Look at converted waves from an OBN test survey
Bibliographic record
Abstract
Ocean Bottom Node (OBN) surveys have been very successful in improving subsurface imaging in deep as well as in mid to shallow water. Because of the limited number of nodes availability, initial surveys consisted of a coarse grid of receiver stations and very dense grid of source points. Unfortunately, for the Converted waves, this geometry is unfavorable due to the limited number of nodes and the asymmetry of the conversion point, but the high interest in OBN acquisition, led to quick supply of more nodes which would help improve the compressional as well as the Converted waves Geometry. At ConocoPhillips, initial examination of OBN data from the North Sea exhibited excellent signs of the utility of the converted waves to complement the compressional waves by obtaining additional seismic attributes such as Vp/Vs, Lithology, anisotropic parameters and ability to image reservoirs in presence of gas where, unlike P-S, compressional waves are highly attenuated. Initial processing of P-S data from an OBN survey yielded very good results with significant potential for improvement and integration of the compressional and P-SV converted waves to address some of the challenging problems in the North Sea such as Imaging with P-SV waves in presence of Gas and fractures characterization where fractures can be the dominant factor controlling production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".