Exploring acoustic characteristics of different aircraft types by fusing with aircraft tracking data.

In this study, we develop a method that assigns acoustic signals with Automatic Dependent Surveillance-Broadcast (ADS-B) data to build a labeled dataset of acoustic signals from aircraft without expensive ground-truth experiments. An exploration of the resultant labeled dataset enables an assessment of the acoustic characteristics from three types of aircraft. The fusion framework is evaluated using data from an acoustic sensor and collocated ADS-B receiver in the middle of a large urban area at Southern Methodist University in Dallas, Texas. Our results demonstrate the benefit of combining multiple types of data to generate a labeled dataset leveraging open-source aircraft surveillance data. By studying three classes of aircraft, we find that the smaller fixed wing single engine (FWSE) class is mostly detected within approximately 5000 m, while the larger fixed wing multi-engine (FWME) class is commonly detected out to greater distances above 7500 m. The FWSE class has a median source frequency at 100 Hz, compared to FWME class with median source frequency at 80 Hz, while rotorcraft has a source frequency falling into a lower range of 30-100 Hz.

Evaluating factors influencing infrasonic signal detection and automatic processing performance utilizing a regional network.

Physical and deployment factors that influence infrasound signal detection and assess automatic detection performance for a regional infrasound network of arrays in the Western U.S. are explored using signatures of ground truth (GT) explosions (yields). Despite these repeated known sources, published infrasound event bulletins contain few GT events. Arrays are primarily distributed toward the south-southeast and south-southwest at distances between 84 and 458 km of the source with one array offering azimuthal resolution toward the northeast. Events occurred throughout the spring, summer, and fall of 2012 with the majority occurring during the summer months. Depending upon the array, automatic detection, which utilizes the adaptive F-detector successfully, identifies between 14% and 80% of the GT events, whereas a subsequent analyst review increases successful detection to 24%-90%. Combined background noise quantification, atmospheric propagation analyses, and comparison of spectral amplitudes determine the mechanisms that contribute to missed detections across the network. This analysis provides an estimate of detector performance across the network, as well as a qualitative assessment of conditions that impact infrasound monitoring capabilities. The mechanisms that lead to missed detections at individual arrays contribute to network-level estimates of detection capabilities and provide a basis for deployment decisions for regional infrasound arrays in areas of interest.

Explosion yield estimation from pressure wave template matching.

A method for estimating the yield of explosions from shock-wave and acoustic-wave measurements is presented. The method exploits full waveforms by comparing pressure measurements against an empirical stack of prior observations using scaling laws. The approach can be applied to measurements across a wide-range of source-to-receiver distances. The method is applied to data from two explosion experiments in different regions, leading to mean relative errors in yield estimates of 0.13 using prior data from the same region, and 0.2 when applied to a new region.

Analysis and modeling of infrasound from a four-stage rocket launch.

Infrasound from a four-stage sounding rocket was recorded by several arrays within 100 km of the launch pad. Propagation modeling methods have been applied to the known trajectory to predict infrasonic signals at the ground in order to identify what information might be obtained from such observations. There is good agreement between modeled and observed back azimuths, and predicted arrival times for motor ignition signals match those observed. The signal due to the high-altitude stage ignition is found to be low amplitude, despite predictions of weak attenuation. This lack of signal is possibly due to inefficient aeroacoustic coupling in the rarefied upper atmosphere.

Detection of regional infrasound signals using array data: Testing, tuning, and physical interpretation.

This work quantifies the physical characteristics of infrasound signal and noise, assesses their temporal variations, and determines the degree to which these effects can be predicted by time-varying atmospheric models to estimate array and network performance. An automated detector that accounts for both correlated and uncorrelated noise is applied to infrasound data from three seismo-acoustic arrays in South Korea (BRDAR, CHNAR, and KSGAR), cooperatively operated by Korea Institute of Geoscience and Mineral Resources (KIGAM) and Southern Methodist University (SMU). Arrays located on an island and near the coast have higher noise power, consistent with both higher wind speeds and seasonably variable ocean wave contributions. On the basis of the adaptive F-detector quantification of time variable environmental effects, the time-dependent scaling variable is shown to be dependent on both weather conditions and local site effects. Significant seasonal variations in infrasound detections including daily time of occurrence, detection numbers, and phase velocity/azimuth estimates are documented. These time-dependent effects are strongly correlated with atmospheric winds and temperatures and are predicted by available atmospheric specifications. This suggests that commonly available atmospheric specifications can be used to predict both station and network detection performance, and an appropriate forward model improves location capabilities as a function of time.

Extracting changes in air temperature using acoustic coda phase delays.

Blast waves produced by 60 high-explosive detonations were recorded at short distances (few hundreds of meters); the corresponding waveforms show charge-configuration independent coda-like features (i.e., similar shapes, amplitudes, and phases) lasting several seconds. These features are modeled as reflected and/or scattered waves by acoustic reflectors/scatters surrounding the explosions. Using explosion pairs, relative coda phase delays are extracted and modeled as changes in sound speed due to changes in air temperature. Measurements from nearby weather towers are used for validation.

Multivariate acoustic detection of small explosions using Fisher's combined probability test.

A methodology for the combined acoustic detection and discrimination of explosions, which uses three discriminants, is developed for the purpose of identifying weak explosion signals embedded in complex background noise. By utilizing physical models for simple explosions that are formulated as statistical hypothesis tests, the detection/discrimination approach does not require a model for the background noise, which can be highly complex and variable in practice. Fisher's Combined Probability Test is used to combine the p-values from all multivariate discriminants. This framework is applied to acoustic data from a 400 g explosion conducted at Los Alamos National Laboratory.

Development of a matched filter detector for acoustic signals at local distances from small explosions.

A method for acoustic detection of small explosions at local distances is presented combining a matched filter with a p-value representing the conditional probability of detection. Because the physics of signal generation and propagation for small, locally recorded acoustic signals from small explosions is well understood, the single hypothesis to be tested is a signal corrupted by additive noise. A simple analytical signal representation is used where a known signal is assumed with parameters to be determined. The advantage of the approach is that the detector can be combined with other detectors that measure different signal characteristics all under the same unifying hypothesis.

Atmospheric waves and global seismoacoustic observations of the January 2022 Hunga eruption, Tonga.

The 15 January 2022 climactic eruption of Hunga volcano, Tonga, produced an explosion in the atmosphere of a size that has not been documented in the modern geophysical record. The event generated a broad range of atmospheric waves observed globally by various ground-based and spaceborne instrumentation networks. Most prominent was the surface-guided Lamb wave (≲0.01 hertz), which we observed propagating for four (plus three antipodal) passages around Earth over 6 days. As measured by the Lamb wave amplitudes, the climactic Hunga explosion was comparable in size to that of the 1883 Krakatau eruption. The Hunga eruption produced remarkable globally detected infrasound (0.01 to 20 hertz), long-range (~10,000 kilometers) audible sound, and ionospheric perturbations. Seismometers worldwide recorded pure seismic and air-to-ground coupled waves. Air-to-sea coupling likely contributed to fast-arriving tsunamis. Here, we highlight exceptional observations of the atmospheric waves.