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Enregistrement W7039790247

Novel Acoustic Methods for Directly Monitoring Seabed Sediment Transport, Geohazards & Scour

2024· dissertation· en· W7039790247 sur OpenAlexaboutno aff

Notice bibliographique

RevueDurham e-Theses (Durham University) · 2024
Typedissertation
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueFossil Insects in Amber
Établissements canadiensnon disponible
Organismes subventionnairesU.S. Geological SurveyUniversidad Nacional del LitoralOcean University of China
Mots-clésSediment transportSeabedSedimentSubmarine pipelineCurrent (fluid)Acoustic sensorWater columnSound (geography)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In the natural environment, sediment transport processes can pose significant hazards to marine infrastructure, such as offshore wind turbines or seabed cables that carry both power onshore as well as carrying over 99% of global data. These processes are often extremely challenging to measure directly because sensors can be easily damaged by the processes themselves. It would, therefore, be highly advantageous to remotely sense and quantify sediment transport via sensors that are located outside the region of sediment transport. One way to do this is via sensors higher in the water column that detect acoustic signals emitted by sediment transport processes closer to the bed.
\nPrevious work such as Wren et al. (2015), Marineau et al. (2016), and Le Guern et al. (2021) have started to develop passive acoustic methods to record signals from sediment transport, using tools such as hydrophones and acoustic Doppler current profilers (ADCPs). Normally, ADCPs actively emit their own acoustic pulses, and their reflections are used to monitor flow velocities and concentrations. However, with modification to extend their listening times, ADCP’s can also be used to passively record acoustic signals emitted by sediment transport processes. Thus far, the potential of these passive acoustic methods have not been fully developed, and the fundamental controls that determine the type of acoustic signals produced are not yet fully understood. 
\nThis PhD sought to understand what controls the nature (frequencies, strength etc) of these signals and, thus, what they can tell us about sediment transport processes (Thorne, 1985,1986,1990,2014; Rigby et al. 2016). It aims to do this using a combination of laboratory experiments (Chapter 2) and detailed fieldwork (Chapters 3 and 4) using acoustic signals passively emitted by sediment flows. In addition, the thesis includes work testing the use of active acoustic methods to monitor sediment transport processes within the natural environment, specifically seabed sediment flows (called turbidity currents) (Chapter 5). 
\nResults from this thesis found a general relationship between the strength of self-generated noise and flow speed in some types of sediment flows (Chapters 2, 3 and 4). However, the strength of this relationship changes depending on the frequency and details of the environment investigated. Field data from the Río Paraná (Chapter 3) suggested no relationship between bedload flux and acoustic signal strength, nor between acoustic signal strength and friction velocity. This is unexpected because previous research by Sime et al. (2007), Hossein and Rennie (2009), Hatcher (2017), Hay et al. (2021) and Le Guern et al. (2021) proposed links between flow speed (and bed shear stress and bedload transport) and passively detected noise strength.
\nPassive acoustic signals generated by turbidity currents were used to monitor these flows in a set of submarine canyons, which were Bute Inlet (Canada), Monterey Canyon (offshore California), and the Congo Canyon (offshore West Africa) (Chapter 4). Noticeable variations in the level of passively detected noise between these three field sites were observed. These variations are thought to be related to the main sediment grain size present within each canyon, with lower noise being detected with an increasing mud content of the seabed. In addition, differences in noise down submarine canyons suggest that flow processes and concentration could be controlling the level of sediment-generated noise, with implications of flow field dynamics. 
\nChapter 5 uses one of the most detailed (near-daily) series of multibeam swath bathymetry surveys yet collected, which come from within Bute Inlet, Canada, in September 2022. This unusual set of field observations is used to understand the relationship between flow evolution and the initiation mechanism of turbidity currents. For example, the Bute Inlet study supports the findings from Hizzett et al. (2018) that there is no link between the initiation mechanism and runout distance of a turbidity current.
\nFurther research is needed to improve understanding of the controls on acoustic signals in the natural environment, and to also improve our ability to use acoustic signals to monitor sediment transport in a wider range of environments, such as around offshore wind farms.
\n

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,917
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,042
Tête enseignante GPT0,307
Écart entre enseignants0,265 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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