High-dimensional compressive irregular-grid data reconstruction with a fast multidimensional singular spectrum analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it