Multiscale structure in sedimentary basins
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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 it