Quantitative Seafloor Geomorphology and Offshore Geohazards
Notice bibliographique
Résumé
Abstract Seafloor geomorphology reflects the dominant geologic and tectonic processes on continental margins. Using the global, one-minute bathymetric grid (GEBCO), we use geostatistical techniques to quantify the scale of the dominant geomorphic features on several active and passive continental slopes to gain insight into the mechanisms that drive the erosive processes. Once the characteristic dimensional and spatial variability is determined, we can examine the regional physiography to surmise the processes that shape the bathymetry, and predict the occurrence of future mass-wasting events based on the past events that shaped the margin, be it sea-level lowstand canyon incision, large and (geologically) infrequent landslides, or frequent earthquake mass-wasting events. Introduction Our knowledge of offshore geomorphic processes increases as more and more high-quality data becomes available. For example, the 1929 Grand Banks (Newfoundland) passive margin earthquake generated a landslide which in turn produced fast-moving, erosive turbidity currents and a deadly tsunami [1,2]. While the 1998 Papua New Guinea earthquake was not sufficient in itself to generate a tsunami, the triggered landslide produced a wave that killed over 2,000 people [3]. Clearly, submarine landslides pose a significant threat to offshore installations such as cables, pipelines, rigs, etc., and also to coastal populations. Properly assessing the risk of this geohazard entails a comprehensive survey of the frequency and magnitude of past events. The goal of this study is to characterize the scale of dominant bathymetric features on active and passive continental margins at different latitudes. The erosive processes on these different margins (e.g. gullies, canyons, landslides, etc.) leave distinctive geomorphological signatures recognizable in bathymetry data [4,5]. The process that drives the erosion, be it earthquake-generated slope failures [6], climate-induced slope destabilization such as gas hydrate melting [7] or high latitude sediment loading at via glacial outwash [8], should occur with greater frequency in certain areas than others. We hope to build upon the studies of submarine landslide size distributions within given regions [4,9,10], by undertaking a systematic comparison of the scale of erosive features between tectonic regimes and latitude. Erosive processes on the seafloor should follow frequency-magnitude relationships related to driving mechanisms as they do on land (e.g. big and infrequent storms will cause lots of large landslides whereas small, frequent rainfall events will cause smaller landslides), therefore it stands to reason that the large submarine erosive features identified using this method (i.e. landslides) should be caused by infrequent events. Passive margins dominated by gullies and closely-spaced canyons generate semivariograms with relatively small sills and ranges. These erosive features suggest frequent (here, ‘frequent’ being anywhere from 1 per 100 years to 1 per 1000 years), small-scale erosive events, likely triggered by seasonal sedimentation events on the shelf, or perhaps earthquakes that trigger failure of sediment accumulated at the gullies' heads. On active margins, the erosive geomorphology largely depends on the nature of earthquakes (e.g. segmentation and ‘slow’ vs. ‘fast’ events). In contrast, margins with large and well-preserved landslides yield semivariograms with large sills and ranges, and are suggestive of a less frequent process, such as sea level change with interspersed intervals of relative quiescence.
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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 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».