Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Many standard seismic data processing and imaging techniques require regularly sampled data. Prestack seismic data are multidimensional signals that can be represented via low-rank fourth-order tensors in the frequency-space (f-x) domain. We propose to adopt tensor completion strategies to recover unrecorded observations and to improve the signal-to-noise ratio of prestack seismic volumes. Tensor completion can be posed as an inverse problem and solved by minimizing a convex objective function. The objective function contains two terms: a data misfit and a nuclear norm. The data misfit measures the proximity of the reconstructed seismic data to the observations. The nuclear norm constraints the reconstructed data to be a low-rank tensor. In essence, we solve the prestack seismic reconstruction problem via low-rank tensor completion. The cost function of the problem is minimized using the alternating direction method of multipliers. We present synthetic examples to illustrate the behavior of the algorithm in terms of trade-off parameters that control the quality of the reconstruction. We further illustrate the performance of the algorithm with a land data survey from Alberta, Canada.
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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) | 0.001 | 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 it