Volcanic Tremor Source Research Articles

Insights into the dynamics of the 2010 Eyjafjallajökull eruption using seismic interferometry and network covariance matrix analyses

Applying seismic interferometry and network covariance matrix-based analyses to detect and locate the source of volcanic tremor during the 2010 Eyjafjallajökull effusive flank and explosive-effusive summit eruptions has provided new insights into this iconic event. The tremor source locations derived from the network covariance matrix approach were spatially distinct during the two eruptions. The tremor was radiated between the surface and 5–6 km depth during the effusive flank eruption, including an apparently progressive upward migration in early April 2010, but was strictly confined to the surface during the summit eruption. Each phase of the summit eruption left a distinct fingerprint in the seismic records. Effusive phases radiated continuous tremor between 0.6 and 5 Hz, whereas explosive phases produced tremor in a more pulsating fashion over a wider frequency band (0.2–10 Hz). A period of intermittent tremor bursts (called banded tremor) on 15 April, associated with formation of a new vent at the summit, was most likely generated by magma-gas-meltwater interaction within a subglacial enclosure. The banded tremor ceased following an abrupt draining of the newly formed subglacial cauldron, resulting in a large slurry glacial meltwater flood (jökulhlaup). This study highlights the importance of new data processing methodologies for future monitoring of volcanic tremor in real-time.

Sources of Volcanic Tremor Associated With the Summit Caldera Collapse During the 2018 East Rift Eruption of Kīlauea Volcano, Hawai'i

AbstractVolcanic tremor occurring at the beginning of the 2018 Kīlauea eruption is characterized using both seismic and tilt data recorded at the Kīlauea summit. An automatic seismic network‐based approach detects several types of tremor including (a) 0.5–1 Hz long‐period tremor preceding the eruption, located at the south‐southwest edge of Halema'uma'u Crater and attributed to the quasi‐steady radiation from a shallow hydrothermal system and (b) two sequences of gliding tremor at the beginning of the eruption, both with locations on the edges of the crater and within it. The first sequence is attributed to two swarms of low‐amplitude regularly repeating earthquakes induced by the jerky motions of a cylindrical rock piston with radius of 325 m, height of 250 m, and mass of 2.07 × 1011 kg, progressively intruding 12.3 m into the shallow hydrothermal system with volume of 108 m3 and depth extent of 300 m. The second sequence is attributed to a gradual evolution in the properties of a bubbly magma within an east‐striking dike below Halema'uma'u Crater, impacted by repeated roof collapses. A fluid‐filled crack model points to a decrease in gas volume fraction from 4.22% to 1.6 × 10−2% in the magma filling the dike, and a model of gas retro‐diffusion within the melt suggests a two orders of magnitude decrease in bubble number density from 7 × 108 m−3 down to 4 × 106 m−3. Both models feature a quasi to totally degassed magma by May 26.

RETREAT: A REal-Time TREmor Analysis Tool for Seismic Arrays, With Applications for Volcano Monitoring

Volcanic tremor is a sustained seismic signal associated with volcanic unrest and is often linked to movement of magmatic fluids in the subsurface. However, signals with similar spectral content can be generated by other surface processes. Hence, one of the best ways of distinguishing between different possible mechanisms is by tracking the location of its source, which is also important for mitigating volcanic risk. Due to its emergent nature, tremor cannot be located using travel-time based methods, and therefore alternatives such as amplitude-based techniques or array analysis must be used. This work presents a python-based software tool that uses array data and array processing techniques to analyze and locate volcanic tremor signals. RETREAT utilizes existing routines from the open-source ObsPy framework to carry out analysis of seismic array data in real-time and performs f-k (frequency-wavenumber) analysis, or Least-Squares beamforming, to calculate the backazimuth and slowness in overlapping time windows, which can help track the location of volcanic tremor sources. A graphical or web-based interface is used to configure a set of input parameters. These include settings for configuring the data source, pre-processing, timing and update options as well as parameters for the array analysis. Once configured, the tool fetches chunks of waveform data in real-time and updates accordingly. On each update the tool returns plots of the array processing results as well as plots of the waveform, envelope and spectrogram. The array response function, a histogram of the power and a map of the array with dominant backazimuth overlaid can also be produced. The tool has been tested using real-time data from the small aperture SPITS array in Spitsbergen, as well as on data from an array deployed during the 2014 eruption of Bárðarbunga volcano, Iceland. Configuration files and waveform data for these examples are supplied with the distribution. RETREAT can also be used for infrasound and has been tested on infrasonic array data recorded at Mt. Etna, Italy. RETREAT is intended for use in real-time monitoring settings and it is hoped it will facilitate greater use of arrays in tracking volcanic tremor sources in real-time, thereby enhancing monitoring capabilities.

Temporal Change of Spectra and Lyapunov Exponent Volcanic Tremor at Raung Volcano, Indonesia

Spectral Analyses and estimation maximal lyapunov exponent (MLE) of volcanic tremor recorded at Raung Volcano were carried out to investigate dynamical systems regarding to their generating system. Their results of both analyses can explain the temporal change in frequency and deterministic processes of the dynamical system. Spectral analysis of volcanic tremor was estimated by the average periodogram method which includes division, Fast Fourier Transformation and averaging. MLE was estimated by graphing the relationship between Stretching Factor (S) and the number of points in the tractor (N) diagram. Content frequency of volcanic tremor Raung Volcano is range from 2.68 to 3.7 Hz. Temporally, there is no significant change, which means that there is no change in the geometry of the Raung volcanic tremor source. This is also shown by the maximal lyapunov exponent which is temporarily constant and positive. That shows that the source process of Raung volcano is chaotic

Reliability evaluation of volcanic tremor source location determination using cross-correlation functions

Summary Source location determination of volcanic tremor has been a challenge in seismology due to the waveform complexity and difficulties in reading P- and S-wave arrival times. We present a method for locating volcanic tremor recorded at a seismic network distributed around a volcano. The method combines the source-scanning algorithm and cross-correlation analysis. Tremor records are processed using a technique adopted from ambient seismic interferometry to obtain stacked cross-correlation functions (CCFs) for all station pairs, which are expected to show high amplitudes at the lag time that corresponds to the travel time difference between the stations. The best seismic source location is determined from the maximum of the sum of envelope amplitudes of CCFs at predicted travel time differences between all pairs of stations. This method does not compute theoretical amplitudes, assume an initial hypocenter location, or measure the arrival times. To quantitatively evaluate the accuracy of the location determination, we examine the method by using the vertical component seismic data of volcano-tectonic (VT) earthquakes recorded at six seismic stations at Izu-Oshima volcano. The VTs have been previously located by using arrival times of P- and S-waves, and the hypocenters are used as the reference for evaluation and error estimation of the method. The results show that the misfit, which is the distance between our estimated sources and the references, is about 2 km or less when using CCFs at the frequency band of 4–16 Hz which contains the dominant frequencies of direct S-waves. To test whether the method can be used for volcanic tremor, we simulate the tremors by combining the observed VTs that occurred randomly in time in a localized region. The simulated tremors are determined with location errors of approximately 1 km or less, when the sources of VTs are located within a distance of 1 km and CCFs are calculated for a minimum data length of about 2 minutes. The volcanic tremor location method we present here can be used as an alternative tool for volcano monitoring, especially to locate tremors and seismic events with no clear phase arrival.

Depth Migration of Seismovolcanic Tremor Sources Below the Klyuchevskoy Volcanic Group (Kamchatka) Determined From a Network‐Based Analysis

AbstractWe present a method for automatic location of dominant sources of seismovolcanic tremor in 3‐D, based on the spatial coherence of the continuously recorded wavefield at a seismic network. We analyze 4.5 years of records from the seismic network at the Klyuchevskoy volcanic group in Kamchatka, Russia, when four volcanoes experienced tremor episodes. After enhancing the tremor signal with spectral whitening, we compute the daily cross‐correlation functions related to the dominant tremor sources from the first eigenvector of the spectral covariance matrix and infer their daily positions in 3‐D. We apply our technique to the tremors beneath Shiveluch, Klyuchevskoy, Tolbachik, and Kizimen volcanoes and observe the yearlong preeruptive volcanic tremor beneath Klyuchevskoy from deep to shallow parts of the plumbing system. This observation of deep volcanic tremor sources demonstrates that the cross‐correlation‐based method is a very powerful tool for volcano monitoring.

Ground Deformation Detected by Permanent Tiltmeters on Mt. Etna Summit: The August 23-26, 2018, Strombolian and Effusive Activity Case

Over the last few years, three tilt deep stations (27-30 meters) have been set up in the summit area of Mount Etna volcano. The aim of this challenging project is to record the ground deformations of the summit craters activity with high precision. We considered data related to the August 23-26, 2018, Strombolian and effusive activity. In this case, tiltmeters recorded variations in the order of 10−7 radians, not observed at the other stations. These changes suggest a shallow contraction source just south of the Southeast Crater. This result, related to the volcanic tremor source, points to the presence of a gas/magma reservoir feeding the Strombolian activity at 1200 m above sea level.

Insights into lava fountain eruptions at Mt. Etna by improved source location of the volcanic tremor

We analysed volcanic tremor recorded during ten lava fountain eruptions at Mount Etna volcano over the second half of 2011. The seis- mic signal, recorded by a network of 19 broadband stations, was corrected for site effects. The site amplification factors obtained for all the seismic stations were calculated using 18 regional earthquakes. To quantitatively estimate the goodness of the site correction, the vol- canic tremor source locations obtained by using site corrections were compared with those performed without such corrections. The seismic signal was filtered in two different frequency bands before applying correction: (i) the band 1-2 Hz, which contains most of the tremor energy, and (ii) the band 5-10 Hz, in which, likely due to scattering phenomena, the assumption of isotropic radiation behind the volcanic tremor source location method is valid. The results revealed that the seismic signal correction for site effects improved the tremor location results. Such improved tremor locations, together with volcanological and geodetic data, allowed us to identify two dis- tinct periods characterised by different tremor locations during lava fountains (July - August 20 and August 20 - November 15). Such differences were probably due to magma recharge phenomena, taking place during the former period.

A21-12 火山性微動の発生の簡単なモデル

A21-12 火山性微動の発生の簡単なモデル

Tremor and its duration‐amplitude distribution at Popocatépetl volcano, Mexico

AbstractThe duration‐amplitude distribution was calculated for the tremor observed at Popocatépetl volcano during episodes of activity in 2000 and 2012–2014. An exponential function was used to obtain a good fit for the duration‐amplitude distribution, and the source of volcanic tremor is probably generated by the transportation of magmatic fluids and its coupling with the host rock within the volcanic conduit. In particular, harmonic tremor has shown large amplitudes, durations, and mean values of amplitude, more than spasmodic or pulsating tremor. This is due to different generation mechanisms: in the case of harmonic tremor, it is produced during magma ascent and lava dome growth, while spasmodic and pulsating tremors are associated with fragmentation of the lava dome and gas emissions. This paper presents the duration‐amplitude distribution as a method to estimate the intensity of the tremor at Popocatépetl, a volcano with the major risk in all Mexico.

Short-term spatial change in a volcanic tremor source during the 2011 Kirishima eruption

Volcanic tremors are indicators of magmatic behavior, which is strongly related to volcanic eruptions and activity. Detection of spatial and temporal variations in the source location is important for understanding the mechanism of volcanic eruptions. However, short-term temporal variations within a tremor event have not always been detected by seismic array observations around volcanoes. Here, we show that volcanic tremor sources were activated at both the top (i.e., the crater) and the lower end of the conduit, by analyzing seismograms from a dense seismic array 3 km from the Shinmoedake crater, Kirishima volcano, Japan. We observed changes in the seismic ray direction during a volcanic tremor sequence, and inferred two major sources of the tremor from the slowness vectors of the approaching waves. One was located in a shallow region beneath the Shinmoedake crater. The other was found in a direction N30°W from the array, pointing to a location above a pressure source. The fine spatial and temporal characteristics of volcanic tremors suggest an interaction between deep and shallow conduits.

The 2010 ash emissions at the summit craters of Mt Etna: Relationship with seismo‐acoustic signals

Mt Etna volcano (Italy) typically generates lava fountains and Strombolian eruptions from the summit craters, producing significant emissions of tephra which may cause moderate to high impact both in the atmosphere and on the ground. However, we have also witnessed a number of minor ash emissions that, although far less powerful, may pose considerable volcanic hazards. In this paper, we deal with three ash emissions occurring in 2010 (8 April at Southeast Crater, 25 August at Bocca Nuova, and 14–15 November at Northeast Crater), correlating their volcanological features with the associated seismo‐acoustic signals. We provide details regarding the chronology, eruption column, dispersal of the deposit, and texture (grain size, componentry and morphology) of the emitted ash. Each eruptive episode has also been characterized by means of seismo‐acoustic analyses evaluating the volcanic acoustic‐seismic ratio (VASR). Furthermore, the source of volcanic tremor recorded from March to December 2010 was localized. The joint volcanological and seismo‐acoustic analyses allowed distinguishing two main kinds of ash emissions: types a (8 April and 25 August) and b (14–15 November). Regarding the former, the accumulation of gas below a dense cap rock obstructing the conduit vent, giving rise, with the uncorking, to impulsive explosivity, was hypothesized. The latter type instead is characterized by a longer‐lasting and less explosivity, likely due to the existence of open conduit conditions. Therefore, type b ash emissions are less hazardous than type a. This simple model, regarding minor explosive activity, may constitute a starting point to assess the volcanic risk from unexpected explosions.

Monitoring Seismo-volcanic and Infrasonic Signals at Volcanoes: Mt. Etna Case Study

Volcanoes generate a broad range of seismo-volcanic and infrasonic signals, whose features and variations are often closely related to volcanic activity. The study of these signals is hence very useful in the monitoring and investigation of volcano dynamics. The analysis of seismo-volcanic and infrasonic signals requires specifically developed techniques due to their unique characteristics, which are generally quite distinct compared with tectonic and volcano-tectonic earthquakes. In this work, we describe analysis methods used to detect and locate seismo-volcanic and infrasonic signals at Mt. Etna. Volcanic tremor sources are located using a method based on spatial seismic amplitude distribution, assuming propagation in a homogeneous medium. The tremor source is found by calculating the goodness of the linear regression fit (R 2) of the log-linearized equation of the seismic amplitude decay with distance. The location method for long-period events is based on the joint computation of semblance and R 2 values, and the location method of very long-period events is based on the application of radial semblance. Infrasonic events and tremor are located by semblance–brightness- and semblance-based methods, respectively. The techniques described here can also be applied to other volcanoes and do not require particular network geometries (such as arrays) but rather simple sparse networks. Using the source locations of all the considered signals, we were able to reconstruct the shallow plumbing system (above sea level) during 2011.

Time reversal imaging of synthetic volcanic tremor sources

Time reversal of seismic wavefields recorded at active volcanoes may provide images of the source process for long‐period (LP) or volcanic tremor events directly, independent of assumptions about extent, geometry and mechanics of the source. Time reversal methods involve propagating the original time series from many receiver locations back into the medium. The reversed signals then focus at their origin due to constructive interference, elucidating the source location, extent and radiation pattern. In practice, focusing may be blurred due to inaccurate Earth models or sparse sampling of the wavefield at the surface. Here, we investigate the feasibility of this method for several synthetic volcanic tremor sources and present a gallery of time reversal source images. Wave propagation is computed numerically in a 2D volcano model with realistic topography. Our results indicate that time reversal may be applicable in volcanic settings, possibly yielding new insights into volcano‐seismic LP or tremor sources.

Volcanic tremor and local earthquakes at Copahue volcanic complex, Southern Andes, Argentina

In the present paper we describe the results of a seismic field survey carried out at Copahue Volcano, Southern Andes, Argentina, using a small-aperture, dense seismic antenna. Copahue Volcano is an active volcano that exhibited a few phreatic eruptions in the last 20 years. The aim of this experiment was to record and classify the background seismic activity of this volcanic area, and locate the sources of local earthquakes and volcanic tremor. Data consist of several volcano-tectonic (VT) earthquakes, and many samples of back-ground seismic noise. We use both ordinary spectral, and multi-spectral techniques to measure the spectral content, and an array technique [Zero Lag Cross Correlation technique] to measure the back-azimuth and apparent slowness of the signals propagating across the array. We locate VT earthquakes using a procedure based on the estimate of slowness vector components and S–P time. VT events are located mainly along the border of the Caviahue caldera lake, positioned at the South-East of Copahue volcano, in a depth interval of 1–3 km below the surface. The background noise shows the presence of many transients with high correlation among the array stations in the frequency band centered at 2.5 Hz. These transients are superimposed to an uncorrelated background seismic signal. Array solutions for these transients show a predominant slowness vector pointing to the exploited geothermal field of “Las Maquinitas” and “Copahue Village”, located about 6 km north of the array site. We interpret this coherent signal as a tremor generated by the activity of the geothermal field.

Locating sources of volcanic tremor and emergent events by seismic triangulation: Application to Arenal volcano, Costa Rica

We address the issue of locating the sources of volcanic tremor and emergent events with a method requiring a limited amount of equipment. A network of several triangular seismic antennas made of vertical sensors is used. The slowness vectors are estimated at each array on a sliding window by inverting the time delays between the sensors calculated with the cross‐spectral method. A probabilistic approach is adopted whereby each measure and its error are represented by a probability density function (PDF). A weighted summation of the PDFs is carried out in which the stable directions of propagation are enhanced. The effects of the structural heterogeneities are taken into account by introducing an additional error associated to a robust hyperbolic secant (sech)‐type PDF. The resulting PDFs of the back‐azimuth are combined to calculate a PDF of the source location. The maximum likelihood of this PDF is taken as an estimate of the source position and its spread is characterized by a covariance analysis. Data from an experiment carried out at Arenal volcano (Costa Rica) with four arrays are analyzed. The precision and robustness of the method are tested by exploring the influence of the array configuration and other parameters. The mean standard deviation on the position of the sources is 600 m for the tremor and 400 m for the explosions and long‐period (LP) events. Several tremors, explosions and LP events are analyzed and their sources located. The seismogenic zone is located in a 600 m radius area centered on the active crater.

Deterministic non-linear source processes of volcanic tremor signals accompanying the 1996 Vatnajökull eruption, central Iceland

SUMMARY Observations and theoretical considerations have cast doubt on the suggestion that volcanic tremor source processes may be modelled by a linear oscillator that is set into resonance by a sustained disturbance. Volcanic tremor signals that accompanied the 1996 Vatnajokull subglacial eruption, central Iceland, have been analysed using methods from the discipline of non-linear dynamics in order to investigate the possibility that they originated from a non- linear source. The volcano-seismic phenomena associated with the eruption were recorded by a permanent network equipped with broad-band seismometers (HOTSPOT) using a sampling rate of 20 samples s −1 . The eruption was preceded by increased seismic activity for a period of 2 days, which also included a large earthquake with a moment magnitude of 5.6. The tremor during the first 2 days of the eruption has a high signal-to-noise ratio at the nearest station to the eruption site and starts as a continuous signal, later evolving to low-amplitude background tremor interrupted by high-amplitude, cigar-shaped bursts having an average duration of 250 s. The phase space, which describes the evolution of the behaviour of a non-linear system, was reconstructed from the original tremor seismograms using the delay embedding theorem suggested by Takens. The delay time used for the reconstruction was selected after examining the autocorrelation function, which showed a first zero crossing at a timelag of 4 samples and the average mutual information that showed no minimum, indicating that the tremor process may have been undersampled. Based also on phase space portraits for different delay times, a delay time of one sample interval (0.05 s) was used. The sufficient embedding dimension for phase space reconstruction was selected by applying the false nearest-neighbours method, which revealed complete unfolding of the tremor attractor at dimensions 7-8, implying upper bounds of its fractal dimension in the range 3.5-4.0. The phase space prediction errors of different segments of the tremor time-series were compared in order to check whether the attractor dynamics change substantially with time. It was found that for continuous tremor there was almost no dynamic variation, in contrast to the background tremor and the superposed bursts that gave a maximum prediction error when the former was used to predict the latter. This difference in dynamics also had an effect on their spectra: the amplitude spectrum of a burst or continuous tremor has a much sharper decay at high frequencies than that of the background tremor. A possible physical mechanism that may explain these observed characteristics involves turbulent slug flow of magma in a narrow cylindrical conduit, generating the different dynamic regimes as the Reynolds number varies.

Gas bubble dynamics model for shallow volcanic tremor at Stromboli

Volcanic tremor at Stromboli (Aeolian islands, Italy) is correlated to small infrasonic transients [Ripepe et al., 1996] which repeat almost rhythmically in time in a range between 0.8 and 1.2 s. We demonstrate that infrasonic transients are associated to small gas bubble (∼0.5 m) burstings which produces no transients in the seismic signal. Tremor ground displacement attenuates with the inverse of the distance from the craters indicating that the source is shallow. Short‐term energy release shows that infrasonic and seismic signals are linked to the same dynamical process, while at the long‐term scale it is evident that the two signals are controlled by two distinctive mechanisms. We suggest that the possible physical model acts in two steps: first, gas coalescence and, then, gas bursting. In our model, the seismic signal is related to the coalescence of a gas bubble from a layer of small bubbles, while the infrasonic signal is linked to the bursting of the bubble when it reaches the magma surface. Gas bubbles could form by free coalescence in magma or could be forced to coalesce by a structural barrier. We calculate that forced coalescence induces in magma a pressure change (∼104 Pa) 2 orders of magnitude higher than free coalescence, and it explains best the tremor ground displacement (10−5 m) recorded at Stromboli. Moreover, forced coalescence evidences the role of a structural barrier, such as a dike, in volcanic tremor source dynamics. In this gas dynamic process, the delay time of 1–2 s between infrasonic pulses could reflect the gas nucleation interval of basaltic magma [Thomas et al., 1993; Manga, 1996]. We propose that the source function for the shallow volcanic tremor at Stromboli could be the viscoelastic reaction of the magma to the pressure decrease induced by gas bubble growth rate under constant depressurization. The spectrum of our source function is controlled by the time duration of the pressure pulse, which represents the viscoelastic relaxation time of the magma and gas bubble growth rate. The predicted asymptotic decay of the frequency contents fits the spectral behavior of the volcanic tremor ground displacement recorded at Stromboli. We show that the same spectral behavior can be found in ground displacement spectra of volcanic tremor recorded on different volcanoes.

Array tracking of the volcanic tremor source at Deception Island, Antarctica

We have found experimental evidence which shows that the volcanic tremor recorded at Deception Island (South Shetland Islands, Antarctica) is a superposition in time of overlapping hybrid events. We studied data from a small aperture seismic array. Data analysis for tremor and hybrids included: (1) spectral analysis; (2) apparent slowness and back‐azimuth determination by using the zero‐lag cross‐correlation method; and (3) polarization analysis. Both types of events share these common features: (a) two dominant spectral bands at frequencies 1–3 Hz (the most energetic) and 4–8 Hz; (b) several coherent phases with the same back‐azimuth to the source and apparent slowness along the whole signal; (c) in the high frequency band, the apparent slowness is very low (around 0.17 s/km), indicating the propagation of body waves; (d) in the low frequency band, the apparent slowness is high (around 1.6 s/km), consistent with the presence of surface waves; and (e) clear P‐wave onset followed by a complex pattern of Rayleigh waves. Therefore, both types of events are strongly related because they share the same source region, the same wave‐propagation properties, and the same wave composition. Moreover, several arrivals, that resemble a single hybrid event, have been found along the tremor signals. Due to these reasons, we hypothesize that volcanic tremor of Deception Island is a superposition of hybrid type events. The source of both types could be the interaction between thaw water and hot materials in a shallow aquifer.

Locating the sources of volcanic explosions and volcanic tremor at Stromboli volcano (Italy) using beam-forming on diffraction hyperboloids

The wave-field of volcano-induced seismic signals at Stromboli volcano (Italy) was observed using a network of four broadband seismometers. In addition to the standard spectral analysis of the recorded signals, new spectral techniques such as wavelet transform and wavelet coherence analysis were also used in order to discriminate between signals related to volcanic tremor and volcanic explosions. The common features of these two types of signals are characterised by the maximum of the power spectral density values located in the frequency band > 2.0 Hz, and the main orientation of the principal axes of the particle motion ellipsoids in the frequency bands analysed. The seismic signals of the volcanic explosions at Stromboli cover the total frequency band, whereas the spectral content of the volcanic tremor is restricted to narrow frequency bands ranging from 0.3–5.0 Hz. In order to localize both types of signals, a modified inversion technique was used. The result of this beam-forming on diffraction hyperboloids of both types of signals reveal a surprisingly shallow source location ranging from 0.2 km–0.7 km above sea level, suggesting that the sources of the volcanic explosions, as well as of the volcanic tremor, are restricted to the uppermost parts of the volcano. This is in agreement with several nonseismological observations of this volcano. The combination of spectral analysis of volcanic tremor and volcanic explosions with the analysis of ground displacement implies a mechanism which can be described by an ascending bubble of volcanic gas in a nearly closed volcanic system, and finally the forced ejection of the material through a narrow channel at the top of the magma column.