Investigating Spectral Distortion of Local Volcano Infrasound by Nonlinear Propagation at Sakurajima Volcano, Japan

Abstract

Sound waves generated by erupting volcanoes can be used to infer important source dynamics, yet acoustic source‐time functions may be distorted during propagation, even at local recording distances ( <15 km). The resulting uncertainty in source estimates can be reduced by improving constraints on propagation effects. We aim to quantify potential distortions caused by wave steepening during nonlinear propagation, with the aim of improving the accuracy of volcano‐acoustic source predictions. We hypothesize that wave steepening causes spectral energy transfer away from the dominant source frequency. To test this, we apply a previously developed single‐point, frequency domain, quadspectral density‐based nonlinearity indicator to 30 acoustic signals from Vulcanian explosion events at Sakurajima Volcano, Japan, in an 8‐day data set collected by five infrasound stations in 2013 with 2.3‐ to 6.2‐km range. We model these results with a 2‐D axisymmetric finite‐difference method that includes rigid topography, wind, and nonlinear propagation. Simulation results with flat ground indicate that wave steepening causes up to ∼2 dB (1% of source level) of cumulative upward spectral energy transfer for Sakurajima amplitudes. Correction for nonlinear propagation may therefore provide a valuable second‐order improvement in accuracy for source parameter estimates. However, simulations with wind and topography introduce variations in the indicator spectra on order of a few decibels. Nonrandom phase relationships generated during propagation or at the source may be misinterpreted as nonlinear spectral energy transfer. The nonlinearity indicator is therefore best suited to small source‐receiver distances (e.g., <2 km) and volcanoes with simple sources (e.g., gas‐rich strombolian explosions) and topography.

Seismo-Acoustic Characterization of Mount Cleveland Volcano Explosions

“…We note that peak pressures at CLES do not appear to strongly correlate with seismo-acoustic time lags ( Figure 7D), indicating that nonlinear propagation (if present) has minimal effect on the seismio-acoustic time lag for Cleveland explosions. This is consistent with the findings of Maher et al (2020), where nonlinear propagation was found to be a secondary effect on the observed waveform. However, it does appear that the highest peak pressures and those that clipped the infrasound sensor ( Figure 7D, open circles) had lower seismo-acoustic time lags.…”

“…Nonlinear propagation modeling results for a synthetic receiver at 3.8 km from the source (slant distance from the Cleveland summit to station CLES) show the expected distortion due to increasingly nonlinear propagation ( Figures 7B,C). As the source pressure increases, the compression travels faster while the rarefaction travels relatively slower, similar to results in Maher et al (2020). Therefore, the arrival time of the peak compression decreases with higher source pressure ( Figure 7C).…”

“…For very high amplitude acoustic sources such as some Vulcanian explosions, the sound waves produced may travel faster than the speed of sound (i.e., supersonic) and propagate nonlinearly. In nonlinear propagation, the waveform distorts as it travels, where the compressional phase travels faster than the rarefaction, potentially steepening into a shock wave (Atchley, 2005;Reichman et al, 2016;Maher et al, 2020). This shock wave is often described by the Friedlander equation (Friedlander, 1946), which defines the pressure of a shock wave (p(t)) as…”

“…This equation for a theoretical blast wave using t* 0.75 (value chosen to resemble data from Cleveland explosions) is shown as Figure 7A, along with the waveform for Explosion 44 from Cleveland. A recent study by Maher et al (2020) performed a detailed analysis of local infrasound data from Sakurajima volcano and the potential impact of spectral energy transfer to higher frequencies due to nonlinear propagation. They find that the effects of nonlinear propagation have a second-order impact on source quantification, whereas the effects of wind and topography may be more influential on the recorded waveform.…”

Evaluating the applicability of a screen diffraction approximation to local volcano infrasound

“…Iezzi et al 2019a;Johnson and Miller 2014;Kim et al 2015], vent radius [Muramatsu et al 2018], and crater geometry [Johnson et al 2018a;Johnson et al 2018b], but wavefield interactions with topography lead to variable source estimates from signals recorded at different azimuths [e.g. Iezzi et al 2019a;Kim and Lees 2014;Lacanna and Ripepe 2013;Maher et al 2020;McKee et al 2014].…”