Fibre optic sensing of submarine gravity flows
Notice bibliographique
Résumé
Fibre optic sensing has emerged as a powerful technology for detecting energetic processes that modify the seafloor, such as earthquakes and ocean currents. Fibre optic technologies have a transformative potential in seafloor geomorphology because of their ability to characterise fine-scale processes in terms of location, timing and magnitude. Furthermore, they can enable long-term time-lapse monitoring and deployment in logistically challenging areas. However, the use of such technology for studying the rich variety of seafloor processes is still in its early stages. This is due to: (i) the limited availability of subsea cables, access to dark fibres, and interrogators; and (ii) the tendency of submarine cables to be located away from the seafloor processes of interest. Consequently, the sensitivity of cable configurations, interrogator units, and analytical approaches for monitoring seafloor processes remains unexplored. Sedimentary flows, which commonly start with submarine landslides, play a key role in shaping continental margins. They are capable of transporting large volumes of sediment (up to hundreds of km³) at high velocities (up to 20 m/s) over long distances (up to 1000 km). Despite decades of research providing valuable insights into the location, structure, and extent of sedimentary flows, key questions about their initiation, behaviour, and impacts remain unanswered. This abstract presents an overview of two field experiments focused on detecting sedimentary flows and characterising their behaviour using fibre optic sensing: The subsea EMSCS telecommunication cable connecting Malta to eastern Sicily, which intersects submarine canyons on the Malta Escarpment, has been interrogated with laser interferometry on an ongoing basis since September 2022. Signals potentially associated with sedimentary flows have been cross-referenced with onshore and offshore seismometer and meteorological data. The subsea MARS cable in Monterey Bay, operated by MBARI, has been monitored using Distributed Acoustic Sensing since July 2022. This setup's efficacy is being assessed by isolating signals likely associated with sedimentary flows in Monterey Canyon and comparing them with oceanographic and seismological data. A variety of events (e.g. earthquakes, storms) have been observed during these experiments. A significant challenge, however, lies in our uncertainty regarding the expected signal for sedimentary flows and our ability to definitively link signals with known events. Efforts are underway to tackle this issue. One such initiative includes a field experiment planned in early 2025 as part of the Geo-Sense project. This experiment involves the deployment of a portable Distributed Acoustic Sensing interrogator and cable, powered by batteries and equipped with onboard data storage, along the flanks of Monterey Canyon. The effectiveness of the system in detecting sedimentary flows will be evaluated against in-situ measurements by ocean bottom seismometers, hydrophones, and moored acoustic Doppler current profilers. Conducting experiments across two distinct sites using varied techniques and reference measurement tools will aid in identifying the typical signature of sedimentary flows, as well as their unique characteristics.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».