Bibliographic record
Abstract
PS-wave data has continuously increased industrial interests for it can shed light on reservoir characterization. Moreover, as the effective velocity of PS-wave is much lower than that of PP-wave, for same frequency band, the wavelength of PS wavelet should be smaller than that of PP wavelet. Consequently, one would like to expects higher resolution from PS-wave images than PP-wave images. In reality, however, this is not the case. For the strong absorption effects of subsurface on S-wave, the frequency band of PS-wave data is much narrower than that of PP-wave data. In this paper we investigate a new algorithm for the collaborative deconvolution of PP-wave and PS-wave data. We first build the mapping relationship between PP-wave and PS-wave data in time domain by image registration, then in the sparse inversion method, the deconvolved PS-wave reflectivities not only honour PS-wave data, but also constrained by PP-wave data. Presentation Date: Wednesday, October 19, 2016 Start Time: 3:35:00 PM Location: 166 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".