Enhanced Difference Algorithm For Seismic Modeling Based On Fruit Fly Optimization
Bibliographic record
Abstract
ABSTRACT The acoustic wave equation is an important basis for seismic wave propagation, imaging, and migration. This equation has led to the development of finite difference methods, but these methods are prone to numerical instability and grid dispersion problems, with the issue of dispersion being the most vital. Optimization methods can improve the accuracy across a large range of wavenumbers or frequencies. In this paper, we present a novel evolutionary optimization scheme for the acoustic wave equation. Our approach involves combining the fruit fly optimization algorithm (FOA) and sampling approximation (SA) to obtain the optimal difference operator for a wide range of wavenumbers. The difference coefficients are optimized to be extracted by the FOA, and the function of fitness, which is used to introduce an iterative process to determine the best smells, is evaluated by acoustic wave simulations. Based on the space-domain dispersion relation, we prove that the accuracy with which the absolute error is minimized is the same as that of the relative error. Within a given range of wavenumbers, we propose a fitness function by minimizing the absolute errors of the space-domain dispersion relation, and the dispersion analysis reveals that this scheme is superior to the Taylor-expansion scheme. We conduct two experiments by applying homogeneous and complex models, respectively. Further, the modeling results indicate that the fruit fly optimization approach in conjunction with sampling approximation preserve a higher accuracy for a small operator length and reduce the numerical artifacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".