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SUMMARYOne of the main constraints on the use of satellite radar data for monitoring natural hazards is the existence of atmospheric signals. In particular, volcanic deformation can be difficult to identify because atmospheric phase delays can mask or even mimic ground deformation signals. Eliminating atmospheric signals is particularly crucial for high-relief volcanoes such as Ağrı, Tendürek, Acigöl, Göllüdağ and Hasandağ in the Eastern and Central Anatolia. To overcome the atmospheric effects, we use high-resolution ECMWF weather models coupled with an empirical phase-elevation approach for correcting Sentinel-1 interferograms. We apply these methods to two areas of Turkey, the first of which covers three volcanoes in Central Anatolia (Acigöl, Göllüdağ, Hasandağ) between January 2016 and December 2018 and the second covers two volcanoes in Eastern Anatolia (Ağrı, Tendürek) between September 2016 and December 2018. The reduction in standard deviation (quality factor) is calculated for both ascending and descending tracks and the atmospheric corrections are found to perform better on descending interferograms in both cases. Then, we use a least-squares approach to produce a time-series. For Central Anatolia, we used 416 ascending and 415 descending interferograms to create 144 and 145 cumulative displacement maps, respectively, and for Eastern Anatolia, we used 390 ascending and 380 descending interferograms to produce 137 and 130 cumulative displacement maps, respectively. We find that the temporal standard deviation before atmospheric corrections ranges between 0.9 and 3.7 cm for the five volcanoes in the region and is consistently higher on ascending track data, which is acquired at the end of the day when solar heating is greatest. Atmospheric correction reduces the standard deviation to 0.5–2.5 cm. Residual signals might be due to the ice-cap at Ağrı and agriculture near Acigöl. We conclude that these volcanoes did not experience significant magmatic deformation during this time period, despite the apparent signals visible in individual uncorrected interferograms. We demonstrate that atmospheric corrections are vital when using InSAR for monitoring the deformation of high-relief volcanoes in arid continental climates such as Turkey.

Countries with low to lower-middle income have limited resources to deploy and maintain ground monitoring networks. In this context, satellite-based techniques such as Radar interferometry (InSAR) is a great solution for detecting volcanic ground deformation at regional-scale. With the launch in 2014 of Sentinel-1 mission, regional monitoring of volcanic unrest becomes easier as SAR data are freely available with a revisit time of 6-12 days. Here, we develop a tuned processing workflow to produce Sentinel-1 InSAR time series and to automatically detect volcanic unrest over 80 volcanic systems located along the East African Rift System (EARS). First, we show that the correction of atmospheric signals for the arid and low-elevation EARS volcanoes is less important than for other volcanic environments. For a 5-year times series (between Jan. 2015 and Dec. 2019), we show that statistically uncertainties in InSAR velocities are around 0.1 cm/yr, whereas uncertainties associated with the choice of reference pixel are typically 0.3–0.6 cm/yr. For the automatic detection, we found that volcanic unrest can be detected with high confidence in the case the cumulative displacements exceed three times the temporal noise (threshold of 3σ). Based on this criterion, our survey reveals ground unrest at 16 volcanic centres among the 38 volcanic centres showing historical evidence of eruptive or unrest activity. A large variety of processes causing deformation occurs in the EARS: (1) subsidence due to contraction of magma bodies at Alu-Dalafilla, Dallol, Paka and Silali; (2) subsidence due to lava flows compaction at Kone and Nabro; (3) subsidence due to fluid migration at Olkaria and Aluto or fault-fluids interactions at Haludebi and Gada Ale; (4) rapid inflation due to magma intrusions at Erta Ale and Fentale; (5) short-lived inflation of shallow reservoirs at Nabro and Suswa; (6) long-lived inflation of large magmatic systems at Corbetti, Tullu Moje and Dabbahu. Except Olkaria and Kone, all these volcanoes were identified as deforming by previous satellites missions (between late 90’s and early 2000), which is an indication of the persistence of activity over long-time scales (>10 years). Finally, we fit the time series using simple functional forms and classify seven of the volcano time series as linear, six as sigmoidal and three as hybrid, enabling us to discriminate between steady deformation and short-term pulses of deformation. We found that the characteristics of the unrest signals are independent of the expected processes, which means that additional information (structural geology, seismicity, eruptive history and source modelling) will be necessary to characterize the processes causing the unrest. Our final objective will be to improve the transfer of this information to local scientists in Africa, which can be achieved by integrating our tools to an existing monitoring system and by developing web-platform where the InSAR products can be freely available.

Long-term magma storage as a cooler, predominantly crystalline mush provides a reasonable model to represent an inherently complex and heterogeneous overall system. The proposed mush consists of a crystalline framework holding large volumes of interstitial melt and volatiles throughout, whose phase, temperature and geochemistry can vary widely within a single system. This magma mush introduces new complexities to the mechanical response following injection or extraction of melt within a system, especially when directly compared against classical solutions for magma reservoir models. These solutions, such as Mogi (1958), Yang et al. (1988) and McTigue (1987) – represent a simple melt pressure source held within an elastic crust. Accounting for a mush’s crystalline framework introduces poro-elastic rheology into the system, whilst the interstitial melt movement throughout this framework causes visco-elastic behaviour (Dragoni & Magnanensi, 1989; Segall, 2016; Liao et al., 2018, 2021). Physical properties such as melt viscosity, melt and mush compressibility, reservoir and injection depth and reservoir geometry can each affect a system’s dynamic response to perturbation, and consequently how it may present as surface deformation (Segall, 2016; Mullet & Segall, 2022; Alshembari et al., 2022). Using finite element modelling, this research investigates the roles of magma and mush properties, such as compressibility/porosity, viscosity, and injection volume, on post-perturbation deformation. Similarly, the influence of the structure of the magmatic system, in terms of geometry, size and thermal distribution, on any subsequent surface deformation will be explored. These results will be applied against geodetic deformation patterns from regions of suspected existing mush zones globally, to examine any potentially identifiable patterns that may result from subsurface magma migration.

Satellite-based earth observation sensors are increasingly able to monitor geophysical signals related to natural hazards, and many groups are working on rapid data acquisition, processing, and dissemination to data users with a wide range of expertise and goals. A particular challenge in the meaningful dissemination of Interferometric Synthetic Aperture Radar (InSAR) data to non-expert users is its unique differential data structure and sometimes low signal to noise ratio. In this study, we evaluate the online dissemination of ground deformation measurements from InSAR through Twitter, alongside the provision of open access InSAR data from the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) processing system. Our aim is to evaluate (1) who interacts with disseminated InSAR data, (2) how the data are used and (3) to discuss strategies for meaningful communication and dissemination of open InSAR data. We found that InSAR Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where InSAR measurements of ground deformation were disseminated, often using wrapped and unwrapped interferograms. For earthquake events, Sentinel-1 data were acquired, processed, and tweeted within 4.7±2.8 days (shortest was one day). Open access Sentinel-1 data dominated the InSAR tweets and were applied to volcanic and earthquake events in the most engaged with (retweeted) content. Open access InSAR data provided by LiCSAR was widely accessed, including automatically processed and tweeted interferograms and interactive event pages revealing ground deformation following earthquake events. The further work required to integrate dissemination of InSAR data into longer-term disaster risk reduction strategies is highly specific, both to hazard-type, international community of practice, and local political setting and civil protection mandates. Notably, communication of uncertainties and processing methodologies are still lacking. We conclude by outlining the future direction of COMET LiCSAR products to maximise their useability.

East Africa hosts significant reserves of untapped geothermal energy. Most exploration has focused on geologically young (

Active fault data are commonly used in seismic hazard assessments, but there are challenges in deriving the slip rate, geometry, and frequency of earthquakes along active faults. Herein, we present the open-access geospatial Malawi Seismogenic Source Model (MSSM; https://doi.org/10.5281/zenodo.5599616), which describes the seismogenic properties of faults that formed during ongoing east African rifting in Malawi. We first use empirically derived constraints to geometrically classify active faults into section, fault, and multifault seismogenic sources. For sources in the North Basin of Lake Malawi, slip rates can be derived from the vertical offset of a seismic reflector that dated lake cores indicate is 75 ka. Elsewhere, slip rates are constrained from advancing a systems-based approach that partitions geodetically derived rift extension rates in Malawi between seismogenic sources using a priori constraints on a regional strain distribution and a hanging wall flexural extension in magma-poor continental rifts. Slip rates are then combined with source geometry and empirical scaling relationships to estimate earthquake magnitudes and recurrence intervals, and their uncertainty is described from the variability in logic tree outcomes used in these calculations. Sources in the MSSM are 5–269 km long, which implies that large-magnitude (Mw 7–8) earthquakes may occur in Malawi. However, low slip rates (0.05–2 mm yr−1) mean that the frequency of such events will be low (recurrence intervals of ∼103–104 years). We also find that, for 9 out of 11 faults in Lake Malawi's North Basin, differences in the slip rates, when estimated independently from the geodetic data and the offset seismic reflector, are not statistically significant. The MSSM represents an important resource for investigating Malawi's increasing seismic risk and provides a framework for incorporating active fault data into seismic hazard assessment elsewhere in the East African Rift and other tectonically active regions.

Radar (SAR) satellites systematically acquire imagery that can be used for volcano monitoring, characterising magmatic systems and potentially forecasting eruptions on a global scale. However, exploiting the large dataset is limited by the need for manual inspection, meaning timely dissemination of information is challenging. Here we automatically process ~ 600,000 images of 1000 volcanoes acquired by the Sentinel-1 satellite in a 5-year period (2015-2020) and use the dataset to demonstrate the applicability and limitations of machine learning for detecting deformation signals. Of the 16 volcanoes flagged most often, 5 experienced eruptions, 6 showed slow deformation, 2 had non-volcanic deformation and 3 had atmospheric artefacts. The detection threshold for the whole dataset is 5.9 cm, equivalent to a rate of 1.2 cm/year over the 5-year study period. We then use the large testing dataset to explore the effects of atmospheric conditions, land cover and signal characteristics on detectability and find that the performance of the machine learning algorithm is primarily limited by the quality of the available data, with poor coherence and slow signals being particularly challenging. The expanding dataset of systematically acquired, processed and flagged images will enable the quantitative analysis of volcanic monitoring signals on an unprecedented scale, but tailored processing will be needed for routine monitoring applications.The online version contains supplementary material available at 10.1007/s00445-022-01608-x.

The TanDEM-X satellite mission has revolutionized DEM generation from spaceborne synthetic aperture radar. In addition to the primary objective of generating a consistent digital elevation model with global coverage and unprecedented accuracy, the mission has acquired time series of topographic data over several volcanoes, providing an excellent opportunity to test the use of this innovative dataset for volcano monitoring and research. In this article, we review the utilization of the single-pass TanDEM-X data for studying various kinds of volcanic activity, such as basaltic lava flows, the formation and destruction of lava domes and related pyroclastic density currents, and subsurface magma withdrawal and intrusion. We then consider the uses of these data for hazard assessment and forecasting. Our goal is to highlight the importance of timely and repeated topographic information in volcanology, and to suggest the development of future spaceborne bistatic synthetic aperture radar satellite missions, such as ESA's Earth Explorer 10 candidate mission, “Harmony.”

Tectonic and paleo-environmental reconstructions of rift evolution typically rely on the interpretation of sedimentary sequences, but this is rarely possible in early-stage rifts where sediment volumes are low. To overcome this challenge, we use geomorphology to investigate landscape evolution and the role of different forcing mechanisms during basin development. Here, we focus on the humid Middle Shire River basin, located within the zone of progressive interaction and linkage between the southern Malawi Rift and Shire Rift Zone, East Africa. We used a digital elevation model to map knickpoints and knickpoint morphologies in the Middle Shire River basin and examined the relationships with pre-rift and syn-rift structures within the rift interaction zone. The main axial stream, Shire River, descends steeply, 372 m over a 50 km distance, across exposed metamorphic basement along the rift floor, exhibiting a strongly disequilibrated longitudinal elevation profile with both ‘mobile’ and ‘fixed’ knickpoints. In particular, we identify two clusters of mobile knickpoints, which we interpret as associated with baselevel fall events at the downstream end of the exposed basement that triggered knickpoint migration through the fluvial network since at least the Mid. Pleistocene. We infer that after the integration of the axial stream across the Middle Shire Basin, the knickpoints migrate upstream in response to fault-related subsidence in the Shire Rift Zone. Conversely, the fixed knickpoints are interpreted to reflect local differential bedrock erodibility at lithologic contacts or basement-hosted fault scarps along the basin floor. The results suggest that Middle Shire basin opening, associated with rift linkage, is likely a recent event (at least Mid. Pleistocene) relative to the Late Oligocene activation of Cenozoic rifting in the East African Rift's Western Branch. These findings support the hypothesis that the Western Branch developed from the gradual propagation, linkage and coalescence of initially nucleated distinct rift basins.

Seismic hazard assessment in slow straining regions is challenging because earthquake catalogues only record events from approximately the last 100 years, whereas earthquake recurrence times on individual faults can exceed 1000 years. Systematic mapping of active faults allows fault sources to be used within probabilistic seismic hazard assessment, which overcomes the problems of short-term earthquake records. We use Shuttle Radar Topography Mission (SRTM) data to analyse surface deformation in the Luangwa Rift in Zambia and develop the Luangwa Rift Active Fault Database (LRAFD). The LRAFD is an open-source geospatial database containing active fault traces and their attributes and is freely available at https://doi.org/10.5281/zenodo.6513691. We identified 18 faults that display evidence for Quaternary activity, and empirical relationships suggest that these faults could cause earthquakes up to Mw 8.1, which would exceed the magnitude of historically recorded events in southern Africa. On the four most prominent faults, the median height of Quaternary fault scarps varies between 12.9 ± 0.4 and 19.2 ± 0.9 m, which suggests they were formed by multiple earthquakes. Deformation is focused on the edges of the Luangwa Rift: the most prominent Quaternary fault scarps occur along the 207 km long Chipola and 142 km long Molaza faults, which are the rift border faults and the longest faults in the region. We associate the scarp on the Molaza Fault with possible surface ruptures from two 20th century earthquakes. Thus, the LRAFD reveals new insights into active faulting in southern Africa and presents a framework for evaluating future seismic hazard.

During earthquakes on low (

Recent studies have shown that large tectonic earthquakes are capable of triggering volcanic eruptions (i.e. increasing the number of eruptions within a defined time period) up to hundreds of kilometres away. However, the prevalence of eruption triggering is less clear, with findings ranging from little evidence for triggered eruptions, to a fourfold increase in the number of eruptions following nearby large earthquakes. Some of this variability is likely due to differences in definitions of what constitutes a triggered volcanic eruption, including a lack of consensus on the maximum distance and time lag between an earthquake and a triggered volcanic eruption, the minimum magnitude of earthquake considered, and how aftershocks are incorporated into the analysis. A further source of variability arises from the different datasets used, including regional versus global studies, and the inclusion of incomplete earthquake and eruption records from before the modern instrumental era. To help address these issues, we provide a comprehensive statistical study of how large earthquakes affect volcanic eruption rates, using complete and unbiased global datasets spanning 1960-2021. We take a systematic approach to investigating how parameters such as the maximum distance and time lag between earthquake-eruption pairs, the minimum earthquake magnitude considered, and the declustering of aftershocks affects the results. We also investigate how previously unstudied earthquake parameters such as source depth and mechanism affect the prevalence of eruption triggering. Our results are placed in statistical context through the use of Monte Carlo simulations using randomised earthquake and eruption catalogues. Preliminary results indicate that, contrary to a previous focus on large subduction megathrust earthquakes, deep normal faulting earthquakes have the greatest eruption triggering tendency. However, when compared with randomised earthquake and eruption catalogues, the overall statistical significance of observed eruption triggering is fairly low.

In low strain rate regions, extrapolation of incomplete instrumental earthquake records is a limitation for probabilistic seismic hazard analysis (PSHA). This limitation can be addressed by incorporating geologic and geodetic data into fault-based seismogenic sources, although it is not always clear how on-fault magnitude-frequency distributions should be described and, if the seismogenic layer is especially thick, how these sources should be extrapolated down-dip. We explore these issues in the context of a new PSHA for Malawi, where regional extensional rates are 0.5-2 mm/yr, the seismogenic layer is 30-40 km thick, the instrumental catalog is ~60 years long, and fault-based sources were recently collated in the Malawi Seismogenic Source Database. Furthermore, Malawi is one of several countries along the East African Rift where exposure to seismic hazard is growing, but PSHA typically considers instrumental records alone. We use stochastic event catalogs to explore different fault-source down-dip extents and magnitude-frequency distributions. These indicate that hazard levels are highest for a Gutenberg-Richter on-fault magnitude-frequency distribution, even at low probabilities of exceedance (2% in 50 years), whilst seismic hazard levels are also sensitive to how relatively short (

In September 2017, volcanic unrest in the vicinity of Mount Agung, Bali, Indonesia, increased drastically as a dike intruded between Agung and Batur volcanoes. This intrusion was followed by 5 weeks of declining activity before the eventual explosive eruption from Agung’s summit starting on November 21, 2017. We use high-resolution satellite SAR imagery to detect pre-eruptive intra-crater uplift at Agung volcano. We show that deformation of the crater floor occurred together with the dike intrusion to the northwest of the volcano. We attribute the deformation to a hydrothermal system less than 300 m below the surface that was activated by the injection of magmatic gasses. This finding indicates that Agung’s shallow magmatic system was active from the start of the increased unrest. Additionally, we observe a pulse of intra-crater uplift within 3-0.5 days prior to the onset of the eruption. The second pulse of uplift was one of the only precursors to the eruption and was probably caused by interaction between the hydrothermal system and the ascending magma. The detection of localized deformation during a volcanic crisis has important implications for eruption and unrest forecasting at Mount Agung and similar volcanoes and argues for monitoring with high-resolution SAR, which is capable of achieving both outstanding spatial resolution and, if sufficient satellites are used, excellent temporal coverage.

Aseismic afterslip is postseismic fault sliding that may significantly redistribute crustal stresses and drive aftershock sequences. Afterslip is typically modeled through geodetic observations of surface deformation on a case-by-case basis, thus questions of how and why the afterslip moment varies between earthquakes remain largely unaddressed. We compile 148 afterslip studies following 53 Mw6.0–9.1 earthquakes, and formally analyze a subset of 88 well-constrained kinematic models. Afterslip and coseismic moments scale near-linearly, with a median Spearman's rank correlation coefficient (CC) of 0.91 after bootstrapping (95% range: 0.89–0.93). We infer that afterslip area and average slip scale with coseismic moment as urn:x-wiley:21699313:media:jgrb55593:jgrb55593-math-0001 and urn:x-wiley:21699313:media:jgrb55593:jgrb55593-math-0002, respectively. The ratio of afterslip to coseismic moment (Mrel) varies from 300% (interquartile range: 9%–32%). Mrel weakly correlates with Mo (CC: −0.21, attributed to a publication bias), rupture aspect ratio (CC: −0.31), and fault slip rate (CC: 0.26, treated as a proxy for fault maturity), indicating that these factors affect afterslip. Mrel does not correlate with mainshock dip, rake, or depth. Given the power-law decay of afterslip, we expected studies that started earlier and spanned longer timescales to capture more afterslip, but Mrel does not correlate with observation start time or duration. Because Mrel estimates for a single earthquake can vary by an order of magnitude, we propose that modeling uncertainty currently presents a challenge for systematic afterslip analysis. Standardizing modeling practices may improve model comparability, and eventually allow for predictive afterslip models that account for mainshock and fault zone factors to be incorporated into aftershock hazard models.

Active fault data are commonly used in seismic hazard assessments, but there are challenges in deriving the slip rate, geometry, and frequency of earthquakes along active faults. Herein, we present the open-access geospatial Malawi Seismogenic Source Database (MSSD), which describes the seismogenic properties of faults that have formed during East African rifting in Malawi. We first use empirical observations to geometrically classify active faults into section, fault, and multi-fault seismogenic sources. For sources in the North Basin of Lake Malawi, slip rates can be derived from the vertical offset of a seismic reflector that is estimated to be 75 ka based on dated core. Elsewhere, slip rates are constrained from advancing a ‘systems-based’ approach that partitions geodetically-derived rift extension rates in Malawi between seismogenic sources using a priori constraints on regional strain distribution in magma-poor continental rifts. Slip rates are then combined with source geometry and empirical scaling relationships to estimate earthquake magnitudes and recurrence intervals, and their uncertainty is described from the variability of outcomes from a logic tree used in these calculations. We find that for sources in the Lake Malawi’s North Basin, where slip rates can be derived from both the geodetic data and the offset seismic reflector, the slip rate estimates are within error of each other, although those from the offset reflector are higher. Sources in the MSSD are 5–200 km long, which implies that large magnitude (MW 7–8) earthquakes may occur in Malawi. Low slip rates (0.05–2 mm/yr), however, mean that the frequency of such events will be low (recurrence intervals ~103–104 years). The MSSD represents an important resource for investigating Malawi’s increasing seismic risks and provides a framework for incorporating active fault data into seismic hazard assessment in other tectonically active regions.

Over the past two decades, multidisciplinary studies have unearthed a rich history of volcanic activity and unrest in the densely-populated East African Rift System, providing new insights into the influence of rift dynamics on magmatism, the characteristics of the volcanic plumbing systems and the foundation for hazard assessments. The raised awareness of volcanic hazards is driving a shift from crisis response to reducing disaster risks, but a lack of institutional and human capacity in sub-Saharan Africa means baseline data are sparse and mitigating geohazards remains challenging., New observations of volcanic and magmatic activity in Africa are changing our views of continental rifting and raising awareness of the associated hazards. However, despite a shift from crisis response to reducing disaster risks, limited capacity means mitigating geohazards remains challenging.

Southern Africa is typically considered to belong to a single tectonic plate, Nubia, despite active faulting along the southwestern branch of the East African Rift System. We analyze regional Global Navigation Satellite System (GNSS) measurements, and find that the “San” microplate, situated south of the southwestern branch of the East African Rift, is statistically distinct from Nubia, with 0.4–0.7 mm/yr of extension across the boundary. Adding nine new campaign GNSS sites, we show that the extension rate across the southern Malawi Rift is 2.2 ± 0.3 mm/yr, with 75% of the relative velocity occurring over 890 km, despite the surface expression of faulting being

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

At the global scale, large tectonic earthquakes and volcanic eruptions are believed to be random and independent events. Here, we compare global time-series of large earthquakes (Mw ≥ 7) and explosive volcanic eruptions (VEI ≥ 2) spanning 1960–2019. Both time-series exhibit decadal timescale trends, over which annual earthquake and eruption rates vary by a factor of ≈2. Moreover, global seismic moment release is positively correlated with global eruption rate, with Monte Carlo permutation tests showing that this correlation is significant with a P-value of 1000 km) by earthquake-induced dynamic stress changes, modulation of global earthquake and eruption rates by variations in Earth's rotational velocity, or natural synchronization of events over repeating cycles, are therefore required to explain the correlation.