Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially at the local scale, as maintaining and deploying networks is costly. With increasing interest in smaller-yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosive Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either “explosion”, “ambient”, or “other” with true positive rates (recall) greater than 96% for all three categories.

Sample return capsules (SRCs) entering Earth’s atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 2023 September 24 arrival of the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing, and Global Positioning System technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the data set collected during this campaign will improve entry and propagation models and augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources.

Attritable sensor systems with microphones are ubiquitous and can detect a variety of moving targets from ground vehicles to unmanned autonomous systems (UAS), aircraft, and spacecraft. Smartphone platforms have open data collection interfaces and can use cellular, Wi-Fi, and other public communications to relay data. In addition, acoustic signatures are more difficult to mask than transmissions in radio-frequency bands, making sound a passive complement to RF in an early warning system. This presentation discusses the development, deployment, and ongoing AI/ML work behind RedVox, a smartphone app available through the Apple App Store and Google Play Store. RedVox records data using the smartphone's onboard sensors, including the accelerometer, gyroscope, magnetometer, barometer, and microphone; these data packets can be streamed to interested parties in real-time and analyzed online using RedVox's open-source software tools. One of RedVox's unique features is the ability to collect infrasonic data at frequencies below human hearing, which has been shown to detect natural disasters, explosions, and motorized vehicles. The RedVox app is rapidly deployable at scale in regions of interest. Two current focus areas are Guam and Coconut Island in Oahu, where networks of smartphones can perform edge processing and dispersed sensing of airborne and maritime targets. The RedVox ecosystem has matured over the past decade across diverse environments and currently provides advanced edge and cloud analytics. Further development could lead to a lightweight machine learning model that flags anomalous entities for human review, offering a resilient ad hoc sensor network for defense or emergency response applications.

For prompt detection of large (>1 kt) above-ground explosions, infrasound microphone networks and arrays are deployed at surveyed locations across the world. Denser regional and local networks are deployed for smaller explosions, however, they are limited in number and are often deployed temporarily for experiments. With the expanded interest in smaller yield explosions targeted at vulnerable areas such as population centers and key infrastructures, the need for more dense microphone networks has increased. An “attritable” (affordable, reusable, and replaceable) and flexible alternative can be provided by smartphone networks. Explosion signals from a fuel air explosive (thermobaric bomb) and a high explosive with trinitrotoluene equivalent yields of 6.35 and 3.63 kg, respectively, were captured on both an infrasound microphone and a network of smartphones. The resulting waveforms were compared in time, frequency, and time-frequency domains. The acoustic waveforms collected on smartphones produced a filtered explosion pulse due to the smartphone's diminishing frequency response at infrasound frequencies (<20 Hz) and was found difficult to be used with explosion characterization methods utilizing waveform features (peak overpressure, impulse, etc.). However, the similarities in time frequency representations and additional sensor inputs are promising for other explosion signal identification and analysis. As an example, a method utilizing the relative acoustic amplitudes for source localization using the smartphone sensor network is presented.

Chemical explosions create blast waves with large overpressure disturbances. It is important to develop a standard blast model based on data to accurately predict acoustic blast-wave amplitudes near detonations and invert for explosion energy from distant observations of blast-wave signals. However, open data from large, controlled chemical explosions with reliable ground truth can be challenging to find. The lack of access to such data could limit the number of contributions to related research and potentially stifle the rate of discoveries or validation of existing models. To address these data scarcity problem, we have curated and compiled a standardized set of 817 blast-wave waveforms from 19 distinct high-explosive events. The blast-wave waveforms are standardized to a 1 kg trinitrotoluene explosion using scaling laws and corrections for location effects. A brief overview of the dataset is presented along with explosion feature models as well as recommendations for extracting explosion features. The resulting dataset is distributed to an open repository in both Seismic Analysis Code and pandas DataFrame formats containing the waveforms, the scaled distances, and the sample rates.

The ability to detect, measure, and locate the source of contaminants, especially heavy metals and radionuclides, is of ongoing interest. A common tool for contaminant identification and bioremediation is vegetation that can accumulate and indicate recent and historic pollution. However, large-scale sampling can be costly and labor-intensive. Hence, non-invasive in-situ techniques such as laser-induced fluorescence (LIF) are becoming useful and effective ways to observe the health of plants through the excitation of organic molecules, e.g., chlorophyll. The technique presented utilizes images collected of LIF in moss to identify different metals and environmental stressors. Analysis through image processing of LIF response was key to identifying Cu, Zn, Pb, and a mixture of the metals at nmol/cm2 levels. Specifically, the RGB values from each image were used to create density histograms of each color channel’s relative pixel abundance at each decimal code value. These histograms were then used to compare color shifts linked to the successful identification of contaminated moss samples. Photoperiod and extraneous environmental stressors had minimal impact on the histogram color shift compared to metals and presented with a response that differentiated them from metal contamination.

Persistent low-frequency (<180 Hz) acoustic detection took place within the boundaries of Oak Ridge National Laboratory to monitor full power operations of the High Flux Isotope Reactor. Three acoustic sensors were installed at distances of 69, 101, and 914 m from the northeast corner of the cooling towers to monitor and assess four reactor power generation cycles. Features were extracted from power spectral density calculations where data were collected during reactor on and off operations. Diverse spectral features were present during full reactor power, including a 21.4 Hz fundamental frequency and ascending harmonics. Using bandpass filters, these related frequencies were isolated and summed, and the root mean square energy was calculated. The method of isolating and summing characteristic features provided a significant improvement in identifying acoustic behavior related to reactor power when the raw signals were obscured by noise.

Fission Sphere (FS-3), an array of forty organic stilbene detectors operated in time-coincidence. The FS-3 is used to measure the prompt emissions of neutrons and gamma rays from Cf-252 spontaneous fission. These new data will be used to validate physics-based prediction codes, including CGMF and FREYA, and will be useful in future ENDF and ENSDF evaluations. This paper will present results from the first experiments using FS-3 and a Cf-252 spontaneous fission source. It will compare the experimental results with predictions from theory and Monte Carlo simulation. Specifically, we will describe the correlations among energy, multiplicity, and angles of emitted particles. We will also discuss the application of these correlations in multiplicity counting techniques, which are widely used in nuclear safeguards.

Abstract A helium‐filled mylar balloon carrying a smartphone and infrasound sensors ascended to a stratospheric height of 35 km over the surface detonation of a chemical explosive, with a total acoustic propagation distance of 127 km. The smartphone was configured to collect multi‐modal data at high rates from internal sensors. Analysis of the data shows successful collection of the explosion signal by both the smartphone's microphone and its accelerometers, the first from an ascending balloon. Comparison of the acoustic signal with that collected by other infrasound sensors, both airborne and ground‐based, provides insight into the possibilities and limitations of collecting acoustic data from the stratosphere.

The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time–frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications.

The eruption of Hunga volcano, Tonga is the most energetic event recorded by the infrasound component of the global International Monitoring System (IMS). Infrasound, acoustic-gravity and Lamb waves were recorded by all 53 operational stations after circling four times the globe. The atmospheric waves recorded globally exhibit amplitude and period comparable to the ones observed following the 1883 Krakatoa eruptions. In the context of the future verification of the Comprehensive Nuclear-Test-Ban Treaty, this event provides a prominent milestone for studying in detail infrasound propagation around the globe for almost one week as well as for calibrating the performance of the IMS network in a broad frequency band.

Mechanical draft cooling towers (MDCTs) serve a critical heat management role in a variety of industries. For nuclear reactors in particular, the consistent, predictable operation of MDCTs is required to avoid damage to infrastructure and reduce the potential for catastrophic failure. Accurate, reliable measure-ment of MDCT fan speed is therefore an important maintenance and safety requirement. To that end, we have developed an algorithm for automatically predicting the rotational speeds of multiple, simulta-neously operating fan rotors using contactless, infrasound measurements. The algorithm is based on identifying the blade passing frequencies (BPFs), their harmonics, as well as the motor frequencies (MFs) for each fan in operation. Using the algorithm, these frequencies can be automatically identified in the acoustic waveform's short-time Fourier transform spectrogram. Attribution is aided by a set of fil-ters that rely on the unique spectral and temporal characteristics of fan operation, as well as the intrinsic frequency ratios of the BPF harmonics and the BPF/MF signals. The algorithm was tested against infra -sound data acquired from infrasound sensors deployed at two research reactors: the Advanced Test Reactor (ATR) located at Idaho National Laboratory (INL) and the High Flux Isotope Reactor (HFIR) located at Oak Ridge National Laboratory (ORNL). After manually identifying the MDCT gearbox ratio, the algo-rithm was able to quickly yield fan speeds at both reactors in good agreement with ground truth. Ultimately, this work demonstrates the ease by which MDCT fans may be monitored in order to optimize operational conditions and avoid infrastructure damage.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

A smartphone plummeted from a stratospheric height of 36 km, providing a near-real-time record of its rapid descent and ground impact. An app recorded and streamed useful internal multi-sensor data at high sample rates. Signal fusion with external and internal sensor systems permitted a more detailed reconstruction of the Skyfall chronology, including its descent speed, rotation rate, and impact deceleration. Our results reinforce the potential of smartphones as an agile and versatile geophysical data collection system for environmental and disaster monitoring IoT applications. We discuss mobile environmental sensing capabilities and present a flexible data model to record and stream signals of interest. The Skyfall case study can be used as a guide to smartphone signal processing methods that are transportable to other hardware platforms and operating systems.

The explosive eruption of the Hunga Tonga-Hunga Ha’apai volcano on 15th of January 2022 impacted the Earth, its oceans and atmosphere on a global scale. Witnesses report an audible “bang” as a result of the event in distances of up to several thousand kilometers. With infrasound sensors this sound wave can be detected where the frequency content or the amplitude of the signal renders the event inaudible to the human ear. Infrasound sensors are distributed globally, a selection of these stations upload their data in real time to publicly available servers. In combination with Open Source libraries such as obspy or scipy it is possible to use these data sources to observe the atmospheric disturbances caused by the eruption on a global scale in near real time. With a minimum of data processing not only the first arrival peak of the atmospheric lamb wave can be identified at most stations but also further passes of the wave as it propagates around the planet several times. Having large amounts of publicly available data is crucial in that process. New data chunks can be analyzed and displayed immediately while the signal is still ongoing because data access requests are not required. Additionally, having immediate access to a large dataset allows for big data analysis and reduces the necessity to consider outliers at individual stations and increases the chance to identify the signal after multiple days when overall signal to noise ratios have decreased.