Thin-skinned extension, salt dynamics and deformation in dynamic depositional systems at passive margins
Bibliographic record
Abstract
Gravity-driven extension, syntectonic sedimentation and salt mobilisation at passive margins control the evolution of complex fault and salt structures. The associated sedimentary basins have proven to be important hydrocarbon exploration targets. The Salt Dynamics Group utilises 4D modelling with scaled physical experiments to simulate the structural evolution and migration of depositional systems on salt substratum. Fault kinematics, surface deformation and subsidence are monitored by high-resolution 2D/3D optical strain monitoring techniques (PIV-Particle Imaging Velocimetry) to study the timing and mechanisms of faulting, the role of variable sedimentation patterns and rates, and the coupling between extension in the shelf and slope and compression in the deepwater basin. The experiments simulate the evolution of complex structures in passive margin salt basins and demonstrate that a strong feedback exists between sedimentation, strain rate and structural styles. The structural patterns of fault and salt structures, in certain cases have the potential to give insights into the dynamics of the depositional systems. The integration of innovative 4D physical simulations with 2D/3D PIV monitoring, structural modelling and seismic interpretation form the basis for the development of mechanical concepts and interpretation templates to improve the understanding of salt deformation processes and salt/sediment interaction. Knowledge gained from the study will be especially relevant for hydrocarbon exploration in salt basins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".