Velocity-depth trends in Mesozoic and Cenozoic sediments from the Norwegian Shelf
Bibliographic record
Abstract
Abstract Sonic velocity, density, and resistivity log data from 60 wells on the Norwegian Shelf have been used to investigate velocity-depth trends in sedimentary rocks as a function of sediment composition, porosity, pore-pressure, burial-history, and compaction processes. A first-order linear velocity-depth trend line has been estimated from published velocity data. The data analyzed in this study, however, show significant variations from this trend line, indicating that no general velocity-depth function can be used when performing more accurate analyses like depth conversion of seismic data, pore-pressure prediction, or basin modeling. Lower Tertiary smectitic sediments from the northern North Sea and Haltenbanken are characterized by relatively low velocities compared to the overlying Pliocene and Pleistocene sediments, causing a distinct velocity inversion. A significant velocity increase at a burial depth corresponding to 70–100°C was found and may reflect the alteration of smectite to illite and the initial precipitation of quartz cement in both sandstones and shales. Overpressured Jurassic sediments from Haltenbanken have lower velocities than equivalent hydrostatically pressured sequences but no significant porosity difference. The reduced velocities may be a direct response to lower effective stresses and, thus, reduced elastic compaction. Low velocities in source rocks are mainly attributed to the relatively soft kerogen and resulting velocity anisotropy. The high velocity/depth ratio of Barents Sea sediments (after correcting for Tertiary exhumation) is explained by the burial history of the area, the subsequent thermal exposure of the sediments over time, and thus, the amount of quartz cementation.
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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 | 0.002 | 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) | 0.001 | 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".