Using synthetic aperture sonar as an effective tool for pipeline inspection survey projects
Notice bibliographique
Résumé
Synthetic Aperture Sonar (SAS) is becoming a powerful tool for hydrographic and pipeline inspection surveys in addition to its original use as a mine detection sensor. This technology can provide very high resolution seafloor imagery and bathymetry over the full extent of the swath. SAS lends itself for use with Autonomous Underwater Vehicles (AUVs) because of the stable nature of these platforms. As the hydrographic industry begins to adopt AUV technology, especially given the efficiencies they can bring, it is clear that SAS will have a greater role to play. This trend is driving a requirement to support this data effectively in the data processing software tools that are used by the hydrographic and Oil and Gas industries. This involves new requirements and workflows to handle the increased data volumes that result from the centimeter level resolutions that these sensors are capable of generating. The data processing emphasis from a survey utilizing a SAS is different from traditional vessel based operations. For example, early experiences suggest that more attention needs to be given to dataset combining rather than data cleaning. Another important observation is understanding how to run a survey with SAS, what line configurations are appropriate when conducting a route survey and how does this differ for an area based survey for the purposes of charting. There are also some technological advancements that could streamline the SAS workflow and aid its adoption as a hydrographic tool. One such technology is variable resolution surface creation. This emerging terrain modelling technique would allow high resolution SAS data and lower resolution multibeam data to be stitched seamlessly together into a single terrain model allowing for more efficient data transfers, more integrated and robust data cleaning techniques and easier object detection. Another advancement that will benefit surveys utilizing this technology is onboard and near real-time data processing solutions. This is being driven by the nature of this data; its density and the autonomous way in which it is collected. This technology is already in use in Brazil for pipeline inspection projects as an alternative solution to video capture and review which has been a time consuming part of pipeline inspection survey projects with remotely operated vehicles (ROVs). By using an AUV that is capable of capturing a combination of SAS imagery, bathymetry and on-demand high definition still photography and delivering these as high resolutions co-referenced datasets there is the potential to save data review time and provide data from the field in a more coherent and modern way. The paper's aim will be to not only highlight the new considerations that need to be understood when using this technology as a hydrographic mapping tool, but to also demonstrate this through an appropriate use case.
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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 | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
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 ».