Seismic attribute expression of differential compaction
Bibliographic record
Abstract
In a marine environment, topographic features on the sea floor will usually be covered by a thick layer of shale with the rise of sea level, resulting in a uniform, nearly flat surface. Evaporating seas may bury sea-floor topography with a thick layer of salt. In a fluvial-deltaic environment, channels are cut and filled with a lithology that may be different from that through which it is cut, followed by subsequent burial with (perhaps) a more uniform sedimentary layer. With continued burial and overburden, pore sizes are reduced and water is squeezed out of the rocks, reducing the rock volume. Different lithologies have different original porosity, pore shapes, and mineral matrix composition, and thus different responses to burial. Lateral changes in lithology give rise to lateral changes in compaction, or simply “differential compaction.” For this reason, easily mapped flooding and other surfaces that were originally flat can exhibit measurable, and often significant structural relief. These maps give rise to lateral “structural” anomalies. Recognition of differential compaction forms a key component in modern seismic interpretation workflows based on geomorphology with excellent publications showing the expression of differential compaction on vertical slices. Mapping the 3D expression of compaction features takes considerable time and is thus less well reported while the use of 3D geometric attributes to map compaction features is underutilized. In this article, we illustrate the attribute expression of the more common differential compaction features over channels and carbonate reefs using examples from the Western Canadian Sedimentary Basin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".