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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: closed (30 April 2026) | Viewed by 4039

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 for Computational & Data Sciences, The Pennsylvania State University, State College, PA 16803, USA
Interests: artificial intelligence; intelligent automation and agentic systems; data science; subsurface energy systems; distributed acoustic sensing (DAS)
Special Issues, Collections and Topics in MDPI journals

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 1800 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 (3 papers)

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30 pages, 4358 KB  
Article
A Bi-LSTM Attention Mechanism for Monitoring Seismic Events—Solving the Issue of Noise & Interpretability
by Nimra Iqbal, Izzatdin Bin Abdul Aziz and Muhammad Faisal Raza
Technologies 2026, 14(4), 199; https://doi.org/10.3390/technologies14040199 - 26 Mar 2026
Viewed by 931
Abstract
The nonlinearity and the extreme variability of seismic signals makes the detection of earthquakes difficult. Although the conventional deep-learning models can be used to extract useful features, they cannot be used in early-warning systems due to their non-interpretability. In this study, a Bidirectional [...] Read more.
The nonlinearity and the extreme variability of seismic signals makes the detection of earthquakes difficult. Although the conventional deep-learning models can be used to extract useful features, they cannot be used in early-warning systems due to their non-interpretability. In this study, a Bidirectional Long Short-Memory network with an attention system (Bi-LSTM-Attn) is proposed to detect seismic events using the ConvNetQuake dataset. To improve the quality of data, the entire preprocessing pipeline, such as signal filtering, segmentation, normalization, and noise reduction is employed. The model was optimized using hyperparameter tuning of sequence length, learning rates, and attention weighting to achieve the best number of true-positive detections and a minimum number of false alarms. The accuracy, precision and recall, F1-score, MSE, and ROC curves were used to assess the performance and the attention weight visualization allowed interpreting the model. It is proven through experiments that the Bi-LSTM-Attn model provides more credible and comprehensible forecasting in relation to baseline LSTM and GRU models. Making the high-accuracy classification and the transparent decision behavior, the approach will increase the level of trust to the automated seismic surveillance, as well as help to build the reliable global networks of earthquake early-warnings. Full article
(This article belongs to the Special Issue Application and Development of Distributed Acoustic Sensing (DAS) Technology)
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34 pages, 7227 KB  
Article
Real-Time Sand Transport Detection in an Offshore Hydrocarbon Well Using Distributed Acoustic Sensing-Based VSP Technology: Field Data Analysis and Operational Insights
by Dejen Teklu Asfha, Abdul Halim Abdul Latiff, Hassan Soleimani, Abdul Rahim Md Arshad, Alidu Rashid, Ida Bagus Suananda Yogi, Daniel Asante Otchere, Ahmed Mousa and Rifqi Roid Dhiaulhaq
Technologies 2026, 14(3), 175; https://doi.org/10.3390/technologies14030175 - 13 Mar 2026
Viewed by 1023
Abstract
Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. [...] Read more.
Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article
(This article belongs to the Special Issue Application and Development of Distributed Acoustic Sensing (DAS) Technology)
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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
Cited by 2 | Viewed by 978
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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