Improved principal component analysis for 3D seismic data simultaneous reconstruction and denoising
Bibliographic record
Abstract
The principal component analysis (PCA) is an effective proper orthogonal decomposition (POD) method for data analysis. The target of the PCA is to reduce the dimensionality of a data set and retain the variance presented in the data set as much as possible. We assume the random noise and irregularly missing data are additive and uncorrelated with the signal, and utilize the PCA method to simultaneously reconstruct and de-noise seismic data. In fact, PCA is to find a lower dimensional optimal approximation of the initial data in the least-squares sense. However, the signal has a deflection to the optimal approximation in this lower dimensional space. For this reason, we derive a fine-tuned operator acting on the extracted principal components to make the reconstructed data closer to the signal. Application of this proposed improved method on synthetic and field seismic data demonstrates a superior performance comparing with the traditional PCA. Presentation Date: Tuesday, October 18, 2016 Start Time: 1:00:00 PM Location: 148 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".