Evolution of Venusian rifts: Insights from Numerical Modeling
Bibliographic record
Abstract
Venus is a terrestrial planet with dimensions similar to the Earth and, although it is generally assumed that it does not host plate-tectonics, there are indications that Venus might have experienced, or still does experience, some form of tectonics. In fact, there are widespread observations of rifts on Venus called ‘chasma’ (plural ‘chasmata’), from radar-image interpretation of normal-fault-bounded graben structures (Harris & Bédard, 2015).The rifts on Venus have been likened to continental rifts on Earth such as the East African (e.g., Basilevsky & McGill, 2007) and Atlantic rift system prior to ocean opening (Graff et al., 2018), even if they are commonly wider than their terrestrial equivalent (e.g., Foster & Nimmo, 1996). However, despite being a prominent feature on its surface, little is known about the mechanisms responsible for creating rifts on Venus beyond the assumption that they are extensional features (Magee & Head, 1995).Since rifting on Earth in both continental and oceanic settings has been extensively studied through modeling, we adapted 2D thermo-mechanical numerical models of rifting on Earth to Venus in order to study how rifting structures observed on the Venusian surface could have been formed. More specifically, we investigated how rifting evolves under the high pressure and temperature conditions of the Venusian surface and the lithospheric structure proposed for Venus.Our results show that a strong crustal rheology such as diabase is needed to localize strain and to develop a rift under the harsh surface conditions of Venus. The evolution of the rift formation is predominantly controlled by the crustal thickness, with a 25 km-thick diabase crust required to produce mantle upwelling and melting. Lastly, we compared the surface topography produced by our models with the topography profiles of different Venusian chasmata. We observed a good fit between models characterised by different crustal thicknesses and the Ganis and Devana Chasmata, suggesting that differences in rift features on Venus could be due to different crustal thicknesses. ReferencesBasilevsky, A. T., & McGill, G. E. (2007). Surface evolution of Venus. In Exploring Venus as a terrestrial planet (p. 23-43). American Geophysical Union. doi: 10.1029/176GM04Foster, A., & Nimmo, F. (1996). Comparisons between the rift systems of East Africa, Earth and Beta Regio, Venus. Earth and Planetary Science Letters, 143 (1), 183-195. doi: 10.1016/0012-821X(96)00146-XGraff, J., Ernst, R., & Samson, C. (2018). Evidence for triple-junction rifting focussed on local magmatic centres along Parga Chasma, Venus. Icarus, 306 , 122-138. doi: 10.1016/j.icarus.2018.02.010Harris, L. B., & Bédard, J. H. (2015). Interactions between continent-like ‘drift’, rifting and mantle flow on Venus: gravity interpretations and Earth analogues. In: Volcanism and Tectonism Across the Inner Solar System. Geological Society of London. doi: 10.1144/SP401.9Magee, K. P., & Head, J. W. (1995). The role of rifting in the generation of melt: Implications for the origin and evolution of the Lada Terra-Lavinia Planitia region of Venus. Journal of Geophysical Research: Planets, 100 (E1), 1527-1552. doi: 10.1029/94JE02334
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".