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Record W4409359331 · doi:10.1016/j.jsg.2025.105428

Cooling induced fracturing in impact melt dikes derived from drone photogrammetry and Python simulation: Example from the Lesutoskraal Granophyre Dike in South Africa

2025· article· en· W4409359331 on OpenAlexaboutno aff
Martin D. Clark, Elizaveta Kovaleva, Matthew S. Huber, Stephanus Riekert, F. Fourie

Bibliographic record

VenueJournal of Structural Geology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
FundersDepartment of Agriculture, Western Cape GovernmentHans Merensky FoundationNational Aeronautics and Space AdministrationMinistry of Economy, Trade and IndustryUSDA Rural Development
KeywordsDikeGeologyPython (programming language)DronePhotogrammetrySeismologyGeomorphologyMining engineeringGeochemistryRemote sensing

Abstract

fetched live from OpenAlex

Large meteorite impact events produce significant amounts of crustal melt, which can be emplaced as dikes below the crater floor over protracted time periods following the cratering process. Their emplacement is theorized to be controlled by stresses associated with the presence and opening of crustal-scale fractures, hydrostatic pressures associated with the overlying melt sheet, and lithostatic stresses of the impacted crust. At least two compositionally distinct phases of impact melt are present within the impact melt dikes at the Sudbury and Vredefort Impact Structures, underpinning the debated concept of a prolonged and multi-phase emplacement process. In this study, cooling fractures within the Lesutoskraal impact melt dike at Vredefort are investigated as a possible pathway to facilitate multi-phase emplacement. Through a combination of high-resolution (0.612 mm/pixel) drone orthophotography and numerical simulation of stress induced during cooling of impact melt shows that (1) the dominant fracture orientation within the impact melt dike is parallel to dike margins, related to a perpendicular and tensional cooling stress, and (2) the magnitude of the tensional cooling stress could reach up to −75 MPa, sufficient to overcome the lithostatic stresses at the observed depth of dike emplacement. Depending on simulation parameters such as the initial temperature of the impact melt, cooling fractures in the impact melt are shown to form within 150 days after their emplacement representing a possible mechanism for emplacement of later phases of impact melt into the centre of earlier impact melt phase. • Dike-central emplacement of later phases of impact melt is a result of cooling-induced fracturing. • Tensile stress within impact melt derived from simulation show for superheated impact melt to 1800 °C, tensile stress can increase to −75 MPa within 350 days. • Orthophoto (0.612 mm/pixel) of the Lesutoskraal Granophyre Dike in Vredefort, South Africa, show dike-central clustering of fractures.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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
Published2025
Admission routes1
Has abstractyes

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