Reconstruction of Nearshore Surface Gravity Wave Heights From Distributed Acoustic Sensing Data

Abstract Distributed Acoustic Sensing (DAS) is a photonics technology converting seafloor telecommunications and optical fiber cables into dense arrays of strain sensors, allowing to monitor various oceanic physical processes. Yet, several applications are hindered by the limited knowledge of the tr...

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Main Authors: Samuel Meulé, Julián Pelaez‐Quiñones, Frédéric Bouchette, Anthony Sladen, Aurélien Ponte, Annika Maier, Itzhak Lior, Paschal Coyle
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2024-11-01
Series:Earth and Space Science
Subjects:
Online Access:https://doi.org/10.1029/2024EA003589
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author Samuel Meulé
Julián Pelaez‐Quiñones
Frédéric Bouchette
Anthony Sladen
Aurélien Ponte
Annika Maier
Itzhak Lior
Paschal Coyle
author_facet Samuel Meulé
Julián Pelaez‐Quiñones
Frédéric Bouchette
Anthony Sladen
Aurélien Ponte
Annika Maier
Itzhak Lior
Paschal Coyle
author_sort Samuel Meulé
collection DOAJ
description Abstract Distributed Acoustic Sensing (DAS) is a photonics technology converting seafloor telecommunications and optical fiber cables into dense arrays of strain sensors, allowing to monitor various oceanic physical processes. Yet, several applications are hindered by the limited knowledge of the transfer function between geophysical variables and DAS measurements. This study investigates the quantitative relationship between surface gravity DAS‐recorded wave‐generated strain signals along the seafloor and the pressure at a colocated sensor. A remarkable linear correlation is found over various sea conditions allowing us to reliably determine significant wave heights from DAS data. Utilizing linear wave potential theory, we derive an analytical transfer function linking cable deformation and wave kinematic parameters. This transfer function provides a first quantification of the effects related to surface gravity waves and fiber responses. Our results validate DAS's potential for real‐time reconstruction of the surface gravity wave spectrum over extended coastal areas. It also enables the estimation of waves hydraulic parameters at depth without the need from offshore deployments.
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institution Kabale University
issn 2333-5084
language English
publishDate 2024-11-01
publisher American Geophysical Union (AGU)
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series Earth and Space Science
spelling doaj-art-5d64ecea19aa4b2cacc3b9eb30a574b42024-12-14T03:25:53ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842024-11-011111n/an/a10.1029/2024EA003589Reconstruction of Nearshore Surface Gravity Wave Heights From Distributed Acoustic Sensing DataSamuel Meulé0Julián Pelaez‐Quiñones1Frédéric Bouchette2Anthony Sladen3Aurélien Ponte4Annika Maier5Itzhak Lior6Paschal Coyle7Aix Marseille Univ CNRS IRD INRAE CEREGE Aix‐en‐Provence FranceObservatoire de la Côte d'Azur Géoazur CNRS IRD Universite Côte d'Azur Valbonne FranceGeosciences‐Montpellier CNRS University of Montpellier Montpellier FranceObservatoire de la Côte d'Azur Géoazur CNRS IRD Universite Côte d'Azur Valbonne FranceLaboratoire d'Océanographie Physique et Spatiale CNRS IFREMER IRD IUEM Université de Brest Brest FranceKarlsruhe Institute of Technology Karlsruhe GermanyInstitute of Earth Sciences The Hebrew University Jerusalem IsraelCNRS/IN2P3 CPPM Aix‐Marseille Univ. Marseille FranceAbstract Distributed Acoustic Sensing (DAS) is a photonics technology converting seafloor telecommunications and optical fiber cables into dense arrays of strain sensors, allowing to monitor various oceanic physical processes. Yet, several applications are hindered by the limited knowledge of the transfer function between geophysical variables and DAS measurements. This study investigates the quantitative relationship between surface gravity DAS‐recorded wave‐generated strain signals along the seafloor and the pressure at a colocated sensor. A remarkable linear correlation is found over various sea conditions allowing us to reliably determine significant wave heights from DAS data. Utilizing linear wave potential theory, we derive an analytical transfer function linking cable deformation and wave kinematic parameters. This transfer function provides a first quantification of the effects related to surface gravity waves and fiber responses. Our results validate DAS's potential for real‐time reconstruction of the surface gravity wave spectrum over extended coastal areas. It also enables the estimation of waves hydraulic parameters at depth without the need from offshore deployments.https://doi.org/10.1029/2024EA003589distributed acoustic sensing (DAS)pressure sensorwave‐generated strain signalscoastal waveslinear theorytransfer function
spellingShingle Samuel Meulé
Julián Pelaez‐Quiñones
Frédéric Bouchette
Anthony Sladen
Aurélien Ponte
Annika Maier
Itzhak Lior
Paschal Coyle
Reconstruction of Nearshore Surface Gravity Wave Heights From Distributed Acoustic Sensing Data
Earth and Space Science
distributed acoustic sensing (DAS)
pressure sensor
wave‐generated strain signals
coastal waves
linear theory
transfer function
title Reconstruction of Nearshore Surface Gravity Wave Heights From Distributed Acoustic Sensing Data
title_full Reconstruction of Nearshore Surface Gravity Wave Heights From Distributed Acoustic Sensing Data
title_fullStr Reconstruction of Nearshore Surface Gravity Wave Heights From Distributed Acoustic Sensing Data
title_full_unstemmed Reconstruction of Nearshore Surface Gravity Wave Heights From Distributed Acoustic Sensing Data
title_short Reconstruction of Nearshore Surface Gravity Wave Heights From Distributed Acoustic Sensing Data
title_sort reconstruction of nearshore surface gravity wave heights from distributed acoustic sensing data
topic distributed acoustic sensing (DAS)
pressure sensor
wave‐generated strain signals
coastal waves
linear theory
transfer function
url https://doi.org/10.1029/2024EA003589
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AT anthonysladen reconstructionofnearshoresurfacegravitywaveheightsfromdistributedacousticsensingdata
AT aurelienponte reconstructionofnearshoresurfacegravitywaveheightsfromdistributedacousticsensingdata
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