Julia Application in Calculating Reflectivity from High Order Anisotropic Interface
Bibliographic record
Abstract
Summary Elastic anisotropy in the Earth causes many artifacts in an isotropic seismic processing and interpretation. To date, these issues have been ameliorated by using approximations to the full solutions for wave propagation and reflectivity for special material symmetries. This algorithm has been written to extend these capabilities to the general case of reflectivity from the interface between two anisotropic slabs of arbitrary symmetry and orientation. To achieve this, the algorithm solves for polarization, amplitude and slowness of all the wave modes generated by a plane wave incident to the interface. In the first step, the plane-wave velocities and polarizations of all three orthogonal wave modes are calculated for a given incidence angle. Second, the algorithm determines the reflection and transmission angles of all of the possible scattered modes followed by their respective velocities and polarization vectors. With this information, the algorithm solves system of equations incorporating the imposed boundary conditions to arrive at the scattered wave amplitudes. We have tested this algorithm in some of the most computationally difficult models to ensure there is no energy leakage in the system of calculations. This algorithm could be used in seismic processing, migration, and inversion of seismic data in anisotropic media.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".