The influence of deep impact cratering on Martian intraplate faults
Bibliographic record
Abstract
Martian tectonic structures such as wrinkle ridges and lobate scarps exhibit differences in fault morphology and spacing across the Martian crustal dichotomy. On Earth, inherited structures from ancient tectonic activity may act as primary control in intraplate deformation. This hypothesis is yet to be explored for intraplate settings on other terrestrial planets, such as Mars. Large impact events during the Late Heavy Bombardment may have deeply fractured the Martian crust and mantle-lithosphere creating weakened inherited structures. Here, we hypothesize that Martian fault morphology may be controlled by subsurface deep impact cratering, and test using lithospheric-scale numerical models.In this study, we use the open-source geodynamic code ASPECT (Advanced Solver for Planetary Evolution, Convection, and Tectonics) to investigate the role of impact-related inherited structures on intraplate fault morphology in different lithospheric settings. We present a suite of 2-D numerical lithospheric models under horizontal shortening comparable to that of global contraction. Model parameters are constrained by past Martian modelling efforts and seismic data from the recent InSight mission. However, given the uncertainty in thermal and rheological model parameters for the Martian lithosphere, we extensively test appropriate ranges to analyze their potential role and focus on the lithospheric thickness across the Martian dichotomy. Our modelling results show appropriate faulting at the surface that may be related to Martian wrinkle ridge and lobate scarp morphology and also offer potential subsurface scenarios of deep lithosphere fault networks on Mars. Similar to Earth tectonics, we indicate that deep lithospheric inheritance may provide control over surface fault morphology and spacing, providing new insight into the growth of intraplate faults across the Martian crustal dichotomy.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".