Classifying and removing unwanted arrivals propagating through a drill collar for semblance processing
Bibliographic record
Abstract
Understanding the characteristics of an acoustic wave propagating through drill collars is important for formation evaluation with logging-while-drilling (LWD) sonic tools. Knowing the frequency-slowness information of different types of waves propagating through the collar, the unwanted wave propagations can be minimized by processing and robustly identifying the formation compressional and shear arrivals. Extensional modes of the steel drill collar in water are generally dispersive and range from approximately 40 μs/ft to approximately 120 μs/ft depending on the frequency band. A fundamental torsional mode of the drill collar is nondispersive, but its slowness is sensitive to the geometry of the drill collar. Depending on the geometry and shear modulus of the material, the slowness of the torsional mode can be slower than 100 μs/ft. For identifying the slowness of the formation arrivals, the different slownesses of the waves propagating through the collar need to be identified separately from those of the wave propagating through the formation. By examining the various types of acoustic waves propagating through a drill collar, it was determined that the waves can be properly muted for semblance processing with LWD sonic tools.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".