A case study of the 3D cable reconstruction in the presence of acoustic anomalies for imaging exploration targets in complex geologic settings Offshore Newfoundland, Canada
Bibliographic record
Abstract
Resolution limits for final migrated seismic data volumes to be utilized for interpretation depend on a number of factors, including usable frequency content, seismic velocities, and the spatial sampling of the input and output data. Marine towed-streamer acquisition systems are generally well sampled in the inline direction, but poorly sampled in the cross-cable direction due to economic or operational constraints. Various methods are available that aim to improve cross-cable sampling. These include acquisition strategies, such as triple source techniques (Langhammer and Bennion, 2015), and processing-based methods such as interpolation are commonly used. Multimeasurement three component towed-streamer systems also offers the opportunity to improve cross-cable sampling by combining different measurements of the seismic wavefield (Ozbek et al., 2010). This approach benefits from multimeasurement-constrained joint interpolation and deghosting in the shot domain. It allows the cross-cable sampling interval to be controlled early in the processing workflow, compared to methods that operate in the common offset or image domains. In an exploration environment, efficient coverage of the prospect area is frequently judged to be more important than high spatial resolution. Typical acquisition geometries use streamer configurations with nominal separations of 100m (or even more) to deliver data volumes with 25m cross-cable cmp interval after migration. However, even in these settings, denser cross-cable sampling can benefit prospect identification and evaluation so long as decision time frames are not impacted. Multimeasurement streamers support this goal by generating 3D deghosted shot records output onto a target geometry that comprises both real and virtual cables, with a smaller sampling interval for subsequent processing. This paper presents a case study of applying this approach on a large-scale exploration survey. The area is prone to changes in water velocity (>20m/s) across the thermocline zone in the water column. We evaluate the enhancements in the migrated image resolution by comparing the natural 25m cross-cable cmp sampling versus an equivalent volume generated at 12.5m cmp bins. We also discuss how the measurements, coupled with a processing workflow, enabled a robust deghosting solution in this challenging thermocline environment. Presentation Date: Tuesday, September 26, 2017 Start Time: 3:30 PM Location: 360A Presentation Type: ORAL
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".