A hybrid method applied to a 2.5D scalar wave equation
Bibliographic record
Abstract
The 3D acoustic wave equation for a heterogeneous medium is used for the seismic modeling of compressional (P-) wave propagation in complex subsurface structures. A combination of finite difference and finite integral transform methods is employed to obtain a “2.5D” solution to the 3D equation. Such 2.5D approaches are attractive because they result in computational run times that are substantially smaller than those for the 3D finite difference method. The acoustic parameters of the medium are assumed to be constant in one of the three Cartesian spatial dimensions. This assumption is made to reduce the complexity of the problem, but still retain the salient features of the approach. Simple models are used to address the computational issues that arise in the modeling. The conclusions drawn can also be applied to the more general fully inhomogeneous problem. Although similar studies have been carried out by others, the work presented here is new in the sense that (i) it applies to subsurface models that are both vertically and laterally heterogeneous, and (ii) the computational issues that need to be addressed for efficient computations, which are not trivial, are examined in detail, unlike previous works. We find that it is feasible to generate true-amplitude synthetic seismograms using the 2.5D approach, with computational run times, storage requirements, and other factors, being at reduced and acceptable levels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".