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Satellite radar backscatter has the potential to provide useful information about the progression of volcanic eruptions when optical, ground-based, or radar phase-based measurements are limited. However, backscatter changes are complex and challenging to interpret: explosive deposits produce different signals depending on pre-existing ground cover, radar parameters and eruption characteristics. We use high temporal- and spatial-resolution backscatter imagery to examine the emplacement and alteration of pyroclastic density currents (PDCs), lahar and ash deposits from the June 2018 eruption of Volcan de Fuego, Guatemala, using observatory reports and rainfall gauge data to ground truth our observations. We use a temporally dense time series of backscatter data to reduce noise and extract deposit areas. We observe backscatter changes in six drainages, the largest deposit was 11.9-km-long that altered an area of 6.3 urn:x-wiley:21699313:media:jgrb55183:jgrb55183-math-0001 and had a thickness of 10.5 urn:x-wiley:21699313:media:jgrb55183:jgrb55183-math-00022 m in the lower sections as estimated from radar shadows. The 3 June eruption also produced backscatter signal over an area of 40 urn:x-wiley:21699313:media:jgrb55183:jgrb55183-math-0003, consistent with reported ashfall. We use transient patterns in backscatter time series to identify nine periods of high lahar activity in a single drainage system between June and October 2018. We find that the characterisation of subtle backscatter signals associated with explosive eruptions are best observed with (1) radiometric terrain calibration, (2) speckle correction, and (3) consideration of pre-existing scattering properties. Our observations demonstrate that SAR backscatter can capture the emplacement and subsequent alteration of a range of explosive deposits, allowing the progression of an explosive eruption to be monitored.

A major challenge in using satellite interferometry (InSAR) for volcanic monitoring in the tropics is distinguishing volcano deformation from atmospheric noise. We reanalysed InSAR time series from 2007-2009 from Agung volcano, Indonesia, which had previously been used as evidence for unrest. Using uncorrected data, we find an apparent velocity of 5.0 ± 2.7 cm/yr consistent with previous reports, but we show that this signal is consistent with predictions of atmospheric contributions derived from weather models (ECMWF-HRES). Following the correction, we find a velocity of 1.4 ± 4.2 cm/yr and conclude that there was no significant deformation related to the inflation of a shallow magma source from 2007-2009. We discuss the implications for the inferred magma storage system at Agung and consider which other reported signals might have been wrongly attributed to deformation and should be reanalyzed.

Volcanoes have dormancy periods that may last decades to centuries meaning that eruptions at volcanoes with no historical records of eruptions are common. Baseline monitoring to detect the early stages of reawakening is therefore important even in regions with little recent volcanic activity. Satellite techniques, such as InSAR, are ideally suited for routinely surveying large and inaccessible regions, but the large datasets typically require expert interpretation. Here we focus on Turkey where there are 10 Holocene volcanic systems, but no eruptions since 1855 and consequently little ground-based monitoring. We analyse data from the first five years of the European Space Agency Sentinel-1 mission which collects data over Turkey every 6 days on both ascending and descending passes. The high relief edifices of Turkey’s volcanoes cause two challenges: 1) snow cover during the winter months causes a loss of coherence and 2) topographically-correlated atmospheric artefacts could be misinterpreted as deformation. We propose mitigation strategies for both. The raw time series at Hasan Dag volcano shows uplift of ~ 10 cm between September 2017 and July 2018, but atmospheric corrections based on global weather models demonstrate that this is an artefact and reduce the scatter in the data to

Space-borne Synthetic Aperture Radar (SAR) Interferometry (InSAR) is now a key geophysical tool for surface deformation studies. The European Commission’s Sentinel-1 Constellation began acquiring data systematically in late 2014. The data, which are free and open access, have global coverage at moderate resolution with a 6 or 12-day revisit, enabling researchers to investigate large- scale surface deformation systematically through time. However, full exploitation of the potential of Sentinel-1 requires specific processing approaches as well as the efficient use of modern computing and data storage facilities. Here we present LiCSAR, an operational system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR is designed to automatically produce geocoded wrapped and unwrapped interferograms and coherence estimates, for large regions, at 0.001° resolution (WGS-84 system). The products are continuously updated in a frequency depending on prioritised regions (monthly, weekly or live update strategy). The products are open and freely accessible and downloadable through an online portal. We describe the algorithms, processing, and storage solutions implemented in LiCSAR, and show several case studies that use LiCSAR products to measure tectonic and volcanic deformation. We aim to accelerate the uptake of InSAR data by researchers as well as non-expert users by mass producing interferograms and derived products., This work was partially undertaken on ARC4, part of the High Performance Computing facilities at the University of Leeds, UK.

Radar satellites, such as Sentinel-1, are now able to produce time-series of ground deformation at any volcano around the world, but atmospheric effects still limit the real time detection of unrest at tropical volcanoes. Here, we test two approaches to correct atmospheric errors - phase-elevation correlations and global weather models - and assess the ability of InSAR time-series to detect deformation anomalies using either a fixed threshold or a cumulative sum control chart. We use the 2017-2018 crisis at Agung volcano as a case example because strong atmospheric signals were originally misidentified as true deformation, and obscured the subtle deformation pattern associated with magmatic activity. We assess the Receiver Operating Characteristics (ROC) of each method and found the average area under the ROC curve to be about 0.5 for the uncorrected data (corresponding to no discrimination capability), around 0.8 after combined atmospheric corrections (weather model and phase-elevation approaches), and more than 0.95 using a cumulative sum control chart (where 1 corresponds to ideal separation between classes). Our results retrospectively show that uplift could have been detected to a 95% level of confidence for both ascending and descending time series by October 2017, 15 days after the start of the seismic swarm and one month prior to the eruption. Thus our approach successfully flags anomalous behavior without relying on visual inspection or selection of an arbitrary threshold, and hence shows potential as a monitoring tool for volcano observatories globally.

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. Recent developments in technology as well as improved computational power have resulted in unprecedented quantities of monitoring data, which can no longer be inspected manually. 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 than many other applications. In this paper, we address this problem using synthetic interferograms to train the AlexNet CNN. 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 (PPV of 100%). Thus, we demonstrate that training with synthetic examples can improve the ability of CNNs to detect volcano deformation in satellite images, and propose an efficient workflow for the development of automated systems.

Forecasting explosive eruptions relies on using monitoring data to interpret the patterns and timescales of magma transport and mixing. In September 2017, a distal seismic swarm triggered the evacuation of around 140,000 people from Agung volcano, Bali. From satellite imagery and 3D numerical models, we show that seismicity was associated with a deep, sub-vertical magma intrusion between Agung and its neighbour Batur. This, combined with observations of the 1963 eruption which caused more than thousand fatalities, suggests a vertically and laterally interconnected system experiencing recurring magma mixing. The geometry of the 2017 dyke is consistent with transport from a deep mafic source to a shallow andesitic reservoir controlled by stresses induced by the topographic load, but not the regional tectonics. The ongoing interactions between Agung and Batur have important implications for interpretation of distal seismicity, the links between closely spaced arc volcanoes, and the potential for cascading hazards., 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 estimation of the volume of volcanic flows during an ongoing eruption is challenging but this information is crucial for improving risk assessment and for forecasting future events. Although previous studies have shown the ability of TanDEM-X satellite data to derive the thickness and the volume of lava flow fields during effusive eruptions, the method has not been explored yet for pyroclastic flows. Using bi-static interferometry, we produce TanDEM-X DEM on Fuego volcano (Guatemala) to measure the significant topographic changes caused by the 3rd June 2018 eruption, which destroyed the town of San Miguel Los Lotes. We estimate the volume of the Pyroclastic Density Currents (PDCs) to be 15.1 ± 4.2 × 106 m3. The deposits are likely to be the source of lahars during future rainy seasons. We identify the main channel of deposition (positive elevation changes) and the source region of pyroclastic material, areas of significant substrate erosion, and vegetation destruction (negative elevation changes). Our results show that the June 3rd 2018 pyroclastic flow was predominantly composed of material which had gravitationally collapsed from a location close to the vent. The eroded material increased the volume of the flow (bulking) and likely caused the run-out distance of the 2018 PDC to be larger than previous eruptions (1999–2017). This study highlights the potential of remote sensing techniques for actively monitoring topography changes in inaccessible locations and to rapidly derive deposit volumes.

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.

The strong tidal triggering of mid-ocean ridge earthquakes has remained unexplained because the earthquakes occur preferentially during low tide, when normal faulting earthquakes should be inhibited. Using Axial Volcano on the Juan de Fuca ridge as an example, we show that the axial magma chamber inflates/deflates in response to tidal stresses, producing Coulomb stresses on the faults that are opposite in sign to those produced by the tides. When the magma chamber’s bulk modulus is sufficiently low, the phase of tidal triggering is inverted. We find that the stress dependence of seismicity rate conforms to triggering theory over the entire tidal stress range. There is no triggering stress threshold and stress shadowing is just a continuous function of stress decrease. We find the viscous friction parameter A to be an order of magnitude smaller than laboratory measurements. The high tidal sensitivity at Axial Volcano results from the shallow earthquake depths., Tidal triggering of earthquakes at Axial Volcano on the Juan de Fuca ridge is shown to be driven by tidally induced magma chamber inflation. Fitting the data to theory requires that the frictional parameter A be much smaller than laboratory measurements indicate.

Recent improvements in the frequency, type and availability of satellite images mean it is now feasible to routinely study volcanoes in remote and inaccessible regions, including those with no ground-based monitoring. In particular, Interferometric Synthetic Aperture Radar (InSAR) data can detect surface deformation, which has a strong statistical link to eruption. However, the dataset produced by the recently-launched Sentinel-1 satellite is too large to be manually analysed on a global basis. In this study, we systematically process >30,000 short-term interferograms at over 900 volcanoes and apply machine learning algorithms to automatically detect volcanic ground deformation. We use a convolutional neutral network (CNN) to classify interferometric fringes in wrapped interferograms with no atmospheric corrections. We employ a transfer learning strategy, and test a range of pretrained networks, finding that AlexNet is best suited to this task. The positive results are checked by an expert and fed back for model updating. Following training with a combination of both positive and negative examples, this method reduced the number of interferograms to ~100 which required further inspection, of which at least 39 are considered 'true positives'. We demonstrate that machine learning can efficiently detect large, rapid deformation signals in wrapped interferograms, but further development is required to detect slow or small deformation patterns which do not generate multiple fringes in short duration interferograms. This study is the first to use machine learning approaches for detecting volcanic deformation in large datasets, and demonstrates the potential of such techniques for developing alert systems based on satellite imagery.

Sentinel-1 represents a major step forward in enabling us to monitor the Earth's hazardous tectonic and volcanic zones. Here, we present the latest progress from the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET), where we provide deformation results to the community for volcanoes and the tectonic belts. For the estimation of seismic hazard, we require relative accuracy on the order of 1 mm/yr between points 100 km apart. This requires mass processing of long time series of radar acquisitions. As of January 2018, we are producing in-terferograms systematically for the entire Alpine-Himalayan belt (~9000 × 2000 km) and the majority of subaerial volcanoes. Currently we make interferograms and coherence products available to the community, but we plan to also provide average deformation rates and displacement time series, in the future. The results are made available through a dedicated COMET portal, and we are in the process of linking them to the ESA G-TEP and EPOS. COMET also responds routinely to significant continental earthquakes, larger than ~Mw 6.0. The short repeat interval of Sentinel-1, together with the rapid availability of the data, allows us to do this within a few days for most earthquakes. For example, after the Mw 7.8 Kaikoura earthquake we supplied a processed interferogram to the community just 5 hours and 37 minutes after the Sentinel-1 acquisition. In this paper we provide an overview of some of the latest results for tectonics and volcanism and discuss how the accuracy of these products will improve as the number of data products acquired by Sentinel-1 increases.

We use numerical models to study the mechanical stability of magma reservoirs embedded in elastic host rock. We quantify the overpressure required to open tensile fractures (the failure overpressure), as a function of the depth and the size of the reservoir, the loading by the volcanic edifice, and the pore fluid pressure in the crust. We show that the pore fluid pressure is the most important parameter controlling the magnitude of the failure overpressure rather than the reservoir depth and the edifice load. Under lithostatic pore fluid pressure conditions, the failure overpressure is on the order of the rock tensile strength (a few tens of megapascals). Under zero pore fluid pressure conditions, the failure overpressure increases linearly with depth (a few hundreds of megapascals at 5 km depth). We use our models to forecast the failure displacement (the cumulative surface displacement just before an eruption) on volcanoes showing unrest: Sinabung and Agung (Indonesia) and Okmok and Westdahl (Aleutian). By comparison between our forecast and the observation, we provide valuable constraint on the pore fluid pressure conditions on the volcanic system. At Okmok, the occurrence of the 2008 eruption can be explained with a 1,000 m reservoir embedded in high pore fluid pressure, whereas the absence of eruption at Westdahl better suggests that the pore fluid pressure is much lower than lithostatic. Our finding suggests that the pore fluid pressure conditions around the reservoir may play an important role in the triggering of an eruption by encouraging or discouraging the failure of the reservoir.

Stress transfer between separate magma bodies is evaluated by considering how pressure changes related to magma accumulation/propagation influence the stability of a separate nearby magma body. Three-dimensional numerical models are used to evaluate the stability evolution of a magma body through the calculation of two variables: (i) the variation of the threshold pressure needed to cause failure around the magma body and (ii) the magma pressure change. A parametric study indicates that stress interactions are strongly dependent on the distance between magma bodies as well as the body's shape. Such models are then applied to evaluate stress influence of intrusive activity in 1994, 1999, and 2010 at Eyjafjallajokull volcano, which preceded two eruptions there in 2010. Two cases are considered: influence of these intrusions on (i) a magma reservoir at 20 km distance under the Katla volcano and (ii) a silicic magma body under Eyjafjallajokull. The distance between the Eyjafjallajokull intrusions and the Katla reservoir is sufficiently long to reduce the stress interaction to insignificant levels, with an amplitude of the same order as Earth tides (a few kilopascals). However, cumulative stress transfer due to the intrusions to a remnant silicic shallow body situated below the Eyjafjallajokull is much larger (0.5 2.5 MPa). This mechanical transfer could have contributed to the failure of the silicic body and promoted the chemical mixing/mingling between different magma types, which is commonly interpreted as the main cause of the 2010 explosive eruption of Eyjafjallajokull.

International audience; The behaviour of a magma plumbing system during a cycle of volcanic edifice growth is investigated with a simple physical model. Loading by an edifice at Earth's surface changes stresses in the upper crust and pressures in a magma reservoir. In turn, these changes affect magma ascent from a deep source to the reservoir and from reservoir to Earth's surface. The model plumbing system is such that a hydraulic connection is maintained at all times between the reservoir and a deep magma source at constant pressure. Consequently the input rate of magma into the reservoir is predicted by the model rather than imposed as an input parameter. The open hydraulic connection model is consistent with short-term measurements of deformation and seismicity at several active volcanoes. Threshold values for the reservoir pressure at the beginning and end of eruption evolve as the edifice grows and lead to long-term changes of eruption rate. Depending on the dimensions and depth of the reservoir, the eruption rate follows different trends as a function of time. For small reservoirs, the eruption rate initially increases as the edifice builds up and peaks at some value before going down. The edifice size at the peak eruption rate provides a constraint on the reservoir shape and depth. Edifice decay or destruction leads to resumption of eruptive activity and a new eruption cycle. A simple elastic model for country rock deformation is valid over a whole eruptive cycle extending to the cessation of eruptive activity. For large reservoirs, an elastic model is only valid over part of an eruptive cycle. Long-term stress changes eventually lead to reservoir instability in the form of either roof collapse and caldera formation or reservoir enlargement in the horizontal direction.

International audience; We reanalyzed the surface displacements observed at the Lazufre volcanic complex in the Southern Andean Central Volcanic Zone using GPS measurements made between 2006 and 2008 and a large InSAR data set. We performed a detailed spatiotemporal analysis of the displacements using a principal component analysis inversion method (PCAIM). The PCAIM reveals a source with no significant changes in shape and dimension and with a remarkably linear strength increase over the whole period of observation (i.e., 2003-2010). Then we used a three-dimensional mixed boundary element method (MBEM) to invert the first component of surface displacement as obtained from PCAIM. We explored a continuum of geometries from a shallow elliptic crack to a deep massive truncated elliptical cone that could represent a sill or a large magma chamber, respectively. The best models indicate a large flat-topped source with a roof area between 40 and 670 km 2 and a depth of between 2 and 14 km below ground surface. Lastly, on the basis of the limited data available for the thermomechanical structure of the crust in the Southern Andean Central Volcanic Zone, we consider some possible scenarios to explain the spatial and temporal pattern of displacements at Lazufre.

Arenal 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).

International audience; Major 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.

SUMMARY We investigate how surface load variations around volcanoes act on shallow magma chambers. Numerical calculations are carried out in axisymmetric geometry for an elliptical chamber embedded in an elastic medium. Magma compressibility is taken into account. For variable chamber shape, size and depth, we quantify how unloading events induce magmatic pressure change as well as variation of the threshold pressure required for dyke initiation at the chamber wall. We evaluate the triggering effect of these surface events on onset of eruptions and find it depends strongly on the surface load location and the magma chamber shape. We apply this model to two active Icelandic subglacial volcanoes: Gr´ omsv¨ otn and Katla. The 2004 eruption of Gr´ omsv¨ otn was immediately preceded by a j¨ okulhlaup, a glacial outburst flood of 0.5 km 3 . We show that this event may have triggered the eruption only if the system was very close to failure conditions. Katla volcano is covered by the M´ yrdalsj¨ okull ice cap. An annual cycle, with up to 6 m change in snow thickness, occurs from winter to summer. As the seasonal snow load is reduced, a pressure decrease of the same order of magnitude as the load is induced within the magma storage zone. Our model predicts that, in the case of a spherical or horizontally elongated magma chamber, eruptions are more likely when the snow cover is smallest, which appears consistent with the fact that all the last nine major historical eruptions at Katla occurred during the summer period. The model predicts an increase in Coulomb stress around the caldera, up to 7 km from its centre, during unloading periods, enough to trigger earthquakes. Stress due to snow load variations, with focusing of it in weak zones near the caldera boundary, is considered a contributing factor to seasonal seismicity observed beneath M´ yrdalsj¨ okull.

Pressure influences both magma production and the failure of magma chambers. Changes in pressure interact with the local tectonic settings and can affect magmatic activity. Present-day reduction in ice load on subglacial volcanoes due to global warming is modifying pressure conditions in magmatic systems. The large pulse in volcanic production at the end of the last glaciation in Iceland suggests a link between unloading and volcanism, and models of that process can help to evaluate future scenarios. A viscoelastic model of glacio-isostatic adjustment that considers melt generation demonstrates how surface unloading may lead to a pulse in magmatic activity. Iceland’s ice caps have been thinning since 1890 and glacial rebound at rates exceeding 20 mm yr−1is ongoing. Modelling predicts a significant amount of ‘additional’ magma generation under Iceland due to ice retreat. The unloading also influences stress conditions in shallow magma chambers, modifying their failure conditions in a manner that depends critically on ice retreat, the shape and depth of magma chambers as well as the compressibility of the magma. An annual cycle of land elevation in Iceland, due to seasonal variation of ice mass, indicates an annual modulation of failure conditions in subglacial magma chambers.