Christopher E. Petrin

Passive acoustic sensing with arrays has applications in many fields, including atmospheric monitoring of low frequency sounds (i.e., infrasound). Beamforming of array signals to gain spatial information about the signal is common, but the performance is often degraded due to limited resources (e.g., number of sensors, array size). Such sparse arrays create ambiguities due to reduced resolution and spatial aliasing. While previous work has focused on either maximizing array resolution or minimizing spatial aliasing, the current study demonstrates how evolutionary algorithms can be utilized to identify array configurations that optimize for both properties. The non-dominated sorting genetic algorithm II (NSGA-II) was used with the beamwidth and maximum sidelobe level as the fitness functions to iteratively identify a group of optimized synthesized array configurations. This group is termed a Pareto-front and is optimized such that one fitness function cannot be improved without a decrease in the other. These optimized solutions were studied for a single frequency (8 Hz) and a multi-frequency (3 to 20 Hz) signal using either a 36-element or 9-element array with a 60 m aperture. The performance of the synthesized arrays was compared against established array configurations (baseline) with most of the Pareto-front solutions outperforming these baseline configurations. The largest improvements to array performance using the synthesized configurations were with fewer array elements and the multi-frequency signal.

A three-microphone acoustic array (OSU1), with microphones that have a flat response from 0.1 to 200 Hz, was deployed for 6 years (2016–2022) at Oklahoma State University (OSU) in Stillwater, Oklahoma, and sampled at 1000 Hz. This study presents a new dataset of acoustic measurements in a high interest region (e.g., study of tornado infrasound), provides a broad overview of acoustic detections and the means to identify them, and provides access to these recordings to the broader scientific community. A wide variety of infrasound and low-audible sources were identified and characterized via analysis of time traces, power spectral densities, spectrograms, and beamforming. Low, median, and high noise models were compared with global noise models. Detected sources investigated include natural (microbaroms, bolides, earthquakes, and tornadoes) and anthropomorphic (fireworks, airplanes, and munition detonations) phenomena. Microbarom detections showed consistency with literature (~0.2 Hz with peak amplitude in the winter) and evidence that the frequency was inversely related to the amplitude. Fireworks and airplanes served as verified local events for the evaluation of data quality and processing procedures. Infrasound from munition detonations, that occur nearly daily at a location 180 km southeast of OSU1, matched the available ground truth on days with favorable propagation to OSU1. A clear bolide detection with an estimated position of approximately 300 km from OSU1 was shown. Most detected earthquakes were seismic arrivals due to sensor vibrations; however, the largest earthquake in Oklahoma history showed an acoustic arrival. Finally, data from multiple tornadoes are discussed, including a previously unpublished quasi-linear convective system tornado.

Most tornado related deaths occur in the southeastern United States, not Tornado Alley. This is due, in part, to the hilly terrain that limits the use of weather radar in this region. Deaths are likely to rise since there is evidence indicating that Tornado Alley is shifting eastward. This has motivated a return to considering infrasound (sound below human hearing) from tornadoes as a potential means of improving warnings in hilly terrain. Current warnings do not make use of these sounds due to the lack of understanding of the physical mechanism responsible for its production. This paper presents recent observations of tornadoes and a funnel cloud from fixed and mobile infrasound sensors. Then a review of the potential mechanisms for the infrasound is presented and discussed in light of the observations.

Infrasound emitted by tornado-producing storms has been observed before tornadogenesis, and can be detected beyond line-of-sight due to low atmospheric attenuation. Therefore, passive infrasound monitoring shows potential for the study and prediction of tornadoes, provided received infrasound signals can be correlated to the tornado flow-field. Herein, observations of atmospheric infrasound during four severe storms are reported briefly. The storms were selected as a sample of severe weather; two produced tornadoes, one produced merely a funnel cloud, and the last was a hail storm with no significant rotation. In general, the observations are in agreement with previous literature indicating that tornadoes cause increased infrasound levels between 0.5 Hz and 10 Hz. However, the underlying physical mechanism responsible for tornado infrasound emissions remains elusive. Thus, this paper presents a review of postulated mechanisms for meteorological infrasound, which are evaluated in light of the reported storms. Electromagnetic sources are discounted due to their impulsive nature. Co-rotating vortex models and vortex-seismic coupling are similarly rejected. Due to its previous success, turbulent jet noise is considered a likely mechanism, though in the audible range. Finally, radial vortex vibrations, latent-heat effects, and non-equilibrium effects are all considered possible mechanisms in light of available observations.