Consequences of volcano sector collapse on magmatic storage zones: insights from numerical modeling

International audienceMajor volcano flank collapses strongly affect the underlying magmatic plumbing system. Here, we consider the magma storage zone as a liquid pocket embedded in an elastic medium, and we perform numerical simulations in two-dimensional axisymmetric geometry as well as in three dimensions in order to evaluate the consequences of a major collapse event. We quantify the pressure decrease induced within and around a magma reservoir by a volcano flank collapse. This pressure reduction is expected to favor replenishment with less evolved magma from deeper sources. We also estimate the impact of the magma pressure decrease, together with the stress field variations around the reservoir, on the eruptive event associated with the edifice failure. We show that, for a given magma reservoir geometry, the collapse of a large strato-volcano tends to reduce the volume of the simultaneous eruption; destabilization of large edifices may even suppress magma emission, resulting in phreatic eruptions instead. This effect is greater for shallow reservoirs, and is more pronounced for spherical reservoirs than for vertically-elongated ones. It is reduced for compressible magmas containing a large amount of volatiles. Over a longer time scale, the modification of pressure conditions for dyke initiation at the chamber wall may also explain an increase in eruption rate as well as an apparent change of magma storage location

Magmatic Processes in the East African Rift System: Insights from a 2015-2020 Sentinel-1 InSAR survey

Abstract The East African Rift System (EARS) is composed of around 78 Holocene volcanoes, but relatively little is known about their past and present activity. This lack of information makes it difficult to understand their eruptive cycles, their roles in continental rifting and the threat they pose to the population. Although previous InSAR surveys (1990–2010) showed sign of unrest, the information about the dynamics of the magmatic systems remained limited by low temporal resolution and gaps in the data set. The Sentinel‐1 SAR mission provides open‐access acquisitions every 12 days in Africa and has the potential to produce long‐duration time series for studying volcanic ground deformation at regional scale. Here, we use Sentinel‐1 data to provide InSAR time series along the EARS for the period 2015–2020. We detect 18 ground deformation signals on 14 volcanoes, of which six are located in Afar, six in the Main Ethiopian Rift, and two in the Kenya‐Tanzanian Rift. We detected new episodes of uplift at Tullu Moje (2016) and Suswa (mid‐2018), and enigmatic long‐lived subsidence signals at Gada Ale and Kone. Subsidence signals are related to a variety of mechanisms including the posteruptive evolution of magma reservoirs (e.g., Alu‐Dallafila), the compaction of lava flows (e.g., Nabro), and pore‐pressure changes related to geothermal or hydrothermal activity (e.g., Olkaria). Our results show that ∼20% of the Holocene volcanoes in the EARS deformed during this 5‐years snapshot and demonstrate the diversity of processes occurring

A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets

Satellites enable widespread, regional or global surveillance of volcanoes and can provide the first indication of volcanic unrest or eruption. Here we consider Interferometric Synthetic Aperture Radar (InSAR), which can be employed to detect surface deformation with a strong statistical link to eruption. The ability of machine learning to automatically identify signals of interest in these large InSAR datasets has already been demonstrated, but data-driven techniques, such as convolutional neutral networks (CNN) require balanced training datasets of positive and negative signals to effectively differentiate between real deformation and noise. As only a small proportion of volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine learning for detecting volcanic unrest is more challenging. In this paper, we address this problem using synthetic interferograms to train the AlexNet. The synthetic interferograms are composed of 3 parts: 1) deformation patterns based on a Monte Carlo selection of parameters for analytic forward models, 2) stratified atmospheric effects derived from weather models and 3) turbulent atmospheric effects based on statistical simulations of correlated noise. The AlexNet architecture trained with synthetic data outperforms that trained using real interferograms alone, based on classification accuracy and positive predictive value (PPV). However, the models used to generate the synthetic signals are a simplification of the natural processes, so we retrain the CNN with a combined dataset consisting of synthetic models and selected real examples, achieving a final PPV of 82%. Although applying atmospheric corrections to the entire dataset is computationally expensive, it is relatively simple to apply them to the small subset of positive results. This further improves the detection performance without a significant increase in computational burden

Continuous subsidence associated to the long lasting eruption of Arenal volcano (Costa Rica) observed by dry tilt stations

International audienceArenal Volcano is a small (~1750 m above sea level, ~10 km3) stratovolcano that continuously erupted between July 1968 and October 2010. During this longlasting eruption (over 42 yr), a large volume of material--~5.6 × 108 m3 of dense rock equivalent--has been extruded and has produced a thick and extended lava fi eld, mainly on the western fl ank of the edifi ce. Measurements of ground deformation obtained using a network of dry-tilt stations are presented for the period 1986-2000. They show a continuous subsidence of the volcano with maximal amplitude on the western side. The load effect of the lava fi eld is calculated and explains the largest part of the observed tilts. Once the data are corrected by this load effect, pressure source models are not supported by the observations and by quality criteria on the models. Although the dry-tilt data from Arenal Volcano give limited constraints on the deformation models, they are representative of a long period of activity that cannot be recovered by other means. Moreover, the corresponding interpretative model is consistent with results obtained by geotechnical studies and modern ground deformation methods like interferometric synthetic aperture radar (InSAR)

Split-Band Interferometric SAR Processing Using TanDEM-X Data

Most recent SAR sensors use wide band signals to achieve metric range resolution. One can also take advantage of wide band to split it into sub-bands and generate several lower-resolution images, centered on slightly different frequencies, from a single acquisition. This process, named Multi Chromatic Analysis (MCA) corresponds to performing a spectral analysis of SAR images. Split-Band SAR interferometry (SBInSAR) is based on spectral analysis performed on each image of an InSAR pair, yielding a stack of sub-band interferograms. Scatterers keeping a coherent behaviour in each subband interferogram show a phase that varies linearly with the carrier frequency, the slope being proportional to the absolute optical path difference. This potentially solves the problems of phase unwrapping on a pixelper-pixel basis. In this paper, we present an SBInSAR processor and its application using TanDEM-X data over the Nyiragongo volcano.Fil: Derauw, Dominique Maurice. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación en Paleobiología y Geología; Argentina. Centre Spatial de Liège; BélgicaFil: Kervyn, François. Royal Museum Of Central Africa; BélgicaFil: d'Oreye, Nicolas. European Centre For Geodynamics And Seismology; Luxemburgo. National Museum of Natural History; LuxemburgoFil: Smets, Benoit. Royal Museum Of Central Africa; Bélgica. European Centre For Geodynamics And Seismology; Luxemburgo. Vrije Unviversiteit Brussel; BélgicaFil: Albino, Fabien. Royal Museum Of Central Africa; BélgicaFil: Barbier, Christian. Centre Spatial de Liège; BélgicaAdvances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR WorkshopFrascatiItaliaEuropean Space Agenc

Dyke intrusion between neighbouring arc volcanoes responsible for 2017 pre-eruptive seismic swarm at Agung

Using seismic data and numerical modelling, here, the authors characterize the three-month period of unrest occurring prior to the 2017 Agung eruption (Bali, Indonesia). They observe a large uplift signal located at ~5 km from Agung summit corresponding to the emplacement of a 10 km deep magma intrusion between Agung edifice and Batur caldera, suggesting a potential magmatic connection between the two volcanic systems

The mechanism of tidal triggering of earthquakes at mid-ocean ridges

Evidence for the triggering of earthquakes by tides has been largely lacking for the continents but detectable in the oceans where the tides are larger. By far the strongest tidal triggering signals are in volcanic areas of mid-ocean ridges. These areas offer the most promise for the study of this process, but even the most basic mechanism of tidal triggering at the ridges has been elusive. The triggering occurs at low tides, but as the earthquakes are of the normal faulting type, low tides should inhibit rather than encourage faulting. Here, treating the most well documented case, Axial Volcano on the Juan de Fuca ridge, we show that the axial magma chamber inflates or deflates in response to tidal stresses and produces Coulomb stresses on normal faults opposite in sign to those produced by the tidal stresses. If the bulk modulus of the magma chamber is below a critical value, the magma chamber Coulomb stresses will exceed the tidal ones and the phase of tidal triggering will be inverted. The stress dependence of seismicity rate agrees with triggering theory with unprecedented faithfulness, showing that there is no triggering threshold

The application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Timeseries

Automated systems for detecting deformation in satellite InSAR imagery could be used to develop a global monitoring system for volcanic and urban environments. Here we explore the limits of a CNN for detecting slow, sustained deformations in wrapped interferograms. Using synthetic data, we estimate a detection threshold of 3.9cm for deformation signals alone, and 6.3cm when atmospheric artefacts are considered. Over-wrapping reduces this to 1.8cm and 5.0cm respectively as more fringes are generated without altering SNR. We test the approach on timeseries of cumulative deformation from Campi Flegrei and Dallol, where over-wrapping improves classication performance by up to 15%. We propose a mean-filtering method for combining results of different wrap parameters to flag deformation. At Campi Flegrei, deformation of 8.5cm/yr was detected after 60days and at Dallol, deformation of 3.5cm/yr was detected after 310 days. This corresponds to cumulative displacements of 3 cm and 4 cm consistent with estimates based on synthetic data