Patricia M. Gregg

The build-up of magma beneath a volcano can be revealed by ground surface deformation, and the recorded surface displacement can be modelled to infer details of the magma system dynamics. Constraints on magmatic processes can then be used to aid hazard assessment and eruption forecasting. However, inferring the processes occurring in the magma plumbing system during volcano deformation episodes is inherently dependent on the modelling approach used to interpret the recorded deformation data, and in particular the choices of rheology used to represent the solid and fluid parts of the magmatic and host rock system. Here, we explore the elastic, viscoelastic, and poroelastic rheologies typically implemented in volcano deformation analyses, and assess how their choices impact the interpretation of recorded volcano deformation data. Different viscoelastic rheologies can produce drastically different predicted surface deformation patterns, but all viscoelastic rheologies will typically lead to different source pressurisation estimates compared to a linear elastic rheology. Poroelastic source implementations can produce surface deformation even after supply to a reservoir has stopped, due to diffusive redistribution of pore pressures. Both viscous and poroelastic processes add a time-dependent component to the stress-strain evolution, which changes model predictions of temporal volcano deformation. Consequently, when applied to interpret recorded deformation, viscous and poroelastic rheologies can suggest non-linear magma system dynamics that are not captured by a simpler purely elastic model rheology. Issues persist with reliably parameterising different rheological approaches but their importance in modifying surface deformation predictions cannot be overlooked.

Spatiotemporal correlations between moderate-to-large earthquakes and volcanic eruptions indicate that earthquake-induced stress changes can trigger eruptions. Triggering is rarely instantaneous, with time-dependent processes inferred to mediate magma ascent to the surface, such as fracture propagation or fluid pressurization. While various processes have been proposed, observational constraints on the specific triggering mechanisms remain limited. Here we integrate an earthquake catalogue, generated by machine-learning and template-matching techniques to enhance microseismicity detections, with geodetic data to show that seismic triggering of the 2018 eruption of Sierra Negra Volcano, Galápagos Islands, was facilitated by a cryptic phase of fluid activity. Following 13 years of near-continuous magmatic inflation, totalling > 6.5 m, stress changes induced by a moment magnitude 5.4 intra-caldera earthquake on 26 June 2018 did not immediately trigger magma release from the pressurized magma reservoir. Instead, long-period earthquake swarms revealed post-seismic fluid activity along reservoir-bounding faults in the northwestern caldera, locally promoting edifice failure and facilitating magma intrusion that initiated 8 hours later. These observations demonstrate that even when a pressurized reservoir experiences significant stress perturbation, cascading processes could potentially be essential to initiate volcanic eruptions.

In their article entitled “ Trapdoor Fault Activation : A Step Toward Caldera Collapse at Sierra Negra , Galapagos , Ecuador ” Shreve and Delgado (2023, https://doi.org/10.1029/2023jb026437 ) examine co‐eruptive deformation during the 2018 eruption of Sierra Negra Volcano. One of their major conclusions is that the 2018 eruption, and specifically co‐eruptive faulting, represents the initial stages of caldera collapse. They reach this conclusion because they focus their analysis solely on co‐eruptive deformation, and do not investigate the total (net) deformation for the 2005 to 2018 eruption cycle. Bell, La Famina, et al. (2021, https://doi.org/10.1038/s41467‐021‐21596‐4 ) investigated both the pre‐ and co‐eruptive phases of the 2018 eruption and showed that net deformation was one of caldera resurgence, not subsidence. In this comment, we demonstrate that the conclusion of collapse, or even initiation of collapse, is attributable to not accounting for pre‐eruptive deformation on the intra‐caldera Trapdoor Fault system and incorrectly assuming that the volcano‐tectonic dynamics of Sierra Negra mimic those of other basaltic calderas.

Studying the interactions between magmatic and volcano-tectonic processes is critical to understanding the behavior of volcanic systems and the triggering of eruptions. Sierra Negra Volcano, Galápagos is one of the largest basaltic calderas on Earth, and its latest eruption in 2018 marked the first time an eruption in the Galápagos was recorded by both GNSS and seismic networks. These unprecedented data sets provide a unique opportunity to investigate the dynamics of eruption triggering and test hypotheses for dike initiation and migration. Observational and modeling studies of Sierra Negra’s previous eruption in 2005 hypothesize that tensile opening induced by a Mw5.3 earthquake on the southern intra-caldera trapdoor fault system resulted in magmatic dike initiation and eruption along the northern rim of the caldera (Gregg et al., GRL, 2018). In the ~13 years following the 2005 eruption, the caldera of Sierra Negra uplifted ~6.5 m (Bell et al., Nat. Comm., 2021). Sierra Negra’s inflation coincided with an increase in Mw > 4 earthquakes located along the intra-caldera fault system from late 2017 to June 2018 and a Mw5.4 earthquake occurred ~9 hours prior to the first eruptive fissure opening on 26 June 2018 (Bell et al., JGR, 2021). The similarities between the 2005 and 2018 pre-eruption sequences provide important clues about the dynamics of eruption triggering at Sierra Negra. In this study, we build upon previous numerical analyses of the 2005 and 2018 eruption (Gregg et al., GRL, 2018; Gregg et al., Sci. Adv., 2020; Bell et al., Nat. Comm., 2021) to model stress changes associated with the 2018 pre-eruptive Mw5.4 earthquake and begin to investigate dike initiation and magma migration leading to the first fissure opening. Specifically, thermomechanical finite element models are implemented in COMSOL Multiphysics to test the hypothesis that the Mw5.4 thrust fault earthquake promoted tensile failure and dike rupture in the northern region of the magma system. Future numerical models will test the hypothesis that the resulting stress field of the Mw5.4 earthquake controlled the pathway of dike migration. Previous numerical deformation models which exclude fault rupture (e.g., Gregg et al., Sci. Adv., 2018) do not result in system failure leading to eruption (i.e., tensile failure in the model space between the magma chamber and surface). Our new numerical model includes a segment of the intra-caldera Trapdoor Fault, and we solve for fault rupture parameters of the 26 June 2018 Mw5.4 earthquake, such as fault geometry and slip, and calculate the resulting stress and strain. The best-fit earthquake parameters are estimated by comparing the observed (cGPS-derived) and modeled co-seismic deformation. The stress field induced by both magma system inflation and fault rupture is evaluated to investigate failure of the volcanic edifice and optimal dike propagation pathways. Preliminary results demonstrate the importance of including fault displacements in model calculations of Sierra Negra’s stress evolution. In future numerical models, we aim to model and constrain magmatic intrusion geometries using pre- and syn-eruptive cGPS data to better understand the impact of static stress transfer in eruption triggering.

Geodetic observations are key for assessing the unrest status of volcanoes worldwide, providing critical information about magmatic systems and the potential for magma migration and eruption. Analysing these signals relies on a robust data-model framework. One such approach is the Ensemble Kalman Filter (EnKF), a data assimilation method that has been adapted for analyses of volcanic deformation. The EnKF sequentially assimilates and inverts geodetic observations, using a series of model states to ‘nudge’ the model parameters with each iteration, reducing the misfit between the model and observation. We construct thermomechanical Finite Element (FE) models of volcanic regions, providing the necessary flexibility to incorporate complex 3D geometries and material heterogeneity. However, these simulations are computationally expensive when incorporated into the EnKF workflow, where an ensemble of >200 model states can take several hours to evaluate. This is particularly problematic for the analysis of observational data with high temporal resolution, such as daily GPS measurements.Here, we aim to reduce the computational cost of the EnKF-FE workflow by using regression machine learning algorithms (MLAs), focusing on reducing the number of model states that need to be evaluated by the FE models. We start by using the ‘Mogi’ deformation model, a simple analytical expression that calculates the three-component displacement field (Ux, Uy, and Uz) due to a point source. We employ a tuneable nearest-neighbour approach to identify model states that occupy a ‘similar’ parameter space, using MLAs to predict the resultant displacements. The Mogi model has significantly reduced complexity compared to that of a FE model, providing a simple platform to test different machine learning approaches. Preliminary results suggests that the k-Nearest Neighbours and Linear Regression algorithms can significantly improve the computational efficiency of the EnKF-FE workflow, with negligible impact on the inferred best-fit model parameters, when a magmatic system is in a steady-state (i.e., static overpressure). Future modelling efforts will consider FE models with a 3D deformation source within a flat-topped domain, and time-varying overpressure scenarios.

Leveraging a cloud‐based interferometric synthetic aperture radar time‐series processing framework, we map the surface deformation along the western and central Aleutian volcanoes from 2015 to 2021. The observed crustal deformation from more than 15 volcanoes is attributed to a wide range of magmatic or tectonic processes, for example, magma accumulation in the magmatic reservoir, steady cooling or degassing of magma or hydrothermal systems, and faulting. More vigorous magmatism in the central Aleutian is noticed and appears to be related to higher magma production rates or higher magma ascent rates as a result of oblique subduction. New deformation patterns never observed in previous studies are detected and modeled at Tanaga, Great Sitkin and Yunaska. This study showcases the cloud‐processing capability to generate interferograms at scale and processing tools to analyze these time series over large, tectonically active areas.

A document by Jiahui Wang. Click on the document to view its contents.

As the volume of data collected at monitored volcanoes continues to expand, researchers will need quick, reliable, and automated methods of inverting those data into useful models of the underlying magma systems. Recently adapted from other fields for use in volcanology, the Ensemble Kalman Filter (EnKF) is one such inversion technique that has been used to produce several successful forecasts and hind‐casts of volcanic unrest, correlating geodetic deformation with mechanical stresses around the magma reservoir. However, given the similarity in which changes to a reservoir's size and pressure are expressed at the surface, the filter can have trouble fully resolving magmatic conditions. In this study, we therefore test several different published variations of the EnKF workflow to produce an optimal configuration for use in future forecasting efforts. By generating synthetic observations of ground deformation under known conditions and then assimilating them through different implementations of the EnKF, we find that many variants favored in other fields underperform for this specific application. We conclude that correlations between model parameters that develop within the EnKF's Monte Carlo ensemble distort the filter's ability to correctly update the model state, causing the filter to systematically favor changes in some parameters over others and ultimately converge to a partially inaccurate solution. This effect can be somewhat mitigated by interrupting these parameter correlations, and the filter remains sensitive to many aspects of the magma system regardless. However, further research and novel approaches will be needed to truly optimize the EnKF for use in volcanology.

Volcanic seamount chains on the flanks of mid‐ocean ridges record variability in magmatic processes associated with mantle melting over several millions of years. However, the relative timing of magmatism on individual seamounts along a chain can be difficult to estimate without in situ sampling and is further hampered by Ar⁴⁰/Ar³⁹ dating limitations. The 8°20’N seamount chain extends ∼170 km west from the fast‐spreading East Pacific Rise (EPR), north of and parallel to the western Siqueiros fracture zone. Here, we use multibeam bathymetric data to investigate relationships between abyssal hill formation and seamount volcanism, transform fault slip, and tectonic rotation. Near‐bottom compressed high‐intensity radiated pulse, bathymetric, and sidescan sonar data collected with the autonomous underwater vehicle Sentry are used to test the hypothesis that seamount volcanism is age‐progressive along the seamount chain. Although sediment on seamount flanks is likely to be reworked by gravitational mass‐wasting and current activity, bathymetric relief and Sentry vehicle heading analysis suggest that sedimentary accumulations on seamount summits are likely to be relatively pristine. Sediment thickness on the seamounts' summits does not increase linearly with nominal crustal age, as would be predicted if seamounts were constructed proximal to the EPR axis and then aged as the lithosphere cooled and subsided away from the ridge. The thickest sediments are found at the center of the chain, implying the most ancient volcanism there, rather than on seamounts furthest from the EPR. The nonlinear sediment thickness along the 8°20’N seamounts suggests that volcanism can persist off‐axis for several million years.

Understanding the stress evolution of extinct volcanoes can improve efforts to forecast flank eruptions on active systems. Field, petrographic, and seismic data are combined with numerical modeling to investigate the paleo‐stress field of New Zealand's Akaroa Volcano, or Akaroa Volcanic Complex. Field mapping identifies 86 radially oriented dikes and seven lava domes found only within a narrow elevation range along Akaroa's erosional crater rim. These observations suggest that crater rim dike emplacement resulted from lateral deflection of vertically ascending intrusions from a centralized magma source, which in turn may have facilitated formation of the lava domes, as well as two scoria cones. We postulate that dike deflection occurred along a stress barrier, as neither a compositional change nor structural boundary are present. We use a finite element model (FEM) simulating Akaroa to test how different factors may have influenced the system's stress state and dike geometry. Elastic, non‐flexural (“roller”) model configurations containing a large, oblate, and shallow magma chamber produce stress barriers most conducive to radial dike emplacement along Akaroa's crater rim. These configurations also simulate rapid edifice construction above a preexisting lithospheric “bulge.” Conversely, simulating flexural stresses exerted on the lithosphere by Akaroa's large mass hinder rather than promote radial dike emplacement. Temperature‐dependent viscoelastic relaxation promotes gradual increases in stress barrier elevation, though this effect is strongly dependent on magma chamber parameters. These results suggest that Akaroa was constructed rapidly (within ∼100 kyr) prior to crater rim dike emplacement, which occurred throughout the volcano's remaining active lifespan.