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Distributed Acoustic Sensing and Applications
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Distributed Acoustic Sensing and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 5168

Special Issue Editor

Prof. Dr. Ning Wu

E-Mail Website
Guest Editor
College of Communication Engineering, Jilin University, Changchun, China
Interests: distributed acoustic sensing (DAS); data processing; signal processing

Special Issue Information

Dear Colleagues,

Distributed Acoustic Sensing (DAS) technology is a fiber-based sensing technology that can monitor the acoustic wave and vibration information along the length of the optical fiber in real time and continuously. DAS technology has rapidly developed over the last decade. This development enables a vast range of applications, which has revolutionized how we monitor and record vibrations across various environments.

This special issue aims to collect the latest scientific and technological advances in any relevant enabling technology impinging on the development and improvement of DAS. This includes but is not limited to, the development of novel algorithms, innovative sensor technologies, real-time processing techniques, and applications across various domains such as environmental monitoring, industrial inspection, and underwater applications. The topics include but are not limited to:

  • Distributed Acoustic Sensing Networks
  • Deep Learning Analysis of DAS
  • Near-Surface Applications of DAS
  • Distributed and Online Acoustic Enhancement and Analysis
  • Intelligent Processing of DAS Data

Prof. Dr. Ning Wu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • distributed acoustic sensing (DAS)
  • signal Processing
  • machine learning
  • distributed acoustic sensing networks

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Published Papers (3 papers)

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Research

22 pages, 1861 KB  
Article
Real-Time Signal Processing for Distributed Acoustic Sensing and Acoustic Sensing Systems Under Non-Stationary Noise
by Samuel Yaw Mensah, Tao Zhang, Xin Zhao and Nahid Al Mahmud
Sensors 2026, 26(4), 1372; https://doi.org/10.3390/s26041372 - 21 Feb 2026
Viewed by 772
Abstract
Real-time acoustic signal enhancement in non-stationary noise remains challenging, especially for sensing systems that must be causal, low latency, and interpretable. This paper proposes a unified Bayesian–Kalman estimator (UBKE) that analytically fuses a spectral Bayesian MMSE estimator with a temporal Kalman state-space tracker [...] Read more.
Real-time acoustic signal enhancement in non-stationary noise remains challenging, especially for sensing systems that must be causal, low latency, and interpretable. This paper proposes a unified Bayesian–Kalman estimator (UBKE) that analytically fuses a spectral Bayesian MMSE estimator with a temporal Kalman state-space tracker via a variance optimal fusion weight α(k). The UBKE is derived in closed form from a shared probabilistic model, yielding an estimator that adaptively balances spectral and temporal information as noise statistics evolve. We establish theoretical properties including bias–variance behavior, stability conditions, and analytical expressions for output SNR, SNR improvement, and log-spectral distortion. Under typical short-time processing (32 ms frame, 50% overlap), the proposed method operates causally with an algorithmic delay of 16 ms and real-time factors below 0.5 on a modern CPU. Analytical and empirical results show that UBKE achieves up to +9.8 dB ΔSNR and approximately +17% PESQ improvement over a baseline MMSE estimator in highly non-stationary noise, while also reducing log-spectral distortion. Experiments on standard speech corpora with real-world noise confirm that the empirical trends closely follow the analytical predictions, with small mismatch between theoretical and measured gains. The UBKE thus offers an interpretable, low-latency, and quantitatively validated framework for real-time acoustic sensing and speech enhancement, and can serve as a foundation for future hybrid model-driven and learning-augmented systems. Full article
(This article belongs to the Special Issue Distributed Acoustic Sensing and Applications)
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20 pages, 8262 KB  
Article
Seismic Measurements Using Distributed Acoustic Sensing (DAS) for Underwater Soft Sediment Characterization: Insights from Laboratory- and Field-Scale Measurements
by Edwin Obando Hernandez, Matteo Rossi, Roeland Nieboer, Manos Pefkos, Wiebe de Boer and Pieter Doornenbal
Sensors 2025, 25(23), 7234; https://doi.org/10.3390/s25237234 - 27 Nov 2025
Cited by 1 | Viewed by 1415
Abstract
Scholte wave surveys were conducted at both the laboratory and field scales to evaluate the reliability of distributed acoustic sensing (DAS) with a fiber-optic cable resting on top of unconsolidated sedimentary deposits to determine the distribution of S-wave velocity underneath. Laboratory measurements performed [...] Read more.
Scholte wave surveys were conducted at both the laboratory and field scales to evaluate the reliability of distributed acoustic sensing (DAS) with a fiber-optic cable resting on top of unconsolidated sedimentary deposits to determine the distribution of S-wave velocity underneath. Laboratory measurements performed in a controlled environment at the Deltares Laboratory facility demonstrated that DAS retrieves low- and high-frequency energy associated with Scholte and guided waves. The recorded DAS signals provided consistent Scholte wave signals, which depicted coherent phase velocity energy that was used to accurately depict S-wave velocity layering. We observed the presence of guided waves at higher frequencies, which appeared to be enhanced as the source position was moved away from the fiber-optic cable. A field survey was carried out using a linear set-up in a shallow lake, where a fiber-optic cable was placed on top of a sediment layer with a thickness of 5–10 m. The results from DAS were validated using standard hydrophone measurements performed simultaneously. The 2D S-wave velocity cross-section retrieved by DAS appeared to be in good agreement with the results obtained from hydrophone measurements, especially when detecting the main velocity transition occurring at a 7–10 m depth from the free surface. Full article
(This article belongs to the Special Issue Distributed Acoustic Sensing and Applications)
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22 pages, 3959 KB  
Article
A Feasibility Study of Automated Detection and Classification of Signals in Distributed Acoustic Sensing
by Hasse B. Pedersen, Peder Heiselberg, Henning Heiselberg, Arnhold Simonsen and Kristian Aalling Sørensen
Sensors 2025, 25(17), 5445; https://doi.org/10.3390/s25175445 - 2 Sep 2025
Cited by 2 | Viewed by 2297
Abstract
Distributed Acoustic Sensing (DAS) is an emerging technology in the maritime domain, enabling the use of existing fiber optic cables to detect acoustic signals in the marine environment. In this study, we present an automated signal detection and classification framework for DAS data [...] Read more.
Distributed Acoustic Sensing (DAS) is an emerging technology in the maritime domain, enabling the use of existing fiber optic cables to detect acoustic signals in the marine environment. In this study, we present an automated signal detection and classification framework for DAS data that supports near-real-time processing. Using data from the SHEFA-2 cable between the Faroe and Shetland Islands, we develop a method to identify acoustic signals and generate both labeled and unlabeled datasets based on their spectral characteristics. Principal component analysis (PCA) is used to explore separability in the labeled data, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is applied to classify unlabeled data. Experimental validation using clustering metrics shows that with the full dataset, we can achieve a Davies–Bouldin Index of 0.828, a Silhouette Score of 0.124, and a Calinski–Harabasz Index of 189.8. The clustering quality degrades significantly when more than 20% of the labeled data is excluded, highlighting the importance of maintaining sufficient labeled samples for robust classification. Our results demonstrate the potential to distinguish between signal sources such as ships, vehicles, earthquakes, and possible cable damage, offering valuable insights for maritime monitoring and security. Full article
(This article belongs to the Special Issue Distributed Acoustic Sensing and Applications)
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