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Technologies
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Application and Development of Distributed Acoustic Sensing (DAS) Technology
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Application and Development of Distributed Acoustic Sensing (DAS) Technology

A special issue of Technologies (ISSN 2227-7080).

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1026

Special Issue Editors

Prof. Dr. Zhipeng Wu

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, University of Manchester, Oxford Road, Manchester M13 9PL, UK
Interests: antennas; electromagnetic wave propagation; microwave circuit and devices; internet of things; wireless sensors; machine-to-machine communications; industrial informatics; wireless communication technologies; 5G/6G and future communications; AI-enabled wireless communications
Dr. Daniel Asante Otchere

E-Mail Website
Guest Editor
Institute of Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA
Interests: artificial intelligence (AI); machine learning (ML); distributed acoustic sensing (DAS); subsurface monitoring; CO2 storage; fiber optic technology; geophysical exploration; predictive analytics; reservoir characterization; geothermal energy

Special Issue Information

Dear Colleagues,

Distributed Acoustic Sensing (DAS) technology leverages optical fibers to transform them into high-resolution, continuous arrays of vibration sensors. This innovative approach enables real-time monitoring of dynamic processes across vast distances with unprecedented spatial and temporal resolution. DAS has rapidly expanded beyond its origins in oil and gas reservoir monitoring to permeate diverse fields, including seismology, infrastructure health assessment, environmental monitoring, and security, due to its versatility and cost-effectiveness.

This Special Issue invites contributions on data processing techniques, machine learning integration, hardware development, and challenges in deployment and interpretation exploring the latest advancements, and interdisciplinary applications of DAS. Topics of interest include, but are not limited to:

  • Geophysical Applications: Earthquake detection, subsurface imaging, and CO₂ storage monitoring.
  • Civil Infrastructure: Pipeline integrity, structural health monitoring of bridges and tunnels, and smart city traffic/utility monitoring.
  • Energy Sector: Reservoir characterization, hydraulic fracturing optimization, and geothermal energy monitoring.
  • Defense and Security: Border surveillance, intrusion detection, and critical infrastructure protection.
  • Environmental Science: Glaciology, oceanography, and permafrost thaw tracking.
  • AI Integration: Machine learning for noise reduction, event classification, and predictive analytics in DAS data.
  • Emerging Frontiers: Biomedical sensing, aerospace structural testing, and mining safety.

We welcome original research, reviews, and case studies that highlight the transformative potential of DAS across disciplines that push the boundaries of this transformative technology.

Prof. Dr. Zhipeng Wu
Dr. Daniel Asante Otchere
Guest Editors

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. Technologies is an international peer-reviewed open access monthly 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 1600 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)
  • seismic imaging and monitoring
  • infrastructure health monitoring
  • Environmental Monitoring
  • CO2 storage
  • surveillance
  • mining safety
  • telecommunication
  • transportation
  • artificial intelligence

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Published Papers (1 paper)

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Research

21 pages, 2679 KB  
Article
Intelligent Feature Extraction and Event Classification in Distributed Acoustic Sensing Using Wavelet Packet Decomposition
by Artem Kozmin, Pavel Borozdin, Alexey Chernenko, Sergei Gostilovich, Oleg Kalashev and Alexey Redyuk
Technologies 2025, 13(11), 514; https://doi.org/10.3390/technologies13110514 - 11 Nov 2025
Viewed by 394
Abstract
Distributed acoustic sensing (DAS) systems enable real-time monitoring of physical events across extended areas using optical fiber that detects vibrations through changes in backscattered light patterns. In perimeter security applications, these systems must accurately distinguish between legitimate activities and potential security threats by [...] Read more.
Distributed acoustic sensing (DAS) systems enable real-time monitoring of physical events across extended areas using optical fiber that detects vibrations through changes in backscattered light patterns. In perimeter security applications, these systems must accurately distinguish between legitimate activities and potential security threats by analyzing complex spatio-temporal data patterns. However, the high dimensionality and noise content of raw DAS data presents significant challenges for effective feature extraction and event classification, particularly when computational efficiency is required for real-time deployment. Traditional approaches or current machine learning methods often struggle with the balance between information preservation and computational complexity. This study addresses the critical need for efficient and accurate feature extraction methods that can identify informative signal components while maintaining real-time processing capabilities in DAS-based security systems. Here we show that wavelet packet decomposition (WPD) combined with a cascaded machine learning approach achieves 98% classification accuracy while reducing computational load through intelligent channel selection and preliminary filtering. Our modified peak signal-to-noise ratio metric successfully identifies the most informative frequency bands, which we validate through comprehensive neural network experiments across all possible WPD channels. The integration of principal component analysis with logistic regression as a preprocessing filter eliminates a substantial portion of non-target events while maintaining high recall level, significantly improving upon methods that processed all available data. These findings establish WPD as a powerful preprocessing technique for distributed sensing applications, with immediate applications in critical infrastructure protection. The demonstrated gains in computational efficiency and accuracy improvements suggest broad applicability to other pattern recognition challenges in large-scale sensor networks, seismic monitoring, and structural health monitoring systems, where real-time processing of high-dimensional acoustic data is essential. Full article
(This article belongs to the Special Issue Application and Development of Distributed Acoustic Sensing (DAS) Technology)
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