Formation of Complex Craters in Layered Targets With Material Anisotropy
Bibliographic record
Abstract
Abstract Meteorite impacts often occur in layered targets, where the strength of the target varies as a function of depth, but this complexity is often not represented in numerical impact simulations because of the high computational cost of resolving thin layers. To address this limitation, we developed a method to approximate the effect of multiple thin weak layers within a sedimentary sequence using a single material layer to represent the entire sequence. Our approach, implemented in the iSALE (impact‐Simplified Arbitrary Lagrangian Eulerian) shock physics code, combines an anisotropic yield criterion with a cell‐based method to track the orientation of layers. To demonstrate the efficacy of the method and constrain parameters of the anisotropic strength model required to replicate the effects of thin, weak layers, we compare results of simulations of an ~20‐ to 25‐km diameter complex crater on Earth using the new method to those from simulations that explicitly resolve multiple thin weak layers. We show that our approach allows for a reduction in computational cost, negating the need for an increase in spatial resolution to resolve thin layers in the target, while replicating crater formation and final morphology from the high‐resolution models. In keeping with field observations, we also find that anisotropic layers may be responsible for a lack of central uplift expression observed at many craters formed in targets with thick sedimentary layers (e.g., the Haughton and Ries impact structures).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".