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Tilde is a new in-house developed solution that the GNS Science’s GeoNet program has recently developed to provide storage and access to low sample rate datasets used to monitor tsunami, landslides, and volcanoes in Aotearoa New Zealand. It includes datasets covering sample rates of 15 s or longer. Time series data are stored and disseminated in JSON and CSV formats, and users can access these through an application programming interface (API) and through graphical user interfaces (GUIs). Tilde’s GUIs were created to allow technical and non-technical users easy access to the available data. The introduction of the Tilde system as one of the GeoNet program delivery channels has represented a big step forward for GeoNet’s volcano data holdings by providing a single point to access all low to medium-sample rate volcano-specific monitoring data. We designed the system and developed a domain model, an API, a graphical data discovery interface, and associated data tutorials. This work leverages the open-by-default data policy for data generated through the GeoNet program. This paper is intended to highlight how we made many of the key decisions that shaped the Tilde system, how they were impacted by our multi-hazard monitoring requirements, and how they have improved access to volcano monitoring data. We conclude with some open questions about the need to develop common standards to share analysis-ready time series data within different disciplines in volcanology and geophysics.

The Seismic Research Centre (SRC), formerly known as the Seismic Research Unit (SRU), of the University of the West Indies is located on the island of Trinidad in the Eastern Caribbean. The centre has been operating its volcanological and seismological surveillance network since 1953. Since that time, the network has been upgraded five times resulting in five generations of seismic network topologies (i.e., Classes). Class 1 consisted of autonomously operated photographic recording stations, a purely analogue configuration. From Class 2 to Class 5 (current class) the network has continuously grown in scope, sophistication and capability. The evolution of the network was carried out using a combination of state-of-the-art instruments as well as trailing edge technology (e.g., analogue transmission) used in a manner that allows for sustainability. In this way, the network has been able to address the scientific and technical challenges associated with operating in an island arc subduction zone which is exposed to other natural hazards such as hurricanes. To counter its operational constrains the SRC has developed several strategies, which contribute to: (i) expand the network to meet the demand for more timely and accurate surveillance of geohazards, (ii) broaden the range of monitoring techniques (e.g., cGPS, geochemical), (iii) capture research grade scientific data and (iv) reduce operational costs.

The application of deep‐learning‐based seismic phase pickers has surged in recent years. However, the efficacy of these models when applied to monitoring volcano seismicity has yet to be fully evaluated. Here, we first compile a data set of seismic waveforms from various volcanoes globally. We then show that the performances of two widely used deep‐learning pickers deteriorate systematically as the earthquakes' frequency content decreases. Therefore, the performances are especially poor for long‐period earthquakes often associated with fluid/magma movement. Subsequently, we train new models which perform significantly better, including when tested on two data sets where no training data were used: volcanic earthquakes along the Cascadia subduction zone and tectonic low‐frequency earthquakes along the Nankai Trough. Our model/workflow can be applied to improve monitoring of volcano seismicity globally while our compiled data set can be used to benchmark future methods for characterizing volcano seismicity, especially long‐period earthquakes which are difficult to monitor. Plain Language Summary Earthquake activity at volcanic regions is often monitored to indicate volcanic activity. Identifying the time when the energy radiated from an earthquake source arrives at a seismometer is essential for locating the earthquake, which can be difficult for volcanic earthquakes because of high noise levels, high event rates, and obscured onsets. Previous studies have demonstrated that deep learning can excel in picking the arrival times of regular earthquakes. However, it is unclear how sensitive these detectors are to earthquakes in volcanic regions. Here, we first compile a data set of earthquakes from various volcanoes globally. We then show that existing deep‐learning‐based detectors can miss a large fraction of these earthquakes, especially those without an abrupt change in signal amplitude. We then provide two new models which can better detect volcanic earthquakes than existing models. Our model/workflow can be applied to improve monitoring of volcanic earthquakes globally. Key Points We compile a data set of seismic waveforms from various volcanic regions globally We show that existing deep‐learning phase pickers' performances deteriorate with decreasing earthquake frequency content Our retrained models perform better and are more generalizable for monitoring volcano seismicity, especially long‐period earthquakes

Identifying the observables that warn of volcanic eruptions is a major challenge in natural hazard management. A potentially important observable is the release of heat through volcano surfaces, which represents a major energy source at quiescent volcanoes. However, it remains unclear whether surface heat emissions respond to pre-eruptive processes and vary before eruption. Here we show through a statistical analysis of satellite-based long-wavelength (10.780–11.280 μm) infrared data that the last magmatic and phreatic eruptions of five different volcanoes were preceded by subtle but significant long-term (years), large-scale (tens of square kilometres) increases in their radiant heat flux (up to ~1 °C in median radiant temperature). Large-scale thermal unrest is detected even before eruptions that were not anticipated from other volcano monitoring methods, such as the 2014 phreatic eruption of Ontake (Japan) and the 2015 magmatic eruption of Calbuco (Chile). We attribute large-scale thermal unrest to the enhancement of underground hydrothermal activity, and suggest that such analysis of satellite-based infrared observations can improve constraints on the thermal budget of volcanoes, early detection of pre-eruptive conditions and assessments of volcanic alert levels. Large-scale radiant heat flux increased in the years prior to eruptions at five volcanoes, probably due to enhanced underground hydrothermal activity, according to an analysis of satellite infrared data.

Unrest began in July 2021 at Askja volcano in the Northern Volcanic Zone (NVZ) of Iceland. Its most recent eruption, in 1961, was predominantly effusive and produced ∼0.1 km3 lava field. The last plinian eruption at Askja occurred in 1875. Geodetic measurements between 1983 and 2021 detail subsidence of Askja, decaying in an exponential manner. At the end of July 2021, inflation was detected at Askja volcano, from GNSS observations and Sentinel‐1 interferograms. The inflationary episode can be divided into two periods from the onset of inflation until September 2023. An initial period until 20 September 2021 when geodetic models suggest transfer of magma (or magmatic fluids) from within the shallowest part of the magmatic system (comprising an inflating and deflating source), potentially involving silicic magma. A following period when one source of pressure increase at shallow depth can explain the observations. Plain Language Summary Askja volcano, situated in the Northern Volcanic Zone in Iceland, has been quiet since its last eruption in 1961, with surface deformation measurements from 1983 to 2021 displaying a decaying subsidence signal within the Askja caldera. However, at the end of July 2021, the volcano began to inflate. This was detected on both GNSS and satellite observations. As of September 2023, ∼65 cm of uplift had been measured at GNSS station OLAC. Modeling of surface deformation measurements indicates that the inflation was triggered by upward migration of melt (or magmatic fluids). Key Points At the end of July 2021, Askja volcano began to inflate—detected on both GNSS and satellite observations, ending 1983–2021 subsidence Geodetic modeling indicates upward migration of magma, feeding a magma body at an inferred depth of 2.5–3.1 km under the main Askja caldera Start of unrest was associated with magma transfer within the upper part of the system, followed by possible additional influx from depth

Identifying precursory phenomena is central to the short-range assessment and anticipation of volcanic hazards. The chemistry of gases, which may separate from magma at depth, is operationally monitored at many volcanoes worldwide to manage risk. However, owing to the complexity of volcanic degassing, decoding the message of gas geochemistry has proven challenging. Here, we report an approach to restoration of measured volcanic gas compositions that enables tracking of variations in the temperature and/or oxidation state of the source magma. We validate the approach with reference to independent estimates of melt oxidation state obtained by X-ray absorption near-edge structure (XANES) spectroscopy at the iron K-edge. We then apply the method to a global database of high temperature volcanic gases and to extended gas geochemical timeseries at Unzen, Aso, and Asama volcanoes, identifying hitherto unreported but significant changes in magma intensive parameters that preceded or accompanied changes in volcanic activity. Restoration of volcanic gas compositions offers a promising complement to monitoring strategies at active volcanoes, calling for more systematic operational surveillance of redox-sensitive gas species.

Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO2 gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards.

The degassing of CO2 and S from arc volcanoes is fundamentally important to global climate, eruption forecasting, ore deposits, and the cycling of volatiles through subduction zones. However, all existing thermodynamic/empirical models have difficulties reproducing CO2‐H2O‐S trends observed in melt inclusions and provide widely conflicting results regarding the relationships between pressure and CO2/SO2 in the vapor. In this study, we develop an open‐source degassing model, Sulfur_X, to track the evolution of S, CO2, H2O, and redox states in melt and vapor in ascending mafic‐intermediate magma. Sulfur_X describes sulfur degassing by parameterizing experimentally derived sulfur partition coefficients for two equilibria: RxnI. FeS (m) + H2O (v) →$\\to $  H2S (v) + FeO (m), and RxnII. CaSO4(m) →$\\to $  SO2 (v) + O2 (v) + CaO (m), based on the sulfur speciation in the melt (m) and co‐existing vapor (v). Sulfur_X is also the first to track the evolution of fO2 and sulfur and iron redox states accurately in the system using electron balance and equilibrium calculations. Our results show that a typical H2O‐rich (4.5 wt.%) arc magma with high initial S6+/ΣS ratio (>0.5) will degas much more (∼2/3) of its initial sulfur at high pressures (>200 MPa) than H2O‐poor ocean island basalts with low initial S6+/ΣS ratio (<0.1), which will degas very little sulfur until shallow pressures (<50 MPa). The pressure‐S relationship in the melt predicted by Sulfur_X provides new insights into interpreting the CO2/ST ratio measured in high‐T volcanic gases in the run‐up to the eruption. Plain Language Summary Understanding how CO2 and S are emitted from volcanoes, called degassing, is important in interpreting the CO2/ST gas precursors to volcanic eruptions and quantifying the total amount of gases released into the atmosphere that are climatically important. However, existing models show significant discrepancies in predicting the behavior of sulfur during degassing. In this study, we employ a new approach to describe sulfur behavior during magma degassing and develop a new model, Sulfur_X, that successfully reproduces the distinct S, CO2, and H2O degassing behavior recorded in melts from different volcanoes. Sulfur_X shows that sulfur can either degas early at high pressure or late at low pressure during magma ascent to the surface, depending on the initial sulfur speciation and H2O contents in the magma. In addition, sulfur is one of the most commonly measured volcanic gas components used for volcano monitoring. Therefore, the predicted compositional evolution of co‐existing vapor by Sulfur_X during magma ascent bears directly on the interpretation of CO2/ST ratio measured in high‐T volcanic gases and the development of eruption forecast models. Key Points Sulfur_X is a new open‐source magma degassing model that accurately predicts the volatile and redox evolution of ascending arc magmas Sulfur_X shows that sulfur can start degassing in the lower crust or near‐surface, depending on the initial S6+/ΣS and H2O in the melt The vapor compositions predicted by Sulfur_X can be used to interpret the CO2/ST ratios in high‐T volcanic gases, an eruption precursor

We explore the capabilities of volcano opto‐acoustics, a promising technique for measuring explosion and infrasound resonance phenomena at open‐vent volcanoes. Joint visual and infrasound study at Yasur Volcano (Vanuatu) demonstrate that even consumer‐grade cameras are capable of recording infrasound with high fidelity. Passage of infrasonic waves, ranging from as low as 5 Pa to hundreds of Pa, from both explosions and persistent tremor, pressurizes and depressurizes ambient plumes inducing visible vaporization and condensation respectively. Optical tracking of these pressure wavefields can be used to identify spectral characteristics, which vary within Yasur's two deep craters and are distinct for explosion and tremor sources. Wavefield maps can illuminate the propagation of blasts as well as the dynamics of persistent infrasonic tremor associated with standing waves in the craters. We propose that opto‐acoustic monitoring is useful for extraction of near‐vent infrasound signal and for tracking volcanic unrest from a remote distance. Plain Language Summary Open‐vent volcanoes often have lava lakes or vents where magma is exposed at the bottom of a crater. These volcanoes degas continuously and explode intermittently producing sounds that are low frequency in nature, often below the threshold of human hearing. Such infrasounds are used by volcano scientists to monitor eruptive behavior over time and estimate eruption style and intensity. This current study uses data from Yasur Volcano (Vanuatu) to demonstrate that it is possible to measure infrasound robustly and accurately using cameras, rather than infrasonic microphones. We observe that infrasonic pressure waves induce detectable changes in the clouds or volcanic plume and we process the video imagery to extract infrasound records from remote vantage points. This is an emerging field we call volcano opto‐acoustics and it has potential utility for volcano monitoring at other open‐vent volcanoes worldwide. Key Points Time series pixel brightness data from cameras show a strong correlation with co‐located infrasound records Video image processing can be used to extract the spatial infrasound wavefield produced at open‐vent volcanoes Radiation of sound waves, existence of standing waves, and crater acoustic response may be investigated with volcano opto‐acoustics

The Alaska Volcano Observatory (AVO) uses multidisciplinary data to monitor and study dozens of active and potentially active volcanoes. Here, we provide an overview of internally and externally generated data types, tools and resources used in their management, and challenges faced. Data sources include the following: (1) a multiparameter (seismic, infrasound, GNSS, web cameras) ground-based monitoring network that spans 3000 km and transmits data in real time; (2) a variety of satellite-borne sensors that provide information about surface change and volcanic emissions; (3) geologic and gas field campaigns; and (4) other external data products that provide situation awareness. Each data type requires distinct acquisition, processing, storage, visualization, and archiving approaches. AVO uses a variety of externally and internally developed tools to handle individual data types as well as multidisciplinary volcanological data. A primary tool is the Geologic Database of Information on Volcanoes in Alaska (GeoDIVA), which stores detailed, searchable information on more than 140 volcanoes and over 1000 eruptions and unrest events, including images, eruption descriptions, and geologic station and sample data, metadata, and analyses. It interacts with other internal tools that store monitoring reports and other operational records. Additional data management resources used by AVO assist with alarms and alerts, state-of-health monitoring, and multiparameter visualization. Requirements for 24/7 accessibility, the ever-expanding portfolio of data, and transitioning new tools from development to operations are all challenges faced by AVO and other volcano observatories. AVO strives to meet FAIR data practices and ensure that data are available to national and international community efforts using external repositories as well as those hosted by AVO and its parent institutions.