Here we investigate the application of ground-coupled airwaves observed by seismoacoustic stations at local to near-regional scales to detect signals of interest and determine back-azimuth information. Ground-coupled airwaves are created from incident pressure waves traveling through the atmosphere that couple to the earth and transmit as a seismic wave with retrograde elliptical motion. Previous studies at sub-local scales (<10 km from a source of interest) found the back-azimuth to the source could be accurately determined from seismoacoustic signals recorded by acoustic and 3-component seismic sensors spatially separated on the order of 10 to 150 m. The potential back-azimuth directions are estimated from the coherent signals between the acoustic and vertical seismic data, via a propagation-induced phase shift of the seismoacoustic signal. A unique solution is then informed by the particle motion of the 3-component seismic station, which was previously found to be less accurate than the seismoacoustic-sensor method. We investigate the applicability of this technique to greater source-receiver distances, from 50-100 km and up to 400 km, which contains pressure waves with tropospheric and stratospheric ray paths, respectively. Specifically, we analyze seismoacoustic sources with ground truth from rocket motor fuel elimination events at the Utah Test and Training Range (UTTR) as well as a 2020 rocket launch in Southern California. From these sources we observe evidence that while coherent signals can be seen from both sources on multiple seismoacoustic station pairs, the determined ground-coupled airwave back-azimuths are more complicated than results at more local scales. Our findings suggest more complex factors including incidence angle, coupling location, subsurface material, and atmospheric propagation effects need to be fully investigated before the ground-coupled airwave back-azimuth determination method can be applied or assessed at these further distances.
Infrasound is generated by a variety of natural and anthropogenic sources. Infrasonic waves travel through the dynamic atmosphere, which can change on the order of minutes to hours. Infrasound propagation largely depends on the wind and temperature structure of the atmosphere. Numerical weather prediction models are available to provide atmospheric specifications, but uncertainties in these models exist and they are computationally expensive to run. Machine learning has proven useful in predicting tropospheric weather using Long Short-Term Memory (LSTM) networks. An LSTM network is utilized to make atmospheric specification predictions up to ~30 km for three different training and testing scenarios: (a) the model is trained and tested using only radiosonde data from the Albuquerque, NM, USA station, (b) the model is trained on radiosonde stations across the contiguous US, excluding the Albuquerque, NM, USA station, which was reserved for testing, and (c) the model is trained and tested on radiosonde stations across the contiguous US. Long Short-Term Memory predictions are compared to a state-of-the-art reanalysis model and show cases where the LSTM outperforms, performs equally as well, or underperforms in comparison to the state-of-the-art. Regional and temporal trends in model performance across the US are also discussed. Results suggest that the LSTM model is a viable tool for predicting atmospheric specifications for infrasound propagation modeling.
Infrasound, or low frequency sound 20 Hz, is produced by a variety of natural and anthropogenic sources. Wind also generates signals within this frequency band and serves as a persistent source of infrasonic noise. Infrasound sensors measure pressure fluctuations, which scale with the ambient density and velocity fluctuations of ground winds. Here we compare four different wind noise reduction systems, or "filters", and make recommendations for their use in temporary infrasound deployments. Our results show that there are two filters that are especially effective at reducing wind noise: (1) a Hyperion high frequency (HF) shroud with a 1 m diameter metal mesh dome placed on top and (2) a Hyperion Four Port Garden Hose shroud with 4 Miracle-Gro Soaker System garden hoses. We also find that placing a 5-gallon bucket over the HF wind shroud should not be done as it provides a negligible decrease in noise up to ~ 1 Hz and then an increase in noise. We conclude that it is up to the researcher to determine which of the other filters is best for their needs based on location and expense. We anticipate this study will be used as a resource for future deployments when a wind noise reduction method is necessary but only needed for a limited time period.
Standard meteorological balloons can deliver small scientific payloads to the stratosphere for a few tens of minutes, but achieving multihour level flight in this region is more difficult. We have developed a solarpowered hot-air balloon named the heliotrope that can maintain a nearly constant altitude in the upper troposphere–lower stratosphere as long as the sun is above the horizon. It can accommodate scientific payloads ranging from hundreds of grams to several kilograms. The balloon can achieve float altitudes exceeding 24 km and fly for days in the Arctic summer, although sunset provides a convenient flight termination mechanism at lower latitudes. Two people can build an envelope in about 3.5 h, and the materials cost about $30. The low cost and simplicity of the heliotrope enables a class of missions that is generally out of reach of institutions lacking specialized balloon expertise. Here, we discuss the design history, construction techniques, trajectory characteristics, and flight prediction of the heliotrope balloon. We conclude with a discussion of the physics of solar hot-air balloon flight.
Low-frequency sound ≤20 Hz, known as infrasound, is generated by a variety of natural and anthropogenic sources. Following an event, infrasonic waves travel through a dynamic atmosphere that can change on the order of minutes. This makes infrasound event classification a difficult problem, as waveforms from the same source type can look drastically different. Event classification usually requires ground-truth information from seismic or other methods. This is time consuming, inefficient, and does not allow for classification if the event locates somewhere other than a known source, the location accuracy is poor, or ground truth from seismic data is lacking. Here,we compare the performance of the state of the art for infrasound event classification, support vector machine (SVM) to the performance of a convolutional neural network (CNN), a method that has been proven in tangential fields such as seismology. For a 2-class catalog of only volcanic activity and earthquake events, the fourfold average SVM classification accuracy is 75%, whereas it is 74% when using a CNN. Classification accuracies from the 4-class catalog consisting of the most common infrasound events detected at the global scale are 55% and 56% for the SVM and CNN architectures, respectively. These results demonstrate that using a CNN does not increase performance for infrasound event classification. This suggests that SVM should be the preferred classification method, as it is a simpler and more trustworthy architecture and can be tied to the physical properties of the waveforms. The SVM and CNN algorithms described in this article are not yet generalizable to other infrasound event catalogs. We anticipate this study to be a starting point for development of large and comprehensive, systematically labeled, infrasound event catalogs, as such catalogs will be necessary to provide an increase in the value of deep learning on event classification.
Low frequency sound %3C zo Hz, known as infrasound, is generated by a variety of natural and anthropogenic sources. Following an event, infrasonic waves travel through a dynamic atmosphere that can change on the order of minutes. This makes infrasound event classification a difficult problem as waveforms from the same source type can look drastically different. Event classification usually requires ground truth information from seismic or other methods. This is time consuming, inefficient, and does not allow for a classification if the event locates somewhere other than a known source, the location accuracy is poor, or ground truth from seismic data is lacking. Here we compare the performance of the state of the art for infrasound event classification, support vector machine (SVM), to the performance of a convolutional neural network (CNN), a method that has been proven in tangential fields such as seismology. For a 1-class catalog consisting of only volcanic activity and earthquake events, the 4-fold average SVM classification accuracy is 75%, while it is 74% when using a CNN. Classification accuracies from the 4-class catalog consisting of the most common infrasound events detected at the global scale are 55% and 56% for the SVM and CNN architectures, respectively. These results demonstrate that using a CNN does not increase performance for infrasound event classification. This suggests that SVM should be the preferred classification method as it is a simpler and more trustworthy architecture and can be tied to the physical properties of the waveforms. The SVM and CNN algorithms described in this paper are not yet generalizable to other infrasound event catalogs. We anticipate this study to be a starting point for the development of large and comprehensive, systematically labeled, infrasound event catalogs as such catalogs will be necessary to provide an increase in the value of deep learning on event classification.
We outline a method using gradient flow independent component analysis (ICA) to separate signals comprising the coda in a topographically complex setting.We also identify the sources of scattered signals by tracking signal backazimuths over time. The gradient flow ICA method is shown to effectively separate signals in the acoustic coda. The method correctly identifies the backazimuth of the first arrival from two 800 kg TNT equivalent explosions as well as subsequent signals scattered by the surrounding topography. Circular statistics is used to analyse the variance, mean and uniformity of calculated backazimuths. These results have strong implications for understanding the acoustic wavefield by identifying scatterers and inverting for atmospheric conditions.
Bowman, Daniel B.; Young, Eliot F.; Krishnamoorthy, Siddharth K.; Lees, Jonathan L.; Albert, Sarah A.; Komjathy, Attila K.; Cutts, James A.
Balloon-borne infrasound research began again in 2014 with a small payload launched as part of the High Altitude Student Platform (HASP; Bowman and Lees(2015)). A larger payload was deployed through the same program in 2015. These proof of concept experiments demonstrated that balloon-borne microbarometers can capture the ocean microbarom (a pervasive infrasound signal generated by ocean waves) even when nearby ground sensors are not able to resolve them (Bowman and Lees, 2017). The following year saw infrasound sensors as secondary payloads on the 2016 Ultra Long Duration Balloon flight from Wanaka, New Zealand (Bowman and Lees, 2018; Lamb et al., 2018) and the WASP 2016 balloon flight from Ft. Sumner, New Mexico (Young et al., 2018). Another payload was included on the HASP 2016 flight as well. In 2017, the Heliotrope project included a four element microbarometer network drifting at altitudes of 20-24 km on solar hot air balloons (Bowman and Albert, 2018). At the time of this writing the Trans-Atlantic Infrasound Payload (TAIP, operated by Sandia National Laboratories) and the Payload for Infrasound Measurement in the Arctic (PIMA, operated by Jet Propulsion Laboratory) are preparing to fly from Sweden to Canada aboard the PMC-Turbo balloon. The purpose of this experiment is to cross-calibrate several different infrasound sensing systems and test whether wind noise events occur in the stratosphere.