Infrasonic Source Research Articles

A quarter wave infrasonic noise generator

The far-field prediction of infrasonic noise, e.g. from large emitters such as wind turbines, requires precise measurement of the sources. A method to determine the source power based on near-field holography techniques are currently under development in our group. However, tuning the method requires a calibrated infrasound source. In this work, we present the concept of a traceable loudspeaker driven infrasound source using the quarter wave resonator principle in a pipe. The power emitted by the source is measured in situ based on the two-port theory. The infrasonic frequencies can be tuned by adjusting the length of the resonator pipe. Results of the free-field measurements at multiple points at varying distances away from the source are presented. The results suggest that the infrasonic source can be used as a calibrated reference for method development and validation.

The Korean infrasound catalogue (1999–2022)

SUMMARY The Korean infrasound catalogue (KIC) covers 1999–2022 and characterizes a rich variety of source types as well as document the effects of the time-varying atmosphere on event detection and location across the Korean Peninsula. The KIC is produced using data from six South Korean infrasound arrays that are cooperatively operated by Southern Methodist University and Korea Institute of Geoscience and Mineral Resources. Signal detection relies on an Adaptive F-Detector that estimates arrival time and backazimuth, which draws a distinction between detection and parameter estimation. Detections and associated parameters are input into a Bayesian Infrasonic Source Location procedure. The resulting KIC contains 38 455 infrasound events and documents repeated events from several locations. The catalogue includes many anthropogenic sources such as an industrial chemical explosion, explosions at limestone open-pit mines and quarries, North Korean underground nuclear explosions and other atmospheric or underwater events of unknown origin. Most events in the KIC occur during working hours and days, suggesting a dominance of human-related signals. The expansion of infrasound arrays over the years in South Korea and the inclusion of data from the International Monitoring System infrasound stations in Russia and Japan increase the number of infrasound events and improve location accuracy because of the increase in azimuthal station coverage. A review of selected events and associated signals at multiple arrays provides a location quality assessment. We quantify infrasound events that have accompanying seismic arrivals (seismoacoustic events) to support the source type assessment. Ray tracing using the Ground-to-Space (G2S) atmospheric model generally predicts observed arrivals when strong stratospheric winds exist, although the predicted arrival times have significant discrepancies. In some cases, local atmospheric data better captures small-scale variations in the wind velocity of the shallow atmosphere and can improve arrival time predictions that are not well matched by the G2S model. The analysis of selected events also illustrates the importance of topographic effects on tropospheric infrasound propagation at local distances. The KIC is the first infrasound catalogue compiled in this region, and it can serve as a valuable data set in developing more robust infrasound source localization and characterization methods.

Infrasound analysis of the 2018-Dec-18 49 kiloton Bering Sea bolide: Misassociations and celerity-range models

Automatic detections at the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO) are often misassociated. This occasionally leads to single events being split into multiple events. Infrasound signals generated from a large bolide over the Bering Sea on 2018-Dec-18 were recorded on more than 15 of the CTBTO International Monitoring System (IMS) stations. The bolide was also reported by the Center for Near Earth Object Studies (http://cneos.jpl.nasa.gov/fireballs) with a calculated total impact energy value of 49 kt. Signals from this bolide were automatically detected and associated at the IDC, into two different events, with the origin time differing by approximately 7.5 minutes and location by ∼125 km. To investigate why detections from this bolide event were misassociated in the automatic bulletins, we analyze infrasound observations from the IMS infrasound arrays and compare our arrival time observations to the automatic detections from the IDC. We use the Bayesian Infrasonic Source Localisation method and employ different celerity-range models, to investigate signal association for this event. Through comparison with the associations based on IDC detections, we aim to improve our understanding of the causes of misassociations and identify potential improvements to future event association algorithms.

A Wavelet Transform-based Feature Extraction Pipeline for Elephant Rumble Detection

Elephants generate infrasonic vocalisations that traverse through the air for long distances. Utilising this phenomenon, a previous work proposed a system, called Eloc, to localise and track elephants in the wild. The Eloc system has been demonstrated to be accurate in calculating the location of infrasonic sources. However, it still lacks the capability to accurately distinguish elephant infrasonic calls from various other infrasonic sources using limited computing power on board. Addressing this problem, the work presented in this paper introduces an approach to distinguish elephant infrasonic calls with a high accuracy on low-resourced hardware. Firstly, a sequence of operations are performed to reduce the effect of noise in the infrasonic signal captured by an Eloc node. Secondly, a wavelet-based signal reconstruction technique is applied to extract spectral features from the infrasonic signal. Finally, the extracted features are fed to a pre-trained machine learning classifier to distinguish the infrasonic vocalisations of elephants. The experimental evaluation using Asian elephant (Elephas Maximus Maximus) infrasonic vocalisation datasets demonstrates that the proposed approach is capable of accurately distinguishing elephant infrasonic calls on low-resourced hardware platform of the Eloc system, with accuracy levels over 82% under varying environmental conditions.

Quantification of spatial and seasonal trends in the atmosphere and construction of statistical models for infrasonic propagation

SUMMARY Infrasonic waves are influenced by variations in the density, pressure and temperature as well as the ambient winds. Modelling infrasonic propagation can be challenging due to the dynamic nature of the atmosphere as well as the sparseness of measurements which result in variability and notable uncertainty. A framework is presented to quantify spatial and seasonal trends in atmospheric structure via analysis of the effective sound speed profile and identification of temporal trends in the middle atmospheric waveguide produced by the circumpolar vortex winds. Seasonal definitions identifying typical atmospheric structures during the summer, winter and spring/fall transition periods are identified using atmospheric data from 2010 through 2020. Seasonal trend analysis is conducted for a number of locations across the contiguous United States to quantify spatial variations in atmospheric structure that impact infrasonic propagation. For each season and location, empirical orthogonal function analysis is used to reduce the historical archive of atmospheric data into a smaller representative set that can be analysed using numerical tools more efficiently. Infrasonic ray tracing and finite-frequency modal propagation analyses are applied to construct propagation path geometry and transmission loss statistics which are useful in localization and yield estimation for infrasonic sources, respectively. An example application is detailed in which transmission loss statistics are combined with an explosive source model and noise statistics to quantify the capability of a network to detect nearby sources.

Multi-Band Acoustic Monitoring of Aerial Signatures

The acoustic monitoring, omni-directional system (AMOS) in the Galileo Project is a passive, multi-band, field microphone suite designed to aid in the detection and characterization of aerial phenomena. Acoustic monitoring augments the Project’s electromagnetic sensors suite by providing a relatively independent physical signal modality with which to validate the identification of known phenomena and to more fully characterize detected objects. The AMOS system spans infrasonic frequencies down to 0.05[Formula: see text]Hz, all of audible, and ultrasonic frequencies up to 190[Formula: see text]kHz. It uses three distinct systems with overlapping bandwidths: infrasonic (0.05[Formula: see text]Hz – 20[Formula: see text]Hz), audible (10[Formula: see text]Hz – 20[Formula: see text]kHz), and ultrasonic (16[Formula: see text]kHz – 190[Formula: see text]kHz). The sensors and their capture devices allow AMOS to monitor and characterize the tremendous range of sounds produced by natural and human-made aerial phenomena, and to encompass possible acoustic characteristics of novel sources. Sound signals from aerial objects can be captured and classified with a single microphone under the following conditions: the sound reaches the sensor; the sound level is above ambient noise; and the signal has not been excessively distorted by the transmission path. A preliminary examination of the signal and noise environment required for the detection and characterization of aerial objects, based on theoretical and empirical equations for sound attenuation in air, finds that moderately loud audible sources (100[Formula: see text]dB) at 1[Formula: see text]km are detectable, especially for frequencies below 1[Formula: see text]kHz and in quiet, rural environments. Infrasonic sources are detectable at much longer distances and ultrasonic at much shorter distances. Preliminary aircraft recordings collected using the single, omni-directional audible microphone are presented, along with basic spectral analysis. Such data will be used in conjunction with flight transponder data to develop algorithms and corresponding software for quickly identifying known aircraft and characterizing the sound transmission path. Future work will include multi-sensor audible and infrasonic arrays for sound localization; analysis of larger and more diverse data sets; and exploration of machine learning and artificial intelligence integration for the detection and identification of many more types of known phenomena in all three frequency bands.

Lighthill's theory applied to tornadoes as an infrasonic source

It has been shown that tornadoes radiate infrasound, which can be detected at great distances. The physical mechanism underlying the radiation is not well understood. In this work, we use Lighthill's acoustic analogy to predict the pressure signals emitted from numerically simulated tornadoes. Several simulations will be considered including simulations produced using an LES model and provided by researchers from the Penn State Department of Meteorology and Atmospheric Science. The acoustical source produced from the LES tornado simulations shows interesting patterns in a narrow low-frequency band. By comparing different numerical models of tornadoes, we hope to determine what in a tornado is causing the infrasound radiation.

A climatology of microbarom detections at the Kerguelen Islands: unravelling the ambient noise wavefield

SUMMARY The ambient infrasonic noise field is complex due to the interference of spatially distributed infrasound sources. Microbaroms are one of the most dominant omnipresent infrasonic sources within this wavefield. These microbaroms are generated by nonlinear ocean surface wave interactions, and have a characteristic and continuous signature within the infrasound spectrum. Under noisy conditions, microbaroms can mask infrasonic signals of interest, such as infrasound from volcanoes or explosions, which limits detection and identification of such sources. This study performs an infrasonic climatology for infrasound array I23FR, using five years of data between 2015–2020. The array is located on the Kerguelen Islands, within the Southern Ocean, and is part of the International Monitoring System for the verification of the Comprehensive Nuclear-Test-Ban Treaty. The climatology analysis addresses the expected ambient noise levels, propagation paths and potential sources within the vicinity of an infrasound sensor. Time- and frequency-domain beamforming methods have been applied to analyse the infrasonic wavefield from the I23FR observations. A recently introduced method is applied to compute so-called soundscapes, to be compared with beamform results. Although the comparison indicates a disagreement in amplitude, there is a good agreement in directionality and frequency between both.

Fitting Jet Noise Similarity Spectra to Volcano Infrasound Data

AbstractInfrasound (low‐frequency acoustic waves) has proven useful to detect and characterize subaerial volcanic activity, but understanding the infrasonic source during sustained eruptions is still an area of active research. Preliminary comparison between acoustic eruption spectra and the jet noise similarity spectra suggests that volcanoes can produce an infrasonic form of jet noise from turbulence. The jet noise similarity spectra, empirically derived from audible laboratory jets, consist of two noise sources: large‐scale turbulence (LST) and fine‐scale turbulence (FST). We fit the similarity spectra quantitatively to eruptions of Mount St. Helens in 2005, Tungurahua in 2006, and Kīlauea in 2018 using nonlinear least squares fitting. By fitting over a wide infrasonic frequency band (0.05–10 Hz) and restricting the peak frequency above 0.15 Hz, we observe a better fit during times of eruption versus non‐eruptive background noise. Fitting smaller overlapping frequency bands highlights changes in the fit of LST and FST spectra, which aligns with observed changes in eruption dynamics. Our results indicate that future quantitative spectral fitting of eruption data will help identify changes in eruption source parameters such as velocity, jet diameter, and ash content which are critical for effective hazard monitoring and response.

Local Explosion Detection and Infrasound Localization by Reverse Time Migration Using 3-D Finite-Difference Wave Propagation

Infrasound data are routinely used to detect and locate volcanic and other explosions, using both arrays and single sensor networks. However, at local distances (<15 km) topography often complicates acoustic propagation, resulting in inaccurate acoustic travel times leading to biased source locations when assuming straight-line propagation. Here we present a new method, termed Reverse Time Migration-Finite-Difference Time Domain (RTM-FDTD), that integrates numerical modeling into the standard RTM back-projection process. Travel time information is computed across the entire potential source grid via FDTD modeling to incorporate the effects of topography. The waveforms are then back-projected and stacked at each grid point, with the stack maximum corresponding to the likely source. We apply our method to three volcanoes with different network configurations, source-receiver distances, and topography. At Yasur Volcano, Vanuatu, RTM-FDTD locates explosions within ∼20 m of the source and differentiates between multiple vents. RTM-FDTD produces a more accurate location for the two Yasur subcraters than standard RTM and doubles the number of detected events. At Sakurajima Volcano, Japan, RTM-FDTD locates the source within 50 m of the active vent despite notable topographic blocking. The RTM-FDTD location is similar to that from the Time Reversal Mirror method, but is more computationally efficient. Lastly, at Shishaldin Volcano, Alaska, RTM and RTM-FDTD both produce realistic source locations (<50 m) for ground-coupled airwaves recorded on a four-station seismic network. We show that RTM is an effective method to detect and locate infrasonic sources across a variety of scenarios, and by integrating numerical modeling, RTM-FDTD produces more accurate source locations and increases the detection capability.

Detection and source parametrization of small-energy fireball events in Western Alps with ground-based infrasonic arrays

SUMMARY We present infrasound signals generated by four fireball events occurred in Western Alps between 2016 and 2019 and that were recorded by small aperture arrays at source-to-receiver distances <300 km. Signals consist in a series of short-lived infrasonic arrivals that are closely spaced in time. Each arrival is identified as a cluster of detections with constant wave parameters (backazimuth and apparent velocity), that change however from cluster to cluster. These arrivals are likely generated by multiple infrasonic sources (fragmentations or hypersonic flow) along the entry trajectory. We developed a method, based on 2-D ray tracing and on the independent optically determined time of the event, to locate the source position of the multiple arrivals from a single infrasonic array data and to reconstruct the 3-D trajectory of a meteoroid in the Earth's atmosphere. The trajectories derived from infrasound array analysis are in excellent agreement with trajectories reconstructed from eyewitnesses reports for the four fireballs. Results suggest that the trajectory reconstruction is possible for meteoroid entries located up to ∼300 km from the array, with an accuracy that depends on the source-to-receiver distance and on the signal-to-noise level. We also estimate the energy of the four fireballs using three different empirical laws, based both on period and amplitude of recorded infrasonic signals, and discuss their applicability for the energy estimation of small energy fireball events ($\le 1{\rm{kt\,\,TNT\,\,equivalent}}$).

The Effects of Atmospheric Models on the Estimation of Infrasonic Source Functions at the Source Physics Experiment

ABSTRACTWe invert infrasound signals for an equivalent seismoacoustic source function using different atmospheric models to produce the necessary Green’s functions. The infrasound signals were produced by a series of underground chemical explosions as part of the Source Physics Experiment (SPE). In a previous study, we inverted the infrasound data using so-called predictive atmospheric models, which were based on historic, regional-scaled, publicly available weather observations interpolated onto a 3D grid. For the work presented here, we invert the same infrasound data, but using atmospheric models based on weather data collected in a time window that includes the approximate time of the explosion experiments, which we term postdictive models. We build two versions of the postdictive models for each SPE event: one that is based solely on the regional scaled observations, and one that is based on both regional scaled observations combined with on-site observations obtained by a weather sonde released at the time of the SPE. We then invert the observed data set three times, once for each atmospheric model type. We find that the estimated seismoacoustic source functions are relatively similar in waveform shape regardless of which atmospheric model that we used to construct the Green’s functions. However, we find that the amplitude of the estimated source functions is systematically dependent on the atmospheric model type: using the predictive atmospheric models to invert the data generally yields estimated source functions that are larger in amplitude than those estimated using the postdictive models.

Evaluation of a pair-based, joint-likelihood association approach for regional infrasound event identification

SUMMARYA Bayesian framework for the association of infrasonic detections is presented and evaluated for analysis at regional propagation scales. A pair-based, joint-likelihood association approach is developed that identifies events by computing the probability that individual detection pairs are attributable to a hypothetical common source and applying hierarchical clustering to identify events from the pair-based analysis. The framework is based on a Bayesian formulation introduced for infrasonic source localization and utilizes the propagation models developed for that application with modifications to improve the numerical efficiency of the analysis. Clustering analysis is completed using hierarchical analysis via weighted linkage for a non-Euclidean distance matrix defined by the negative log-joint-likelihood values. The method is evaluated using regional synthetic data with propagation distances of hundreds of kilometres in order to study the sensitivity of the method to uncertainties and errors in backazimuth and time of arrival. The method is found to be robust and stable for typical uncertainties, able to effectively distinguish noise detections within the data set from those in events, and can be made numerically efficient due to its ease of parallelization.

CLEAN beamforming for the enhanced detection of multiple infrasonic sources

SUMMARY The detection and characterization of signals of interest in the presence of (in)coherent ambient noise is central to the analysis of infrasound array data. Microbaroms have an extended source region and a dynamical character. From the perspective of an infrasound array, these coherent noise sources appear as interfering signals that conventional beamform methods may not correctly resolve. This limits the ability of an infrasound array to dissect the incoming wavefield into individual components. In this paper, this problem will be addressed by proposing a high-resolution beamform technique in combination with the CLEAN algorithm. CLEAN iteratively selects the maximum of the f/k spectrum (i.e. following the Bartlett or the Capon method) and removes a percentage of the corresponding signal from the cross-spectral density matrix. In this procedure, the array response is deconvolved from the f/k spectral density function. The spectral peaks are retained in a ‘clean’ spectrum. A data-driven stopping criterion for CLEAN is proposed, which relies on the framework of Fisher statistics. This allows the construction of an automated algorithm that continuously extracts coherent energy until the point is reached that only incoherent noise is left in the data. CLEAN is tested on a synthetic data set and is applied to data from multiple International Monitoring System infrasound arrays. The results show that the proposed method allows for the identification of multiple microbarom source regions in the Northern Atlantic that would have remained unidentified if conventional methods had been applied.

Infrasonic source altitude localization based on an infrasound ray tracing propagation model.

Based on a three-dimensional Hamiltonian ray tracing program in spherical coordinates, the effective sound speed is confirmed as the determining factor that influences the infrasound propagation path, and a theoretical model for the back-projected infrasound propagation trajectory is established. In this paper, the mass spectrometer incoherent scatter radar model (MSISE00) and horizontal wind model (HWM93) are first used to obtain the effective sound speed at different altitudes in summer and winter in Beijing. Then, the propagation paths generated by an infrasound source in Beijing are simulated with a conventional model. Some of the simulated rays are used to verify the back-projection model, and the verification has a high degree of coincidence with the simulation. Finally, using an experiment to validate the back-projection model, the altitude localization essentially conforms to the true value, proving the feasibility of the ray tracing back-projection algorithm.

A rapid detection method of earthquake infrasonic wave based on decision-making tree and the BP neural network

In this paper, we propose a rapid automatic detection method based on decision-making tree combined with BP neural network for the earthquake infrasonic wave. Three factors of frequency (F), duration period (P) and amplitude (A) of seismic infrasonic waves were selected as the network input parameters in the three-layered BP neural network. A total of 30 different infrasonic waves were tested in this model. The results indicate that the successful decision rates can reach 0.8 with input parameters F, P and A. When using proper thresholds for the input parameters, such as F = 0.005 Hz, P = 500 s and A = 5 Pa, the detection results are very closed to the true input signals, and the infrasonic sources as well as their main characteristics can be effectively recognised and classified rapidly. This new method could provide clues and thoughts for the short-term earthquake infrasonic wave detection.

A rapid detection method of earthquake infrasonic wave based on decision-making tree and the BP neural network

In this paper, we propose a rapid automatic detection method based on decision-making tree combined with BP neural network for the earthquake infrasonic wave. Three factors of frequency (F), duration period (P) and amplitude (A) of seismic infrasonic waves were selected as the network input parameters in the three-layered BP neural network. A total of 30 different infrasonic waves were tested in this model. The results indicate that the successful decision rates can reach 0.8 with input parameters F, P and A. When using proper thresholds for the input parameters, such as F = 0.005 Hz, P = 500 s and A = 5 Pa, the detection results are very closed to the true input signals, and the infrasonic sources as well as their main characteristics can be effectively recognised and classified rapidly. This new method could provide clues and thoughts for the short-term earthquake infrasonic wave detection.

Detecting Lightning Infrasound Using a High‐Altitude Balloon

AbstractAcoustic waves with a wide range of frequencies are generated by lightning strokes during thunderstorms, including infrasonic waves (0.1 to 20 Hz). The source mechanism for these low‐frequency acoustic waves is still debated, and studies have so far been limited to ground‐based instruments. Here we report the first confirmed detection of lightning‐generated infrasound with acoustic instruments suspended at stratospheric altitudes using a free‐flying balloon. We observe high‐amplitude signals generated by lightning strokes located within 100 km of the balloon as it flew over the Tasman Sea on 17 May 2016. The signals share many characteristics with waveforms recorded previously by ground‐based instruments near thunderstorms. The ability to measure lightning activity with high‐altitude infrasound instruments has demonstrated the potential for using these platforms to image the full acoustic wavefield in the atmosphere. Furthermore, it validates the use of these platforms for recording and characterizing infrasonic sources located beyond the detection range of ground‐based instruments.

Estimation of explosive yield using regional distance infrasound

Infrasound signals from large above-ground explosions are known to propagate to significant distances while remaining at observable levels due to a combination of the large source energy for such events and decreased absorption for acoustic energy at lower frequencies. Previously, propagation-based, stochastic path geometry and travel time models have been used to improve estimates of location and time for infrasonic sources with promising results. A similar approach has been applied to develop stochastic transmission loss models for infrasonic signals. The resulting models have been utilized in a Bayesian framework to compute a near-source estimate of the acoustic signal, which can then be used to compute a probability distribution for possible explosive yields of the source. Details of the propagation models and yield estimation framework will be presented along with results for application to a number of explosive events.

Towards an accurate real-time locator of infrasonic sources

Infrasonic signals propagate from an atmospheric source via media with stochastic and fast space-varying conditions. Hence, their travel time, the amplitude at sensor recordings and even manifestation in the so-called “shadow zones” are random. Therefore, the traditional least-squares technique for locating infrasonic sources is often not effective, and the problem for the best solution must be formulated in probabilistic terms. Recently, a series of papers has been published about Bayesian Infrasonic Source Localization (BISL) method based on the computation of the posterior probability density function (PPDF) of the source location, as a convolution of a priori probability distribution function (APDF) of the propagation model parameters with likelihood function (LF) of observations. The present study is devoted to the further development of BISL for higher accuracy and stability of the source location results and decreasing of computational load. We critically analyse previous algorithms and propose several new ones. First of all, we describe the general PPDF formulation and demonstrate that this relatively slow algorithm might be among the most accurate algorithms, provided the adequate APDF and LF are used. Then, we suggest using summation instead of integration in a general PPDF calculation for increased robustness, but this leads us to the 3D space-time optimization problem. Two different forms of APDF approximation are considered and applied for the PPDF calculation in our study. One of them is previously suggested, but not yet properly used is the so-called “celerity-range histograms” (CRHs). Another is the outcome from previous findings of linear mean travel time for the four first infrasonic phases in the overlapping consecutive distance ranges. This stochastic model is extended here to the regional distance of 1000 km, and the APDF introduced is the probabilistic form of the junction between this travel time model and range-dependent probability distributions of the phase arrival time picks. To illustrate the improvements in both computation time and location accuracy achieved, we compare location results for the new algorithms, previously published BISL-type algorithms and the least-squares location technique. This comparison is provided via a case study of different typical spatial data distributions and statistical experiment using the database of 36 ground-truth explosions from the Utah Test and Training Range (UTTR) recorded during the US summer season at USArray transportable seismic stations when they were near the site between 2006 and 2008.