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Record W2597803407 · doi:10.7282/t34x5b31

Experimental modeling of salt flow subparallel to basement-involved faults

2016· article· en· W2597803407 on OpenAlexaboutno aff
Mattathias David Needle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyRiftFault (geology)BasementPetrologyMining engineeringStructural basinGeomorphologySeismologyEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Rock salt exhibits highly ductile behavior at shallow crustal levels, and its presence profoundly affects the structural development of rift basins. Basement-involved normal faults strongly influence the initial thickness and distribution of the synrift salt. Subsequent deformation and deposition during and after rifting cause the salt to flow. This work uses experimental (analog) modeling to examine the secondary structures that develop in the sedimentary cover above synrift salt that flows subparallel to the strike of basement-involved faults. In the models, silicone polymer simulates salt, wet clay simulates the sedimentary cover, and rigid blocks represents basement-involved faults. Extension imposed at the base of the models causes the silicone polymer to flow subparallel to the rigid blocks. The models indicate that two zones of deformation form within the sedimentary cover: 1) a shear zone with oblique-slip faults that trends (sub)parallel to the strike of the underlying faults; and 2) an extensional domain with predominantly normal faults that strike (sub)perpendicular to the flow direction of the ductile unit. At the clay surface, some faults in the shear zone and extensional domain link, forming curved fault surfaces. The initial thickness and distribution of the highly ductile unit affect the development of secondary structures in the overlying cover. The thickness of the ductile unit controls the degree of decoupling between shallow and deep structures. Where the ductile unit is thick, the extensional domain and shear zone in the cover are broad, whereas where the ductile unit is thin, the extensional domain and shear zone in the cover are narrow and form directly above the deep structures. Varying the initial distribution of the ductile unit produces subsequent asymmetrical deformation in the overlying cover. The orientation of pre-existing faults, relative to the flow direction of the highly ductile unit, also has an influence on the development of secondary structures. When the flow of the ductile unit (relative to a pre-existing fault) produces highly oblique extension at depth, deformation is distributed broadly in the extensional domain, and the shear zone forms above and trends parallel to the pre-existing fault. However, when the flow of the ductile unit (relative to the pre-existing fault) produces highly oblique shortening at depth, 1) the trend of the shear zone in the cover is not parallel to the strike of the underlying pre-existing fault, and 2) secondary features in the extensional domain are muted in the cover. The latter suggests that the ductile unit distributes the deformation and, thus, subdues the expression of both shortening and extensional features at the surface. Comparisons of the modeling results in this thesis to the deformation in the Jeanne d’Arc basin of offshore Newfoundland, Canada, suggest that the synrift Argo Salt flowed parallel to the basin’s long-axis. The salt flow produced secondary structures in the sedimentary cover above the salt including trans-basin normal faults and shear zones above basement-involved faults.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.031
GPT teacher head0.237
Teacher spread0.206 · 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 designSimulation or modeling
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
Published2016
Admission routes1
Has abstractyes

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