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The submarine volcano Axial Seamount has exhibited an inflation predictable eruption cycle, which allowed for the successful forecast of its 2015 eruption. However, the exact triggering mechanism of its eruptions remains ambiguous. The inflation predictable eruption pattern suggests a magma reservoir pressure threshold at which eruptions occur, and as such, an overpressure eruption triggering mechanism. However, recent models of volcano unrest suggest that eruptions are triggered when conditions of critical stress are achieved in the host rock surrounding a magma reservoir. We test hypotheses of eruption triggering using 3-dimensional finite element models which track stress evolution and mechanical failure in the host rock surrounding the Axial magma reservoir. In addition, we provide an assessment of model sensitivity to various temperature and non-temperature-dependent rheologies and external tectonic stresses. In this way, we assess the contribution of these conditions to volcanic deformation, crustal stress evolution, and eruption forecasts. We conclude that model rheology significantly impacts the predicted timing of through-going failure and eruption. Models consistently predict eruption at a reservoir pressure threshold of 12–14 MPa regardless of assumed model rheology, lending support to the interpretation that eruptions at Axial Seamount are triggered by reservoir overpressurization.

Near-axis seamounts provide a unique setting to investigate three-dimensional mantle processes associated with the formation of new oceanic crust and lithosphere. Here, we investigate the characteristics and evolution of the 8˚20’N Seamount Chain, a lineament of seamounts that extends ~ 175 km west of the East Pacific Rise (EPR) axis, just north of the fracture zone of the Siqueiros Transform Fault. Shipboard gravity, magnetic, and bathymetric data acquired in 2016 are utilized to constrain models of seamount emplacement and evolution. Geophysical observations indicate that these seamounts formed during four distinct episodes of volcanism coinciding with changes in regional plate motion that are also reflected in the development of intra-transform spreading centers (ITSCs) along the Siqueiros transform fault (Fornari et al. 1989; Pockalny et al. 1997). Although volcanism is divided into distinct segments, the magnetic data indicate continuous volcanic construction over long portions of the chain. Crustal thickness variations along the chain up to 0.75 km increase eastward, inferred from gravity measurements, suggest that plate reorganization has considerably impacted melt distribution in the area surrounding the Siqueiros-EPR ridge transform intersection. This appears to have resulted in increased volcanism and the formation of the 8˚20’N Seamounts. These findings indicate that melting processes in the mantle and subsequently the formation of new oceanic crust and lithosphere are highly sensitive to tectonic stress changes in the vicinity of fast-spreading transform fault offsets.

In this study, synthetic numerical experiments are conducted to investigate how well the Ensemble Kalman Filter (EnKF) data assimilation approach distinguishes between two potential drivers of ground deformation at volcanic systems: pressurization and lateral reservoir expansion. Numerical models indicate that pressure-driven inflation creates larger radial displacements relative to inflation driven by lateral expansion. However, the introduction of noise can obscure these differences in simulated geodetic data. Although the EnKF does not fully reproduce the original synthetic models, it remains sensitive to changes in the magma reservoir's aspect ratio and is able to distinguish between the two inflation mechanisms. Ultimately, there remains significant non-uniqueness in how changes in reservoir pressure and size are reflected in surface deformation for any given aspect ratio, but future innovations may continue to improve filter performance.

In recent years, the advent of ensemble-based methods in volcanology has greatly facilitated the use of numerical models within data assimilation frameworks that had previously been limited, either computationally or mathematically, to simpler analytical models. Because numerical models can simulate stress conditions throughout the model space, recent inversions based on assimilated volcanic deformation data are able to track not only the basic parameters of a magma reservoir, but also how those parameters affect the overall mechanical stability of the system. Although this approach has produced successful forecasts and hind-casts of volcanic eruptions, much work remains to be done in assessing its full capabilities and limitations. In particular, non-uniqueness in how source parameters are reflected in surface deformation can significantly impair the inversion’s ability to resolve the magma system’s true state and, by extension, the likelihood of eruption. While this problem is nearly intractable for deep reservoirs, for which changes in pressure and size are indistinguishable from deformation alone, preliminary synthetic tests at shallower systems have demonstrated a limited ability to resolve the main inflation mechanism. In this study, we investigate how the performance of an Ensemble Kalman Filter (EnKF) data assimilation framework varies under a wider range of experimental conditions than used in these initial investigations. In particular, we test how different mathematical implementations of the filter and how different levels of data availability affect the EnKF’s ability to distinguish inflation drivers and to accurately resolve reservoir parameters. To implement this experiment, two time series of synthetic GPS and InSAR data are generated, one in which deformation is driven by excess pressure and another in which it is driven by lateral expansion of the reservoir. For each filter implementation these datasets are down-sampled and given random noise prior to inversion, and after assimilation the resulting model is compared to the original synthetic conditions. We find that newer deterministic formulations of the EnKF are more accurate and consistent than the original stochastic implementation, although the improvement is relatively small. Moreover, some amount of parameter inflation is required to avoid model collapse, but more sophisticated adaptive inflation schemes do not produce better results than more basic formulations. Finally, we show that while increased data sampling does improve performance, this effect is subject to diminishing returns. In particular, data resolution near the center of inflation is more important than overall range of coverage. As new inversion techniques are developed or adapted from other fields, rigorous testing as demonstrated here will be a key step in being able to interpret future results and develop new forecasting frameworks for volcanic eruptions.

Ensemble based data assimilation approaches, such as the Ensemble Kalman Filter (EnKF), have been widely and successfully implemented to combine observations with dynamic models to investigate the evolution of a system’s state. Such inversions are powerful tools for providing forecasts as well as “hindcasting” events such as volcanic eruptions to investigate source parameters and triggering mechanisms. In this study, a high performance computing (HPC) adaptation of the EnKF is used to assimilate ground deformation observations from interferometric synthetic-aperture radar (InSAR) into high-fidelity, multiphysics finite element models to evaluate the prolonged unrest and June 26, 2018 eruption of Sierra Negra volcano, Galápagos. The stability of the Sierra Negra magma system is evaluated at each time step by estimating variations in reservoir overpressure, Mohr-Coulomb failure in the host rock, and tensile stress and failure along the reservoir boundary. The deformation of Sierra Negra is tracked over a decade, during which almost 5 meters of surface uplift has been recorded. The EnKF reveals that the evolution of the stress state in the host rock surrounding the Sierra Negra magma reservoir likely controlled the timing of the eruption. While increases in magma reservoir overpressure remained modest (< 10 MPa) throughout the data assimilation time period, significant Mohr-Coulomb failure is indicated in the lead up to the eruption coincident with increased seismicity along both trapdoor faults within Sierra Negra’s caldera and along the caldera’s ring faults. During the final stages of pre-eruptive unrest, the EnKF models indicate limited tensile failure, with no tensile failure along the northern portion of the magma system where the eruption commenced. Most strikingly, model calculations of significant through-going Mohr-Coulomb failure correspond in space and time with a Mw 5.4 earthquake recorded in the hours preceding the 2018 eruption. Subsequent stress modeling implicates the Mw 5.4 earthquake along the southern intra-caldera trapdoor fault as the potential catalyst for tensile failure and dike initiation along the reservoir to the north. In conclusion, the volcano EnKF approach successfully tracked the evolving stability of Sierra Negra, indicating great potential for future forecasting efforts.

Ground deformation observed using near-real time geodetic methods, such as InSAR and GPS, can provide critical information about the evolution of a magma chamber prior to volcanic eruption. Rapid advancement in numerical modeling capabilities has resulted in a number of finite element models targeted at better understanding the connection between surface uplift associated with magma chamber pressurization and the potential for volcanic eruption. Robust model-data fusion techniques are necessary to take full advantage of the numerical models and the volcano monitoring observations currently available. In this study, we develop a 3D data assimilation framework using the Ensemble Kalman Filter (EnKF) approach in order to combine geodetic observations of surface deformation with geodynamic models to investigate volcanic unrest. The EnKF sequential assimilation method utilizes disparate data sets as they become available to update geodynamic models of magma reservoir evolution. While the EnKF has been widely applied in hydrologic and climate modeling, the adaptation for volcano monitoring is in its initial stages. As such, our investigation focuses on conducting a series of sensitivity tests to optimize the EnKF for volcano applications and on developing specific strategies for assimilation of geodetic data. Our numerical experiments illustrate that the EnKF is able to adapt well to the spatial limitations posed by GPS data and the temporal limitations of InSAR, and that specific strategies can be adopted to enhance EnKF performance to improve model forecasts. Specifically, our numerical experiments indicate that: (1) incorporating additional iterations of the EnKF analysis step is more efficient than increasing the number of ensemble members; (2) the accuracy of the EnKF results are not affected by initial parameter assumptions; (3) GPS observations near the center of uplift improve the quality of model forecasts; (4) occasionally shifting continuous GPS stations to provide variability in the locations of observations results in better model predictions than utilizing fixed locations when the number of available instruments is limited; (5) spotty InSAR data coverage on the flanks of a volcano due to topographic shadows and/or atmospheric interference does not adversely impact the effectiveness of EnKF if the available coverage is > 50%; and (6) snow or glacial obstruction in the center of uplift can adversely impact EnKF forecasts. By utilizing these strategies, we conclude that the EnKF is an effective sequential model-data fusion technique for assimilating multiple geodetic observations to forecast volcanic activity at restless volcanoes.

The accumulation of sizeable volumes of magma in the upper crust may produce plutons and/or result in supereruptions. Geophysical observations provide constraints on the rates, volumes, and melt distributions in magmatic systems, but they suffer from limited resolution and inherent nonuniqueness. Different, yet complementary, geophysical approaches must be combined with petrological, laboratory, and geochemical measurements. We summarize the results from such a combined approach from the central Andes. Taking a global perspective on large silicic systems reveals that several have >10% partial melt over large areas (10s of km2), and there may be localized zones with 50% or more.

The New Madrid Seismic Zone (NMSZ) in the Midwestern United States was the site of several major M 6.8–8 earthquakes in 1811–1812, and remains seismically active. Although this region has been investigated extensively, the ultimate controls on earthquake initiation and the duration of the seismicity remain unclear. In this study, we develop a finite element model for the Central United States to conduct a series of numerical experiments with the goal of determining the impact of heterogeneity in the upper crust, the lower crust, and the mantle on earthquake nucleation and rupture processes. Regional seismic tomography data (CITE) are utilized to infer the viscosity structure of the lithosphere which provide an important input to the numerical models. Results indicate that when differential stresses build in the Central United States, the stresses accumulating beneath the Reelfoot Rift in the NMSZ are highly concentrated, whereas the stresses below the geologically similar Midcontinent Rift System are comparatively low. The numerical observations coincide with the observed distribution of seismicity throughout the region. By comparing the numerical results with three reference models, we argue that an extensive mantle low velocity zone beneath the NMSZ produces differential stress localization in the layers above. Furthermore, the relatively strong crust in this region, exhibited by high seismic velocities, enables the elevated stress to extend to the base of the ancient rift system, reactivating fossil rifting faults and therefore triggering earthquakes. These results show that, if boundary displacements are significant, the NMSZ is able to localize tectonic stresses, which may be released when faults close to failure are triggered by external processes such as melting of the Laurentide ice sheet or rapid river incision.

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

Active volcanoes pose a constant risk to populations living in their vicinity. Significant effort has been spent to increase monitoring and data collection campaigns to mitigate potential volcano disasters. To utilize these datasets to their fullest extent, a new generation of model-data fusion techniques is required that combine multiple, disparate observations of volcanic activity with cutting-edge modeling techniques to provide efficient assessment of volcanic unrest. The purpose of this paper is to develop a data assimilation framework for volcano applications. Specifically, the Ensemble Kalman Filter (EnKF) is adapted to assimilate GPS and InSAR data into viscoelastic, time-forward, finite element models of an evolving magma system to provide model forecasts and error estimations. Since the goal of this investigation is to provide a methodological framework, our efforts are focused on theoretical development and synthetic tests to illustrate the effectiveness of the EnKF and its applicability in physical volcanology. The synthetic tests provide two critical results: (1) a proof of concept for using the EnKF for multi dataset assimilation in investigations of volcanic activity; and (2) the comparison of spatially limited, but temporally dense, GPS data with temporally limited InSAR observations for evaluating magma chamber dynamics during periods of volcanic unrest. Results indicate that the temporally dense information provided by GPS observations results in faster convergence and more accurate model predictions. However, most importantly, the synthetic tests illustrate that the EnKF is able to swiftly respond to data updates by changing the model forecast trajectory to match incoming observations. The synthetic results demonstrate a great potential for utilizing the EnKF model-data fusion method to assess volcanic unrest and provide model forecasts. The development of these new techniques provides: (1) a framework for future applications of rapid data assimilation and model development during volcanic crises; (2) a method for hind-casting to investigate previous volcanic eruptions, including potential eruption triggering mechanisms and precursors; and (3) an approach for optimizing survey designs for future data collection campaigns at active volcanic systems.

Recent analytical investigations have suggested that magma buoyancy is critical for triggering catastrophic caldera forming eruptions. Through detailed assessment of these approaches, we illustrate how analytical models have been misapplied for investigating buoyancy and are, therefore, incorrect and inconclusive. Nevertheless, the hypothesis that buoyancy is the critical trigger for larger eruptions warrants further investigation. As such, we utilize viscoelastic finite element models that incorporate buoyancy to test overpressure evolution and mechanical failure in the roof due to the coalescence of large buoyant magma bodies for two model cases. In the first case, we mimic empirical approaches and include buoyancy as an explicit boundary condition. In the second set of models, buoyancy is calculated implicitly due to the density contrast between the magma in the reservoir and the host rock. Results from these numerical experiments indicate that buoyancy promotes only minimal overpressurization of large silicic magma reservoirs ( 100 km3) they surpass a rheological threshold where their subsequent evolution is controlled by host rock mechanics. Consequently, this results in a thermomechanical division between small systems that are triggered “internally” by magmatic processes and large systems that are triggered “externally” by faulting related to roof uplift or tectonism. Finally, critical assessment of recent analytical approaches illustrates that care must be used when applying previously derived analytical solutions to ensure that assumptions used in the original formulation are not violated during application to new geologic problems.

Highlights: New data reveal for the first time a history of the last ~33.7 ky of uplift of Samosir. Minimum uplift rates were high (4.9 cm/year) for the first 11.2 ky but diminished after that to