Apollo Seismic Data Interpretation Using an Elastodynamic Space-Time Spectral Element Technique and Dispersion Image Inversion Method
Bibliographic record
Abstract
The data collected during the Apollo seismic experiments can provide important information regarding the lunar subsurface conditions and the corresponding mechanical properties. This paper aims to determine the shear wave velocity of the shallow subsurface using an elastodynamic space-time spectral element forward solver and the trust region reflective algorithm for back-calculation. The elastodynamic forward solver provides a semi-analytical solution for the wave propagation through subsurface materials. The trust region reflective algorithm is a bounded non-linear least square algorithm that effectively reduces the difference (Euclidean distance) between measured and predicted data by iteratively improving the prediction of material properties. In this paper, the seismic data is used to generate a dispersion image of the lunar subsurface, which provides the relation between the phase (group) velocity and frequency. Such dispersion images can be used to derive the shear wave velocity in each layer.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".