Robust singular spectrum analysis via the bifactored gradient descent algorithm
Bibliographic record
Abstract
Several rank-reduction techniques have been proposed to simultaneously denoise and reconstruct seismic datasets. We reformulate the Singular Spectrum Analysis (SSA) filter as a convex optimization problem constraining the associated Hankel matrix to be of low-rank. The Hankel matrix is written as the product of two matrices of lower dimension, which are obtained using a gradient descent algorithm, called the bifactored gradient descent (BFGD). The BFGD is an efficient nonconvex method which can be easily adaptable to include sampling operators within robust measures as cost functions, thus simultaneously handling missing traces and erratic noise. We evaluate the BFGD-based SSA in the simultaneous reconstruction and denoising of a 3D field dataset and compare it with the MSSA interpolation method. The results support that the BFGD does have a competitive performance for seismic data processing applications. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Start Time: 3:05 PM Location: Poster Station 13 Presentation Type: Poster
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".