Submarine canyon-fill reconstruction from integrated seismic-stratigraphic analysis – application to Banquereau formation, Scotian basin – offshore Canada.
Bibliographic record
Abstract
Building geological models (integrating stratigraphic, structural and paleo-environmental 3D models) that allow the interpretation of sand bodies deposited by turbidity currents along submarine canyons or channels, is one of the most useful tools used by geoscientists for the definition of new drilling opportunities in both exploration and development phases. In this context, the integration of methodologies such as sequence stratigraphyand seismic attributes, together with well-log and core information, outline the basis for the interpretation of sand-body lithostratigraphy and chronostratigraphy. Similarly, these models allow the interpreter to reconstruct the depositional environment and deformation history of a sedimentary basin [1]. Based on a series of chronostratigraphic stages, this paper proposes a 3D model for the sedimentation history of the Banquereau Formation.This model is based on the integration of seismic stratigraphy, seismic attribute interpretation and well-log analysis. Also, a set of system tracts and corresponding transgression and regression phases were identified for the sedimentary interval of interest. The available dataset provided the information to identify the geometry and changes in the sedimentation patterns of the stratigraphic sequences from the Tertiary to the present, thus defining a 3D model of the sedimentological and structural architecture of this interval. Last but not least, theresulting 3D stratigraphic model made possible the identification and description of an amalgamated channel complex filling a submarine canyon associated with a fluvio-deltaic setting. This sort of analysis might be used as an analog for similar reservoirs, providing key insights and vital information for decision making.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".