Publications

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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.

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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 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.

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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.

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This report provides details of the algorithms in the Bloodhound package for infrasound data analysis. The report provides a detailed description of the algorithms, general instructions on tuning Bloodhound for different signal types, and a complete listing of all input parameters and the complete output schema. Several Jupyter notebooks are provided with the distribution for illustrating how to use Bloodhound for different workflows.

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.

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The first solar hot air balloon was constructed in the early 1970s (Besset, 2016). Over the following decades the Centre National d'Etudes Spatiales (CNES) developed the Montgolfiere Infrarouge (MIR) balloon, which flew on solar power during the day and infrared radiation from the Earth's surface at night (Fommerau and Rougeron, 2011). The balloons were capable of flying for over 60 days and apparently reached altitudes of 30 km at least once (Malaterre, 1993). Solar balloons were the subject of a Jet Propulsion Laboratory study that performed test flights on Earth (Jones and Wu 1999) and discussed their mission potential for Mars, Jupiter, and Venus (Jones and Heun, 1997). The solar balloons were deployed from the ground and dropped from hot air balloons; some were altitude controlled by means of a remotely-commanded air valve at the top of the envelope. More recently, solar balloons have been employed for infrasound studies in the lower stratosphere (see Table 1). The program began in 2015, when a prototype balloon reached an altitude of 22 kilometers before terminating just prior to float (Bowman et al., 2015). An infrasound sensor was successfully deployed on a solar balloon during the 2016 SISE/USIE experiment, in which an acoustic signal from a ground explosion was captured at a range of 330 km (Anderson et al. 2018; Young et al. 2018). This led to the launch of a 5-balloon infrasound network during the Heliotrope experiment (Bowman and Albert, 2018). The balloons were constructed by the researchers themselves at a materials of less than $50 per envelope.

A variety of Earth surface and atmospheric sources generate low-frequency sound waves that can travel great distances. Despite a rich history of ground-based sensor studies, very few experiments have investigated the prospects of free floating microphone arrays at high altitudes. However, recent initiatives have shown that such networks have very low background noise and may sample an acoustic wave field that is fundamentally different than that at Earth's surface. The experiments have been limited to at most two stations at altitude, making acoustic event detection and localization difficult.We describe the deployment of four drifting microphone stations at altitudes between 21 and 24 km above sea level. The stations detected one of two regional ground-based chemical explosions as well as the ocean microbarom while travelling almost 500 km across the American Southwest. The explosion signal consisted of multiple arrivals; signal amplitudes did not correlate with sensor elevation or source range. The waveforms and propagation patterns suggest interactions with gravity waves at 35-45 km altitude. A sparse network method that employed curved wave front corrections was able to determine the backazimuth from the free flying network to the acoustic source. Episodic signals similar to those seen on previous flights in the same region were noted, but their source remains unclear. Background noise levels were commensurate with those on infrasound stations in the International Monitoring System below 2 s.

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This research uses the acoustic coda phase delay method to estimate relative changes in air temperature between explosions with varying event masses and heights of burst. It also places a bound on source-receiver distance for the method. Previous studies used events with different shapes, height of bursts, and masses and recorded the acoustic codas at source-receiver distances less than 1 km. This research further explores the method using explosions that differ in mass (by up to an order of magnitude) and are placed at varying heights. Source-receiver distances also cover an area out to 7 km. Relative air temperature change estimates are compared to complementary meteorological observations. Results show that two explosions that differ by an order of magnitude cannot be used with this method because their propagation times in the near field and their fundamental frequencies are different. These differences are expressed as inaccuracies in the relative air temperature change estimates. An order of magnitude difference in mass is also shown to bias estimates higher. Small differences in height of burst do not affect the accuracy of the method. An upper bound of 1 km on source-receiver distance is provided based on the standard deviation characteristics of the estimates.

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