4D Wheeler diagrams: concept and applications
Bibliographic record
Abstract
Abstract The conventional Wheeler diagram aids the construction of a spatiotemporal framework of strata. The diagrams are created manually by studying outcrops, wells, or seismic data. For the latter case, automated methods now exist, which support the construction of 2D, as well as 3D Wheeler diagrams. Seismic data contains information in three dimensions, X , Y and Z , where ‘ Z ’ is either two-way time or depth. Seismic horizons are correlated surfaces that often follow geological time lines. In this case, a set of interpreted seismic horizons contains information in four dimensions ( X , Y , Z , and Geological Time). In the mapping from the structural domain to Wheeler space, information about Z (thickness) is lost. This means that one dimension is missing in the conventional Wheeler diagram. This paper describes a method to add information from the Z dimension to the Wheeler domain. It is done by computing stratigraphic thicknesses per sequence stratigraphic unit and displaying these as colour-coded overlays in the Wheeler domain. Thus displayed, thickness variations help in understanding changes in accommodation, sedimentation rate, and depositional trends. 3D Wheeler displays with colour-coded thickness information are referred to as 4D Wheeler diagrams. In this article, the method is described and applied to a case study from the southern North Sea.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".