Reduced memory implementation of a local elastic finite-difference solver
Bibliographic record
Abstract
ABSTRACT The recovery of elastic properties from seismic data often requires the iterative use of seismic modeling. Finite-difference (FD) simulation is a common component in seismic modeling, and it is usually the most computationally expensive step in methodologies such as inversion or reverse time migration. Local solvers attempt to reduce the cost of FD simulations by reducing the computational domain to small areas, updating the model within these areas without recomputing throughout the full domain. We have implemented a local elastic solver that allows us to propagate the elastic wavefield within a subvolume after local alterations of the model. We determine how the scattered wavefield due to the alterations can be extrapolated from the local domain to surface receivers. We extend existing works by using the method of multiple point sources to recompute the wavefield within the local domain. This method is memory efficient because it only requires the global wavefield to be recorded along the local domain boundary. By injecting these recordings as point sources, the global wavefield is emulated within the local domain. Thus, the method requires no modifications of standard FD solvers, merely the ability to record and inject data. We evaluate the capability of the local elastic solver to reconstruct the wavefield in a subvolume of the elastic SEAM model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".