Geostatistical mapping and spatial variability of surficial sediment types on the Beaufort Sea shelf based on grain size data
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
The nearshore Beaufort Sea is a sensitive marine environment that is also the focus of oil and gas exploration. Offshore, the Beaufort Sea contains large potential reserves of hydrocarbons. Any future exploitation of these resources will present unique engineering challenges and will require an understanding of the processes that govern stability, nearshore morphology and sediment properties in the extensive shallow coastal zone of the Beaufort Sea shelf. Knowledge of the surficial sediment distribution is, therefore, necessary to provide a framework for understanding sediment stability, sediment transport, platform foundation conditions and to balance engineering challenges with environmental concerns, resource development and precautionary sustainable management. We describe an approach for a quality controlled mapping of grain sizes and sediment textures for the Beaufort Sea shelf in the Canadian Arctic. The approach is based on grain size data sampled during the period 1969-2008. A replenishment of grain size data since the 1980’s, as well as the consideration of correlating parameters (bathymetry, slope and sediment input) to a cokriging algorithm, amends the former way of mapping the surficial sediments of the Beaufort Sea shelf. \nSubsequent to data processing and applying autocorrelation, four single grids (clay, silt, sand and gravel) were generated from grain size data by ordinary kriging and cokriging. Cokriging also considered parameters that influence sediment texture such as bathymetry, slope, cost distance from the Mackenzie River and data anisotropy (directional dependency). The cokriging algorithm expressed as a variogram was quality controlled by cross-validation and predicted standard errors (PSEs). PSE values express a maximum deviation of modeled from the real values and therefore help to estimate the quality in these regions regarding the interpolation results for each grain size range. A sediment type classification scheme applied to the set of clay, silt, sand and gravel content maps resulted in a sediment type map of the Beaufort Sea shelf. \nThe PSEs of ordinary kriging and cokriging have been compared and showed that the cokriging technique provided superior interpolation results for silt and slightly improved results for clay and sand. Cokriging was able to capture most of the small variations in the sediment texture distribution. Furthermore, reduced nugget effects confirmed that the cost distance grid was a better indicator for sediment texture when compared to bathymetry and slope. For gravel, ordinary kriging achieved better prediction probabilities and was, therefore, used for generation of the distribution map for this grain size class. \nThe use of cokriging and ordinary kriging greatly enhanced interpolation estimates without additional sampling. Especially in nearshore regions, like the Beaufort Sea shelf, geostatistical interpolation techniques are very useful for evaluating seabed sediment texture because sampling is often difficult or impossible due to ice conditions or even prohibited near oil platforms. The described methodology along with the inclusion of recent data, provided an improved mapping of the surficial sediments of the Beaufort Sea shelf.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découle