Volumetric estimates of seismic reflector rotation and convergence—tools for mapping rotation about faults and seismic stratigraphy
Bibliographic record
Abstract
Geometric attributes such as coherence and curvature are commonly used for mapping structural deformation and depositional environment. Coherence proves useful for identification of faults, channel edges, reef edges, and collapse features while curvature images folds, flexures, subseismic conjugate faults that appear as drag or as folds adjacent to faults, rollover anticlines, diagenetically altered fractures, karst, and differential compaction over channels. Unfortunately, these two attributes have limited value in imaging classic seismic stratigraphy features such as progradation and erosional truncation. Seismic stratigraphy refers to the analysis of the configuration and termination of seismic reflection events, packages of which are then interpreted as stratigraphic patterns. These packages are then correlated to well-known patterns such as toplap, onlap, downlap, hummocky clinoforms, and so forth, which in turn represent architectural elements of a depositional environment (Mitchum et al., 1977). By integrating these elements with well control as well as modern and paleo analogs, the interpreter produces a probability map of lithofacies. Seismic facies also thicken and thin with increasing and decreasing accommodation space. Rotation of lithologic units about faults can provide increased accommodation space or subject uplifted areas to erosion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".