Frequency‐Domain Finite‐Difference Acoustic Modeling with Free Surface Topography using Embedded Boundary Method
Bibliographic record
Abstract
In this paper, we present a method to model acoustic wave propagation in the frequency-domain in the presence of free surface topography using the embedded boundary method. The advantage of this method is to solve for the pressure field at each frequency on regular finite difference grids but with sub-cell resolution (up to 2nd order accuracy) for the irregular free surface. The topographic free surface condition is implemented in 2nd order accuracy, as in the regular domain and that global 2nd order accuracy is guaranteed. We use the level set method to obtain the projection points and normal directions corresponding to the ghost points in the scheme in the irregular domain. The computational cost for solving the modified sparse matrix for the pressure field increases very little compared to that for a flat surface. We have benchmarked our solver with a 2-D Gaussian hill model and simulated wave propagating in a modified Canadian foothills model. This solver can be used as the forward engine in the full waveform inversion, and we are working underway to perform full waveform inversions with real land survey data with considerable presence of surface topography.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".