An overview of volcano infrasound: From hawaiian to plinian, local to global

Journal of Volcanology and Geothermal Research 249 (2013) 123–139

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Review

An overview of volcano infrasound: From hawaiian to plinian, local to global David Fee a,⁎, Robin S. Matoza b a b

Wilson Infrasound Observatories, Alaska Volcano Observatory, Geophysical Institute, University of Alaska Fairbanks, AK, USA Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, La Jolla, CA, USA

a r t i c l e

i n f o

Article history: Received 2 April 2012 Accepted 4 September 2012 Available online 10 September 2012 Keywords: Infrasound Volcano Acoustics Review Explosive eruptions Propagation

a b s t r a c t Volcano infrasound is an increasingly useful technique for detecting, locating, characterizing, and quantifying eruptive activity, and can be used to constrain eruption source parameters. In recent years, studies of infrasound data from active volcanoes have shown clear progress towards mitigating volcanic hazards and understanding volcanic source processes. Volcano acoustic sources are shallow or aerial, thus volcano infrasound data provide valuable information on eruption dynamics and are readily combined with direct and remote observations of gas, ash, and other eruptive phenomena. The infrasound signals produced by volcanoes are indicative of the eruption style and dynamics. Here we review the diversity of infrasound signals generated by a wide variety of volcanic eruptions, from hawaiian to plinian, and the physical processes inferred to produce them. We place particular emphasis on regional (15–250 km distance) and global (>250 km distance) volcano infrasound studies, as recent work in this area has made significant advances in monitoring and characterizing remote and difficult-to-monitor eruptions. Long-range infrasonic detection of explosive volcanic eruptions is possible due to the energetic source mechanisms involved, minor atmospheric attenuation at low frequencies, and the existence of waveguides in the atmosphere. However, accurate characterization of the atmosphere and its spatiotemporal variability is required for reliable long-range sound propagation modeling and correct interpretation of global infrasound recordings. Conversely, because volcanic explosions are energetic and sometimes repetitive infrasound sources, they can be used to validate atmospheric and acoustic propagation models. © 2012 Elsevier B.V. All rights reserved.

Contents 1. 2. 3. 4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Infrasound and acoustics primer . . . . . . . . . . . . . . . . . . . Brief history of volcano infrasound . . . . . . . . . . . . . . . . . . Hawaiian to plinian infrasound . . . . . . . . . . . . . . . . . . . . 4.1. Volcano infrasound terminology: tremor, jet noise, and explosions 4.2. Hawaiian . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Strombolian . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Vulcanian . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Subplinian–plinian . . . . . . . . . . . . . . . . . . . . . . 4.6. Infrasound from long-period (LP) seismic events . . . . . . . . 4.7. Ultra-Long-Period (ULP) infrasound and acoustic gravity waves . 4.8. PDCs and rockfalls . . . . . . . . . . . . . . . . . . . . . . 5. Regional and global volcano infrasound and propagation . . . . . . . . 5.1. Atmospheric structure and variability . . . . . . . . . . . . . 5.2. Long-range sound propagation . . . . . . . . . . . . . . . . . 5.3. Local propagation effects . . . . . . . . . . . . . . . . . . . 5.4. Remote detection of explosive volcanism . . . . . . . . . . . . 5.5. Atmospheric structure inferred from volcano infrasound . . . . . 6. Future studies and directions . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

⁎ Corresponding author. Tel.: +1 907 474 7564; fax: +1 907 474 7290. E-mail address: [email protected] (D. Fee). 0377-0273/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jvolgeores.2012.09.002

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1. Introduction It is well-established that volcanoes are prodigious sources of infrasound, defined as acoustic, or sound, waves below 20 Hz. As magma rises within the earth, pressure oscillations are created from various fluid-dynamic, thermodynamic, and elastodynamic processes. These oscillations couple into the ground in the form of seismic energy, and energy from shallow sources may propagate into the atmosphere in the form of acoustic energy. In particular, the shallow exsolution and release of volcanic gases produces extensive atmospheric perturbations including short-duration, impulsive bursts (explosions) and long-duration vibrations (tremor). Lava dome collapses, Pyroclastic Density Currents (PDCs), and rockfalls generate infrasound. Aerial volcanic processes, such as volcanic jets and plumes, can produce significant acoustic energy. Quantification of volcano acoustics therefore provides information on shallow processes within the conduit and hydrothermal system as well as above the vent. The infrasound source location is also noteworthy in that it permits comparison with direct and remote observations of gas, ash, and other eruptive phenomena. In contrast, volcano seismology involves subsurface sources, which are usually impossible to observe directly. Due to the large length scales involved with volcanic eruptions, the majority of volcano acoustic oscillations occur at infrasonic frequencies. The relatively low acoustic attenuation in the atmosphere at these frequencies allows infrasound from large eruptions to propagate long distances and to be recorded globally. Recent studies have demonstrated how infrasound can be used to detect, locate, characterize, and quantify volcanic eruptions, as well as constrain various eruption source parameters. These studies have shown clear progress in hazard mitigation, and knowledge on volcano-infrasound source processes is expanding. In addition, volcanic signals have been used to study the propagation of sound and atmospheric structure. Numerous sources of infrasound exist, and may be recorded both locally and globally. Examples of man-made infrasonic sources include explosions (Ceranna et al., 2009), high-speed aircraft (Liszka and Waldemark, 1995), rockets (Balachandran and Donn, 1971), and industrial activity (Liszka, 1974). Natural sources of infrasound are diverse: aurora (Wilson, 1967), lightning and sprites (Farges and Blanc, 2010), surf (Garces et al., 2006), wave–wave ocean interactions (microbaroms) (Waxler and Gilbert, 2006), avalanches (Scott et al., 2007), meteors (ReVelle, 1975), mountain-associated waves (Wilson et al., 2010) earthquakes (Mutschlecner and Whitaker, 2005), tsunamis (Le Pichon et al., 2005b), and of course volcanoes. The reader is referred to Le Pichon et al. (2010) for in-depth discussions of global infrasound research and monitoring. The diverse nature of volcanic activity produces a wide variety of infrasound signals, and nearly all types of active volcanism have been documented to produce infrasound. In this manuscript we describe the various types of volcano infrasound signals and the physical processes inferred to produce them. We define local infrasound recordings as those within ~15 km of the volcano, regional from ~15 to 250 km distances, and global for >250 km distances. Here we emphasize regional and global volcano infrasound observations and modeling, and document the recent progress in this area. The dynamic atmosphere affects the propagation of infrasound at all observation ranges. Generally, however, as propagation range increases, the spatiotemporal variability of the atmosphere becomes increasingly significant, such that accurate characterization of the atmosphere from 0 to 140 km altitude is necessary to interpret long-range infrasound recordings. This manuscript is not intended to be a comprehensive review of the volcano infrasound literature; the reader is referred to Johnson and Ripepe (2011), and Garces et al. (in press) for more references and information on the subject and Arrowsmith et al. (2010) for a review on seismoacoustics. A primer on infrasound and acoustics is given in Section 2, followed by a description of the history of volcano infrasound in Section 3. Section 4 describes typical signals and models for various types of volcanic activity,

while Section 5 focuses on regional and global observations and propagation modeling of volcano infrasound. We conclude with a discussion of future directions in the field (Section 6). 2. Infrasound and acoustics primer Acoustic energy is produced by a multitude of oscillatory processes. Once generated, acoustic energy propagates as a mechanical pressure wave through a medium. In the atmosphere, sound waves propagate within a gas, compressing and rarifying the medium. Acoustic energy in the atmosphere propagates as a compressional or longitudinal wave as its motion is in the same direction as its propagation. No shear waves are supported. Sound is also produced over a wide range of frequencies: audible between ~20–20,000 Hz, ultrasound above 20,000 Hz, and infrasound below 20 Hz. At even lower frequencies (and longer wavelengths) gravity begins to act as a restoring force on the pressure wave, creating acoustic-gravity waves (Pierce, 1981). The speed of sound c, in an ideal gas is given by: c¼

pffiffiffiffiffiffiffiffiffi γRT

ð1Þ

where γ is the specific heat ratio, R is the universal gas constant, and T is the temperature (Pierce, 1981). The sound speed is thus proportional to the square root of the temperature, and is 343 m/s in a typical 20 °C atmosphere at sea level. Extremely high amplitude sources can produce supersonic waves that propagate faster than the speed of sound and produce nonlinear shock waves, which decay and eventually transition to linear sound waves. In addition to temperature (Eq. (1)), wind also affects the propagation path and travel times, and will be discussed along with long-range sound propagation in Section 5. Acoustic energy loss in the atmosphere (attenuation) results from two main processes: absorption and geometrical spreading. Absorption in the atmosphere is further divided into two types: classical and rotational. Classical losses are associated with the transfer of energy from the kinetic energy of the gas molecules to heat, while rotational losses are associated with excitation of the gas molecules' energy states. Absorption of acoustic energy varies in the atmosphere with height, and at low frequencies decreases with frequency approximately to the power of 2. The relatively low amount of absorption in the infrasound band (~ 10 −6 dB/km at 0.1 Hz vs. ~ 2.4 dB/km at 125 Hz) (Sutherland and Bass, 2004) allows infrasound to propagate long distances (up to thousands of kilometers). Geometric spreading occurs as a consequence of wavefront expansion. For a point source in an unbounded homogeneous medium, spherical spreading prevails and the pressure decreases as 1/r, where r is the distance. Cylindrical spreading can result from either an extended (line) source or where the normally spherically spreading wave propagates in a waveguide (duct) (Section 5). The pressure loss for cylindrical spreading occurs as 1/√r (Pierce, 1981). Energy in the wave is conserved as it spreads but not as it is absorbed. Energy losses due to ground reflections are typically negligible at infrasonic frequencies; consequently, little energy is lost to ground-bounces in long-range ducting. Scattering of acoustic energy from turbulence in the atmosphere and diffraction from and around topography are additional sources of attenuation (Salomons, 2001). Volcanic topography is often pronounced, resulting in amplitude losses and waveform distortion, which require accounting for at local (b15 km) propagation ranges (Matoza et al., 2009b). Two other relevant concepts are that of the compact source and far-field. An acoustic source can be considered compact when its largest dimension, l, is much smaller than the radiated sound wavelength: klb b 1, where k = 2π/λ is the wavenumber. This relationship is usually met in volcanic environments, unless the source is extremely large. Plane-wave propagation occurs when an acoustic wave is in the far-field. This assumes the acoustic field quantities change with time and with one spatial dimension, but do not change with position normal

D. Fee, R.S. Matoza / Journal of Volcanology and Geothermal Research 249 (2013) 123–139

to the propagation direction. The far-field occurs for kr≫1, where r is the distance (Pierce, 1981). For a 0.5 Hz sound wave, the far-field occurs at r≫109 m. The total acoustic source energy of a compact source in the far-field can be estimated given assumptions about the wave propagation from source to receiver. First we define the acoustic intensity as the average rate of flow of energy through a unit area normal to the direction of propagation, I = p 2/ρc, where p is the excess pressure and ρ the density of the medium. The acoustic energy, Ea, is then the intensity integrated over time, T, and the region it propagates through: Ea ¼

Ω T 2 ∫ Δp ðt Þdt ρc 0

ð2Þ

where Δp is the change in pressure and Ω is the solid angle (surface area of radiated sound projected onto the unit sphere). For an unbounded source in free space (i.e., neglecting volcano topography and assuming a homogeneous atmosphere), sound radiates through a perfect sphere and Ω = 4πr 2 where r = distance to the source, while for a source radiating into a half-space (hemisphere) Ω =2πr 2. In real volcanic environments, different values of Ω have been used, and the reader is referred to Johnson and Ripepe (2011) and Garces et al. (in press) for further discussion on this subject. The acoustic power can be calculated by differentiating Ea with respect to time. For more in-depth information on acoustics the reader is referred to Morse and Ingard (1968), Pierce (1981) and Kinsler et al. (1982). Specialized sensors are deployed to detect infrasound. These sensors measure pressure changes (p) relative to the ambient atmospheric pressure (P0). Compared to the ambient atmospheric pressure at sea level (~105 Pa), the amplitude of recorded infrasound signals at typical distances from volcanoes (> 2 km) are generally in the range of 0.1–100 Pa. Multiple types of infrasound sensors have been used. Electret condenser elements are cheap and have moderate signal-noise (i.e. higher noise floors), but have limited frequency ranges. Also low-cost are microelectromechanical systems (MEMS) silicon chips. MEMS sensors have been used more recently and are broadband, with lower noise levels than the electrets. The global International Monitoring System (IMS) infrasound network (Section 3) and some temporary arrays use high-resolution, very low-noise, broadband MB2000 and Chaparral Physics sensors, but these are more expensive and, depending on the sensor, less portable (Ponceau and Bosca, 2010; Johnson and Ripepe, 2011). Infrasound sensors are deployed either as single sensors or as part of a network or array. Networks consist of multiple individual sensors deployed nearby and surrounding the source, and can permit accurate absolute source location. Various techniques, such as semblance (Neidell and Tarner, 1971; Ripepe and Marchetti, 2002), have been successfully used for source localization and discrimination. The semblance is a measure of the coherence between sensors by considering the energy ratio between time-shifted waveform stacks. An array is a group of sensors placed in a systematic configuration to detect coherent acoustic waves. Fig. 1b shows an example array configuration at IS53, Fairbanks, Alaska. Once the signal is recorded, numerous techniques can be employed to detect coherent acoustic waves traveling across the array, determine their propagation speed (trace velocity), and identify their bearing from north to the source (back-azimuth). Beamforming of array data is also used to increase the signal-to-noise ratio (Johnson and Dudgeon, 1992). A plane wave approximation is made for most array processing techniques. The two most common detection methods in use by the nuclear-monitoring infrasound community are the Progressive Multi-Channel Correlation Method (PMCC) (Cansi, 1995) and the Fisher Ratio (Melton and Bailey, 1957). When an array is close to the source and in the near-field, the plane wave approximation is no longer valid and other methods should be used, e.g. those based on wavefront curvature (Szuberla et al., 2006) and grid search techniques (e.g. Ripepe and Marchetti, 2002; Cannata et al., 2009).

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The primary source of recorded non-acoustic noise is wind, and extensive research has been done to reduce the effects of wind-induced noise (Christie and Campus, 2010; Walker and Hedlin, 2010). Arrays are designed so that wind noise will be incoherent between elements (i.e. sensors), while longer wavelength acoustic waves will be coherent. Noise is commonly reduced at the array element by spatially averaging the pressure field by connecting multiple pipes with inlet ports or attaching microporous hoses. However, site selection in low-wind environments, often in dense forests where wind noise is greatly reduced, often proves to be the best solution (Walker and Hedlin, 2010). 3. Brief history of volcano infrasound In 1883, barometers around the world recorded low-frequency pressure signals from the eruption of Krakatau Volcano, Indonesia. Cannonlike sounds were audible as far away as 5000 km, while acousticgravity waves (periods >1 min) propagated at least seven times around the world (Strachey, 1888). In a pioneering study of earthquakes and airborne explosions at Mount Asama, Japan, Omori (1912) used seismometers and barometers to discriminate between seismic signals associated with explosions and non-explosion earthquakes. Many of the explosion events were audible at distances of 200–300 km, and some were powerful enough to break doors and windows. Omori used this information to map the sound propagation and acoustic shadow zones, and even considered the effects of wind and topography on the acoustic signals; these are on-going research areas in modern volcano infrasound. The use of weather barometers and infrasonic microphone arrays to study low-frequency (b1 Hz) atmospheric pressure waves from volcanic explosions at regional to global ranges (tens to thousands of kilometers) was continued throughout the 20th century, e.g., Mount Pelee, Martinique, 1902 (Tempest and Flett, 1903); Bezymianny, Russia, 1956 (Gorshkov, 1960); Mount St. Helens, USA, 1980 (e.g. Reed, 1987); Mount Tokachi, Japan, 1988; Sakurajima, Japan, 1989; Mount Pinatubo, Philippines, 1991; and Ruapehu, New Zealand, 1995 (Morrissey and Chouet, 1997). Wilson et al. (1966), and Wilson and Forbes (1969) provided the first infrasonic microphone array observations of volcanoes in the low infrasound band (0.01–0.1 Hz). The 1963 eruption of Mount Agung, Bali was recorded 14,700 km away in Boulder, Colorado; and the 1967 eruptions of Redoubt and Trident Volcanoes, Alaska, were recorded in Fairbanks, Alaska. The main emphasis of these studies was the atmospheric propagation of the signals. Infrasonic microphone arrays were also installed at Kariya, Japan (Tahira, 1982). Although limited to the band 0.1–1 Hz, the Kariya array routinely detected explosions from Sakurajima Volcano at a range of 710 km and also recorded the 1991 Pinatubo, Philippines eruptions at a range of 2770 km. These data were used to infer the eruptive time history when visual or instrumental observations close to the volcano were impossible (Tahira et al., 1996). In addition to these lower frequency observations, volcano-acoustic signals at audible (>20 Hz) frequencies have also received attention. Frank Perret made probably the first recordings of volcano acoustic signals using moving-coil microphones at Vesuvius Volcano, Italy, eventually also recording signals at Etna and Stromboli Volcanoes, Italy; Kilauea Volcano, Hawaii; Sakurajima Volcano, Japan; Mt. Pelee Volcano, Martinique; and Soufriere Hills Volcano, Montserrat (Perret, 1950). The first tape recordings of volcanic sounds were apparently made by the NHK Broadcasting Bureau of Japan (Snodgrass and Richards, 1956). In 1952, a program of volcanic acoustics was initiated by James Snodgrass at the Scripps Institution of Oceanography, leading to a decade's worth of underwater and airborne acoustic recordings of volcanic sounds (Richards, 1963), however, the sonobuoy-based recording systems had a poor frequency response below 50 Hz. Richards (1963) provided a table summarizing the results of their investigations, which describes acoustic signals recorded from various idealized styles of volcanic activity. We have attempted to provide an update of the table by Richards as Table 1, incorporating recent advances in volcano acoustics and infrasonics (Section 4). Also considering higher frequency audible acoustic

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Fig. 1. Global map of infrasound arrays. a) Map of IMS arrays (black circles) and volcanoes discussed in detail in this paper (red triangles). An example IMS array configuration (from IS53, Fairbanks, AK) is shown in b). The array consists of eight elements arranged in an outer pentagon and inner triangle.

signals, a pioneering study by Woulff and McGetchin (1976) represents the first attempt to make a quantitative link between acoustic radiation and fluid mechanics at volcanoes using equivalent source theory. From about the 1980s, near infrasound (1–20 Hz) from volcanoes has been recorded (e.g. Dibble et al., 1984; Firstov and Kravchenko, 1996). Since then, it has become increasingly clear that the frequency band from about 0.1 to 20 Hz is a very rich band for volcano acoustics. As with volcano seismology, a diverse range of signals are recorded in this frequency band resulting from a variety of active volcanic processes (Sections 4–5). Iguchi and Ishihara (1990) and Yamasato (1997) installed infrasonic microphones at distances of 2–5 km from Sakurajima, Suwanosejima, and Unzen Volcanoes in Japan, recording numerous explosions, pyroclastic flows (Yamasato, 1997), harmonic infrasonic tremor (Sakai et al., 1996), and impulsive signals associated with long period (LP) events (Iguchi and Ishihara, 1990; Yamasato, 1998). Acoustic studies began at Stromboli Volcano in the early 1990s (Braun and Ripepe, 1993; Vergniolle and Brandeis, 1994; Buckingham and Garces, 1996). Kamo et al. (1994) proposed to integrate infrasound into volcanic eruption early warning systems, similar to more recent work by Garces et al. (2008).

In 1996, the Comprehensive Nuclear-Test-Ban Treaty (CTBT) was opened for signature by the United Nations to ban all testing of nuclear explosions. To enforce this treaty, the International Monitoring System (IMS) was constructed and consists of global networks of seismic, infrasound, hydroacoustic, and radionuclide stations. The infrasound portion consists of 60 planned arrays dispersed throughout the globe to detect atmospheric nuclear tests (Fig. 1a). The development of the IMS has led to a surge in infrasound research and technology advancements (Le Pichon et al., 2010). Construction of the IMS infrasound network has also led to a somewhat practical interest in volcano acoustics. Explosive volcanic eruptions are large, naturally occurring, infrasonic sources, which may be used to validate and improve methods for infrasound propagation modeling, signal detection, and source location of arbitrary remote explosion sources. 4. Hawaiian to plinian infrasound Traditionally, volcanic eruptions have been broadly assigned to categories based on the eruption style, tephra deposits, and magma viscosity. These idealized types of volcanic activity (e.g. hawaiian, strombolian,

Table 1 General eruption styles and their associated acoustic (infrasonic) character and source processes. See text for details and references on acoustic character and source processes. Activity

Description

Generalized acoustic character of eruptions

Hawaiian

Lava fountaining from eruptive fissures or vents; often have long-lived, non-explosive, effusive phases; occassional explosions or ”degassing bursts”

Strombolian

Short-duration, recurring explosions of gas slugs producing jets of gas and magma fragments; frequency and intensity variable

Vulcanian

Short-duration, energetic explosions that can produce high ash clouds and PDCs; source either the explosive failure of a “capped” conduit (lava plug) or the interaction of magma and external water; often associated with lava dome destruction High-energy, sustained eruptions producing massive eruption clouds that may extend high into the atmosphere; momentum-driven jet transitions to buoyancy driven plume Lateral flows of hot gas and particles from fountain collapse, lava dome collapse, or lateral blasts

Hissing fumeroles (audible), gas jetting from fountains; postulated tremor sources include 1) bubble cloud oscillation, 2) helmholtz and other resonance of cavities above degassing surfaces, 3) gravity-driven oscillations; bursting slugs and shallow gas accumulations may cause explosions Short-duration compression and rarefaction Explosions from bursting of gas slug at surface followed by short-duration jetting; degassing with codas of a few to tens of seconds; relatively low-amplitude; tremor during ex- through sealed caps (e.g., Karymsky) generates muffled explosions with more complex tended degassing episodes waveforms Explosive bursting of capped viscous lava surface, Short-duration, high-amplitude compresfollowed by jetting and/or tremor; chugging from sion and rarefaction, often long codas with harmonic spectra; “chugging” tremor is au- repeated pressure pulses dible with pulsating infrasound

Subplinian–plinian

Pyroclastic Density Currents (PDCs)

Acoustic source processes

Fountains produce sustained, broadband infrasound; effusion produces variable tremor, from harmonic to broadband; explosions can have complex signatures

High-amplitude, sustained (minutes to hours) infrasound able to propagate and be recorded globally; broadband with characteristic “jet noise” spectral shape Moving source with broadband spectra

Turbulence-related sound from the volcanic jet (jet noise)

Relatively unknown, likely from turbulent processes within the flow

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etc.) each have characteristic acoustic signals as well, as first documented by Richards (1963). In this section we describe the typical infrasound signals associated with the predominant types of volcanic activity, as well as the models used to explain them. We stress that these categories and descriptions are by no means comprehensive, and individual volcanoes and eruptions can exhibit multiple types of activity. Table 1 briefly describes each type of activity and summarizes the dominant acoustic characteristics and inferred source processes. Figs. 2 and 3 shows the acoustic waveforms from a variety of eruptions, demonstrating the observed variability in time, energy, and overall character. 4.1. Volcano infrasound terminology: tremor, jet noise, and explosions Volcano acoustic nomenclature generally follows that first developed in volcano seismology (e.g. Chouet, 2003). Infrasonic tremor is a common and varied volcano acoustic signal, and is broadly defined as a continuous vibration of the atmosphere lasting from seconds to months. Numerous types of volcanic tremor exist, largely classified based on their time and frequency domain characteristics. Harmonic tremor is distinguished by a frequency spectrum containing a fundamental peak and associated harmonics, while monochromatic (or monotonic) tremor has a relatively simple spectrum with a single peak. Harmonic tremor is more common than monochromatic tremor. Gliding refers to tremor in which the spectral peaks change frequency over time. Broadband tremor has a wide frequency range with no sharp spectral peaks. Spasmodic tremor is continuous in time with amplitude variations. Banded

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or episodic tremor has bursts of tremor separated in time, with the bursts resembling “bands” on a spectrogram (Konstantinou and Schlindwein, 2002). Explosions are transient events characterized by a rapid volume expansion (compression) into the atmosphere followed by a subsequent rarefaction (decompression). A coda of a few seconds to minutes follows. Volcanic explosions have a wide variety of characteristics and at times complicated source-time functions, but typically have impulsive onsets and durations of seconds to minutes. They are commonly associated with strombolian and vulcanian eruptions (Sections 4.3–4.4), but can occur during nearly every type of activity. Sustained volcanic explosions generally produce broadband tremor and jet-noise-like signals, where jet noise is defined as sound produced by a turbulent jet flow itself and is discussed further in Section 4.6. 4.2. Hawaiian Acoustic records of lava fountaining and fissure eruptions are somewhat rare. Fountaining from low-level fissure eruptions produces a tremor-like signal with broadband spectra (Cannata et al., 2009; Fee et al., 2011a). Movement of eruptive activity along fissures, including the rupture of new fissure segments, has been tracked using infrasound arrays and networks (Cannata et al., 2011; Fee et al., 2011a), providing insight into the eruption location and intensity otherwise not available during periods of darkness and/or poor visibility. Acoustic source generation during fountaining is poorly understood.

a) b) c) d) e)

f) g)

Fig. 2. Infrasound waveforms from hawaiian to plinian activity. Vertical scale in each case is acoustic pressure in (Pa). The respective recording distance to the volcano (r) is listed to the right of the waveform. a) 0.1–15 Hz harmonic tremor from Halema'uma'u Vent, Kilauea Volcano. The tremor begins with an impulsive degassing burst that cleared the vent (Fee et al., 2010a). b) Infrasound pulses associated with LP seismic “drumbeat” events at Mount St. Helens. The events are broadband and have durations of ~10 s (Matoza et al., 2009b). Strombolian activity from c) Stromboli Volcano, Italy (Ripepe and Marchetti, 2002) and d) Tungurahua Volcano, Ecuador. Harmonic tremor follows several of the Tungurahua explosions for a couple to tens of minutes (Fee et al., 2010b). e) Complex explosion waveforms from Karymsky Volcano, Russia. Although the signals could be classified as strombolian, the explosive onset is asymmetric (larger compression than rarefaction) and the durations are extended, possibly due to jetting. f) Subplinian eruption from Tungurahua Volcano. Multiple pulses of sustained, broadband infrasound occur, with large discrete explosions intermixed (Matoza et al., 2009a; Fee et al., 2010b). g) Subplinian–plinian eruption of Tungurahua Volcano. Sustained, broadband, high-amplitude infrasound gradually builds and peaks near hour 11.75 with a ~45 minute paroxysmal plinian phase (Matoza et al., 2009a; Fee et al., 2010b). Note a–e) represent 1 h of data, while f–g) 14 h.

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a)

b)

c)

d)

e)

f)

Fig. 3. Transient infrasound signals. Vertical scale in each case is acoustic pressure in (Pa). The respective recording distance to the volcano (r) is listed to the right of the waveform. a) Infrasound pulse associated with an LP seismic “drumbeat” event at Mount St. Helens (Matoza et al., 2009b). b) Rockfall signal from Mount St. Helens. Very-Long-Period (>30 sec) energy is clearly present (Moran et al., 2008). c) Typical impulsive, short-duration explosion from Stromboli Volcano, Italy (Ripepe and Marchetti, 2002) d) Explosion from Karymsky Volcano, with the compression much larger than rarefaction. Jetting continues for ~2 min after the explosion onset. e) Explosion from Tungurahua, with tremor or jetting after the onset (Matoza et al., 2009a; Fee et al., 2010b). f) Large vulcanian explosion from Augustine Volcano, Alaska. A sharp, high-amplitude explosive onset is followed by tremor or jetting (Petersen et al., 2006).

The majority of acoustic signals documented on the Island of Hawaii have actually been from effusive activity or passive degassing. Kilauea Volcano, Hawaii produces extensive infrasound from its principal degassing locations, with the character of the sound being largely controlled by the cavities and conduits confining gas escape. Nearcontinuous tremor was recorded from the onset of the opening of a new eruptive vent at Halema'uma'u Crater, Kilauea in 2008 (Fee et al., 2010a). The infrasonic tremor was modeled to be due to Helmholtz resonance (volume oscillation) of a large cavity beneath the vent. The Helmholtz resonance frequency, fh, can be approximated by: fh ¼

ccav 2π

rffiffiffiffiffiffiffiffi S Vk′

ð3Þ

where ccav is the sound speed within the cavity, S is the area of the “neck” above the cavity, k′ is the effective length of the neck, and V is the cavity volume. Estimates of the cavity volume (and subsequent changes in lava level) were made by evaluating changes in fh. At Halema'uma'u, the gas was visibly pushed out of and sucked into the vent at frequencies consistent with the peak infrasonic tremor frequency (Fee et al., 2010a). The Halema'uma'u tremor waveforms are shown in Fig. 2a and the spectrum in Fig. 5c. Note the large spectral peak at ~0.5 Hz, attributed to Helmholtz resonance. Similarly, Villarica Volcano, Chile produced high-amplitude, monotonic infrasound modeled to result from Helmholtz resonance (Goto and Johnson, 2011).

Matoza et al. (2010) recognized that the harmonic tremor from Pu'u 'Ō'ō Crater, Kilauea may be related to persistent effusive degassing and the unique crater geometry and proposed a mechanism consisting of self-sustained gas-flow-induced oscillations. In small-scale laboratory experiments, “hole tones” result from a low-velocity gas jet flowing from one boundary and impinging on a second boundary with a hole in it. The aeroacoustic loop frequency (fa) can be estimated by (Rossiter, 1964): L L 1 þ ¼ Uj c f a

ð4Þ

where L is the distance between the jet nozzle and solid boundary and Uj is the jet velocity. Considering realistic values for crater dimensions and gas flux at Pu'u 'Ō'ō crater in early 2007, frequencies consistent with those recorded (~0.2–1 Hz) are obtained. If these laboratory-scale processes operate at large volcanic length-scales (hence infrasonic frequencies) as suggested by Matoza et al. (2010), this offers a potential excitation mechanism for Helmholtz modes of near-surface volcanic cavities. The Helmholtz resonator (Eq. (3)) still requires a trigger or excitation mechanism to sustain the resonance. Villarica Volcano, Chile; as well as Halema'uma'u Crater and Pu'u O'o Crater, Hawaii, all hosted roiling, degassing lava lakes, which also potentially excited the cavities into resonance. Other potential infrasonic tremor sources proposed for volcanoes exhibiting hawaiian style behavior include collective oscillations of bubbles in the shallow volcanic conduit (Matoza et al., 2010) and gravity-driven oscillations of bubbly magma (Ripepe et al., 2010b). Lava tube skylights may also radiate infrasound (Garces et al., 2003; Matoza et al., 2010). Transitory explosions or “degassing bursts” also occur at low-silica content volcanoes. The opening of the vent at Halema'uma'u (Fig. 2a) is one such example, with numerous complex, long-duration degassing bursts documented (Fee et al., 2010a). Patrick et al. (2011) used infrasound as a proxy for shallow degassing of the Halema'uma'u lava lake, and combined it with visual and seismic data to provide timing and source constraints on episodic tremor and degassing bursts. 4.3. Strombolian Strombolian eruptions typically produce short-duration infrasonic signals consisting of sharp compressional onsets, followed by a slightly longer duration rarefaction and coda of a few to tens of seconds (Figs. 2c, 3c, Table 1). At Stromboli Volcano, Italy, the type locale for strombolian eruptions, two principal explosions signals have been observed: 1) high amplitude (20–80 Pa at 350 m) with short durations (3–5 s) and 2) lower amplitude (10–30 Pa at 350 m) with longer (>10 s), more complex codas (Ripepe and Marchetti, 2002; Harris and Ripepe, 2007a). Degassing at the surface is characterized by the episodic release of discrete gas accumulations (slugs). If the slug arrives with minimal overpressure, active “puffing” events occur, while over-pressurized slugs result in explosive bubble bursting at the surface (Harris and Ripepe, 2007b). Simple bursting of the slug likely produces the first type of signal at Stromboli, while more sustained gas flow may generate more complex bursting observed in the second type of signals (Ripepe et al., 1996). Johnson et al. (2008a) used high resolution video and acoustic data to model the source of strombolian eruptions at Mt. Erebus, Antarctica as over-pressured gas slugs reaching the surface and bursting followed by short duration gas jetting. The acoustic waveforms at Erebus displayed the typical strombolian explosion features. Seismoacoustic sources within the conduit, rather than from bursting at the top, have also been proposed. Buckingham and Garces (1996) postulated that if a bubble rises within the magma column and passes into a region of much lower density, the bubble will undergo rapid, violent expansion and produce an “explosion” signal. The bubble then overshoots

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its equilibrium radius and collapses, only to overshoot its equilibrium radius again, beginning a new expansion/contraction cycle. The pressure perturbation would radiate into the earth as a seismic signal and assuming an open-vent system, into the atmosphere as an acoustic wave. This process is analogous to underwater explosions, and although unlikely for gas slugs, it may occur in some volcanic systems. Vergniolle and Brandeis (1994) explain sound from strombolian eruptions as the result of oscillation of the slug near the surface, followed by its subsequent burst. Note there is often variety in the acoustic waveforms from “typical” strombolian eruptions, demonstrating the variability in shallow source processes at these volcanoes and the potential pitfalls of grouping signals simply based upon general eruption categories (e.g. Table 1). Karymsky Volcano, Russia shows significant variation in its explosions signals, including “chugging” events composed of a series of discrete pulses that can be intermittent or continuous and resemble harmonic tremor (Johnson and Lees, 2000; Lees et al., 2004). Figs. 2e and 3d show explosions from Karymsky in 2011. The initial compressions from these explosions are larger than the subsequent rarefaction, and the durations are often long and complex, possibly related to jetting. Infrasonic tremor is also common in strombolian systems (e.g. Garces et al., 1998), and may be due to gas release at the lava/air interface (Ripepe et al., 1996). Attempts to estimate mass flux for volcanic eruptions have been made using infrasound data. Assuming a compact acoustic source (Section 2), the excess pressure as a function of time and distance, p(r,t), can be estimated by (Lighthill, 1978): pðr; t Þ ¼

Q ðt−r=cÞ Ω

ð5Þ

where Q is the source strength or density times the volumetric acceleration. The mass flux and cumulative mass flux can then be estimated by integrating Q over the eruption time interval (Johnson et al., 2004). Numerous studies have used this relationship to model mass flux and volumes from monopole sources (e.g. Firstov and Kravchenko, 1996; Johnson et al., 2003; Dalton et al., 2010). Although this method is useful and attractive for estimating mass flux, it is not valid for multipole (anisotropic) sources. Gerst et al. (2008) integrated radar, video, and infrasound data to study the directivity of eruptions at Erebus Volcano, Antarctica and found that significant directivity exists for the majority of the explosions, indicating a dominant non-monopole component. This method for estimating mass flux may thus be accurate only for the most simple and idealized volcano-acoustic sources and recording environments. 4.4. Vulcanian Acoustic signals from vulcanian eruptions typically consist of a short duration, high-amplitude compression and subsequent rarefaction, followed by a longer duration coda up to tens of minutes (Table 1). Fig. 3f shows an infrasound signal from a vulcanian explosion; this example is from the 11 January 2006 1344 UTC eruption of Augustine Volcano, Alaska (Petersen et al., 2006). The high-amplitude, impulsive onset represents significant overpressure and burst at the source, while the longer duration coda likely represent jetting or tremor. Often the coda consists of harmonic spectra that exhibit gliding. Large vulcanian eruptions can also produce nonlinear shock-waves characterized by an “N-shape” compression and rarefaction (Morrissey and Chouet, 1997). Note these N-waves are defined by extremely fast compressional rise times characteristic of supersonic propagation, compared to impulsive, but slower compressional rise times for typical vulcanian and strombolian acoustic signals. Additionally, at Sakurajima Volcano, Japan, the site of many vulcanian explosions, a precursory compression just before the explosion of a lava plug is visible in the infrasound records. Yokoo et al. (2009) used high resolution video imagery and infrasound to explain the precursory

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compression as the expansion of a gas volume beneath the lava plug causing the surface to swell. Some recent high-energy, short-duration infrasound signals from “vulcanian” eruptions differ from typical explosions in that they have emergent onsets and peak amplitudes occurring some time after the onset (e.g. Petersen et al., 2006; Fee et al., 2011b), possibly caused by gradual dome collapse. Note the acoustic signals from vulcanian and strombolian explosions can contain similar characteristics, and no clear dividing line exists between the two. Marchetti et al. (2009) demonstrate how using infrasound and thermal infrared data can help discriminate between strombolian and vulcanian explosion style and give insight into jet and plume dynamics. 4.5. Subplinian–plinian The relative infrequency and hazard associated with subplinian and plinian eruptions make high-resolution (local-regional) infrasound recordings of these events difficult to obtain and rare, yet highly valuable for hazard mitigation. Because subplinian and plinian eruptions produce high-amplitude, sustained (minutes to hours) broadband infrasound (Fig. 2f–g), they are more commonly recorded up to thousands of kilometers from the source (e.g. Vergniolle and Caplan-Auerbach, 2006; Fee et al., 2010b; Fee et al., 2010c; Matoza et al., 2011a). In addition to delineating explosion timing and chronology (e.g., Fee et al., 2010a; Matoza et al., 2011a), of particular relevance is the observed correlation between sustained, low-frequency acoustic energy and ash cloud height during large eruptions (e.g. Fee et al., 2010b). Fig. 4 shows the a) waveforms, b) spectrogram, and c) acoustic power (black line) and ash cloud height (green lines) from the August 2006 eruption of Tungurahua Volcano, Ecuador. Increases in acoustic power correlate well with increases in ash cloud height. Further, the paroxysmal plinian phase of the eruption produced a clear shift in the acoustic spectrum to lower frequencies (Fig. 4d) coincident with a ~200 km wide and ~25 km high ash cloud (Fig. 4c,e) (Fee et al., 2010b). Only recently has high-resolution infrasound from these large eruptions been recorded and analyzed at local and regional distances (Garces et al., 2008; Matoza et al., 2009a; Fee et al., 2010b). This particular type of broadband infrasound accompanying highenergy, sustained volcanic eruptions has been likened to a low frequency form of jet noise. Jet noise is generated, for example, from the high-speed flows exiting man-made jet engines and rockets. Jet noise has been studied extensively because of its critical role in commercial and military aviation noise control issues. Woulff and McGetchin (1976) first hypothesized that acoustic signals from some small-scale volcanic eruptions (e.g., strombolian) may originate from turbulent interactions within the volcanic jet itself, i.e., jet noise. However, Woulff and McGetchin (1976) considered only the audible acoustic range and did not consider infrasound. Reports of audible volcanic eruption phenomena commonly include jetting sounds or sounds akin to a jet engine. Matoza et al. (2009a) analyzed signals from vulcanian, subplinian, and plinian eruptions at Mount St. Helens, USA and Tungurahua Volcano, Ecuador. The spectral content of the broadband infrasonic tremor appears to have a characteristic shape consisting of a broad spectral peak and a characteristic roll-off at high frequencies (Fig. 5a–b). A similar relative spectral shape is observed for higher (audible) acoustic frequencies from man-made jet noise recorded in the laboratory and from flight vehicles. Two predominant sources of jet mixing noise are recorded from laboratory jets, each with a characteristic spectral shape determined from empirical fits to a large suite of laboratory jet noise data (Tam et al., 1996). The two mixing noise sources are known as fine-scale turbulence noise and large-scale turbulence noise. These jet noise spectra exhibit self-similarity, meaning that the characteristic spectral shape is in theory observed for jets of all scales, and the absolute frequency range of the spectral shape scales with the size (diameter) and velocity of the jet flow. The relative shape of the similarity spectra remains

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b)

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2006/8/17 UTC Time Fig. 4. 16–17 August 2006 Eruption of Tungurahua Volcano. a) Infrasound waveform, b) spectrogram, and c) acoustic power (black) and ash plume height (green lines for estimated minimum/maximum). A spectrogram focusing on the end of the eruption is shown in d). The eruption begins gradually at ~16 August 2000 UTC, slowly building in acoustic power and ash plume height. The eruption ends with a ~45 min paroxysmal phase, evident in the increased pressure amplitudes in a), sharp rise in acoustic power in c), and the shift to lower frequencies in d). Note the lower frequency, logarithmic frequency scale (0.06–5 Hz) in d). The ash plume from the paroxysmal phase in e) depicts a very large (~200 km wide) plume that has pierced the stratosphere to ~25 km height. Acoustic power and ash plume height broadly scale for this and other subplinian–plinian eruptions. Figure adapted from Fee et al. (2010b) and Steffke et al. (2010).

fixed for a translation along the frequency axis, but this spectral shape had not previously been compared to data at infrasonic frequencies. Matoza et al. (2009a) found that the acoustic spectra from the eruptions fit the characteristic jet noise similarity spectra reasonably well overall (Fig. 5a–b), although with significant departures at the peak frequency and in some cases the high-frequency roll-off is not well-matched. This reasonable fit supports the hypothesis that the infrasonic signals resemble, to first order, a low frequency form of jet noise. Fee et al. (2010b) further analyzed the data at Tungurahua Volcano and suggested that multiple turbulence-related processes may help explain some of the complexities (i.e., departures from the spectrum expected from man-made jet noise) found in the volcanic jet noise spectrum. Further, they analyzed remote sensing data and found that the jet noise spectrum was observed coincident with high-altitude ash plumes (Fig. 4). Long-range infrasound from the plinian eruption of Kasatochi Volcano, Alaska in 2008 also resembled the characteristic jet noise spectra and was coincident with high-altitude ash emissions (Fee et al., 2010c).

Further studies of this volcanic form of jet noise show promise in providing quantitative estimates of eruption parameters. The observed jet noise peak frequency, fj, is commonly scaled according to the Strouhal number, St, as: St ¼

f j Dj Uj

ð6Þ

where Dj is the jet diameter and Uj is the jet velocity. Assuming that the Strouhal number of volcanic jet noise radiation could be determined, and does not vary too significantly from eruption to eruption, this highlights the possibility of estimating volcanic jet diameter and jet velocity (or at least their ratio) using infrasound data. Note however that volcanic jets differ from jet flows from jet engines, with volcanic jets having significant additional complexities, including (but not limited to) multiphase and multicomponent flow, high temperatures, and strong interactions with volcanic craters (Matoza et al., 2009a).

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a)

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Fig. 5. Spectra for sustained infrasound. a) Mount St. Helens (MSH) Volcano eruption infrasound at r = 13.4 km (solid black) with jet noise similarity spectra for Large Scale Turbulence (LST-red) and Fine Scale Turbulence (FST-gray), along with background spectrum (dotted line). The MSH eruption spectrum roughly matches the shape of the LST spectrum. b) Tungurahua Volcano eruption infrasound at r = 37 km (solid black) with similarity spectra and background spectrum. Although some differences exist, the Tungurahua eruption spectral shape is roughly consistent with the LST spectrum. c) Halema'uma'u harmonic tremor at r=6.8 km (HV-solid line) and Pu'u O'o tremor at r=12.5 km (dashed line). Multiple tremor sources may be responsible for the complex spectral shape.

4.6. Infrasound from long-period (LP) seismic events Shallow (b2 km) long-period (LP, 0.5–5 Hz) seismic events at volcanoes are often attributed to the activity of magmatic and hydrothermal fluids in subsurface conduits and cracks, and play a pivotal role in eruption forecasting (e.g. Chouet, 1988; Neuberg et al., 2000; Kumagai et al., 2005). LP seismic events are transient, volumetric signals, with a broadband onset lasting ~10 s, followed by a decaying harmonic coda lasting tens of seconds to a few minutes and containing pronounced spectral peaks that are independent of azimuth and distance to the source (Chouet, 1996). This is usually interpreted as a broadband pressure excitation mechanism, followed by the resonant response of a fluid-filled cavity. Although the fluid response is understood quantitatively in terms of solid–fluid interface waves, called crack waves (e.g. Chouet, 1988), the physics of the driving mechanism initiating LP resonance is far from understood (Chouet, 2003). Several studies have documented infrasound signals associated with LP seismic signals at volcanoes (Iguchi and Ishihara, 1990; Yamasato, 1998; Garces et al., 1999; Petersen and McNutt, 2007; Johnson et al., 2008b, 2009; Matoza et al., 2009b). These studies have typically explained the infrasound by a gas-release mechanism accompanying the shallow LP seismicity. At Mount Unzen, Japan, Yamasato (1998) found impulsive infrasonic signals associated with LP events but not from VT (high-frequency) earthquakes. The observed ratio of infrasonic amplitude to seismic amplitude could not be explained by a simple volume change due to ground displacement above the seismic source. Because the seismic signals also appeared to have mixed first motions, Yamasato (1998) interpreted the infrasonic pulse as resulting from emission of volcanic gas during seismic rupture and fracture of the gas-charged lava dome material. Petersen and McNutt (2007) also observed impulsive infrasonic signals associated with LP events at Shishaldin Volcano, Alaska. They correlated seismoacoustic events with visual observations of discrete “gas puffing” from the open-conduit system, and attributed the signals to degassing explosions in a shallow hydrothermal conduit system. Broadband infrasonic pulses were also found to accompany sustained “drumbeat” LP seismicity during the 2004–2008 eruption of Mount

St. Helens, Washington (Figs. 2b, 3a) (Matoza et al., 2007). Matoza et al. (2009b) performed finite-difference simulations of the seismoacoustic wavefield at Mount St. Helens, considering the effects of topography, near-surface seismic velocity structure, wind, and source configuration. Conceptually similar to the work by Yamasato (1998), the aim of these simulations was to test whether the ratio of infrasonic to seismic amplitude could be explained by seismic–acoustic wave conversion from a shallow-buried source. Matoza et al. (2009b) concluded that the broadband infrasonic pulses were unlikely to be generated by simple seismoacoustic coupling from a shallow-buried LP source. Instead they proposed a mechanism by which rapid venting of steam from a hydrothermal crack generates the acoustic signals and simultaneously triggers the collapse of the crack. The crack collapse triggers resonance of the fluid remaining in the crack, and generates the seismic LP event (Waite et al., 2008). The infrasonic signals are therefore interpreted as a record of the trigger mechanism initiating LP resonance. LP seismic signals are commonly observed in direct relation to shallow degassing processes, such as jets or puffs of steam and ash occurring repetitively from cracks or vents (e.g. Gil Cruz and Chouet, 1997; Neuberg et al., 2000; Petersen and McNutt, 2007; Johnson et al., 2008b). This is consistent with numerous studies that have highlighted links between a range of volcano-seismic and volcano-acoustic phenomena and observed explosions or outgassing (e.g., Yuan et al., 1984; McNutt, 1986; Mori et al., 1989; Ripepe et al., 1996; Johnson et al., 2008a; Ripepe et al., 2010b). 4.7. Ultra-Long-Period (ULP) infrasound and acoustic gravity waves Atmospheric perturbations with periods between ~ 50 and 230 s produced from volcanic activity are termed Ultra Long Period (ULP) infrasound signals. Moderate-large eruptions at Redoubt Volcano, Alaska, show significant ULP energy (Fee et al., 2011b), as do many subplinian–plinian eruptions (e.g. Fee et al., 2010c). The source of energy at these periods may be related to extremely large, high velocity jetting or plume oscillations (e.g. Fee et al., 2010b; Matoza et al., 2011a). Acoustic-gravity waves typically have periods between ~230 and 300 s

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and have been recorded historically from very large eruptions, such as the 1991 Mt. Pinatubo, Philippines eruption (e.g. Kanamori et al., 1994). Gravity waves have even longer periods, from 300 s to several minutes, and propagate at much slower velocities (tens of m/s). The 2008 eruption of Okmok Volcano, Alaska produced clear gravity waves recorded on seismometers and observed with satellites, and were modeled to result from gravity oscillations produced by the emplacement of a large eruption column (De Angelis et al., 2011). Although large eruptions typically produce gravity waves, moderate eruptions have been shown to be an efficient gravity wave source as well (Ripepe et al., 2010a). 4.8. PDCs and rockfalls The first acoustic recordings of Pyroclastic Density Currents (PDC) (Table 1) were apparently made at Mt. Unzen Volcano, Japan and were used in conjunction with seismicity to track their movement (Yamasato, 1997). Oshima and Maekawa (2001) combined infrasound and video records to show that both falling blocks and a PDC produced clear infrasound. Ripepe et al. (2010a) tracked and derived run-out velocities of PDCs from Soufriere Hills Volcano, Montserrat using a single infrasound array. The source of low-frequency sound from PDCs remains poorly understood, but appears to produce broadband infrasound reminiscent of turbulent processes. Note concurrent jetting (and hence volcanic jet noise) may often mask infrasound from PDCs (Fee et al., 2010b). Although infrasound from PDCs is relatively unstudied, its potential to aid in hazard mitigation and constraining PDC mechanisms is clear. Large rockfalls have been shown to produce significant infrasound (Moran et al., 2008; Fee et al., 2011a). Moran et al. (2008) modeled a 50 s period Very Long Period (VLP) infrasound signal (Fig. 3b) accompanying a large rockfall at Mount St. Helens. Using Eq. (5) they estimated the volume of displaced air to be ~ 10 7 m 3, consistent with an independent estimate of the air volume displaced by the rockfall itself. 5. Regional and global volcano infrasound and propagation Long-range detection of explosive volcanic eruptions is possible due to the energetic source mechanisms involved, minor atmospheric attenuation at low frequencies, and the existence of waveguides in the atmosphere. In this section we detail the recent work done in regional and global volcano infrasound studies, and describe the various propagation modeling methods employed. In particular we note the recent progress made in detecting and characterizing volcanic activity in remote regions using infrasound. However, before analyses of remote volcano infrasound data can be interpreted, propagation from the volcanic source to receiver must be addressed. We begin by reviewing the structure and variability of the atmosphere, and then discuss the various long-range sound propagation modeling techniques. Turning this problem around, volcanoes provide powerful and sometimes repetitive sources of infrasound for testing models of atmospheric propagation and making inferences about atmospheric structure. The detection and recognition of the signals presented in Section 4 is also discussed. 5.1. Atmospheric structure and variability The atmosphere can be roughly divided into five principal layers based upon temperature gradients: troposphere (ground to ~7–17 km height), stratosphere (~7–17 to ~50 km), mesosphere (~50 to ~85 km), thermosphere (~85 to 350–800 km), and exosphere (350–800 to 10,000 km) (Fig. 6). For infrasound studies we are only interested in the lowermost ~140 km of the atmosphere due to the substantial acoustic attenuation above this height, so the upper thermosphere and exosphere are ignored here. Temperature (and thus sound speed) generally decrease with increasing height in the troposphere, increase in the stratosphere, decrease in the mesosphere, and then increase again in the thermosphere

(Fig. 6). Dividing the bottom four layers from bottom to top are the tropopause, stratopause, and mesopause. Fig. 6 shows the sound speed and horizontal winds above Mount St. Helens (46.20°N, 122.18°W) on 1 February 2012 0000 UTC, representing a typical mid-winter atmosphere at mid-latitudes. Accurate, high-resolution atmospheric specifications are necessary for understanding long-range sound propagation. The Naval Research Laboratory Ground to Space (G2S) semi-empirical model was developed to integrate near-real-time atmospheric data with empirical models to characterize the lowermost ~170 km of the atmosphere. Due to the difficulty of sampling the upper atmosphere, the G2S models rely on empirical models based upon multi-year averages above ~65 km, thus the upper atmosphere is not as well characterized as the lower atmosphere (Drob et al., 2003). The G2S specifications provide global atmospheric variables necessary for quantifying acoustic propagation (winds, temperature, density, and pressure) 4 times daily at 1° latitude/ longitude and 0.5 km height increments. The European Centre for Medium-Range Weather Forecasts (ECMWF) provides alternative atmospheric specifications, which are gaining use in the infrasound community. Radiosonde instruments directly measure atmospheric variables but are limited in space, time, and height (typically b 35 km). Atmospheric winds vary spatially and temporally. Horizontal winds are divided into two components: zonal (east–west, with positive zonal winds moving towards the east) and meridional (north–south, positive meridional winds moving towards the north) (Fig. 6a). Significant, consistent zonal wind jets occur in both the troposphere and stratosphere. In the troposphere, the dominant wind jet relevant for long-range acoustic propagation is the jet stream, which exists at mid-high latitudes at ~12 km height. Zonal stratospheric wind jets are common at mid-high latitudes and occur predominantly during the solstice periods. During the summer, the zonal stratospheric jet blows to the west, then switches directions during the winter. The stratospheric jet results in significant infrasound ducting, and has been found to be the primary factor controlling global infrasound detections (e.g. Drob et al., 2003; Le Pichon et al., 2009). Zonal winds near the equator are much smaller in magnitude. Meridional wind jets are often smaller in magnitude than, and not as consistent as, middle to high latitude zonal jets. The G2S profiles in Fig. 6 demonstrate a typical mid-winter, mid-latitude wind and sound speed structure, with a strong tropospheric and stratospheric jet blowing east. On average, vertical winds have much lower velocities and are routinely ignored for infrasound propagation. Tidal variations in the thermosphere (e.g. Assink et al., 2012) and stratosphere (e.g. Donn and Rind, 1971) cause minor, but observable variations in infrasound propagation and signal characteristics. 5.2. Long-range sound propagation Sound radiating linearly from a source will propagate at the local sound speed. Variations in sound speed will then cause sound to refract, which can be predicted to a first-order using Snell's Law. Due to the general decrease with temperature as a function of height in the troposphere (Fig. 6), sound from sources near the ground primarily refract upward. When the sound speed at height exceeds that at the source, sound will refract back down towards the ground, which together with ground-surface reflections creates a waveguide or duct. The ducting height will thus be where the sound speed at altitude exceeds that at the source. As mentioned above, winds in the atmosphere can be a significant fraction of the static sound speed and must be taken into account. The effective sound speed (ceff) corresponds to the static sound speed plus the vector component of the horizontal wind in that direction: →

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Fig. 6. G2S atmospheric specifications above Mount St. Helens on 1 February 2012 0000 UTC. a) Zonal (east–west, positive corresponds to easterly blowing) (solid line) and meridional (north–south, positive northerly) (solid-dot) winds show a broad, strong easterly wind jet in the stratosphere and strong easterly tropospheric jet at ~10 km (the jet stream). b) Static (c) and effective sound speed profiles for propagation to the east (ceff_90) and west (ceff_270). The static sound speed has a typical shape, with c decreasing with height in the troposphere, increasing in the stratosphere, decreasing in the mesosphere, and then increasing again in the thermosphere. The effective sound speed profiles reflect the strong zonal wind jets, with a duct predicted to the east (ceff_90) in the troposphere at ~10 km and a deep duct beginning at ~45 km height. Thermospheric ducting is predicted for propagation in both directions at ~110 km.

A variety of techniques have been developed to model long-range sound propagation (Salomons, 2001). The most common is geometric acoustics, or ray tracing. Here sound energy is treated as “rays” in a high-frequency approximation and sound energy is propagated through the atmosphere. Ray tracing provides a useful representation of propagation paths and acoustic travel times. However, it does not account for diffraction and scattering, which can be significant at low frequencies, and thus predicts “shadow zones” where no sound will propagate. Continuous wave methods such as the Parabolic Equation (PE) and Normal Mode (NM) (e.g. Jensen et al., 1994) have also seen wide use. These methods are able to account for diffraction and scattering and predict the amount of sound lost (termed transmission loss), and are able to resolve travel times if extended to a time-domain approach. Both diffraction and scattering may be significant in the atmosphere at infrasonic frequencies. The standard implementation of PE and NM methods results in simulation at a single acoustic frequency. Time-domain PE and NM modeling typically consists of: 1) inputting a source waveform, 2) running the PE or NM model at a number of frequencies, 3) synthesizing the output in the frequency domain, and then 4) inverse Fourier transforming the output back to the time domain to produce a waveform. Time-domain models are useful in that they predict full-waveforms, but they are sensitive to the source waveform and can be computationally expensive (Norris et al., 2010). Finite difference time domain models have also been developed to model infrasound and gravity wave propagation (e.g. de Groot-Hedlin, 2008). Atmospheric structure of course varies as a function of distance, therefore long-range sound propagation (typically >500 km) should use 3-D (range-dependent) atmospheric specifications. To illustrate the effect of various wind jets on atmospheric propagation we perform ray-tracing and PE modeling for the G2S profiles shown in Fig. 6. Ray tracing is performed using the 3-D Hamiltonian Ray Tracing Program for Acoustic Waves in the Atmosphere (HARPA), modified from Jones et al. (1986), with rays “launched” between 0 and 60° from horizontal at 1° intervals. PE modeling at 0.5 Hz is performed using a wide-angle, sound-speed-insensitive method after West et al. (1992).

Both models are part of the InfraMAP package (Gibson and Norris, 2002), developed and maintained by BBN Technologies. Fig. 7a shows sound propagating to the east is ducted in three regions: the troposphere at ~10 km, stratosphere at ~45 km, and thermosphere at ~120 km. The first two ducts are a consequence of the strong wind jets at those altitudes, while the third is due to the increase in temperature (and sound speed) in the thermosphere (Fig. 6). An infrasound array located at >200 km to the east would thus be predicted to record arrivals from three propagation paths with different ducting heights, and with corresponding variations in arrival time and signal amplitude. Note the high amplitudes (low transmission loss) predicted in the troposphere and stratosphere, while greater atmospheric absorption and longer path lengths increase the attenuation above ~60 km height. Sound propagating west is only ducted in the thermosphere at ~110–130 km height and predicted amplitudes along the ground are quite low. No sound is predicted to arrive at the ground until ~250 km west of the source where thermospheric arrivals reach the ground, demonstrating the often predicted shadow zone. A pressure sensor >250 km west is predicted to observe only thermospheric arrivals. Fig. 7b shows the transmission loss at the ground from the PE modeling, as well as spherical (1/r) and cylindrical (1/√r) spreading loss estimates. The transmission loss at the ground is clearly much less towards the east, and its values are less than predicted by spherical spreading due to the strong ducting. Transmission losses along propagation paths to the west are significantly greater than predicted by spherical spreading due to strong atmospheric absorption. This figure illustrates that the theoretical spherical and cylindrical spreading laws are not valid at long ranges. Further, it demonstrates the effect of winds and absorption on predicted sound levels, and shows the complexity and often anisotropic nature of long range infrasound propagation. 5.3. Local propagation effects Local variability in infrasound propagation should also be considered. The Pu'u O'o crater complex of Kilauea Volcano, Hawaii has been

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a persistent source of infrasound presumably since the eruption onset in 1983. Fee and Garces (2007) and Matoza et al. (2010) showed that although the infrasonic source at Pu'u O'o was persistent, an array 12 km away recorded infrasonic tremor with clear diurnal amplitude variations. These amplitude variations were likely caused by diurnal variations in the atmospheric boundary layer and were not observed at a near-source (2.4 km) array (Matoza et al., 2010). Nonlinear propagation from very high amplitude sources may also be relevant for sensors near the source (Morrissey and Chouet, 1997). 5.4. Remote detection of explosive volcanism Long-range infrasound has been shown effective in detecting, locating, and characterizing moderate and large eruptions. Section 3 describes historical recordings of large volcanic eruptions at global distances. In this section we discuss recent advances on this topic. The first long-range volcano-acoustic monitoring system was proposed by Kamo et al. (1994) to monitor explosive activity from Japanese volcanoes. The Acoustic Surveillance of Hazardous Eruptions (ASHE) project was a pilot project with the goal of mitigating volcanic ash hazards (Garces et al., 2008). This project deployed two arrays in Ecuador and two in Washington, USA (near Mount St. Helens). Numerous infrasonic events from the 2004 to 2008 eruption of Mount St. Helens were recorded at both a local (13 km) and remote (~250 km) array (Matoza et al., 2007). The RIOE array in Ecuador recorded over 20,000 explosions from Tungurahua Volcano between 2006 and 2008 at a distance of 37 km, while the more distant LITE array recorded over 3000 of

these events (Fee et al., 2010b). Fee et al. (2010b) and Steffke et al. (2010) compared acoustic power from Tungurahua Volcano with ash cloud heights for five different eruption styles during this period and found that: 1) acoustic power broadly scales with eruption intensity (in particular ash cloud height) and 2) sustained, low frequency infrasound and the volcanic jet noise spectral shape are coincident with highaltitude ash emissions (Fig. 4). Using these findings they were able to set up an automated infrasound detection and notification system, which successfully detected and notified the authorities of a hazardous eruption in February 2008. Fee et al. (2010c) analyzed remote IMS infrasound data for the 2008 plinian eruptions of Kasatochi and Okmok Volcanoes, Alaska. Similar to the ASHE project, they found evidence for the characteristic jet noise spectrum at Kasatochi Volcano, and again that sustained, low-frequency infrasound coincided with high-altitude ash emissions. Arnoult et al. (2010) analyzed data from these eruptions as well and noted that they were detected by multiple IMS arrays out to ~5000 km. However, we note that studies have obtained a wide range of results when correlating plume height and infrasound-derived parameters, with some studies suggesting poor correlation (Tupper et al., 2003; Petersen et al., 2006; Vergniolle and Caplan-Auerbach, 2006), possibly due to variable atmospheric conditions affecting the plume height and the complex nature of infrasound generation from explosive eruptions. The 2009 eruption of Sarychev Peak Volcano, Kurile Islands occurred in a very remote region, such that no local monitoring instruments were on or near the volcano. Satellite remote sensing was the only tool used to monitor the eruption and provide information on the significant

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aviation hazard presented by the volcano's emissions. Matoza et al. (2011a) analyzed the IMS infrasound data for this eruption and found that it was clearly recorded by seven arrays at distances from 640 to 6400 km. The infrasound detections correlate well with the satellite observations, and in fact provide a higher temporal resolution explosion chronology. Multiple array cross-bearings and atmospheric corrections were used to locate the infrasound source to within ~15 km of Sarychev Peak, an encouraging location considering propagation variability and the distances involved. This study highlights the utility of infrasound data for constraining the chronology of explosive events, and for providing eruption notifications using sparse ground-based infrasound sensor networks. The 2010 eruption of Eyjafjallajökull, Iceland created a significant aviation hazard and had an immense societal impact. This eruption was recorded on 14 infrasound arrays between 1700 and 3700 km. Although the eruption was well-monitored locally, Matoza et al. (2011b) show how the IMS and other infrasound arrays throughout the region detected the eruption well, further demonstrating the potential for long-range volcano infrasound monitoring, even for moderate sized eruptions. The combination of numerous active volcanoes and long-running infrasound arrays has made Alaska a prime location for regional and global studies. In addition to the previously mentioned studies, Wilson et al. (2006) detected 12 explosive eruptions from Augustine Volcano, Alaska in 2006. Recent explosive activity from the remote Cleveland Volcano, Aleutian Islands, Alaska was detected by numerous regional and global infrasound stations, as well as ground-coupled airwaves on seismic stations. Cleveland has no local monitoring network and frequent cloudy conditions make it difficult to monitor this regularly active volcano. De Angelis et al. (in review) used regional and global stations from the Alaska Volcano Observatory, University of Alaska Fairbanks, and IMS network to identify and characterize previously undetected eruptions of Cleveland Volcano. Automatic alarms were eventually created and successfully detected multiple eruptions, demonstrating successful regional and global volcano acoustic monitoring. Although global volcanic infrasound is often detected, the signal characteristics are changed as they propagate long distances through the atmosphere. Fee et al. (2011b) took advantage of the 2009 explosive eruption of Redoubt Volcano, Alaska being recorded on both local (12 km, DFR) and global (547 km, IMS array IS53) distances to examine this, as the local data provides an estimate of the acoustic source. This eruption had 19 numbered explosive events all recorded at both distances, most with very high amplitudes (>3 Pa peak–peak at IS53). Fig. 8 displays the time-aligned 0.05–5 Hz local (gray) and remote (black) infrasound waveforms a) and spectra c) for Event 10. Propagation modeling for this event indicates strong stratospheric ducting and a single ground reflection between the source and 547 km. After this ground bounce, geometric acoustics predicts the signal to pass through a caustic (Kinney and Pierce, 1980), causing a 90° phase change equivalent to a Hilbert Transform of the source waveform. Fig. 8b shows the local and 90° phase-shifted remote data, which now strongly resembles the local data (cross-correlation coefficient of 0.78). This study demonstrates that under fairly typical meteorological conditions (strong stratospheric ducting), global infrasound data can provide a high-quality estimate of the acoustic source waveform using basic propagation theory. Note however that current absorption prediction models often do not match observations (Fee et al., 2010c; Norris et al., 2010), thus careful inference on source spectra should be taken from global infrasound recordings. Additionally, weak atmospheric ducting (e.g. thermospheric) may not permit robust estimates of volcano acoustic sources at global distances. Lastly, Fee et al. (2011b) found a strong correlation between satellite-derived sulfur-dioxide (SO2) masses and infrasound energy at Redoubt Volcano during its 2009 eruption, further demonstrating the potential to use remote infrasound as a proxy for elevated emissions of ash and gas. Building upon previous work, Dabrowa et al. (2011) carried out a comprehensive study of volcano infrasound recorded on the IMS

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infrasound network, focusing on the relationship between recorded infrasound and plume height. After systematically analyzing 110 volcanic events recorded across the IMS, they found multiple correlations generally consistent across a wide-range of distances and eruption styles: 1) recorded distance increases with ash plume height, 2) the lowest detected infrasonic frequency decreases with increasing plume height, and 3) total acoustic energy and distance-corrected amplitude increase as a function of plume height. Fig. 9 has the detected frequency, distance-corrected amplitude, and acoustic energy plotted as a function of plume height from Dabrowa et al. (2011). Similar to other studies, they also found a correlation between increasing acoustic energy and decreasing frequency. Although some assumptions must be made, this study indicates that the IMS network can provide useful source constraints for a wide variety of eruptions at multiple distances. 5.5. Atmospheric structure inferred from volcano infrasound In addition to providing information on the eruptions themselves, infrasound can give insight into the structure of the atmosphere and help validate atmospheric models. Section 3 discusses historical studies of large eruptions that provided information on atmospheric structure (e.g. Wilson et al., 1966). Since the advent of the IMS, volcanoes have become increasingly used as infrasound sources to study the atmosphere. In particular, multiple studies have taken advantage of persistent explosive activity to validate atmospheric models. Explosions from Yasur Volcano, Vanuatu have been used to examine the effect of stratospheric winds on infrasound detections. These data were used to validate the G2S models by looking at compared vs. predicted azimuth deviations (Le Pichon et al., 2005a; Antier et al., 2007) and suggest that the stratospheric wind jet is underestimated in both magnitude and height by the G2S models. Assink et al. (2012) used the ASHE arrays in Ecuador and persistent explosion signals from Tungurahua Volcano recorded at 251 km distance to examine upper atmospheric dynamics. Periodic variations in acoustic travel time correlate well with upper atmospheric tide periodicities. Mesospheric arrivals are also recorded but not predicted by the G2S models. Infrasound observations of activity from Mt. Etna Volcano in 2001 by arrays in northern Europe revealed azimuth deviations consistent with those predicted by westerly blowing stratospheric winds. Further, it was shown that the number and locations of array elements processed helps determine how well a signal is detected (Evers and Haak, 2005). Validating absorption models is critical to understanding amplitude variations. Fee et al. (2010c) show how thermospheric absorption predictions are inconsistent with remote recordings from the 2008 eruption of Kasatochi Volcano, suggesting that the generally accepted absorption model of Sutherland and Bass (2004) overestimates thermospheric absorption. Although the source was not volcanic, Haney (2009) performed ambient noise interferometery on data from two infrasound microphones separated by ~13 km on Fourpeaked Volcano, Alaska. This commonly used method in seismology allowed inversion for atmospheric structure between the stations, providing information on the temperature and wind structure of the atmospheric boundary layer. Marcillo and Johnson (2010) examined phase delays between sensors of a 3-element infrasound network at Kilauea to invert for changes in atmospheric conditions. Their novel technique was roughly consistent with local meteorological observations, and their 2-D network provided unique information on the spatially averaged wind in the region not available by ground-based meteorological stations. 6. Future studies and directions It is clear that local, regional, and remote volcano infrasound can be used effectively to detect, locate, characterize, and quantify eruptive activity, as well as to constrain various eruption source parameters. In this section we discuss some future directions in volcano infrasound.

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Infrasound is readily combinable with other technologies. Direct observation through video and infrared cameras should continue to be correlated with infrasound to advance understanding of shallow conduit and aerial eruptive activity. High-resolution, time-synced recordings of infrasound and gas and ash measurements will be valuable in understanding the relationship between volcanic emissions and infrasound, in particular validating infrasound estimates of mass flux and volume. The effect of solid particles and complex crater morphologies on acoustic sources and near-source propagation is currently poorly understood. Seismoacoustic studies can help determine eruption source depths and energy partitioning. New methods, such as cross-correlating collocated seismic and acoustic sensors (Ichihara et al., 2012), appear useful for signal discrimination even with single-station seismo-acoustic recordings. Independent estimates of volcanic jet velocity, composition, mass flux, and diameter could all be correlated with acoustic records, potentially leading to infrasound-derived real-time information on tephra fallout, column stability, gas emission mass, and ash plume height. Comparison of infrasound with remote sensing data is particularly valuable for remote regions or regions with poor visibility. Near real-time estimates of eruption onset, duration, and changes in intensity could be implemented as input into volcanic ash and gas transport models. High-resolution infrasound recordings and models of PDCs can also help with hazard mitigation and should be explored further. Regional and remote source characterization relies on accurate longrange sound propagation modeling, therefore continued work is necessary to validate and refine both atmospheric and propagation models. Higher resolution atmospheric specifications, such as those provided by radionsondes, will also be useful. Studies that integrate accurate source constraints (e.g. local observations) with global observations

and modeling show particular promise in this direction. The potential to use the IMS infrasound network to monitor and characterize volcanic eruptions has been demonstrated. Future work should incorporate other arrays to increase the network density and decrease detection latency. Volcano monitoring using the IMS is particularly relevant in remote regions, so regional and remote volcano infrasound recordings should continue to be exploited in these regions. However, the biggest potential hindrance in real-time monitoring is propagation time, as it takes an acoustic wave ~ 25–30 min to propagate 500 km. Again, higher-density networks transmitting data in real-time can help reduce the propagation time and latency. Similarly, more and higher-quality local infrasound networks and arrays will also aid in infrasound source characterization. A growing number of researchers and volcano observatories are now using infrasound as a tool to study and monitor volcanic eruptions and validate atmospheric models. Continued quantitative work in these areas using high-quality infrasound data and comparison with other technologies will lead to a better understanding of the atmosphere, acoustic propagation, volcano-acoustic source processes, and volcanic eruption mechanisms, which will in turn increase the utility of infrasound technology for mitigating volcanic hazards. Acknowledgments The ASHE project led to numerous results presented in this manuscript, and we are in particular grateful to the Instituto Geofísico–EPN for help in collecting and interpreting data. We thank Milton Garces for cultivating our interest in volcano acoustics. The authors wish to thank KBGS, IVS, and Taryn Lopez for help in collecting the Karymsky

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data, AVO for the Augustine and Redoubt data, and Maurizio Ripepe for the Stromboli data. Section 3 uses previously unpublished material from the PhD thesis of RSM. Silvio de Angelis and Taryn Lopez gave helpful early reviews of the manuscript, and Doug Drob provided the

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G2S models. Funding for DF was provided by NSF grant EAR-1113294 and the Geophysical Institute. RSM gratefully acknowledges support from the Cecil H. and Ida M. Green Foundation at the Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography. We also thank Maurizio Ripepe and an anonymous reviewer for helpful comments.

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