Multiscale structure in sedimentary basins
Bibliographic record
Abstract
Abstract Hierarchies of superimposed structures are found in maps of geological horizons in sedimentary basins. Mapping based on three‐dimensional (3D) seismic data includes structures that range in scale from tens of metres to hundreds of kilometres. Extraction of structures from these maps without a priori knowledge of scale and shape is analogous to pattern recognition problems that have been widely researched in disciplines outside of Geoscience. A number of these lessons are integrated and applied within a geological context here. We describe a method for generating multiscale representations from two‐dimensional sections and 3D surfaces, and illustrate how superimposed geological structures can be topologically analysed. Multiscale analysis is done in two stages – generation of scale‐space as a geometrical attribute, followed by identification of significant scale‐space objects. Results indicate that Gaussian filtering is a more robust method than conventional moving average filtering for deriving multiscale geological structure. We introduce the concept of natural scales for identifying the most significant scales in a geological cross section. In three dimensions, scale‐dependent structures are identified via an analogous process as discrete topological entities within a four‐dimensional scale‐space cube. Motivation for this work is to take advantage of the completeness of seismic data coverage to see ‘beyond the outcrop’ and yield multiscale geological structure. Applications include identifying artefacts, scale‐specific features and large‐scale structural domains, facilitating multiscale structural attribute mapping for reservoir characterisation, and a novel approach to fold structure classification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".