Improving seismic images for frontier exploration in East Coast Canada: Lessons learned
Bibliographic record
Abstract
Frontier exploration involves many unknowns, making seismic image quality all the more critical. Image quality is determined by the input data, velocity model, and migration algorithm, with the velocity update typically being the most difficult part. In Orphan Basin, a deep-water exploration frontier off the east coast of Canada, many of the velocity challenges were resolved with Time-lag Full-waveform Inversion (TLFWI, Zhang et al., 2018), which resulted in improved Kirchhoff images. However, while TLFWI resolved most of the velocity issues, we also encountered challenges with the migration algorithm and input data. In areas with high-contrast velocity details, ray tracing breaks down and Kirchhoff migration images deteriorate. Reverse Time Migration (RTM) overcomes the ray tracing limitations through wavefield extrapolation but still suffers from low illumination due to complex overburdens. We utilized FWI Imaging (Zhang et al., 2020) to resolve the imaging issues, producing migration images superior to both Kirchhoff and RTM. Regarding the migration input, we were faced with the inherent limitations of the available narrowazimuth streamer data, as well as a hard sea floor that made the high-order multiples from previous shots strong enough to contaminate the primary energy of current shots. To address this, we applied a multi-stage surface-related multiple elimination flow to remove the high-order multiples, resulting in better high-frequency Kirchhoff images with improved signal-to-noise ratio (S/N).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".