Efficient Multidimensional Deconvolution with an H2-Like Parametrization
Bibliographic record
Abstract
Summary This study presents a new approach to improving the efficiency of Multidimensional Deconvolution (MDD) for seismic wavefield redatuming. While MDD offers more accurate results than traditional methods, it is often limited by high computational demands due to the large and complex matrices involved in the process. We introduce an innovative technique that uses low-rank and H2-like parametrization to compress these matrices, reducing both memory usage and computational costs. Our method focuses on representing the operator, right-hand side, and unknowns in a low-rank format, allowing for the solution of smaller linear systems in the frequency domain. This approach is tested on 2D and 3D synthetic seismic datasets, demonstrating significant reductions in computational complexity with only a slight decrease in solution quality. The potential impact of this method is substantial—it could make MDD a more viable tool for large-scale geophysical applications, offering significantly improved efficiency. By using H2-like matrix compression, we enable faster and more resource-effective seismic wavefield reconstructions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".