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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.

Funding: W. Hutchison is funded by a UKRI Future Leaders Fellowship (MR/S033505/1). E.R.D. Ogilvie was supported by a St Andrews Research Internship Scheme (StARIS) grant from the University of St Andrews. East Africa hosts significant reserves of untapped geothermal energy. Exploration has focused on geologically young (

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.

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

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.”

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.

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.

Funder: Leverhulme Trust; Id: http://dx.doi.org/10.13039/501100000275, Funder: Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics, Funder: University of Cambridge Jesus College, University of Cambridge (Jesus College); Id: http://dx.doi.org/10.13039/501100000644, Two of the most widely observed co‐eruptive volcanic phenomena—Ground deformation and volcanic outgassing—Are fundamentally linked via the mechanism of magma degassing and the development of compressibility, which controls how the volume of magma changes in response to a change in pressure. Here we use thermodynamic models—Constrained by petrological data—To reconstruct volatile exsolution and the consequent changes in magma properties. We use the fraction of SO2 exsolved during decompression to predict co‐eruptive SO2 flux and magma compressibility to predict co‐eruptive surface deformation (both normalized by erupted volume). We conduct sensitivity tests using properties of typical basalts to assess how varying magma volatile content, crustal properties, and chamber geometry affect co‐eruptive deformation and degassing. We find that magmatic H2O content has the most impact on both SO2 flux and volume change. Our findings have general implications for typical basaltic systems in arc and ocean island settings. The higher water content of arc magmas makes them more compressible than ocean island magmas and leads to muted or non‐existent deformation being observed during arc eruptions. Our models are consistent with observation: Deformation has been detected during 48% of basaltic eruptions in ocean island settings (16/33) during the satellite era (2005–2020), but only 11% of basaltic eruptions in arc settings (7/61).

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.

Since the launch of Sentinel-1 mission, automated processing systems have been developed for near real-time monitoring of ground deformation signals. Here, we perform a regional analysis of 5 years over 64 volcanic centres located along the East African Rift System (EARS). We show that the correction of atmospheric signals for the arid and low-elevation EARS volcanoes is less important than for other volcanic environments. We find that the amplitude of the cumulative displacements exceeds three times the temporal noise of the time series (3σ) for 16 of the 64 volcanoes, which includes previously reported deformation signals, and two new ones at Paka and Silali volcanoes. From a 5-year times series, uncertainties in rates of deformation are ∼0.1 cm/yr, whereas uncertainties associated with the choice of reference pixel are typically 0.3–0.6 cm/yr. 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. This study provides a framework for routine volcano monitoring using InSAR on a continental scale. Here, we focus on Sentinel-1 data from the EARS, but the framework could be expanded to include other satellite systems or global coverage.

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

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

The final stage of continental breakup is often accompanied by abundant magmatism. Erta 'Ale volcano lies on the Nubia‐Arabia extensional boundary in Afar, Ethiopia, an incipient mid‐ocean ridge. A fissure on the south flank of Erta 'Ale began erupting on 21 January 2017 and has remained active until at least July 2019. We use Sentinel‐1 synthetic aperture radar acquisitions to create a time series of ground displacement measurements at Erta 'Ale from October 2014 to June 2019, covering the eruption and its buildup. In the preeruption period, we observe gradual extension centered on the lava lake, consistent with the opening of an axis‐aligned dike. Using synthetic aperture radar intensity shadows, we show that the long‐lived Erta 'Ale lava lake was stable in this period, indicative of a steady pressure state in the shallow plumbing system. During the initial eruption, we observe surface displacements consistent with a shallow dike intrusion below the eruption site and conduit contraction below the lava lake. The pressure change associated with the cointrusive drop in the lava lake level is sufficient to reproduce the deformation pattern suggesting that the lava lake is well connected to the shallow plumbing system. Subsidence and contraction during the long‐lived eruption indicates the presence of an off‐rift vertically extensive source. We suggest that this may represent a system of stacked sources throughout the upper crust, with melt being more distributed. We also propose that high magma flux on the slow‐spreading Erta 'Ale segment may be facilitating the presence of shallow axial magma bodies.

Southern Mozambique is the southernmost expression of the continental East African Rift. Here, extension rates are low and rifting is achieved through normal faulting. Incipient rift environments provide an ideal location to investigate the role of reactivated pre-existing structures, aftershock sequences, and fault interactions in rift development. In 2016 a Mw 5.6 earthquake occurred in the Zinave region of southern Mozambique, ∼10 km south-east of the Mw 7.0 2006 Machaze earthquake. We reanalyse ENVISAT InSAR observations of the Machaze earthquake, together with new Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) observations of the Zinave earthquake, and solve for uniform and distributed slip models for both events. We find the Machaze earthquake occurred on a steeply dipping (∼75◦) fault, in agreement with other studies, but that the Zinave earthquake occurred on a ∼60◦ dipping fault. The occurrence of the Zinave earthquake at the same depth as afterslip following the Machaze earthquake suggests laterally heterogeneous crustal frictional properties. The Machaze earthquake caused a Coulomb stress increase of ∼0.2 MPa on the Zinave fault. The full >10 year record of seismicity following the Machaze event can be fit by the Omori law, showing that the Zinave earthquake is part of a decade-long aftershock sequence, consistent with long-duration aftershock sequences in other slowly straining regions. Aftershocks represent a major hazard that needs to be considered if a large earthquake were to occur in the southern East African Rift system today.

With the advances of ESA’s Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) mission there are freely available remote sensing ground deformation observations all over the globe that allows continuous monitoring of natural hazards and structural instabilities. The Digital Environment initiative in the UK aims to include these remote sensing data in the effort of forecasting and mitigating hazards across the UK.However, analyses of low coherence areas (e.g. forested and vegetated areas) with conventional InSAR methodologies are difficult to perform due to the limiting factor of temporal and geometric decorrelation. Even the application of the permanent scatterer (PS) technique may not be successful when there is a low density of stable radar targets. Using artificial reflectors with high radar cross section (RCS) can be a way of overcome this limitation and achieve measurements with a good signal-to-clutter ratio (SCR).In order to be able to include Sentinel-1 data in the UK’s Digital Environment it is important to understand the advantages and limitations of these observations and interpret them appropriately. The Hollin Hill landslide observatory in North Yorkshire is used by the British Geological Survey in their efforts to understand landslide processes, and to trial new technologies and methodologies for slope stability characterisation and monitoring.We present InSAR results of the Hollin Hill landslide where a variety of ground-based geophysical measurements (e.g. GPS, Electric resistivity tomography, meteorological observations) are available for comparison with InSAR data. We use Sentinel-1 InSAR data acquired between Oct 2015 and Jan 2021 to study the behaviour of this landslide. We find that the Line of Sight component of the down-slope movement is 2.7 mm/yr in the descending track, and 7.5-7.7 mm/yr in the ascending track. The InSAR measurements also highlight the seasonal behaviour of this landslide.In July 2019 six corner reflectors were installed to improve the coherence of the InSAR measurements, especially in the ascending acquisition mode. We present comparison with ground-based measurements such as the movement recorded by the GPS measurements of the pegs of the ERT survey or the moisture recorded by the various instruments at the site, and show the improvement introduced by the corner reflectors.In addition we present results of an experiment that explores the use of smaller corner reflectors for potential urban applications of infrastructure monitoring. A single corner reflector needs to be at least ~67cm wide and tall to be seen by the Sentinel-1 satellites. We show that by placing 4 reflectors with 33cm dimensions in the same pixel coherent signal can be acquired. It is feasible to install small reflectors on bridges, tall buildings, or incorporate “corner-like” features in newly built structures,but care needs to be taken on the precise spacing of the reflectors to avoid destructive interference. Continuous monitoring of infrastructure with remote sensing and machine learning can alert to potential failures where further investigation is needed.

Time series of displacement are now routinely available from satellite InSAR and are used for flagging anomalous ground motion, but not yet forecasting. We test conventional time series forecasting methods such as SARIMA and supervised machine learning approaches such as LSTM compared to simple function extrapolation. We focus initially on forecasting seasonal signals and begin by characterising the time‐series using sinusoid fitting, seasonal decomposition and autocorrelation functions. We find that the three measures are broadly comparable but identify different types of seasonal characteristic. We use this to select a set of 310 points with highly seasonal characteristics and test the three chosen forecasting methods over prediction windows of 1‐9 months. The lowest overall median RMSE values are obtained for SARIMA when considering short term predictions ( 6 months). Machine learning methods (LSTM) perform less well. We then test the prediction methods on 2000 randomly selected points with a range of seasonalities and find that simple extrapolation of a constant function performed better overall than any of the more sophisticated time series prediction methods. Comparisons between seasonality and RMSE show a small improvement in performance with increasing seasonality. This proof‐of‐concept study demonstrates the potential of time‐series prediction for InSAR data but also highlights the limitations of applying these techniques to non‐periodic signals or individual measurement points. We anticipate future developments, especially to shorter timescales, will have a broad range of potential applications, from infrastructure stability to volcanic eruptions.

Large earthquakes alter the crustal stress field across great distances (hundreds to thousands of kilometers) over geologically short timescales (seconds to years). These stress changes can affect magmatic systems, triggering (or suppressing) volcanic unrest and eruption, along with other deeper processes. We use simple kinematic source models in an isotropic elastic half‐space to assess earthquake‐induced static stress changes (>1 kPa) over the entire thickness of the lithosphere and consider the implications for magma ascent and storage. Modeling subduction zone earthquakes, we calculate static normal stress changes with depth on three mutually perpendicular end‐member magma pathways: vertical arc‐parallel, vertical arc‐perpendicular, and horizontal. From this, we define seven stress change regimes within the adjacent volcanic arc. Three of these regimes may strongly encourage magma ascent in dykes by inducing unclamping (decreased compressive normal stress) of vertical pathways which increases in magnitude toward the surface and clamping of horizontal pathways. Two of the regimes may encourage stalling and storage of magma in sills near the base of the crust by inducing unclamping of horizontal pathways at depth. The spatial distribution of the regimes is largely dependent on earthquake magnitude, but also varies with slip distribution and interface dip. We show how the responses of magmatic systems to earthquakes also depends on the stress change magnitude and the state of the magmatic system, with a greater impact expected for larger stress changes acting on weaker, more thermally mature systems.

Seismic hazard is frequently characterised using instrumental seismic records. However, in regions where the instrumental record is short relative to earthquake repeat times, extrapolating it to estimate seismic hazard can misrepresent the probable location, magnitude, and frequency of future large earthquakes. Although paleoseismology can address this challenge, this approach requires certain geomorphic settings and carries large inherent uncertainties. Here, we outline how fault slip rates and recurrence intervals can be estimated through an approach that combines fault geometry, earthquake-scaling relationships, geodetically derived regional strain rates, and geological constraints of regional strain distribution. We then apply this approach to the southern Malawi Rift where, although no on-fault slip rate measurements exist, there are theoretical and observational constraints on how strain is distributed between border and intrabasinal faults. This has led to the development of the South Malawi Active Fault Database (SMAFD), the first database of its kind in the East African Rift System (EARS) and designed so that the outputs can be easily incorporated into Probabilistic Seismic Hazard Analysis. We estimate earthquake magnitudes of MW 5.4–7.2 for individual fault sections in the SMAFD, and MW 6.0–7.8 for whole fault ruptures. These potentially high magnitudes for continental normal faults reflect southern Malawi's 11–140 km long faults and thick (30–35 km) seismogenic crust. However, low slip rates (intermediate estimates 0.05–0.8 mm/yr) imply long recurrence intervals between events: 102–105 years for border faults and 103–106 years for intrabasinal faults. Sensitivity analysis indicates that the large range of these estimates can be reduced most significantly from an improved understanding of the rate and partitioning of rift-extension in southern Malawi, earthquake scaling relationships, and earthquake rupture scenarios. Hence these are critical areas for future research. The SMAFD provides a framework for using geological and geodetic information to characterize seismic hazard in low strain rate settings with few on-fault slip rate measurements, and could be adapted for use elsewhere in the EARS or globally.

Magmatic intrusions play a vital role not only in accommodating extensional stresses in continental rifts but also in feeding volcanic systems. The location, orientation, and timescale of dike intrusions are dictated by the interaction of regional and local stresses, the effect of pre‐existing weaknesses, and the composition of magma. Observing active intrusions can provide important information regarding the interaction between magmatic processes and the tectonic stress field during continental rifting. We focus on a seismic swarm that occurred in 2015 to the northeast of Fentale volcano, in the Main Ethiopian Rift (MER), and use radar interferometry to study surface deformation associated with the seismic swarm. Interferograms show a pattern of dike‐induced deformation, with a model estimate of volume change of 33×106±0.6×106m3 at a depth range of 5.4 to 8 km. We use a small baseline subset algorithm to calculate line of sight time series and find that the displacements decay exponentially with a decay constant of ∼83 days. Coupled source‐sink models suggest that such slow dike intrusions require a high viscosity rhyolitic magma. The difference in behavior between Fentale and other caldera systems in the MER, which show multi‐year cycles of inflation and deflation, suggests fundamental differences in magma composition and architecture of the plumbing system. This is the first direct observation of a dike intrusion in the MER and provides new constraints on the temporal‐spatial patterns of stress and strain that occur during continental rifting. Whether this activity is transient or a long‐term feature associated with rift evolution is an open question.

Time series of displacement are now routinely available from satellite InSAR and are used for flagging anomalous ground motion, but not yet for forecasting. Here we test the capabilities of conventional time series analysis and forecasting methods such as SARIMA and supervised machine learning approaches such as Long Short Term Memory (LSTM) in comparison to simple function extrapolation methods. For our initial tests, we focus on forecasting periodic signals and begin by characterising the time-series using sinusoid fitting, seasonal decomposition and autocorrelation functions. We find that the three measures are broadly comparable but identify different types of seasonal characteristic. We use this to select a set of 310 points with highly seasonal characteristics and test the three chosen forecasting methods over prediction windows of 1-9 months. The lowest overall RMSE values are obtained for SARIMA when considering short term predictions ($$6 months). Machine learning methods (LSTM) perform less well, as is often the case for non-stationary signals. We then test the prediction methods on 2000 randomly selected points with a range of seasonalities and find that simple extrapolation of a constant function performed better overall than any of the more sophisticated time series prediction methods. Comparisons between seasonality and RMSE show a statistically significant improvement in performance with increasing seasonality. This proof-of-concept study demonstrates the potential of time-series prediction for InSAR data but also highlights the limitations of applying these techniques to non-periodic signals or individual measurements points. We anticipate future developments, especially to shorter timescales, will have a broad range of potential applications, from infrastructure stability to volcanic eruptions.

The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of nonexpert stakeholders a challenge. Here, we explore the applicability of deep learning approaches by adapting a pretrained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the U.K. where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides, and tunneling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exist to construct a balanced training data set, and the deformation signals are slower and more localized than in previous applications. We propose three enhancement methods to tackle these problems: 1) spatial interpolation with modified matrix completion; 2) a synthetic training data set based on the characteristics of the real U.K. velocity map; and 3) enhanced overwrapping techniques. Using velocity maps spanning 2015-2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides, and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of nonexpert stakeholders a challenge. Here, we explore the applicability of deep learning approaches by adapting a pretrained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the U.K. where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides, and tunneling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exist to construct a balanced training data set, and the deformation signals are slower and more localized than in previous applications. We propose three enhancement methods to tackle these problems: 1) spatial interpolation with modified matrix completion; 2) a synthetic training data set based on the characteristics of the real U.K. velocity map; and 3) enhanced overwrapping techniques. Using velocity maps spanning 2015-2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides, and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.

Large silicic magma reservoirs preferentially form in the upper crust of extensional continental environments. However, our quantitative understanding of the link between mantle magmatism, silicic reservoirs, and surface deformation during rifting is very limited. Here, we focus on Corbetti, a peralkaline caldera in the densely populated Main Ethiopian Rift, which lies above a focused zone of upper mantle partial melt and has been steadily uplifting at a maximum rate of 6.6±1.2 cm yr−1 for more than 10 yr. Numerical modeling shows that a maximum concomitant residual gravity increase of 9±3 μGal yr−1 by the intrusion of mafic magma at ∼7 km depth into a compressible and inelastic crystal mush best explains the observed deformation and gravity changes. The derived magma mass flux of ∼1011 kg yr−1 is anomalously high and at least 1 order of magnitude greater than the mean long‐term mass eruption rate. This study demonstrates that periodic and high‐rate magmatic rejuvenation of upper‐crustal mush is a significant and rapid contributor to mature continental rifting.

Aftershock sequences following large tectonic earthquakes exhibit considerable spatio-temporal complexity and suggest causative mechanisms beyond co-seismic, elasto-static Coulomb stress changes in the crust. Candidate mechanisms include dynamic triggering and postseismic processes such as viscoelastic relaxation, poroelastic rebound and aseismic afterslip, which has garnered particular interest recently. Aseismic afterslip – whereby localized frictional sliding within velocity-strengthening rheologies acts to redistribute lithospheric stresses in the postseismic phase – has been suggested by numerous studies to exert dominant control on aftershock sequence evolution, including productivity, spatial distribution and temporal decay.As evidence is based overwhelmingly on individual case study analysis, we wish to systematically compare key metrics of aseismic afterslip and corresponding aftershock sequences to investigate this relationship. We specifically look for any empirical relationship between the seismic-equivalent moment of aseismic afterslip episodes and the corresponding aftershock sequence productivity. We first compile published afterslip models into a database containing moment estimates over varying time periods, as well as spatial distributions, temporal decays and modelling methodology as a supplementary resource. We then identify the corresponding aftershock sequence from the globally comparable USGS PDE catalog. As expected, coseismic moment exerts an obvious control on both afterslip moment and aftershock productivity – an effect we control for by normalising by mainshock moment and expected productivity (the Utsu-Seki law) respectively. Preliminary results suggest broad variability of both afterslip moment and aftershock productivity with no obvious control of afterslip on aftershocks beyond the scaling with mainshock size, including when separated by mainshock mechanism or region. As this study is insensitive to spatial and temporal distributions, we cannot rule out the potential influence afterslip exerts in these but find no evidence that afterslip drives overall productivity of aftershock sequences.

Large silicic magma reservoirs preferentially form in the upper crust of extensional continental environments. However, our quantitative understanding of the link between mantle magmatism, silicic reservoirs and surface deformation during rifting is very limited. Here, we focus on Corbetti, a peralkaline caldera in the densely-populated Main Ethiopian Rift, which lies above a focused zone of upper mantle partial melt and has been steadily uplifting at ≤6.6±1.2 cm yr−1 for more than ten years. We show that a concomitant residual gravity increase of ≤9±3 μGal yr−1 by the intrusion of mafic magma at ∼7 km depth into a compressible and inelastic crystal mush best explains the uplift. The derived magma mass flux of ∼10^11 kg yr−1 is anomalously high and at least one order of magnitude greater than the mean long-term mass eruption rate. We demonstrate that periodic and high-rate magmatic rejuvenation of upper-crustal mush is a significant and rapid contributor to mature continental rifting.

Satellite interferometric synthetic aperture radar (InSAR) can be used for measuring surface deformation for a variety of applications. Recent satellite missions, such as Sentinel-1, produce a large amount of data, meaning that visual inspection is impractical. Here we use deep learning, which has proved successful at object detection, to overcome this problem. Initially we present the use of convolutional neural networks (CNNs) for detecting rapid deformation events, which we test on a global dataset of over 30,000 wrapped interferograms at 900 volcanoes. We compare two potential training datasets: data augmentation applied to archive examples and synthetic models. Both are able to detect true positive results, but the data augmentation approach has a false positive rate of 0.205% and the synthetic approach has a false positive rate of 0.036%. Then, I will present an enhanced technique for measuring slow, sustained deformation over a range of scales from volcanic unrest to urban sources of deformation such as coalfields. By rewrapping cumulative time series, the detection performance is improved when the deformation rate is slow, as more fringes are generated without altering the signal to noise ratio. We adapt the method to use persistent scatterer InSAR data, which is sparse in nature, by using spatial interpolation methods such as modified matrix completion Finally, future perspectives for machine learning applications on InSAR data will be discussed.

We present new insights on the geometry, initiation and growth of the Thyolo fault, an 85 km long active border fault in the southern Malawi Rift, from high-resolution topography, field and microstructural observations. The Thyolo fault is located towards the edge of the Proterozoic Unango Terrane, and is the border fault of the Lower Shire Graben, which has experienced four phases of extension since the Jurassic. Recent activity is demonstrated by an 18.6 ± 7.7 m high fault scarp, with two substantial reductions in scarp height along strike. However, the segment boundaries suggested by these displacement measurements do not coincide with changes in fault strike. Elsewhere, a ∼5 km long fault perpendicular scarp joins two overlapping sections, yet the scarp height in this linking section is similar to the bounding sections, and there is no evidence of significant pre-linkage strain accumulation. Microstructural analyses along the fault show a 15–45 m thick footwall damage zone with a 0.7 m thick core. We suggest that favourably-oriented, pre-existing shallow structures control changes in surface geometry and the narrow fault core, whereas exploitation of weak ductile zones at depth, possibly associated with the terrane boundary, control the displacement profile of the fault.

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

Seismicity can be used to better understand interactions between magma bodies, hydrothermal systems and their host rocks—key factors influencing volcanic unrest. Here, we use earthquake data to image, for the first time, the seismic velocity structure beneath Aluto, a deforming volcano in the Main Ethiopian Rift. Traveltime tomography is used to jointly relocate seismicity and image 3D P- and S-wave velocity structures and the ratio between them (V P/V S). At depths of 4–9 km, the seismicity maps the top of a large low velocity zone with high V P/V S, which we interpret as a more ductile and melt-bearing region. A shallow (

In continental rifts structural heterogeneities, such as pre-existing faults and foliations, are thought to influence shallow crustal processes, particularly the formation of rift faults, magma reservoirs and surface volcanism. We focus on the Corbetti caldera, in the southern central Main Ethiopian Rift. We measure the surface deformation between 22nd June 2007 and 25th March 2009 using ALOS and ENVISAT SAR interferograms and observe a semi-circular pattern of deformation bounded by a sharp linear feature cross-cutting the caldera, coincident with the caldera long axis. The signal reverses in sign but is not seasonal: from June to December 2007 the region south of this structure moves upwards 3 cm relative to the north, while from December 2007 until November 2008 it subsides by 2 cm. Comparison of data taken from two different satellite look directions show that the displacement is primarily vertical. We discuss potential mechanisms and conclude that this deformation is associated with pressure changes within a shallow (

The mechanism and evolution of fault linkage is important in the growth and development of large faults. Here we investigate the role of coseismic stress changes in shaping the hard-links between parallel normal fault segments (or faults), by comparing numerical models of the Coulomb stress change from simulated earthquakes on two en echelon fault segments to natural observations of hard-linked fault geometry. We consider three simplified linking fault geometries: 1) fault bend; 2) breached relay ramp; and 3) strike-slip transform fault. We consider scenarios where either one or both segments rupture and vary the distance between segment tips. Fault bends and breached relay ramps are favoured where segments underlap, or when the strike-perpendicular distance between overlapping segments is less than 20% of their total length, matching all documented examples. Transform fault linkage geometries are preferred when overlapping segments are laterally offset at larger distances. Few transform faults exist in continental extensional settings, and our model suggests that propagating faults or fault segments may first link through fault bends or breached ramps before reaching sufficient overlap for a transform fault to develop. Our results suggest that Coulomb stresses arising from multi-segment ruptures or repeated earthquakes are consistent with natural observations of the geometry of hard-links between parallel normal fault segments

Volcano-tectonic (VT) earthquake swarms are usual precursors to volcanic eruptions and their dynamic phenomenologies are an invaluable eruption forecasting tool. A power-law relationship between cumulative seismic moment release and intrusion volume has been proposed for both well injection sites and VT swarms. Here we compile data from 17 geodetically-studied VT swarms and use a Bayesian methodology to assess the relationship between cumulative moment release and subsurface volume change. We find that the empirical relationship derived from injection sites systematically underpredicts volume changes observed during VT swarms near volcanoes. A new relationship derived specifically from VT swarms provides a better fit, but large uncertainties mean that estimates of intruded volume range by three orders of magnitude (95% confidence) for a given cumulative seismic moment release. We further subdivide the dataset, and although the sample size is too small to meet conventional statistic hypothesis significance tests, qualitative inspection suggests that the seismic moment release is proportionally larger for unrest swarms which culminate in eruption. We conclude that the currently available dataset is too sparse and the uncertainties too large for use for reliably forecasting eruption magnitude; however, as the dataset of cases grows, further analysis along these same lines may provide improved diagnostic insights into eruption-related processes that may be driving seismic moment release in VT swarms.

Lava extrusion at erupting volcanoes causes rapid changes in topography and morphology on the order of tens or even hundreds of meters. Satellite radar provides a method for measuring changes in topographic height over a given time period to an accuracy of meters, either by measuring the width of radar shadow cast by steep sided features, or by measuring the difference in radar phase between two sensors separated in space. We measure height changes, and hence estimate extruded lava volume flux, at El Reventador, Ecuador, between 2011 and 2016, using data from the RADARSAT‐2 and TanDEM‐X satellite missions. We find that 39 new lava flows were extruded between 9 February 2012 and 24 August 2016, with a cumulative volume of 44.8M m3 dense rock equivalent and a gradually decreasing eruption rate. The average dense rock rate of lava extrusion during this time is 0.31 ± 0.02 m3 s−1, which is similar to the long‐term average from 1972 to 2016. Apart from a volumetrically small dyke opening event between 9 March and 10 June 2012, lava extrusion at El Reventador is not accompanied by any significant magmatic ground deformation. We use a simple physics‐based model to estimate that the volume of the magma reservoir under El Reventador is greater than 3 km3. Our lava extrusion data can be equally well fit by models representing a closed reservoir depressurising during the eruption with no magma recharge, or an open reservoir with a time‐constant magma recharge rate of up to 0.35 ± 0.01 m3 s−1.

The silicic volcanic centres of the Main Ethiopian Rift (MER) play a central role in facilitating continental rifting. Many of these volcanoes host geothermal resources and are located in heavily populated regions. InSAR studies have shown several are deforming, but regional seismic networks have detected little seismicity. A local network of 12 seismometers was deployed at Aluto Volcano from 2012 to 2014, and detected 2142 earthquakes within a 24-month period. We locate the events using a 1D velocity model that exploits a regional model and information from geothermal boreholes and calculate local magnitudes, b-values and focal mechanisms. Event depths generally range from the near surface to 15 km with most of the seismicity clustering in the upper 2 km. A significant amount of seismicity follows the Artu Jawa Fault Zone, which trends in alignment with the Wonji Fault Belt, NNE–SSW and is consistent with previous studies of strain localisation in the MER. Focal mechanisms are mostly normal in style, with the mean T-axes congruent to the orientation of extension in the rift at this latitude. Some show relatively small left-lateral strike-slip components and are likely associated with the reactivation of NE-ENE structures at the southern tip of the Aluto-Gedemsa segment. Events range from − 0.40 to 2.98 in magnitude and we calculate an overall b-value of 1.40 ± 0.14. This relatively elevated value suggests fluid-induced seismicity that is particularly evident in the shallow hydrothermal reservoir and above it. Subdividing our observations according to depth identifies distinct regions beneath the volcanic edifice: a shallow zone (− 2–0 km) of high seismicity and high b-values that corresponds to the hydrothermal system and is influenced by a high fluid saturation and circulation; a relatively aseismic zone (0–2 km) with low b-values that is impermeable to ascending volatiles; a region of increased fluid-induced seismicity (2–9 km) that is driven by magmatic intrusion from below and a deeper zone (below 9 km) that is interpreted as a partially crystalline, magmatic mush. These observations indicate that both the magmatic and hydrothermal systems of Aluto volcano are seismically active and highlight the need for dedicated seismic monitoring at volcanoes in the MER.

Currently, it is only possible to look inside an active volcano using indirect geophysical methods. One such method is to measure surface deformation, which results from subsurface magmatic or hydrothermal processes. Modern satellite data allows deformation to be measured at hundreds of volcanoes without relying on limited ground instrumentation. As a result, the number of known deforming volcanoes has increased from 44 in 1997 to over 220 in 2016. This article reviews the diverse ways by which volcanoes can deform, the typical rates and durations of such deformations, and the processes that drive deformation.