- Open Access
Synthesis of global satellite observations of magmatic and volcanic deformation: implications for volcano monitoring & the lateral extent of magmatic domains
© The Author(s). 2018
- Received: 4 August 2017
- Accepted: 9 January 2018
- Published: 6 February 2018
Global Synthetic Aperture Radar (SAR) measurements made over the past decades provide insights into the lateral extent of magmatic domains, and capture volcanic process on scales useful for volcano monitoring. Satellite-based SAR imagery has great potential for monitoring topographic change, the distribution of eruptive products and surface displacements (InSAR) at subaerial volcanoes. However, there are challenges in applying it routinely, as would be required for the reliable operational assessment of hazard. The deformation detectable depends upon satellite repeat time and swath widths, relative to the spatial and temporal scales of volcanological processes. We describe the characteristics of InSAR-measured volcano deformation over the past two decades, highlighting both the technique’s capabilities and its limitations as a monitoring tool. To achieve this, we draw on two global datasets of volcano deformation: the Smithsonian Institution Volcanoes of the World database and the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics volcano deformation catalogue, as well as compiling some measurement characteristics and interpretations from the primary literature.
We find that a higher proportion of InSAR observations capture non-eruptive and non-magmatic processes than those from ground-based instrument networks, and that both transient (< month) and long-duration (> 5 years) deformation episodes are under-represented. However, satellite radar is already used to assess the development of extended periods of unrest and long-lasting eruptions, and improved spatial resolution and coverage have resulted in the detection of previously unrecognised deformation at both ends of the spatial scale (~ 10 to > 1000 km2). ‘Baseline’ records of past InSAR measurements, including ‘null’ results, are fundamental for any future interpretation of interferograms in terms of hazard‚ both by providing information about past deformation at an individual volcano, and for assessing the characteristics of deformation that are likely to be detectable (and undetectable) using InSAR.
More than half of all InSAR deformation signals attributed to magmatic processes have sources in the shallow crust (< 5 km depth). While the depth distribution of InSAR-derived deformation sources is affected by measurement limitations, their lateral distribution provides information about the extent of active magmatic domains. Deformation is common (24% of all potentially magmatic events) at loci ≥5 km away from the nearest active volcanic vent. This demonstrates that laterally extensive active magmatic domains are not exceptional, but can comprise the shallowest part of trans-crustal magmatic systems in a range of volcanic settings.
Monitoring data have been described as the “only scientifically valid basis for short-term forecasts of a future eruption, or of possible changes during an ongoing eruption” (Tilling, 2008). Ideally, such data should be collected and analysed in real (or near-real) time and be done so consistently over long periods. In practise, continuous measurements from seismometers, tiltmeters and Global Positioning System (GPS) form the basis of most monitoring data streams, occasionally supplemented by field campaigns, gas and geochemical measurements (e.g., Sparks et al., 2012). Satellite-based Synthetic Aperture Radar (SAR) measurements have the potential to make a significant contribution to volcano monitoring (e.g., Pinel et al., 2014), especially in the form of regional surveys and for remote volcanoes with limited monitoring infrastructure.
The focus of most research in the application of SAR imagery in volcanology has been Interferometric SAR (InSAR), where the change in phase between time-separated radar images is used to measure displacements of the Earth’s surface on a centimetre to millimetre scale using interferograms (e.g., Bürgmann et al., 2000). Such measurements capture a wide range of magmatic, hydrothermal and structural processes (e.g., Prichard & Simons, 2004; Biggs et al., 2009), including deformation during pre-eruptive unrest and during eruptions (e.g., Lu et al., 2010; Sigmundsson et al., 2010). Deformation measurements make a broad range of contributions to hazard assessment at volcanoes, from providing the sole evidence that a magmatic system is active (e.g., Pritchard & Simons, 2004; Biggs et al., 2011), to distinguishing between tectonic and magmatic deformation mechanisms (e.g., Biggs et al., 2009, Ebmeier et al., 2016) to making eruption forecasts from analysis of variation in system overpressure (e.g., Hreinsdottir et al.).
InSAR measurements have now been made at over 500 volcanoes worldwide (including null results; Fournier et al., 2010; Biggs et al., 2014; supplemental material in Biggs and Pritchard, 2017; Global Volcanism Program, 2013) and have advanced our understanding of many of the physical processes that drive deformation (e.g., Pinel et al., 2014; Biggs & Pritchard, 2017 and references therein). However, the use of interferograms for volcano monitoring, especially in a decision-making context, remains challenging due to delays between data acquisition and delivery of ‘raw’ imagery, the historical lack of freely available imagery, and to a dearth of the experience needed to interpret diverse satellite datasets in many observatories (e.g., Dzurisin, 2000).
This article describes global datasets of InSAR observations of volcano deformation and draws primarily on the Smithsonian Institution Global Volcanism Program (GVP) Volcanoes of the World database (from here on referred to as VOTW) and for the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) volcano deformation catalogue. Some additional deformation measurement characteristics were compiled for this article (data sources are described). From a snapshot of these databases, we describe the temporal and spatial characteristics of InSAR-measured deformation at volcanoes. This allows us to examine two issues concerning the measurement and recording of deformation: (1) the type of magmatic processes we are most likely to be able to detect with InSAR and how these compare to ground-based measurements; and (2) considerations for constructing catalogues or databases of volcano deformation to provide accurate baseline data for future monitoring. We also discuss the implications of InSAR observations for our understanding of the location and lateral extent of magmatic systems.
SAR data & volcano monitoring
InSAR methods require at least a pair, (and preferably a longer time series), of SAR images to analyse the variation in phase. Variations in radar phase are a consequence of relative ground motion and/or changes to the surface scattering properties (as well as nuisance factors including path delays through the atmosphere due to changes in refractivity). Measurements of displacements are made from interferograms, maps of shifts in radar phase between images acquired at different times. As phase is cyclical, phase differences take the form of repeating ‘fringes’ with values between 0 and 2π, resulting in an ambiguity of 2π in phase measurements. The main challenges of analysing InSAR data include solving for this ambiguity and identifying or correcting the contributions that other factors, such as variations in satellite position or atmospheric composition, make to phase (InSAR methods are reviewed by Simons & Rosen, 2007). In addition, changes in the scattering properties of the Earth’s surface may result in phase incoherence, making deformation measurement impossible. This is particularly problematic on steep volcanoes with frequent explosive eruptions or rockfalls, on volcanoes with ice caps and in regions of dense vegetation (e.g., Pinel et al., 2011; Ebmeier et al., 2013; Lu & Dzurisin). Measurement thresholds have been lowered from centimetres to millimetres through the development of methods to correct atmospheric phase contributions (e.g., Parker et al., 2015; Bekaert et al., 2015), limit analysis only to stable (‘persistent’) scatterers (e.g., Hooper, 2008), and solve for deformation as part of a time series (approaches reviewed by Osmanoglu et al., 2016). Increasing numbers of systematic acquisitions by constellations of SAR satellite platforms offer the chance to improve on these detection thresholds, and provide the opportunity to apply signal processing approaches (e.g., independent component analysis, Ebmeier, 2016) to the analysis of large, multi-temporal InSAR datasets.
Other types of measurement (beyond ground displacements), have also been derived from the phase component of SAR imagery and successfully demonstrated for use in volcano monitoring, including topographic differences and variations in the coherence of the phase signal (Figure 1). Changes to topography on a metre-scale related to dome growth or the emplacement of flow deposits can be retrieved from the phase component of SAR images that have close temporal but large spatial separation (e.g., Ebmeier et al., 2012; Arnold et al., 2016, Naranjo et al., 2016). This is most successfully achieved with bistatic image pairs (close to simultaneously acquired, but spatially separated pairs of images) acquired by satellite missions designed for the measurement of topography (e.g. DLR’s TanDEM-X, Albino et al., 2015). Although the effectiveness of SAR measurements of topography during an effusive eruption have been demonstrated (Poland, 2014), such bistatic data are not yet widely available for use in monitoring. Finally, interferometric phase coherence captures how rapidly surface scatterers are changing and can in some circumstances be used to track the development of fresh lava flows as they are emplaced, settle and cool (e.g. Dietterich et al., 2012). Incoherence, caused by the emplacement of fresh explosive deposits including pyroclastic flows and ash fall, also has potential for tracking eruption progress, especially in conjunction with SAR amplitude measurements.
The contribution that InSAR and SAR data make to volcano monitoring varies between economic settings. In some regions, especially where volcano observatories1 employ specialist staff, interferograms are integrated into the data streams relied upon for decision-making, notably the Alaska Volcano Observatory (Meyer et al., 2015), the Hawai’i Volcano Observatory (Poland et al., 2008), Italian volcanoes (Buongiorno et al., 2008), at La Réunion (Peltier et al., 2010) and in Iceland (Sigmundsson et al., 2015b). The utilisation of new technology (Poland, 2014), developments in analysis and near real-time data integration (Meyer et al., 2015) and use of geodetic measurements in remote settings (Lu & Dzurisin, 2014) have all been driven by monitoring goals. For some observatories where SAR data are not integrated into monitoring streams, especially in Middle Income Countries (as defined by the Organisation for Economic Co-operation and Development Development Assistance Committee, OECD-DAC), SAR measurements have nevertheless been used to supplement ground-based measurements, especially where instrumental networks are sparse (e.g., Delgado et al., 2017; Ebmeier et al., 2016). Where local observatories do not routinely processes and analyse SAR imagery, external organisations or research groups may do so (e.g., U.S.G.S. Volcano Disaster Assistance Program (VDAP) or Volcano Hazards Program, and Committee for Earth Observations Satellites (CEOS) Volcano Pilot). Projects such as the CEOS Volcano Pilot have demonstrated that SAR data provide unique information about volcanic unrest, which is complementary to established monitoring networks and valued by observatory scientists (Pritchard et al., n.d., under review). Where volcanoes are unmonitored, especially in Least Developed Countries, and where there have not been recent eruptions, InSAR measurements may provide the only source of information about volcanic unrest (e.g., Biggs et al., 2011). Such measurements often come from retrospective analyses of SAR data archives, and thus do not constitute true monitoring, although some pilot schemes are getting closer to that goal.
Displacement measurements from interferograms are included in analyses of multi-year trends in a volcano’s activity by some volcano observatories (e.g., Wicks et al., 2006; Neri et al., 2009; Lu et al., 2010). In a few regions, over time windows of weeks to months, InSAR deformation measurements have contributed to datasets used to forecast the development of eruption (e.g., at Bar∂abunga, 2014, Sigmundsson et al., 2015a) or unrest (e.g., at Chiles-Cerro Negro, Ebmeier et al., 2016). At all volcanoes an understanding of deformation baseline is critical for interpreting new geodetic observations, and is especially important in absence of other geophysical datasets.
Some parameters of historical and current SAR satellite instruments used to measure volcano deformation
Swath width (km)
Ground pixel resolution (m)
Repeat time (days)
1991- 2000; 1995-2011 (gyroscope malfunction 2000 severely limits use)
26 x 6
18 x 6
1995-2013 (1); 2008 – (2)
multiple modes, including: 50 (Fine Beam Mode); 100 (Standard); 500 (ScanSAR Wide).
multiple modes including: 5 × 8 (Fine Beam Mode); 25 × 28 (Standard); 100 (ScanSAR Wide)
100 (image mode); 400 (wide swath)
30 × 30 (image mode); 150 × 150 (wide swath)
70 (Fine Beam Single); 350 (ScanSAR)
10 (Fine Beam Single); 100 (ScanSAR)
55 (Ultra Fine Beam Single); 490 (ScanSAR single)
3 (Ultra Fine Beam Single); 60 (ScanSAR single)
2008 – (TSX); 2010- (TDX)
4 (Starting Spotlight); 30 (Stripmap); 270 (Wide ScanSAR)
0.25 (Staring Spotlight); 3 (Stripmap); 40 (Wide ScanSAR)
2014 – date (a), 2016- date (b)
80 (Stripmap); 250 (interferometric wide swath); 400 (Extra wide swath)
5 × 5 (Stripmap); 5 × 20 (interferometric wide swath); 25 × 100 (extra wide swath)
12 (6 with 1b)
Constellation staggered launches between 2007 and 2010, active to present.
10 (Spotlight-2); 40 (Stripmap); 100 (ScanSAR)
1 (Spotlight-2); 3-5 (Stripmap); 30 (ScanSAR)
16 (≥ 1 for full constellation)
Identifying and interpreting magmatic and volcanic deformation
Deformation identified as volcanic or magmatic is generally in a geographic region with evidence of past volcanic activity and is additionally either (1) localised on a volcanic edifice/deposits or (2) consistent with a pressure change within the Earth’s crust. Most displacement signals that meet these criteria are related to structural (edifice growth and collapse), hydrothermal or magmatic processes. In most cases the spatial and temporal characteristics of magmatic deformation have limited overlap with those caused by tectonic or anthropogenic processes. However, ambiguities sometimes exist between magmatic and, for example, isostatic rebound (e.g., Lu & Dzurisin, 2014) or hydrothermal deformation signals (e.g., Biggs et al., 2011). Distinguishing between hydrothermal and magmatic volume changes is particularly challenging, and not usually possible from geodetic data alone without additional constraints from gravity or conductivity measurements. Separating out the magmatic and tectonic elements of a deformation event may also be non-trivial, for example, during an episode of rifting (e.g., Biggs et al., 2009) or during moderate earthquakes that sometimes accompany volcanic unrest or eruption (e.g., Diez et al., 2005; Ebmeier et al., 2016).
Magmatic deformation can be broadly defined as relating to pressure changes within a magmatic system. A first order interpretation of deformation at a volcano is often one of a change in volume within a long-lived magma chamber or reservoir (which could be loosely defined as a site of repeated intrusion). However, InSAR displacements have also been attributed to other physical mechanisms, including, but not limited to, one-off emplacement of dykes or sills (often called simply ‘intrusions’), phase changes (e.g., Caricchi et al., 2014), and the interaction of magma with a conduit system (e.g., Stephens et al., 2017). Parts of the crust where magma is stored are currently understood to include zones of partially crystalline mush, more mobile lenses of lower crystal fraction, and a higher crystal fraction framework. Zones of magma storage are expected to be spatially complex, with temporally varying types of connectivity between different parts of the system, allowing both the development of distinct chemical compositions and the assembly of mobile (and therefore eruptible) magma. Such systems as a whole have been designated ‘trans-crustal magmatic systems’ (e.g., Cashman et al., 2017), while magma storage zones of unknown structure and connectivity have been termed ‘magmatic domains’ (e.g., Sigmundsson, 2016).
The links between geodetic sources and the characteristics of complex magmatic domains are inherently ambiguous. A single observation of uplift, for example, does not allow us to discriminate between a one-off intrusion and the latest cycle of growth of a long-lived reservoir. Furthermore, we cannot necessarily discriminate between a pressure change throughout an entire system and one limited to some small section of a larger whole, i.e. we do not know what fraction of the magmatic system is involved in the deformation. However, if a deforming system can also be imaged tomographically (e.g., as a zone of low seismic velocity, Sigmundsson, 2016), then the relationship between the actively deforming sections and broader magmatic domain can be assessed.
This article synthesises volcanic and magmatic displacement measurements made around the world by a broad range of authors with diverse frameworks for the interpretation of measurements made over the past two decades. Here, we therefore describe ‘deformation source’ characteristics, rather than imposing terminology associated with different physical interpretations by different authors, e.g., ‘chamber’, ‘reservoir’ or ‘intrusion’. We refer to the full extent of magma storage throughout the crust as a trans-crustal magmatic system, but use the term ‘magmatic domain’ more generally to describe a part of that system - for example, a region examinable from a particular type of measurement. We discuss the implications of the global InSAR deformation dataset for understanding magmatic processes in the discussion.
Insights from global InSAR volcano deformation measurements
InSAR measurements have provided a diverse picture of magmatic processes in different tectonic settings. One major advance provided by InSAR data is the possibility of regional surveys, which have now been carried out at most of the Earth’s major volcanic arcs (Alaska/Aleutians, Mexico/Central America, Northern-Central-Southern-Austral Andes, Indonesia & Japan). The incorporation of ‘null’ results into such studies has allowed global statistical demonstration of the association between deformation and eruption (Biggs et al., 2014) and the observations that some smaller volume (< VEI 3) eruptions at stratovolcanoes take place without generating measurable deformation (e.g., Pritchard & Simons, 2004, Moran et al., 2006, Ebmeier et al., 2013).
InSAR studies have revealed differences in magmatic systems related to tectonic settings. At volcanic arcs InSAR measurements have provided evidence for deformation sources at a range of depths (e.g., Lu & Dzurisin, 2014), including the growth of mid-to lower crustal plutons (e.g., Pritchard & Simons, 2004; Ruch et al., 2008) and shallow, extensive bodies aligned with the arc azimuth that subside during great earthquakes (Pritchard et al., 2013; Takada and Fukushima, 2013). Both long term (e.g., Parker et al., 2014) and transient subsidence (Caricchi et al., 2014) have been interpreted in terms of phase changes related to the cooling of an intrusion.
InSAR observations have provided major insights into the progression of rifting episodes and fissure eruptions in East Africa (e.g., Wright et al., 2006; Biggs et al., 2009; Pagli et al., 2012) and Iceland (Sigmundsson et al., 2015a, b). These have included the observations that magma is supplied to the crust intermittently, rather than steadily, and that its storage is distributed over multiple locations and depths in the lead up to a rifting episode (Wright et al., 2012; Biggs et al., 2016). Ascent into the shallow crust encompasses both repeated sill intrusion and multiple, interacting dykes (e.g., Hamling et al., 2010). Rift zones are extensively intruded by interconnected lenses of melt (e.g., Heise et al., 2007), but InSAR measurements have nevertheless shown the importance of repeated deformation originating beneath volcanoes and of repeated sill intrusion, which also seem to play a role in rifting events. For example, uplift attributed to sill intrusion at Alu and Gabho in Ethiopia preceded the Alu-Dalafilla and Dabbahu rifting episodes, respectively (Pagli et al., 2012), and similarly a deformation source beneath dormant Gelai volcano was active during rifting in Tanzania (Biggs et al., 2013). In contrast, neither long term uplift nor subsidence in the Taupo Volcanic Zone New Zealand, are associated with major volcanic features, but are centred beneath the Bay of Plenty and the boundaries between calderas in the Taupo volcanic zone, respectively (Hamling et al., 2016; Hamling et al., 2015).
The measurement of deformation at ocean islands with InSAR presents particular challenges due to the lack of far field observations, especially where only a fraction of the full displacement field may be discernible (e.g., Gonzalez, et al., 2013; Lu & Dzurisin, 2014). Long-lived hotspot eruptions at Kilauea, Hawai’i (e.g., Poland et al., 2008) and Piton de la Fournaise, Réunion (e.g., Peltier et al., 2010), as well as repeated episodes of intrusion in the Galapagos (e.g., Bagnardi et al., 2013) dominate the small number of systems worldwide where multiple cycles of eruption have been observed geodetically (although this also includes non-hotpot volcanoes such as Okmok, Aleutians, Biggs et al., 2010a). As well as cycles of pre-eruptive uplift followed by co-eruptive subsidence, such long-lived eruptions have also provided evidence for endogenous growth, change in intrusion location in response to varying stress fields (e.g., Bagnardi et al., 2013) and cycles of feedback related to topographic structures (e.g., Jónsson, 2009; Lénat et al., 2012).
Freely accessible catalogues and databases of past volcanic eruptions, activity and unrest are critical for identifying gaps in current observations and for understanding the context of recent activity (e.g., Venezky & Newhall, 2007; Brown et al., 2014; Loughlin et al., 2015). The dataset compiled for the Smithsonian Institution GVP’s VOTW relational database (Global Volcanism Program, 2013) is being developed to allow deformation to be compared to eruptive parameters and other information including petrological and emission data. It contains data about all of the volcanoes known to have erupted in the past 10,000 years and is housed at the Department of Mineral Sciences at the National Museum of Natural History, Smithsonian Institution, Washington, D.C., USA. The addition of deformation (and emissions) data to this database has required the adoption of new database structures to allow database entries to be made for deformation and emission periods as well as eruptions. Any number of deformation periods can be added to the database for a particular volcano, with entries made for a set number of parameters including: start and end dates, method of observation (InSAR, GPS, tilt, etc.), direction of displacement, displacement and/or displacement rate, location of centre of deformation, spatial extent of deformation, likely cause of deformation, and remarks. In addition to this basic information, the database also aims to record modelling information where available, as well as sample images and appropriate references. VOTW currently includes 422 records of deformation measured at 198 different volcanoes. Users will be able to search for deformation data on the Smithsonian Institution GVP website (volcano.si.edu) and submit published measurements through a Microsoft Excel upload tool for GVP staff to approve and commit.
The COMET volcano deformation catalogue is, at this stage, an inventory of past observations of volcano deformation, designed to accompany and provide context for the release of the most recent Sentinel-1 interferograms for volcanoes around the world. In the long term the COMET catalogue should provide feeder information for the VOTW database. The COMET catalogue is currently hosted by the University of Bristol, UK (volcanodeformation.blogs.ilrt.org) and is designed as a repository for information about past observations of deformation, primarily, but not exclusively, from InSAR measurements, and largely recorded in ‘free text’ format. The catalogue is designed to allow community contributions, with most information recorded in ‘free text’ boxes (sample guidelines for contributors to the COMET deformation database are shown in Appendix 1 ). Volcano names, numbers and locations use the VOTW database conventions to ease future compatibility. Contributors are asked to select multiple options from a list of categories to describe the types of deformation measurement and the range of inferred causes before describing deformation characteristics in as much detail as possible. Catalogue entries are organised by volcano, with multiple observations recorded in the same entry. The COMET database currently includes entries for 1011 volcanoes, of which 464 have reported geodetic measurements (including null results). Although the majority of the information in the catalogue comes from publications, our intention is to provide a forum for sharing information from the ‘grey’ literature, including, for example, observations from otherwise unpublished student theses, conference abstracts, observatory reports and personal communications, which are allowed as references. The COMET catalogue records ‘null’ results – that is, a measurement of no deformation with a quantified uncertainty (or measurement threshold) over a particular time period (e.g., Moran et al., 2006; Ebmeier et al., 2013). Such data are critical for robust probabilistic analyses of links between measured deformation and outcomes in term of volcanic activity, but are rarely published.
Here we present a synthesis of observations based on a ‘snapshot’ subset of information from the VOTW and COMET databases (from March 20172). Our combined dataset encompasses InSAR measurements of 339 episodes of deformation at 160 different subaerial volcanoes and is spread globally across all arcs, rifts and oceanic islands (note that as this encompasses just InSAR measurements, this total is lower than number of records in either database, and lower than the 485 episodes of deformation in the appendix to Biggs and Pritchard, 2017, which combines both InSAR and ground-based measurements). This work is focussed on parameters of individual deformation episodes including deformation episode duration, maximum deformation rate, approximate signal area (‘footprint’) and inferred depth of the associated source. Not all of this information is available for every deformation episode, or even every volcano. The uncertainties on some of the properties are also highly variable between catalogue entries. For example, the values for maximum displacement rate may be reported directly in publications or estimated from publication figures, and may therefore depend on the figure resolution and quality as well as the processing and reporting choices made by the authors. Inferred depth is non-unique, dependent on choice of model and optimisation method, as well as a normally incomplete knowledge of crustal properties, and can trade-off with estimated volume change (e.g., Masterlark, 2007; Hickey and Gottsmann, 2014). The majority of inverse modelling of geodetic data relies on analytical solutions for particular geometries and assumptions that the Earth’s crust behaves as a homogeneous uniform half-space (e.g., Segall, 2010). The reliability of geodetically determined depths may therefore depend on assumptions about crustal rheology and whether any independent constraints on source depth and location inform the modelling. We discuss some of the challenges of classifying deformation data for a catalogue or database, along with strategies for making such exercises useful in the discussion section, below.
In this article we discuss both direct observations of InSAR-measured signals, such as signal area and duration of displacements, as well as parameters derived from the InSAR measurements, such as deformation source location, displacement rate and source depth. We also use contextual information such as dates of eruptions and published interpretations of deformation signals in our discussions. This section describes considerations for defining parameters than best characterise both InSAR measurements and the episodes of deformation that they capture.
Direct parameters of InSAR signals
Displacement signal area is a critical consideration for designing monitoring networks, measurement campaigns and tasking satellite acquisitions. Signal area is reported in some entries of the COMET deformation catalogue, and we have supplemented this information from the primary literature (e.g., Additional file 1: Table S1). Whilst signal area can be inferred directly from InSAR data, it depends on both the properties of the deformation source (depth, volume or pressure change and geometry) and on the detection threshold for deformation in the InSAR data (which depends in turn on radar wavelength, time span of observations, look direction of satellite, number of acquisitions in a time series, ability to reduce atmospheric noise, and level of coherence). Ideally it would be defined using a threshold displacement magnitude, relative to a far field location, but it is rarely possible to extract such information from publications. We therefore approximate deformation footprint areas by using the signal diameters provided by authors (or estimated from publication figures), assuming either a circular or elliptical shape. As signal areas have a range that spans orders of magnitude, this crude approximation is sufficient for a first order comparison with other properties (e.g., maximum displacement rate in Fig. 2B).
Derived deformation source parameters
The VOTW database records both mean and maximum displacement rate (in cm/yr), where it has been reported in the source publications. Mean displacement rates derived from total displacement in a single interferogram (or from the sum of a chain of images) can be calculated for most episodes in the dataset. However, mean displacement rates are only physically meaningful if the duration of deformation is equal to or greater than the time spanned by the interferogram(s). Maximum displacement rates estimated by the authors of InSAR studies and recorded in both VOTW and COMET databases are more likely to represent true deformation rates, but are limited by satellite repeat time. For example, the maximum displacement rate of a transient, but rapid, acceleration is likely to be underestimated if its duration was a small fraction of satellite repeat time. The highest deformation rates in our datasets (metres per day) were detected during caldera collapse at Bar∂abunga (Gudmundsson et al., 2016), and dyke opening during eruptions or rifting events (e.g., Pagli et al., 2012) where duration could be defined from independent data sources. Despite measurement limitations, some general trends can be observed in the relationship between displacement rate and other deformation signal parameters. There is a broad anti-correlation between displacement rate and duration (e.g., Fournier et al., 2010; Biggs & Pritchard, 2017), as deformation rates above a few 10s cm/yr tend not to continue longer than a few weeks to months (Laguna del Maule, 28 cm/yr., is a notable exception, e.g., Feigl et al., 2014). Deformation signals with large spatial footprints (> 1000 km2) have low rates (Figure 2B) and have been attributed to the growth of plutonic bodies in the mid to lower crust (e.g., Pritchard & Simons, 2004).
One of the most useful derived parameters is the depth of the inferred deformation source. Apparent depth of deformation depends both on the processes detected (e.g., the intrusion of a sill, overpressure of (part of) a reservoir or ascent of a diapir) and on the approach taken to modelling the geodetic data. Source depth depends on a range of factors relating to buoyancy, magma composition, stress conditions and pre-existing structural features (e.g., Chaussard & Amelung, 2014), as well as conditions that promote the amalgamation of lenses of melt and volatiles in a mush system to form larger bodies of eruptible magma (e.g., Cashman et al., 2017). We do not discuss estimations of volume change here, because it is strongly dependent on the reservoir or intrusion compressibility, which is very poorly constrained, and depends on both magma and exsolved gas compressibilities as well as source geometry and material properties of the surrounding rock (e.g., Rivalta and Segall, 2008; Amoruso and Crescentini, 2009; McCormick Kilbride et al., 2016). Modelled source depth is recorded for some deformation episodes by both VOTW and COMET, and we build on this by including further preferred deformation source depths from publications (including any estimation of uncertainty provided). Such depths are affected by the assumptions required for modelling, as well as the spatial and temporal resolution of geodetic data (and therefore the SAR instrument used for measurement) and processing methods (e.g., single interferogram, time series, persistent scatterer). We do not attempt to separate out data from magmatic and hydrothermal sources here, but note the authors’ interpretation of the deformation. Some of the shallower depths reported are therefore likely to relate to hydrothermal systems, so that any magma storage is actually deeper (e.g., at Campi Flegrei, Trasatti et al., 2008; Gottsmann et al., 2006). Figure 2C and D illustrate the distribution of inferred depths of deformation sources. For this figure we have used the maximum estimated source depth where a range of possible values have been evaluated, or where multiple depths have been suggested for the same deformation events. Only depths associated with potential sites of magma storage are included here (in practice, the majority of these are modelled as point, spherical, ellipsoidal, penny-shaped or sill-like sources - the depth ranges estimated for opening dykes are excluded).
Summary of InSAR signal characteristics
If we were to describe a ‘modal’ InSAR deformation signal from the global datasets described above, it would be attributed to the movement of magmatic or hydrothermal fluids within the shallow crust (< 5 km depth), not associated with an eruption, with a rate of a few cm/yr. and footprint area < 100 km2. The properties of this modal signal are primarily the consequence of the historical spatial and temporal boundaries of InSAR measurement, rather than evidence for a universally common deformation sources. In fact, we expect melt to be distributed at different fractions throughout the crust, with significant differences between rift zones, where melt fraction is expected to be high over large areas, and in arcs, where accumulation of higher melt fraction lenses will form melt-rich reservoirs at depths where stress conditions are favourable. However, characterisation of the depths and setting where most geodetic signals originate is a good starting point for assessing the processes for which SAR data are likely to be most useful for monitoring.
There is a peak in the number of displacement episodes in our study that suggest a maximum deformation source depth of 4-5 km (Fig. 2C), and only half of the deformation signals were thought to be deeper than this. That half of potentially magmatic source detected with InSAR are thought to lie in the shallow crust (< 5 km) is unsurprising, given that pressure changes in shallow systems cause higher magnitude surface displacements, and are therefore more likely to be detected by InSAR measurement (i.e. small volumes changes in the mid to lower crust are unlikely to be detectable). It is likely that very shallow sources are underreported due to both the lower spatial sampling of many historical measurements and particularly where high deformation rates or eruptive products cause phase incoherence near active vents. Although the ranges of co-eruptive, pre-eruptive (here defined as occurring in the year before an eruption) and inter-eruptive inferred maximum depths largely overlap (Figure 2D), the mean value for deformation sources involved in co-eruptive deformation is ~ 2 km shallower than that for those not associated with eruption. Whilst there are examples of volcanoes where co-eruptive subsidence is shallower than the pre-eruptive uplift (e.g., Eyjafjalljökull 2010, Sigmundsson et al., 2010), this is not always the case, and the difference in mean values is more likely to be because most examples of mid-lower crustal deformation are non-eruptive. Deformation sources with maximum depths deeper than ~ 12 km were inferred only from displacements that occurred during inter-eruptive periods.
About 60% of InSAR measurements of deformation catalogued here (Fig. 2A) have estimated durations that exceed a year, and only 14% have durations shorter than a month (39 of 285 entries for which we have signal durations). Those thought to have lasted less than a month were almost all high magnitude (i.e. much larger that half radar wavelength) and produced displacements that endured at least as long as the satellite repeat time. Ground-based deformation measurements record a slightly higher proportion of deformation lasting less than a month (20%). Pre-eruptive unrest lasting days to months, especially where deformation is low magnitude or transient, is likely to be under-represented in historical InSAR data. However, newer satellite missions with shorter repeat intervals (TerraSAR-X repeat = 11 days, COSMOSkyMed constellation minimum repeat = 1-16 days) have recently made the measurement of rapid processes, particularly those connected to lava dome growth (e.g., Salzer et al., 2014) or conduit processes (e.g., Stephens et al., 2017), possible for the first time. At the other end of the scale, the duration of satellite missions (typically 5-10 years) is also potentially a limiting factor for detecting low-rate, long duration deformation. Ground-based measurements have captured 10% more deformation signals lasting > 5 years than InSAR, suggesting that the longest duration processes also remain under-represented. The planned long duration of current missions, especially the European Space Agency’s 20 year commitment to the Sentinel-1 programme, should improve this in the coming decades. A small proportion of volcanoes have levelling survey data that extends back decades, providing evidence of many decades of subsidence (e.g., > 50 years at Medicine Lake, Parker et al., 2014; Santorini, Parks et al., 2012), uplift (decades e.g., Long Valley, Newman et al., 2006) or complex deformation sequences (e.g., 100s years, Campi Flegrei, Lundgren et al., 2001 and references therein). InSAR measurement has been possible since 1992 at the earliest, so the longest possible duration of InSAR deformation observations is 25 years, although in effect, measurements have been possible for a much shorter time in some parts of the world due to dense vegetation and low frequencies or gaps in image acquisitions.
List of the 33 InSAR detected deformation sources that are more than 5 km from the nearest Holocene volcano. Displacements that lie beneath Pleistocene volcanoes, or apparently recent volcanic deposits are indicated with a star, and details provided in the ‘context of deformation’ column
Approximate offset distance (km)
Point of reference for offset
Context of deformation
Description of Footprint
Estimated deformation source depth (km)
Subsidence triggered by Tohoku earthquake, near two Cenozoic calderas
Ellipse, primary axis ~ 55 km
1.8 to top of pluton
Takada and Fukushima 2013; Ozawa and Fujita 2013
Korovin stratocone – site of eruption
Co-eruptive subsidence (2006) and subsequent uplift
Roughly concentric on Kliuchef cone
Mogi sources 3-4 km (subsidence); 5-8 km (uplift)
Lu and Dzurisin 2014
Uplift – no associated activity
Morales Rivera et al., 2016
Subsidence triggered by Maule earthquake
Ellipse, primary axis ~ 10 km
Shallow point source
Pritchard et al. 2013
“Bay of Plenty”, Off axis Taupo Volcanic Zone
Uplift on margin of Taupo Volcanic Zone
Roughly 40 km diameter
Point source, 9.5 km
Hamling et al., 2016
Bora-Bericcio volcano (nearest GVP)
Uplift in low-lying area between Bora Berrichio and Tulla Moje
Penny shaped crack 0.9-1.3 km
Biggs et al. 2011
Subsidence beneath Pleistocene collapse caldera, triggered by Maule earthquake
Ellipse, primary axis ~ 30 km
Mogi source, 7.2 km
Pritchard et al. 2013
Chillán, Nevados de
Subsidence triggered by Maule earthquake
Ellipse, primary axis ~ 15 km
Spherical source, 7 to 10 km
Pritchard et al. 2013
Cordón del Azufre volcano (nearest GVP)
Uplift between Lastarria and Cordon del Azufre, spans wide area encompassing three active volcanic centres and 2 older calderas
Ellipse, primary axis ~ 45 km
Various analytical models, 6 to 15 km
Uplift 1992-1997 attributed to intrusion on SW flank. This is most likely a one-off sill intrusion, not connected to reservoir formation
Roughly circular, ~ 5 km diameter
Okada planar sources used to model intrusions, ~ 1 km depth
Fourpeaked volcano (nearest GVP)
Uplift between Fourpeaked and Douglas volcanoes
Roughly circular, diameter approximately 15 km
Mogi source, ~ 7 km
Lu and Dzurisin 2014
Hertali volcano (nearest GVP)
Epsiodes of uplift and subsidence between Hertali, fresh looking lava flows above signal
Roughly elliptical, primary axis ~ 8 km
Sill, 2.7 to 8.8 km
Biggs et al. 2011
“Hualca Hualca” *
Sabancaya (nearest GVP), 2.5 km East of Hualca hualca summit
Uplift 2 cm/yr. 1992-1996, unclear if pluton has any connection to either Sabancaya or Hualca hualca
Roughly circular, ~ 90 km diameter
Various EHS source geometries, 11 to 13 km
Pritchard & Simons, 2004
Uplift during seismic swarm
Roughly circular 12 km diameter
Mogi source, 7.9 km
Nishimura et al. 2001
Pre-eruptive uplift (2011)
Miyagi et al., 2013
Subsidence triggered by Tohoku earthquake, displacement well-aligned with Cenozoic caldera rim
Ellipse, primary axis ~ 40 km
Takada and Fukushima 2013; Ozawa and Fujita 2013
Variable rate subsidence SE of Lassen Peak
Roughly circular, 30-40 km diameter
Various elastic half space models, 8.18-11.6 km
Ol Doinyo Lengai summit
Deflation of reservoir during Gelai rifting event, beneath Pleistocene shield volcano
Complex signal due to coincidence slip on fault and dyke opening
Mogi source, 4-8 km
Biggs et al., 2013
Volcano summit and most recent eruptive vent
Episodes of uplift and subsidence
Roughly circular, variable diameter
Mogi and Sill sources in range of 5.0 to 7.4 km
Lu & Dzurisin, 2014
Subsidence triggered by Tohoku earthquake
Ellipse, primary axis 15 km
Ellipsoidal pluton, top depth 3.8 km
Takada and Fukushima 2013; Ozawa and Fujita 2013
Nevado del Ruiz
Temporally correlated unrest at Nevado del Ruiz, but nearer Santa Isabel
Uplift during unrest and eruption at Ruiz volcano
Roughly circular, 20 km
Point or spheroidal source,14 km
Lundgren et al., 2015a
Cordón de Puntas Negras (El Laco volcanic complex is ~ 10 km to the SW).
Uplift 1992-2003, subsidence 2003-2010, SE terminus of Puntas Negras
Roughly circular, 17 to 20 km
Mogi source, 9 km pre-2003 uplift, 13 km post 2003 subsidence
Henderson and Pritchard 2013
2011-2012 eruptive vent
Three episodes of pre-eruptive uplift, co-eruptive subsidence and an event triggered by the 2010 Maule earthquake
Various geometries and areas of signals between 1996 and 2012, areas from 20 to 400 km2, some sources beneath past vents
Various analytical sources, depths 5-9 km.
Jay et al., 2014; Delgado et al., 2016
7 and 12
Uplift near Geyser Bight and Hot Springs Cove (smaller patch of subsidence superimposed)
Ellipse, primary axis 7 km
Lu and Dzurisin 2014
Cerro Blanco volcano (note some difference in the application of name ‘Robledo’ to caldera and lava dome in publications)
Long term subsidence
Roughly circular, diameter 20 km
Various analytical models, 4.5 to 6 km
Pyre’s peak, site of most recent eruptions
Pulses of uplift and subsidence in eastern caldera
Roughly circular, diameter 10 to 15 km
Mogi source, 5.5 km during uplift, 2 km during subsidence
15 and 30
Uplift East of Shishaldin and NW of Shishaldin (close to Fisher) temporally correlated with 2004 eruption
Gong et al. 2015
Uplift during seismic swarm NE of Spurr, near Strandline Lake
Approximate diameter of 35 km
Mogi source, 12 to 16 km
Lu and Dzurisin 2014
Uplift centred between Tanaga and Takawangha spanning both
Approximate diameter of 16 km
Mogi and Yang sources, 5 to 8 km
Lu and Dzurisin 2014
Uplift with variable rate, WSW of volcano
Ellipse, primary axis 20 km
Analytical and numerical models, 5 to 7 km
Subsidence triggered by Maule earthquake
Ellipse, primary axis 20 km
Spherical source, 8 to 10 km
Pritchard et al. 2013
Post-eruptive uplift, near Holocene crater rim
Uplift SE of volcano
Mogi source, ~ 4.2 km-
Delgado et al., 2017
Subsidence triggered by Tohoku earthquake
Subsidence triggered by Tohoku earthquake
Ellipse, primary axis 30 km
Takada and Fukushima 2013; Ozawa and Fujita 2013
Of the 152 observations of deformation made with ground-based instruments from our snapshot of the VOTW database, 98 are from Developed Countries (DCs) and a further 19 from Upper Middle Income Countries (UMICS) and territories (2017 OECD-DAC list of Official Development Assistance recipients, www.oecd.org/dac/stats/daclist.htm), and < 2% of measurements come from Least Developed Countries (LDCs), although these countries have roughly 8% of Holocene volcanoes. LDCs are better represented in the global InSAR catalogue with 15% of all records (DCs and UMICS make up ~ 70% of observations). This illustrates the potential that satellite remote sensing has for monitoring volcanoes in LDCs, where funding for ground-based infrastructure is particularly limited.
The processes that eventually lead to eruption, including the intrusion, migration and ascent of magmatic fluids, take place on a range of timescales from days to decades (indicative red bars on Fig. 4A). For monitoring, the most important of these is arguably the duration of any pre-eruptive unrest. Passarelli & Brodsky, 2012 find that the duration of pre-eruptive unrest is generally longer for volcanoes with higher silica contents: from minutes to a year for basalts, and from days to years for andesites. Phillipson et al., (2013) find that the median duration of pre-eruptive unrest captured by monitoring networks varied significantly between different types of volcanoes, with pre-eruptive unrest at complex volcanoes lasting an average of just two days before eruption, about a month at stratovolcanoes and > 2 months at shield volcanoes and calderas. The timescales over which an eruption evolves in character are also important for assessing volcanic hazard. Decrease in reservoir overpressure, for example, has been estimated from GPS (e.g. Hreinsdottir et al., 2014a; Mastin et al., 2008) and InSAR measurements (e.g., Sigmundsson et al., 2015a, b) over weeks to months. Deformation of a lava dome (hours to days, e.g. Salzer et al., 2014), change in effusion rate (days to months, e.g., Poland, 2014) or slip during mass wasting (over years, e.g., Froger et al., 2001; Solaro et al., 2010 or minutes e.g., Voight et al., 1981) may also indicate changes in hazard level during an ongoing eruption. Changes in such deformation signals may occur rapidly, or gradually over years, requiring measurement at regular intervals for detection. Satellite repeat intervals that exceed the duration of target deformation signals limit the timescales over which InSAR is appropriate for monitoring. Historically, satellite repeat times were 35-46 days (ERS, ENVISAT-ASAR, ALOS-PALSAR, Table 1) with actual measurement frequency being even lower in areas with seasonal snow cover or flooding and for instruments with limited power and acquisition strategy that did not prioritise volcanic hazard. The higher number of InSAR measurements for signal durations of > 12 months (Fig. 4B) reflects the under-representation of signals lasting days to months in our records. The lack of a significant increase in the cumulative number of InSAR observations between one and two decades is a consequence of the limited time over which InSAR measurement has been possible. Presently, multiple cycles of eruption and intrusion have only been observed at a handful of volcanoes (e.g., Sigmundsson et al., 2010; Lu et al., 2010; Biggs et al., 2010a, b; Bagnardi et al., 2013), although cycles of activity within long-lasting eruption have been observed at several more (e.g., Poland et al., 2008; Peltier et al., 2010).
Pre-eruptive deformation can take a range of forms, but its utility for volcano monitoring and ultimately forecasting eruptions depends on our ability to interpret the process responsible and forecast its evolution. A simple definition would be any deformation that occurs in a specified time period leading up to an eruption, but at active, frequently erupting systems, it may be unclear whether this is related to the previous eruption or simply baseline activity.
About half of the deformation episodes in our catalogue occur in the year before a volcanic eruption or span the eruption itself (excluding flow deposit and gravity-driven subsidence). However, a more useful definition of ‘pre-eruptive’ might require the demonstration of a causal link to the eruption. This is relatively straight-forward where pre-eruptive uplift continues until the onset of eruption, and is followed by similar co-eruptive subsidence (e.g., at Okmok, 2008; Lu et al., 2010) or when the onset of deformation is correlated with seismicity (e.g., at El Hierro, Gonzalez et al., 2013). However, gaps of days to months between a period of observed inflation and the eventual eruption (e.g., at Kerinci, Sinabung and Slamet, Indonesia, Chaussard et al., 2013) make a connection with eruption harder to infer and interpret. Uplift preceding eruption can be of long duration (e.g., 6 years before the 2011 eruption of Hudson, Delgado et al., 2014), or continue after an eruption has taken place (c.f. 2012 phreatic eruption at Copahue, Velez et al., 2016). There are a growing number of observations of episodes of uplift occurring both months and years before the onset of eruption: the 2010 eruption of Eyjafjallajökull tapped sills both emplaced gradually in the three months before eruption, and a decade earlier (Sigmundsson et al., 2010). Such intrusions could only be classified as ‘pre-eruptive’ in retrospect. Distinguishing between a shallow pre-existing reservoir being ‘charged’ by an intrusion (e.g., as interpreted at Santorini, Parks et al., 2012) and endogenous growth of a volcanic edifice (e.g., Tungurahua, Biggs et al., 2010a, b; Fernandina, Bagnardi et al., 2013) therefore requires an understanding of the pre-existing magmatic plumbing. These historical deformation measurements provide a useful baseline for interpreting future episodes of uplift in particular. For future monitoring, InSAR is likely to be useful for ‘pulsed’ reservoir charging, where the time between intrusion and eruption greatly exceeds satellite repeat time (but is also less than the total monitoring duration) and measurement of repeated intrusion allows a picture of the volume and location of eruptible magma to be built up.
Calderas typically have spatially and temporally variable deformation signals, as well as some of the highest rates of non-eruptive deformation detected using InSAR (Wicks et al., 2006; Le Mével et al., 2015; Jay et al., 2014; Biggs, et al., 2009). At larger calderas (> 10 km diameter) deformation is frequently centred on caldera rims and extends beyond their topographic boundaries. These include displacements attributed to both magmatic and hydrothermal processes where deformation signals occur outside caldera boundaries, including at Uzon caldera, Kamchatkta (Lundgren & Lu, 2006) and at Yellowstone, USA (Wicks et al., 2006). The peaks of displacement signals at active calderas are rarely focussed on the most recent eruption vent, although some do correspond to the location of a resurgent dome (e.g., Newman et al., 2006).
16% of observations are between a surface radial distance of 5 and 10 km from the nearest volcano, and are largely at upper crustal (< 10 km) depths. These examples are more likely to provide an indication of the lateral extent of shallow magma storage at currently active volcanoes, and where associated with eruption, have been interpreted as evidence for connections between bodies of melt-rich magma over distances exceeding 5 km (e.g., at Puyehue, Jay et al., 2014; Seguam, Makushin, Korovin, Lu & Dzurisin, 2014; Kirishimayama, Kato & Yamasato, 2013). This includes many mature caldera systems (Atka, Lu & Dzurisin, 2014; Puyehue-Cordón Caulle, Jay et al., 2014), but also several stratovolcanoes (Three Sisters, Wicks et al., 2002; Villarica, Delgado et al., 2017 and Fourpeaked, Lu & Dzurisin, 2014). Deformation ≥5 km from the nearest volcano is roughly half as likely to be co-eruptive (16% of deformation ≥5 km from nearest volcano), as deformation within 5 km of the edifice (39% of deformation < 5 km from nearest volcano).
An implicit assumption behind the design of most instrument networks and monitoring strategies is that a persistent body of melt (a chamber) or melt-rich magma (part of a reservoir) that feeds eruptions is located directly beneath the volcanic edifice and/or active vent. This assumption is particularly common for volcanoes without either recent eruption or a record of unrest, and where information about subsurface plumbing is therefore minimal. The InSAR record of volcano deformation provides evidence about deformation signal characteristics that allow us to refine this at individual volcanoes, and more generally provides temporal and spatial constraints for monitoring network design, and the characteristics of deformation we can expect to detect using InSAR.
Implications for the use of InSAR in volcano monitoring
Regular acquisition strategies (e.g., Sentinel-1, COSMO-SkyMed volcano background mission, ENVISAT in some parts of the world) has meant that InSAR measurements have already been used for monitoring the development of unrest or eruptions lasting weeks to years (e.g., Poland, 2014; Sigmundsson et al., 2015a, b). If satellite acquisition timings are fortuitous, InSAR may also provide important deformation information in near to real-time during a volcanic crisis, but serendipitous overflight times cannot be relied upon for monitoring. Data from multiple satellites or constellations are likely to be required for the detection of pre-eruptive deformation that occurs on similar timescales to the satellite repeat times. Given that almost one in five InSAR-detected deformation episodes are thought to exceed the duration of the window over which measurements were made, the proportion of deformation episodes found to last multiple years or even decades is likely to increase in the future. The number of short duration, transient deformation episodes detected is also likely to increase due to the impact of shorter repeat time instruments and especially the application of constellations of similar satellites (e.g., COSMO-SkyMed 1-4 and Sentinel-1a,b). Improving our detection limits and establishing good baseline measurements, are also critical for being able to recognise pre-eruptive deformation.
About half of the deformation sources ≥5 km from the nearest volcano did not encompass the edifice itself. Such signals are likely to be missed by observations made from higher resolution instruments with narrower swath widths (Table 1), especially in TerraSAR-X and COSMO-SkyMed’s Spotlight modes. For example, uplift southeast of Nevado del Ruiz, Colombia, was outside of the footprint of both the GPS network and the COSMO-SkyMed imagery acquired over the volcano during unrest, but was captured by RADARSAT imagery over the same time period (Lundgren et al., 2015). Observation footprints require a radius of > 20 km about the volcano of interest, to capture at least part of > 90% of the deformation signals in the historical InSAR catalogue. Good practice for monitoring could encompass the integration of higher resolution imagery (e.g, Spotlight mode TerraSAR-X) over active volcanoes with less frequent, broader swath imagery to identify any distal deformation processes (e.g., Sentinel-1 or ALOS-2).
Considerations for recording volcanic and magmatic deformation
The usefulness of any record of volcano deformation depends heavily on the quality of reporting. Publications from scientific journals generally provide sufficient information for measurements to be reproduced, but often lack description of signal parameters (e.g., precise location, area, discrimination between mean and maximum deformation rate). This information can often be extracted from figures, although this adds additional uncertainty associated with figure scale and labelling. Informal (‘grey’) literature, including conference abstracts, volcano observatory reports and personal communications may record that an observation of deformation was made, but often lack any further description. Furthermore, observations of a lack of deformation are rarely reported. An important goal of the production of catalogues and databases of volcanic and magmatic deformation is therefore to encourage community contributions of records of InSAR measurements. One purpose of this article is to engage the international community in reporting deformation to be recorded in the COMET and GVP VOTW databases.
A fully relational database is the most desirable format for recording volcano and magmatic deformation data, as it would allow future users to search for deformation episodes on the basis of location, as well as properties such as rate, duration, area or inferred depth. However, the construction of a database requires defining categories that impose some interpretation on observations. For example, recording a deformation rate, requires that a new entry is made every time a new rate is measured. Similarly, periodic pulses of uplift and subsidence attributed to the same source would require multiple database entries. A suitable approach at least for preliminary catalogue entries, is to allow a ‘free text’ format, so that as much information can be recorded as possible, allowing for unexpected styles of deformation that do not conform to previously established definitions. This also allows for a description of uncertainty, which would currently be challenging to define uniformly for InSAR measurements due to the diversity of data, processing and analysis methods widely used. However, community contributions provided in this manner are likely to vary in consistency more than entries to a database with clear definitions. The paths taken to describe deformation by GVP’s VOTW database (orange) and COMET deformation catalogue (red) drawn upon here, are indicated on the flowchart in Fig. 7, which shows the areas of overlap and difference between the two approaches. A further consideration for the dissemination of InSAR data is the challenge of storing and sharing the very large data files. The seismic community generally share event catalogues in the first instance, and an analogous approach for InSAR may be to share deformation signal parameters or heavily downsampled data.
Characteristics of shallow magmatic domains
InSAR measurements allow us to identify distinct sites of magma storage at high spatial resolution. As relatively few magmatic systems have been imaged tomographically (e.g., Lees, 2007; Sigmundsson, 2016), at many volcanoes this provides the best evidence of the lateral extent of many active magmatic domains. The decrease in the number of deformation sources found with increasing depth (e.g., Figure 6) may reflect a transition away from elastic rheology, but is also a consequence of measurement detection thresholds (i.e., deep sources will only cause measurable deformation if volume changes are very large). The lateral distribution of sources, however, is not correlated to systematic measurement uncertainty or modelling choices, and is therefore expected to capture real characteristics of magmatic systems. Laterally extensive magmatic domains encompassing multiple deformation sources (Figure 6) are consistent with the upper levels of a complex, variable melt fraction system (e.g., Cashman et al., 2017). Mechanisms for deformation in such systems include the ascent and intrusion of juvenile magma, the migration of mobile melt/volatiles and phase transitions (e.g., crystallisation, exsolution).
Broad zones of magma storage have previously been identified by tomographic studies that found low velocity zones > 10 km in diameter beneath active volcanoes (e.g., Lees, 2007) and by magnetotelluric studies (e.g., Aoki et al., 2013) that have detected large conductive bodies up to 20 km away from the locations of recent eruptions. Calderas where the manifestations of unrest (e.g., seismicity, thermal anomalies, fumerolic gas emission) extend over a large area are expected to sit over large, complex magmatic domains (e.g., Wicks et al., 2006). InSAR observations of laterally offset deformation at some stratovolcanoes (e.g., Delgado et al., 2017) have provided new evidence of magmatic domain extent at smaller, younger volcanoes. Deformation associated with an eruption is more likely to be ‘central’ (< 5 km from the volcano) than ‘offset’ (≥ 5 km distant), although there are a few notable examples of distal deformation accompanying eruption (e.g., Jay et al., 2014). ‘Offset’ deformation not associated with eruptions have been attributed to both magmatic and hydrothermal processes (notably, Pritchard et al., 2013 and Takada and Fukushima, 2013), so distinguishing between these origins at upper crustal depths is an important problem for understanding the shape and characteristics of magmatic domains.
That ~ 24% of potentially magmatic InSAR signals are centred over 5 km from the nearest volcano demonstrates that laterally extensive active magmatic zones are not exceptional. Such ‘offset’ deformation sources have so far been found in both rift zones and volcanic arcs, and at volcanoes with a range of maturities. We expect melt-rich lenses to be localised in zones with favourable stress conditions in volcanic arcs, in contrast to rift zones where high melt fractions are likely to extend over larger areas. This suggests that locations of deformation will vary more in rift zones than in volcanic arcs, where stress conditions may favour persistent magma reservoirs. Seismic reflection and field observations suggest that felsic sill complexes have total lateral extents of < 20 km, while shallow complexes of mafic sills may have much greater lateral extents (e.g., Magee et al., 2016). This suggests that there are likely to be major differences in the eventual geometry and extent of mature magmatic domains in different tectonics settings and for different magma compositions. The increasing total duration of global InSAR coverage will provide data with which to examine variations between active systems, as well as the impact of differences in melt supply and magmatic system maturity.
Analysis of SAR images and InSAR measurements of deformation are increasingly used in volcano monitoring, and are swelling the number of geodetically monitored subaerial volcanoes, improving the spatial resolution of routine observations and contributing to our understanding of the diversity of ‘pre-eruptive’ deformation signals. We consider a reliable and detailed record of past observations of volcanic and magmatic deformation - preferably incorporating uncertainties and description of ‘null’ results - to be essential for the future interpretation of interferograms in volcanic settings. Our synthesis of global InSAR measurements of volcanic and magmatic deformation has implications both for the design of volcano monitoring strategies and for our understanding of the distribution of magma in the crust.
We use the historical InSAR catalogue as a ‘baseline’ for assessing the contexts for which SAR is most useful for monitoring applications. Deformation lasting more than a few months, but less than a decade is best represented in the InSAR records, with spatial extents of hundreds of metres to hundreds of kilometres. However, the number of deformation episodes that are (1) transient, lasting days or less and not producing permanent deformation, (2) at magnitudes of a few mm per year and (3) located several kilometres from the nearest volcano may be underrepresented. The combination of data from multiple satellites or constellations, longer total duration of satellite missions and use of broad survey-mode regional measurements can mitigate these gaps for future monitoring.
Of the InSAR deformation signals attributed to magmatic processes, 24% are centred at distances ≥5 km from the nearest active volcano, and most have sources in the shallow crust (< 10 km depth). Such ‘offset’ deformation fields, many of which encompass the volcano, are found across different tectonic settings and include co-eruptive subsidence, episodes of uplift, and subsidence triggered by great earthquakes. This constitutes independent evidence that magmatic processes take place over broad regions of the shallow crust (at least over many 10s of kilometres) at many volcanoes, and is consistent with a vision of magmatic processes that incorporates spatially complex, dynamic zones of varying crystal, melt and volatile fraction. Future analysis of the global set of InSAR measurements have potential to provide useful constraints on the characteristics of the shallowest parts of trans-crustal magmatic systems.
We use the term ‘volcano observatory’ to refer to organizations with statutory responsibilities that include monitoring volcanic hazards. In different countries this may be a national geological survey or meteorological office, an academic institution, or branch of the civil service.
The initial public release of the deformation data on 31 March 2017 was updated to add figures and references on 8 May 2017 (VOTW version 4.5.6, unchanged with the 4.6 update)
SKE is funded by a Leverhulme Early Career Fellowship and a European Space Agency Living Planet fellowship (formerly held at the University of Bristol) co-funded by the NERC-BGS Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET). JB is also funded by COMET and by NERC grant Strengthening Resilience in Volcanic Areas (STREVA, NE/J020052/1). The development of the COMET deformation catalogue was supported by a NERC Impact Acceleration Account from the University of Bristol. J.A.J., M.A.F and M.E.P. were partly supported by NASA grants NNX12AO31G and NNX12AM24G issued through the Science Mission Directorate’s Earth Science Division. J.A.J. was also partly supported by a postdoctoral fellowship from the Smithsonian Institution Global Volcanism Program. EC is grateful for support from the Deep Carbon Observatory.
Availability of data and materials
Unless otherwise stated, the observations discussed in this article are drawn from the Smithsonian Institution Global Volcanism Programs Volcanoes of the World database (volcano.si.edu) and the NERC-BGS Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics Volcano Deformation Database (volcanodeformation.blogs.ilrt.org).
S.K.E. wrote the paper, compiled information about spatial characteristics of deformation signals and, with J.B., designed and initiated the COMET volcano deformation database. J.J., M.F., M.E.P., B.J.A., E.V., and E.C. designed the deformation component of the VOTW database and contributed database entries. A.L.P., D.W.D.A., R.L., J.H., E.R., M.C.A., C.C. and J.L.W. contributed entries to the COMET volcano deformation database. All authors read and approved the final manuscript.
None of the authors have financial or non-financial competing interests in this manuscript.
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- Albino F, Smets B, d'Oreye N, Kervyn F. High resolution TanDEM-X DEM: an accurate method to estimate lava flow volumes at Nyamulagira volcano (DR Congo). J Geophys Res Solid Earth. 2015;120(6):4189–207.View ArticleGoogle Scholar
- Amelung F, Jónsson S, Zebker H, Segall P. Widespread uplift and ‘trapdoor’ faulting on Galapagos volcanoes observed with radar interferometry. Nature. 2000;407(6807):993–6.View ArticleGoogle Scholar
- Amoruso, A., & Crescentini, L. (2009). Shape and volume change of pressurized ellipsoidal cavities from deformation and seismic data. Journal of Geophysical Research: Solid Earth, 114(B2).Google Scholar
- Aoki Y, Takeo M, Ohminato T, Nagaoka Y, Nishida K. Magma pathway and its structural controls of Asama volcano, Japan. Geol Soc Lond, Spec Publ. 2013;380(1):67–84.View ArticleGoogle Scholar
- Arnold DWD, Biggs J, Wadge G, Ebmeier SK, Odbert HM, Poland MP. Dome growth, collapse, and valley fill at Soufrière Hills volcano, Montserrat, from 1995 to 2013: contributions from satellite radar measurements of topographic change. Geosphere. 2016;12(4):1300–15.View ArticleGoogle Scholar
- Arnold DWD, Biggs J, Anderson K, Vargas SV, Wadge G, Ebmeier SK, Naranjo MF, and Mothes P (2017), Decaying lava extrusion rate at El Reventador Volcano, Ecuador measured using high-resolution satellite radar, J. Geophys. Res., 122, doi:https://doi.org/10.1002/2017JB014580.
- Bagnardi M, Amelung F, Poland MP. A new model for the growth of basaltic shields based on deformation of Fernandina volcano, Galápagos Islands. Earth Planet Sci Lett. 2013;377:358–66.View ArticleGoogle Scholar
- Bekaert DPS, Walters RJ, Wright TJ, Hooper AJ, Parker DJ. Statistical comparison of InSAR tropospheric correction techniques. Remote Sens Environ. 2015;170:40–7.View ArticleGoogle Scholar
- Biggs J, Pritchard ME. Global volcano monitoring: what does it mean when volcanoes deform? Elements. 2017;13(1):17–22.View ArticleGoogle Scholar
- Biggs J, Amelung F, Gourmelen N, Dixon TH, Kim SW. InSAR observations of 2007 Tanzania rifting episode reveal mixed fault and dyke extension in an immature continental rift. Geophys J Int. 2009;179(1):549–58.View ArticleGoogle Scholar
- Biggs, J., Mothes, P., Ruiz, M., Amelung, F., Dixon, T. H., Baker, S., & Hong, S. H. (2010a). Stratovolcano growth by co-eruptive intrusion: The 2008 eruption of Tungurahua Ecuador. Geophysical Research Letters, 37(21).Google Scholar
- Biggs, J., Lu, Z., Fournier, T., & Freymueller, J. T. (2010b). Magma flux at Okmok Volcano, Alaska, from a joint inversion of continuous GPS, campaign GPS, and interferometric synthetic aperture radar. Journal of Geophysical Research: Solid Earth, 115(B12).Google Scholar
- Biggs J, Bastow ID, Keir D, Lewi E. Pulses of deformation reveal frequently reccurring shallow magmatic activity beneath the main Ethiopian rift. Geochem Geophys Geosyst. 2011;12:9.View ArticleGoogle Scholar
- Biggs J, Chivers M, Hutchinson MC. Surface deformation and stress interactions during the 2007–2010 sequence of earthquake, dyke intrusion and eruption in northern Tanzania. Geophys J Int. 2013;195(1):16–26.View ArticleGoogle Scholar
- Biggs, J., Ebmeier, S. K., Aspinall, W. P., Lu, Z., Pritchard, M. E., Sparks, R. S. J., & Mather, T. A. (2014). Global link between deformation and volcanic eruption quantified by satellite imagery. Nature communications, 5.Google Scholar
- Biggs J, Robertson E, Cashman K. The lateral extent of volcanic interactions during unrest and eruption. Nat Geosci. 2016;9(4):308–11.View ArticleGoogle Scholar
- Brown SK, Crosweller HS, Sparks RSJ, Cottrell E, Deligne NI, Guerrero NO, Hobbs L, Kiyosugi K, Loughlin SC, Siebert L, Takarada S. Characterisation of the quaternary eruption record: analysis of the large magnitude explosive volcanic eruptions (LaMEVE) database. J Appl Volcanol. 2014;3(1):1–22.View ArticleGoogle Scholar
- Brunori CA, Bignami C, Stramondo S, Bustos E. 20 years of active deformation on volcano caldera: Joint analysis of InSAR and AInSAR techniques. Int J Appl Earth Obs Geoinf. 2013;23:279–87.View ArticleGoogle Scholar
- Buongiorno, M.F., Musacchio, M., Vignoli, S., Zoffoli, S., Amodio, A., Cardaci, C., Pugnaghi, S., Teggi, S., Sansosti, E., Puglisi, G. and Borgstrom, S., 2008. Volcanic risk system (SRV): ASI pilot project to support the monitoring of volcanic risk in Italy by means of EO data. In Use of Remote Sensing Techniques for Monitoring Volcanoes and Seismogenic Areas, 2008. USEReST 2008. Second Workshop on (pp. 1-5). IEEE.Google Scholar
- Bürgmann R, Rosen PA, Fielding EJ. Synthetic aperture radar interferometry to measure Earth’s surface topography and its deformation. Annu Rev Earth Planet Sci. 2000;28(1):169–209.View ArticleGoogle Scholar
- Caricchi L, Biggs J, Annen C, Ebmeier SK. The influence of cooling, crystallisation and re-melting on the interpretation of geodetic signals in volcanic systems. Earth Planet Sci Lett. 2014;388:166–74.View ArticleGoogle Scholar
- Cashman, K. V., Sparks, R. S. J., & Blundy, J. D. (2017). Vertically extensive and unstable magmatic systems: A unified view of igneous processes. Science, 355(6331).Google Scholar
- Chaussard E, Amelung F. Regional controls on magma ascent and storage in volcanic arcs. Geochem Geophys Geosyst. 2014;15(4):1407–18.View ArticleGoogle Scholar
- Chaussard E, Amelung F, Aoki Y. Characterization of open and closed volcanic systems in Indonesia and Mexico using InSAR time series. J Geophys Res Solid Earth. 2013;118(8):3957–69.View ArticleGoogle Scholar
- Delgado F, Pritchard M, Lohman R, Naranjo JA. The 2011 Hudson volcano eruption (southern Andes, Chile): pre-eruptive inflation and hotspots observed with InSAR and thermal imagery. Bull Volcanol. 2014;76(5):815.View ArticleGoogle Scholar
- Delgado F, Pritchard ME, Basualto D, Lazo J, Córdova L, Lara LE. Rapid reinflation following the 2011–2012 rhyodacite eruption at Cordón Caulle volcano (Southern Andes) imaged by InSAR: Evidence for magma reservoir refill. Geophys Res Lett. 2016;43(18):9552–62.View ArticleGoogle Scholar
- Delgado, F., Pritchard, M. E., Ebmeier, S., González, P., & Lara, L. (2017). Recent unrest (2002–2015) imaged by space geodesy at the highest risk Chilean volcanoes: Villarrica, Llaima, and Calbuco (Southern Andes). Journal of Volcanology and Geothermal Research.Google Scholar
- Dietterich HR, Poland MP, Schmidt DA, Cashman KV, Sherrod DR, Espinosa AT. Tracking lava flow emplacement on the east rift zone of Kilauea, Hawai’i, with synthetic aperture radar coherence. Geochem Geophys Geosyst. 2012;13(5)Google Scholar
- Diez M, La Femina PC, Connor CB, Strauch W, Tenorio V. Evidence for static stress changes triggering the 1999 eruption of Cerro Negro volcano, Nicaragua and regional aftershock sequences. Geophys Res Lett. 2005;32(4)Google Scholar
- Dzurisin D. Volcano geodesy: challenges and opportunities for the 21st century. Philosophical transactions of the Royal Society of London a: mathematical. Phys Eng Sci. 2000;358(1770):1547–66.View ArticleGoogle Scholar
- Dzurisin D, Lisowski M, Wicks CW. Continuing inflation at Three Sisters volcanic center, central Oregon Cascade Range, USA, from GPS, leveling, and InSAR observations. Bull Volcanol. 2009;71(10):1091–110.View ArticleGoogle Scholar
- Ebmeier SK. Application of independent component analysis to multitemporal InSAR data with volcanic case studies, J. Geophys. Res. Solid Earth. 2016;121:8970–86. https://doi.org/10.1002/2016JB013765.View ArticleGoogle Scholar
- Ebmeier, S. K., Biggs, J., Mather, T. A., Wadge, G., & Amelung, F. (2010). Steady downslope movement on the western flank of Arenal volcano, Costa Rica. Geochemistry, Geophysics, Geosystems, 11(12).Google Scholar
- Ebmeier SK, Biggs J, Mather TA, Elliott JR, Wadge G, Amelung F. Measuring large topographic change with InSAR: lava thicknesses, extrusion rate and subsidence rate at Santiaguito volcano, Guatemala. Earth Planet Sci Lett. 2012;335:216–25.View ArticleGoogle Scholar
- Ebmeier SK, Biggs J, Mather TA, Amelung F. Applicability of InSAR to tropical volcanoes: insights from central America. Geol Soc Lond, Spec Publ. 2013;380(1):15–37.View ArticleGoogle Scholar
- Ebmeier SK, Biggs J, Muller C, Avard G. Thin-skinned mass-wasting responsible for widespread deformation at Arenal volcano. Front Earth Sci. 2014;2:35.View ArticleGoogle Scholar
- Ebmeier SK, Elliott JR, Nocquet JM, Biggs J, Mothes P, Jarrín P, Yépez M, Aguaiza S, Lundgren P, Samsonov SV. Shallow earthquake inhibits unrest near Chiles–Cerro Negro volcanoes, Ecuador–Colombian border. Earth Planet Sci Lett. 2016;450:283–91.View ArticleGoogle Scholar
- Elliott, J. R., R. J. Walters & T. J. Wright (2016). The role of space-based observation in understanding and responding to active tectonics and earthquakes, Nature Communications, 7, doi:https://doi.org/10.1038/ncomms13844.
- Fearnley CJ, McGuire WJ, Davies G, Twigg J. Standardisation of the USGS Volcano Alert Level System (VALS): analysis and ramifications. Bull Volcanol. 2012;74(9):2023–36.View ArticleGoogle Scholar
- Feigl KL, Le Mével H, Ali ST, Córdova L, Andersen NL, DeMets C, Singer BS. Rapid uplift in Laguna del Maule volcanic field of the Andean southern volcanic zone (Chile) 2007–2012. Geophys J Int. 2014;196(2):885–901.View ArticleGoogle Scholar
- Fournier, T. J., Pritchard, M. E., & Riddick, S. N. (2010). Duration, magnitude, and frequency of subaerial volcano deformation events: New results from Latin America using InSAR and a global synthesis. Geochemistry, Geophysics, Geosystems, 11(1).Google Scholar
- Froger JL, Merle O, Briole P. Active spreading and regional extension at Mount Etna imaged by SAR interferometry. Earth Planet Sci Lett. 2001;187(3):245–58.View ArticleGoogle Scholar
- Froger J-L, Remy D, Bonvalot S, Legrand D. Two scales of inflation at Lastarria-Cordon del Azufre volcanic complex, central Andes, revealed from ASAR-ENVISAT interferometric data. Earth Planet Sci Lett. 2007;255(1-2):148–63.View ArticleGoogle Scholar
- Global Volcanism Program, 2013. Venzke, E (ed.)., Volcanoes of the World, v. 4.6.0. Smithsonian Institution. Downloaded 19 Jun 2017. https://doi.org/10.5479/si.GVP.VOTW4-2013
- Gong W, Meyer FJ, Lee C-W, Lu Z, Freymueller J. Measurement and interpretation of subtle deformation signals at Unimak Island from 2003 to 2010 using weather model-assisted time series InSAR. J Geophys Res Solid Earth. 2015;120(2):1175–94.View ArticleGoogle Scholar
- González PJ, Samsonov SV, Pepe S, Tiampo KF, Tizzani P, Casu F, Fernández J, Camacho AG, Sansosti E. Magma storage and migration associated with the 2011–2012 el Hierro eruption: implications for crustal magmatic systems at oceanic island volcanoes. J Geophys Res Solid Earth. 2013;118(8):4361–77.View ArticleGoogle Scholar
- Gottsmann J, Folch A, Rymer H. Unrest at Campi Flegrei: a contribution to the magmatic versus hydrothermal debate from inverse and finite element modelling. J Geophys Res Solid Earth. 2006;111(B7)Google Scholar
- Gudmundsson MT, Jónsdóttir K, Hooper A, Holohan EP, Halldórsson SA, Ófeigsson BG, Cesca S, Vogfjörd KS, Sigmundsson F, Högnadóttir T, Einarsson P. Gradual caldera collapse at Bárdarbunga volcano, Iceland, regulated by lateral magma outflow. Science. 2016;353(6296):aaf8988.View ArticleGoogle Scholar
- Hamling IJ, Wright TJ, Calais E, Bennati L, Lewi E. Stress transfer between thirteen successive dyke intrusions in Ethiopia. Nat Geosci. 2010;3(10):713–7.View ArticleGoogle Scholar
- Hamling IJ, Hreinsdottir S, Fournier N. The ups and downs of the TVZ: geodetic observations of deformation around the Taupo volcanic zone, New Zealand. J Geophys Res Solid Earth. 2015;120(6):4667–79.View ArticleGoogle Scholar
- Hamling IJ, Hreinsdóttir S, Bannister S, Palmer N. Off-axis magmatism along a subaerial back-arc rift: observations from the Taupo volcanic zone, New Zealand. Sci Adv. 2016;2(6):e1600288.View ArticleGoogle Scholar
- Heise, W., H. M. Bibby, T. Grant Caldwell, S. C. Bannister, Y. Ogawa, S. Takakura, T. Uchida. (2007). Melt distribution beneath a young continental rift: The Taupo Volcanic Zone, New Zealand. Geophys. Res. Lett. 34, .Google Scholar
- Henderson ST, Pritchard ME. Decadal volcanic deformation in the Central Andes volcanic zone revealed by InSAR time series. Geochem Geophys Geosyst. 2013;14(5):1358–74.View ArticleGoogle Scholar
- Hickey J, Gottsmann J. Benchmarking and developing numerical Finite Element models of volcanic deformation. J Volcanol Geotherm Res. 2014;280:126–30.View ArticleGoogle Scholar
- Hooper, A. (2008). A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophysical Research Letters, 35(16).Google Scholar
- Hreinsdóttir S, Sigmundsson F, Roberts MJ, Björnsson H, Grapenthin R, Arason P, Árnadóttir T, Hólmjárn J, Geirsson H, Bennett RA, Gudmundsson MT, Oddsson B, Ófeigsson BG, Villemin T, Jónsson T, Sturkell E, Höskuldsson Á, Larsen G, Thordarson T, Óladóttir BA. Volcanic plume height correlated with magmapressure change at Grimsvotn Volcano, Iceland. Nat Geosci. 2014a;7(3):214–8.View ArticleGoogle Scholar
- Jay J, Costa F, Pritchard ME, Lara L, Singer B, Herrin J. Locating magma reservoirs using InSAR and petrology before and during the 2011–2012 Cordón Caulle silicic eruption. Earth Planet Sci Lett. 2014;395:254–66.View ArticleGoogle Scholar
- Jónsson S. Stress interaction between magma accumulation and trapdoor faulting on sierra Negra volcano, Galápagos. Tectonophysics. 2009;471(1):36–44.View ArticleGoogle Scholar
- Jónsson S, Zebker H, Cervelli P, Segall P, Garbeil H, Mouginis-Mark P, Rowland S. A shallow-dipping dike fed the 1995 flank eruption at Fernandina Volcano, Galapagos, observed by satellite radar interferometry. Geophys Res Lett. 1999;26(8):1077–80.View ArticleGoogle Scholar
- Kato K, Yamasato H. The 2011 eruptive activity of Shinmoedake volcano, Kirishimayama, Kyushu, Japan—overview of activity and volcanic alert level of the Japan meteorological agency—. Earth, Planets and Space. 2013;65(6):489–504.View ArticleGoogle Scholar
- Le Mével H, Feigl KL, Córdova L, DeMets C, Lundgren P. Evolution of unrest at Laguna del Maule volcanic field (Chile) from InSAR and GPS measurements, 2003 to 2014. Geophys Res Lett. 2015;42(16):6590–8.View ArticleGoogle Scholar
- Lee CW, Lu Z, Won JS, Jung HS, Dzurisin D. Dynamic deformation of Seguam Island, Alaska, 1992–2008, from multi-interferogram InSAR processing. J Volcanol Geotherm Res. 2013;260:43–51.View ArticleGoogle Scholar
- Lees JM. Seismic tomography of magmatic systems. J Volcanol Geotherm Res. 2007;167(1):37–56.View ArticleGoogle Scholar
- Lénat JF, Bachèlery P, Peltier A. The interplay between collapse structures, hydrothermal systems, and magma intrusions: the case of the central area of piton de la Fournaise volcano. Bull Volcanol. 2012;74(2):407–21.View ArticleGoogle Scholar
- López, C., M. J. Blanco, R. Abella, B. Brenes, V. M. Cabrera Rodríguez, B. Casas, I. Domínguez Cerdeña et al. (2012). "Monitoring the volcanic unrest of El Hierro (Canary Islands) before the onset of the 2011–2012 submarine eruption." Geophysical Research Letters 39, no. 13.Google Scholar
- Loughlin, S. C., Sparks, S., Brown, S. K., Vye-Brown, C., & Jenkins, S. F. (Eds.). (2015). Global volcanic hazards and risk. Cambridge University Press.Google Scholar
- Lu, Z. and Dzurisin, D., 2014. InSAR imaging of Aleutian volcanoes: monitoring a volcanic arc from space. Springer Science & Business Media.Google Scholar
- Lu, Z., Dzurisin, D., Biggs, J., Wicks, C., & McNutt, S. (2010). Ground surface deformation patterns, magma supply, and magma storage at Okmok volcano, Alaska, from InSAR analysis: 1. Intereruption deformation, 1997‚Äì2008. Journal of Geophysical Research: Solid Earth (1978-2012), 115(B5).Google Scholar
- Lundgren, P., & Lu, Z. (2006). Inflation model of Uzon caldera, Kamchatka, constrained by satellite radar interferometry observations. Geophysical Research Letters, 33(6).Google Scholar
- Lundgren P, Usai S, Sansosti E, Lanari R, Tesauro R, Fornaro G, Berardino P. Modeling surface deformation observed with synthetic aperture radar Interferometry at Campi Flegrei caldera. J Geophys Res Solid Earth. 2001;106(B9):19355–66.View ArticleGoogle Scholar
- Lundgren, P., Casu, F., Manzo, M., Pepe, A., Berardino, P., Sansosti, E., & Lanari, R. (2004). Gravity and magma induced spreading of Mount Etna volcano revealed by satellite radar interferometry. Geophysical Research Letters, 31(4).Google Scholar
- Lundgren P, Samsonov SV, López Velez CM, Ordoñez M. Deep source model for Nevado del Ruiz volcano, Colombia, constrained by interferometric synthetic aperture radar observations. Geophys Res Lett. 2015;42(12):4816–23.View ArticleGoogle Scholar
- Magee C, Muirhead JD, Karvelas A, Holford SP, Jackson CAL, Bastow ID, Schofield N, Stevenson CTE, McLean C, McCarthy W, Shtukert O. Lateral magma flow in mafic sill complexes. Geosphere. 2016;12(3):809–41.View ArticleGoogle Scholar
- Masterlark, T. (2007). Magma intrusion and deformation predictions: Sensitivities to the Mogi assumptions. Journal of Geophysical Research: Solid Earth, 112(B6).Google Scholar
- Masterlark T, Lu Z. Transient volcano deformation sources imaged with interferometric synthetic aperture radar: Application to Seguam Island, Alaska. J Geophys Res Solid Earth. 2004;109:B01401.View ArticleGoogle Scholar
- Mastin LG, Roeloffs E, Beeler NM, Quick JE. Constraints on the size, overpressure, and volatile content of the Mount St. Helens magma system from geodetic and dome-growth measurements during the 2004–2006+ eruption. A volcano rekindled: the renewed eruption of Mount St. Helens, 2004–2006; 2008. p. 461–78.Google Scholar
- McCormick Kilbride B, Edmonds M, Biggs J. Observing eruptions of gas-rich compressible magmas from space. Nat Commun. 2016;7Google Scholar
- Meyer FJ, McAlpin DB, Gong W, Ajadi O, Arko S, Webley PW, Dehn J. Integrating SAR and derived products into operational volcano monitoring and decision support systems. ISPRS J Photogramm Remote Sens. 2015;100:106–17.View ArticleGoogle Scholar
- Miyagi Y, Ozawa T, Kozono T, Shimada M (2013) DInSAR/PSInSAR Observations of Kirishima, Shinmoe-dake Volcano, Japan. EGU General Assembly abstract EGU2013-4658, Vienna, Austria.Google Scholar
- Morales Rivera AM, Amelung F, Mothes P. Volcano deformation survey over the Northern and Central Andes with ALOS InSAR time series. Geochem Geophys Geosyst. 2016;17(7):2869–83.View ArticleGoogle Scholar
- Moran SC, Kwoun O, Masterlark T, Lu Z. On the absence of InSAR-detected volcano deformation spanning the 1995–1996 and 1999 eruptions of Shishaldin volcano, Alaska. J Volcanol Geotherm Res. 2006;150(1):119–31.View ArticleGoogle Scholar
- Naranjo MF, Ebmeier SK, Vallejo S, Ramón P, Mothes P, Biggs J, Herrera F. Mapping and measuring lava volumes from 2002 to 2009 at el Reventador volcano, Ecuador, from field measurements and satellite remote sensing. J Appl Volcanol. 2016;5(1):1–11.View ArticleGoogle Scholar
- Neri, M., Casu, F., Acocella, V., Solaro, G., Pepe, S., Berardino, P., Sansosti, E., Caltabiano, T., Lundgren, P. and Lanari, R., 2009. Deformation and eruptions at Mt. Etna (Italy): a lesson from 15 years of observations. Geophysical Research Letters, 36(2).Google Scholar
- Newman AV, Dixon TH, Gourmelen N. A four-dimensional viscoelastic deformation model for Long Valley caldera, California, between 1995 and 2000. J Volcanol Geotherm Res. 2006;150(1):244–69.View ArticleGoogle Scholar
- Nishimura T, Fujiwara S, Murakami M, Tobita M, Nakagawa H, Sagiya T, Tada T. The M6.1 earthquake triggered by volcanic inflation of Iwate Volcano, northern Japan, observed by satellite radar interferometry. Geophys Res Lett. 2001;28(4):635–8.View ArticleGoogle Scholar
- Osmanoğlu B, Sunar F, Wdowinski S, Cabral-Cano E. Time series analysis of InSAR data: methods and trends. ISPRS J Photogramm Remote Sens. 2016;115:90–102.View ArticleGoogle Scholar
- Ozawa T, Fujita E. Local deformations around volcanoes associated with the 2011 off the Pacific coast of Tohoku earthquake. J Geophys Res Solid Earth. 2013;118(1):390–405.View ArticleGoogle Scholar
- Ozawa T, Kozono T. Temporal variation of the Shinmoe-dake crater in the 2011 eruption revealed by spaceborne SAR observations. Earth, Planets and Space. 2013;65(6):5.View ArticleGoogle Scholar
- Pagli C, Wright TJ, Ebinger CJ, Yun SH, Cann JR, Barnie TL, Ayele A. Shallow axial magma chamber at the slow-spreading Erta Ale Ridge. Nat Geosci. 2012;5(4):284–8.View ArticleGoogle Scholar
- Pallister, J.S., Schneider, D.J., Griswold, J.P., Keeler, R.H., Burton, W.C., Noyles, C., Newhall, C.G. and Ratdomopurbo, A. (2013). Merapi 2010 eruption‚ ÄîChronology and extrusion rates monitored with satellite radar and used in eruption forecasting. J Volcanol Geotherm Res, 261, 144-152.Google Scholar
- Parker AL, Biggs J, Lu Z. Investigating long-term subsidence at medicine Lake volcano, CA, using multitemporal InSAR. Geophys J Int. 2014;199(2):844–59.View ArticleGoogle Scholar
- Parker AL, Biggs J, Walters RJ, Ebmeier SK, Wright TJ, Teanby NA, Lu Z. Systematic assessment of atmospheric uncertainties for InSAR data at volcanic arcs using large-scale atmospheric models: application to the Cascade volcanoes, United States. Remote Sens Environ. 2015;170:102–14.View ArticleGoogle Scholar
- Parker AL, Biggs J, Lu Z. Time-scale and mechanism of subsidence at Lassen volcanic center, CA, from InSAR. J Volcanol Geotherm Res. 2016;320:117–27.View ArticleGoogle Scholar
- Parks MM, Biggs J, Mather TA, Pyle DM, Amelung F, Monsalve ML, Medina LN. Co-eruptive subsidence at Galeras identified during an InSAR survey of Colombian volcanoes (2006–2009). J Volcanol Geotherm Res. 2011;202(3):228–40.View ArticleGoogle Scholar
- Parks MM, Biggs J, England P, Mather TA, Nomikou P, Palamartchouk K, Papanikolaou X, Paradissis D, Parsons B, Pyle DM, Raptakis C. Evolution of Santorini volcano dominated by episodic and rapid fluxes of melt from depth. Nat Geosci. 2012;5(10):749.View ArticleGoogle Scholar
- Passarelli L, Brodsky EE. The correlation between run-up and repose times of volcanic eruptions. Geophys J Int. 2012;188(3):1025–45.View ArticleGoogle Scholar
- Peltier, A., Bianchi, M., Kaminski, E., Komorowski, J. C., Rucci, A., & Staudacher, T. (2010). PSInSAR as a new tool to monitor pre-eruptive volcano ground deformation: Validation using GPS measurements on Piton de la Fournaise. Geophysical Research Letters, 37(12).Google Scholar
- Phillipson G, Sobradelo R, Gottsmann J. Global volcanic unrest in the 21st century: An analysis of the first decade. J Volcanol Geotherm Res. 2013;264:183–96.View ArticleGoogle Scholar
- Pinel V, Hooper A, De la Cruz-Reyna S, Reyes-Davila G, Doin MP, Bascou P. The challenging retrieval of the displacement field from InSAR data for andesitic stratovolcanoes: case study of Popocatepetl and Colima Volcano, Mexico. J Volcanol Geotherm Res. 2011;200(1):49–61.View ArticleGoogle Scholar
- Pinel V, Poland MP, Hooper A. Volcanology: lessons learned from synthetic aperture radar imagery. J Volcanol Geotherm Res. 2014;289:81–113.View ArticleGoogle Scholar
- Poland MP. Time-averaged discharge rate of subaerial lava at Kilauea volcano, Hawai’i, measured from TanDEM-X interferometry: implications for magma supply and storage during 2011-2013. J Geophys Res Solid Earth. 2014;119(7):5464–81.View ArticleGoogle Scholar
- Poland M, Miklius A, Orr T, Sutton J, Thornber C, Wilson D. New episodes of volcanism at Kilauea volcano, Hawaii. EOS Trans Am Geophys Union. 2008;89(5):37–8.View ArticleGoogle Scholar
- Poland MP, Lisowski M, Dzurisin D, Kramer R, McLay M, Pauk B. Volcano geodesy in the Cascade arc, USA. Bull Volcanol. 2017;79(8) https://doi.org/10.1007/s00445-017-1140-x.
- Pritchard, M.E. and Simons, M., 2004. An InSAR-based survey of volcanic deformation in the central Andes. Geochemistry, Geophysics, Geosystems, 5(2).Google Scholar
- Pritchard ME, Jay JA, Aron F, Henderson ST, Lara LE. Subsidence at southern Andes volcanoes induced by the 2010 Maule, Chile earthquake. Nat Geosci. 2013;6(8):632–6.View ArticleGoogle Scholar
- Pritchard ME, Biggs J, Wauthier C, Sansosti E, Arnold DWD, Delgado F, Ebmeier SK, Henderson ST, Stephens K, Wnuk K, Amelung F, Mothes P, Macedo O, Lara L, Poland MP, Zoffoli S. Towards coordinated regional multi-satellite InSAR volcano observations: Results from the Latin America pilot project. n.d.: In prep.Google Scholar
- Riddick SN, Schmidt DA. Time-dependent changes in volcanic inflation rate near Three Sisters, Oregon, revealed by InSAR. Geochem Geophys Geosyst. 2011;12(12):Q12005.View ArticleGoogle Scholar
- Rivalta E, Segall P. Magma compressibility and the missing source for some dike intrusions. Geophys Res Lett. 2008;35(4)Google Scholar
- Ruch J, Anderssohn J, Walter TR, Motagh M. Caldera-scale inflation of the Lazufre volcanic area, South America: evidence from InSAR. J Volcanol Geotherm Res. 2008;174(4):337–44.View ArticleGoogle Scholar
- Segall, P. (2010). Earthquake and volcano deformation. Princeton University Press.Google Scholar
- Sigmundsson, F. (2016), New insights into magma plumbing along rift systems from detailed observations of eruptive behavior at Axial volcano, Geophys. Res. Lett., 43, doi:https://doi.org/10.1002/%202016GL071884.
- Sigmundsson F, Hreinsdóttir S, Hooper A, Árnadóttir T, Pedersen R, Roberts MJ, Eskarsson N, Auriac A, Decriem J, Einarsson P, Geirsson H. Intrusion triggering of the 2010 Eyjafjallajokull explosive eruption. Nature. 2010;468(7322):426–30.View ArticleGoogle Scholar
- Sigmundsson F, Hooper A, Hreinsdóttir S, Vogfjörd KS, Ófeigsson BG, Heimisson ER, Dumont S, et al. Segmented lateral dyke growth in a rifting event at Barδarbunga volcanic system, Iceland. Nature. 2015a;517(7533):191–5.View ArticleGoogle Scholar
- Sigmundsson F, Hooper A, Hreinsdóttir S, Vogfjörd KS, Ófeigsson B, Rafn Heimisson E, Dumont S, Parks M, Spaans K, Gu∂mundsson GB, Drouin V. Contribution of the FUTUREVOLC project to the study of segmented lateral dyke growth in the 2014 rifting event at bar∂arbunga volcanic system, Iceland. In: EGU general assembly conference abstracts, vol. 17; 2015b. p. 11846.Google Scholar
- Simons, M., & Rosen, P. A. (2007). Interferometric synthetic aperture radar geodesy.Google Scholar
- Solaro, G., Acocella, V., Pepe, S., Ruch, J., Neri, M., & Sansosti, E. (2010). Anatomy of an unstable volcano from InSAR: Multiple processes affecting flank instability at Mt. Etna, 1994–2008. Journal of Geophysical Research: Solid Earth, 115(B10).Google Scholar
- Sparks RSJ, Biggs J, Neuberg JW. Monitoring volcanoes. Science. 2012;335(6074):1310–1.View ArticleGoogle Scholar
- Stephens, K. J., Ebmeier, S. K., Young, N. K., & Biggs, J. (2017). Transient deformation associated with explosive eruption measured at Masaya volcano (Nicaragua) using Interferometric Synthetic Aperture Radar. Journal of Volcanology and Geothermal Research.Google Scholar
- Takada Y, Fukushima Y. Volcanic subsidence triggered by the 2011 Tohoku earthquake in Japan. Nat Geosci. 2013;6(8):637–41.View ArticleGoogle Scholar
- Tilling RI. The critical role of volcano monitoring in risk reduction. Adv Geosci. 2008;14:3–11.View ArticleGoogle Scholar
- Trasatti, E., F. Casu, C. Giunchi, S. Pepe, G. Solaro, S. Tagliaventi, P. Berardino et al. (2008)"The 2004–2006 uplift episode at Campi Flegrei caldera (Italy): Constraints from SBAS-DInSAR ENVISAT data and Bayesian source inference." Geophysical Research Letters 35, no. 7 .Google Scholar
- Velez, M. L., Euillades, P., Blanco, M., & Euillades, L. (2016). Ground deformation between 2002 and 2013 from InSAR observations. In Copahue Volcano (pp. 175-198). Springer Berlin Heidelberg.Google Scholar
- Voight, B., Glicken, H., Janda, R. J., & Douglass, P. M. (1981). Catastrophic rockslide avalanche of May 18. In The 1980 Eruptions of Mount St. Helens, Washington (Vol. 1250, pp. 347-377). US Geol. Surv. Prof. Pap.Google Scholar
- Wadge G, Scheuchl B, Cabey L, Palmer MD, Riley C, Smith A, Stevens NF. Operational use of InSAR for volcano observatories: experience from Montserrat. In: Proc. FRINGE99 symposium (p. 8); 1999.Google Scholar
- Wang, T., Poland, M. P. and Lu, Z. (2015). Dome growth at Mount Cleveland, Aleutian Arc, quantified by time series TerraSAR-X imagery. Geophysical Research Letters, Vol. 42, 24, pp 10,614‚Äì10,621, doi: https://doi.org/10.1002/2015GL066784
- Whelley PL, Jay J, Calder ES, Pritchard ME, Cassidy NJ, Alcaraz S, Pavez A. Post-depositional fracturing and subsidence of pumice flow deposits: Lascar Volcano, Chile. Bull Volcanol. 2012;74(2):511–31.View ArticleGoogle Scholar
- Wicks, C. W., Dzurisin, D., Ingebritsen, S., Thatcher, W., Lu, Z., & Iverson, J. (2002). Magmatic activity beneath the quiescent Three Sisters volcanic center, central Oregon Cascade Range, USA. Geophysical Research Letters, 29(7).Google Scholar
- Wicks CW, Thatcher W, Dzurisin D, Svarc J. Uplift, thermal unrest and magma intrusion at Yellowstone caldera. Nature. 2006;440(7080):72–5.View ArticleGoogle Scholar
- Wright TJ, Ebinger C, Biggs J, Ayele A, Yirgu G, Keir D, Stork A. Magma-maintained rift segmentation at continental rupture in the 2005 afar dyking episode. Nature. 2006;442(7100):291–4.View ArticleGoogle Scholar
- Wright TJ, Sigmundsson F, Pagli C, Belachew M, Hamling IJ, Brandsdóttir B, Keir D, Pedersen R, Ayele A, Ebinger C, Einarsson P, Lewi E, Calais E. Geophysical constraints on the dynamics of spreading centres from rifting episodes on land. Nat Geosci. 2012;5(4):242–50.View ArticleGoogle Scholar