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Effect of the interplay between ultra-slow spreading ridge and transform faults on seafloor morphology

2020· article· en· W3160968949 on OpenAlexaboutno aff
Stéphane J. Beaussier, Andreia Plaza‐Faverola, Taras Gerya, Stefan Büenz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsSeafloor spreadingRidgeTransform faultGeologyLithosphereMid-ocean ridgeGeodynamicsRidge pushCrustMagma chamberDepth soundingMagmaSeismologyMid-Atlantic RidgeGeophysicsFault (geology)TectonicsPaleontologyVolcanoOceanography

Abstract

fetched live from OpenAlex

Slow and ultra-slow spreading systems gives way to complexes seafloor morphologies characteristic of different modes of tectono-magmatic activity at the ridge: crust accretion by episodic magma supply, low-angle brittle/ductile normal faulting and high-angle normal faults leading to the formation of oceanic core complexes (OCC). Previous studies have established that the magma supply exert a first order control on the tectono-magmatic activity at ultra-slow ridges (Howell et al., 2019; Lavier et al., 2000). However, other parameters are likely to play a significant role in the mode of spreading and therefore the seafloor morphology. For instant, transform faults are ubiquitous in slow spreading systems and are therefore likely to impact the mode of spreading by redistributing the stress field in the oceanic lithosphere. This seems to be supported by the observation that OCC are typically occurring in the inside corners of intersections between the ridge axis and major transform faults (Tremblay et al., 2009). Yet, little work has been done to investigate this question, leaving a significant gap in the understanding of slow and ultra-slow spreading systems. This contribution investigates the interaction between ultra-slow spreading ridge and transform faults within the framework of a case study of the Fram Strait using high-resolution 3D numerical modelling. This study rely on the latest advances in geodynamics, namely the grain-damage rheology (Bercovici and Ricard, 2012) – which allows for internally consistent modelling of long-lived transformed faults. Numerical experiments are compared to the tectonic history of the Fram Strait over the last 10 Ma. A significant amount of geophysical and geological data available in the region allows us to asses how well the models reproduce observable structures in near-surface. Results show that ridge obliquity and ridge-transform interplay strongly affect the ridge spreading mode. Oblique ridge favour the formation of OCC over low-angle detachment fault and are systematically formed in the vicinity of major transform faults. Overall, results are in accordance with the highly complex seafloor morphology of the Fram Strait, in particular in the vicinity of the Molloy ridge. This study opens the way for a better understanding of complex ridge and abyssal hills structures in ultra-slow and slow spreading systems. Bibliography Bercovici, D., Ricard, Y., 2012. Mechanisms for the generation of plate tectonics by two-phase grain-damage and pinning. Phys. Earth Planet. Inter. 202–203, 27–55. doi:10.1016/j.pepi.2012.05.003 Howell, S.M., Olive, J.-A., Ito, G., Behn, M.D., Escartín, J., Kaus, B., 2019. Seafloor expression of oceanic detachment faulting reflects gradients in mid-ocean ridge magma supply. Earth Planet. Sci. Lett. 516, 176–189. doi:10.1016/J.EPSL.2019.04.001 Lavier, L.L., Buck, W.R., Poliakov, A.N.B., 2000. Factors controlling normal fault offset in an ideal brittle layer. J. Geophys. Res. Solid Earth 105, 23431–23442. doi:10.1029/2000JB900108 Tremblay, A., Meshi, A., Bédard, J.H., 2009. Oceanic core complexes and ancient oceanic lithosphere: Insights from Iapetan and Tethyan ophiolites (Canada and Albania). Tectonophysics 473, 36–52. doi:10.1016/J.TECTO.2008.08.003

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.016
GPT teacher head0.233
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2020
Admission routes1
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