2011-2020: A Decade of global volcanic events observations at the IMS Infrasound network

2011-2020: A Decade of global volcanic events observations at the IMS Infrasound network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 2011-2020: A Decade of global volcanic events observations at the IMS Infrasound network Sandro Matos, Paola Campus, Maurizio Ripepe, Linda Silva, Nicolau Wallenstein This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9281628/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract The Global Volcanism Program (GVP) includes a comprehensive list of the 1281 Earth’s active volcanoes and their eruptions over the last 12,000 years. In this work, we used the web-based GVP database of the Smithsonian Institute to correlate detections in the period 2011–2020. According to GVP data, 360 eruptions (or confirmed eruptive activity) occurred on 138 volcanoes around the world. Among those, we selected 67 confirmed eruptions originated from 46 volcanoes, with Volcanic Explosive Index (VEI) above 3. Data from 43 IMS infrasound stations were processed and analysed in the specified time window using the Progressive Multi-Channel Correlation (PMCC) algorithm. A station-to-source back-azimuth deviation of 5° was considered, using a cross-bearing azimuth methodology. The IMS network infrasound detections of the 67 selected volcanic events are presented, as well as the correspondence of the volcanic events with the lists of in the Late Event Bulletin (LEB, 52 events), Standard Event Lists (SEL3, 13 events) and Reviewed Event Bulletin (REB, 30 events) produced by the CTBTO International Data Centre (IDC). Volcanic Eruptions IMS Infrasound CTBTO IDC Bulletins Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction According to the Smithsonian Institution Global Volcanism Program (GVP) database, 1,281 potentially active volcanoes are currently identified worldwide. Those volcanoes are mainly distributed along major tectonic plate boundaries and concentrated within four linear volcanic belts: the Circum-Pacific, the Mid-Oceanic Ridge, the Mediterranean-Himalayan and the East African Rift Valley (Lowman Jr., 1980). Although subaerial volcanoes represent approximately 10 to 20% of all volcanoes on Earth, they interact directly with the atmosphere and can pose significant hazards to the surrounding areas, many of which densely populated and so considered high-risk regions (Freire et al., 2019 ). Eruptive activity can be classified according to its magnitude using the Volcanic Explosivity Index (VEI; Newhall and Self, 1982 ), which describes eruptions in terms of the erupted volume (magnitude) and the eruption plume height (intensity). The VEI is widely used in volcanic studies and it was adopted by GVP in its catalogues covering volcanic activity over the last 10,000 years (Siebert et al., 2010 ). Traditionally, eruptive activity is broadly classified according to eruption style into Hawaiian, Strombolian, Vulcanian, Sub-Plinian, Plinian and Ultra-Plinian types (Walker, 1973 , 1980 ; Bonadonna et al., 2016 ). These styles reflect different eruption dynamics and generate a wide range of volcanic hazards. Effusive events, typically associated with Hawaiian activity, produce fluid lava flows or lava domes that mainly affect areas close to the vent. In contrast, more explosive styles (e.g., Vulcanian, Sub-Plinian, or Plinian), can generate sustained eruptive columns, expanding volcanic ash clouds, and pyroclastic density currents. Consequently, the impacts of volcanic eruptions may range from local (< few kilometres from the source) to regional (< few hundreds of km) scales. Considering the occurrence of, approximately, 50 to 80 eruptions per year, ash clouds produced by major volcanic explosive eruptions can be blown by winds and spread hundreds of kilometers away from the volcanic source, often crossing national and international borders. Eruptions with VEI equal and above 3 can represent a major concern for aviation safety, hazard mitigation, and global situational awareness. This led the International Civil Aviation Organization (ICAO) to establish 9 Volcanic Ash Advisory Centres (VAAC) that have the duty of identifying volcanic clouds and issuing timely hazard alerts to civil aviation. VAACs perform extensive use of satellite images and interact with volcano observatories worldwide (Evans, 1991 ; ICAO, 2023 ). Due to the resemblance of volcanic and meteorological clouds, the rapid identification of explosive eruptions is crucial, as the eruptions might potentially inject ash in the atmosphere, causing severe impacts on aircrafts flying over the affected areas. Despite considerable advances in real-time monitoring networks, only a small fraction of the potentially active world’s volcanoes is continuously monitored in real time (Pallister and McNutt, 2015 ). Many active volcanoes, particularly those located in remote regions, lack local ground-based monitoring systems. Here, remote-sensing techniques provide the only practical means of volcanic surveillance. In this context, large-scale volcanic eruptions have the potential to inject pressured gas and volcanic clasts into the atmosphere and generate shock and acoustic waves propagating over long distances, in some cases circulating around the Earth. Such phenomena have been documented for major eruptions including Krakatau in 1883 (Yokoyama, 1981 ), Bezymianny in 1956 (Murayama, 1968 ), Mount Saint Helens in 1980 (Bolt and Tanimoto, 1981 ; Donn and Balachandran, 1981 ), and the Hunga Tonga-Hunga Ha’apai eruption on January 15th, 2022 (Matoza et al., 2022 ; Vergoz et al., 2022 ). Acoustic waves with frequencies lower than the acoustic threshold of the human ear, (~ 20 Hz), are called infrasound waves (Campus and Christie, 2010 ; Johnson and Ripepe, 2011 ; Fee and Matoza, 2013 ). Due the reduced effect of attenuation, infrasound waves have long wavelengths ranging from tens of metres at 10 Hz to several tens of kilometres near 0.01 Hz that propagate for thousands of kilometres in the atmosphere (Drob et al., 2003 ; Campus and Christie, 2010 ; de Groot-Hedlin et al., 2010 ). Three dominant atmospheric waveguides, established between the ground, high temperature and wind speed regions (troposphere, stratosphere and thermosphere) can be identified. In downwind conditions along the direction of propagation, these waves can travel for distances greater than 1,000 kilometres (Le Pichon et al., 2012 ), in case of 1) signals that are refracted in the troposphere (0–16 km), mainly bound by the mid-latitude jet stream around the tropopause (~ 10 km); 2) signals that are refracted in the stratosphere (10–60 km) as the result of the wind and the temperature increase, induced by the presence of ozone; and 3) signals that are refracted in the thermosphere (110–160 km) where infrasound attenuation is naturally stronger (Garcés et al., 2004 ; Assink et al., 2012 ). Tropospheric waveguides depend mainly on diurnal variations in temperature and wind speeds, while stratospheric waveguides are influenced by the presence of seasonal variations in stratospheric zonal winds (blowing from east to west and vice versa), thus indicating that long-range infrasound propagation depends predominantly from the stratospheric duct (de Groot-Hedlin et al., 2011 ). In a horizontally stratified atmosphere, the wind's effects on the speed of sound can be explained through the effective speed of sound, v eff (Wilson, 2003 ; Evers, 2008 ; Hupe, 2022). The effective sound speed ratio, v eff−ratio , can be defined, for a given layer, by the relation between the effective sound speed at a reference altitude z and the effective sound speed at ground level, (Le Pichon et al., 2012 ). Refraction of a signal, propagated by an upward wind towards the ground is predicted in the condition of v eff−ratio ≥1 (Wilson, 2003 ; Le Pichon et al., 2012 ). The state of the atmosphere strongly controls infrasound propagation through attenuation, refraction driven by sound-speed gradients, ground reflections, atmospheric turbulence, and wind-induced azimuthal deviations (de Groot-Hedlin, 2008). A propagating infrasound wave loses energy through absorption and geometrical spreading, is deflected by the wind, and is refracted according to its velocity profile. Despite these effects, efficient low-frequency ducting enables infrasound to propagate over long distances and be detected as small pressure fluctuations of a few millipascals (mPa) using arrays of highly sensitive pressure sensors (microbarometers). Since 1990, infrasound-based technology has been included in the geophysical toolkit and incorporated by worldwide observatories for volcano monitoring, rapidly evolving from an academic research area into a usable, well-established, and valuable real-time monitoring tool (Ripepe & Marchetti, (GRL) 2002; Ripepe et al., (JGR) 2007; Cannata et al., 2009 ; Matoza and Roman, 2022 ). The development and establishment of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) IMS Infrasound network, designed with 60 stations and, currently consisting of 54 certified stations, has demonstrated the capability of detecting volcanic eruptions across a wide range of released energies at very large source-receiver distances (Christie and Campus, 2010 ; Campus and Christie, 2010 ; Dabrowa et al., 2011 ), high temporal resolution data on ongoing activity at remote volcanoes (Matoza et al., 2011 ), and global coverage for different scale volcanic eruptions (Dabrowa et al., 2011 ). This global network can provide near–real-time information on volcanic events and valuable long-range observations of activity at remote or poorly monitored volcanoes. Such information is particularly important for VAACs, as it supports the timely identification of explosive activity and potential ash emissions. Despite the increasing use of infrasound for volcanic monitoring, the detection and characterization of explosive eruptions at large distances remain challenging, particularly for remote or poorly monitored volcanoes. Improving our understanding of long-range infrasound propagation and detection capabilities is, therefore, essential for enhancing global volcanic surveillance. In this framework, this study evaluates the effectiveness of remote detection of explosive volcanic activity using infrasound monitoring. An automatic detection algorithm has been applied to eruptions with VEI ≥ 3 reported in the GVP database between 2011 and 2020. The algorithm has been applied to recordings from IMS infrasound stations to identify coherent infrasonic signals potentially associated with explosive volcanic activity, taking advantage of the network’s global coverage to assess the performance of the detection algorithm worldwide. A distance of 4,500 km has been adopted to ensure that each volcano included in the study has been covered by at least three stations for analysis. The resulting detections have been cross validated against bulletins from the CTBTO International Data Centre (IDC): Standard Event List (SEL3), Reviewed Event Bulletin (REB) and Latest Event Bulletin (LEB) to assess the robustness of the detection algorithm. 2 Data and methodology 2.1. Data sources A detection algorithm was developed to integrate three complementary data sources: volcanic eruption information from the GVP database, infrasound observations from the CTBT IMS network and atmospheric conditions derived from meteorological reanalysis datasets produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). 2.1.1 Volcanic Eruption Dataset The GVP maintains one of the most comprehensive databases of worldwide volcanic activity. The database provides information on volcano locations, eruption chronologies, eruptive behaviour or the VEI, based on reports updated on a daily, weekly and multi-monthly basis. Due to its global scope, the GVP database is widely used as a reference source for research on volcanic activity, risk assessment and statistical analysis of eruptive events. In this study, database was examined to identify volcanoes that experienced eruptive activity between 2011 and 2020. Only confirmed eruptions with a VEI ≥ 3 were considered for the analysis. 2.1.2 IMS Infrasound Data The IMS comprises a global network of 337 monitoring facilities that use four verification technologies: seismic (170 stations), hydroacoustic (11 stations), infrasound (60 stations), and radionuclide (80 stations and 16 laboratories). These stations are connected to the IDC in Vienna, enabling the continuous transmission of near–real-time data through the Global Communications Infrastructure (GCI). Within the IMS, the infrasound network is designed to ensure the reliable detection and location of atmospheric explosions with yields greater than 1 kiloton TNT equivalent anywhere on Earth by at least two stations (Christie et al., 2001 ; Le Pichon et al., 2009 ; Green and Bowers, 2010 ). For this study, the selection and processing of infrasound data has been based on data availability, data quality and regional relevance for the period 2011–2020. The objective has been the identification of the stations with the highest potential to detect volcanic infrasound for each region and each volcano. Although the CTBTO IMS infrasound network consisted of 53 certified stations during the period of study, only data from 43 stations located within a source–receiver distance up to 4,500 km have met the required criteria and have been included in the dataset. Therefore, nine stations have been not considered, due to distance constraints. 2.1.3 Atmospheric Data Meteorological reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) have been used to describe the atmospheric state at the time of each event. ERA-Interin and ERA5 reanalysis datasets have been used, providing global atmospheric fields such as temperature and horizontal wind components relevant for infrasound propagation. ERA-Interim provides data from 1979 to present, with a global spatial resolution of approximately 80 km in 60 vertical levels up to 0.1 hPa available in 6-hour analyses. Data from ERA5 is presented on a 31 km grid (0.28° × 0.28°) and using 137 levels from the surface up to a height of 80 km (0.01 hPa) hourly available (Hersbach et al., 2020 ). Data from the 60 levels of the atmospheric model, the temperature, the horizontal wind components and sound speed (adiabatic and effective speed of sound) have been retrieved and stored for all the discrete events. 2.2 Algorithm development An automatic detection algorithm has been developed through three main workflow phases: (i) compilation of an explosive volcanic activity database, (ii) infrasound data processing and propagation analysis, and (iii) cross-validation with IDC bulletins. 2.2.1 Phase 1: GVP explosive activity compilation The database has been examined to identify volcanoes with confirmed eruptive activity between 2011 and 2020, based on records reported in the GVP bulletins. The selection has been based on four criteria: (i) the eruptive volcano must have an assigned name; (ii) the eruption must be confirmed, excluding discredited or uncertain events; (iii) only activity occurring within the selected time window has been considered, even if the eruptive period began earlier; and (iv) eruptions must have VEI ≥ 3. Following the selection phase, volcanoes have been grouped into predefined GVP zones according to their geographical and geodynamic setting. The terminology adopted follows the GVP classification to ensure consistency in the subsequent analysis. A general overview of the selected volcanoes has been compiled, as shown in Table 1 with an example of the Northwest Pacific Volcanic Region. The dataset includes key information, such as the GVP Number, Volcano Name, location, Eruption timing, VEI and Duration. Table 1 Overview of selected volcano characteristics in the Northwest Pacific volcanic region. GVP number Volcano_Name Lat Lon Eruption period time VEI Duration Northwest Pacific Volcanic Region − 9 volcanoes 300250 Bezymianny 55,972 160,595 2012 Feb 12–2013 Jun 20 3 1 y, 4 m, 8 d 2016 Dec 5–2021 Feb 1 3 4 y, 1 m, 27 d 290360 Chikurachki 50,324 155,461 2015 Feb 16–2015 Feb 18 3 3 d 290260 Chirinkotan 48,98 153,48 2016 Nov 29–2017 Apr 7 3 4 m, 9 d 300010 Kambalny 51,306 156,875 2017 Mar 24–2017 Apr 23 3 30 d 300130 Karimsky 54,049 159,443 2017 Jun 4–2018 Sep 30 3 1 y, 3 m, 26 d 2020 Apr 1–2022 Aug 7 3 2 y, 4 m, 6 d 300260 Klyuchevskoy 56,056 160,642 2013 Aug 15–2013 Dec 20 3 4 m, 5 d 2015 Aug 28–2018 Jul 14 3 2 y, 10 m, 16 d 290250 Raikoke 48,292 53,25 2019 Jun 22–2019 Jul 1 3 10 d 300240 Tolbachik 55,832 60,326 2012 Nov 27–2013 Sep 5 3 9 m, 9 d 300120 Zhupanovsky 53,589 159,15 2014 Jun 6–2015 Aug 6 3 1 y, 2 m 2015 Nov 28–2016 Mar 25 3 3 m, 26 d 2016 Nov 20–2016 Nov 20 3 1 d As the duration of eruptive episodes varies significantly, an additional processing step has been required to identify discrete eruptive events suitable for global infrasound analysis. This procedure has included identifying the eruptive periods from the GVP database, selecting associated discrete events with VEI ≥ 3, and determining the onset time (t₀) of each event (Table 2 ). Table 2 Example of the catalogue of selected eruptive events and corresponding onset times (t₀). Volcano_Name eruption period time Episod period time Date (dd.mm.yyy) t0 (UTC) VEI Bezymianny 2012 Feb 12–2013 Jun 20 2012 Feb 12–2012 Jun 7 08.03.2012 21:40 3 2012 Jul 29–2013 Jun 20 01.09.2012 19:16 3 2016 Dec 5–2021 Feb 1 2016 Dec 5–2017 Apr 21 15.12.2016 10:00 3 09.03.2017 03:23 3 2017 Jun 9–2017 Oct 5 16.06.2017 04:53 3 2017 Dec 18–2018 Nov 15 20.12.2017 03:55 3 2019 Jan 15–2019 Nov 15 20.01.2019 16:10 3 15.03.2019 17:30 3 2020 Aug 26–2021 Feb 1 21.10.2020 20:22 3 To ensure consistent processing, the script has applied logical checks during the iteration. If no additional events were identified within a given eruptive period, the analysis has proceeded to the next eruptive period or volcano. Once all volcanoes have been processed, the Events List has been saved in a structured format. This catalogue forms the basis for the correlation phase, in which the onset time of each event (t₀) is compared with the infrasound detections recorded by IMS stations to assess detectability, temporal consistency, and potential source-to-station associations. 2.1.2 Phase 2: IMS infrasound processing and propagation analysis In general, an IMS infrasound station comprises an array of 4 to 15 elements (sites) spatially distributed over apertures of, approximately, ranged from 1 to 3 km, arranged in different geometric layouts. Each site includes a protected vault, equipped with a high sensitivity microbarometer, data acquisition systems (DAS) and communication equipment and a wind noise reduction system (WNRS). Meteorological parameters such as temperature, wind speed and wind direction are also recorded at one array element to characterize local background conditions. The sensors are designed to detect pressure variations of less than 1 mPa and operate over a wide temperature range. Wind noise reduction is achieved through pipe-array systems, typically arranged in a rosette configuration, which represent the current IMS standard (Christie et al., 2001 ; Christie and Campus, 2010 ; Marty et al., 2012 ). Data from each site are transmitted to a central recording facility where they are buffered, formatted, digitally signed, and forwarded to the IDC through the GCI. For this study selected arrays have an average aperture of 2.15 km, with 4 to 10 elements per station, with main apertures spanning from 1.13 km (IS32) to 3.37 km (IS60). The daily volume of raw data analysed has been more than 508×10 6 samples at a sampling rate of 20 Hz (Matos, 2026). Signal processing has been performed using the Progressive Multi-Channel Correlation (PMCC) algorithm, an array-processing method designed to detect coherent low-amplitude acoustic waves embedded in non-coherent noise (Cansi, 1995 ). The PMCC parameters have been adjusted to volcanic infrasound characteristics, typically transient and dominated by frequencies between 0.5 and 5 Hz (Le Pichon et al., 2008 ; Pilger et al., 2018 ). To reduce false detections and ensure the coherence of signal association, three key parameters have been optimized: the duration of the correlation time window (WindowLength), the time shift between two successive windows (TimeStep), and the consistency threshold (Threshold consistency), which defines the maximum acceptable deviation before a detection pixel is rejected. Frequency-dependent settings have been implemented using 15 logarithmically spaced frequency bands between 0.07 and 5 Hz, with time-window lengths ranged from 150 s to 25 s, following a 1/f scaling and a 90% window overlap and Threshold consistency of 0.2 s. From the array processing results, the apparent phase velocity is given by the time delay between coherent signals arrivals to the different sensors. The back-azimuth indicates the sensor-to-source bearing of a detected signal. When detections are available from at least two stations a potential source region can be estimated by applying a cross-bearing method. The more stations contribute to the detection, the more accurate the localisation will be. Unlike other natural processes, where infrasound sources can vary in with time of day or season (e.g., microbarom or Mountain-Air Waves (MAW)), volcanoes have well-defined locations and their monitoring largely depends on the source-to-receiver propagation conditions. A back-azimuth (θ) tolerance of θ ± 5° relative to the volcano’s reference has been applied (Fig. 1 ), to account for uncertainties associated with back-azimuth, which may result from sensor malfunction, array response characteristics, or atmospheric wind conditions along the propagation path (Le Pichon et al., 2005 ). For each volcano, a data sheet was prepared linking the volcano to the closest stations. Parameters such as source-receiver distance, back-azimuth and waveform propagation times (assuming speed of 0,34 km/s), have been calculated for all selected volcanoes. An example for Ambae volcano is presented in Table 3 . Table 3 Source–receiver distance, back-azimuth, and propagation time for Ambae volcano and the nearest IMS stations. Ambae Volcano 1st _sta 2nd _sta 3rd _sta 4th _sta 5th _sta 6th _sta 7th _sta IMS Station I22FR I40PG I36NZ I05AU I07AU I60US I39PW Distance (Kms) 759 2123 3494 3569 3585 3838 4462 Back-azimuth (°) 8 126 330 39 87 178 125 Prop. Time (Hour) 00:37:13 01:44:04 02:51:16 02:54:57 02:55:43 03:08:09 03:38:43 Infrasound propagation over long distances is strongly controlled by atmospheric conditions, including the direction and amplitude of vertical wind gradients and background temperature fields (Brown et al., 2002 ; Drob et al., 2008 ). Vertical wind gradients and temperature structures influence the formation of atmospheric waveguides that enable signals to propagate over large distances (Drob et al., 2003 ). It is therefore important to know the atmospheric conditions at the time of the event. To support this analysis, MATLAB® scripts were developed to process meteorological data (temperature, zonal and meridional winds) and to compute parameters such as the sound speed (adiabatic and effective speed of sound, Fig. 2 ). ERA-Interim and ERA5 datasets have been used to characterize atmospheric conditions, in this case the stratospheric temperature and horizontal wind fields. The retrieved parameters have been then used to calculate the effective speed of sound ratio \(\:{v}_{eff-ratio\:}\) and atmospheric attenuation (Fig. 3 ) at the locations of the events and the selected IMS stations (Le Pichon et al 2012 ). The Phase 2 focuses on the detection script developed to correlate the onset times of volcanic events with infrasound detections recorded at IMS stations. The script uses two key inputs: (i) the volcanic event catalogue (Event List) obtained in Phase 1 and (ii) the station detection lists generated using the adapted PMCC algorithm (Station Bulletins). The detection workflow follows these steps: (1) Station selection – For each volcano in the Event List, the script evaluates all IMS stations individually. The volcano-station distance is calculated (within 4,500 km and available data) and sorted in ascending order of distance. (2) Event time window definition – For each discrete event the onset time (t₀) is taken. The script then verifies whether valid detection data is available in the screened list. If no data is found, the script moves on to the next station. Otherwise, processing is carried out within a time window defined from 12 hours before (t_start) to 24 hours after the event onset (t_end). (3) Back-azimuth filtering – The expected back-azimuth of the volcano (θₙ) for each station is calculated based on their geographical coordinates, and a directional tolerance range window of θₙ ± 5° is applied. ( 4) Detection screening - Station detection lists are analysed to identify detections falling within both the defined time window and azimuth range. (5) Event association - The screened detections are automatically saved and associated with the specific event and station. These outputs are later used in the interpretation and evaluation phases (Fig. 4 ). 2.2.2 Phase 3: Cross-validation with IDC bulletins (SEL3, REB, LEB) At the IDC, the waveform received from the infrasound station are subjected to quality control verifications and then processed by the DFX-PMCC (Data Feature eXtraction-Progressive Multi-Channel Correlation) application technology (Branchet et al., 2010) based on the PMCC algorithm (Cansi, 1995 ). Incoming arrivals are used as input for the network processing performed through the Global Association (GA) software, where events are built up from associated arrivals and reported in automatic bulletins (SEL2 and SEL3). Following this, analyst review process is summarised in the LEB, and event definition criteria are then applied to produce the REB. Events that don't fulfil REB's event definition criteria ( e.g. , minimum number of primary IDC station defining arrivals associated with them) are included in the LEB but are not listed in the REB. The selected events were stored in text files, with associated parameters including Event Identification (Event Id), Event Location (Location), Date, Time, RMS, Latitude (Lat), Longitude (Lon), Azimuth of event (Az), Number of stations (Nsta), Detected stations code (Sta) and their distance (Dist), Phase, Arrival time (Time), Back-Azimuth (Baz), Slowness (Slow), SNR, Signal Amplitude (Amp), among others (Table 4 ). These data were subsequently used to compile a catalogue of events to be tentatively correlated with volcanic events identified on Phase 1 and detections obtained in Phase 2. Table 4 Example of a LEB event information, for Ambae volcanic activity. Event Id Location Date Time RMS Lat Long Az Nsta 16493999 VANUATU ISLANDS 2018-10-30 07:43:05.56 193.5 -16.20 168.11 22 4 Sta Dist Phase Time Baz Slow SNR Amp I22FR 6.07 I 08:15:45.000 8.8 320.4 31.7 1.2 I40PG 19.75 I 09:40:10.714 125.4 271.9 3.9 29.9 I07AU 32.30 I 11:04:50.000 87.8 310.2 8.3 0.02 I21FR 50.91 I 13:10:41.786 260.8 300.1 10.2 0.1 The IDC CTBTO bulletins were collected to assess their ability to register and provide information on events that could be potentially associated with the events detected and processed in the previous phases. In this context, among the three IDC bulletins, the LEB was used as a reference dataset to validate the robustness of the detection algorithm and to assess its potential as a first-order approach for early warning notifications. As a final step, detections has been correlated with the reported events of the IDC bulletins. The number of IMS station detections that can be associated with volcanic events, their validation or not through IDC bulletins will indicate how effective the assessment tool can be applied as a reference for detection response and as an early warning notification system (Fig. 5 ). 3 RESULTS During the studied period (2011–2020), 360 eruptions were recorded from 138 volcanoes (GVP). Among these, 67 eruptions from 46 volcanoes had VEI ≥ 3 and were selected for this study (53 events were ranked with VEI = 3, 13 events with VEI = 4 and 1 event with 1 VEI = 5). Out from the 67 confirmed eruptions, 186 discrete events were identified. Of the 67 eruptive time intervals, 71.6% (n = 48) were successfully identified, while 28.4% (n = 19) were not. At the event scale, 54.8% (n = 102) of the 186 volcanic events were detected, compared to 45.2% (n = 84) that were not. The different discrete eruptive events identified show the episodic and complex dynamics of the volcanic activity. The selected volcanoes were grouped into 11 pre-defined GVP zones according to their geography, geodynamic settings, classification on the effectiveness of the algorithm in identifying discrete events (Fig. 6 ). For 27 volcanoes, all reported eruptions were successfully detected by the algorithm, comprising 21 VEI 3 events, 10 VEI 4 events, and 1 VEI 5 event, including eruptions at Tolbachik (2012), Villarica (2014), Chirinkotan (2016), and Cleveland (2020), highlighting the system’s capability to identify diverse eruptive events under different geographic and atmospheric contexts, pointing to favourable conditions for long-distance infrasound propagation and effective network coverage. On the other hand, for 8 volcanoes only a subset of eruptions was detected by the algorithm, probably reflecting variations in the eruption intensities, in the atmospheric conditions at the time of the event, or in the stations network layout used. For the remaining 11 volcanoes, the algorithm did not detect any events (9 VEI 3 and 2 VEI 4), despite the confirmed activity. Such cases can be explained by factors such as low-energy eruption, complex propagation patterns, or sparse network coverage (Table 4 ). Table 4 Summary of selected volcanoes by region, eruptive periods, and associated detections. Volcanic Region Volcano_Name Eruption time period Eruption detected Overall Detection Northwest Pacific Bezymianny 2012 Feb 12–2013 Jun 20 Det Detected 2016 Dec 5–2021 Feb 1 Det Chikurachki 2015 Feb 16–2015 Feb 18 Det Detected Chirinkotan 2016 Nov 29–2017 Apr 7 Det Detected Kambalny 2017 Mar 24–2017 Apr 23 Det Detected Karymsky 2017 Jun 4–2018 Sep 30 Det Parcially Detected 2020 Apr 1–2022 Aug 7 Ndet Klyuchevskoy 2013 Aug 15–2013 Dec 20 Det Detected Raikoke 2019 Jun 22–2019 Jul 1 Det Detected Tolbachik 2012 Nov 27–2013 Sep 5 Det Detected Zhupanovsky 2014 Jun 6–2015 Aug 6 Det Parcially Detected 2015 Nov 28–2016 Mar 25 Det 2016 Nov 20–2016 Nov 20 Ndet Western Pacific Asosan 2016 Oct 7–2016 Nov 12 Det Detected Kirishimayama 2011 Jan 19–2011 Sep 7 Det Parcially Detected 2018 Mar 1–2018 Jun 22 Ndet Kuchinoerabujima 2015 May 29–2015 Jun 19 Det Parcially Detected 2018 Oct 21–2019 Feb 3 Det 2020 Jan 11–2020 May 13 Ndet Ontakesan 2014 Sep 27–2014 Oct 14 Ndet Not Detected Soputan 2011 Jul 3–2011 Aug 15 Det Parcially Detected 2012 Aug 26–2012 Sep 19 Ndet 2015 Jan 6–2015 Mar 9 Det 2016 Jan 2–2016 Feb 7 Det 2018 Oct 2–2018 Dec 16 Det Taal 2020 Jan 12–2020 Jan 22 Det Detected Southwest Pacific Ambae 2017 Sep 6–2018 Oct 30 Det Detected Manam 2010 Aug 10 − 2013 Dec 15 Ndet Parcially Detected 2014 Jun 29–2018 Jan 10 Det Rabaul 2014 Jul 7–2014 Sep 18 Det Detected Tinakula 2017 Oct 21–2017 Oct 26 Det Detected Ulawun 2019 Jun 26–2019 Oct 5 Det Detected Eastern Pacific Wolf 2015 May 25–2015 Jul 16 Ndet Not Detected Sunda-Banda Agung 2017 Nov 21–2019 Jun 13 Ndet Not Detected Kelud 2014 Feb 13–2014 Feb 15 Det Detected Krakatau 2018 Jun 18–2020 Apr 17 Ndet Not Detected Merapi 2013 Nov 18–2013 Nov 18 Ndet Parcially Detected 2014 Mar 9–2014 Apr 20 Det 2018 May 11–2020 Jun 21 Det Paluweh 2012 Oct 8–2013 Oct 31 Ndet Not Detected Sangeang Api 2014 May 30–2015 Nov 5 Det Detected Semeru 2017 Jun 6–2024 Dec 19 (ongoing) Ndet Not Detected Sinabung 2013 Sep 15–2018 Jun 22 Det Detected 2019 Feb 6–2019 Jun 9 Det Eastern Africa Nabro 2011 Jun 13–2012 Jun 3 Det Detected Atlantic Ocean Grimsvotn 2011 May 21–2011 May 25 Det Detected European Etna 2010 Aug 25–2013 Apr 27 Det Detected 2013 Sep 3–2022 Jun 17 Det North America Bogoslof 2016 Dec 20–2017 Aug 30 Det Detected Cleveland 2020 Jun 1–2020 Jun 1 Det Detected Pavlof 2013 May 13–2013 Jun 26 Ndet Parcially Detected 2014 May 31–2014 Jun 6 Det 2014 Nov 12–2014 Nov 15 Det 2016 Mar 27–2016 Jul 30 Det Shishaldin 2019 Jul 23–2020 May 4 Det Detected Veniaminof 2013 Jun 13–2013 Oct 12 Ndet Not Detected Middle America-Caribbean Colima 2013 Jan 6–2017 Mar 7 Ndet Not Detected San Miguel 2013 Dec 29–2014 Jul 28 Ndet Not Detected Turrialba 2015 Mar 8–2019 Dec 7 Det Detected South America Calbuco 2015 Apr 22–2015 May 26 Det Detected Nevado del Ruiz 2012 Feb 22–2013 Jul 12 Ndet Not Detected Puyehue Cordon Caulle 2011 Jun 4–2012 Apr 21 Det Detected Sabancaya 2016 Nov 6–2024 Dec 19 (ongoing) Ndet Not Detected Tungurahua 2011 Apr 20–2011 May 26 Det Detected 2011 Nov 27–2012 Sep 4 Det 2012 Dec 14–2016 Mar 16 Det Villarrica 2014 Dec 2 − 2024 Dec 13 (ongoing) Det Detected Overall, these results point out the heterogeneous performance of the detection algorithm at global scale and underline the importance of considering the source's features, the network's configuration and the atmosphere's status at the time of the event. Analysis of the IDC automatic bulletin (SEL3) identified 13 events as potentially related to the eruptive periods under study. These events were associated to 10 active volcanoes in five of the eleven GVP regions, while no events were identified in association with those located in the Western Pacific, Eastern Pacific, Atlantic Ocean, North America or Middle America–Caribbean regions. By analysing the LEB bulletin, a larger number of associations were identified, with 52 events as potentially related to the eruptive periods of 28 volcanoes, representing approximately 61% of the volcanoes analysed. No events were found to be associated with volcanoes in the Eastern Pacific Region (Wolf volcano) or in the Central America–Caribbean region (Colima, San Miguel and Turrialba volcanoes). According with IDC criteria, events that do not fulfil the REB definition criteria - such as those not detected by at least three primary stations or with a cumulative weight of less than 4.6 - are kept in the LEB but are excluded from the published REB. Consequently, 31 events were identified as potentially related to eruptive periods of 21 volcanoes. No events were identified for volcanoes located in the Southwest Pacific, Eastern Pacific, or Middle America–Caribbean volcanic regions. The comparison between IDC bulletins highlights significant differences in detection capability, with LEB providing the highest number of potential associations, demonstrating its value as an independent reference for validating the detection algorithm used in this work and assessing its performance. 4 DISCUSSION The results demonstrated the high reliability of the algorithm in multiple years, particularly in 2011, 2014, 2015 and 2019 where detection rates exceeding 87% and reaching 100% in 2019. The results indicate a high success rate in eruptive activity detection, highlighting the robustness of the algorithm in identifying sustained volcanic activity. This consistent performance supports the suitability of the system for detecting signals associated with long-lasting or high-energy eruptions, even at great distances from the source. In contrast, detections associated to discrete events are more variable, reflecting the episodic nature of volcanic activity and the susceptibility of success with short-lived signals, due to changes in source strength and atmospheric propagation conditions. Different types of volcanic activity can produce variable energy levels which can generate and propagate infrasound waves. Despite their low explosiveness and limited long-range propagation, Hawaiian-style eruptions were successfully detected by the algorithm in a few cases, including the lava fountaining at Mount Etna volcano (2013 and 2016). Strombolian activity at Villarrica volcano (2015) was detected at distances of approximately 3,700 km (stations IS42 and I13CL), demonstrating the capability of the network to capture moderate explosive activity. However, Strombolian and Vulcanian gas-driven outbursts, which dominate discrete events associated with VEI 3, show more variable detection performance, reflecting variability in source strength and propagation conditions. For large explosive events (VEI 4) that were more consistently detected, the algorithm successfully identified 11 out of 13 c eruptions, including Grimsvötn and Nabro (2011), Kelud (2014), and Taal (2020). On the other hand, two eruptions, Wolf (2015) and Semeru (2017), were not identified by the algorithm. The network coverage near those volcanoes (e.g., the nearest station to Wolf volcano is located approximately 3,000 km away, once I20EC was not yet operational) and unfavourable atmospheric conditions at the time of the events, may explain the lack of detections. A VEI 5 Plinian eruption, associated to Puyehue Cordon Caulle event (June 4th, 2011), was successfully detected. Detection trends show a clear hemisphere and seasonal tendency in accordance with the atmosphere propagation effects. In the Northern Hemisphere, 31 eruptions (66%) with 79 discrete events (77.5%) were identified mainly during winter periods, when stratospheric wind patterns enhance long-distance infrasound propagation. The Northwest Pacific is the most active region, followed by the Western Pacific, reflecting both high volcanic activity and favourable propagation paths. In contrast, regions such as North America, Europe and Central America–Caribbean show detections during the Northern Hemisphere summer months, which may be related by a combination of network configuration and seasonal variability in atmospheric propagation conditions. Other regions, including East Africa and the Atlantic Ocean, contribute minimally, with 1 eruption or event per region. A similar seasonal pattern is observed in the Southern Hemisphere with 17 eruptions (34%) with 23 discrete events (22.5%), with detections clustered during winter, particularly in the South-West Pacific and South America, consistent with propagation enhancements expected under favourable stratospheric wind patterns: in the Sunda-Banda arc region detections occurred across the Southern summer, Southern winter and even isolated detections during the Northern winter and Northern summer, reflecting its location closer to equatorial regions and its exposure to both seasonal hemispheric seasonal variations. Also, in equatorial region, no activity was detected in the Eastern Pacific, possibly due to a combination of lower eruptive activity during the analysis period and long oceanic distances to the nearest stations, which potentially limited the detection capability. In addition to source characteristics and network layout, atmospheric conditions play a critical role in controlling infrasound propagation and detection capability. Favourable propagation paths, especially stratosphere ducts, improves the propagation of long-distance signals, while unfavourable wind and temperature can attenuate or deflect the acoustic waves. Variations in the effective sound speed ratio ( v eff−ratio ) and atmospheric attenuation significantly influence whether signals are refracted back to the ground or are lost at higher altitudes. All these factors provide a plausible explanation for the variability in detection performance observed in this work, including events where moderate or even energetic eruptions failed to be detected. Seasonal and regional variations in stratospheric winds, as well as local atmospheric conditions at the time of each event, can contribute to substantial variability in propagation efficiency. 5 CONCLUSION The results demonstrate that the proposed algorithm provides reliable detection of infrasound signals generated by explosive volcanic activity across periods of high and low eruptive frequency. The algorithm has demonstrated higher detection capabilities for more energetic eruptions but also identifies points for improvement in dealing with the complexity and diversity of volcanic activity worldwide. The IMS network provides relatively uniform global coverage, with a mean of six stations contributing to detections per volcano. Detection patterns show a clear hemispheric and seasonal dependence, consistent with known variations in atmospheric propagation conditions, with enhanced detectability during winter months in each hemisphere. Overall, the developed detection algorithm has proven to be an effective instrument for detecting infrasound events in a wide range of volcanic regions worldwide and demonstrates its potential for global infrasound-based volcanic monitoring. Its conception, represents a practical and scalable framework for routine monitoring, can be adapted to specific volcanoes or regions through the incorporation of atmospheric information. This adaptability and flexibility make it suitable for integration into early warning systems and real-time monitoring programs based on IMS data. Future improvements should focus on the integration of eruption source parameters and regional propagation conditions to enhance detection performance across a broader spectrum of eruptive styles. Such advances will strengthen the role of infrasound monitoring as a key component of global volcanic surveillance. Declarations Author Contribution Conceptualization: SM,NW; Data curation: SM; Formal analysis: SM, LS; Funding acquisition: SM, NW; Supervision: NW; Investigation: SM; Methodology: SM, NW, PC, MR;Software: SM, LS; Visualization: SM, LS; Writing—original draft: SM; Writing—editing and review: All authors reviewed the manuscript. Acknowledgement This work was supported by FCT, I.P., the Portuguese national funding agency for science, research, and technology, under the Projects UID/00643/2023 and by Instituto de Investigação em Vulcanologia e Avaliação de Riscos (IVAR), Universidade dos Açores. SM was supported by FCT – Foundation for Science and Technology by PhD Grant UI/BD/151384/2021 (https://doi.org/10.54499/UI/BD/151384/2021). SM was also supported by CTBTO contract No. 2012-1694.The views expressed herein are those of the authors and not necessarily reflect the views of the CTBTO Preparatory Commission. Data Availability Global Volcanism Program (GVP) data are available from the Smithsonian Institution Global Volcanism Program database (https://volcano.si.edu/).For the atmospheric models we used the CDS API tools freely provided by European Centre for Medium-RangeWeather Forecasts (ECMWF) to obtain the necessary ERA5 reanalysis profiles, publicly available foracademic research (https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5).IMS data are available on request from the CTBTO Preparatory Commission for scientific purposes through the virtual Data Exploitation Centre (vDEC): https://www.ctbto.org/specials/vdec/ References Assink J. D., Waxler R., Drob D. (2012). On the sensitivity of infrasonic travel times in the equatorial region to the atmospheric tides. Journal of Geophysical Research, 117. D01110, doi:10.1029/2011JD016107. Bolt, B. A., & Tanimoto, T. (1981). Atmospheric oscillations after the May 18, 1980 eruption of Mount St. Helens, Eos Trans. AGU, 62(23), 529–530. Bonadonna, C., Cioni, R., Costa, A., Druitt, T., Phillips, J., Pioli, L., Andronico, D., Harris, A., Scollo, S., Bachmann, O., Bagheri, G., Biass, S., Brogi, F., Cashman, K., Dominguez, L., Dürig, T., Galland, O., Giordano, G., Gudmundsson, M., … Wallenstein, N. (2016). MeMoVolc report on classification and dynamics of volcanic explosive eruptions. Bulletin of Volcanology, 78(11), 84. https://doi.org/10.1007/s00445-016-1071-y Brachet N, Brown D, Le Bras R, Mialle P, Coyne J (2010). Monitoring the earth’s atmosphere with the global IMS infrasound network. In: Le Pichon A., Blanc E., Hauchecorne A. (eds.) Infrasound Monitoring for Atmospheric Studies. Springer, Dordrecht, pp. 73–114. Brown D., Katz C., Le Bras R., Flanagan M., Wang J., Gault A. (2002). Infrasonic signal detection and source location at the prototype international data centre”. Pure and Applied Geophysics , 159: 1081–1125. https://doi.org/10.1007/978-3-0348-8144-9_10 Campus, P. & Christie, D. (2010). Worldwide Observations of Infrasonic Waves. In: Le Pichon A., Blanc E., Hauchecorne A. (eds.) Infrasound Monitoring for Atmospheric Studies. Springer, Dordrecht. DOI: https:// doi10.1007/978-1-4020-9508-5_6. Cannata, A., Montalto, P., Privitera, E., Russo, G., & Gresta, S. (2009). Tracking eruptive phenomena by infrasound: May 13, 2008 eruption at Mt. Etna. Geophysical Research Letters, 36(5). https://doi.org/10.1029/2008GL036738 Cansi Y (1995). An automatic seismic event processing for detection and location: the PMCC method. Geophysical Research Letters. 22(9):1021–1024. Christie, D. R., Vivas Veloso, J. A., Campus, P., Bell, M., Hoffmann, T., Langlois, A., Martysevich, P., Demirovic, E. & Carvalho, J. (2001). Detection of atmospheric nuclear explosions: The infrasound component of the International Monitoring System. Kerntechnik. 66. 96-101. 10.1515/kern-2001-0058. Christie, D.R. & Campus, P. (2010) The IMS Infrasound Network: Design and Establishment of Infrasound Stations. In: Le Pichon A., Blanc E., Hauchecorne A. (eds.) Infrasound Monitoring for Atmospheric Studies. Springer, Dordrecht. DOI: https:// doi10.1007/978-1-4020-9508-5_6. Dabrowa, A. L., Green, D.N., Rust, A.C., Phillips, J.C. (2011). A global study of volcanic infrasound characteristics and the potential for long-range monitoring. Earth and Planetary Science Letters. 310. 369-379. DOI:10.1016/j.epsl.2011.08.027. de Groot-Hedlin, C.D., Hedlin, M.A., Walker, K.T., Drob, D.P. & Zumberge, M.A. (2008). Evaluation of infrasound signals from the shuttle Atlantis using a large seismic network. The Journal of the Acoustical Society of America, 124 3, 1442-51. de Groot-Hedlin C. D., Hedlin, M. A. H. & Drob, D. P. (2010). Atmospheric Variability and Infrasound Monitoring. In: Le Pichon A., Blanc E., Hauchecorne A. (eds) Infrasound Monitoring for Atmospheric Studies. Springer, Dordrecht. DOI: https://doi.org/10.1007/978-1-4020-9508-5_15. de Groot-Hedlin C., Hedlin, M. A. H. & Walker, K. (2011). Finite difference synthesis of infrasound propagation through a windy, viscous atmosphere: application to a bolide explosion detected by seismic networks. Geophysical Journal International. 185 (1). 305–320. https://doi.org/10.1111/j.1365-246X.2010.04925.x. Donn, W. & Balachandran, N. (1981). Mount St. Helens eruption of 18 May 1980: air waves and explosive yield. Science, 213:539–541. Drob, D. P., Picone J. M., Garcés M. (2003). Global morphology of infrasound propagation. Journal of Geophysical Research. 108. D2. 4680. DOI:10/1029/2002JD003307 Drob, D. P., Emmert J. T., Crowley G., Picone J. M., Shepherd G. G., Skinner W., Hays P., Niciejewski R.J., Larsen M., She C. Y., Meriwether J. W., Hernandez G., Jarvis M. J., Sipler D.P., Tepley C. A., O’Brien M. S., Bowman J. R., Wu Q., Murayama Y., Kawamura S., Reid I. M. & Vincent R. A. (2008). An empirical model of the Earth’s horizontal wind fields: HWM07. Journal of Geophysical Research. 113. A12304. doi:10.1029/2008JA013668. Drob, D. P., Broutman D., Hedlin M. A., Winslow N. W. & Gibson R. G. (2013). A method for specifying atmospheric gravity wavefields for long-range infrasound propagation calculations. Journal of Geophysical Research: Atmospheres. 118. 3933–3943. DOI:10.1029/2012JD018077. Evans, J. E. (1991). Development of a real-time ATC volcanic ash advisory system based on the future aviation weather system: in Casadevall, T. J., (ed.), Volcanic ash and aviation safety: Proceedings of the first international symposium on volcanic ash and aviation safety, U.S. Geological Survey Bulletin. 2047, p. 157-161. Evers, L. (2008). The inaudible symphony: on the detection and source identification of atmospheric infrasound. [Dissertation (TU Delft), Delft University of Technology]. TU Delft, DEOS. Fee, D. & Matoza, R. S. (2013). An overview of volcano infrasound: From Hawaiian to Plinian, local to global. Journal of Volcanology and Geothermal Research. 249. 123-139. https://doi.org/10.1016/j.jvolgeores.2012.09.002. Freire, S., Florczyk, A. J., Pesaresi, M., & Sliuzas, R. (2019). An Improved Global Analysis of Population Distribution in Proximity to Active Volcanoes, 1975–2015. ISPRS International Journal of Geo-Information, 8 (8), 341. https://doi.org/10.3390/ijgi8080341. Garcés M., Bass H., Drob D., Hetzer C., Hedlin M., Le Pichon A., Lindquist K., North R., Olson, J. (2004). Forensic studies of infrasound from massive hypersonic sources. EOS 85(43):433. Global Volcanism Program, 2025. [Database] Volcanoes of the World (v. 5.3.4; 30 Dec 2025). Distributed by Smithsonian Institution, compiled by Venzke, E. https://doi.org/10.5479/si.GVP.VOTW5-2025.5.3 Green, D.N., Bowers, D. (2010). Estimating the detection capability of the International Monitoring System infrasound network. Journal of Geophysical Research, 115:D18116. https://doi.org/10.1029/ 2010JD014017. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J-N. (2020). ERA5 hourly data on model levels from 1940 to present [Conjunto de dados]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi.org Hupe, P., Ceranna, L., Le Pichon, A., Matoza, R. S., and Mialle, P. (2022). International Monitoring System infrasound data products for atmospheric studies and civilian applications, Earth System Science Data, 14, 4201–4230, https://doi.org/10.5194/essd-14-4201-2022. ICAO (2023) Handbook on the international airways volcano watch (IAVW). Operational procedures and contact list. https:// www.icao. int/ airna vigat ion/ METP/ MOG% 20IAVW% 20Ref erence%20Doc uments/ Handb ook% 20on% 20the% 20IAV W,% 20Doc%209766. pdf. Accessed Jan 2026 Johnson, J. & Ripepe, M. (2011). Volcano infrasound: a review. Journal of Volcanology Geothermal Research, 206 (34): 61–69. http://dx.DOI.org/10.1016/.2011.06.006. Le Pichon, A., Blanc, E., Drob, D., Lambotte, S., Dessa, J., Lardy, M., Bani, P. & Vergniolle, S. (2005). Infrasound monitoring of volcanoes to probe high-altitude winds, Journal of Geophysical Research, 110, D13106, doi:10.1029/2004JD005587. Le Pichon, A., J. Vergoz, P. Herry, and L. Ceranna (2008), Analyzing the detection capability of infrasound arrays in Central Europe, J. Geophys. Res., 113, D12115, doi:10.1029/2007JD009509. Le Pichon A, Vergoz J, Blanc E, Guilbert J, Ceranna L, Evers L, Brachet N. (2009). Assessing the performance of the International Monitoring System infrasound network: geographical coverage and temporal variabilities. Journal of Geophysical Research, 114: D08112. https://doi.org/10.1029/ 2008JD010907. Le Pichon, A., Ceranna, L., Vergoz, J. (2012). Incorporating numerical modeling into estimates of the detection capability of the IMS infrasound network. Journal of Geophysical Research, 117: D05121. https://doi. org/10.1029/2011JD016670. Lowman, Paul D & Goddard Space Flight Center. (1980). Global tectonic and volcanic activity of the last one million years / Paul D. Lowman. [cartographic material]. Marty, Julien & Martysevich, Pavel & Kramer, A. & Haralabus, G. (2012). Engineering and development projects for the sustainment and enhancement of the IMS infrasound network. 11204-. Geophysical Research Abstracts, Vol. 14.EGU General Assembly 2012. Matos, S. (2025). Contribution to real-time long-range erupting volcanoes monitoring based on infrasound , PhD thesis, Universidade dos Açores, Portugal, 187pp., https://doi.org/10.54499/UI/BD/151384/2021. Matoza, R. S., Vergoz, J., le Pichon, A., Ceranna, L., Green, D. N., Evers, L. G., Ripepe, M., Campus, P., Liszka, L., Kvaerna, T., Kjartansson, E., & Höskuldsson, Á. (2011). Long-range acoustic observations of the Eyjafjallajökull eruption, Iceland, April-May 2010. Geophysical Research Letters, 38(6), n/a-n/a. https://doi.org/10.1029/2011GL047019 Matoza, R.S., Roman, D.C. (2022). One hundred years of advances in volcano seismology and acoustics. Bulletin of Volcanology, 84, 86. https://doi.org/10.1007/s00445-022-01586-0 Matoza, R. S., Fee, D., Assink, J. D., Iezzi, A. M., Green, D. N., Kim, K., ... & Witze, A. (2022). Atmospheric waves and global seismoacoustic observations of the January 2022 Hunga Tonga-Hunga Ha’apai eruption. Science , 377 (6601), 95-100. doi.org Murayama, N. (1968). Propagation of atmospheric pressure waves produced by the explosion of volcano Bezymianny of March 30, 1956 and transport of the volcanic ashes. Q. J. Seismol. 33, 1–11 (PDF) Atmospheric pressure waves in the field of volcanology. Newhall, C.G., Self, S., (1982). The volcanic explosivity index (VEI): an estimate of explosive magnitude for historical volcanism. Journal of Geophysical Research. 87, 1231e1238. Pallister, J., McNutt, S.R. (2015). Synthesis of Volcano Monitoring, Chapter 66 of Encyclopaedia of Volcanoes, 2nd Edition, Sigurdsson, H., B. Houghton, S.R. McNutt, H. Rymer, and J. Stix (eds.), Elsevier, p. 1151-1171. Pilger, C., Gaebler, P., & Ceranna, L. (2018). Real-time infrasound processing with the PMCC algorithm and a dedicated processing pipeline. In CTBT: Science and Technology 2017 Conference (SnT2017). CTBTO Preparatory Commission. Ripepe, M., & Marchetti, E. (2002). Array tracking of infrasonic sources at Stromboli volcano. Geophysical Research Letters , 29 (22), 2076, doi:10.1029/2002GL01545 Ripepe, M., E. Marchetti, and G. Ulivieri (2007), Infrasonic monitoring at Stromboli volcano during the 2003 effusive eruption: Insights on the explosive and degassing process of an open conduit system, J. Geophys. Res., 112, B09207, doi:10.1029/2006JB004613 Siebert, L., Simkin, T., & Kimberly, P. (2010). Volcanoes of the World. University of California Press. Tsuya, H., 1955. Geological and petrological studies of volcano Fuji, V. Bulletin of the Earthquake Research Institute, Tokyo 33, 341 and 383. Vergoz, J., Hupe, P., Listowski, C., Le Pichon, A., Garcés, M. A., Marchetti, E., et al. (2022). IMS observations of infrasound and acoustic-gravity waves produced by the January 2022 volcanic eruption of Hunga, Tonga:A global analysis. Earth andPlanetary Science Letters, 591, 117639. https://doi.org/10.1016/j.epsl.2022.117639 Walker, G. P. L. (1973). Explosive volcanic eruptions - a new classification scheme. Geologische Rundschau, 2:431–446. Walker, G. P. L. (1980). The Taupo pumice: product of the most powerful known (ultraplinian) eruption? Journal of Volcanology and Geothermal Research, 8:69–94. Wilson, D. K. (2003). The sound-speed gradient and refraction in the near-ground atmosphere. The Journal of the Acoustical Society of America, 113. 750-757. https://doi.org/10.1121/1.1532028. Yokoyama, I. (1981). A geophysical interpretation of the 1883 Krakatau eruption. Journal of Volcanology and Geothermal Research. 9, 359–378. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 May, 2026 Reviews received at journal 20 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 31 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9281628","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619572764,"identity":"ebedae38-1ead-4b5e-bedd-e620c88d225f","order_by":0,"name":"Sandro Matos","email":"data:image/png;base64,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","orcid":"","institution":"Instituto de Investigação em Vulcanologia e Avaliação de Riscos (IVAR), Universidade dos Açores","correspondingAuthor":true,"prefix":"","firstName":"Sandro","middleName":"","lastName":"Matos","suffix":""},{"id":619572765,"identity":"2481137a-20cc-411a-9990-0d3837f0cb6b","order_by":1,"name":"Paola Campus","email":"","orcid":"","institution":"Dipartimento di Scienze della Terra, Università degli Studi di Firenze","correspondingAuthor":false,"prefix":"","firstName":"Paola","middleName":"","lastName":"Campus","suffix":""},{"id":619572766,"identity":"b9338d90-6d36-4b79-9b29-7b655b953ba7","order_by":2,"name":"Maurizio Ripepe","email":"","orcid":"","institution":"Dipartimento di Scienze della Terra, Università degli Studi di Firenze","correspondingAuthor":false,"prefix":"","firstName":"Maurizio","middleName":"","lastName":"Ripepe","suffix":""},{"id":619572767,"identity":"aa9cf83a-bb37-4978-818c-845f5d255cba","order_by":3,"name":"Linda Silva","email":"","orcid":"","institution":"Instituto de Investigação em Vulcanologia e Avaliação de Riscos (IVAR), Universidade dos Açores","correspondingAuthor":false,"prefix":"","firstName":"Linda","middleName":"","lastName":"Silva","suffix":""},{"id":619572768,"identity":"0ee98cef-14f8-4288-9355-949f398fd6f7","order_by":4,"name":"Nicolau Wallenstein","email":"","orcid":"","institution":"Instituto de Investigação em Vulcanologia e Avaliação de Riscos (IVAR), Universidade dos Açores","correspondingAuthor":false,"prefix":"","firstName":"Nicolau","middleName":"","lastName":"Wallenstein","suffix":""}],"badges":[],"createdAt":"2026-03-31 14:56:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9281628/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9281628/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106645010,"identity":"2a142fee-38b0-4ca3-9742-e06a8e4a7de0","added_by":"auto","created_at":"2026-04-10 19:46:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":149635,"visible":true,"origin":"","legend":"\u003cp\u003ea) Cross-bearing method approach schematic illustration. Solid lines refer to the volcano main azimuth, and the dashed lines refers to 5° deviation b) Great circle bearings from three IMS stations example, related to the Chirinkotan event in 2016 November 29\u003csup\u003eth\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9281628/v1/cb9b239cb6c20c6fbc70106a.png"},{"id":106645012,"identity":"9e000b4b-7906-455c-9ad9-fd256ff565d9","added_by":"auto","created_at":"2026-04-10 19:46:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155250,"visible":true,"origin":"","legend":"\u003cp\u003eAtmospheric profiles (temperature, wind speed, and sound speed) during the 26 November 2017 eruption of Agung volcano\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9281628/v1/dc4ca40eb4d1ad6482330e8d.png"},{"id":106645011,"identity":"fdfa48af-0ae4-46ad-9ef4-eb6ada68abc5","added_by":"auto","created_at":"2026-04-10 19:46:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":641701,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of effective sound speed ratio (a) and atmospheric attenuation (b) for the Agung eruption on 2017 November 26\u003csup\u003eth\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9281628/v1/9c3abb7305b0223d41f4945a.png"},{"id":106645014,"identity":"1c61b5dc-8f96-44ae-b4fe-4087d0b6c1fa","added_by":"auto","created_at":"2026-04-10 19:46:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":221616,"visible":true,"origin":"","legend":"\u003cp\u003eProcessing results for the Grímsvötn eruption (May 2011). IMS stations with valid data are presented by distance within 4,500 km. The red dashed line indicates the expected back-azimuth, while vertical lines represent celerities of 260 m/s (red) and 330 m/s (blue). Detections are plotted by frequency (Hz), and the green vertical line marks the event onset time (t₀).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9281628/v1/35ca78c5025adc6c64d0b750.png"},{"id":106727465,"identity":"ecf8d01d-aaaf-4102-97ca-4dca4434d2a8","added_by":"auto","created_at":"2026-04-12 18:39:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":149833,"visible":true,"origin":"","legend":"\u003cp\u003eChirinkotan 2016 November 29\u003csup\u003eth\u003c/sup\u003e eruption example: a) Three closest stations cross-bearing of the main back-azimuths and of 5° back-azimuths deviation. b) Ellipse of 50 km centred on volcano showing back-azimuths interpolation of best-fit reconstructed locations (red area) and LEB results locations (red crosses)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9281628/v1/b46c65b99f9fed074642a674.png"},{"id":106727464,"identity":"7d7d27a6-ff4b-4133-ae25-d5e28ae02eaf","added_by":"auto","created_at":"2026-04-12 18:39:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":482513,"visible":true,"origin":"","legend":"\u003cp\u003eWorld map showing the selected volcanoes and IMS infrasound stations (blue pentagrams). Volcanoes are classified with all eruptions detected (green triangles), partially detected eruptive periods (yellow triangles), and with no detections (red triangles).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9281628/v1/ca056933ecb79ee64d817de6.png"},{"id":106728517,"identity":"84a8f100-6334-441b-90fe-186ccf9e4014","added_by":"auto","created_at":"2026-04-12 18:43:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2974101,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9281628/v1/198b73cf-c481-40e7-961a-00e463747eb3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"2011-2020: A Decade of global volcanic events observations at the IMS Infrasound network","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAccording to the Smithsonian Institution Global Volcanism Program (GVP) database, 1,281 potentially active volcanoes are currently identified worldwide. Those volcanoes are mainly distributed along major tectonic plate boundaries and concentrated within four linear volcanic belts: the Circum-Pacific, the Mid-Oceanic Ridge, the Mediterranean-Himalayan and the East African Rift Valley (Lowman Jr., 1980). Although subaerial volcanoes represent approximately 10 to 20% of all volcanoes on Earth, they interact directly with the atmosphere and can pose significant hazards to the surrounding areas, many of which densely populated and so considered high-risk regions (Freire et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEruptive activity can be classified according to its magnitude using the Volcanic Explosivity Index (VEI; Newhall and Self, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), which describes eruptions in terms of the erupted volume (magnitude) and the eruption plume height (intensity). The VEI is widely used in volcanic studies and it was adopted by GVP in its catalogues covering volcanic activity over the last 10,000 years (Siebert et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditionally, eruptive activity is broadly classified according to eruption style into Hawaiian, Strombolian, Vulcanian, Sub-Plinian, Plinian and Ultra-Plinian types (Walker, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1973\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Bonadonna et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These styles reflect different eruption dynamics and generate a wide range of volcanic hazards. Effusive events, typically associated with Hawaiian activity, produce fluid lava flows or lava domes that mainly affect areas close to the vent. In contrast, more explosive styles (e.g., Vulcanian, Sub-Plinian, or Plinian), can generate sustained eruptive columns, expanding volcanic ash clouds, and pyroclastic density currents. Consequently, the impacts of volcanic eruptions may range from local (\u0026lt;\u0026thinsp;few kilometres from the source) to regional (\u0026lt;\u0026thinsp;few hundreds of km) scales. Considering the occurrence of, approximately, 50 to 80 eruptions per year, ash clouds produced by major volcanic explosive eruptions can be blown by winds and spread hundreds of kilometers away from the volcanic source, often crossing national and international borders. Eruptions with VEI equal and above 3 can represent a major concern for aviation safety, hazard mitigation, and global situational awareness. This led the International Civil Aviation Organization (ICAO) to establish 9 Volcanic Ash Advisory Centres (VAAC) that have the duty of identifying volcanic clouds and issuing timely hazard alerts to civil aviation. VAACs perform extensive use of satellite images and interact with volcano observatories worldwide (Evans, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; ICAO, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Due to the resemblance of volcanic and meteorological clouds, the rapid identification of explosive eruptions is crucial, as the eruptions might potentially inject ash in the atmosphere, causing severe impacts on aircrafts flying over the affected areas. Despite considerable advances in real-time monitoring networks, only a small fraction of the potentially active world\u0026rsquo;s volcanoes is continuously monitored in real time (Pallister and McNutt, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Many active volcanoes, particularly those located in remote regions, lack local ground-based monitoring systems. Here, remote-sensing techniques provide the only practical means of volcanic surveillance.\u003c/p\u003e \u003cp\u003eIn this context, large-scale volcanic eruptions have the potential to inject pressured gas and volcanic clasts into the atmosphere and generate shock and acoustic waves propagating over long distances, in some cases circulating around the Earth. Such phenomena have been documented for major eruptions including Krakatau in 1883 (Yokoyama, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), Bezymianny in 1956 (Murayama, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1968\u003c/span\u003e), Mount Saint Helens in 1980 (Bolt and Tanimoto, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Donn and Balachandran, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), and the Hunga Tonga-Hunga Ha\u0026rsquo;apai eruption on January 15th, 2022 (Matoza et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vergoz et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcoustic waves with frequencies lower than the acoustic threshold of the human ear, (~\u0026thinsp;20 Hz), are called infrasound waves (Campus and Christie, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Johnson and Ripepe, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fee and Matoza, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Due the reduced effect of attenuation, infrasound waves have long wavelengths ranging from tens of metres at 10 Hz to several tens of kilometres near 0.01 Hz that propagate for thousands of kilometres in the atmosphere (Drob et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Campus and Christie, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; de Groot-Hedlin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Three dominant atmospheric waveguides, established between the ground, high temperature and wind speed regions (troposphere, stratosphere and thermosphere) can be identified. In downwind conditions along the direction of propagation, these waves can travel for distances greater than 1,000 kilometres (Le Pichon et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), in case of 1) signals that are refracted in the troposphere (0\u0026ndash;16 km), mainly bound by the mid-latitude jet stream around the tropopause (~\u0026thinsp;10 km); 2) signals that are refracted in the stratosphere (10\u0026ndash;60 km) as the result of the wind and the temperature increase, induced by the presence of ozone; and 3) signals that are refracted in the thermosphere (110\u0026ndash;160 km) where infrasound attenuation is naturally stronger (Garc\u0026eacute;s et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Assink et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Tropospheric waveguides depend mainly on diurnal variations in temperature and wind speeds, while stratospheric waveguides are influenced by the presence of seasonal variations in stratospheric zonal winds (blowing from east to west and vice versa), thus indicating that long-range infrasound propagation depends predominantly from the stratospheric duct (de Groot-Hedlin et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In a horizontally stratified atmosphere, the wind's effects on the speed of sound can be explained through the effective speed of sound, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eeff\u003c/em\u003e\u003c/sub\u003e (Wilson, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Evers, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hupe, 2022). The effective sound speed ratio, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eeff\u0026minus;ratio\u003c/em\u003e\u003c/sub\u003e, can be defined, for a given layer, by the relation between the effective sound speed at a reference altitude z and the effective sound speed at ground level, (Le Pichon et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Refraction of a signal, propagated by an upward wind towards the ground is predicted in the condition of \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eeff\u0026minus;ratio\u003c/em\u003e\u003c/sub\u003e \u0026ge;1 (Wilson, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Le Pichon et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe state of the atmosphere strongly controls infrasound propagation through attenuation, refraction driven by sound-speed gradients, ground reflections, atmospheric turbulence, and wind-induced azimuthal deviations (de Groot-Hedlin, 2008). A propagating infrasound wave loses energy through absorption and geometrical spreading, is deflected by the wind, and is refracted according to its velocity profile. Despite these effects, efficient low-frequency ducting enables infrasound to propagate over long distances and be detected as small pressure fluctuations of a few millipascals (mPa) using arrays of highly sensitive pressure sensors (microbarometers).\u003c/p\u003e \u003cp\u003eSince 1990, infrasound-based technology has been included in the geophysical toolkit and incorporated by worldwide observatories for volcano monitoring, rapidly evolving from an academic research area into a usable, well-established, and valuable real-time monitoring tool (Ripepe \u0026amp; Marchetti, (GRL) 2002; Ripepe et al., (JGR) 2007; Cannata et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Matoza and Roman, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe development and establishment of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) IMS Infrasound network, designed with 60 stations and, currently consisting of 54 certified stations, has demonstrated the capability of detecting volcanic eruptions across a wide range of released energies at very large source-receiver distances (Christie and Campus, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Campus and Christie, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dabrowa et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), high temporal resolution data on ongoing activity at remote volcanoes (Matoza et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and global coverage for different scale volcanic eruptions (Dabrowa et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This global network can provide near\u0026ndash;real-time information on volcanic events and valuable long-range observations of activity at remote or poorly monitored volcanoes. Such information is particularly important for VAACs, as it supports the timely identification of explosive activity and potential ash emissions.\u003c/p\u003e \u003cp\u003eDespite the increasing use of infrasound for volcanic monitoring, the detection and characterization of explosive eruptions at large distances remain challenging, particularly for remote or poorly monitored volcanoes. Improving our understanding of long-range infrasound propagation and detection capabilities is, therefore, essential for enhancing global volcanic surveillance.\u003c/p\u003e \u003cp\u003eIn this framework, this study evaluates the effectiveness of remote detection of explosive volcanic activity using infrasound monitoring. An automatic detection algorithm has been applied to eruptions with VEI\u0026thinsp;\u0026ge;\u0026thinsp;3 reported in the GVP database between 2011 and 2020. The algorithm has been applied to recordings from IMS infrasound stations to identify coherent infrasonic signals potentially associated with explosive volcanic activity, taking advantage of the network\u0026rsquo;s global coverage to assess the performance of the detection algorithm worldwide. A distance of 4,500 km has been adopted to ensure that each volcano included in the study has been covered by at least three stations for analysis. The resulting detections have been cross validated against bulletins from the CTBTO International Data Centre (IDC): Standard Event List (SEL3), Reviewed Event Bulletin (REB) and Latest Event Bulletin (LEB) to assess the robustness of the detection algorithm.\u003c/p\u003e"},{"header":"2 Data and methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data sources\u003c/h2\u003e \u003cp\u003eA detection algorithm was developed to integrate three complementary data sources: volcanic eruption information from the GVP database, infrasound observations from the CTBT IMS network and atmospheric conditions derived from meteorological reanalysis datasets produced by the European Centre for Medium-Range Weather Forecasts (ECMWF).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Volcanic Eruption Dataset\u003c/h2\u003e \u003cp\u003eThe GVP maintains one of the most comprehensive databases of worldwide volcanic activity. The database provides information on volcano locations, eruption chronologies, eruptive behaviour or the VEI, based on reports updated on a daily, weekly and multi-monthly basis. Due to its global scope, the GVP database is widely used as a reference source for research on volcanic activity, risk assessment and statistical analysis of eruptive events. In this study, database was examined to identify volcanoes that experienced eruptive activity between 2011 and 2020. Only confirmed eruptions with a VEI\u0026thinsp;\u0026ge;\u0026thinsp;3 were considered for the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 IMS Infrasound Data\u003c/h2\u003e \u003cp\u003eThe IMS comprises a global network of 337 monitoring facilities that use four verification technologies: seismic (170 stations), hydroacoustic (11 stations), infrasound (60 stations), and radionuclide (80 stations and 16 laboratories). These stations are connected to the IDC in Vienna, enabling the continuous transmission of near\u0026ndash;real-time data through the Global Communications Infrastructure (GCI). Within the IMS, the infrasound network is designed to ensure the reliable detection and location of atmospheric explosions with yields greater than 1 kiloton TNT equivalent anywhere on Earth by at least two stations (Christie et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Le Pichon et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Green and Bowers, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For this study, the selection and processing of infrasound data has been based on data availability, data quality and regional relevance for the period 2011\u0026ndash;2020. The objective has been the identification of the stations with the highest potential to detect volcanic infrasound for each region and each volcano. Although the CTBTO IMS infrasound network consisted of 53 certified stations during the period of study, only data from 43 stations located within a source\u0026ndash;receiver distance up to 4,500 km have met the required criteria and have been included in the dataset. Therefore, nine stations have been not considered, due to distance constraints.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Atmospheric Data\u003c/h2\u003e \u003cp\u003eMeteorological reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) have been used to describe the atmospheric state at the time of each event. ERA-Interin and ERA5 reanalysis datasets have been used, providing global atmospheric fields such as temperature and horizontal wind components relevant for infrasound propagation. ERA-Interim provides data from 1979 to present, with a global spatial resolution of approximately 80 km in 60 vertical levels up to 0.1 hPa available in 6-hour analyses. Data from ERA5 is presented on a 31 km grid (0.28\u0026deg; \u0026times; 0.28\u0026deg;) and using 137 levels from the surface up to a height of 80 km (0.01 hPa) hourly available (Hersbach et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Data from the 60 levels of the atmospheric model, the temperature, the horizontal wind components and sound speed (adiabatic and effective speed of sound) have been retrieved and stored for all the discrete events.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Algorithm development\u003c/h2\u003e \u003cp\u003eAn automatic detection algorithm has been developed through three main workflow phases: (i) compilation of an explosive volcanic activity database, (ii) infrasound data processing and propagation analysis, and (iii) cross-validation with IDC bulletins.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Phase 1: GVP explosive activity compilation\u003c/h2\u003e \u003cp\u003eThe database has been examined to identify volcanoes with confirmed eruptive activity between 2011 and 2020, based on records reported in the GVP bulletins. The selection has been based on four criteria: (i) the eruptive volcano must have an assigned name; (ii) the eruption must be confirmed, excluding discredited or uncertain events; (iii) only activity occurring within the selected time window has been considered, even if the eruptive period began earlier; and (iv) eruptions must have VEI \u0026ge; 3. Following the selection phase, volcanoes have been grouped into predefined GVP zones according to their geographical and geodynamic setting. The terminology adopted follows the GVP classification to ensure consistency in the subsequent analysis.\u003c/p\u003e \u003cp\u003eA general overview of the selected volcanoes has been compiled, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e with an example of the Northwest Pacific Volcanic Region. The dataset includes key information, such as the GVP Number, Volcano Name, location, Eruption timing, VEI and Duration.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of selected volcano characteristics in the Northwest Pacific volcanic region.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGVP number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolcano_Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEruption period time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVEI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNorthwest Pacific Volcanic Region\u0026thinsp;\u0026minus;\u0026thinsp;9 volcanoes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e300250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBezymianny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e55,972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e160,595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2012 Feb 12\u0026ndash;2013 Jun 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 y, 4 m, 8 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2016 Dec 5\u0026ndash;2021 Feb 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 y, 1 m, 27 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e290360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChikurachki\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50,324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155,461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2015 Feb 16\u0026ndash;2015 Feb 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e290260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChirinkotan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2016 Nov 29\u0026ndash;2017 Apr 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 m, 9 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e300010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKambalny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017 Mar 24\u0026ndash;2017 Apr 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e300130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKarimsky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e54,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e159,443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017 Jun 4\u0026ndash;2018 Sep 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 y, 3 m, 26 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020 Apr 1\u0026ndash;2022 Aug 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 y, 4 m, 6 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e300260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKlyuchevskoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e56,056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e160,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2013 Aug 15\u0026ndash;2013 Dec 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 m, 5 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2015 Aug 28\u0026ndash;2018 Jul 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 y, 10 m, 16 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e290250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaikoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48,292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2019 Jun 22\u0026ndash;2019 Jul 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e300240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTolbachik\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55,832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60,326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2012 Nov 27\u0026ndash;2013 Sep 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 m, 9 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e300120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZhupanovsky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e53,589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e159,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2014 Jun 6\u0026ndash;2015 Aug 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 y, 2 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2015 Nov 28\u0026ndash;2016 Mar 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 m, 26 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2016 Nov 20\u0026ndash;2016 Nov 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs the duration of eruptive episodes varies significantly, an additional processing step has been required to identify discrete eruptive events suitable for global infrasound analysis. This procedure has included identifying the eruptive periods from the GVP database, selecting associated discrete events with VEI\u0026thinsp;\u0026ge;\u0026thinsp;3, and determining the onset time (t₀) of each event (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExample of the catalogue of selected eruptive events and corresponding onset times (t₀).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolcano_Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeruption period time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEpisod period time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDate (dd.mm.yyy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et0 (UTC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVEI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eBezymianny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2012 Feb 12\u0026ndash;2013 Jun 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2012 Feb 12\u0026ndash;2012 Jun 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08.03.2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21:40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2012 Jul 29\u0026ndash;2013 Jun 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e01.09.2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e2016 Dec 5\u0026ndash;2021 Feb 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2016 Dec 5\u0026ndash;2017 Apr 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.12.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e09.03.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e03:23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017 Jun 9\u0026ndash;2017 Oct 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.06.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e04:53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017 Dec 18\u0026ndash;2018 Nov 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.12.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e03:55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2019 Jan 15\u0026ndash;2019 Nov 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.01.2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16:10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.03.2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17:30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020 Aug 26\u0026ndash;2021 Feb 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.10.2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20:22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo ensure consistent processing, the script has applied logical checks during the iteration. If no additional events were identified within a given eruptive period, the analysis has proceeded to the next eruptive period or volcano. Once all volcanoes have been processed, the Events List has been saved in a structured format. This catalogue forms the basis for the correlation phase, in which the onset time of each event (t₀) is compared with the infrasound detections recorded by IMS stations to assess detectability, temporal consistency, and potential source-to-station associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Phase 2: IMS infrasound processing and propagation analysis\u003c/h2\u003e \u003cp\u003eIn general, an IMS infrasound station comprises an array of 4 to 15 elements (sites) spatially distributed over apertures of, approximately, ranged from 1 to 3 km, arranged in different geometric layouts. Each site includes a protected vault, equipped with a high sensitivity microbarometer, data acquisition systems (DAS) and communication equipment and a wind noise reduction system (WNRS). Meteorological parameters such as temperature, wind speed and wind direction are also recorded at one array element to characterize local background conditions. The sensors are designed to detect pressure variations of less than 1 mPa and operate over a wide temperature range. Wind noise reduction is achieved through pipe-array systems, typically arranged in a rosette configuration, which represent the current IMS standard (Christie et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Christie and Campus, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Marty et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Data from each site are transmitted to a central recording facility where they are buffered, formatted, digitally signed, and forwarded to the IDC through the GCI. For this study selected arrays have an average aperture of 2.15 km, with 4 to 10 elements per station, with main apertures spanning from 1.13 km (IS32) to 3.37 km (IS60). The daily volume of raw data analysed has been more than 508\u0026times;10\u003csup\u003e6\u003c/sup\u003e samples at a sampling rate of 20 Hz (Matos, 2026).\u003c/p\u003e \u003cp\u003eSignal processing has been performed using the Progressive Multi-Channel Correlation (PMCC) algorithm, an array-processing method designed to detect coherent low-amplitude acoustic waves embedded in non-coherent noise (Cansi, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The PMCC parameters have been adjusted to volcanic infrasound characteristics, typically transient and dominated by frequencies between 0.5 and 5 Hz (Le Pichon et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pilger et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo reduce false detections and ensure the coherence of signal association, three key parameters have been optimized: the duration of the correlation time window (WindowLength), the time shift between two successive windows (TimeStep), and the consistency threshold (Threshold consistency), which defines the maximum acceptable deviation before a detection pixel is rejected. Frequency-dependent settings have been implemented using 15 logarithmically spaced frequency bands between 0.07 and 5 Hz, with time-window lengths ranged from 150 s to 25 s, following a 1/f scaling and a 90% window overlap and Threshold consistency of 0.2 s.\u003c/p\u003e \u003cp\u003eFrom the array processing results, the apparent phase velocity is given by the time delay between coherent signals arrivals to the different sensors. The back-azimuth indicates the sensor-to-source bearing of a detected signal. When detections are available from at least two stations a potential source region can be estimated by applying a cross-bearing method. The more stations contribute to the detection, the more accurate the localisation will be.\u003c/p\u003e \u003cp\u003eUnlike other natural processes, where infrasound sources can vary in with time of day or season (e.g., microbarom or Mountain-Air Waves (MAW)), volcanoes have well-defined locations and their monitoring largely depends on the source-to-receiver propagation conditions. A back-azimuth (θ) tolerance of θ\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg; relative to the volcano\u0026rsquo;s reference has been applied (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), to account for uncertainties associated with back-azimuth, which may result from sensor malfunction, array response characteristics, or atmospheric wind conditions along the propagation path (Le Pichon et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor each volcano, a data sheet was prepared linking the volcano to the closest stations. Parameters such as source-receiver distance, back-azimuth and waveform propagation times (assuming speed of 0,34 km/s), have been calculated for all selected volcanoes. An example for Ambae volcano is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSource\u0026ndash;receiver distance, back-azimuth, and propagation time for Ambae volcano and the nearest IMS stations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmbae Volcano\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st _sta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2nd _sta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3rd _sta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4th _sta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5th _sta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6th _sta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7th _sta\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIMS Station\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI22FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI40PG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI36NZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI05AU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI07AU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI60US\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI39PW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance (Kms)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBack-azimuth (\u0026deg;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProp. Time (Hour)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e00:37:13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01:44:04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e02:51:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e02:54:57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e02:55:43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e03:08:09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e03:38:43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInfrasound propagation over long distances is strongly controlled by atmospheric conditions, including the direction and amplitude of vertical wind gradients and background temperature fields (Brown et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Drob et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Vertical wind gradients and temperature structures influence the formation of atmospheric waveguides that enable signals to propagate over large distances (Drob et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). It is therefore important to know the atmospheric conditions at the time of the event. To support this analysis, MATLAB\u0026reg; scripts were developed to process meteorological data (temperature, zonal and meridional winds) and to compute parameters such as the sound speed (adiabatic and effective speed of sound, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eERA-Interim and ERA5 datasets have been used to characterize atmospheric conditions, in this case the stratospheric temperature and horizontal wind fields. The retrieved parameters have been then used to calculate the effective speed of sound ratio \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{v}_{eff-ratio\\:}\\)\u003c/span\u003e\u003c/span\u003e and atmospheric attenuation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) at the locations of the events and the selected IMS stations (Le Pichon et al \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Phase 2 focuses on the detection script developed to correlate the onset times of volcanic events with infrasound detections recorded at IMS stations. The script uses two key inputs: (i) the volcanic event catalogue (Event List) obtained in Phase 1 and (ii) the station detection lists generated using the adapted PMCC algorithm (Station Bulletins). The detection workflow follows these steps:\u003c/p\u003e \u003cp\u003e \u003cb\u003e(1) Station selection\u003c/b\u003e \u0026ndash; For each volcano in the Event List, the script evaluates all IMS stations individually. The volcano-station distance is calculated (within 4,500 km and available data) and sorted in ascending order of distance.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(2) Event time window definition\u003c/b\u003e \u0026ndash; For each discrete event the onset time (t₀) is taken. The script then verifies whether valid detection data is available in the screened list. If no data is found, the script moves on to the next station. Otherwise, processing is carried out within a time window defined from 12 hours before (t_start) to 24 hours after the event onset (t_end).\u003c/p\u003e \u003cp\u003e \u003cb\u003e(3) Back-azimuth filtering\u003c/b\u003e \u0026ndash; The expected back-azimuth of the volcano (θₙ) for each station is calculated based on their geographical coordinates, and a directional tolerance range window of θₙ \u0026plusmn; 5\u0026deg; is applied.\u003c/p\u003e \u003cp\u003e(\u003cb\u003e4) Detection screening\u003c/b\u003e - Station detection lists are analysed to identify detections falling within both the defined time window and azimuth range.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(5) Event association\u003c/b\u003e - The screened detections are automatically saved and associated with the specific event and station. These outputs are later used in the interpretation and evaluation phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Phase 3: Cross-validation with IDC bulletins (SEL3, REB, LEB)\u003c/h2\u003e \u003cp\u003eAt the IDC, the waveform received from the infrasound station are subjected to quality control verifications and then processed by the DFX-PMCC (Data Feature eXtraction-Progressive Multi-Channel Correlation) application technology (Branchet et al., 2010) based on the PMCC algorithm (Cansi, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Incoming arrivals are used as input for the network processing performed through the Global Association (GA) software, where events are built up from associated arrivals and reported in automatic bulletins (SEL2 and SEL3).\u003c/p\u003e \u003cp\u003eFollowing this, analyst review process is summarised in the LEB, and event definition criteria are then applied to produce the REB. Events that don't fulfil REB's event definition criteria (\u003cem\u003ee.g.\u003c/em\u003e, minimum number of primary IDC station defining arrivals associated with them) are included in the LEB but are not listed in the REB.\u003c/p\u003e \u003cp\u003eThe selected events were stored in text files, with associated parameters including Event Identification (Event Id), Event Location (Location), Date, Time, RMS, Latitude (Lat), Longitude (Lon), Azimuth of event (Az), Number of stations (Nsta), Detected stations code (Sta) and their distance (Dist), Phase, Arrival time (Time), Back-Azimuth (Baz), Slowness (Slow), SNR, Signal Amplitude (Amp), among others (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These data were subsequently used to compile a catalogue of events to be tentatively correlated with volcanic events identified on Phase 1 and detections obtained in Phase 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExample of a LEB event information, for Ambae volcanic activity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEvent Id\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLong\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNsta\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16493999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eVANUATU ISLANDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018-10-30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e07:43:05.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e193.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-16.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e168.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDist\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBaz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSNR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAmp\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI22FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e08:15:45.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e320.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI40PG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e09:40:10.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e271.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI07AU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11:04:50.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e310.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI21FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13:10:41.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e260.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe IDC CTBTO bulletins were collected to assess their ability to register and provide information on events that could be potentially associated with the events detected and processed in the previous phases. In this context, among the three IDC bulletins, the LEB was used as a reference dataset to validate the robustness of the detection algorithm and to assess its potential as a first-order approach for early warning notifications.\u003c/p\u003e \u003cp\u003eAs a final step, detections has been correlated with the reported events of the IDC bulletins. The number of IMS station detections that can be associated with volcanic events, their validation or not through IDC bulletins will indicate how effective the assessment tool can be applied as a reference for detection response and as an early warning notification system (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cp\u003eDuring the studied period (2011\u0026ndash;2020), 360 eruptions were recorded from 138 volcanoes (GVP). Among these, 67 eruptions from 46 volcanoes had VEI\u0026thinsp;\u0026ge;\u0026thinsp;3 and were selected for this study (53 events were ranked with VEI\u0026thinsp;=\u0026thinsp;3, 13 events with VEI\u0026thinsp;=\u0026thinsp;4 and 1 event with 1 VEI\u0026thinsp;=\u0026thinsp;5). Out from the 67 confirmed eruptions, 186 discrete events were identified. Of the 67 eruptive time intervals, 71.6% (n\u0026thinsp;=\u0026thinsp;48) were successfully identified, while 28.4% (n\u0026thinsp;=\u0026thinsp;19) were not. At the event scale, 54.8% (n\u0026thinsp;=\u0026thinsp;102) of the 186 volcanic events were detected, compared to 45.2% (n\u0026thinsp;=\u0026thinsp;84) that were not. The different discrete eruptive events identified show the episodic and complex dynamics of the volcanic activity.\u003c/p\u003e \u003cp\u003eThe selected volcanoes were grouped into 11 pre-defined GVP zones according to their geography, geodynamic settings, classification on the effectiveness of the algorithm in identifying discrete events (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor 27 volcanoes, all reported eruptions were successfully detected by the algorithm, comprising 21 VEI 3 events, 10 VEI 4 events, and 1 VEI 5 event, including eruptions at Tolbachik (2012), Villarica (2014), Chirinkotan (2016), and Cleveland (2020), highlighting the system\u0026rsquo;s capability to identify diverse eruptive events under different geographic and atmospheric contexts, pointing to favourable conditions for long-distance infrasound propagation and effective network coverage. On the other hand, for 8 volcanoes only a subset of eruptions was detected by the algorithm, probably reflecting variations in the eruption intensities, in the atmospheric conditions at the time of the event, or in the stations network layout used. For the remaining 11 volcanoes, the algorithm did not detect any events (9 VEI 3 and 2 VEI 4), despite the confirmed activity. Such cases can be explained by factors such as low-energy eruption, complex propagation patterns, or sparse network coverage (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of selected volcanoes by region, eruptive periods, and associated detections.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolcanic Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolcano_Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEruption time period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEruption detected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall Detection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e \u003cp\u003eNorthwest Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBezymianny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2012 Feb 12\u0026ndash;2013 Jun 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016 Dec 5\u0026ndash;2021 Feb 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChikurachki\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015 Feb 16\u0026ndash;2015 Feb 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChirinkotan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016 Nov 29\u0026ndash;2017 Apr 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKambalny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017 Mar 24\u0026ndash;2017 Apr 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKarymsky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017 Jun 4\u0026ndash;2018 Sep 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParcially Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020 Apr 1\u0026ndash;2022 Aug 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKlyuchevskoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2013 Aug 15\u0026ndash;2013 Dec 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaikoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019 Jun 22\u0026ndash;2019 Jul 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTolbachik\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2012 Nov 27\u0026ndash;2013 Sep 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZhupanovsky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 Jun 6\u0026ndash;2015 Aug 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParcially Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015 Nov 28\u0026ndash;2016 Mar 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016 Nov 20\u0026ndash;2016 Nov 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e \u003cp\u003eWestern Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsosan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016 Oct 7\u0026ndash;2016 Nov 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKirishimayama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011 Jan 19\u0026ndash;2011 Sep 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParcially Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018 Mar 1\u0026ndash;2018 Jun 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eKuchinoerabujima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015 May 29\u0026ndash;2015 Jun 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParcially Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018 Oct 21\u0026ndash;2019 Feb 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020 Jan 11\u0026ndash;2020 May 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOntakesan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 Sep 27\u0026ndash;2014 Oct 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSoputan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011 Jul 3\u0026ndash;2011 Aug 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eParcially Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2012 Aug 26\u0026ndash;2012 Sep 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015 Jan 6\u0026ndash;2015 Mar 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016 Jan 2\u0026ndash;2016 Feb 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018 Oct 2\u0026ndash;2018 Dec 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020 Jan 12\u0026ndash;2020 Jan 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSouthwest Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmbae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017 Sep 6\u0026ndash;2018 Oct 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eManam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010 Aug 10 \u0026minus;\u0026thinsp;2013 Dec 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParcially Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 Jun 29\u0026ndash;2018 Jan 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRabaul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 Jul 7\u0026ndash;2014 Sep 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTinakula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017 Oct 21\u0026ndash;2017 Oct 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUlawun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019 Jun 26\u0026ndash;2019 Oct 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWolf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015 May 25\u0026ndash;2015 Jul 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eSunda-Banda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017 Nov 21\u0026ndash;2019 Jun 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKelud\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 Feb 13\u0026ndash;2014 Feb 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKrakatau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018 Jun 18\u0026ndash;2020 Apr 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMerapi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2013 Nov 18\u0026ndash;2013 Nov 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParcially Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 Mar 9\u0026ndash;2014 Apr 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018 May 11\u0026ndash;2020 Jun 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaluweh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2012 Oct 8\u0026ndash;2013 Oct 31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSangeang Api\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 May 30\u0026ndash;2015 Nov 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSemeru\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017 Jun 6\u0026ndash;2024 Dec 19 (ongoing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSinabung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2013 Sep 15\u0026ndash;2018 Jun 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019 Feb 6\u0026ndash;2019 Jun 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNabro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011 Jun 13\u0026ndash;2012 Jun 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtlantic Ocean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrimsvotn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011 May 21\u0026ndash;2011 May 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEtna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010 Aug 25\u0026ndash;2013 Apr 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2013 Sep 3\u0026ndash;2022 Jun 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBogoslof\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016 Dec 20\u0026ndash;2017 Aug 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCleveland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020 Jun 1\u0026ndash;2020 Jun 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePavlof\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2013 May 13\u0026ndash;2013 Jun 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eParcially Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 May 31\u0026ndash;2014 Jun 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 Nov 12\u0026ndash;2014 Nov 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016 Mar 27\u0026ndash;2016 Jul 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShishaldin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019 Jul 23\u0026ndash;2020 May 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVeniaminof\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2013 Jun 13\u0026ndash;2013 Oct 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMiddle America-Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2013 Jan 6\u0026ndash;2017 Mar 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSan Miguel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2013 Dec 29\u0026ndash;2014 Jul 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurrialba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015 Mar 8\u0026ndash;2019 Dec 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eSouth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalbuco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015 Apr 22\u0026ndash;2015 May 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNevado del Ruiz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2012 Feb 22\u0026ndash;2013 Jul 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePuyehue Cordon Caulle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011 Jun 4\u0026ndash;2012 Apr 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSabancaya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016 Nov 6\u0026ndash;2024 Dec 19 (ongoing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNdet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTungurahua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011 Apr 20\u0026ndash;2011 May 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011 Nov 27\u0026ndash;2012 Sep 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2012 Dec 14\u0026ndash;2016 Mar 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVillarrica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014 Dec 2 \u0026minus;\u0026thinsp;2024 Dec 13 (ongoing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, these results point out the heterogeneous performance of the detection algorithm at global scale and underline the importance of considering the source's features, the network's configuration and the atmosphere's status at the time of the event.\u003c/p\u003e \u003cp\u003eAnalysis of the IDC automatic bulletin (SEL3) identified 13 events as potentially related to the eruptive periods under study. These events were associated to 10 active volcanoes in five of the eleven GVP regions, while no events were identified in association with those located in the Western Pacific, Eastern Pacific, Atlantic Ocean, North America or Middle America\u0026ndash;Caribbean regions.\u003c/p\u003e \u003cp\u003eBy analysing the LEB bulletin, a larger number of associations were identified, with 52 events as potentially related to the eruptive periods of 28 volcanoes, representing approximately 61% of the volcanoes analysed. No events were found to be associated with volcanoes in the Eastern Pacific Region (Wolf volcano) or in the Central America\u0026ndash;Caribbean region (Colima, San Miguel and Turrialba volcanoes).\u003c/p\u003e \u003cp\u003eAccording with IDC criteria, events that do not fulfil the REB definition criteria - such as those not detected by at least three primary stations or with a cumulative weight of less than 4.6 - are kept in the LEB but are excluded from the published REB. Consequently, 31 events were identified as potentially related to eruptive periods of 21 volcanoes. No events were identified for volcanoes located in the Southwest Pacific, Eastern Pacific, or Middle America\u0026ndash;Caribbean volcanic regions.\u003c/p\u003e \u003cp\u003eThe comparison between IDC bulletins highlights significant differences in detection capability, with LEB providing the highest number of potential associations, demonstrating its value as an independent reference for validating the detection algorithm used in this work and assessing its performance.\u003c/p\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eThe results demonstrated the high reliability of the algorithm in multiple years, particularly in 2011, 2014, 2015 and 2019 where detection rates exceeding 87% and reaching 100% in 2019. The results indicate a high success rate in eruptive activity detection, highlighting the robustness of the algorithm in identifying sustained volcanic activity. This consistent performance supports the suitability of the system for detecting signals associated with long-lasting or high-energy eruptions, even at great distances from the source. In contrast, detections associated to discrete events are more variable, reflecting the episodic nature of volcanic activity and the susceptibility of success with short-lived signals, due to changes in source strength and atmospheric propagation conditions.\u003c/p\u003e \u003cp\u003eDifferent types of volcanic activity can produce variable energy levels which can generate and propagate infrasound waves. Despite their low explosiveness and limited long-range propagation, Hawaiian-style eruptions were successfully detected by the algorithm in a few cases, including the lava fountaining at Mount Etna volcano (2013 and 2016). Strombolian activity at Villarrica volcano (2015) was detected at distances of approximately 3,700 km (stations IS42 and I13CL), demonstrating the capability of the network to capture moderate explosive activity. However, Strombolian and Vulcanian gas-driven outbursts, which dominate discrete events associated with VEI 3, show more variable detection performance, reflecting variability in source strength and propagation conditions.\u003c/p\u003e \u003cp\u003eFor large explosive events (VEI 4) that were more consistently detected, the algorithm successfully identified 11 out of 13 c eruptions, including Grimsv\u0026ouml;tn and Nabro (2011), Kelud (2014), and Taal (2020). On the other hand, two eruptions, Wolf (2015) and Semeru (2017), were not identified by the algorithm. The network coverage near those volcanoes (e.g., the nearest station to Wolf volcano is located approximately 3,000 km away, once I20EC was not yet operational) and unfavourable atmospheric conditions at the time of the events, may explain the lack of detections. A VEI 5 Plinian eruption, associated to Puyehue Cordon Caulle event (June 4th, 2011), was successfully detected.\u003c/p\u003e \u003cp\u003eDetection trends show a clear hemisphere and seasonal tendency in accordance with the atmosphere propagation effects. In the Northern Hemisphere, 31 eruptions (66%) with 79 discrete events (77.5%) were identified mainly during winter periods, when stratospheric wind patterns enhance long-distance infrasound propagation. The Northwest Pacific is the most active region, followed by the Western Pacific, reflecting both high volcanic activity and favourable propagation paths. In contrast, regions such as North America, Europe and Central America\u0026ndash;Caribbean show detections during the Northern Hemisphere summer months, which may be related by a combination of network configuration and seasonal variability in atmospheric propagation conditions. Other regions, including East Africa and the Atlantic Ocean, contribute minimally, with 1 eruption or event per region.\u003c/p\u003e \u003cp\u003eA similar seasonal pattern is observed in the Southern Hemisphere with 17 eruptions (34%) with 23 discrete events (22.5%), with detections clustered during winter, particularly in the South-West Pacific and South America, consistent with propagation enhancements expected under favourable stratospheric wind patterns: in the Sunda-Banda arc region detections occurred across the Southern summer, Southern winter and even isolated detections during the Northern winter and Northern summer, reflecting its location closer to equatorial regions and its exposure to both seasonal hemispheric seasonal variations. Also, in equatorial region, no activity was detected in the Eastern Pacific, possibly due to a combination of lower eruptive activity during the analysis period and long oceanic distances to the nearest stations, which potentially limited the detection capability.\u003c/p\u003e \u003cp\u003eIn addition to source characteristics and network layout, atmospheric conditions play a critical role in controlling infrasound propagation and detection capability. Favourable propagation paths, especially stratosphere ducts, improves the propagation of long-distance signals, while unfavourable wind and temperature can attenuate or deflect the acoustic waves. Variations in the effective sound speed ratio (\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eeff\u0026minus;ratio\u003c/em\u003e\u003c/sub\u003e) and atmospheric attenuation significantly influence whether signals are refracted back to the ground or are lost at higher altitudes. All these factors provide a plausible explanation for the variability in detection performance observed in this work, including events where moderate or even energetic eruptions failed to be detected. Seasonal and regional variations in stratospheric winds, as well as local atmospheric conditions at the time of each event, can contribute to substantial variability in propagation efficiency.\u003c/p\u003e"},{"header":"5 CONCLUSION","content":"\u003cp\u003eThe results demonstrate that the proposed algorithm provides reliable detection of infrasound signals generated by explosive volcanic activity across periods of high and low eruptive frequency. The algorithm has demonstrated higher detection capabilities for more energetic eruptions but also identifies points for improvement in dealing with the complexity and diversity of volcanic activity worldwide.\u003c/p\u003e \u003cp\u003eThe IMS network provides relatively uniform global coverage, with a mean of six stations contributing to detections per volcano. Detection patterns show a clear hemispheric and seasonal dependence, consistent with known variations in atmospheric propagation conditions, with enhanced detectability during winter months in each hemisphere.\u003c/p\u003e \u003cp\u003eOverall, the developed detection algorithm has proven to be an effective instrument for detecting infrasound events in a wide range of volcanic regions worldwide and demonstrates its potential for global infrasound-based volcanic monitoring. Its conception, represents a practical and scalable framework for routine monitoring, can be adapted to specific volcanoes or regions through the incorporation of atmospheric information. This adaptability and flexibility make it suitable for integration into early warning systems and real-time monitoring programs based on IMS data.\u003c/p\u003e \u003cp\u003eFuture improvements should focus on the integration of eruption source parameters and regional propagation conditions to enhance detection performance across a broader spectrum of eruptive styles. Such advances will strengthen the role of infrasound monitoring as a key component of global volcanic surveillance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: SM,NW; Data curation: SM; Formal analysis: SM, LS; Funding acquisition: SM, NW; Supervision: NW; Investigation: SM; Methodology: SM, NW, PC, MR;Software: SM, LS; Visualization: SM, LS; Writing\u0026mdash;original draft: SM; Writing\u0026mdash;editing and review: All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by FCT, I.P., the Portuguese national funding agency for science, research, and technology, under the Projects UID/00643/2023 and by Instituto de Investiga\u0026ccedil;\u0026atilde;o em Vulcanologia e Avalia\u0026ccedil;\u0026atilde;o de Riscos (IVAR), Universidade dos A\u0026ccedil;ores. SM was supported by FCT \u0026ndash; Foundation for Science and Technology by PhD Grant UI/BD/151384/2021 (https://doi.org/10.54499/UI/BD/151384/2021). SM was also supported by CTBTO contract No. 2012-1694.The views expressed herein are those of the authors and not necessarily reflect the views of the CTBTO Preparatory Commission.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eGlobal Volcanism Program (GVP) data are available from the Smithsonian Institution Global Volcanism Program database (https://volcano.si.edu/).For the atmospheric models we used the CDS API tools freely provided by European Centre for Medium-RangeWeather Forecasts (ECMWF) to obtain the necessary ERA5 reanalysis profiles, publicly available foracademic research (https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5).IMS data are available on request from the CTBTO Preparatory Commission for scientific purposes through the virtual Data Exploitation Centre (vDEC): https://www.ctbto.org/specials/vdec/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAssink J. D., Waxler R., Drob D. (2012). On the sensitivity of infrasonic travel times in the equatorial region to the atmospheric tides. Journal of Geophysical Research, 117. D01110, doi:10.1029/2011JD016107.\u003c/li\u003e\n\u003cli\u003eBolt, B. A., \u0026amp; Tanimoto, T. (1981). Atmospheric oscillations after the May 18, 1980 eruption of Mount St. Helens, Eos Trans. AGU, 62(23), 529\u0026ndash;530.\u003c/li\u003e\n\u003cli\u003eBonadonna, C., Cioni, R., Costa, A., Druitt, T., Phillips, J., Pioli, L., Andronico, D., Harris, A., Scollo, S., Bachmann, O., Bagheri, G., Biass, S., Brogi, F., Cashman, K., Dominguez, L., D\u0026uuml;rig, T., Galland, O., Giordano, G., Gudmundsson, M., \u0026hellip; Wallenstein, N. (2016). MeMoVolc report on classification and dynamics of volcanic explosive eruptions. Bulletin of Volcanology, 78(11), 84. https://doi.org/10.1007/s00445-016-1071-y\u003c/li\u003e\n\u003cli\u003eBrachet N, Brown D, Le Bras R, Mialle P, Coyne J (2010). Monitoring the earth\u0026rsquo;s atmosphere with the global IMS infrasound network. In: Le Pichon A., Blanc E., Hauchecorne A. (eds.) Infrasound Monitoring for Atmospheric Studies. Springer, Dordrecht, pp. 73\u0026ndash;114.\u003c/li\u003e\n\u003cli\u003eBrown D., Katz C., Le Bras R., Flanagan M., Wang J., Gault A. (2002). Infrasonic signal detection and source location at the prototype international data centre\u0026rdquo;. \u003cem\u003ePure and Applied Geophysics\u003c/em\u003e, 159: 1081\u0026ndash;1125. https://doi.org/10.1007/978-3-0348-8144-9_10\u003c/li\u003e\n\u003cli\u003eCampus, P. \u0026amp; Christie, D. (2010). Worldwide Observations of Infrasonic Waves. In: Le Pichon A., Blanc E., Hauchecorne A. (eds.) Infrasound Monitoring for Atmospheric Studies. Springer, Dordrecht. DOI: https:// doi10.1007/978-1-4020-9508-5_6.\u003c/li\u003e\n\u003cli\u003eCannata, A., Montalto, P., Privitera, E., Russo, G., \u0026amp; Gresta, S. (2009). Tracking eruptive phenomena by infrasound: May 13, 2008 eruption at Mt. Etna. Geophysical Research Letters, 36(5). https://doi.org/10.1029/2008GL036738\u003c/li\u003e\n\u003cli\u003eCansi Y (1995). An automatic seismic event processing for detection and location: the PMCC method. Geophysical Research Letters. 22(9):1021\u0026ndash;1024.\u003c/li\u003e\n\u003cli\u003eChristie, D. R., Vivas Veloso, J. A., Campus, P., Bell, M., Hoffmann, T., Langlois, A., Martysevich, P., Demirovic, E. \u0026amp; Carvalho, J. (2001). Detection of atmospheric nuclear explosions: The infrasound component of the International Monitoring System. Kerntechnik. 66. 96-101. 10.1515/kern-2001-0058.\u003c/li\u003e\n\u003cli\u003eChristie, D.R. \u0026amp; Campus, P. (2010) The IMS Infrasound Network: Design and Establishment of Infrasound Stations. In: Le Pichon A., Blanc E., Hauchecorne A. (eds.) Infrasound Monitoring for Atmospheric Studies. Springer, Dordrecht. DOI: https:// doi10.1007/978-1-4020-9508-5_6.\u003c/li\u003e\n\u003cli\u003eDabrowa, A. L., Green, D.N., Rust, A.C., Phillips, J.C. (2011). A global study of volcanic infrasound characteristics and the potential for long-range monitoring. Earth and Planetary Science Letters. 310. 369-379. DOI:10.1016/j.epsl.2011.08.027.\u003c/li\u003e\n\u003cli\u003ede Groot-Hedlin, C.D., Hedlin, M.A., Walker, K.T., Drob, D.P. \u0026amp; Zumberge, M.A. (2008). Evaluation of infrasound signals from the shuttle Atlantis using a large seismic network. The Journal of the Acoustical Society of America, 124 3, 1442-51.\u003c/li\u003e\n\u003cli\u003ede Groot-Hedlin C. D., Hedlin, M. A. H. \u0026amp; Drob, D. P. (2010). Atmospheric Variability and Infrasound Monitoring. In: Le Pichon A., Blanc E., Hauchecorne A. (eds) Infrasound Monitoring for Atmospheric Studies. Springer, Dordrecht. DOI: https://doi.org/10.1007/978-1-4020-9508-5_15.\u003c/li\u003e\n\u003cli\u003ede Groot-Hedlin C., Hedlin, M. A. H. \u0026amp; Walker, K. (2011). Finite difference synthesis of infrasound propagation through a windy, viscous atmosphere: application to a bolide explosion detected by seismic networks. Geophysical Journal International. 185 (1). 305\u0026ndash;320. https://doi.org/10.1111/j.1365-246X.2010.04925.x.\u003c/li\u003e\n\u003cli\u003eDonn, W. \u0026amp; Balachandran, N. (1981). Mount St. Helens eruption of 18 May 1980: air waves and explosive yield. Science, 213:539\u0026ndash;541.\u003c/li\u003e\n\u003cli\u003eDrob, D. P., Picone J. M., Garc\u0026eacute;s M. (2003). Global morphology of infrasound propagation. Journal of Geophysical Research. 108. D2. 4680. DOI:10/1029/2002JD003307\u003c/li\u003e\n\u003cli\u003eDrob, D. P., Emmert J. T., Crowley G., Picone J. M., Shepherd G. G., Skinner W., Hays P., Niciejewski R.J., Larsen M., She C. Y., Meriwether J. W., Hernandez G., Jarvis M. J., Sipler D.P., Tepley C. A., O\u0026rsquo;Brien M. S., Bowman J. R., Wu Q., Murayama Y., Kawamura S., Reid I. M. \u0026amp; Vincent R. A. (2008). An empirical model of the Earth\u0026rsquo;s horizontal wind fields: HWM07. Journal of Geophysical Research. 113. A12304. doi:10.1029/2008JA013668.\u003c/li\u003e\n\u003cli\u003eDrob, D. P., Broutman D., Hedlin M. A., Winslow N. W. \u0026amp; Gibson R. G. (2013). A method for specifying atmospheric gravity wavefields for long-range infrasound propagation calculations. Journal of Geophysical Research: Atmospheres. 118. 3933\u0026ndash;3943. DOI:10.1029/2012JD018077.\u003c/li\u003e\n\u003cli\u003eEvans, J. E. (1991). Development of a real-time ATC volcanic ash advisory system based on the future aviation weather system: in Casadevall, T. J., (ed.), Volcanic ash and aviation safety: Proceedings of the first international symposium on volcanic ash and aviation safety, U.S. Geological Survey Bulletin. 2047, p. 157-161.\u003c/li\u003e\n\u003cli\u003eEvers, L. (2008). The inaudible symphony: on the detection and source identification of atmospheric infrasound. [Dissertation (TU Delft), Delft University of Technology]. TU Delft, DEOS.\u003c/li\u003e\n\u003cli\u003eFee, D. \u0026amp; Matoza, R. S. (2013). An overview of volcano infrasound: From Hawaiian to Plinian, local to global. Journal of Volcanology and Geothermal Research. 249. 123-139. https://doi.org/10.1016/j.jvolgeores.2012.09.002.\u003c/li\u003e\n\u003cli\u003eFreire, S., Florczyk, A. J., Pesaresi, M., \u0026amp; Sliuzas, R. (2019). An Improved Global Analysis of Population Distribution in Proximity to Active Volcanoes, 1975\u0026ndash;2015. ISPRS International Journal of Geo-Information, 8 (8), 341. https://doi.org/10.3390/ijgi8080341.\u003c/li\u003e\n\u003cli\u003eGarc\u0026eacute;s M., Bass H., Drob D., Hetzer C., Hedlin M., Le Pichon A., Lindquist K., North R., Olson, J. (2004). Forensic studies of infrasound from massive hypersonic sources. EOS 85(43):433.\u003c/li\u003e\n\u003cli\u003eGlobal Volcanism Program, 2025. [Database] Volcanoes of the World (v. 5.3.4; 30 Dec 2025). Distributed by Smithsonian Institution, compiled by Venzke, E. https://doi.org/10.5479/si.GVP.VOTW5-2025.5.3\u003c/li\u003e\n\u003cli\u003eGreen, D.N., Bowers, D. (2010). Estimating the detection capability of the International Monitoring System infrasound network. Journal of Geophysical Research, 115:D18116. https://doi.org/10.1029/ 2010JD014017.\u003c/li\u003e\n\u003cli\u003eHersbach, H., Bell, B., Berrisford, P., Hirahara, S., Hor\u0026aacute;nyi, A., Mu\u0026ntilde;oz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., \u0026hellip; Th\u0026eacute;paut, J-N. (2020). ERA5 hourly data on model levels from 1940 to present [Conjunto de dados]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi.org\u003c/li\u003e\n\u003cli\u003eHupe, P., Ceranna, L., Le Pichon, A., Matoza, R. S., and Mialle, P. (2022). International Monitoring System infrasound data products for atmospheric studies and civilian applications, Earth System Science Data, 14, 4201\u0026ndash;4230, https://doi.org/10.5194/essd-14-4201-2022.\u003c/li\u003e\n\u003cli\u003eICAO (2023) Handbook on the international airways volcano watch (IAVW). Operational procedures and contact list. https:// www.icao. int/ airna vigat ion/ METP/ MOG% 20IAVW% 20Ref erence%20Doc uments/ Handb ook% 20on% 20the% 20IAV W,% 20Doc%209766. pdf. Accessed Jan 2026\u003c/li\u003e\n\u003cli\u003eJohnson, J. \u0026amp; Ripepe, M. (2011). Volcano infrasound: a review. Journal of Volcanology Geothermal Research, 206 (34): 61\u0026ndash;69. http://dx.DOI.org/10.1016/.2011.06.006.\u003c/li\u003e\n\u003cli\u003eLe Pichon, A., Blanc, E., Drob, D., Lambotte, S., Dessa, J., Lardy, M., Bani, P. \u0026amp; Vergniolle, S. (2005). Infrasound monitoring of volcanoes to probe high-altitude winds, Journal of Geophysical Research, 110, D13106, doi:10.1029/2004JD005587.\u003c/li\u003e\n\u003cli\u003eLe Pichon, A., J. Vergoz, P. Herry, and L. Ceranna (2008), Analyzing the detection capability of infrasound arrays in Central Europe, J. Geophys. Res., 113, D12115, doi:10.1029/2007JD009509.\u003c/li\u003e\n\u003cli\u003eLe Pichon A, Vergoz J, Blanc E, Guilbert J, Ceranna L, Evers L, Brachet N. (2009). Assessing the performance of the International Monitoring System infrasound network: geographical coverage and temporal variabilities. Journal of Geophysical Research, 114: D08112. https://doi.org/10.1029/ 2008JD010907.\u003c/li\u003e\n\u003cli\u003eLe Pichon, A., Ceranna, L., Vergoz, J. (2012). Incorporating numerical modeling into estimates of the detection capability of the IMS infrasound network. Journal of Geophysical Research, 117: D05121. https://doi. org/10.1029/2011JD016670.\u003c/li\u003e\n\u003cli\u003eLowman, Paul D \u0026amp; Goddard Space Flight Center. (1980). Global tectonic and volcanic activity of the last one million years / Paul D. Lowman. [cartographic material]. \u003c/li\u003e\n\u003cli\u003eMarty, Julien \u0026amp; Martysevich, Pavel \u0026amp; Kramer, A. \u0026amp; Haralabus, G. (2012). Engineering and development projects for the sustainment and enhancement of the IMS infrasound network. 11204-. Geophysical Research Abstracts, Vol. 14.EGU General Assembly 2012.\u003c/li\u003e\n\u003cli\u003eMatos, S. (2025). \u003cem\u003eContribution to real-time long-range erupting volcanoes monitoring based on infrasound\u003c/em\u003e, PhD thesis, Universidade dos A\u0026ccedil;ores, Portugal, 187pp., https://doi.org/10.54499/UI/BD/151384/2021. \u003c/li\u003e\n\u003cli\u003eMatoza, R. S., Vergoz, J., le Pichon, A., Ceranna, L., Green, D. N., Evers, L. G., Ripepe, M., Campus, P., Liszka, L., Kvaerna, T., Kjartansson, E., \u0026amp; H\u0026ouml;skuldsson, \u0026Aacute;. (2011). Long-range acoustic observations of the Eyjafjallaj\u0026ouml;kull eruption, Iceland, April-May 2010. Geophysical Research Letters, 38(6), n/a-n/a. https://doi.org/10.1029/2011GL047019\u003c/li\u003e\n\u003cli\u003eMatoza, R.S., Roman, D.C. (2022). One hundred years of advances in volcano seismology and acoustics. Bulletin of Volcanology, 84, 86. https://doi.org/10.1007/s00445-022-01586-0\u003c/li\u003e\n\u003cli\u003eMatoza, R. S., Fee, D., Assink, J. D., Iezzi, A. M., Green, D. N., Kim, K., ... \u0026amp; Witze, A. (2022). Atmospheric waves and global seismoacoustic observations of the January 2022 Hunga Tonga-Hunga Ha\u0026rsquo;apai eruption. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e377\u003c/em\u003e(6601), 95-100. doi.org\u003c/li\u003e\n\u003cli\u003eMurayama, N. (1968). Propagation of atmospheric pressure waves produced by the explosion of volcano Bezymianny of March 30, 1956 and transport of the volcanic ashes. Q. J. Seismol. 33, 1\u0026ndash;11 (PDF) Atmospheric pressure waves in the field of volcanology. \u003c/li\u003e\n\u003cli\u003eNewhall, C.G., Self, S., (1982). The volcanic explosivity index (VEI): an estimate of explosive magnitude for historical volcanism. Journal of Geophysical Research. 87, 1231e1238.\u003c/li\u003e\n\u003cli\u003ePallister, J., McNutt, S.R. (2015). Synthesis of Volcano Monitoring, Chapter 66 of Encyclopaedia of Volcanoes, 2nd Edition, Sigurdsson, H., B. Houghton, S.R. McNutt, H. Rymer, and J. Stix (eds.), Elsevier, p. 1151-1171.\u003c/li\u003e\n\u003cli\u003ePilger, C., Gaebler, P., \u0026amp; Ceranna, L. (2018). Real-time infrasound processing with the PMCC algorithm and a dedicated processing pipeline. In CTBT: Science and Technology 2017 Conference (SnT2017). CTBTO Preparatory Commission.\u003c/li\u003e\n\u003cli\u003eRipepe, M., \u0026amp; Marchetti, E. (2002). Array tracking of infrasonic sources at Stromboli volcano. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(22), 2076, doi:10.1029/2002GL01545\u003c/li\u003e\n\u003cli\u003eRipepe, M., E. Marchetti, and G. Ulivieri (2007), Infrasonic monitoring at Stromboli volcano during the 2003 effusive eruption: Insights on the explosive and degassing process of an open conduit system, J. Geophys. Res., 112, B09207, doi:10.1029/2006JB004613\u003c/li\u003e\n\u003cli\u003eSiebert, L., Simkin, T., \u0026amp; Kimberly, P. (2010). Volcanoes of the World. University of California Press.\u003c/li\u003e\n\u003cli\u003eTsuya, H., 1955. Geological and petrological studies of volcano Fuji, V. Bulletin of the Earthquake Research Institute, Tokyo 33, 341 and 383.\u003c/li\u003e\n\u003cli\u003eVergoz, J., Hupe, P., Listowski, C., Le Pichon, A., Garc\u0026eacute;s, M. A., Marchetti, E., et al. (2022). IMS observations of infrasound and acoustic-gravity waves produced by the January 2022 volcanic eruption of Hunga, Tonga:A global analysis. Earth andPlanetary Science Letters, 591, 117639. https://doi.org/10.1016/j.epsl.2022.117639\u003c/li\u003e\n\u003cli\u003eWalker, G. P. L. (1973). Explosive volcanic eruptions - a new classification scheme. Geologische Rundschau, 2:431\u0026ndash;446.\u003c/li\u003e\n\u003cli\u003eWalker, G. P. L. (1980). The Taupo pumice: product of the most powerful known (ultraplinian) eruption? Journal of Volcanology and Geothermal Research, 8:69\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eWilson, D. K. (2003). The sound-speed gradient and refraction in the near-ground atmosphere. The Journal of the Acoustical Society of America, 113. 750-757. https://doi.org/10.1121/1.1532028.\u003c/li\u003e\n\u003cli\u003eYokoyama, I. (1981). A geophysical interpretation of the 1883 Krakatau eruption. Journal of Volcanology and Geothermal Research. 9, 359\u0026ndash;378.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pure-and-applied-geophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paag","sideBox":"Learn more about [Pure and Applied Geophysics](https://www.springer.com/journal/24)","snPcode":"24","submissionUrl":"https://submission.nature.com/new-submission/24/3","title":"Pure and Applied Geophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Volcanic Eruptions, IMS, Infrasound, CTBTO, IDC Bulletins","lastPublishedDoi":"10.21203/rs.3.rs-9281628/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9281628/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Global Volcanism Program (GVP) includes a comprehensive list of the 1281 Earth\u0026rsquo;s active volcanoes and their eruptions over the last 12,000 years. In this work, we used the web-based GVP database of the Smithsonian Institute to correlate detections in the period 2011\u0026ndash;2020. According to GVP data, 360 eruptions (or confirmed eruptive activity) occurred on 138 volcanoes around the world. Among those, we selected 67 confirmed eruptions originated from 46 volcanoes, with Volcanic Explosive Index (VEI) above 3. Data from 43 IMS infrasound stations were processed and analysed in the specified time window using the Progressive Multi-Channel Correlation (PMCC) algorithm. A station-to-source back-azimuth deviation of 5\u0026deg; was considered, using a cross-bearing azimuth methodology. The IMS network infrasound detections of the 67 selected volcanic events are presented, as well as the correspondence of the volcanic events with the lists of in the Late Event Bulletin (LEB, 52 events), Standard Event Lists (SEL3, 13 events) and Reviewed Event Bulletin (REB, 30 events) produced by the CTBTO International Data Centre (IDC).\u003c/p\u003e","manuscriptTitle":"2011-2020: A Decade of global volcanic events observations at the IMS Infrasound network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 19:46:25","doi":"10.21203/rs.3.rs-9281628/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T12:06:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T08:52:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T08:34:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34288568560286179290955929728752953994","date":"2026-04-08T10:19:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40795452425451547179856621230771107570","date":"2026-04-06T11:10:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T06:49:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T06:44:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T11:22:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pure and Applied Geophysics","date":"2026-03-31T14:42:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pure-and-applied-geophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paag","sideBox":"Learn more about [Pure and Applied Geophysics](https://www.springer.com/journal/24)","snPcode":"24","submissionUrl":"https://submission.nature.com/new-submission/24/3","title":"Pure and Applied Geophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"73d7dc1e-f06a-4e09-bf64-e6c9447604c2","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T12:06:30+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T12:10:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 19:46:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9281628","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9281628","identity":"rs-9281628","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}