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Record W2000906845 · doi:10.4043/18736-ms

Quantitative Seafloor Geomorphology and Offshore Geohazards

2007· article· en· W2000906845 on OpenAlexaboutno aff
Eugene Morgan, Brian G. McAdoo, Laurie G. Baise, Don J. DeGroot

Bibliographic record

VenueOffshore Technology Conference · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsnot available
FundersOffice of International Science and EngineeringNational Science Foundation
KeywordsGeohazardMass wastingGeologyBathymetrySubmarine landslideSeafloor spreadingSubmarine pipelineLandslideContinental marginTectonicsMass movementSubmarine canyonContinental shelfGeomorphologyCanyonPassive marginSeismologyOceanographyRift

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.253
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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