ElasWave3D: A GPU-accelerated 3D finite-difference elastic wave solver for complex topography using irregular subdomain index arrays
Bibliographic record
Abstract
Simulating seismic wave propagation in complex geological structures is a challenging task in exploration geophysics, especially in foothill regions characterized by rough topography, irregular bedrock interfaces, low-velocity surface sediments, and significant heterogeneities. Although existing numerical methods can address such scenarios, they often require highly refined grids that lead to elevated computational costs. To addres this, we introduce ElasWave3D, a three-dimensional solver based on the finite difference method for elastic wave propagation in the presence of irregular topography, specifically designed for GPU acceleration. The solver employs a novel Irregular Subdomain Index Array (ISIA) strategy to implement the parameter-modified (PM) formulation, thus enforcing the free-surface condition for arbitrary topographic variations. We validated ElasWave3D against the well-known SPECFEM3D solver in scenarios with rough topography and heterogeneous media, observing misfit errors below 1% and correlation values exceeding 99% in most cases. Additionally, our solver achieves more than an order-of-magnitude speedup (13×) over its CPU-OpenMP implementation on 24 cores. Consequently, ElasWave3D enables cost-effective, realistic, and detailed simulations of near-surface seismic scattering in heterogeneous Earth models with irregular 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) | 0.001 | 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) | 0.005 | 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".