displacement data are obtained by double integration and then used to derive the permanent slip distribution on the earthquake fault. Comparisons with the corresponding GPS‐based solutions yield a quantitative estimation of uncertainties of the empirical baseline correction. Furthermore, a dozen nearby GPS and strong‐motion station pairs are selected to demonstrate that the information in their time series agrees with each other. Finally, methods for combining both types of ground‐motion observation systems are discussed, and the wide applicability of this approach is highlighted.