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Record W7066049425

Gravity and Magnetic Signatures of Different Types of Spreading in the Atlantic: Characterisation of Ocean-Continent Transition

2021· dissertation· en· W7066049425 on OpenAlexaboutno aff

Bibliographic record

VenueWhite Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsBathymetryTectonicsOceanic crustContinental crustAccretion (finance)CrustTerrainMid-ocean ridgeFault (geology)
DOInot available

Abstract

fetched live from OpenAlex

Magmatic accretion and tectonic extension have been recognised as the driving forces that forms the oceanic crust at mid-ocean ridges. At slow-spreading ridges, as the melt supply falls below a critical level, the plate separation is accommodated by long-lived detachment faulting rather than the typical magmatic accretion. The detachment fault accommodates exhumation of lower-crust and upper-mantle rocks to the ocean floor, forming domed structures known as Oceanic Core Complexes (OCCs). These domed structures are commonly found at one side of the spreading axis, indicating the occurrence of asymmetric spreading, as opposed to the symmetric fault-bounded abyssal hills commonly found over magmatic crust. Meanwhile, parts of the ultra-slow-spreading ridges are completely devoid of magmatism, where large detachment faults form continually at both axis flanks to facilitate the plate separation. At passive continental margins, these crustal morphologies are not recognisable by shipboard multibeam bathymetry data, as they have been buried by sediments deposited from the continental crust. Hence, this study aims to classify the types of oceanic crust based on the gravity and magnetic characteristics observed over the spreading axis by: (1) characterising the different types of spreading by quantifying parameters observed in shipboard multibeam bathymetry of an active slow-spreading ridge; (2) assessing and improving established gravity and magnetic data enhancement techniques to characterise and classify crustal types of a slow-spreading ridge, and; (3) applying the assessed enhancement techniques to the available gravity and magnetic data over a passive continental margin.
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\nA novel automatic terrain classification technique, namely the slope-weighted eccentricity (SWE) is established based on the parameterisation of the shape, directionality, and curvature of the ocean floor where shipboard multibeam bathymetry data has been made available. Investigation by means of gravity and magnetic anomalies are also conducted to investigate spreading evolution and the crustal thickness variation. Crustal thickness is computed from the isostatic mantle Bouguer anomaly (IMBA), a type of gravity anomaly developed in this study by removing the gravity effects observed within the water-crust and crust-mantle interfaces. The evolution of the alternating spreading modes is then identified by comparing the SWE number and computed crustal thickness over time through the interpreted magnetic chrons. The crustal thickness computation a well as a number of existing gravity and magnetic data enhancement techniques are also applied to a larger set of data over the Labrador Basin, where a composite of field magnetic surveys is made available. Consistent with the recognised characteristics of ultra-slow-spreading ridge morphology, a significant area of thin crust is identified across the basin, where upper-mantle rocks are likely to be exhumed through large detachment faulting and went through a high degree of serpentinisation.
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\nThis thesis has contributed to the establishment of a new grid-based interpretation technique that is tested and ready to be applied to shipboard multibeam bathymetry at various areas, as well as testing and applying several existing gravity and magnetic interpretation techniques to identify and characterise crustal structures.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.189
Teacher spread0.182 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2021
Admission routes1
Has abstractyes

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