A study on lossy compression for background wavefield storage in LSM
Bibliographic record
Abstract
Least-squares reverse time migration (LSRTM) stands out as an effective method for delineating complex geological formations, offering a way to offset the limitations posed by limited offset data. However, a significant challenge arises from the computation of gradients in each iteration, which demands storing the entire background wavefield. For voluminous 3D models, this requirement can escalate to storing terabytes of data for every shot, underscoring the limitations even when employing advanced strategies like optimal checkpointing, enhanced boundary conditions, and reduced wavefield reconstruction due to the massive size of these models. Albeit a simple approach, archiving the entire background wavefield on disk becomes increasingly relevant. Nonetheless, this approach introduces several complications, including concerns over the lifespan of storage mediums, performance bottlenecks, and space usage constraints. An alternative strategy involves leveraging computation in exchange for an expanded storage footprint, in terms of both space and durability, by applying compression techniques. Nevertheless, when it comes to scientific data, employing lossless compression on floatingpoint numbers often falls short in significantly reducing space requirements, which brings to light the potential benefits and necessity for lossy compression methods. This study focuses on exploring the impact of varying degrees of lossy compression, specifically utilizing the ZFP compression algorithm, on the convergence, performance, and quality of LSM images, with the goal of providing a deeper understanding through practical experiments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".