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Monitoring the activity of subglacial volcanoes along the Aleutian Arc in Alaska is important to the safety of local populations, as well as air traffic flying through the region. However, observations of volcanic unrest are limited by accessibility and resources, particularly at glacier-covered systems, making investigations of their stability challenging. Westdahl Peak, a subglacial volcano on Unimak Island in the Aleutian Arc has experienced significant unrest and uplift since its most recent VEI three eruption in 1991-1992. Given the magnitude of observed uplift, previous investigations suggested the potential for eruption by 2010, but no such event has occurred. One hypothesis to explain this prolonged unrest is that the 1-km thick glacier may increase the stability of the magma system. However, the impact of ice caps and glaciers on the short-term stability of volcanoes is not well understood. In this study, thermomechanical finite element models are used to evaluate how the stability of a glaciated volcano is impacted by variations in ice cap thickness, magma chamber depth, geometry, magma flux rate, and seasonal changes in ice cover thickness. Our numerical experiments indicate that the presence of an ice cap (1–3 km thick) increases the average repose interval for a magma system. Among models with different magma chamber geometries, depths, and flux rates, the greatest increases in repose interval are observed in prolate systems where the increase is up to 57% for a chamber located at 5 km-depth. Spherical and oblate also experience smaller, yet significant, increases in repose interval. Additionally, the percentage increase in repose interval is not impacted by variations in magma flux rate for a given ice cap thickness and magma chamber geometry. However, flux rates do influence the timing of eruptions when the system is experiencing seasonal variations in ice thickness. Our results show that systems with low flux rates are more likely to fail when the ice thickness is at its lowest. The numerical estimates further suggest that the ice cap on Westdahl Peak, which is ∼1 km, may slightly increase the stability of the magma system. In general, given flux rates and magma chamber geometries estimated for the Westdahl system, the repose interval can increase by ∼7 years due to the Westdahl glacier. This increase is small on a geologic scale but is significant on human time scales and the impact of glaciers must be considered in future forecasting efforts. 

Numerical modeling framework tracks volcanic activity and forecasts the 2018 eruption of Sierra Negra Volcano, Galapagos. 

Ensemble based data assimilation approaches, such as the Ensemble Kalman Filter (EnKF), have been widely and successfully implemented to combine observations with dynamic forecast models. In this study the EnKF is adapted to assimilate ground deformation observations from interferometric synthetic-aperture radar (InSAR) and GPS into thermomechanical finite element models (FEM) to evaluate volcanic unrest. Two eruption hindcasts are investigated: the 2008 eruption of Okmok volcano, Alaska and the 2018 eruption of Sierra Negra volcano, Galápagos, Ecuador. At Okmok, EnKF forecasts tensile failure and the lateral movement of the magma from a central pressure source in the lead up to its 2008 eruption indicating potential for diking. Alternatively, at Sierra Negra, the EnKF forecasts significant shear failure coincident with a Mw 5.4 earthquake that preceded the 2018 eruption. These successful hindcasts highlight the flexibility and potential of the volcano EnKF approach for near real time monitoring and hazard assessment at active volcanoes worldwide. 

We describe the design, deployment and operation of a computer system built to efficiently run deep learning frameworks. The system consists of 16 IBM POWER9 servers with 4 NVIDIA V100 GPUs each, interconnected with Mellanox EDR InfiniBand fabric, and a DDN all-flash storage array. The system is tailored towards efficient execution of the IBM Watson Machine Learning enterprise software stack that combines popular open-source deep learning frameworks. We build a custom management software stack to enable an efficient use of the system by a diverse community of users and provide guides and recipes for running deep learning workloads at scale utilizing all available GPUs. We demonstrate scaling of a PyTorch and TensorFlow based deep neural networks to produce state-of-the-art performance results. 

Abstract Surface deformation and seismicity provide critical information to understand the dynamics of volcanic unrest. During 2006–2007, >80 mm/yr uplift was observed by interferometric synthetic aperture radar (InSAR) at the central Atka volcanic center, Alaska, coinciding with an increasing seismicity rate. On November 25, 2006, a phreatic eruption occurred at the Korovin volcanic vent, 5‐km north of the central Atka, following the drainage of its crater lake a month prior to the eruption. The InSAR data are assimilated into three‐dimensional finite element models using the Ensemble Kalman Filter to investigate: (1) the pressure source creating the surface deformation; (2) the triggering of the volcano‐tectonic (VT) earthquakes in the Atka volcanic center; and (3) the triggering of the phreatic eruption at Korovin. The models show that the pressure source required to create the surface deformation is a NE‐tilted, oblate ellipsoid, which rotated from steep to gentle dipping from June to November 2006 before the eruption. The modeled dilatancy in a pre‐existing weak zone, coinciding with the Amlia‐Amukta fault, driven by the pressure source has a spatial and temporal correlation with the evolution of the VT earthquakes during the unrest. The fault dilatancy may have increased the connected porosity and permeability of the fault zone allowing fluid injection which triggered the observed seismicity. In addition, the dilatated fault may have increased the fluid capacity of the fault zone by ∼10⁵ m³, causing the discharge of the crater lake at Korovin. Consequently, the phreatic eruption of the Korovin volcano may have been triggered.