Acoustic Sensor Research Articles

Illuminating Urban Near-Surface with Distributed Acoustic Sensing Multimodal Noise Surface-Wave Imaging

Abstract Urban subsurface exploration requires high spatial and temporal resolution, cost-effective operation, and minimal interference with urban activities. Distributed acoustic sensing (DAS)—an innovative seismic observation tool—emerges as a promising solution for urban surveys. In this study, we repurposed a 7.9 km telecommunication cable traversing Hefei into a seismic observation array with 3850 channels spaced at 2 m intervals. Noise cross-correlation functions (NCFs) were constructed from recordings by iDAS2 and ZD-DAS interrogators along the entire cable. Spatial variation in the NCFs was observed and attributed to different traffic conditions. Employing the recently developed modified frequency–Bessel transform method to NCFs from the 2 km southern subsection of the optic cable, we extracted broadband, high-resolution multimodal dispersion curves. The inverted near-surface structure beneath the cable unveiled a sediment thinning trend from the center to the periphery of the Hefei basin, consistent with borehole inspections. The three-station interferometry (C3) method and beamforming with the Bessel kernel function are applied to mitigate challenges arising from the weak coupling between the cable and the Earth, as well as persistent localized noise sources. These techniques facilitated the acquisition of broadband surface waves. Distinct secondary scatters are observed in NCFs near channels 2090 and 2287, accompanied by a substantial velocity contrast of 30%–40%, suggesting the existence of a blind fault. The study reaffirms the significant potential of DAS arrays for high-resolution imaging of subsurface structures in challenging urban environments, emphasizing the importance of advanced processing techniques to enhance imaging accuracy and robustness.

Seismic Monitoring of a Deep Geothermal Field in Munich (Germany) Using Borehole Distributed Acoustic Sensing

Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a geothermal field located in Munich, Germany. We leverage the operator’s cloud infrastructure for DAS data management and processing. We introduce a comprehensive workflow for the automated processing of DAS data, including seismic event detection, onset time picking, and event characterization. The latter includes the determination of the event hypocenter, origin time, seismic moment, and stress drop. Waveform-based parameters are obtained after the automatic conversion of the DAS strain-rate to acceleration. We present the results of a 6-month monitoring period that demonstrates the capabilities of the proposed monitoring set-up, from the management of DAS data volumes to the establishment of an event catalog. The comparison of the results with seismometer data shows that the phase and amplitude of DAS data can be reliably used for seismic processing. This emphasizes the potential of improving seismic monitoring capabilities with hybrid networks, combining surface and downhole seismometers with borehole DAS. The inherent high-density array configuration of borehole DAS proves particularly advantageous in urban and operational environments. This study stresses that realistic prior knowledge of the seismic velocity model remains essential to prevent a large number of DAS sensing points from biasing results and interpretation. This study suggests the potential for a gradual extension of the network as geothermal exploitation progresses and new wells are equipped, owing to the scalability of the described monitoring system.

Temperature Dependency of Insect’s Wingbeat Frequencies: An Empirical Approach to Temperature Correction

This study examines the relationship between the wingbeat frequency of flying insects and ambient temperature, leveraging data from over 302,000 insect observations obtained using a near-infrared optical sensor during an eight-month field experiment. By measuring the wingbeat frequency as well as wing and body optical cross-sections of each insect in conjunction with the ambient temperature, we identified five clusters of insects and analyzed how their average wingbeat frequencies evolved over temperatures ranging from 10 °C to 38 °C. Our findings reveal a positive correlation between temperature and wingbeat frequency, with a more pronounced increase observed at higher wingbeat frequencies. Frequencies increased on average by 2.02 Hz/°C at 50 Hz, and up to 9.63 Hz/°C at 525 Hz, and a general model is proposed. This model offers a valuable tool for correcting wingbeat frequencies with temperature, enhancing the accuracy of insect clustering by optical and acoustic sensors. While this approach does not account for species-specific responses to temperature changes, our research provides a general insight, based on all species present during the field experiment, into the intricate dynamics of insect flight behavior in relation to environmental factors.

The development of sensitive graphene-based surface acoustic wave sensors for NO2 detection at room temperature

Bilayer graphene (Bl-Gr) and sulphur-doped graphene (S-Gr) have been integrated with LiTaO3 surface acustic wave (SAW) sensors to enhance the performance of NO2 detection at room temperature. The sensitivity of the Bl-Gr SAW sensors toward NO2, measured at room temperature, was 0.29º/ppm, with a limit of detection of 0.068 ppm. The S-Gr SAW sensors showed 0.19º/ppm sensitivity and a limit of detection of 0.140 ppm. The origin of these high sensitivities was attributed to the mass loading and elastic effects of the graphene-based sensing materials, with surface changes caused by the absorption of the NO2 molecules on the sensing films. Although there are no significant differences regarding the sensitivity and detection limit of the two types of sensors, the measurements in the presence of interferent gases and various humidity conditions outlined much better selectivity and sensing performances towards NO2 gas for the Bl-Gr SAW sensors.Graphical

Digital twin of surface acoustic wave transceivers for a computational design of an optimal wave guiding layer thickness

AbstractDetection of biomarkers is exploited in lab-on-a-chip devices by means of Love type Surface Acoustic Waves (SAW). Finger type arrangement of electrodes, used for InterDigital-Transducers (IDT), perform well to create and detect SAW by using electro-mechanical coupling. Efficiency of such a transceiver depends on design parameters such as chosen material orientation, thickness, placement of electrodes. An optimized design reduces production costs, hence, we need a digital twin of the device with multiphysics simulations that compute deformation and electric field. In this study, we develop a framework with the open-source package called FEniCS for modal and transient analyses of IDTs by using the Finite Element Method (FEM). Specifically, we discuss all possible sensor design parameters and propose a computational design guideline that determines the “best” thickness parameter by maximizing mass sensitivity, thus, efficiency for a Love surface acoustic wave sensor.

Effective ecological monitoring using passive acoustic sensors: Recommendations for conservation practitioners

AbstractPassive acoustic recorders have emerged as powerful tools for ecological monitoring. However, effective monitoring is not simply an act of recording sounds. To have meaning for conservation and management, acoustic monitoring needs to be properly planned and analyzed to yield high quality information. Here, we provide a set of considerations for the design of an effective acoustic monitoring program. We argue that such a program, has the following attributes: (1) has established appropriate partnerships with landowners, Traditional Owners, researchers, or other relevant stakeholders, (2) is based on clear objectives and questions, (3) is explicit in its target sound signals, (4) has considered in‐field sensor placement for a range of factors, including experimental design, statistical power, background noise, and potential impacts on human privacy and animal disturbance, (5) has a justified recording schedule and periodicity, (6) has methods to process sound data in line with objectives, and (7) has protocols for permanent data storage and access. Acoustic monitoring is increasingly used in large‐scale programs and will be important in addressing global biodiversity targets and new biodiversity markets. It is critical that new monitoring programs are designed to effectively and efficiently capture data that address pertinent and emerging issues in conservation.

Rabi‐Like Splitting in Acoustic Cavity Coupled with Membrane‐Type Metasurface

Rabi‐like splitting effect in acoustics is demonstrated in a hybrid metasurface. The hybrid metasurface is composed of a membrane‐type acoustic metasurface and a Fabry–Perot (F–P) cavity. It is found that the transmission resonance of the F–P cavity is modulated to spectrum with two peaks around the resonant frequencies. The transmission valley in the middle of the two transmission peaks is caused by the antiresonance effect, and the phenomenon is similar to the Rabi‐splitting effect in quantum physics. The results are promising for utilization in designing acoustic wave devices like filtering and acoustic sensors with new mechanisms in hybrid metasurfaces.

Recent Advances in Ferroelectret Fabrication, Performance Optimization and Applications.

The growing demand for wearable devices has sparked a significant interest in ferroelectret films. They possess flexibility and exceptional piezoelectric properties due to strong macroscopic-dipoles formed by charges trapped at the interface of their internal cavities. This review of ferroelectrets focuses on the latest progress in fabrication techniques for high temperature resistant ferroelectrets with regular and engineered cavities, strategies for optimizing their piezoelectric performance, and novel applications. The charging mechanisms of bipolar and unipolar ferroelectrets with closed and open-cavity structures are explained first. Next, the preparation and piezoelectric behavior of ferroelectret films with closed, open and regular cavity structures using various materials are discussed. Three widely used models for predicting the piezoelectric coefficients (d33) are outlined. Methods for enhancing the piezoelectric performance such as optimized cavity design, utilization of fabric electrodes, injection of additional ions, application of DC bias voltage and synergy of foam structure and ferroelectric effect are illustrated. A variety of applications of ferroelectret films in acoustic devices, wearable monitors, pressure sensors and energy harvesters are presented. Finally, the future development trends of ferroelectrets towards fabrication and performance optimization are summarized along with its potential for integration with intelligent systems and large-scale preparation. This article is protected by copyright. All rights reserved.

Advancing Smart Lithium-Ion Batteries: A Review on Multi-Physical Sensing Technologies for Lithium-Ion Batteries

Traditional battery management systems (BMS) encounter significant challenges, including low precision in predicting battery states and complexities in managing batteries, primarily due to the scarcity of collected signals. The advancement towards a “smart battery”, equipped with diverse sensor types, promises to mitigate these issues. This review highlights the latest developments in smart sensing technologies for batteries, encompassing electrical, thermal, mechanical, acoustic, and gas sensors. Specifically, we address how these different signals are perceived and how these varied signals could enhance our comprehension of battery aging, failure, and thermal runaway mechanisms, contributing to the creation of BMS that are safer and more reliable. Moreover, we analyze the limitations and challenges faced by different sensor applications and discuss the advantages and disadvantages of each sensing technology. Conclusively, we present a perspective on overcoming future hurdles in smart battery development, focusing on appropriate sensor design, optimized integration processes, efficient signal transmission, and advanced management systems.

Interference Fading Suppression with Multi-Subcarrier Pulse in Distributed Acoustic Sensor

Interference Fading Suppression with Multi-Subcarrier Pulse in Distributed Acoustic Sensor

Research on Partial Discharge Detection Based on Distributed Optical Fiber Acoustic Sensor

Abstract Distributed acoustic sensors (DAS) have the advantage of long-distance and distributed monitoring of vibration signals. However, there are few studies on the application of DAS for partial discharge monitoring of power equipment. There is also a lack of related theoretical and methodological studies. In this paper, the DAS-measured phase signals are theoretically analyzed from the frequency and energy perspectives, and the method of partial discharge broadband signal coverage using spectrum and energy accumulation is proposed. Fiber optic rings are employed for the detection of partial discharge signals, and the precise localization of these signals is achieved by calculating the DAS energy spectrum. The fiber optic ring is wrapped around the cable joint to achieve the detection and locating of the partial discharge signal. The results indicate that the use of a fiber optic ring can realize the sensitive detection and locating of partial discharge. The signal strength is maximum when the length of the fiber ring is equal to the spatial resolution of DAS. The sensitivity of partial discharge detection in cable joints can reach 40 pC, and the simultaneous detection and localization of partial discharge at different locations can be realized. The research can provide support for theoretical and experimental for the monitoring of partial discharge in power equipment.

Streets classification models by urban features for road traffic noise estimation

Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.

Improved Underwater Single-Vector Acoustic DOA Estimation via Vector Convolution Preprocessing

Remote passive sonar detection with underwater acoustic vector sensor (UAVS) has attracted increasing attention due to its merit in measuring the full sound field information. However, the accurate estimation of the direction-of-arrival (DOA) remains a challenging problem, especially under low signal-to-noise ratio (SNR) conditions. In this paper, a novel convolution (COV)-based single-vector acoustic preprocessing method is proposed on the basis of the single-vector acoustic preprocessing model. In view of the theoretical analysis of the classical single-vector acoustic DOA estimation method, the principle of preprocessing can be described as “to achieve an improved denoising performance in the constraint of equivalent amplitude gain and phase response.” This can be naturally guaranteed by our proposed COV method. In addition, the upper bound with matched filtering (MF) preprocessing is provided in the consideration of the optimal linear signal processing for weak signal detection under Gaussian noise. Numerical analyses demonstrate the effectiveness of our proposed preprocessing method with both vector array signal processing-based and intensity-based methods. Experimental verification conducted in South China Sea further verifies the effectiveness of our approach for practical applications. This work can lay a solid foundation in improving underwater remote vector acoustic DOA estimation under low SNR, and can provide important guidance for future research work.

An Efficient Framework for Localization Based Optimized Opportunistic Routing Protocol in Underwater Acoustic Sensor Networks

An Efficient Framework for Localization Based Optimized Opportunistic Routing Protocol in Underwater Acoustic Sensor Networks

Short-period flow oscillation during eruptions of Onikobe geyser, NE Japan: Insights from thermal infrared observation and acoustic measurements

Geysers are episodic features with variable eruption intervals that range from minutes to years. Although many previous studies on geysers have focused on subsurface properties and processes such as plumbing geometry and recharge process, it is known that erupting fluid column exhibits short-term behaviors such as individual water jets from the vent. In the present study, we conducted observations at Onikobe geyser, NE Japan, a relatively small geyser (geysering well) erupting water up to ∼6–8 m every ∼10 min and observed the erupting fluid column using a thermal infrared camera and an acoustic sensor. We succeed in tracking the water jets by analyzing spatio-temporal temperature map obtained from the thermal infrared observation, which clearly shows the eruptions at Onikobe geyser are not completely stationary but rather a series of intermittent jets with a short period interval of <1 s. We estimate the exit velocity by fitting a ballistic model under the air drag condition of inertial resistance to the jet trajectory. The exit velocity and the averaged gas volume fraction of the erupting fluid are estimated to be ∼4–79 m/s and ∼0.93–0.96, respectively. The exit velocity is ∼30 m/s during the first bursts, then it rapidly increases to ∼50–80 m/s for ∼15 s, and then decreases to ∼20–30 m/s until ∼10 s before the eruption ends. Time series analyses of the thermal infrared and acoustic signals during an eruption indicate harmonic spectra with integer multiple peaks. The fundamental frequency showing ∼4 Hz at the beginning gradually decreases to ∼2 Hz for ∼15 s, keeps almost constant in the following 30 s, and then slightly increases near the end of the eruption. These harmonic spectra may be caused by a resonance mechanism, non-linear fluid motion, and/or subsurface two-phase flow. We discuss the case of a closed organ-pipe resonance in a subsurface crack and attribute the frequency decrease to a decrease in the water level in the crack (an increase in the length of the resonating liquid-vapor column) and/or a decrease in the gas volume fraction of the liquid-vapor mixture in the crack. Since such harmonic characteristics are observed at other geysers, further simultaneous observation of the short-period oscillations on the erupting column proposed in this study with seismic observations of harmonic tremor may provide an improved understanding of geyser subsurface phenomena.

Robust acoustic directional sensing enabled by synergy between resonator-based sensor and deep learning

We demonstrate enhanced acoustic sensing arising from the synergy between resonator-based acoustic sensor and deep learning. We numerically verify that both vibration amplitude and phase are enhanced and preserved at and off the resonance in our compact acoustic sensor housing three cavities. In addition, we experimentally measure the response of our sensor to single-frequency and siren signals, based on which we train convolutional neural networks (CNNs). We observe that the CNN trained by using both amplitude and phase features achieve the best accuracy on predicting the incident direction of both types of signals. This is even though the signals are broadband and affected by noise thought to be difficult for resonators. We attribute the improvement to a complementary effect between the two features enabled by the combination of resonant effect and deep learning. This observation is further supported by comparing to the CNNs trained by the features extracted from signals measured on reference sensor without resonators, whose performances fall far behind. Our results suggest the advantage of this synergetic approach to enhance the sensing performance of compact acoustic sensors on both narrow- and broad-band signals, which paves the way for the development of advanced sensing technology that has potential applications in autonomous driving systems to detect emergency vehicles.

Directional Multi-Resonant Micro-Electromechanical System Acoustic Sensor for Low Frequency Detection.

This paper reports on the design, modeling, and characterization of a multi-resonant, directional, MEMS acoustic sensor. The design builds on previous resonant MEMS sensor designs to broaden the sensor's usable bandwidth while maintaining a high signal-to-noise ratio (SNR). These improvements make the sensor more attractive for detecting and tracking sound sources with acoustic signatures that are broader than discrete tones. In-air sensor characterization was conducted in an anechoic chamber. The sensor was characterized underwater in a semi-anechoic pool and in a standing wave tube. The sensor demonstrated a cosine-like directionality, a maximum acoustic sensitivity of 47.6 V/Pa, and a maximum SNR of 88.6 dB, for 1 Pa sound pressure, over the bandwidth of the sensor circuitry (100 Hz-3 kHz). The presented design represents a significant improvement in sensor performance compared to similar resonant MEMS sensor designs. Increasing the sensitivity of a single-resonator design is typically associated with a decrease in bandwidth. This multi-resonant design overcomes that limitation.

Accounting for sub-wavelength heterogeneities in full waveform inversion based on wavefield gradient measurements

Summary Seismic gradient measurements from distributed acoustic sensors and rotational sensors are becoming increasingly available for field surveys. These measurements provide a wealth of information and are currently being considered for many applications such as earthquake detection and subsurface characterizations. In this work, using a simple 2-D numerical approach, we tackle the implications of such wavefield gradient measurements on full waveform inversion (FWI) techniques using a simple two-dimensional numerical test. In particular, we study the impact of the wavefield gradient measurement sensitivity to heterogeneities that are much smaller than the minimum wavelength. Indeed, as shown through the homogenisation theory, small-scale heterogeneities induce an unexpected coupling of the strain components to the wavefield gradient measurement. We further show that this coupling introduces a potential limitation to the FWI results if it is not taken into account. We demonstrate that a gradient measurement-based FWI can only reach the accuracy of a classical displacement field-based FWI if the coupling coefficients are also inverted. Furthermore, there appears to be no specific gain in using gradient measurements instead of conventional displacement (or velocity, acceleration) measurements to image structures. Nevertheless, the inverted correctors contain fine-scale heterogeneities information that could be exploited to reach an unprecedented resolution, particularly if an array of receivers is used.

Miniaturized and highly sensitive fiber-optic Fabry–Perot sensor for mHz infrasound detection

Infrasound detection is important in natural disasters monitoring, military defense, underwater acoustic detection, and other domains. Fiber-optic Fabry–Perot (FP) acoustic sensors have the advantages of small structure size, long-distance detection, immunity to electromagnetic interference, and so on. The size of an FP sensor depends on the transducer diaphragm size and the back cavity volume. However, a small transducer diaphragm size means a low sensitivity. Moreover, a small back cavity volume will increase the low cut-off frequency of the sensor. Hence, it is difficult for fiber-optic FP infrasound sensors to simultaneously achieve miniaturization, high sensitivity, and extremely low detectable frequency. In this work, we proposed and demonstrated a miniaturized and highly sensitive fiber-optic FP sensor for mHz infrasound detection by exploiting a Cr-Ag-Au composite acoustic-optic transducer diaphragm and a MEMS technique-based spiral micro-flow hole. The use of the spiral micro-flow hole as the connecting hole greatly reduced the volume of the sensor and decreased the low-frequency limit, while the back cavity volume was not increased. Combined with the Cr-Ag-Au composite diaphragm, a detection sensitivity of −123.19 dB re 1 rad/μPa at 5 Hz and a minimum detectable pressure (MDP) of 1.2 mPa/Hz1/2 at 5 Hz were achieved. The low detectable frequency can reach 0.01 Hz and the flat response range was 0.01–2500 Hz with a sensitivity fluctuation of ±1.5 dB. Moreover, the size of the designed sensor was only 12  mm×Φ12.7  mm. These excellent characteristics make the sensor have great practical application prospects.

A Quantitative and Qualitative Experimental Framework for the Evaluation of Urban Soundscapes: Application to the City of Sidi Bou Saïd

This research introduces an experimental framework based on 3D acoustic and psycho-acoustic sensors supplemented with ambisonics and sound morphological analysis, whose objective is to study urban soundscapes. A questionnaire that highlights the differences between what has been measured and what has been perceiveSd by humans complements the quantitative approach with a qualitative evaluation. The comparison of the measurements with the questionnaire provides a global vision of the perception of these soundscapes, as well as differences and similarities. The approach is experimented within the historical center of the Tunisian city of Sidi Bou Saïd, demonstrating that from a range of complementary protocols, a soundscape environment can be qualified. This framework provides an additional dimension to urban planning studies.