PP-wave and PS-wave 5D reconstruction and denoising to assist registration
Bibliographic record
Abstract
Mapping post-stack PS-wave data to the PP-wave time domain is a critical step before joint interpretation and inversion. Registration techniques are often constrained by having access to a known Vp/Vs ratio. When an accurate Vp/Vs ratio is not provided, one can solve the problem of seismic data registration by minimizing the difference between the PP-wave and the warped PS-wave data with a smoothing constraint applied on the warping function. However, one possible limitation of the applicability of registration algorithms on field multicomponent seismic data is the low signal-to-noise-ratio (SNR) of the PS-wave data. To deal with this issue, we propose to attenuate the noise of the pre-stack seismic data via 5D interpolation/ reconstruction. In our processing flow, we recommend adopting a tensor completion method to reconstruct and enhance pre-stack PP and PS-wave data. Then one can implement registration algorithms on the SNR-enhanced post-stack seismic data to obtain a stable estimation of the warping function. To demonstrate the effectiveness of the proposed workflow, we test it on a 3D land multicomponent seismic data acquired in Central Alberta, Canada. Presentation Date: Monday, October 15, 2018 Start Time: 1:50:00 PM Location: 213A (Anaheim Convention Center) 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".