Understanding the timing of critical changes in volcanic systems, such as the beginning and end of eruptive behavior, is a key goal of volcanic monitoring. Traditional approaches to forecasting these changes have used models motivated by the underlying physics of eruption onset, which assume that geophysical precursors will consistently display similar patterns prior to transition in volcanic state. We present a machine learning classification approach for detecting significant changes in patterns of volcanic activity, potentially signaling transitions during the onset or end of volcanic activity, which does not require a model of the physical processes underlying critical changes. We apply novelty detection, where models are trained only on data prior to eruption, to the precursory unrest at Augustine Volcano, Alaska in 2005. This approach looks promising for geophysically monitored volcanic systems which have been in repose for some time, as no eruptive data is required for model training. We compare novelty detection results with multi-class classification, where models are trained on examples of both non-eruptive and eruptive data. We contextualize the results of these classification models using constraints from petrological, satellite and visual observations from the 2006 eruption of Augustine Volcano. The transition from non-eruptive to eruptive behavior we identify in mid-November 2005 is in agreement with previous estimates of the initiation of dike intrusion prior to the 2006 eruption. We find that models which include multiple types of data (seismic, deformation, and gas emissions) can better distinguish between non-eruptive and eruptive data than models formulated on single data types.

Vanguard efforts in forecasting volcanic eruptions are turning to physics-based models, which require quantitative estimates of magma conditions during pre-eruptive storage. Below active arc volcanoes, observed magma storage depths vary widely (similar to 0 to 20 kilometers) and are commonly assumed to represent levels of neutral buoyancy. Here we show that geophysically observed magma depths (6 +/- 3 kilometers) are greater than depths of neutral buoyancy, ruling out this commonly assumed control. Observed depths are instead consistent with predicted depths of water degassing. Intrinsically wetter magmas degas water and crystallize deeper than dry magmas, resulting in viscosity increases that lead to deeper stalling of ascending magma. The water-depth relationship provides a critical constraint for forecasting models by connecting depth of eruption initiation to its volatile fuel.

On June 15, 2020, at 21:16 UTC, a locally-felt earthquake of magnitude 4.2 struck Unalaska Island, Alaska,similar to 15 km west of the town of Unalaska and the large fishing port of Dutch Harbor. The event was followed by a M4.1 earthquake at 00:34 UTC and several M3+ aftershocks, initiating a prolific sequence with hundreds of earthquakes recorded into late December. The earthquakes all locate about 12 km southeast of the summit of Makushin Volcano at 7 to 10 km depth. To date, no eruptive activity or other surface changes have been observed at the volcano in webcam images, GPS or InSAR. Seismic bursts close to volcanoes are often associated with the onset of unrest that can lead to eruption. However, determining whether seismicity reflects magmatic rather than tectonic stresses is often challenging, although critical for hazard assessments and risk management strategies. To investigate the triggering mechanisms of the recent Makushin seismicity, we integrate information from space-time patterns of the earthquake hypocenters with their fault-plane solutions. We relocate the swarm events using double-difference relocation techniques and a 3D velocity model and find that the earthquakes, although they seem to follow two predominant orientations (NW-SE and SW-NE), do not show clear clustering into preferred alignments. Similarly, we do not observe pronounced migration in time and space. Fault-plane solutions (FPS) for all but one M2.5+ earthquakes have P-axis orientations consistent with subhorizontal NW-SE oriented regional maximum compression, whereas many of the lower-magnitude earthquakes have P-axes perpendicular to regional maximum compression. This provides evidence for the presence of a local stress field likely induced by magma intrusion. Results from Coulomb stress modeling are also consistent with dike inflation modulated by stresses induced by the M4+ earthquakes. The seismic swarm is thus likely linked to a superposition of driving stresses from both magmatic and tectonic processes on pre-existing faults. The case of the 2020 Makushin swarm, with its unusual characteristics, challenges traditional swarm classification schemes and suggests that a reconsideration of the definition of seismic swarms as having the maximum magnitude event in the middle of the swarm is warranted. (c) 2022 The Authors. Published by Elsevier B.V.

Understanding the subsurface processes that generate volcanic unrest, including surface deformation, earthquakes, temperature increase, and gas emissions, is essential to improve the forecasting of volcanic eruptions. Volcanic gases exsolved from magma reservoirs can transfer heat towards the surface when the system is open, or pressurize the volcano and lead up to eruptions when the system is closed. Hence, the nature of the observed precursory signals is greatly dependent on whether exsolved volatiles accumulate or escape. In this study, we develop a two-dimensional finite element model to calculate the thermal and poroelastic responses of a volcano to gases that exsolve from depth and migrate to the surface through a pre-existing fractured conduit. This model is explored through a set of sensitivity tests to quantify the controls of gas fluxes and permeability on geophysical observables; and is used to interpret surface deformation (GPS), ground temperature, and seismicity data recorded before the 2006 eruption of Augustine volcano, Alaska, by utilizing the Ensemble Kalman Filter data assimilation technique and Coulomb stress calculations. Our results show that the permeable transfer of gas through a fractured conduit can yield a measurable thermal anomaly at the surface for at least one year before the eruption, consistent with ground- and remote sensing-based data. Moreover, gas flux increased about ten times around three months before the eruption, which might have accelerated hydrothermal alteration and reduced permeability of the conduit by several orders of magnitude, thus accumulating gases inside the volcanic edifice, generating surface deformation, and triggering volcano-tectonic earthquakes. Eventually, failure of the sealed pathways due to high overpressure led to the eruption. Multi-physical numerical models coupling gas flow with host rock deformation and heat transfer are useful tools to understand the triggering mechanisms of volcanic eruptions driven by volcanic gases. (c) 2022 Elsevier B.V. All rights reserved.

To examine controls on the local stress field at Augustine Volcano, Alaska, before its 2006 eruption, we calculated fault plane solutions for volcano-tectonic earthquakes from 2002 to 2006. The P-axis orientation was first aligned to the regional maximum compression (NW) and then rotated by about 90 degrees (perpendicular to the dike alignment) after the onset of surface deformation in mid-August 2005. Using 3D finite element models, we systematically evaluated the effects of tectonic stresses, volcanic edifice densities, and dike overpressures on the local stress field orientation. Combining data and models to generate "phase diagrams" of different stress controls by these competing effects, we argue that moderate tectonic stress of 2-3 MPa at 600 m above sea level slightly exceeded the edifice loading before the precursory deformation and was then overprinted by a local stress field from dike opening with an overpressure of similar to 15 MPa.

This article is composed of a commentary about the state of Integrated, Coordinated, Open, and Networked (ICON) principles (Goldman et al., 2021, https://doi.org/10.1029/2021ea002099) in Volcanology, Geochemistry, and Petrology (VGP), and discussion on the opportunities and challenges of adopting them. VGP encompasses a broad field that addresses volcanic, magmatic, hydrothermal, geomicrobial systems; process investigations that span the physical, geochemical and biological realms, including planetary geology; and one that is extensively supported by state-of-the-art research facilities. We suggest that an open, inclusive, collaborative and evolving model of an international coordinated network is critical to answering the most pressing challenges in VGP. In this commentary piece, we begin to discuss the elements of, challenges to, and path forward in developing such a model. For this team, ICON means collaboration, equitable access to data for the entire scientific community, and forging of partnerships that potentially contribute to more innovative ways of coordinating and sharing research. It also means bringing more equity to science, by implementing effective measures which consider access to funding, analytical equipment, resources, and mentors. More importantly, ICON to us means having important conversations around what we value in the advancement of science, perhaps exploring outside the idea of meritocracy and evaluating what individual traits can contribute to science outside what has traditionally been considered the norm.

Since the 1919 foundation of the International Association of Volcanology and Chemistry of the Earth's Interior (IAVCEI), the fields of volcano seismology and acoustics have seen dramatic advances in instrumentation and techniques, and have undergone paradigm shifts in the understanding of volcanic seismo-acoustic source processes and internal volcanic structure. Some early twentieth-century volcanological studies gave equal emphasis to barograph (infrasound and acoustic-gravity wave) and seismograph observations, but volcano seismology rapidly outpaced volcano acoustics and became the standard geophysical volcano-monitoring tool. Permanent seismic networks were established on volcanoes (for example) in Japan, the Philippines, Russia, and Hawai'i by the 1950s, and in Alaska by the 1970s. Large eruptions with societal consequences generally catalyzed the implementation of new seismic instrumentation and led to operationalization of research methodologies. Seismic data now form the backbone of most local ground-based volcano monitoring networks worldwide and play a critical role in understanding how volcanoes work. The computer revolution enabled increasingly sophisticated data processing and source modeling, and facilitated the transition to continuous digital waveform recording by about the 1990s. In the 1970s and 1980s, quantitative models emerged for long-period (LP) event and tremor sources in fluid-driven cracks and conduits. Beginning in the 1970s, early models for volcano-tectonic (VT) earthquake swarms invoking crack tip stresses expanded to involve stress transfer into the wall rocks of pressurized dikes. The first deployments of broadband seismic instrumentation and infrasound sensors on volcanoes in the 1990s led to discoveries of new signals and phenomena. Rapid advances in infrasound technology; signal processing, analysis, and inversion; and atmospheric propagation modeling have now established the role of regional (15-250 km) and remote (> 250 km) ground-based acoustic systems in volcano monitoring. Long-term records of volcano-seismic unrest through full eruptive cycles are providing insight into magma transport and eruption processes and increasingly sophisticated forecasts. Laboratory and numerical experiments are elucidating seismo-acoustic source processes in volcanic fluid systems, and are observationally constrained by increasingly dense geophysical field deployments taking advantage of low-power, compact broadband, and nodal technologies. In recent years, the fields of volcano geodesy, seismology, and acoustics (both atmospheric infrasound and ocean hydroacoustics) are increasingly merging. Despite vast progress over the past century, major questions remain regarding source processes, patterns of volcano-seismic unrest, internal volcanic structure, and the relationship between seismic unrest and volcanic processes.

The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental in determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellan-Alvarez et al., 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis, over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.

SUMMARY: We developed grenepipe, an all-in-one Snakemake workflow to streamline the data processing from raw high-throughput sequencing data of individuals or populations to genotype variant calls. Our pipeline offers a range of popular software tools within a single configuration file, automatically installs software dependencies, is highly optimized for scalability in cluster environments, and runs with a single command.