Abstract

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.

Highlights

  • About 1500 volcanoes are considered active worldwide [1], with about 50–85 erupting volcanoes each year [2]

  • MOUNTS focuses on the automatic detection of processes generating large ground deformations in short time intervals, which typically imprint on interferograms as many colored fringes

  • We present an operational volcano monitoring system, based on the automated download and processing of multisensor satellite-based data (Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 short-wave infrared (SWIR), Sentinel-5P TROPOMI)

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Summary

Introduction

About 1500 volcanoes are considered active worldwide [1], with about 50–85 erupting volcanoes each year [2]. Volcanoes considered dormant (no recent eruption) or extinct (no eruption for >10,000 years) are commonly not instrumentally monitored, but may experience large and unexpected eruptions, as it was the case for Chaiten (Chile) in 2008 which erupted after 8000 years of inactivity [7] In this regard, satellite remote sensing can provide crucial observations when ground-based monitoring is limited or lacking, due to remote environments and/or limited resources. Many volcanoes are located on political boundaries between neighboring countries and states, and leave uncertainties concerning the responsibilities during eruptive crisis In this regard, only a few satellite-based platforms have the scope to monitor volcanoes on a global scale. We hereafter briefly describe a selection of relevant operating satellite-based platforms which provide open data access on a global scale (Table 1), and expose afterwards the rationale behind MOUNTS

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