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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

Monitoring data are critical for understanding volcanic unrest and eruption, but they often lack the ability to constrain the pre‐eruptive magma processes. As such, an increasing number of studies couple monitoring data with petrological tools to obtain insights into the causes and durations of magmatic processes. The 2018–2020 eruption of the submarine Fani Maoré (FM) volcano, Comoros Archipelago, is an excellent location to correlate diffusion chronometry with real‐time monitoring data. Zoned olivine in FM testify that deep basanitic magma interacted with a more evolved reservoir. Diffusion modeling in olivine shows an increase in the magma interaction times (0.5–3 to ∼20 months) as the eruption progresses, implying that basanitic magma stalled near the more evolved reservoir. Additionally, two major intrusive events were identified—in March 2019 and March 2020. The first intrusion occurred as seismic and deformation signals decreased possibly due to enhanced mixing and unlocking of the more evolved mushy reservoir after intrusion. The second intrusion correlates with a change in the locations of seismic events before seismicity and deformation become minor, implying that this intrusion occurred before magma drainage and waning of the eruption. Lastly, by coupling diffusion geochronometry and geophysical monitoring, the magmatic reservoir at FM can be placed at a greater depth (∼30 km) than previously thought, which is consistent with seismic events. Hence, our integrated approach of petrologic tools and real‐time monitoring aids in providing new insights into the magma storage locations and magma‐mush interactions at submarine volcanos.

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 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.

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.

Quantifying topographic changes of volcano surfaces provides important information about volcanic deposits and mass wasting processes, which has specific implications for forecasting volcanic hazards. EarthDEM and ArcticDEM are Digital Elevation Models (DEMs) derived from commercial Maxar stereo-optical satellite data. These DEMs allow for potential global volcanic monitoring of topography at a high resolution (2 m) but have not been used routinely to study volcanoes up to now. Here we show how these DEMs may be used to detect and quantify volcanic activity and describe the successes and challenges of using these data. We studied 9 volcanoes, in locations ranging from equatorial to polar in Indonesia, Galápagos (Ecuador), Kamchatka (Russia) and the Aleutian arc (USA). These volcanoes experienced a wide range of volcanic eruptions that generated different eruptive deposits (lava flows, lava domes, pyroclastic density currents), mass-wasting (lahars and debris avalanches), and erosional features (collapse scars, channels, etc.). The 2 m DEM resolution allowed us to detect topographic changes associated with different volcanic activity, often in difficult environmental conditions (e.g. snow cover). Cloudless, artifact-free DEMs are most successful in quantifying height and volumes changes, including for small and narrow regions (e.g. channels). These DEMs perform well in detecting height changes ≥ 0.5–2 m, which is the range of vertical data errors. Our results demonstrate the value of EarthDEM and ArcticDEM in detecting and quantifying unique signals related to volcanic activity in different environments. Acquisition of high resolution DEMs on a more frequent basis could significantly improve our ability to document time-dependent topographic changes at volcanoes worldwide.

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.

Tsunamis generated by volcanic eruptions have risen to prominence since the December 2018 tsunami generated by the flank collapse of Anak Krakatau during a moderate eruption and then the global tsunami generated by the explosive eruption of the Hunga volcano in the Tongan Archipelago in January 2022. Both events cause fatalities and highlight the lack in tsunami warning systems to detect and warn for tsunamis induced by volcanic mechanisms. Following the Hunga Tonga—Hunga Ha’apai eruption and tsunami, an ad hoc working group on Tsunamis Generated by Volcanoes was formed by the Intergovernmental Oceanographic Commission of UNESCO. Volcanic tsunamis differ from seismic tsunamis in that there are a wide range of source mechanisms that can generate the tsunamis waves and this makes understanding, modelling and monitoring volcanic tsunamis much more difficult than seismic tsunamis. This paper provides a review of both the mechanisms behind volcanic tsunamis and the variety of modelling techniques that can be used to simulate their effects for tsunami hazard assessment and forecasting. It gives an example of a volcanic tsunami risk assessment undertaken for Stromboli, outlines the requirement of volcanic monitoring to warn for tsunami hazard and provides examples of volcanic tsunami warning systems in Italy, the Hawaiian Island (USA), Tonga and Indonesia. The paper finishes by highlighting the need for implementing monitoring and warning systems for volcanic tsunamis for locations with submarine volcanoes or near-shore volcanoes which could potentially generate tsunamis.