4aPA – Using Sound Waves to Quantify Erupted Volumes and Directionality of Volcanic Explosions

Alexandra Iezzi – amiezzi@alaska.edu
Geophysical Institute, Alaska Volcano Observatory
University of Alaska Fairbanks
2156 Koyukuk Drive
Fairbanks, AK 99775

David Fee – dfee1@alaska.edu
Geophysical Institute, Alaska Volcano Observatory
University of Alaska Fairbanks
2156 Koyukuk Drive
Fairbanks, AK 99775

Popular version of paper 4aPA
Presented Thursday morning, May 16, 2019
177th ASA Meeting, Louisville, KY

Volcanic eruptions can produce serious hazards, including ash plumes, lava flows, pyroclastic flows, and lahars. Volcanic phenomena, especially explosions, produce a substantial amount of sound, particularly in the infrasound band (<20 Hz, below human hearing) that can be detected at both local and global distances using dedicated infrasound sensors. Recent research has focused on inverting infrasound data collected within a few kilometers of an explosion, which can provide robust estimates of the mass and volume of erupted material in near real time. While the backbone of local geophysical monitoring of volcanoes typically relies on seismometers, it can sometimes be difficult to determine whether a signal originates from the subsurface only or has become subaerial (i.e. erupting). Volcano infrasound recordings can be combined with seismic monitoring to help illuminate whether or not material is actually coming out of the volcano, therefore posing a potential threat to society.

This presentation aims to summarize results from many recent studies on acoustic source inversions for volcanoes, including a recent study by Iezzi et al. (in review) at Yasur volcano, Vanuatu. Yasur is easily accessible and has explosions every 1 to 4 minutes making it a great place to study volcanic explosion mechanisms (Video 1).

Video 1 – Video of a typical explosion at Yasur volcano, Vanuatu.

Most volcano infrasound inversion studies assume that sound radiates equally in all directions. However, the potential for acoustic directionality from the volcano infrasound source mechanism is not well understood due to infrasound sensors usually being deployed only on Earth’s surface. In our study, we placed an infrasound sensor on a tethered balloon that was walked around the volcano to measure the acoustic wavefield above Earth’s surface and investigate possible acoustic directionality (Figure 1).

Figure 1 [file missing] – Image showing the aerostat on the ground prior to launch (left) and when tethered near the crater rim of Yasur (right).

Volcanos typically have high topographic relief that can significantly distort the waveform we record, even at distances of only a few kilometers. We can account for this effect by modeling the acoustic propagation over the topography (Video 2).

Video 2 – Video showing the pressure field that results from inputting a simple compressional source at the volcanic vent and propagating the wavefield over a model of topography. The red denotes positive pressure (compression) and blue denotes negative pressure (rarefaction). We note that all complexity past the first red band is due to topography.

Once the effects of topography are constrained, we can assume that when we are very close to the source, all other complexity in the infrasound data is due to the acoustic source. This allows us to solve for the volume flow rate (potentially in real time). In addition, we can examine directionality for all explosions, which may lead to volcanic ejecta being launched more often and farther in one direction than in others. This poses a great hazard to tourists and locals near the volcano and may be mitigated by studying the acoustic source from a safe distance using infrasound.

4aPA4 – Acoustic multi-pole source inversions of volcano infrasound

Keehoon Kim – kkim32@alaska.edu
University of Alaska Fairbanks
Wilson Infrasound Observatory, Alaska Volcano Observatory, Geophysical Institute
903 Koyukuk Drive, Fairbanks, Alaska 99775

David Fee – dfee1@alaska.edu
University of Alaska Fairbanks
Wilson Infrasound Observatory, Alaska Volcano Observatory, Geophysical Institute
903 Koyukuk Drive, Fairbanks, Alaska 99775

Akihiko Yokoo – yokoo@aso.vgs.kyoto-u.ac.jp
Kyoto University
Institute for Geothermal Sciences
Kumamoto, Japan

Jonathan M. Lees – jonathan.lees@unc.edu
University of North Carolina Chapel Hill
Department of Geological Sciences
104 South Road, Chapel Hill, North Carolina 27599

Mario Ruiz – mruiz@igepn.edu.ec
Escuela Politecnica Nacional
Instituto Geofisico
Quito, Ecuador

Popular version of paper 4aPA4, “Acoustic multipole source inversions of volcano infrasound”
Presented Thursday morning, May 21, 2015, at 9:30 AM in room Kings 1
169th ASA Meeting, Pittsburgh
Click here to read the abstract

Volcano infrasound
Volcanoes are outstanding natural sources of infrasound (low-frequency acoustic waves below 20 Hz). In the last few decades local infrasound networks have become an essential part of geophysical monitoring systems for volcanic activity. Unlike seismic networks dedicated to monitoring subsurface activity (c.f., magma or fluid transportation) infrasound monitoring facilitates detecting and characterizing eruption activity at the earth’s surface. Figure 1a shows Sakurajima Volcano in southern Japan and an infrasound network deployed in July 2013. Figure 1b is an image of a typical explosive eruption during the field experiment, which produces loud infrasound.

Sakurajima Volcano - Kim1Figure 1. a) A satellite image of Sakurajima Volcano, adapted from Kim and Lees (2014). Five stand-alone infrasound sensors were deployed around Showa Crater in July 2013, indicated by inverted triangles. b) An image of a typical explosive eruption observed during the field campaign.

Source of volcano infrasound
One of the major sources of volcano infrasound is a volume change in the atmosphere. Mass discharge from volcanic eruptions displaces the atmosphere near and around the vent and this displacement propagates into the atmosphere as acoustic waves. Infrasound signals can, therefore, represent a time history of the atmospheric volume change during eruptions. Volume flux inferred from infrasound data can be further converted into mass eruption rate with the density of the erupting mixture. Mass eruption rate is a critical parameter for forecasting ash-cloud dispersal during eruptions and consequently important for aviation safety. One of the problems associated with the volume flux estimation is that observed infrasound signals can be affected by propagation path effects between the source and receivers. Hence, these path effects must be appropriately accounted for and removed from the signals in order to obtain the accurate source parameter.

Infrasound propagation modeling
vent of Sakurajima Volcano - Kim2Figure 2. a) Sound pressure level in dB relative to the peak pressure at the source position. b) Variation of infrasound waveforms across the network caused by propagation path effects.

Figure 2 shows the results of numerical modeling of sound propagation from the vent of Sakurajima Volcano. The sound propagation is simulated by solving the acoustic wave equation using a Finite-Difference Time-Domain method taking into account volcanic topography. The synthetic wavefield is excited by a Gaussian-like source time function (with 1 Hz corner frequency) inserted at the center of Showa Crater (Figure 2a). Homogeneous atmosphere is assumed since atmospheric heterogeneity should have limited influence in this local range (< 7 km). The numerical modeling demonstrates that both amplitude and waveform of infrasound are significantly affected by the local topography. In Figure 2a, Sound Pressure Level (SPL) relative to the source amplitude is calculated at each computational grid node on the ground surface. The SPL map indicates an asymmetric radiation pattern of acoustic energy. Propagation paths to the northwest of Showa Crater are obstructed by the summit of the volcano (Minamidake), and as a result acoustic shadow zones are created northwest of the summit. Infrasound waveform also shows significant variation across the network. In Figure 2b, synthetic infrasound signals computed at the station positions (ARI – SVO) show bipolar pulses followed by oscillations in pressure while the pressure time history at the source location exhibits only a positive unipolar pulse. This result indicates that the oscillatory infrasound waveforms can be produced by not only source effects but also propagation path effects. Hence, this waveform distortion must be considered for source parameter inversion.

Volume flux estimates
Because wavelengths of volcano infrasound are usually longer than the dimension of source region, the acoustic sources are typically treated as a monopole, which is a point source approximation of volume expansion or contraction. Then, infrasound data represent the convolution of volume flux history at the source and the response of the propagation medium, called Green’s function. Volume flux history can be obtained by deconvolving the Green’s functions from the data. The Green’s functions can be obtained by two different ways: 3-D numerical modeling considering local topography (Case 1) and the analytic solution in a half-space neglecting volcanic topography (Case 2). Resultant volume histories for a selected infrasound event are compared in Figure 3. Case 1 results in gradually decreasing volume flux curve, but Case 2 shows pronounced oscillation in volume flux. In Case 2, propagation path effects are not appropriately removed from the data leading to misinterpretation of the source effect.

Proper Green’s function is critical for accurate volume flux history estimation. We obtained a reasonable volume flux history using the 3-D numerical Green’s function. In this study only simple source model (monopole) was considered for volcanic explosions. More general representation can be obtained by multipole expansion of acoustic sources. In 169th ASA Meeting presentation, we will further discuss source complexity of volcano infrasound, which requires the higher-order terms of the multipole series.

Kim3Figure 3. Volume flux history inferred from infrasound data. In Case 1, the Green’s function is computed by 3-D numerical modeling considering volcanic topography. In Case 2, the analytic solution of the wave equation in a half-space is used, neglecting the topography.


Kim, K. and J. M. Lees (2014). Local Volcano Infrasound and Source Localization Investigated by 3D Simulation. Seismological Research Letters, 85, 1177-1186