A fast rank reduction method for the reconstruction of <i>5D</i> seismic volumes
Bibliographic record
Abstract
Rank reduction strategies can be employed to attenuate noise and as a basic template for pre-stack regularization of seismic data. We propose to utilize the rank reduction method of Multichannel Singular Spectrum Analysis (MSSA) to implement a fast 5D seismic data reconstruction method by embedding 4D spatial data into a block Toeplitz matrix and rank reduce this matrix via the Lanczos bidiagonalization algorithm, rather than using Singular Value Decomposition (SVD). The computational cost of the Lanczos bidiagonalization is dominated by the cost of multiplying a block Toeplitz matrix by a vector. The latter can be efficiently implemented via multidimensional Fast Fourier Transforms. The proposed algorithm significantly decreases the computational cost of the rank-reduction stage needed for de-noising and reconstruction with respect to algorithms that utilize the Singular Value Decomposition (SVD). In essence, our algorithm exploits the structure of block Toeplitz matrices to accelerate the rank-reduction step of de-noising and reconstruction strategies used by Multichannel Singular Spectrum Analysis (MSSA) or Cadzow matrix completion methods. Synthetic data examples and a field data test were used to examine the proposed algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".