Blom, Philip Stephen | Engineering Papers

DOE OSTI2024

Prediction of regional infrasound produced by supersonic sources using a ray-based Mach cone source

The geometry of the Mach cone produced by a supersonic source is analyzed and mapped into initial conditions used in acoustic ray tracing. The resulting source model is combined with spherical geometry ray tracing methods to enable propagation simulations for infrasonic signals produced by bolides, space debris, rockets, aircraft, and other fast-than-sound sources out to typical infrasonic observation distances of hundreds or thousands of kilometers. Idealized linear and parabolic trajectories typical of bolides and rockets, respectively, are used to demonstrate the calculation of regional infrasonic signals produced by such sources and characteristics of the radiated infrasonic waves are found to vary strongly with the geometry of the trajectory and atmospheric structure. Predicted regional infrasonic signals are compared with those observed from a November 2020 bolide that passed over Scandinavia using a combination of institutionally maintained infrasound stations and “citizen scientist” data from the Raspberry Shake data repository.

42 ENGINEERING↗

DOE OSTI2024

Prediction of regional infrasound produced by supersonic sources using a ray-based Mach cone source

The geometry of the Mach cone produced by a supersonic source is analyzed and mapped into initial conditions used in acoustic ray tracing. The resulting source model is combined with spherical geometry ray tracing methods to enable propagation simulations for infrasonic signals produced by bolides, space debris, rockets, aircraft, and other fast-than-sound sources out to typical infrasonic observation distances of hundreds or thousands of kilometers. Idealized linear and parabolic trajectories typical of bolides and rockets, respectively, are used to demonstrate the calculation of regional infrasonic signals produced by such sources and characteristics of the radiated infrasonic waves are found to vary strongly with the geometry of the trajectory and atmospheric structure. Predicted regional infrasonic signals are compared with those observed from a November 2020 bolide that passed over Scandinavia using a combination of institutionally maintained infrasound stations and “citizen scientist” data from the Raspberry Shake data repository.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Finite-frequency modeling of regional tropospheric infrasound using realistic atmospheres and terrain

Infrasonic waves have been observed to propagate to regional (greater than 15 km) distances through the troposphere. Infrasound propagation in the geometric acoustics approximation has shown that realistic terrain can scatter acoustic energy from tropospheric ducts; however, ray methods cannot intrinsically capture finite-frequency behavior such as diffraction. A two-dimensional finite-difference time-domain (FDTD) method has been developed to solve linearized equations for infrasound propagation with realistic terrain. Acoustic wave propagation over 100 km with both flat terrain and a Gaussian hill was first simulated in order to compare finite-frequency propagation with ray predictions. The effects of realistic terrain and atmospheres on infrasound signals from a 2012 surface explosion at the Utah Testing and Training Range are then investigated. Propagation through the troposphere is suggested by array processing results, but eigenrays are not predicted due to weak to nonexistent ducting conditions. FDTD modeling suggests that the inclusion of terrain and finite frequency effects helps explain much of the observed signal in a realistic scenario. Furthermore, these results suggest that geometric acoustics may underestimate propagation through the troposphere, and that recorded waveforms at regional distances may be noticeably affected by terrain.

58 GEOSCIENCES↗

DOE OSTI2023

Seismoacoustic Analysis of the Large Surface Explosion Coupling Experiment Using a Large- N Seismic Array

Seismoacoustic signals at local distance (<~10 km) are widely used as important constraints on source parameters for near-surface events, yet the seismoacoustic wave generation and energy partitioning are not fully understood. Spatially dense sensors could provide observations in high resolution to capture the full wavefield for better understanding wave propagation and improving source estimation. Recently, spatially dense observations of the local seismoacoustic wavefield produced by a pair of 1-ton surface explosions have been recorded using a large-N seismic array. This large-N array consists of 446 geophones and covers an area of approximately 2×2.5 km 2 . The two surface explosions occur at the same location but at different times with different atmospheric conditions. Both seismic and air–ground coupled acoustic waves from the two surface explosions are well observed. Analyses of signals recorded by the large-N seismic array show different acoustic wave speed and amplitude for the two explosions. A strong spatial variability in acoustic wave speed and amplitude for each explosion is also observed. Finally, the observations suggest the important role of local atmosphere state on wave propagation and source estimation and demonstrate how the use of a large-N capability can improve characterization of the propagation medium and source.

58 GEOSCIENCES↗

DOE OSTI2022

Deep Learning categorization of infrasound array data

An analyst review created a labelled catalog of pseudo-ground truth data with almost 9,000 examples spanning four categories: transient signals, persistent signals, signals from moving sources, and noise.

42 ENGINEERING↗

DOE OSTI2022

Deep learning categorization of infrasound array data

Here we develop a deep learning-based infrasonic detection and categorization methodology that uses convolutional neural networks with self-attention layers to identify stationary and non-stationary signals in infrasound array processing results. Using features extracted from the coherence and direction-of-arrival information from beamforming at different infrasound arrays, our model more reliably detects signals compared with raw waveform data. Using three infrasound stations maintained as part of the International Monitoring System, we construct an analyst-reviewed data set for model training and evaluation. We construct models using a 4-category framework, a generalized noise vs non-noise detection scheme, and a signal-of-interest (SOI) categorization framework that merges short duration stationary and non-stationary categories into a single SOI category. We evaluate these models using a combination of k-fold cross-validation, comparison with an existing “state-of-the-art” detector, and a transportability analysis. Although results are mixed in distinguishing stationary and non-stationary short duration signals, f-scores for the noise vs non-noise and SOI analyses are consistently above 0.96, implying that deep learning-based infrasonic categorization is a highly accurate means of identifying signals-of-interest in infrasonic data records.

47 OTHER INSTRUMENTATION↗