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Record W4322210788 · doi:10.5194/egusphere-egu23-15108

Evolution of Venusian rifts: Insights from Numerical Modeling

2023· preprint· en· W4322210788 on OpenAlexaboutno aff
Alessandro Regorda, Cédric Thieulot, Iris van Zelst, Zoltán Erdös, Julia Maia, Susanne Buiter

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsVenusRiftGeologyLithosphereTectonicsGrabenGeophysicsPlate tectonicsPaleontologyHalf-grabenAstrobiologyPhysics

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.037
GPT teacher head0.243
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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