Distributed acoustic sensing (DAS) technology is gaining popularity for real-time monitoring during the hydraulic fracturing of unconventional reservoirs. By transforming a standard optical fiber into a dense array of acoustic sensors, DAS provides continuous spatiotemporal measurements along the entire wellbore. Although accurate DAS-based real-time diagnosis of multistage hydraulic fracturing is critical for optimizing the efficiency of stimulation operations and mitigating operational risks in horizontal tight oil wells, existing methods often fail to provide integrated qualitative and quantitative insights. To address this gap, we present an original diagnostic workflow that synergistically combines frequency band energy (FBE), low-frequency DAS (LF-DAS), and surface injection data for simultaneous fluid/proppant allocation and key downhole anomaly identification. Field application of the proposed framework in a 47-stage well demonstrates that FBE (50–200 Hz) enables robust cluster-level volume estimation, while LF-DAS (<0.5 Hz) reveals fiber strain signatures indicative of mechanical integrity threats. The workflow can successfully diagnose sand screenout, diversion, out-of-zone flow, and early fiber failure—events often missed by conventional monitoring. By linking distinct acoustic fingerprints to specific physical processes, our approach transforms raw DAS data into actionable operational intelligence. This study provides a reproducible, field-validated framework that enhances understanding in the context of fracture treatment, supports real-time decision making, and paves the way for automated DAS interpretation in complex completions.

The emissions of carbon dioxide (CO₂) are the main factors responsible for climate change, demanding urgent mitigation of its occurrence straight away. The geological carbon storage (GCS), the sequestration of carbon dioxide (CO₂) in subsurface formations, notably saline aquifers, depleted hydrocarbon reservoirs and coal seams, is one of the essential solutions for decreasing the carbon dioxide concentration of the atmosphere. The fulfilment of secure long-term storage with the least change of leakage of carbon dioxide depends upon site selection, site characterization and monitoring of the sites. This review updates the knowledge of the methods of GCS and improves them in the direction of site selection optimization, characterization of the sites given by better procedures and commissioned procedures for the monitoring of the safe site of storage and of the risk of leakage of gases in the atmosphere and into the groundwater. Scientific literature was selected in a systematic search using the Scopus and the Web of Science, (2015-2024), together with technical and miscellaneous literature searches of GCS diversity of GCS projects carried out in Sleipner, Weyburn, Quest and Gorgon. There are various site selection procedures which are more likely to amplify, given the ultra­power requirements and suggest a few important ones, like reservoir level (> 800 m), integrity of caprock (> 40 m shale), emission sources nearby (< 200 km). The GCS three-dimensional geophysical cell was generally mentioned (3D) seismic, geometrical resistivity tomography (ERT) and geochemical modelling; however belonging to the carbon dioxide sufficiency features, suggestions were made that Distributed Acoustic Sensing (DAS) is integrated more easily. "distributed acoustic sensing" with the principles of FAIR (Findable, Accessible, Interoperable, Reusable) data. The monitoring of the "GCS" is achieved by both surface (InSAR, airborne sensors) and underground (pressure sensors, traces) technologies. One of the new technologies for monitoring which is defined is GHGSat, which indicates the gas leak in the atmosphere. The reported results of In Sala… are given respectively.

Cardiovascular disease is a leading cause of mortality worldwide and a major contributor to rising healthcare costs. Early detection of arterial stiffness through metrics such as Pulse Wave Velocity (PWV) and Augmentation Index (AIx) is essential for cardiovascular disease prevention and treatment. However, conventional measurement methods typically require expensive, specialized equipment and clinical settings. In this paper, we explore the use of everyday earphones to monitor PWV and AIx in daily life by capturing skin displacement waveforms induced by arterial pulsation. However, several key challenges must be addressed. First, detecting subtle skin displacement is difficult due to strong self-interference from the speaker to microphone. To address this, we propose a Doppler shift-based displacement estimation approach to isolate dynamic movements from static interference. Second, estimating PWV requires simultaneous two-point measurements, which is non-trivial with standard earphones. We address this with a hardware-software co-design that connects two earphone pairs via a commercial audio splitter and uses orthogonal frequency division multiplexing (OFDM)-based signal separation to extract displacement signals from both sites. Third, enabling general public use without medical training requires thoughtful system design. To this end, we develop a user-friendly mobile application that provides real-time feedback, along with a 3D-printed enclosure to facilitate ease of use and wide accessibility. We conducted IRB-approved clinical studies with 32 participants, comparing our measurements against ground truths from medical devices. Results show that our method achieves a PWV error of less than 0.5 m/s and an AIx error of less than 4%, meeting established medical standards. Please find the demo of our system here .

Wireless surface acoustic wave gas sensors with 2-phenyl-2,1-borazaronaphthalene for application in vehicle humidity monitoring systems and alcohol locks

Implementation and comparative analysis of surface acoustic wave sensor readout circuit architectures: Delay-line versus resonator configurations

On/In-line monitoring of cleaning in place operation using a passive acoustic emission sensor technology

LoRaWAN and artificial intelligence integrated smart acoustic sensor network for bird species identification and deterrence system for farm protection

A multi-step joint noise reduction method of the distributed acoustic sensor data for flow rate monitoring

Photoacoustic spectroscopy detection based on complementary interdigital cantilever enhanced Fabry-Perot acoustic sensor

Orthogonal phase simultaneous parallel demodulation method of dual Fabry-Perot acoustic sensors

Long-range distributed fiber-optic acoustic sensor with 100 kHz linewidth laser and frequency domain signal-and-kernel phase noise compensation

Deep learning-assisted high sensitivity acoustic sensor for enhanced auditory robot real-time emotion recognition

In this paper, we investigate the physical mechanics of vibration wave detection in distributed acoustic sensing (DAS) systems with the aim of enhancing the interpretation of the quantitative wavefield. We investigate the nonlinear relationship of DAS gauge length and pulse width on the seismic wave response, and the result is explained by the trigonometric relationship of backscattered Rayleigh wave phases. We further demonstrate the influence of spiral winding on DAS performance and also build phase response models for P-waves and S-waves in helically wound cables. These models suggest that the winding angle controls the measurement interval spacing and the angle of wave incidence. Additionally, integration of structural reinforcement improves the amplitude response characteristics and SNR. The experimentally inspired results show, using simulations and field tests, that the same vibration sources can give helically wound cables with larger winding angles the largest phase amplitudes, which would substantially exceed that of straight cables. SNR increased significantly (approximately 10% to 30%). The efficacy of the method was also checked using experiments for different vibration amplitudes and frequencies. Such results provide evidence for the design and installation of fiber-optic cables for use in practical engineering applications involving safety monitoring.

In response to the challenge of changes in the physical and mechanical properties of red sandstone when it comes into contact with water during construction projects, this paper proposes a moisture content detection method for red sandstone based on the knocking method. Taking red sandstone as the research object, this study explores a moisture content detection approach by combining the knocking method with Convolutional Neural Network and Support Vector Machine algorithms (CNN-SVM). Specifically, this research involves knocking the surface of red sandstone specimens with a knocking hammer and precisely capturing the acoustic signals generated during the knocking process using a microphone. Subsequently, an effective detection of the moisture content in red sandstone is achieved through a method based on feature extraction from knocking sound signals and a Convolutional Neural Network classification model. This method is easy to operate. By utilizing modern signal processing techniques combined with the CNN-SVM model, it enables accurate identification and non-destructive testing of the moisture content in red sandstone even with small sample datasets. Mel Frequency Cepstral Coefficients (MFCCs) and Continuous Wavelet Transform (CWT) were separately used as features for detecting red sandstone specimens with different moisture contents. The detection results show that the classification accuracy of red sandstone moisture content using MFCCs as the feature reaches as high as 94.4%, significantly outperforming the classification method using CWT as the feature. This study validates the effectiveness and reliability of the proposed method, providing a novel and efficient approach for rapid and non-destructive detection of the moisture content in red sandstone.

ABSTRACT Underwater Acoustic Sensor Networks (UASNs) are essential for offshore engineering, military surveillance, environmental monitoring, and oceanographic exploration. However, challenges including high latency, congested networks, low bandwidth, and energy limitations make it difficult to communicate effectively in UASNs. In order to tackle these problems, this study suggests a new Energy‐Efficient Double‐Level Deep Reinforcement Learning with Chaotic Chimp Optimization (E2D2RL‐ChCo) model that is intended to improve localization precision, reduce packet loss, and effectively handle network congestion. The proposed E2D2RL‐ChCo model uses transformer‐based Markov Decision Processes (MDPs) for node localization at the top level and congestion‐aware routing at the bottom level to guarantee efficient task scheduling and energy‐efficient data transfer. By integrating Chaotic Chimp Optimization (ChCo), learning parameters are adjusted to enhance convergence and solution quality. Simulation results demonstrate up to 33.8% reduction in energy consumption, 61.5% decrease in end‐to‐end delay, and over 52% improvement in successful packet delivery compared to baseline models. The proposed model also improves localization error to 0.0075, outperforming existing strategies. This work fills gaps in existing literature by integrating deep reinforcement learning and metaheuristics to optimize both localization and congestion in real‐time UASN scenarios. This approach enables more sustainable and reliable UASN operations, paving the way for enhanced performance in real‐world aquatic environments.

Among the various completion strategies used in multi-stage hydraulic fracturing of horizontal wells, the limited entry design has become one of the most common approaches to promote more uniform slurry distribution. This method involves reducing the number of perforations so that higher perforation friction is generated at each entry point. The increased pressure drops force fluid and proppant to be diverted across multiple clusters rather than concentrating at only a few, thereby enhancing stimulation efficiency along the lateral. In this study, Computational Fluid Dynamics (CFD) simulations were performed to investigate how perforation erosion influences acoustic signals measured by Distributed Acoustic Sensing (DAS). Unlike previous studies that assumed perfectly circular perforations, this work uses oval-shaped geometries to better reflect the irregular erosion observed in the field, which provides more realistic modeling. The workflow involved building wellbore and perforation geometries, generating computational meshes, and solving transient turbulent flow using Large Eddy Simulation (LES) coupled with the Ffowcs Williams–Hawkings (FW-H) acoustic model. Acoustic pressure was then estimated at receiver points and converted into sound pressure level for analysis. The results show that, for a given perforation size, changes in flow rate cause log(q) versus sound pressure level to follow a straight line defined by a constant slope and varying intercept. Even when erosion alters the perforation into an oval shape, the intercept increases logarithmically, resulting in reduced sound amplitude, while the slope remains unchanged. Furthermore, when the cross-sectional area and flow rate are equal, oval perforations produce higher sound amplitudes than circular ones, suggesting that perforation geometry has a measurable influence on the DAS signal. This indicates that even when the same amplitude DAS signal is obtained, assuming circular perforations when estimating the fluid distribution leads to an overestimation if the actual perforation shape is oval. These findings highlight the importance of considering irregular erosion patterns when linking DAS responses to fluid distribution during hydraulic fracturing.

Summary Distributed Acoustic Sensing (DAS) technology has gained widespread attention in seismic exploration due to its high spatial resolution and low deployment cost. However, the presence of coupling noise in DAS data significantly affects the accurate extraction and interpretation of seismic signals. Coupling noise typically appears as narrowband stripe-like or zigzag-like interference and shares similar characteristics with seismic signals in the time-space (T-S) domain, making it challenging for traditional denoising methods to achieve effective signal-noise separation without residual noise or signal leakage. To address these challenges, this paper proposes a deep learning-based dual-domain fusion approach that integrates both T-S and frequency-wavenumber (F-K) domain information to enhance the accuracy of coupling noise separation. The method leverages the narrowband characteristics of coupling noise in the F-K domain while incorporating spatiotemporal information from the T-S domain to achieve cross-domain feature fusion, thereby improving the separability between signals and coupling noise. Experimental results demonstrate that the proposed method significantly improves coupling noise suppression performance on both synthetic and field DAS vertical seismic profile (VSP) data while minimizing signal leakage. Furthermore, in corridor stacking experiments, the method effectively reduces the impact of coupling noise on seismic interpretation, improving the reliability of subsurface formation analysis. Compared to conventional F-K filtering, single-domain network and denoising diffusion model, the proposed approach achieves superior performance in terms of coupling noise suppression and signal amplitude preservation.

Fluvially generated seismo-acoustic waves offer a novel means of investigating river processes, yet interpreting signals from individual seismometers or hydrophones remains challenging. This study demonstrates the potential of distributed acoustic sensing (DAS) for fluvial monitoring. We present strain-rate measurements and power spectra recorded at sub-meter resolution along 160 m of submerged fiber-optic cable in Clear Creek, CO, USA. We find that regions of enhanced turbulence, such as rapids, are associated with broadband signals, whereas reaches with less turbulent flow display spectral power within distinct frequency bands. In three such regions, we observe harmonic frequency banding with pronounced spatio-spectral gliding (i.e., peak frequencies vary systematically along-river). One of these regions is colocated with the source of a recurring impulsive signal characterized by audible "knocking" sounds in the acoustic strain-rate data. We use travel time analysis to determine that this signal is generated by cable-bed impacts due to turbulence-driven cable oscillation. Model results further indicate that along-cable variation in the lags between pulses and their reflections produces the banded spatio-spectral gliding. Our observations highlight the capacity for array methods to interrogate distinct signal sources in DAS data and emphasize the need for improved deployment techniques in dynamic fluvial environments.

Sound Event Localization and Classification Using Wireless Acoustic Sensor Networks in Outdoor Environments

This article focuses on the evolution of telecommunication towers as distributed Smart Environment Intelligence Centers which use complex multi-tier sensor architectures with advanced communications and integrated AI/edge computing. Given the current state of technology and infrastructure, the study defines telecom towers, traditionally passive communication structures, as high-value nodes for environmental, structural, and geospatial intelligence. The review raises awareness of three degrees of sensing: basic environmental sensors for temperature, humidity, air quality, wind profiling and visual surveillance; advanced technologies such as Structural Health Monitoring (SHM), Distributed Acoustic Sensing (DAS) and thermal analytics; and special sensing auto related to the deserts such as sand and dust storms, UV/solar radiation, and high-density crowd analytics. The article highlights the exceptional relevance of these systems in the case of Saudi Arabia with such peculiarities as sandstorms and high winds, deadly hot, and huge areas of critical infrastructures, requiring precise monitoring in real time. Methodologically, the study incorporates hybrid communications protocols (LoRaWAN, NB-IoT, 5G) with edge-based, artificial intelligence-powered processing and application of predictive analytics to showcase a scale-up architecture that can be used for national resilience, public safety and integrated urban planning. The grounds discovered show significant operational advantages, including enhanced hazard forecasting capabilities, asset maintenance through predictive analytics, and diverse new revenue models such as Sensors-as-a-Service (SaaS) and data brokerage for the governmental and commercial sectors. The article concludes that the telecom-based environmental intelligence system provides a high-impact, cost-efficient platform for nations seeking advanced environmental resilience and smart city capabilities.