Generalized cross-dip moveout correction of crooked 2D seismic reflection surveys
Bibliographic record
Abstract
ABSTRACT In hard rock settings, reflection seismic surveys are often acquired on crooked roadways. Acquisition geometry-related noise resulting from these crooked profiles obscures the final image in places where there are crossline dipping reflectors. This noise can be prevented with cross-dip moveout (CDMO) corrections. The conventional practice is to apply corrections on straight processing lines; however, this aggravates reflection duplication and stretching artifacts. We have adopted an efficient method for CDMO correction that operates on any common midpoint (CMP) binning geometry. Our method suppresses reflection duplication in high-fold CMP bins. The strike and dip of the reflectors are decomposed into two horizontal orthogonal components and input into a 3D traveltime equation. Using a synthetic model, a processing workflow was developed to locally apply these generalized CDMO corrections. This workflow was then applied to a seismic profile acquired over the Larder-Lake Cadillac Deformation Zone in the Abitibi Greenstone Belt, Canada. The final processed seismic image showed an increased coherency of reflections rendering them more compatible with the known surface geology of the study area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".