Grain size dynamics using a source-to-sink approach to planform modelling
Notice bibliographique
Résumé
The grain size preserved within the stratigraphic record over thousands to millions of years has a wide breadth of relevance for deciphering past tectonic and climatic events within the continental sedimentary system and within applications for reservoir prediction, groundwater modelling and floodplain weathering. Here, we present a new model for grain size fining predictions that couples a landscape evolution model (with deposition and erosion components (Yuan et al. 2019)) with a self-similar model for gravel and sand (Fedele and Paola 2007). The new model, that we called GravelScape, includes the e"ects on grain size fining of lateral heterogeneity in deposition rate caused by dynamically evolving channels. Through an initial validation, we show that when channel avulsions are prevented, by reducing the planform model to a single, downstream dimension, our new model can reproduce results from past methods (e.g. (Duller et al. 2010)) that assume that fining is controlled by subsidence only. However, deviations in predicted grain size occur when the e"ects of a multi-channel (internal or autogenic dynamics) planform model are considered. The amplitude of these deviations seems to be proportional to the extent of sedimentary system bypass and the shape of the surface topography, which influence the magnitude of across-basin variation within the sedimentary system. Under low bypass and gentle slopes, grain size trends are primarily governed by subsidence, while high bypass and steep topography enhance autogenic influences. Simulating shorter transport lengths (e.g. decreasing orogen discharge, increasing basin area, or increasing transient sediment in the basin) tend to generate more autogenic related fining and variance within a basin. We demonstrate how these dynamics of grain size fining can be mapped using a framework that links by-pass and surface geometry, with practical applications for foreland basin evolution illustrated using data from the Alberta Basin in Western Canada. As the by-pass ratio increases over time, as seen in foreland basins, there is a transition from subsidence to autogenicall dominated grain size fining. We also test under which conditions Milankovitch scale precipitation perturbations are best captured within the continental alluvial fan system considering autogenic variability, perturbation strength, and preservation. We then apply a detailed misfit comparing the model to data in the Grapevine mountain fans of Death Valley, where both climate and autogenic fan responses have been observed. Our research contributes to the interpretation of grain size trends in natural systems and their response to both autogenic and external forces. Prior to this dissertation, no landscape evolution model could e!ciently (within minutes) predict grain sizes preserved within the sedimentary record over large spatial and long temporal scales in response to climatic, tectonic, and internal forcing.
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Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».