Modeling seismic energy propagation in highly scattering environments
Bibliographic record
Abstract
Abstract Meteoroid impacts over millions to billions of years can produce a highly fractured and heterogeneous megaregolith layer on planetary bodies such as the Moon that lack effective surface recycling mechanisms. The energy from seismic events on these bodies undergoes scattering in the fractured layer(s) and generates extensive coda wave trains that follow major seismic wave arrivals. The decay properties of these codas are affected by the planetary body's interior structure. To understand the propagation of seismic waves in such media, we model the transmission of seismic energy in highly scattering environments using an adapted phonon method. In this Monte Carlo simulation approach, we track a large number of seismic wavelets as they leave a source and we record the resulting ground deformation each time a wavelet reaches a surface receiver. Our method provides the first numerical global modeling of 3‐D scattering, with user‐defined power law distributions of scatterer length scales and frequency‐dependent intrinsic attenuation, under the assumption of 1‐D background velocity models. We model synthetic signals for simple, but highly scattering interior models and vary the model parameters independently to assess their individual effects on the coda. Results show that the magnitude of the decay times is most affected by the background velocity model, in particular the presence of shallow low‐velocity layers, the event source depth, and the intrinsic attenuation level. The decay times are also controlled to a lesser extent by the size‐frequency distribution of scatterers, the thickness of the scattering layer, and the impedance contrast at the scatterers.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".