Atmospheric injection of volcanic ash during eruptions is a threat to aviation. Reliable forecast of airborne ash dispersal relies on empirical and numerical models. Key inputs into these models are so-called eruption source parameters such as the rate at which pyroclastic material is ejected from the vent and the height of eruptive columns. Here, we use infrasound data recorded during eruptive activity in June 2021 at Mt. Etna, Italy, to demonstrate its potential for assessment of eruption rates in near-real time. We calculate a time series of flow velocity at the vent using data corrected for topographic scattering, and effect of vent geometry on the acoustic source radiation. We obtain values of flow velocity of 50-125 m/s during a period of sustained, paroxysmal, activity. These values agree with independent estimates from other ground-based remote sensing data. We use infrasound-derived flow velocities as input into a 1D plume model to estimate the maximum height of the eruption column. Our results suggest that infrasound data hold promise to inform modelling of volcanic plumes. We anticipate that implementation of real-time operational workflows based on infrasound data analysis and plume modelling will impact decision-making and risk mitigation at active volcanoes.
Article
Assessment of eruption source parameters using infrasound: a case study from the 2021 eruption of Mt.Etna, Italy
https://doi.org/10.21203/rs.3.rs-3241423/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 13 Nov, 2023
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Challenges in monitoring volcanic plumes and ash transport. Explosive eruptions frequently release significant amounts of fine rock particles and glass fragments, referred to as volcanic ash, into the atmosphere. The emission of volcanic ash, owing to its highly abrasive nature and the low melting point of its glass component, can cause severe damage to aircrafts1,2,3. Nine Volcanic Ash Advisory Centres (VAACs) are deputed to evaluate the presence and extent of volcanic ash in the atmosphere, and to issue ash-cloud warnings to the aviation community around the world1. The ability of these operational centres to forecast the movement of ash in the atmosphere, and thus identify at-risk regions, is underpinned by complex numerical models. Such models require a set of input parameters that represent the meteorology of the atmosphere at the time of eruption (e.g., wind speed and direction, vertical temperature profile) and the nature of the eruptive source (e.g., source location, time and duration of eruption, vent geometry, height of the plume, mass eruption rate, particle size distribution, ash density and shape)4. Constraining the extensive parametrization of atmospheric ash transport models is challenging, and thus, their outputs are affected by substantial uncertainty. Meteorological parameters are usually well-constrained by data and models with temporal and spatial resolutions of the order of hours and few kilometers, respectively (e.g., the Met Office Unified Model5 used by the London VAAC4). Conversely, larger uncertainties are linked to estimates of eruption source parameters6,7 (ESP), in particular mass or volume eruption rate (MER or VER, respectively). Commonly, VER is estimated through inversion of so-called 0D eruption plume models (EPMs), that is empirical scaling relationships between VER and the height reached by the plume above the vent. The simplest, and most widely used, family of EPMs takes the general form \(H=C{V}^{n}\), where \(H\) is the maximum height reached by the plume above the vent, \(V\) is VER (measured as \({m}^{3}\) of dense-rock equivalent per second), \(C\) and \(n\) are constants8,1. For the purpose of informing volcanic ash transport modelling, 1D integral EPMs have also been increasingly tested owing to their ability to include processes such as the entrainment of atmospheric air into the eruption column, and to account for the effect of wind speed and direction on plume rise9,4. The main challenge in the use of EPMs for assessment of VER is that they require validation, which depends on the availability of independent measurements of both plume heights and MER/VER10,11,1. Independent column height estimates are available from either satellite and ground-based (e.g., camera, radar, lidar) measurements albeit with limitations posed by data latency, logistics and weather conditions. The majority of independent VER estimates are average measurements of the ratio between the total volume of erupted tephra and the duration of the eruptive event. These data can exhibit significant scatter12,4, which arises from differences in the techniques employed to evaluate the volume of tephra deposits and the duration of eruptions, and factors such as the impact of wind speed and direction on the rise of volcanic plumes. Therefore, the need for new methods to quantify volcanic emissions has emerged as a critical step towards improving our capacity to assess and mitigate hazards from volcanic ash.
Measurements of volume eruption rate. In recent years, a series of studies have proposed methods to estimate VER/MER from analyses of ground-based radar, thermal infrared (IR), and acoustic data13,14 used echo power data from Doppler Radar measurements to estimate MER at Stromboli and Mt. Etna (Italy) volcanoes, respectively15 proposed a workflow to measure MER using microwave weather radar and infrasound array data collected during eruptions at Eyjafjallajökull (Iceland, 2010), Grímsvötn (Iceland, 2011) and Mt. Etna (2013)16; employed a combination of thermal camera imagery and infrasound array data to evaluate plume exit velocity during the 2010 Eyjafjallajökull eruption17 calculated the exit velocity of the eruptive jet from thermal IR data during paroxysmal activity at Mt. Etna in 2020–2021. Among these methods, infrasound has emerged as a promising tool to quantify VER/MER owing to the comparatively low costs associated with sensor installation and maintenance, data availability in real-time, and its suitability for automated data processing. A substantial body of research has informed continuous development of methods for inversion of acoustic waveforms aimed at quantifying volcanic emissions18,19,16 used scaling laws that link the power radiated by acoustic sources to gas velocity during flow from a volcanic vent20,21,22 used a monopole source model, that is a compact source that radiates acoustic waves hemispherically, to quantify volcanic emissions at Mt. Erebus (Antarctica) and Sakurajima volcano (Japan)23 introduced a waveform inversion method to calculate VER at Tungurahua volcano (Ecuador) based on the Green’s Function solution to the inhomogeneous Helmoltz wave equation in a half-space; the method, based on the representation of the acoustic pressure wavefield as a combination of monopole and dipole terms, was also applied by24 at Santiaguito volcano (Guatemala). Progress in numerical modelling of the acoustic wavefield generated by compact volcanic sources25,26 underpinned additional studies focussed on retrieval of VER via full waveform inversion of infrasound signals using 3D numerical Green’s Functions27,28,29,30. A comprehensive review of these methods and their underlying theoretical frameworks can be found in31. More recently32,33 have shown that VER can be calculated from direct integration of the pressure wavefield after applying corrections for scattering from topography, wavefield directivity (controlled by vent geometry and the acoustic wavenumber) and reflectance at the conduit outlet32 accounted for topographic scattering of the acoustic wavefield at Copahue volcano (Argentina) via the insertion loss (IL) parameter in the screen diffraction approximation33 assumed that topographic scattering at Mt. Etna was negligible as the position of the infrasound array used was in line-of-sight with the erupting vent34, however, demonstrated that the screen diffraction approximation is only appropriate under specific conditions and suggested that numerical simulations are the best tool to estimate IL; a workflow for calculating maps of IL based on numerical modelling of acoustic wave propagation over volcanic topography was proposed by ref.26.
Here, we build on this extensive body of research and demonstrate a workflow to obtain independent estimates of VER from the integration of attenuation-corrected infrasound data recorded at Mt. Etna, Italy. We compare our results to independent estimates of flow velocity at the vent obtained from analyses of ground-based thermal IR imagery. Finally, we show how VER can be used as a direct input into 1D plume models for rapid assessment of maximum column height, thus, providing key monitoring information when other observations may not be available.
Eruptive activity at Mt. Etna: June 2021. Mt. Etna, Italy, is one of the most active volcanoes in the world. One of its distinctive features is the frequent occurrence of so-called paroxysms, that is episodes of intense explosive activity lasting from tens of minutes to many hours35. Paroxysms typically occur in clusters within eruptive periods that can last from few weeks to months36. Between December 2020 and February 2022 Mt. Etna produced 66 paroxysms17. In this study, we focus on an episode on 20 June, 2021, part of a sequence of paroxysms that occurred, with striking regularity, during the second half of June 2021. These events had durations of up to few hours and all followed the characteristic pattern of paroxysmal activity at Mt. Etna. Initial rapid-fire Strombolian explosions evolved into nearly continuous lava fountaining, feeding lava flows and volcanic plumes with heights of up to several kilometers above the vent37. Infrasound and thermal IR data recorded during the paroxysmal activity on 20 June are shown in Figs. 1a-1f. Figures 1c-1d illustrate early Strombolian activity, consisting of discrete explosions clearly distinguishable in both the waveform (Fig. 1c) and spectrogram (Fig. 1d). During the later stages of the paroxysm, the acoustic signal evolves into nearly continuous tremor (Fig. 1e) and individual explosions are only occasionally discerned (Fig. 1f). Figure 1b shows thermal IR images tracking the change from discrete explosions to lava fountains feeding a sustained volcanic plume.
Data
Infrasound array. For this study, we used acoustic data recorded by a temporary 6-element infrasound array deployed in June 2021. The array was installed and operated by the University of Liverpool and the Istituto Nazionale di Geofisica e Vulcanologia (INGV sezione di Pisa and INGV-Osservatorio Etneo) at the Mt. Conca site (Fig. 2a, CONC), approximately 6km from the active vent within the South East Crater (SEC) area (Fig. 2b). The array had an aperture (i.e., max distance between any two sensors) of 50m (Fig. 2c) and was instrumented with IST2018 infrasound microphones38 (full-scale range of 480 Pa peak-to-peak, flat response between 0.1 and 40 Hz). Data were recorded on-site with a sampling frequency of 100 Hz and 24-bit resolution.
Thermal IR. Mt. Etna is routinely monitored by INGV-Osservatorio Etneo (INGV-OE) with an extensive multi-parameter sensor network, including thermal IR cameras. Here, we used thermal imagery of the ENT camera recorded at the site located in Nicolosi (Fig. 2a), approximately 15km from the active summit craters. The site is equipped with a Flir A40M camera recording IR images in the \(7.5-13\mu m\) band, with a field of view of 640x480 pixels and thermal sensitivity of \(80mK\)at 25°17,39. The camera records images with a sampling rate of \(0.5Hz\), and provides a spatial resolution of 1.3 \(\mu rad\) (at the ENT site). We chose this site as it provides the best trade-off between data availability, spatial and temporal resolution, and the ability to track the development of the eruptive plume during paroxysmal activity.
X-band Radar. Volcanic plumes from Etna are also observed using a dual polarimetric X-band radar, managed by the Italian Department of the Civil Protection, part of the national weather radar network40. The X-band dual-polarization radar, located at the international airpot of Catania (distance of about 32 km from the SEC), has a wavelength of 3.1 cm (9.6 GHz), peak power of 50 kW and half-power beam width of 1.3°. The radar scans a volume defined by an area of 160×160 km2 and a height of 20 km, recording data along 12 elevation angles every 10 minutes.
Here, we investigate an episode of paroxysmal activity that occurred on 20 June, 2021 starting at approximately 21:30 (UTC), lasting for about 2 hours (Fig. 3a). For this event, INGV-OE issued two Volcano Observatory Notice for Aviation (VONA) with color code Red (the highest level of concern) at 22:01 and 22:19 UTC41,42; INGV-OE reported lava fountaining at the South East Crater (Fig. 2b, SEC) and a plume drifting towards the East-South East. However, the height of the plume could not be estimated by INGV-OE in real-time during the paroxysm.
VER and flow velocity from infrasound array data analyses. We analysed infrasound data recorded at the CONC site (Fig. 2a, 2c) during paroxysmal activity on 20 June, 2021. Infrasound arrays allow to identify windows of coherent signal and the direction of their source region, providing an effective tool for discrimination of eruptive activity from other signals. We processed the CONC data using slowness inversion 43 to obtain a time series of Direction of Arrival (DOA), that is the direction from which infrasound energy travels while crossing the array. The results, in Fig. 3b, show coherent acoustic energy crossing the array from a stable backazimuth in the direction of the active SEC (~ 205-215o N). We, then, selected data windows corresponding to high quality array locations (MCMM > 0.65) and integrated the pressure signal recorded at the CONC6 microphone (Fig. 2c) to calculate a time series of VER (Fig. 3c). Finally, we used a vent radius of 10m44 to convert VER into flow velocity and calculated its 1-minute median, which is shown in Fig. 3c (yellow circles).
Flow velocity from thermal IR data analyses. Figure 3c shows a time series of measurements of jet velocity at the vent (blue squares) obtained from analyses of thermal IR data recorded at the ENT site (Fig. 2a) during paroxysmal activity on 20 June, 2023. The measurements, performed with a time step of 1 minute, can be compared with the 1-minute median jet velocity obtained from acoustic data (Fig. 3c, yellow circles); the two independent time series show excellent agreement throughout the phase of sustained lava fountaining (~ 22:45 − 23:30). After 23:30, concurrent with waning of lava fountaining, thermal IR estimates of velocity decrease while infrasound-derived jet velocities remain high as acoustic sensors continue detecting individual explosions with no significant associate lava fountaining. Figure 3d shows the thermal IR data from the ENT camera plotted as a time series. The figure, produced by plotting stacks across a vertical section (through the crater area) of each time-lapse thermal IR image (one image every 2 seconds), allows to track the evolution of the paroxysm in space and time, clearly showing its onset (~ 22:00), peak (22:45 − 23:40) and waning (Fig. 3c) phases.
Radar-derived height of the plume and plume model. We retrospectively analysed X-band radar data to estimate the temporal evolution of plume height during paroxysmal activity. Figure 3e shows radar detection of a ~ 5km ash plume starting from 23:00 increasing to a maximum height of 10km at 23:30. The plume top was estimated at 10km until ~ 00:00 (21 June, 2021) when it started to progressively wane during the late stages of the paroxysm (supplementary material, Fig. S1). We used VER and flow velocity estimates from the analysis of infrasound and thermal IR data as input into a 1D model of plume rise9. The model output, displayed in Fig. 4 for values of flow velocities of 50, 75 and 125m/s (VER of \(1.6\cdot {10}^{4}\), \(2.3\cdot {10}^{4}\), and \(3.9\cdot {10}^{4}{m}^{3}/s\) considering a vent radius of \(10m\)) shows the plume drifting towards the East direction (Fig. 4a), extending vertically to a maximum heights of 8-10km above sea level (Fig. 4b). These results agree with both the radar-derived maximum height (Fig. 3e) and observations of the plume drifting eastwards (Fig. 4c) from satellite thermal imagery collected by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI, 10.8\(\mu m\) wavelength).
The potential impact of airborne volcanic ash on aviation and infrastructure is well-known. Mitigation of the associated risks depends critically on the ability to issue timely forecasts of atmospheric ash dispersion, and thus, characterize ESP in real- or near real-time. In this study, we have demonstrated an infrasound-based workflow to obtain measurements of VER that holds potential for real-time implementation. We have analysed acoustic data gathered during a paroxysmal event at Mt. Etna on 20 June, 2021, and have benchmarked infrasound-derived measurements of VER (transformed into flow velocity assuming a vent with circular cross-section) with independent estimates of flow velocity at the vent from thermal IR imagery, one of the tools routinely employed by INGV-OE to detect the onset of eruptions, track their evolution and investigate the height of lava fountains during paroxysms.
Flow velocity at the vent and VER are important parameters to constrain eruption dynamics and to evaluate the height of the eruptive plume. Infrasound is a widely adopted tool for volcano monitoring and provides an effective means to measure VER. The main advantages of infrasound over other methods with comparable resolution, such as Doppler radar, are its low installation costs, and simple and rapid data processing that can easily be automated for real-time implementation. Unlike other methods such as those based on analyses of optical or thermal IR imagers, infrasound is not affected by cloud cover. Strong wind can, indeed, deteriorate infrasound signal quality but its effects can be mitigated through careful sensor installation. Owing to these characteristics, infrasound is used for operational eruption early warning at Mt. Etna45 and worldwide. Implementation of the workflow presented here for VER measurements would be a straightforward addition to the existing monitoring systems. The main limitation of using infrasound to measure VER is that it requires a well-constrained source mechanism, as well as knowledge of topographic scattering and true signal attenuation, and reflectivity effects at the vent-atmosphere interface. In the case study presented here, as well as other volcanoes,32,33, it has been shown that the source is well-represented by an isotropically radiating monopole (see 5 in this manuscript for additional details). The formulation of46 provides a valuable tool to characterize and correct for non-isotropic source radiation patterns and reflectivity at the vent-atmophere interface in terms of the product of two measurable parameters, that is the characteristic dimension of the source and the acoustic wavenumber. Finally, signal attenuation (IL) can be evaluated from numerical modelling of acoustic wave propagation (see 5 in this manuscript for additional details).
The acoustic and thermal IR flow velocity time series shown in Fig. 3c show good agreement until approximately 23:15 on 20 June, 2021. After that time the acoustic data appear to continue tracking eruption while the thermal IR signal decreases abruptly. Infrasound waveforms and array DOAs (Fig. 3a, 3b) confirm that eruptive activity was still ongoing until approximately 23:45; at the same time the X-band radar detected a well-established plume (Fig. 3e). A plausible explanation for the divergence between acoustic and thermal IR data starting from 23:15 is a migration of the fragmentation front deeper into the conduit towards the final stages of paroxysmal activity. In this scenario explosive activity would still be ongoing at depth into the conduit, as detected by infrasound data (Fig. 3a, 3b), but it would only produce low-level lava fountaining at the surface while still feeding an airborne ash plume. Infrasound activity declined at ~ 23:45 marking the waning of paroxysmal activity. A plume between 8–10 km persisted until at least 00:30 (supplementary material fig. S1).
Finally, we have shown that infrasound-derived VER can be used as input into a simple 1D plume rise model to obtain realistic estimates of the maximum height of the ash column. We stress that PlumeRise9, the 1D model used in this study, was capable of producing results for a single model in < 1s, showing its suitability for use in real-time. The results of plume modelling were confirmed by data from ground-based X-band radar (Fig. 3e) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite (Fig. 4c), which confirmed the maximum height (~ 10 km) and direction (East) of the ash plume, respectively. We note that the objective of this study was to provide a proof-of-concept for the combined use of infrasound data and plume rise modelling to assess ESP; a full sensitivity analysis of the plume model employed here to its parametrization was beyond the objectives of this study. Plume height is a key ESP as it defines the spreading height of the volcanic cloud in atmospheric ash dispersal models33 and it is also often used for assessment of eruption rates. At Mt. Etna plume height is presently derived from analyses of optical images47, ground-based radar40 and satellite data48; successful application of these methods depends on whether specific conditions are met. Methods based on visible cameras are contingent on favorable meteorological conditions (e.g., no cloud cover), can only be used during daytime, and the retrievable height of volcanic plumes is dependent on source-camera distance and the camera field-of-view. Radar data are not affected by meteorological conditions and can be used during both daytime and nighttime; however, detection capabilities depend on source-receiver distance and direction of plume dispersion. Satellite-based measurements of plume height are generally delayed compared to other methods as they require a fully developed (opaque) plume to produce reliable height estimates47. Numerical modelling (or alternatively selection of a scenario from an ensemble of pre-computed models) informed by real-time analysis of acoustic data offers a valuable complement to these methods for rapid assessment of plume height.
This study aimed at introducing an alternative workflow for real-time assessment of ESP during eruptions, in particular VER and the top height of the eruptive plume. A number of methods already exist for measurement of ESP; some have briefly been discussed in this manuscript, including advantages and limitations. We have introduced a complementary strategy that relaxes some of the assumptions and simplifications underpinning widely used 0D EPM and overcomes the limitations of other existing techniques. We have demonstrated how analysis of infrasound data allows reliable estimates of VER that can be used to inform 1D plume models, thus, providing real-time estimates of the height of volcanic plumes. Such a workflow would prove especially valuable in the early stages of a volcanic crisis when other measurements are typically not readily available. At Mt. Etna, rapid information on plume height would be of great importance when issuing warnings to aviation authorities (e.g., VONA), and as a preliminary tool to assess plume dynamics and identify the most likely ash dispersal scenario.
We envision that infrasound-based methods for assessment of ESP, along with numerical modelling, will play an increasingly important role in volcano monitoring operations, and suggest that the implementation of our workflow into an operational tool will be transformative in influencing decision making and risk mitigation during future volcanic crises.
Thermal IR estimates of jet velocity. Thermal IR images at the ENT site (Fig. 2a) were processed using the algorithm of Mereu et al.49; they provide a time series of brightness temperature over the course of a paroxysmal event. The Incandescent Jet Region (IJR), that is the near-vent jet that feeds the volcanic plume, is identified from thermal images as the saturated portion within each frame by evaluating the gradient of the brightness temperature as a function of a given threshold. As a first-order approximation, the height of the IJR is assumed to correspond to the height of the lava fountain39, \({H}_{lf}\), and the jet velocity at the vent (\({v}_{jet}\)) is then calculated as:
$${v}_{jet}=\sqrt{2gH}$$
Where \(g\) is the acceleration of gravity. Detection of the IJR and calculation of \({v}_{jet}\) are performed for each thermal IR frame and the results averaged every minute (Fig. 3c).
Infrasound array processing. Infrasound array data were processed following the methodology of De Angelis et al.43. The processing workflow implements least-squares beamforming to evaluate the DOA of coherent energy and the propagation velocity of acoustic waves across the array. The method implements a simple inversion problem:
$$\mathbf{d}=\mathbf{G}\mathbf{m}$$
Where \(\mathbf{d}\) is a data vector of time delay measurements between each pair of sensors within the array, \(\mathbf{m}=({s}_{x},{s}_{y}{)}^{T}=(sin\theta /v,cos\theta /v{)}^{T}\) is a model vector of slowness in the East-West and North-South directions, \(\mathbf{G}\) is a \(N\times 2\) matrix of distances between each pair of sensors (\(N\)is the number of sensors, \(\theta\) is the DOA, \(v\) is the acoustic wave velocity across the array). Delay times for each data window analysed are measured using cross-correlation between all pairs of sensors within the array. The results in Fig. 3b were obtained by processing 10-second sliding windows with 50% overlap; a preliminary bandpass filter (0.7-3Hz, Butterworth, 2-pole) was applied to the data. Only high-quality DOA estimates were selected for plotting, that is those corresponding to a median value of the cross-correlation maximum (MCCM) greater than 0.65.
Infrasound estimates of VER. We estimated VER by direct integration of the infrasound pressure time series recorded at station CONC6, at central element of the CONC array, (Fig. 2c) following the formulation proposed of Lacanna and Ripepe46:
$$IL=20Log\left(\frac{{P}_{topo}}{{P}_{geo}}\right)$$
that is, the ratio between the infrasound pressure calculated at a given position from numerical modelling (\({P}_{topo}\)) and the expected pressure considering only the effect of geometrical spreading (\({P}_{geo}\)). Figure 5 shows a map of IL at Mt. Etna obtained from 3D Finite Difference Time Domain modelling29,28. Similar to Diaz-Moreno et al.29 we used a Blackman-Harris source function with an amplitude of 10000kg/m3 and a cutoff frequency of 4.5 Hz to include the dominant frequency range of the infrasound recorded during the paroxysm (0.7-3Hz). The topography of Mt. Etna was derived merging a 0.55m resolution digital elevation model of the SEC area from UAV surveys conducted in March 202144 with a regional 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer DEM for the rest of the study area29; the resulting area was resampled into a grid of 5 × 5m cells and the acoustic pressure wavefield was propagated over the topography using 20 grid points per wavelength to ensure numerical stability of the procedures50. At each grid point we applied Eq. (3) to produce a map of \(IL\) for the study area. We calculated a value of \(IL=-9.4dB\) at the CONC site.
Plume model and atmospheric data. We used the PlumeRise model of Woodhouse et al.9 to estimate the maximum height reached by the volcanic plume. The model is a 3-dimensional description of the rise of volcanic ash columns based on the fluid dynamics of turbulent buoyant plumes in a windy atmosphere. This is achieved by combining an integral model of pure plumes in a horizontal wind field51 with an integral model of volcanic columns in a static atmosphere52. PlumeRise also accounts for the thermodynamics of heat transfer between hot pyroclasts and the surrounding atmosphere and the effects of a variable atmosphere on the rise of volcanic plumes. Additional details and the full model formulation can be found in ref.9. In this study, we used a model of the atmopshere obtained from the Reanalysis v5 (ERA5) dataset. ERA5, produced by the of the European Centre for Medium-Range Weather Forecasts of the Copernicus Climate Change Service, provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. These data cover the Earth on a 30km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km53; we used data corresponding to the grid node that is closest to Mt. Etna, at 23:00 on 20 June, 2021. Values for all additional parameters required by the model are shown in the supplementary material (Table S1) and are based on previous work on plume modelling at Mt. Etna47.
Plume height measurements with X-band radar. We applied the VARR (Volcanic Ash Radar Retrieval) algorithm to the measured copolar radar reflectivity Zhh (dBZ) to obtain information on the nature and heigt of the volcanic plume54,7. The algorithm performs tephra classification using a maximum a-posteriori probability criterion and estimates tephra features such as its mass concentration and number-weighted mean diameter. The top height of the plume (HTP) is estimated using a threshold algorithm on the measured Zhh of the probed plume and on the retrieved Ct. This estimate equals the central point of the range bin volume. The distance between the radar and the summit craters and the half-power beam width of the radar are used to reconstruct the radar beam cone; an uncertainty on HTP of ± 300 m is evaluated as the half-aperture (radius) with respect to the axis of the truncated cone in the proximal area above the crater55.
Data Availability
The infrasound data and related metadata used in this study are available at https://zenodo.org/record/8207343. The software used for infrasound array processing is hosted at: https://github.com/silvioda/Infrasound-Array-Processing-Matlab. Thermal IR, radar and Digital Elevation Model data can be made available via reasonable request to the corresponding authors.
Acknowledgements
SDA was supported by by NERC Grant NE/W004771/1 and project SINFONIA, progetto Bando Ricerca Libera 2021- Delibera 214/2021-INGV. LZ was supported by project SINFONIA, progetto Bando Ricerca Libera 2021 - Delibera 214/2021-INGV and by the INGV-MIUR project Pianeta Dinamico - “Working Earth” - Sub-project VT - DYNAMO - 2023. We thank all INGV-OE technicians who are involved in the maintenance of the thermal ir and visibile cameras operated by INGV-OE.
Author contributions
SDA and LZ wrote the proposal that funded installation and maintenance of the infrasound array, designed the field experiment and installed all equipment. SDA and LZ jointly designed the study, performed analyses of infrasound data, numerical modelling of acoustic wavefield and plume rise, and prepared all figures. SS and LM analysed thermal IR and radar data. SDA wrote the initial draft of the manuscript and all authors contributed to the final version.
Competing Interests
The authors declare no competing interests.
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No competing interests reported.