Source detection and tracking for underwater distributed acoustic sensing
Source detection and tracking for underwater distributed acoustic sensing
Distributed Optical Fiber Sensing (DOFS) transforms conventional fiber optic cables into an extensive network of continuous sensors. It achieves this by exploiting the spectral, polarization and/or phase sensitivity of the propagating light to measurands of temperature, strain, pressure, vibrations etc. To harness the novel capabilities of optical fibers to remotely capture, process and coherently analyze ambient vibration (e.g., acoustic) fields, it is crucial to address the challenges of the diversity of noise introduced in DOFS measurements, in particular, within the under-explored submarine environment. This research introduces a comprehensive workflow for the detection of active (uncontrolled) acoustic sources, comprised of successive denoising steps that deal with the distinctive properties of such environments. Leveraging the spatio-temporal density of DOFS measurements, we develop a method based on data covariances for the automatic extraction of features in an unsupervised manner, together with additional features introduced to distinguish active source signals from noise. Consequently, this work takes the denoising of underwater DOFS data one step further through the application of a tracking algorithm on real, novel submarine DOFS data, laying the foundation for broader applications of DOFS data analysis in marine environmental sensing and monitoring.
distributed acoustic sensing (DAS), machine learning
Drylerakis, Konstantinos Theofilos
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Belal, Mohammad
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Mestre, Rafael
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Norman, Timothy J.
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Evers, Christine
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Drylerakis, Konstantinos Theofilos
6661ba38-57e2-41a1-b09e-5473ee913dcd
Belal, Mohammad
33550de9-0df1-4c90-bae6-3eb65c62778a
Mestre, Rafael
33721a01-ab1a-4f71-8b0e-abef8afc92f3
Norman, Timothy J.
663e522f-807c-4569-9201-dc141c8eb50d
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Drylerakis, Konstantinos Theofilos, Belal, Mohammad, Mestre, Rafael, Norman, Timothy J. and Evers, Christine
(2024)
Source detection and tracking for underwater distributed acoustic sensing.
32rd European Signal Processing Conference, Lyon Convention Center, Lyon, France.
26 - 30 Aug 2024.
5 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
Distributed Optical Fiber Sensing (DOFS) transforms conventional fiber optic cables into an extensive network of continuous sensors. It achieves this by exploiting the spectral, polarization and/or phase sensitivity of the propagating light to measurands of temperature, strain, pressure, vibrations etc. To harness the novel capabilities of optical fibers to remotely capture, process and coherently analyze ambient vibration (e.g., acoustic) fields, it is crucial to address the challenges of the diversity of noise introduced in DOFS measurements, in particular, within the under-explored submarine environment. This research introduces a comprehensive workflow for the detection of active (uncontrolled) acoustic sources, comprised of successive denoising steps that deal with the distinctive properties of such environments. Leveraging the spatio-temporal density of DOFS measurements, we develop a method based on data covariances for the automatic extraction of features in an unsupervised manner, together with additional features introduced to distinguish active source signals from noise. Consequently, this work takes the denoising of underwater DOFS data one step further through the application of a tracking algorithm on real, novel submarine DOFS data, laying the foundation for broader applications of DOFS data analysis in marine environmental sensing and monitoring.
Text
Eusipco_2024_ktd1g20_cr_final
- Accepted Manuscript
More information
Accepted/In Press date: 21 June 2024
Venue - Dates:
32rd European Signal Processing Conference, Lyon Convention Center, Lyon, France, 2024-08-26 - 2024-08-30
Keywords:
distributed acoustic sensing (DAS), machine learning
Identifiers
Local EPrints ID: 491915
URI: http://eprints.soton.ac.uk/id/eprint/491915
PURE UUID: 4ddb6169-bd52-4f87-b972-80d55413ec0c
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Date deposited: 05 Jul 2024 17:14
Last modified: 12 Jul 2024 02:08
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Contributors
Author:
Konstantinos Theofilos Drylerakis
Author:
Christine Evers
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