Seismic data reconstruction using multidimensional prediction filters
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 In this paper we discuss a beyond‐alias multidimensional implementation of the multi‐step autoregressive reconstruction algorithm for data with missing spatial samples. The multi‐step autoregressive method is summarized as follows: vital low‐frequency information is first regularized adopting a Fourier based method (minimum weighted norm interpolation); the reconstructed data are then used to estimate prediction filters that are used to interpolate higher frequencies. This article discusses the implementation of the multi‐step autoregressive method to data with more than one spatial dimension. Synthetic and real data examples are used to examine the performance of the proposed method. Field data are used to illustrate the applicability of multidimensional multi‐step autoregressive operators for regularization of seismic data.
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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 | 0.001 |
| 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