A geostatistical approach for two-dimensional seismic velocity modelling
Bibliographic record
Abstract
This study tests two geostatistical approaches, kriging with external drift (KED) and cokriging (CK), for building two-dimensional seismic velocity models for the reprocessing of vintage seismic reflection data collected in Canadian Western Arctic Islands between the late 1960s and the early 1980s. The interval thickness between horizons is estimated at all Common Mid Points (CMPs). The interval thickness evaluated at three well is used as the primary variable of kriging and the timethickness estimated from seismic horizon picking at all CMPs is used as the external drift to represent time-depth variations along the seismic line. The depth to horizons estimated by KED honours perfectly the depth evaluated at three wells, while the lateral variations of the horizons in depth closely follow those of the horizons picked in time-depth. In contrast to constant lateral velocity layer models often used in seismic processing, the velocity calculated from the KED allow modelling lateral velocity variations within each layers, providing a more realistic representation of the subsurface geology.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".