Sparsity‐promoting recovery from simultaneous data: A compressive sensing approach
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
Seismic data acquisition forms one of the main bottlenecks in seismic imaging and inversion. The high cost of acquisition work and collection of massive data volumes compel the adoption of simultaneous‐source seismic data acquisition ‐ an emerging technology that is developing rapidly, stimulating both geophysical research and commercial efforts. Aimed at improving the performance of marine‐ and land‐acquisition crews, simultaneous acquisition calls for development of a new set of design principles and post‐processing tools. Leveraging developments from the field of compressive sensing the focus here is on simultaneous‐acquisition design and sequential‐source data recovery. Apart from proper compressive sensing sampling schemes, the recovery from simultaneous simulations depends on a sparsifying transform that compresses seismic data, is fast, and reasonably incoherent with the compressive‐sampling matrix. Using the curvelet transform, in which seismic data can be represented parsimoniously, the recovery of the sequential‐source data volumes is achieved using the sparsity‐promoting program — SPGL1, a solver based on projected spectral gradients. The main outcome of this approach is a new technology where acquisition related costs are no longer determined by the stringent Nyquist sampling criteria.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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