High-dimensional compressive irregular-grid data reconstruction with a fast multidimensional singular spectrum analysis
Bibliographic record
Abstract
ABSTRACT Spatially irregularly sampled seismic data is unavoidable due to natural obstacles or acquisition designed for compressive sensing. Seismic reconstruction aims to regularize field data and map them from an irregular acquisition grid to regular-grid coordinates. We develop reconstructing high-dimensional arbitrary irregular-grid data with a fast multidimensional singular spectrum analysis (FMSSA) algorithm. The FMSSA filtering algorithm, replacing the traditional multidimensional singular spectrum analysis (MSSA) algorithm, acts as a projection operator to avoid explicitly constructing block Hankel matrices, accelerate the rank-reduction procedure, and reduce the memory load. Our method, the interpolated-FMSSA, can reconstruct data deployed on an irregular grid by introducing an interpolation operator adapted to connect irregular-grid observations and desired regular-grid data without losing accurate spatial coordinates information. In addition, two commonly used Fourier-based methods for irregular-grid data reconstruction, a modified projection onto convex sets algorithm and the fast iterative shrinkage-thresholding algorithm, are used for comparison. Synthetic and real data examples show significant improvement in computational efficiency compared to the traditional I-MSSA method and improvement in reconstruction accuracy compared with the Fourier-based methods for 3D and 5D irregular-grid data reconstruction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".