A document by Antoine Turquet. Click on the document to view its contents.

The 15 January 2022 eruption of the Hunga volcano (Tonga) generated a rich spectrum of waves, some of which achieved global propagation. Among numerous platforms mon- itoring the event, two stratospheric balloons flying over the tropical Pacific provided unique observations of infrasonic wave arrivals, detecting five complete revolutions. Combined with ground measurements from the infrasound network of the International Monitor- ing System, balloon-borne observations may provide additional constraint on the scenario of the eruption, as suggested by the correlation between bursts of acoustic wave emis- sion and peaks of maximum volcanic plume top height. Balloon records also highlight previously unobserved long-range propagation of infrasound modes and their dispersion patterns. A comparison between ground- and balloon-based measurements emphasizes superior signal-to-noise ratios onboard the balloons and further demonstrates their po- tential for infrasound studies.

We present an inversion methodology where acoustic observations of infrasound waves are used to update an atmospheric model. We sought a flexible parameterization that permits to incorporate physical and numerical constraints without the need to reformulate the inversion. On the other hand, the optimization conveys an explicit search over the solution space, making the solver computationally expensive. Nevertheless, through a parallel implementation and the use of tight constraints we demonstrate that the methodology is computationally tractable. Constraints to the solution space are derived from the spread (variance) of ERA5 ensemble reanalysis members, which summarize the best current knowledge of the atmosphere from assimilated measurements and physical models. Similarly, the initial model temperature and winds for the inversion are chosen to be the average of these parameters in the ensemble members. The performance of the inversion is demonstrated with the application to infrasound observations from an explosion generated by the destruction of ammunition at Hukkakero, Finland. The acoustic signals are recorded at an array station located at 178 km range, which is within the classical shadow zone distance. The observed returns are assumed to come from stratospheric reflections. Thus, the reflection altitude is also an inverted parameter.

Modelling the spatial distribution of infrasound attenuation (or transmission loss, TL) is key to understanding and interpreting microbarometer data and observations. Such predictions enable the reliable assessment of infrasound source characteristics such as ground pressure levels associated with earthquakes, man-made or volcanic explosion properties, and ocean-generated microbarom wavefields. However, the computational cost inherent in full-waveform modelling tools, such as Parabolic Equation (PE) codes, often prevents the exploration of a large parameter space, i.e., variations in wind models, source frequency, and source location, when deriving reliable estimates of source or atmospheric properties – in particular for real-time and near-real-time applications. Therefore, many studies rely on analytical regression-based heuristic TL equations that neglect complex vertical wind variations and the range-dependent variation in the atmospheric properties. This introduces significant uncertainties in the predicted TL. In the current contribution, we propose a deep learning approach trained on a large set of simulated wavefields generated using PE simulations and realistic atmospheric winds to predict infrasound ground-level amplitudes up to 1000 km from a ground-based source. Realistic range dependent atmospheric winds are constructed by combining ERA5, NRLMSISE-00, and HWM-14 atmospheric models, and small-scale gravity-wave perturbations computed using the Gardner model. Given a set of wind profiles as input, our new modelling framework provides a fast (0.05 s runtime) and reliable (~5 dB error on average, compared to PE simulations) estimate of the infrasound TL.

The International Monitoring System (IMS) infrasound network has been established to detect nuclear explosions and other signals of interest embedded in the station specific ambient noise. The ambient noise can be separated into coherent infrasound (e.g. real infrasonic signals) and incoherent noise (such as that caused by wind turbulence). Previous work statistically and systematically characterizing coherent infrasound recorded by the IMS. This paper expands on this analysis of the coherent ambient infrasound by including updated IMS datasets up to the end of 2020, for all 53 of the currently certified IMS infrasound stations using an updated configuration of the Progressive Multi-Channel Correlation (PMCC) method. This paper presents monthly station dependent reference curves for the back azimuth, apparent speed, and root-mean squared amplitude, which provide a means to determine the deviation from nominal monthly behaviour. In addition, a daily Ambient Noise Stationarity (ANS) factor based on deviations from the reference curves is determined for a quick reference to the data quality compared to the nominal situations. Newly presented histograms provide a higher resolution spectrum, including the observations of the microbarom peak, as well as additional peaks reflecting station dependent environmental noise. The aim of these reference curves is to identify periods of sub-optimal operation (e.g. non-operational sensor) or instances of strong abnormal signals of interest.