Search

The GeoNet programme at GNS Science has monitored and managed data for volcanoes, earthquakes, landslides, and tsunami in Aotearoa New Zealand since 2001. Volcano monitoring data are collected from seismometers, acoustic sensors, GNSS receivers, webcams, remote gas monitoring sensors, and a range of environmental sensors, as well as manually during visits to volcanoes. The primary user of volcano data is the internal cross-specialised Volcano Monitoring Group (VMG), which fulfils the role of the national volcano observatory. GeoNet concentrates on automatic data collection and analysis, while supporting members of the VMG with manual data collection and interpretation. The application of open-data principles to both data and metadata has always been a core aspect of GeoNet; responses have been overwhelmingly positive, despite concerns regarding some high value, manually collected datasets. The website www.geonet.org.nz represents the primary data access portal. Data analysis and delivery applications are organised by data type rather than hazard, with no volcano-specific data applications. Most datasets have web-based and API delivery application options; both provide standard data formats from a cloud-based archive. One of the challenges for volcano data collection and management has been shifting from a reliance on manually collected data to automatic collection. Additionally, awareness of important questions related to Indigenous Māori data governance is increasing, although the associated impact is not yet understood. Overall, the current centralised, cooperative volcano monitoring and data collection and management system, which benefits from improved efficiency, interoperability, and data quality, has proved effective in Aotearoa New Zealand. Ongoing work aims to ensure optimal data collection and management for volcano monitoring and downstream activities. [ABSTRACT FROM AUTHOR]

Temporary seismic networks on Mt. Halla and Ulleung Island volcanoes were deployed, which employ broadband and geophone arrays to monitor potential volcanic activities and to estimate high-resolution magmatic structures beneath these volcanoes. The purpose of this paper is to introduce these networks and present early results through basic seismic analyses, suggesting the potential for future comprehensive seismological studies. The array in Mt. Halla volcano consists of five broadband sensors (JH array), and it has been operational around the Baengnokdam summit crater since October 2020. There was an additional linear geophone array (HL array) installed in September 2021 for detailed shallow subsurface imaging. Ulleung Island volcano had been under observation for two years since June 2021 with a network of nine broadband sensors (UL array) along its coast and in the Nari crater basin, complemented by a 52-geophone array (UG array) deployed in May 2022 for high-resolution subsurface studies. Despite the noisy environments typical of temporary setups, power spectral density analyses confirmed the quality of data as comparable to established reference noise models in permanent stations. Our study aimed to initiate studies uncovering seismic activities and structures beneath Mt. Halla and Ulleung Island volcanoes, specifically regarding volcanic activity. This approach detected no clear sign of volcanic seismicity on both islands, suggesting a period of magmatic dormancy. Seismic velocity variation (dv/v) analyses further indicated that environmental factors, rather than volcanic processes, influenced the changes in the physical properties of the underground structures. Conversely, the receiver function analysis and ambient noise data processing hinted at the presence of complex subsurface structures, potentially indicative of volcanic features, such as partial melting. Despite the lack of direct evidence for active magmatic processes, the collected seismic data provides a crucial baseline for future monitoring and a deeper understanding of the magmatic and tectonic dynamics beneath these volcanoes, offering valuable insights for ongoing volcanic research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Mount Rainier has had at least 11 large lahars over the last 6,000 years, including one occurring without evidence of eruptive activity. This prompted the creation of a lahar detection system that uses a combination of seismic, infrasound, and tripwires. We test a laser rangefinder placed on a river channel bank for detecting and confirming mass movements flowing past a station as an alternative to the physical tripwires. After testing the device at an experimental debris-flow flume, the laser rangefinder successfully captured a small debris flow on Mount Rainier in 2023, confirming its effectiveness as a lahar detection and monitoring tool. Over the 2-month deployment at Mount Rainier, we find that spurious recordings in the laser rangefinder data (noise) tend to correlate with high humidity, and that periods of noise do not correlate with increased co-located seismic amplitude. Therefore, the impact of the noise on future alarms can be mitigated by coupling a laser rangefinder alarm with that of independent datasets.

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity. [ABSTRACT FROM AUTHOR]

Volcanic eruptions carry essential information on the dynamics of volcanic systems. Studies have documented variable eruption styles and eruptive surface deformation. However, co‐eruptive subsurface structural changes remain poorly understood. Here we characterize the seismic velocity changes from July 2019 to July 2023 at Great Sitkin Volcano in the central Aleutian volcanic arc, using single‐station ambient noise interferometry at five three‐component seismic stations. Coincident with the lava effusion since late July 2021, about two months after the explosive eruption on 26 May 2021, we observe a sustained velocity increase, most prominently to the northwest of the caldera. We attribute this velocity increase to the structural changes with magma extrusion, with the spatial variation controlled by the geometry of the magma system or the property of shallow volcaniclastics. Our findings offer insights into understanding co‐eruptive structural modifications at active volcanoes. Plain Language Summary: Volcanic eruptions provide important insights into how volcanoes work and the potential risks they pose. This study looks at the ongoing eruption at Great Sitkin Volcano in the central Aleutian volcanic arc to better understand the changes happening beneath the surface. We measure the changes in the velocity of seismic waves, with data from five seismometers on the island. We collected data from July 2019 to July 2023, covering different eruptive stages and multiple lava effusion episodes during the eruption that started on 26 May 2021. Coincident with the lava effusion since late July 2021, we notice a significant increase in seismic velocities, most prominently at a station northwest of the volcano's central crater. We conclude that this increase reflects the structural change in the shallow subsurface due to magma extrusion, with deflation of the edifice and closure of cracks. The findings in this study help to improve our understanding of how volcanoes behave during eruptions. Key Points: We observe prominent, spatially variable increases in seismic velocity during lava effusions at Great Sitkin Volcano since late July 2021The sustained co‐eruptive increase in seismic velocity reflects structural changes with crack closures as the consequence of magma extrusionThe distinct spatial variation of the velocity increase may be controlled by reservoir geometry or the strength of shallow volcaniclastics [ABSTRACT FROM AUTHOR]

The gravitational instability of hot material deposited during eruptive activity can lead to the formation of glowing avalanches, commonly known as deposit-derived pyroclastic density currents (PDCs). These currents can travel hundreds of metres to several kilometres from the source at exceptionally high temperatures, posing a catastrophic hazard to areas surrounding steep-slope volcanoes. The occurrence of deposit-derived PDCs is often associated with crater rim failure, which can be triggered by various factors such as magma thrust from dike injection, magma fingering, bulging or less commonly, powerful explosions. Here, the in-depth study of data from the multi-parametric monitoring network operating on Stromboli (Italy), including video surveillance, seismicity and ground deformation data, complemented by remote topographic sensing data, has facilitated the understanding of the events leading to the crater rim collapse on 9 October and 4 December 2022. The failures resulted in the remobilisation of 6.4 ± 1.0 × 103 m3 and 88.9 ± 26.7 × 103 m3 of material for the 9 October and the 4 December 2022, respectively, which propagated as PDCs along the NW side of the volcano and reached the sea in a few tens of seconds. These events were characterised by a preparatory phase marked by an increase in magmatic pressure in the preceding weeks, which correlated with an increase in the displacement rate of the volcano's summit. There was also an escalation in explosive degassing, evidenced by spattering accompanied by seismic tremors in the hours before the collapse. These events have been interpreted as an initial increase in magma vesicularity, followed by the release of gas once percolation threshold was reached. The degassing process induced densification of the magma, resulting in increased thrust on the conduit walls due to increased magmastatic pressure. This phase coincided with crater rim collapse, often followed or accompanied by the onset of lava overflow phases. A mechanism similar to the one proposed may shed light on similar phenomena observed at other volcanoes. The analysis performed in this study highlights the need for a multi-parametric and multi-platform approach to fully understand such complex phenomena. By integrating different data sources, including seismic, deformation and remote sensing data, it is possible to identify the phenomena associated with the different phases leading to crater rim collapse and the subsequent development of deposit-derived PDCs. [ABSTRACT FROM AUTHOR]

We present a simple method for identification of seasonal components in GPS displacement time series. The method considers two options for the treatment of long series, considering possible variability in the seasonal phases. The method also proposes a bootstrap scheme to deal approximately with series shorter than one period of the seasonal signal. Tests with synthetic data show that the method gives quite acceptable results, so that results from application to real data, although approximate, should be useful considering the inherent uncertainties in the data. [ABSTRACT FROM AUTHOR]

This study estimated the gravity change expected on the sea surface due to magma accumulation beneath submarine volcanoes. For calculation, a semi-analytical model describing the deformation of an infinite elastic cone was considered, in which a point spherical source was embedded on the axis. The expected gravity change exceeds 0.2 mGal in certain scenarios when |$\delta V = 10^8\, \mathrm{m^3}$| of source volume change occurs 2 km below the summit, and the summit is shallower than 300 m below the sea surface. The steeper the slope of a submarine volcano, the greater the expected gravity change. 0.2 mGal of gravity variation can be detected with recent marine gravimeters such as cold atom interferometers. The computation method for the gravity change based on the integral transform (Mellin type) is elaborated in this paper. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of volcanic activity is crucial for volcano observatories in their efforts to forecast volcanic hazards. Satellite imager data hold promise in offering crucial insights into the thermal behavior of active volcanoes worldwide, facilitating the assessment of volcanic activity levels and identifying significant changes during periods of volcano unrest. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, aboard NASA's Terra and Aqua satellites, provides invaluable data with high temporal and spectral resolution, enabling comprehensive thermal monitoring of eruptive activity. The accuracy of volcanic activity characterization depends on the quality of models used to relate the relationship between volcanic phenomena and target variables such as temperature. Under these circumstances, machine learning (ML) techniques such as decision trees can be employed to develop reliable models without necessarily offering any particular or explicit insights. Here, we present a ML approach for quantifying volcanic thermal activity levels in near real time using thermal infrared satellite data. We develop an unsupervised Isolation Forest machine learning algorithm, fully implemented in Google Colab using Google Earth Engine (GEE) which utilizes MODIS Land Surface Temperature (LST) data to automatically retrieve information on the thermal state of volcanoes. We evaluate the algorithm on various volcanoes worldwide characterized by different levels of volcanic activity. [ABSTRACT FROM AUTHOR]

Volcanic tremor is a semi‐continuous seismic and/or acoustic signal that occurs at time scales ranging from seconds to years, with variable amplitudes and spectral features. Tremor sources have often been related to fluid movement and degassing processes, and are recognized as a potential geophysical precursor and co‐eruptive geophysical signal. Eruption forecasting and monitoring efforts need a fast, robust method to automatically detect, characterize, and catalog volcanic tremor. Here we develop VOlcano Infrasound and Seismic Spectrogram Network (VOISS‐Net), a pair of convolutional neural networks (one for seismic, one for acoustic) that can detect tremor in near real‐time and classify it according to its spectral signature. Specifically, we construct an extensive data set of labeled seismic and low‐frequency acoustic (infrasound) spectrograms from the 2021–2022 eruption of Pavlof Volcano, Alaska, and use it to train VOISS‐Net to differentiate between different tremor types, explosions, earthquakes and noise. We use VOISS‐Net to classify continuous data from past Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022). VOISS‐Net achieves an 81.2% and 90.0% accuracy on the seismic and infrasound test sets respectively, and successfully characterizes tremor sequences for each eruption. By comparing the derived seismoacoustic timelines of each eruption with the corresponding eruption chronologies compiled by the Alaska Volcano Observatory, our model identifies changes in tremor regimes that coincide with observed volcanic activity. VOISS‐Net can aid tremor‐related monitoring and research by making consistent tremor catalogs more accessible. Plain Language Summary: Volcanic tremor is a persistent vibration of the ground, atmosphere, or both that can occur before and during volcanic eruptions. Despite its importance in volcano monitoring and eruption forecasting, volcano observatories do not have a reliable way of automatically detecting and identifying tremor due to the variable intensities and frequencies at which it occurs. In order to accomplish this, we develop and test a pair of machine learning models that classify spectrograms (i.e., images representing a signal's frequency content over time) from seismic and low‐frequency acoustic data. The models are trained on manually labeled images derived from the recent 2021–2022 eruption of Pavlof Volcano, Alaska, which demonstrated substantial signal diversity (e.g., different tremor types, earthquakes, explosions and noise). Our models achieve 81.2% and 90.0% accuracy on the seismic and low‐frequency acoustic test sets respectively, and perform well when applied to data recorded from past Pavlof Volcano eruptions. In addition, transitions in tremor sequences identified from our analysis generally coincide with shifts in eruptive patterns from Pavlof Volcano. Our tools can help volcano observatories systematically monitor tremor, and advance tremor research by making catalogs of their occurrences more consistent and accessible. Key Points: We develop a pair of convolutional neural networks that detect and classify volcano seismic and acoustic signalsWe apply our models to Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022) and derive volcano seismoacoustic timelinesThe seismoacoustic timelines reveal shifts in unrest regimes linked to explosions and effusive activity [ABSTRACT FROM AUTHOR]

Understanding the dynamics of sulfur dioxide (SO2) degassing is of primary importance for tracking temporal variations in volcanic activity. Here we introduce the novel "disk method," which aims at estimating the daily volcanic SO2 mass flux from satellite images (such as those provided by Sentinel‐5P/TROPOspheric Monitoring Instrument [TROPOMI]). The method calculates a "proto‐flux" using a regression, as a function of distance, of SO2 mass integrated in a series of nested circular domains centered on a volcano. After regression, a single multiplication by plume speed suffices to deduce the SO2 mass flux, without requiring a subsequent regression. This way, a range of plume speed and plume altitude scenarios can be easily explored. Noise level in the image is simultaneously evaluated by the regression, which allows for estimating posterior uncertainties on SO2 flux and improving the level of detection for weak sources in noisy environments. A statistical test is also introduced to automatically detect occurrences of volcanic degassing, lowering the risk of false positives. Application to multi‐year time‐series at Etna (2021) and Piton de la Fournaise (2021–2023) demonstrates (a) a reliable quantification of SO2 emissions across a broad range of degassing styles (from passive degassing to effusive or paroxysmal events), and (b) a reasonable day‐to‐day correlation between SO2 flux and seismic energy. The method is distributed as an open‐source software, and is implemented in an interactive web application within the "Volcano Space Observatory Portal," facilitating near‐real‐time exploitation of the TROPOMI archive for both volcano monitoring and assessment of volcanic atmospheric hazards. Plain Language Summary: Volcanic eruptions emit sulfur dioxide gas (SO2) into the atmosphere, which may cause harm to populations and the environment, and need to be monitored. Tracking volcanic emissions is also important for volcanologists to detect changes on a given volcano, and anticipate eruptions. SO2 can be observed by satellites every day, but exploitation of satellite imagery requires complex procedures. Wind speed is a crucial ingredient, but it is often poorly known, leading to large uncertainties in estimated SO2 emissions. Here, a simple algorithm is proposed for analyzing SO2 images provided by satellites. The mass of SO2 is extracted in an area surrounding a volcano (typically 500 km) to estimate the SO2 emission rate, as well as associated uncertainty. Plume speed information can be incorporated after running the algorithm, which facilitates testing different plume speed scenarios. Application to Etna and Piton de la Fournaise volcanoes shows that temporal variations of SO2 emissions follow the same pattern as seismic energy recorded by ground seismometers, which gives confidence in the results. The algorithm is made available to all as open‐source code and in an open‐access interactive web application "SO2 Flux Calculator" within the framework of the "Volcano Space Observatory Portal." Key Points: Daily volcanic SO2 flux is deduced from TROPOspheric Monitoring Instrument satellite imagery by mass‐to‐distance regression, including a noise estimation procedureSO2 emission rates at Etna, during episodes of passive, and eruptive degassing, demonstrate a good correlation with seismic energyThe algorithm is made available to all as an open‐source Python package and on the interactive web application "SO2 Flux Calculator" [ABSTRACT FROM AUTHOR]

With the evolution of interferometric synthetic aperture radar into a tool for active hazard monitoring, new methods are sought to quickly and automatically interpret the large number of interferograms that are created. We present a convolutional neural network (CNN) that is able to both classify the type of deformation, and to locate the deformation within an interferogram in a single step. We achieve this through building a "two headed model," which returns both outputs after one forward pass of an interferogram through the network. We train our model by first creating a data set of synthetic interferograms, but find that our model's performance is improved through the inclusion of real Sentinel‐1 data. We also investigate how model performance can be improved by best organizing interferograms such that they can exploit the three channel nature of computer vision models trained on very large databases of labeled color images, but find that using different data in each of the three input channels degrades performance when compared to the simple case of repeating wrapped or unwrapped phase across each channel. We also release our labeled Sentinel‐1 interferograms as a database named VolcNet, which consists of ∼500,000 labeled interferograms. VolcNet comprises of time series of unwrapped phase and labels of the magnitude, location, and duration of deformation, which allows for the automatic creation of interferograms between any two acquisitions, and greatly increases the amount of data available compared to other labeling strategies. Plain Language Summary: The latest radar satellites provide unprecedented volumes of measurements of ground movement around subaerial volcanoes (referred to as interferograms). These data can be used to detect signs of volcanic unrest, but the number of interferograms produced when monitoring all ∼1,500 subaerial volcanoes requires automation of the required analysis. We have trained a neural network to perform this analysis, and it enhances the state‐of‐the‐art by both differentiating between different styles of volcanic deformation, and by determining the spatial size of a deformation signal. Models of this type are commonly adapted from models that were trained in a domain in which there is far more labeled data. We explore if data that complements our radar derived measurements (such as a digital elevation model of a volcano) can be used with the three inputs that are available in the best performing neural networks from other domains (interpreting color images of everyday life). However, we find that the best performance is achieved when this data is omitted. In adapting an existing model to our problem, we created a novel database of interferograms for multiple volcanoes which we term VolcNet, and make available to the community. Key Points: Passing interferometric synthetic aperture radar (InSAR) data (e.g., phase and digital elevation model) as channels to a pretrained Convolutional neural network (CNN) degrades performanceOur database of InSAR time series contains up to ∼500,000 labeled interferogramsOur CNN uses unwrapped data to differentiate between deformation patterns, and determines their size [ABSTRACT FROM AUTHOR]

Muography is an imaging technique that relies on the attenuation of the muon flux traversing geological or anthropogenic structures. Several simulation frameworks help to perform muography studies by combining specialized codes: for muon generation through muon transport to muon detector performance. This methodology is precise but requires significant computational resources and time. We present an end-to-end python-based MUographY Simulation Code, which implements a muography simulation framework capable of rapidly estimating muograms of any geological structure worldwide. This framework considers the generated muon flux as the observation point; the energy loss of muons passing through the geological target; the integrated muon flux detected by the telescope and estimates the 3-D density distribution of the target using algebraic reconstruction techniques. The simulations ignore the relatively small muon flux variance caused by geomagnetic effects, solar modulation and atmospheric conditions. We validate the code performance by comparing our simulation results with data from other frameworks. [ABSTRACT FROM AUTHOR]

This paper describes the successes and failures after 4 years of continuous operation of a network of sensors, communicating nodes, and gateways deployed on the Etna Volcano in Sicily since 2019, including a period of Etna intense volcanic activity that occurred in 2021 and resulted in over 60 paroxysms. It documents how the installation of gateways at medium altitude allowed for data collection from sensors up to the summit craters. Most of the sensors left on the volcanic edifice during winters and during this period of intense volcanic activity were destroyed, but the whole gateway infrastructure remained fully operational, allowing for a very fruitful new field campaign two years later, in August 2023. Our experience has shown that the best strategy for IoT deployment on very active and/or high-altitude volcanoes like Etna is to permanently install gateways in areas where they are protected both from meteorological and volcanic hazards, that is mainly at the foot of the volcanic edifice, and to deploy temporary sensors and communicating nodes in the more exposed areas during field trips or in the summer season. [ABSTRACT FROM AUTHOR]

Lake Taupō, New Zealand, conceals a large caldera volcano which last erupted in 232 AD. A surveying experiment in 1979 which utilised the surface of the lake for detecting small movements of the lakebed soon became a programme to monitor vertical deformation by making regular observations at 22 fixed points – survey stations – around the lake. The programme continues today with 4 surveys being made annually. In this paper, we review the methodology and present the 42 year-long dataset in graphical forms, and draw conclusions on its implications in terms of volcanic and tectonic deformation. We also make the dataset available for download. The dataset confirms that vertical deformation is occurring in the lakebed. Long periods of slow deformation, dominated by subsidence totalling 140 mm in the Taupō Fault Belt at the northern end of the lake, and to a lesser degree at the southern end, are interrupted by uplift episodes now reaching 160 mm in the northeastern sector containing the most recent active vents. We suggest that the subsidence is primarily tectonic while inflation episodes are driven by upward migration of magma to shallow levels during periods of unrest. [ABSTRACT FROM AUTHOR]

Abstract Volcanic eruptions carry essential information on the dynamics of volcanic systems. Studies have documented variable eruption styles and eruptive surface deformation. However, co‐eruptive subsurface structural changes remain poorly understood. Here we characterize the seismic velocity changes from July 2019 to July 2023 at Great Sitkin Volcano in the central Aleutian volcanic arc, using single‐station ambient noise interferometry at five three‐component seismic stations. Coincident with the lava effusion since late July 2021, about two months after the explosive eruption on 26 May 2021, we observe a sustained velocity increase, most prominently to the northwest of the caldera. We attribute this velocity increase to the structural changes with magma extrusion, with the spatial variation controlled by the geometry of the magma system or the property of shallow volcaniclastics. Our findings offer insights into understanding co‐eruptive structural modifications at active volcanoes.

Abstract With the evolution of interferometric synthetic aperture radar into a tool for active hazard monitoring, new methods are sought to quickly and automatically interpret the large number of interferograms that are created. We present a convolutional neural network (CNN) that is able to both classify the type of deformation, and to locate the deformation within an interferogram in a single step. We achieve this through building a “two headed model,” which returns both outputs after one forward pass of an interferogram through the network. We train our model by first creating a data set of synthetic interferograms, but find that our model's performance is improved through the inclusion of real Sentinel‐1 data. We also investigate how model performance can be improved by best organizing interferograms such that they can exploit the three channel nature of computer vision models trained on very large databases of labeled color images, but find that using different data in each of the three input channels degrades performance when compared to the simple case of repeating wrapped or unwrapped phase across each channel. We also release our labeled Sentinel‐1 interferograms as a database named VolcNet, which consists of ∼500,000 labeled interferograms. VolcNet comprises of time series of unwrapped phase and labels of the magnitude, location, and duration of deformation, which allows for the automatic creation of interferograms between any two acquisitions, and greatly increases the amount of data available compared to other labeling strategies.

Santorini Island (Greece) is an active volcano which has alternated between dormant and active periods over the last 650,000 years with the latest volcanic unrest occurring in 2011–2012. Here we report a geochemical survey of fumarolic gases collected at Nea Kameni islet located in the center of the caldera over the period 2015–2022 in order to study the activity of the volcano and changes in hydrothermal conditions. This period is marked by the absence of significant geochemical anomalies compared to the unrest of 2011–2012, implying that no new magma upwelling has occurred. This is evident from the low CO2/CH4 ratio and H2 concentration of fumaroles. An increase of the atmospheric contribution in gases after the 2011–2012 unrest suggests a decrease of the deep gas flow and the chemical and C-He-isotope compositions are compatible with a model of Rayleigh fractionation in which CO2 dissolves in water at decreasing temperatures over time. These results are consistent with temperature estimates obtained using the H2/N2 geothermometer, seismic and geodetic evidences. This implies a slowing of the degassing of the hydrothermal/volcanic system and a cooling of the magma injected at shallow depth in 2011–2012. All these conclusions support a quiescent state of the Santorini volcano over the period 2015–2022.

Abstract The devastation caused by the January 2022 eruption of Hunga Tonga-Hunga Ha’apai volcano (HTHH) in the Tongan archipelago reminded us of the importance of monitoring shallow-sea volcanic activity. Seismic observations are essential for such monitoring, but there were no operational seismic stations in Tonga at the time of the eruption. There are only a few islands near Tongan volcanoes, and installation and maintenance of seismic stations on remote islands are expensive. Seismic observations based on distributed acoustic sensing (DAS) using a seafloor cable may provide a more practical and economical solution. To investigate the potential of this approach, we made preliminary DAS observations for 1 week using the seafloor domestic broadband telecommunications cable in Tonga. DAS equipment was installed at the landing station of the seafloor cable at Nuku’alofa on Tongatapu, the main island of Tonga. To provide reference data, we installed several seismometers on Tongatapu. The DAS data we obtained showed high noise levels in areas of shallow coral reef, but noise levels decreased greatly in deeper water areas, indicating that DAS is suitable for seismic observations of the deep seafloor. We detected many local and regional earthquakes during our week of observation and determined 17 earthquake hypocenters by picking P- and S-wave arrival times from the DAS and onshore seismic data. Although most of these were tectonic events related to the subduction of the Pacific plate along the Tonga trench, several events were detected around the volcanic chain of the Tongan archipelago including one event beneath the HTHH crater, implying that activity at HTHH has continued since the 2022 eruption. The much lower cost of installation of DAS equipment compared to that for pop-up type ocean-bottom seismometers and the ability of DAS systems to monitor seismic activity in real-time make it an attractive option for monitoring the activity of HTHH and other volcanoes near seafloor cables in the Tongan archipelago. Graphical Abstract

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 subsidenceGeodetic modeling indicates upward migration of magma, feeding a magma body at an inferred depth of 2.5–3.1 km under the main Askja calderaStart of unrest was associated with magma transfer within the upper part of the system, followed by possible additional influx from depth [ABSTRACT FROM AUTHOR]

Monitoring volcanoes is of the most importance in volcano risk mitigation to safeguard lives and economies. Thanks to recent technological advances, both on-ground and in space, our understanding of volcanic processes has improved significantly. Though there is no one-system-fits-all, optimizing infrastructure for efficient monitoring stands as key objective. The impacts of volcanic hazards can span from local to global scales, affecting us both in the short and long term. This highlights the worldwide significance of improving volcano monitoring. Previously reliant on local ground-based instruments, today's monitoring approach is enhanced by remote and space-based techniques such as satellite remote sensing, scanning-Differential Optical Absorption Spectroscopy (DOAS), and infrasound. Designing an effective monitoring infrastructure for volcano observatories involves careful consideration of various factors such as network coverage, type of sensors, data transmission, and power supply to ensure that the targeted parameters meet the specific needs of each volcano (e.g., type of activities, early warning systems). Additionally, fostering collaboration and information sharing within the global scientific community is essential for addressing the current challenges in volcanology. In line with this, we've established the Global Volcano Monitoring Infrastructure Database (GVMID) to compile data from volcano monitoring across the globe. Global Volcano Monitoring Infrastructure Database serves as an integral component of WOVOdat, the global volcano unrest database, aiming to enhance our understanding of eruptive processes and improve eruption forecasts. The database incorporates monitoring metadata comprising networks, stations, and instruments, all standardized and managed using a MySQL relational database management system. Accessed through a web-based interface (https://wovodat.org/gvmid/home.php), GVMID offers an informative snapshot and foundational overview of the techniques and instruments in place at diverse volcanoes. This interactive platform allows for queries, visualizations, and downloads, serving as a valuable resource for the volcano community. GVMID can assist observatories in various ways, by: (a) Facilitating the setup or enhancement of monitoring systems for specific volcanoes. (b) Providing insights into the latest monitoring technologies and instrumentation. (c) Identifying existing monitoring gaps that could be addressed through remote sensing infrastructure and future instrument deployments. We extend an invitation to the global volcano community to actively participate in the development and enrichment of GVMID. Our aim is for it to become a continually updated and indispensable resource that caters to diverse needs within the volcanology community. [ABSTRACT FROM AUTHOR]

The devastation caused by the January 2022 eruption of Hunga Tonga-Hunga Ha'apai volcano (HTHH) in the Tongan archipelago reminded us of the importance of monitoring shallow-sea volcanic activity. Seismic observations are essential for such monitoring, but there were no operational seismic stations in Tonga at the time of the eruption. There are only a few islands near Tongan volcanoes, and installation and maintenance of seismic stations on remote islands are expensive. Seismic observations based on distributed acoustic sensing (DAS) using a seafloor cable may provide a more practical and economical solution. To investigate the potential of this approach, we made preliminary DAS observations for 1 week using the seafloor domestic broadband telecommunications cable in Tonga. DAS equipment was installed at the landing station of the seafloor cable at Nuku'alofa on Tongatapu, the main island of Tonga. To provide reference data, we installed several seismometers on Tongatapu. The DAS data we obtained showed high noise levels in areas of shallow coral reef, but noise levels decreased greatly in deeper water areas, indicating that DAS is suitable for seismic observations of the deep seafloor. We detected many local and regional earthquakes during our week of observation and determined 17 earthquake hypocenters by picking P- and S-wave arrival times from the DAS and onshore seismic data. Although most of these were tectonic events related to the subduction of the Pacific plate along the Tonga trench, several events were detected around the volcanic chain of the Tongan archipelago including one event beneath the HTHH crater, implying that activity at HTHH has continued since the 2022 eruption. The much lower cost of installation of DAS equipment compared to that for pop-up type ocean-bottom seismometers and the ability of DAS systems to monitor seismic activity in real-time make it an attractive option for monitoring the activity of HTHH and other volcanoes near seafloor cables in the Tongan archipelago. [ABSTRACT FROM AUTHOR]

Understanding the composition, texture, and morphology of volcanic rocks that have erupted at the surface better constrains the eruption style and is vital to infer subsurface processes, the development of magma upon ascent, and the potential for future eruptions. The reflectance and emission spectroscopy of these rocks, collected from the near-infrared (NIR) through the thermal infrared (TIR) portion of the electromagnetic (EM) spectrum, provides the data necessary to retrieve composition, micron-scale surface roughness, and particle size. Remote imaging systems enable the analyses of active volcanoes in remote regions, where sample collection for laboratory analysis poses a significant challenge. Laboratory hyperspectral data of samples acquired at volcanic deposits are easily resampled to the spectral resolution of any infrared sensor and provide a means of estimating the composition of volcanoes and their products worldwide, as well as those on other planetary bodies such as the Moon and Mars. In this review paper, we provide an overview of the current use of infrared reflectance and emission spectroscopy as an analysis tool in volcanology, including ground-based imaging systems that acquire unprecedented detail and serve as testbeds for new orbital concepts. We also discuss the potential impact that future satellite missions will have on volcano science as spectral, spatial, and temporal resolutions improve. [ABSTRACT FROM AUTHOR]

Owing to the current lack of plausible and exhaustive physical pre-eruptive models, often volcanologists rely on the observation of monitoring anomalies to track the evolution of volcanic unrest episodes. Taking advantage from the work made in the development of Bayesian Event Trees (BET), here we formalize an entropy-based model to translate the observation of anomalies into probability of a specific volcanic event of interest. The model is quite general and it could be used as a stand-alone eruption forecasting tool or to set up conditional probabilities for methodologies like the BET and of the Bayesian Belief Network (BBN). The proposed model has some important features worth noting: (i) it is rooted in a coherent logic, which gives a physical sense to the heuristic information of volcanologists in terms of entropy; (ii) it is fully transparent and can be established in advance of a crisis, making the results reproducible and revisable, providing a transparent audit trail that reduces the overall degree of subjectivity in communication with civil authorities; (iii) it can be embedded in a unified probabilistic framework, which provides an univocal taxonomy of different kinds of uncertainty affecting the forecast and handles these uncertainties in a formal way. Finally, for the sake of example, we apply the procedure to track the evolution of the 1982–1984 phase of unrest at Campi Flegrei. [ABSTRACT FROM AUTHOR]

Continuous seismic monitoring of volcanoes is challenging due to harsh environments and associated hazards. However, the investigation of volcanic phenomena is essential for eruption forecasting. In seismo-volcanic applications, distributed acoustic sensing (DAS) offers new possibilities for long-duration surveys. We analyse DAS strain rate signals generated by volcanic explosions and tremor at Stromboli volcano (Italy) recorded along 1 km of dedicated fibre-optic cable. We validate DAS recordings with colocated nodal seismometers. Converting node measurements to strain rate, we observe a perfect match in phase between DAS and node waveforms. However, DAS amplitudes appear to be around 2.7 times smaller than those of node records, which we explain as due to the inefficient ground-to-fibre strain transfer in the loose cable. We invert time delays between strain rate waveforms and confirm that the DAS enables us to retrieve a dominant and persistent seismic source in the proximity of active craters. This stable source location is confirmed by node array analyses. Despite an observed high noise level of strain rate signals outside a range of 2–15 Hz, our results demonstrate the potential of this new technology in monitoring volcanic areas. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for developing a volcano-seismic event classification system using a multi-station deep learning approach to support monitoring the Nevados del Chillán Volcanic Complex, which has been active since 2017. A convolutional network of multiple inputs processes the information from an event recorded up to five seismic stations. Each record is represented by its normalized spectrogram; thus, the network may receive from one to five spectrograms as input. The design includes entering additional information into the network, like the stations configuration and the event duration, information not provided by the spectrograms. Finally, this work includes the design and implementation of a relational database to access the continuous traces of events, showing different subsets of data quickly and efficiently. The results show that the classification of an event recorded up to five stations is substantially more effective than a single-station strategy. However, incorporating additional information of the signal does not significantly improve the classification performance. [ABSTRACT FROM AUTHOR]

This article provides an overview of the use of remote sensing technology to monitor and study volcanic emissions. It explains the use of Differential Optical Absorption Spectroscopy (DOAS) to measure the concentration of sulfur dioxide (SO2) gas in volcanic plumes. The article emphasizes the importance of technological advancements and interdisciplinary research in improving our understanding of volcanism and its impact on the atmosphere. However, it also cautions that seasonal snow cover can potentially lead to overestimations of SO2 emissions. Overall, this article offers valuable insights into the role of remote sensing in volcano monitoring and research. [Extracted from the article]

Abstract On 22 December 2018, the major flank collapse of Anak Krakatau volcano generated a tsunami that struck the surrounding coasts of Java and Sumatra islands in Indonesia without warning. It was later suggested that the corresponding seismic event lacked the body-wave arrivals typical of tectonic earthquakes, causing difficulties for the automated detection system to recognize the event. We explore the possibility of detecting the seismic signature of such events without relying on the arrival times of body waves, by measuring seismic amplitudes in a regional seismic network at the expected arrival times from a fixed, potential source and comparing them to the theoretical attenuation of surface waves. We propose a fast detection method and evaluate the method using seismograms recorded during the flank collapse and tsunami episode as well as several known tectonic earthquakes. Detailed examinations of the detection results confirm the seismic signatures of the flank collapse and teleseismic events as suggested by previous studies. We also find more seismic signatures suggesting the occurrence of two possible smaller collapse events and variations in the eruptive activity related to the major flank collapse, as well as body and surface wave signals from two teleseismic earthquakes that were present during this episode. Finally, we construct a timeline of events during this devastating episode, combining our results with previous studies as well as insights from weather radar observations. With the ability to detect and discriminate various types of seismic events from each other, the detection method can be useful in assisting the existing monitoring and early warning systems in detecting major volcano-related tsunamigenic events. Graphical Abstract

Abstract Kirishima volcano consists of more than 20 eruptive centers. Among them, Shinmoe-dake had magmatic eruptions in October 2017 and March 2018. Subsequently, another active cone, Iwo-yama, had phreatic eruptions in April 2018. These events were unique in that the 2018 eruption was the first effusion-dominated eruption of Shinmoe-dake and the first simultaneous activity of two cones of the Kirishima volcanic group ever documented. We report the detailed sequence of the events by combining areal photos, satellite images, and seismo-acoustic data analyses with the other published information. The seismo-acoustic data clarify the eruption onset and the transitions of the behaviors in three stages for each of the 2017 and 2018 eruptions. For both eruptions, we present regularly repeated tremors or ’drumbeat’ earthquakes in the second stage, which interpret as gas separation from magma, leading to the ash-poor plume in the 2017 eruption or the effusive eruption in the 2018 event. We also propose that the 2017 and 2018 eruptions of Shinmoe-dake and the 2018 eruption of Iwo-yama are sequential events linked by the degassing of magma beneath Shinmoe-dake. An eruption like the 2017–2018 eruptions of Shinmoe-dake would leave few geological records and could be captured only by modern techniques. Although Shinmoe-dake has been believed to be an example of less-frequent eruptions, effusive eruptions like the 2018 case might have occurred more frequently in the past , but the following eruptions had obscured their records. The timelines summarized in this study will be useful in future studies of Kirishima volcanoes and world equivalences. Graphical Abstract

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity.

Understanding the dynamics of volcanic activity is crucial for volcano observatories in their efforts to forecast volcanic hazards. Satellite imager data hold promise in offering crucial insights into the thermal behavior of active volcanoes worldwide, facilitating the assessment of volcanic activity levels and identifying significant changes during periods of volcano unrest. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, aboard NASA’s Terra and Aqua satellites, provides invaluable data with high temporal and spectral resolution, enabling comprehensive thermal monitoring of eruptive activity. The accuracy of volcanic activity characterization depends on the quality of models used to relate the relationship between volcanic phenomena and target variables such as temperature. Under these circumstances, machine learning (ML) techniques such as decision trees can be employed to develop reliable models without necessarily offering any particular or explicit insights. Here, we present a ML approach for quantifying volcanic thermal activity levels in near real time using thermal infrared satellite data. We develop an unsupervised Isolation Forest machine learning algorithm, fully implemented in Google Colab using Google Earth Engine (GEE) which utilizes MODIS Land Surface Temperature (LST) data to automatically retrieve information on the thermal state of volcanoes. We evaluate the algorithm on various volcanoes worldwide characterized by different levels of volcanic activity.

Abstract 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.

Monitoring volcanoes is of the most importance in volcano risk mitigation to safeguard lives and economies. Thanks to recent technological advances, both on-ground and in space, our understanding of volcanic processes has improved significantly. Though there is no one-system-fits-all, optimizing infrastructure for efficient monitoring stands as key objective. The impacts of volcanic hazards can span from local to global scales, affecting us both in the short and long term. This highlights the worldwide significance of improving volcano monitoring. Previously reliant on local ground-based instruments, today’s monitoring approach is enhanced by remote and space-based techniques such as satellite remote sensing, scanning-Differential Optical Absorption Spectroscopy (DOAS), and infrasound. Designing an effective monitoring infrastructure for volcano observatories involves careful consideration of various factors such as network coverage, type of sensors, data transmission, and power supply to ensure that the targeted parameters meet the specific needs of each volcano (e.g., type of activities, early warning systems). Additionally, fostering collaboration and information sharing within the global scientific community is essential for addressing the current challenges in volcanology. In line with this, we’ve established the Global Volcano Monitoring Infrastructure Database (GVMID) to compile data from volcano monitoring across the globe. Global Volcano Monitoring Infrastructure Database serves as an integral component of WOVOdat, the global volcano unrest database, aiming to enhance our understanding of eruptive processes and improve eruption forecasts. The database incorporates monitoring metadata comprising networks, stations, and instruments, all standardized and managed using a MySQL relational database management system. Accessed through a web-based interface (https://wovodat.org/gvmid/home.php), GVMID offers an informative snapshot and foundational overview of the techniques and instruments in place at diverse volcanoes. This interactive platform allows for queries, visualizations, and downloads, serving as a valuable resource for the volcano community. GVMID can assist observatories in various ways, by: (a) Facilitating the setup or enhancement of monitoring systems for specific volcanoes. (b) Providing insights into the latest monitoring technologies and instrumentation. (c) Identifying existing monitoring gaps that could be addressed through remote sensing infrastructure and future instrument deployments. We extend an invitation to the global volcano community to actively participate in the development and enrichment of GVMID. Our aim is for it to become a continually updated and indispensable resource that caters to diverse needs within the volcanology community.

Understanding the composition, texture, and morphology of volcanic rocks that have erupted at the surface better constrains the eruption style and is vital to infer subsurface processes, the development of magma upon ascent, and the potential for future eruptions. The reflectance and emission spectroscopy of these rocks, collected from the near-infrared (NIR) through the thermal infrared (TIR) portion of the electromagnetic (EM) spectrum, provides the data necessary to retrieve composition, micron-scale surface roughness, and particle size. Remote imaging systems enable the analyses of active volcanoes in remote regions, where sample collection for laboratory analysis poses a significant challenge. Laboratory hyperspectral data of samples acquired at volcanic deposits are easily resampled to the spectral resolution of any infrared sensor and provide a means of estimating the composition of volcanoes and their products worldwide, as well as those on other planetary bodies such as the Moon and Mars. In this review paper, we provide an overview of the current use of infrared reflectance and emission spectroscopy as an analysis tool in volcanology, including ground-based imaging systems that acquire unprecedented detail and serve as testbeds for new orbital concepts. We also discuss the potential impact that future satellite missions will have on volcano science as spectral, spatial, and temporal resolutions improve.

AbstractThe great potential of the Global Navigation Satellite System (GNSS) in monitoring ground deformation is widely recognized. As with other geophysical data, GNSS time series can be significantly noisy, hiding elusive ground deformation signals. Several denoising techniques have been proposed to improve the signal-to-noise ratio over the years. One of the most effective denoising techniques has been proved to be multi-resolution decomposition through the discrete wavelet transform. However, wavelet analysis requires long data sets to be effective, as well as long computation times, that hinder its use as a real or near real-time monitoring tool. We propose training by a Convolutional Neural Network (CNN) to perform the equivalent of wavelet analysis to overcome these limitations. Once trained, the CNN model provides answers within seconds, making it feasible as a real-time data analysis tool. Our Machine Learning algorithm is tested on daily GNSS time series collected in the Campi Flegrei caldera (Southern Italy), which is a highly volcanic risk area. Without significant gaps, the retrieved RMSE and R2 values vary in the ranges 0.65–0.98 and 0.06–0.52 cm, respectively. These results are encouraging, as they hint at the possibility of applying this methodology in more effective real-time monitoring solutions for active volcanoes.

We report on about 20 yr of relative gravity measurements, acquired on Mt. Somma–Vesuvius volcano in order to investigate the hydrological and volcano-tectonic processes controlling the present-day activity of the volcano. The retrieved long-term field of time gravity change (2003–2022) shows a pattern essentially related to the subsidence, which have affected the central part of the volcano, as detected by the permanent GNSS network and InSAR data. After reducing the observations for the effect of vertical deformation, no significant residuals are found, indicating no significant mass accumulation or loss within the volcanic system. In the north-western sector of the study area, at the border of the volcano edifice, however, significant residual positive gravity changes are detected which are associated to ground-water rebound after years of intense exploitation of the aquifers. On the seasonal timescale, we find that stations within the caldera rim are affected by the seasonal hydrological effects, while the gravity stations at the base of the Vesuvius show a less clear correlation. Furthermore, within the caldera rim a multiyear gravity transient is detected with an increase phase lasting about 4 yr followed by a slower decrease phase. Analysis of rain data seem to exclude a hydrological origin, hence, we hypothesize a deeper source related to the geothermal activity, which can be present even if the volcano is in a quiescent state. We infer the depth and volume of the source by inverting the spatial pattern of the gravity field at the peak of the transient. A volume of fluids of 9.5 × 107 m3 with density of 1000 kg m−3 at 2.3 km depth is capable to fit reasonably well the observations. To explain the gravity transient, simple synthetic models are produced, that simulate the ascent of fluids from a deep reservoir up to the depth of 2.3 km and a successive diffusion within the carbonate aquifer hosting the geothermal system. The whole process appears to not significantly affect the seismicity rate and the deformation of the volcano. This study demonstrates the importance of a 4-D gravity monitoring of a volcano to understand its complex gravity signals that cover different spatial and temporal scales. Discriminating the different contributions that mix up in the observed gravity changes, in particular those due to hydrologic/anthropogenic activities form those due to the geothermal dynamics, is fundamental for a complete and reliable evaluation of the volcano state. [ABSTRACT FROM AUTHOR]

Muography is increasingly used to image the density distribution of volcanic edifices, complementing traditional geophysical tomographies. Here, we present a new muon data processing algorithm, and apply it to a new generation of scintillator-based muon detectors, to image the relative density distribution in La Soufrière de Guadeloupe volcano (Lesser Antilles, France). Our processing method iteratively searches for the best fit of each muon trajectory, accounting for all the hits registered by the detector related to the particular muon event. We test the performance of our algorithm numerically, simulating the interaction of muons with our detector and accounting for its exact assemblage including the scintillator bars and lead shielding. We find that our new data processing mitigates the impact of spurious signals coming from secondary particles, and improves the amount of successfully reconstructed events. The resulting 2-D muon images at La Soufrière have higher angular resolution than previous ones and capture the heterogeneous structure of the dome. They show density anomalies located on the summit southern region, which includes a boiling acid lake and degassing fractures, where the rock is the most porous and fumarolic activity is ongoing. This work shows the importance of combining numerical simulations of muon propagation with precise raw data processing to obtain high-quality results. It is also a first step towards fully assessing the noise contamination sources when performing muon tomography, and their correction, prior to geophysical interpretations. [ABSTRACT FROM AUTHOR]

On 22 December 2018, the major flank collapse of Anak Krakatau volcano generated a tsunami that struck the surrounding coasts of Java and Sumatra islands in Indonesia without warning. It was later suggested that the corresponding seismic event lacked the body-wave arrivals typical of tectonic earthquakes, causing difficulties for the automated detection system to recognize the event. We explore the possibility of detecting the seismic signature of such events without relying on the arrival times of body waves, by measuring seismic amplitudes in a regional seismic network at the expected arrival times from a fixed, potential source and comparing them to the theoretical attenuation of surface waves. We propose a fast detection method and evaluate the method using seismograms recorded during the flank collapse and tsunami episode as well as several known tectonic earthquakes. Detailed examinations of the detection results confirm the seismic signatures of the flank collapse and teleseismic events as suggested by previous studies. We also find more seismic signatures suggesting the occurrence of two possible smaller collapse events and variations in the eruptive activity related to the major flank collapse, as well as body and surface wave signals from two teleseismic earthquakes that were present during this episode. Finally, we construct a timeline of events during this devastating episode, combining our results with previous studies as well as insights from weather radar observations. With the ability to detect and discriminate various types of seismic events from each other, the detection method can be useful in assisting the existing monitoring and early warning systems in detecting major volcano-related tsunamigenic events. [ABSTRACT FROM AUTHOR]

One of the most important tasks when studying volcanic activity is to monitor their thermal radiation. To fix and assess the evolution of thermal anomalies in areas of volcanoes, specialized hardware-thermal imagers are usually used, as well as specialized instruments of modern satellite systems. The data obtained with their help contain information that makes it relatively easy to track changes in temperature and the size of a thermal anomaly. At the same time, due to the high cost of such complexes and other limitations, thermal imagers sometimes cannot be used to solve scientific problems related to the study of volcanoes. In the current paper, day/night video cameras with an infrared-cut filter are considered as an alternative to specialized tools for monitoring volcanoes' thermal activity. In the daytime, a camera operated in the visible range, and at night the filter was removed, increasing the camera's light sensitivity by allowing near-infrared light to hit the sensor. In that mode, a visible thermal anomaly could be registered on images, as well as other bright glows, flares, and other artifacts. The purpose of this study is to detect thermal anomalies on night images, separate them from other bright areas, and find their characteristics, which could be used for volcano activity monitoring. Using the image archive of the Sheveluch volcano as an example, this article presents the results of developing a computer algorithm that makes it possible to find and classify thermal anomalies on video frames with an accuracy of 98%. The test results are presented, along with their validation based on thermal activity data obtained from satellite systems. [ABSTRACT FROM AUTHOR]

At 04:14:45 UT on 2022 January 15, a powerful eruption of the submarine Hunga Tonga–Hunga Ha'apai volcano occurred at about 30 km south of the Ha'apai Islands in the Kingdom of Tonga (at −20.55° N, −175.39° E). This eruption caused atmospheric waves that spread worldwide. In this study, we investigate the the total electron content (TEC) variation over China using the BeiDou Navigation Satellite System. The particularly interesting feature of the data set compared to other ground-based TEC data is the exclusive use of the BeiDou geostationary satellites, which monitor the TEC variations for fixed ionospheric piercing points and can provide more accurate calculations of the travelling speed of the disturbance. For comparison, atmospheric pressure records were examined, which show that the Lamb wave passed by the same stations four times with a constant speed of 310 m s−1. However, the TEC results show that the ionospheric disturbances passing over China four times with different speeds within four days after the eruption, two travelling along the short-path direction and two along the long-path direction. The primary front of the first short-path event travels with a speed of 340 m s−1, which is higher than the Lamb wave. The faster speed suggests that the primary front cannot be fully attributed to the Lamb wave, and further studies need to explore its mechanism. The second short-path and first long-path events travel with speeds of 301 and 310 m s−1, respectively, close to the speed of the Lamb wave, and they may be caused by upward energy leakage during the propagation of the Lamb wave. The second long-path event travels with a speed of 264 m s−1, possibly induced by the gravity waves. [ABSTRACT FROM AUTHOR]