Towards global volcano monitoring using multisensor sentinel missions and artificial intelligence: The MOUNTS monitoring system

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

Generation of Earth’s Surface Three-Dimensional (3-D) Displacement Time-Series by Multiple-Platform SAR Data

In this chapter, the recent advancements of differential synthetic aperture radar interferometry (DInSAR) technique are presented, with the focus on the DInSAR-based approaches leading to the generation of three-dimensional time-series of Earth’s surface deformation, based on the combination of multi-platform line-of-sight (LOS)-projected time-series of deformation. Use of pixel-offset (PO) measurements for the retrieval of North-South deformation components, which are difficult to be extracted from DInSAR data, only, is also discussed. A review of the principal techniques based on the exploitation of amplitude and phase signatures of sequences of SAR images will be first provided, by emphasizing the limitations and strength of each single approach. Then, the interest will be concentrated on the recently proposed multi-track InSAR combination algorithm, referred as minimum acceleration InSAR combination (MinA) approach. The algorithm assumes the availability of two (or more) sets of SAR images acquired from complementary tracks. SAR data are pre-processed through one of currently available multi-temporal DInSAR toolboxes, and the LOS-projected surface deformation time-series are computed. An under-determined system of linear equations is then solved, based on imposing that the 3-D displacement time-series have minimum acceleration (MA). The presented results demonstrate the validity of the MinA algorithm

Report of the panel on volcanology, section 4

Two primary goals are identified as focal to NASA's research efforts in volcanology during the 1990s: to understand the eruption of lavas, gases, and aerosols from volcanoes, the dispersal of these materials on the Earth's surface and through the atmosphere, and the effects of these eruptions on the climate and environment; and to understand the physical processes that lead to the initiation of volcanic activity, that influence the styles of volcanic eruptions, and that dictate the morphology and evolution of volcanic landforms. Strategy and data requirements as well as research efforts are discussed

insar decorrelation to assess and prevent volcanic risk

SAR� can� be� invaluable� describing� pre�eruption� surface� deformation� and� improving� the� understanding� of� volcanic� processes.� This� work� studies� correlation� of� pairs� of� SAR� images� focusing� on� the� inༀ䃻uence� of� surface,� climate� conditions� and� acquisition� band.� Chosen� L�band� and� C�band� images� (ENVISAT,� ERS� and� ALOS)� cover� most� of� the� Yellowstone� caldera� (USA)� over� a� span� of� 4� years,� sampling� all� the� seasons.� Interferograms� and� correlation� maps� are� generated� and� studied� in� relation� to� snow� depth� and� temperature.� To� isolate� temporal� decorrelation� pairs� of� images� with� the� shortest� baseline� are� chosen.� Results� show� good� performance� during� winter,� bad� attitude� towards� wet� snow� and� good� coherence� during� summer� with� L�band� performing� better� over� vegetation

Volcanic deformation and degassing:the role of volatile exsolution and magma compressibility

Integrating multi-parameter observations of volcanic processes is crucial for volcano monitoring. Qualitative models demonstrate that combining observations of volcanic deformation, gas emissions, and other parameters enhances the detection of volcanic unrest and provide insights into the magma plumbing system. Despite the progress made in this field, quantitative models that link these observations are still lacking. Thermodynamic models have been used to constrain the characteristics of magma properties and its plumbing system. In this thesis, I develop models based on melt inclusion data and thermodynamics to reconstruct magma properties such as compressibility, and investigate how magmatic volatile content and magma storage conditions influence observations of volcanic deformation and SO2 degassing.By comparing mafic systems in arc and ocean island settings, I provide evidence for the lack of deformation observed during water-rich arc eruptions. In contrast, despite having low magmatic volatile content, ocean island eruptions have high SO2 emissions due to their high diffusivity, which results in co-eruptive degassing. By comparing model predictions and observations, I show that all magmatic systems experience a certain degree of outgassing prior to an eruption, consistent with current conceptual models of transcrustal magmatic systems. Additionally, integrating time series of deformation, degassing, and extrusion flux can reveal the evolution of magma properties. Using this framework, I provide evidence for the increase in bulk magma compressibility following the removal of the degassed magma during the 2004 eruption of Mount St. Helens. This study contributes to the better understanding of the effects of magmatic volatile content and pre-eruptive gas segregation on the physicochemical properties of magma, and provides a framework for modelling magma properties that can be applied to global volcano monitoring.</div

Monitoring and modelling volcanoes with assessment of their hazards by means of remote sensing and analogue modelling

Many active volcanoes in developing countries are poorly-known and not monitored. This thesis investigates low cost solutions to map the topography, to identify hazards and to document the eruptions at volcanoes with satellite data. Using a combination of remote sensing techniques and analogue modelling, this thesis also contributes to the understanding of volcanic processes such as the controls upon the 3D shape of sub-volcanic intrusive systems, upon the location of eruption outbreaks, upon the variations in eruption intensity through time and upon the transition between contrasted eruptive styles at a single volcano. After reviewing previous applications of low cost remote sensing in volcanology, the accuracy of two topographic datasets derived from contrasted remote sensing data (ASTER and SRTM) is assessed for volcanic terrains. Oldoinyo Lengai, a natrocarbonatite stratovolcano in Tanzania, is used as an illustrative example of poorly-known volcanoes whose hazards need to be assessed and whose eruptive activity has to be monitored. Satellite images enable mapping, constraining volumes and characterizing surface features of three flank collapses and their associated deposits. An existing numerical model is applied to constrain the emplacement dynamics and the velocity of one of those debris avalanche flows. An algorithm is then presented to retrieve daily information about eruptive activity and its variation over an 8-year period using nighttime MODIS satellite data. Analysis of this time series enable to highlight the control of Earth tides on the timing of high intensity eruptions. The same algorithm, combined with field data and petrologic analyses, is used to document a voluminous lava flow eruption that occurred at Oldoinyo Lengai at the end of March 2006, providing insights into the structure of the shallow plumbing system of the volcano. Satellite data are finally combined with laboratory experiments simulating magma propagation in the Earth crust with sand and syrup or gelatin and water, to provide a better understanding of the control exerted by volcanic edifice load upon magma ascent. These experiments also enable to explain the links between magma ascent, volcano load, sub-volcanic intrusions, volcano surface deformation and location of volcanic vents at the base of large volcanoes

地盤変動解析により推定されるストロンボリ火山の異なる様式の噴火に伴う浅部マグマだまりの増圧過程

Tohoku University西村太志課